The Allele Frequencies Website [Search HLA Allele Frequencies]

HLA » Allele Frequency Search

Displaying all results from your selection.

Line	Allele	Population	% of individuals that have the allele	Allele Frequency	Sample Size	Location
1	A*02:17:02:01	Colombia Wayuu NA-DHS_15 (G)	6.7	0.0333	15	11_0_N_73_0_W
2	A*68:01:64	Latvia		0.0019	266	56_50_N_24_45_E
3	A*02:01:216	Latvia		0.0038	266	56_50_N_24_45_E
4	A*02:864:01	Latvia		0.0019	266	56_50_N_24_45_E
5	A*02:01:170	Latvia		0.0019	266	56_50_N_24_45_E
6	A*24:609	Latvia		0.0019	266	56_50_N_24_45_E
7	A*25:68	Latvia		0.0019	266	56_50_N_24_45_E
8	A*30:18:01	Latvia		0.0038	266	56_50_N_24_45_E
9	A*02:17:02:01	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
10	A*03:07:01:01	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
11	A*11:12:01	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
12	A*36:01:01:01	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
13	A*24:07:01:01	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
14	A*24:17:01:01	Pakistan Mixed Punjabi	0.6	0.0026	389	31_0_N_72_0_E
15	A*01:380:01	Russia Moscow Bashkir	0.7	0.0034	442	55_46_N_37_41_E
16	A*02:01:204	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
17	A*02:1113	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
18	A*02:1114	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
19	A*02:974	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
20	A*01:408	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
21	A*11:440	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
22	A*31:210	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
23	A*02:01:170	Russia Moscow Buryat	0.7	0.0034	149	55_46_N_37_41_E
24	A*03:434	Russia Moscow Buryat	0.7	0.0034	149	55_46_N_37_41_E
25	A*11:362	Russia Moscow Buryat	0.7	0.0034	149	55_46_N_37_41_E
26	A*01:01:123	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
27	A*01:01:133	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
28	A*01:380:01	Russia Moscow Kabardin	0.5	0.0024	834	55_46_N_37_41_E
29	A*02:01:170	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
30	A*02:1083	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
31	A*02:07:14	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
32	A*02:948	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
33	A*03:395	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
34	A*02:899	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
35	A*02:903	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
36	A*11:12:01	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
37	A*11:199:03	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
38	A*11:370	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
39	A*11:399	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
40	A*24:515	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
41	A*32:01:43	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
42	A*26:20:02	Russia Moscow Kabardin	0.2	0.0012	834	55_46_N_37_41_E
43	A*26:203	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
44	A*26:01:78	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
45	A*32:148	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
46	A*32:175	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
47	A*33:237	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
48	A*33:245	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
49	A*01:380:01	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
50	A*02:01:185	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
51	A*68:249	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
52	A*03:434	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
53	A*11:362	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
54	A*26:20:02	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
55	A*02:01:170	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
56	A*02:01:178	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
57	A*02:01:199	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
58	A*11:362	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
59	A*24:537	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
60	A*26:203	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
61	A*33:242	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
62	A*33:243	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
63	A*68:164:01	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
64	A*68:249	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
65	A*03:430	Russia Moscow Mansi	2.6	0.0132	38	55_46_N_37_41_E
66	A*26:20:02	Russia Moscow Mansi	2.6	0.0132	38	55_46_N_37_41_E
67	A*33:237	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
68	A*11:12:01	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
69	A*26:20:02	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
70	A*68:278	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
71	A*01:380:01	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
72	A*02:903	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
73	A*03:56:01	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
74	A*11:353	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
75	A*11:356	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
76	A*11:362	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
77	A*11:364	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
78	A*11:373	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
79	A*11:378	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
80	A*11:379	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
81	A*11:385	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
82	A*11:399	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
83	A*11:12:01	Russia Moscow Russians	0.2	0.0009	13,147	55_46_N_37_41_E
84	A*11:126:01	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
85	A*11:199:03	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
86	A*11:208:01N	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
87	A*24:272:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
88	A*24:121:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
89	A*11:440	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
90	A*11:441	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
91	A*23:17:03	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
92	A*24:02:164	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
93	A*26:01:70	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
94	A*26:01:73	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
95	A*26:12:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
96	A*26:198	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
97	A*26:20:02	Russia Moscow Russians	0.2	0.0011	13,147	55_46_N_37_41_E
98	A*26:203	Russia Moscow Russians	0.1	0.0005	13,147	55_46_N_37_41_E
99	A*26:205	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
100	A*26:207	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
101	A*26:208	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
102	A*26:238	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
103	A*30:166	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
104	A*30:167	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
105	A*30:177	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
106	A*30:18:01	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
107	A*30:183	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
108	A*30:186	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
109	A*30:187	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
110	A*30:205	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
111	A*30:210	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
112	A*31:04:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
113	A*31:210	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
114	A*32:01:43	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
115	A*32:03:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
116	A*32:152	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
117	A*32:162	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
118	A*31:164	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
119	A*31:171	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
120	A*31:200	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
121	A*33:226	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
122	A*33:237	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
123	A*33:242	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
124	A*33:243	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
125	A*33:245	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
126	A*33:247	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
127	A*36:01:01	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
128	A*68:114:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
129	A*68:164:01	Russia Moscow Russians	0.1	0.0005	13,147	55_46_N_37_41_E
130	A*68:164:02	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
131	A*68:249	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
132	A*68:253	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
133	A*68:99:02	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
134	A*68:278	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
135	A*01:01:142	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
136	A*01:01:143	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
137	A*01:02:01	Russia Moscow Russians	0.2	0.0010	13,147	55_46_N_37_41_E
138	A*01:01:116	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
139	A*02:01:180	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
140	A*02:01:187	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
141	A*02:01:192	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
142	A*02:01:199	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
143	A*02:01:202	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
144	A*02:01:203	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
145	A*02:01:170	Russia Moscow Russians	0.3	0.0014	13,147	55_46_N_37_41_E
146	A*02:01:174	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
147	A*02:01:178	Russia Moscow Russians	0.1	0.0004	13,147	55_46_N_37_41_E
148	A*02:01:179	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
149	A*02:04:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
150	A*02:1026	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
151	A*02:1044	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
152	A*02:1049	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
153	A*02:1055N	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
154	A*02:1056	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
155	A*02:1060	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
156	A*02:1065	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
157	A*02:1069	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
158	A*02:1004	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
159	A*02:1005	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
160	A*02:1072	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
161	A*02:1077	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
162	A*02:1081	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
163	A*02:1082	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
164	A*02:1083	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
165	A*02:1018	Russia Moscow Russians	0.1	0.0004	13,147	55_46_N_37_41_E
166	A*02:1100	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
167	A*02:1114	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
168	A*02:314:01N	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
169	A*02:648:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
170	A*03:197:02N	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
171	A*03:344:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
172	A*03:344:02	Russia Moscow Russians	0.2	0.0009	13,147	55_46_N_37_41_E
173	A*03:345:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
174	A*03:390:02	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
175	A*03:395	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
176	A*03:422	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
177	A*03:56:01	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
178	A*11:01:115	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
179	A*11:01:120	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
180	A*03:465	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
181	A*03:371	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
182	A*03:375	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
183	A*25:67	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
184	A*25:68	Russia Moscow Russians	0.1	0.0005	13,147	55_46_N_37_41_E
185	A*25:82	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
186	A*01:337	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
187	A*01:348	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
188	A*01:353	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
189	A*01:356	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
190	A*01:363	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
191	A*01:364	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
192	A*01:370	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
193	A*01:375	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
194	A*01:380:01	Russia Moscow Russians	0.2	0.0008	13,147	55_46_N_37_41_E
195	A*01:430	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
196	A*01:391	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
197	A*01:396Q	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
198	A*01:408	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
199	A*01:409	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
200	A*01:419	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
201	A*01:427	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
202	A*02:903	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
203	A*02:904	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
204	A*02:905	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
205	A*02:919	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
206	A*02:930	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
207	A*02:958	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
208	A*02:985	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
209	A*02:993:02	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
210	A*03:01:102	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
211	A*02:864:01	Russia Moscow Russians	0.2	0.0010	13,147	55_46_N_37_41_E
212	A*24:509	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
213	A*24:515	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
214	A*24:516	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
215	A*24:532	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
216	A*24:539	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
217	A*24:548	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
218	A*24:550	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
219	A*24:551	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
220	A*24:555	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
221	A*24:557	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
222	A*24:56:01	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
223	A*24:577	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
224	A*24:585:01	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
225	A*24:594	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
226	A*24:603	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
227	A*24:606	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
228	A*02:878	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
229	A*24:490	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
230	A*24:551	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
231	A*24:515	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
232	A*25:68	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
233	A*25:76:02	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
234	A*25:82	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
235	A*26:195	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
236	A*26:20:02	Russia Moscow Tatars	0.2	0.0012	2,512	55_46_N_37_41_E
237	A*26:203	Russia Moscow Tatars	0.2	0.0008	2,512	55_46_N_37_41_E
238	A*26:205	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
239	A*26:207	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
240	A*26:208	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
241	A*29:156	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
242	A*30:18:01	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
243	A*30:186	Russia Moscow Tatars	0.2	0.0008	2,512	55_46_N_37_41_E
244	A*26:01:64	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
245	A*26:01:73	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
246	A*02:795Q	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
247	A*02:864:01	Russia Moscow Tatars	0.4	0.0020	2,512	55_46_N_37_41_E
248	A*02:985	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
249	A*03:430	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
250	A*03:56:01	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
251	A*03:344:02	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
252	A*03:371	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
253	A*03:395	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
254	A*03:417	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
255	A*11:12:01	Russia Moscow Tatars	0.2	0.0008	2,512	55_46_N_37_41_E
256	A*11:353	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
257	A*33:209	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
258	A*33:237	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
259	A*33:242	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
260	A*31:210	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
261	A*68:164:01	Russia Moscow Tatars	0.2	0.0008	2,512	55_46_N_37_41_E
262	A*68:164:02	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
263	A*68:249	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
264	A*68:35:01	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
265	A*01:02:01	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
266	A*01:380:01	Russia Moscow Tatars	0.2	0.0008	2,512	55_46_N_37_41_E
267	A*01:395	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
268	A*02:01:170	Russia Moscow Tatars	0.2	0.0010	2,512	55_46_N_37_41_E
269	A*02:01:175	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
270	A*02:01:179	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
271	A*02:01:199	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
272	A*01:430	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
273	A*02:1011Q	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
274	A*02:501:02N	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
275	A*01:301Q	Russia Moscow Tuvian	0.9	0.0045	112	55_46_N_37_41_E
276	A*11:362	Russia Moscow Tuvian	0.9	0.0045	112	55_46_N_37_41_E
277	A*03:434	Russia Moscow Tuvian	0.9	0.0045	112	55_46_N_37_41_E
278	A*31:210	Russia Moscow Tuvian	0.9	0.0045	112	55_46_N_37_41_E
279	A*24:585:01	Russia Moscow Tuvian	0.9	0.0045	112	55_46_N_37_41_E
280	A*68:164:02	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
281	A*68:249	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
282	A*11:362	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
283	A*11:12:01	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
284	A*26:01:73	Russia Moscow Uzbek	1.0	0.0051	198	55_46_N_37_41_E
285	A*30:186	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
286	A*33:243	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
287	A*01:406	Russia Moscow Yakut	0.4	0.0021	235	55_46_N_37_41_E
288	A*11:362	Russia Moscow Yakut	7.2	0.0362	235	55_46_N_37_41_E
289	A*03:434	Russia Moscow Yakut	1.3	0.0064	235	55_46_N_37_41_E
290	A*26:01:73	Russia Moscow Yakut	0.4	0.0021	235	55_46_N_37_41_E
291	A*26:207	Russia Moscow Yakut	0.4	0.0021	235	55_46_N_37_41_E
292	A*36:01:01	Russia Moscow Yakut	0.4	0.0021	235	55_46_N_37_41_E
293	A*31:210	Russia Moscow Yakut	0.4	0.0021	235	55_46_N_37_41_E
294	A*01	Albania		0.1000	160	41_0_N_20_0_E
295	A*01	Albania pop 2	18.3	0.0972	432	41_9_N_20_10_E
296	A*01	Albanian Kosovo		0.1542	120	41_50_N_20_1_E
297	A*01	Algeria pop 2	23.0		106	36_42_N_3_13_E
298	A*01	Argentina Buenos Aires	23.2	0.1190	466	34_38_S_58_28_W
299	A*01	Argentina Buenos Aires pop 2	19.3	0.1010	1,216	34_36_S_58_22_W
300	A*01	Argentina Chiriguano	3.8	0.0190	54	23_8_S_64_20_W
301	A*01	Argentina Corrientes	14.8	0.0840	155	27_30_S_58_49_W
302	A*01	Argentina Cuyo Region	17.1	0.0890	420	32_53_S_68_49_W
303	A*01	Argentina La Plata	22.0	0.1150	100	34_55_S_57_57_W
304	A*01	Argentina Rosario Toba	16.3	0.0810	86	32_57_S_60_39_W
305	A*01	Argentina Salta Wichi pop 2	0.0	0.0000	19	23_8_S_64_20_W
306	A*01	Armenia Ghegharkunik		0.1180	242	40_11_N_44_31_E
307	A*01	Armenia living in Iran		0.1470	85	40_11_N_44_31_E
308	A*01	Armenia living in Karabakh		0.1160	445	40_11_N_44_31_E
309	A*01	Armenia living in Lebanon		0.1130	368	40_11_N_44_31_E
310	A*01	Armenia living in USA		0.1290	233	40_11_N_44_31_E
311	A*01	Armenia Lori		0.1270	102	40_11_N_44_31_E
312	A*01	Armenia Shirak		0.1120	76	40_11_N_44_31_E
313	A*01	Armenia Syunik		0.1240	117	40_11_N_44_31_E
314	A*01	Armenia Yerevan		0.1070	445	40_11_N_44_31_E
315	A*01	Australia West Caucasian	33.6	0.1850	891	31_5_S_115_5_E
316	A*01	Austria	27.0	0.1450	200	48_13_N_16_21_E
317	A*01	Austria Cord Bloods		0.1350	1,368	47_4_N_15_26_E
318	A*01	Azerbaijan	17.3	0.0920	2,301	40_39_N_47_47_W
319	A*01	Azores		0.1160	1,050	37_44_N_25_40_W
320	A*01	Azores Terceira Island		0.1090	130	38_44_N_27_19_W
321	A*01	Bangladesh Dhaka Bangalee		0.0950	141	23_42_N_90_22_E
322	A*01	Bolivia La Paz Aymaras		0.0110	87	16_29_S_68_7_W
323	A*01	Bolivia Quechua		0.0435	69	16_30_S_68_9_W
324	A*01	Bosnia and Herzegovina		0.1343	134	43_52_N_18_25_E
325	A*01	Bosnia and Herzegovina pop 2	29.2	0.1458	120	44_32_N_18_41_E
326	A*01	Brazil Paraná Caucasian	19.0	0.0959	641	23_25_S_51_55_W
327	A*01	Brazil Curitiba-Parana Mixed		0.1127	264	25_25_S_49_16_W
328	A*01	Brazil Minas Gerais State Mixed	15.3	0.0810	1,000	19_49_S_43_57_W
329	A*01	Brazil Para Cord Blood Unit	10.6	0.0547	841	6_0_S_52_0_W
330	A*01	Brazil Para State	15.1	0.0672	5,000	6_0_S_52_0_W
331	A*01	Brazil Parana Afro Brazilian	24.7	0.1230	77	25_0_S_49_0_W
332	A*01	Brazil Parana Cafuzo	18.5	0.0920	319	25_0_S_49_0_W
333	A*01	Brazil Parana Caucasian	19.7	0.0990	2,775	25_0_S_49_0_W
334	A*01	Brazil Parana Japanese	3.0	0.0156	192	22_29_S_48_2_W
335	A*01	Brazil Parana Mixed race	10.2	0.0510	186	25_0_S_49_0_W
336	A*01	Brazil Parana Oriental	0.0	0.0000	33	25_0_S_49_0_W
337	A*01	Brazil Pernambuco Mixed		0.0840	101	8_3_S_34_54_W
338	A*01	Brazil Piaui Mixed		0.0710	21,943	2_44_S_40_25_W
339	A*01	Brazil REDOME Acre		0.0850	859	9_11_S_70_52_W
340	A*01	Brazil REDOME Alagoas		0.0872	25,349	9_57_S_36_55_W
341	A*01	Brazil REDOME Amapa		0.0599	17,864	1_0_N_52_0_W
342	A*01	Brazil REDOME Amazonas		0.0659	24,129	5_0_S_63_0_W
343	A*01	Brazil REDOME Bahia		0.0746	47,399	12_31_S_41_41_W
344	A*01	Brazil REDOME Ceara		0.0835	101,217	5_5_S_39_39_W
345	A*01	Brazil REDOME Espirito Santo		0.0897	88,485	20_19_S_40_20_W
346	A*01	Brazil REDOME Federal District		0.0885	29,549	15_46_S_47_56_W
347	A*01	Brazil REDOME Goias		0.0927	88,574	15_56_S_50_8_W
348	A*01	Brazil REDOME Maranhao		0.0798	10,180	2_31_S_44_18_W
349	A*01	Brazil REDOME Mato Grosso		0.0907	34,649	15_35_S_56_4_W
350	A*01	Brazil REDOME Mato Grosso do Sul		0.0882	95,667	20_26_S_54_38_W
351	A*01	Brazil REDOME Minas Gerais		0.0900	211,275	19_49_S_43_57_W
352	A*01	Brazil REDOME Para		0.0638	72,637	5_40_S_52_44_W
353	A*01	Brazil REDOME Paraiba		0.0862	43,868	7_10_S_36_50_W
354	A*01	Brazil REDOME Parana		0.0998	341,639	24_0_S_51_0_W
355	A*01	Brazil REDOME Pernambuco		0.0810	92,332	8_20_S_48_48_W
356	A*01	Brazil REDOME Piaui		0.0721	46,140	8_14_S_43_6_W
357	A*01	Brazil REDOME Rio de Janeiro		0.0857	139,322	22_54_S_43_11_W
358	A*01	Brazil REDOME Rio Grande do Norte		0.0873	46,603	5_44_S_36_33_W
359	A*01	Brazil REDOME Rio Grande do Sul		0.1083	241,329	30_0_S_53_0_W
360	A*01	Brazil REDOME Rondonia		0.0881	54,396	10_54_S_62_46_W
361	A*01	Brazil REDOME Roraima		0.0675	4,140	2_3_N_61_24_W
362	A*01	Brazil REDOME Santa Catarina		0.1110	106,673	27_15_S_50_20_W
363	A*01	Brazil REDOME Sao Paulo		0.0942	800,809	23_33_S_46_38_W
364	A*01	Brazil REDOME Sergipe		0.0812	7,321	10_35_S_37_23_W
365	A*01	Brazil REDOME Tocantins		0.0793	20,692	10_11_S_48_20_W
366	A*01	Brazil Rio Grande do Norte Mestizo		0.0870	12,973	5_47_S_35_12_W
367	A*01	Brazil Rio Grande do Sul Black		0.0420	248	30_0_S_53_0_W
368	A*01	Brazil Rio Grande do Sul Caucasoid		0.1050	4,428	30_0_S_53_0_W
369	A*01	Brazil Rio Grande do Sul Mixed		0.0870	324	30_0_S_53_0_W
370	A*01	Brazil Sao Paulo Bauru Mixed		0.0880	3,542	22_18_S_49_3_W
371	A*01	Brazil Sao Paulo Mixed	13.0	0.0670	239	23_0_S_48_0_W
372	A*01	Brazil South East Cord Blood		0.0881	11,409	14_14_S_51_55_W
373	A*01	Brazil South Ribeirao Preto		0.0930	184	21_11_S_47_49_W
374	A*01	Burkina Faso Fulani		0.1330	49	12_50_N_1_50_W
375	A*01	Burkina Faso Mossi		0.0470	53	12_50_N_1_50_W
376	A*01	Burkina Faso Rimaibe		0.0320	47	12_50_N_1_50_W
377	A*01	Cameroon Bakola Pygmy	6.0		50	4_3_N_9_42_E
378	A*01	Cameroon Bamileke 2		0.0000	35	5_15_N_10_20_E
379	A*01	Cameroon Ewondo		0.0260	38	3_55_N_11_24_E
380	A*01	Cameroon Podokwo		0.0120	43	10_37_N_14_20_E
381	A*01	Cameroon Uldeme		0.0140	35	10_37_N_14_20_E
382	A*01	Chile Santiago		0.1027	920	33_26_S_70_40_W
383	A*01	China Guangdong Province	2.7		403	23_20_N_113_30_E
384	A*01	China Harbin Korean		0.0150	201	45_45_N_126_38_E
385	A*01	China Harbin Manchu		0.0450	172	45_45_N_126_38_E
386	A*01	China Jiangsu Province		0.0310	20,248	33_0_N_120_0_E
387	A*01	China North Han		0.0000	105	39_54_N_116_24_E
388	A*01	China Shaanxi Province Han		0.0450	10,000	34_0_N_109_0_E
389	A*01	China Shandong Province	8.1	0.0410	7,418	36_30_N_118_0_E
390	A*01	China Shandong Province Linqu County	2.2		139	36_0_N_118_0_E
391	A*01	China Shanghai		0.0240	26,266	31_12_N_121_30_E
392	A*01	China Wuhan	3.3	0.0170	121	30_45_N_114_15_E
393	A*01	Colombia Barranquilla		0.0480	188	10_57_N_74_47_W
394	A*01	Colombia San Basilio de Palenque		0.0600	42	10_6_N_75_11_W
395	A*01	Croatia pop 2		0.1570	100	45_0_N_15_0_E
396	A*01	Croatia pop 3	26.7	0.1333	120	45_6_N_15_12_E
397	A*01	Cuba Mixed		0.0800	78	23_8_N_82_23_W
398	A*01	Cuba Mixed pop 2		0.0670	189	23_8_N_82_23_W
399	A*01	Cuba Mixed Race	14.3	0.0710	42	23_8_N_82_22_W
400	A*01	Czech Republic Kurd	29.7	0.1622	74	49_44_N_15_19_E
401	A*01	Czech Republic Romani pop 2	41.3	0.2174	46	49_44_N_15_19_E
402	A*01	Ecuador Amazonia Mixed Ancestry		0.0128	39	0_48_S_76_54_W
403	A*01	Ecuador Andes Mixed Ancestry		0.0413	824	1_28_S_78_49_W
404	A*01	Ecuador Coast Mixed Ancestry		0.0420	238	2_11_S_79_53_W
405	A*01	Ecuador Mixed Ancestry		0.0384	1,173	0_10_S_78_28_W
406	A*01	England Lancaster	38.3		545	54_2_N_2_48_W
407	A*01	England Leeds	34.5		5,024	53_47_N_1_32_W
408	A*01	England Liverpool	35.1		1,343	53_24_N_3_0_W
409	A*01	England London Ashkenazi Jews		0.1260	500	51_30_N_0_7_W
410	A*01	England Manchester	34.3		1,640	53_28_N_2_14_W
411	A*01	England Newcastle	34.8		2,739	54_58_N_1_36_W
412	A*01	England North West Mixed pop 2	33.5	0.1676	379	53_28_N_2_14_W
413	A*01	England Sheffield	35.5		4,755	53_23_N_1_28_W
414	A*01	France Bordeaux	24.3	0.1219	990	44_50_N_0_34_W
415	A*01	France Corsica Island		0.0650	100	42_9_N_9_5_E
416	A*01	France Grenoble	25.9	0.1298	1,000	45_12_N_5_43_E
417	A*01	France Grenoble, Nantes and Rennes		0.1320	6,094	47_13_N_1_33_W
418	A*01	France Lyon	25.3	0.1264	4,813	45_45_N_4_50_E
419	A*01	France Marseille	24.7	0.1235	1,000	43_17_N_5_22_E
420	A*01	France Reims		0.1570	102	49_15_N_4_2_E
421	A*01	France Rennes pop 3		0.1600	200	48_6_N_1_40_W
422	A*01	France Rennes pop 4	25.2	0.1266	1,000	48_6_N_1_40_W
423	A*01	France Southeast	27.7	0.1500	130	46_0_N_5_0_E
424	A*01	Gaza Palestinians		0.1240	165	31_31_N_34_27_E
425	A*01	Georgia Kurds		0.0860	30	41_43_N_44_47_E
426	A*01	Germany	27.7		11,407	50_6_N_8_40_E
427	A*01	Germany Essen	25.9	0.1440	174	51_27_N_7_0_E
428	A*01	Ghana Accre Asutuare Akan		0.0260	429	6_5_N_0_12_E
429	A*01	Greece Crete		0.1200	135	35_0_N_25_0_E
430	A*01	Greece North		0.1000	500	40_0_N_22_0_E
431	A*01	Greece pop 7		0.1049	11,250	37_58_N_23_43_E
432	A*01	Greece pop3	17.5		246	37_58_N_23_43_E
433	A*01	Guatemala Mayan		0.0220	132	14_8_N_91_5_W
434	A*01	Guatemala, Guatemala City Mixed Ancestry	16.5	0.0827	127	14_37_N_90_31_W
435	A*01	India Bikaner Non-Raikas	20.4		127	28_1_N_73_18_E
436	A*01	India Bikaner Raikas	15.1		143	28_1_N_73_18_E
437	A*01	India Jalpaiguri Toto	5.0		40	26_30_N_88_42_E
438	A*01	India Kerala Hindu Ezhava		0.1040	24	8_48_N_76_36_E
439	A*01	India Kerala Hindu Nair		0.0850	41	8_48_N_76_36_E
440	A*01	India Kerala Hindu Namboothiri		0.0630	40	11_14_N_75_44_E
441	A*01	India Kerala Hindu Pulaya		0.0000	16	11_48_N_75_19_E
442	A*01	India Kerala Malabar Muslim		0.0590	34	11_2_N_76_3_E
443	A*01	India Kerala Syrian Christian		0.0480	31	9_35_N_76_31_E
444	A*01	India North Gujarat	35.5	0.1923	338	23_13_N_72_41_E
445	A*01	India North pop 3		0.0860	587	28_38_N_77_9_E
446	A*01	India sub-Himalayan West Bengal Kami	5.1	0.0253	158	27_2_N_88_15_E
447	A*01	Iran pop 1		0.1250	64	32_35_N_54_16_E
448	A*01	Iran pop 4		0.1088	855	35_41_N_51_38_E
449	A*01	Iran Royan Cord Blood Bank	21.9		15,600	35_41_N_51_25_E
450	A*01	Iraq Arabs	22.1	0.1107	149	33_18_N_44_21_E
451	A*01	Iraq Erbil	21.8	0.1090	372	33_33_N_43_43_E
452	A*01	Iraq Kurdistan Region	23.9	0.1320	209	36_51_N_43_0_E
453	A*01	Ireland South pop 2		0.2359	17,624	53_20_N_6_15_W
454	A*01	Israel Ashkenazi Jews pop 2		0.1580	132	31_47_N_35_13_E
455	A*01	Israel Jews pop 2	28.0	0.1520	23,000	32_5_N_34_48_E
456	A*01	Israel Moroccan Jews		0.2590	113	31_47_N_35_13_E
457	A*01	Italy		0.1210	159,311	45_28_N_9_11_E
458	A*01	Italy Bergamo	14.9	0.0750	101	45_42_N_9_40_E
459	A*01	Italy East Sicily	18.0	0.1100	50	37_30_N_15_4_E
460	A*01	Italy Lombardy	22.1	0.1170	674	45_11_N_9_9_E
461	A*01	Italy North Pavia		0.0800	81	45_11_N_9_9_E
462	A*01	Italy North Pavia pop 2	21.1	0.1120	2,416	45_11_N_9_9_E
463	A*01	Italy North Pavia pop 3		0.1136	685	45_11_N_9_9_E
464	A*01	Italy pop 6	23.9	0.1223	184	41_53_N_12_29_E
465	A*01	Italy Rome	24.0	0.1200	100	41_54_N_12_27_E
466	A*01	Italy Sardinia pop3		0.0450	100	39_14_N_9_3_E
467	A*01	Italy South Campania Region	18.4		1,089	40_50_N_14_15_E
468	A*01	Japan Aichi	4.4	0.0220	50	35_11_N_136_54_E
469	A*01	Japan Hyogo	6.3	0.0330	32	34_0_N_135_0_E
470	A*01	Japan Hyogo Osaka and Nagano		0.0100	195	34_41_N_135_30_E
471	A*01	Japan Kyoto and Osaka		0.0090	165	35_1_N_135_46_E
472	A*01	Japan pop 11	1.6		62	35_41_N_139_46_E
473	A*01	Japan pop 2		0.0040	916	35_41_N_139_46_E
474	A*01	Jordan		0.1500	15,141	31_57_N_35_56_E
475	A*01	Macedonia		0.1259	286	41_32_N_21_42_E
476	A*01	Macedonia MBMDR - Albanian	25.8	0.1367	128	41_46_N_22_19_E
477	A*01	Macedonia MBMDR - Macedonian	24.4	0.1321	1,283	41_46_N_22_19_E
478	A*01	Macedonia MBMDR - Macedonian Muslims	25.0	0.1250	76	41_46_N_22_19_E
479	A*01	Macedonia pop 3		0.1370	172	42_0_N_22_0_E
480	A*01	Macedonia pop 4		0.1130	216	42_0_N_21_0_E
481	A*01	Madeira pop 2		0.0890	173	32_44_N_16_58_W
482	A*01	Madeira pop 3		0.1060	538	32_44_N_16_58_W
483	A*01	Malaysia Negeri Sembilan Minangkabau		0.0300	34	2_43_N_101_57_E
484	A*01	Malaysia Perak and Johor Banjar Bugis Jawa		0.0210	94	4_24_N_101_5_E
485	A*01	Malaysia Perak Rawa		0.0000	23	1_29_N_103_45_E
486	A*01	Malaysia pop 2		0.0120	62	3_8_N_101_42_E
487	A*01	Malaysia pop 3		0.0210	1,445	3_8_N_101_42_E
488	A*01	Malaysia Sarawak Iban		0.0100	51	2_18_N_111_49_E
489	A*01	Martinique		0.0700	100	14_40_N_61_0_W
490	A*01	Mexico Aguascalientes state		0.0895	95	22_1_N_102_21_W
491	A*01	Mexico Baja Californa, Mexicali		0.0650	100	32_37_N_115_27_W
492	A*01	Mexico Baja California Rural		0.0700	50	27_31_N_113_40_W
493	A*01	Mexico Baja California, La Paz		0.0667	75	24_8_N_110_18_W
494	A*01	Mexico Baja California, Tijuana		0.0800	25	32_30_N_117_2_W
495	A*01	Mexico Campeche Rural		0.0213	47	18_6_N_89_48_W
496	A*01	Mexico Campeche, Campeche city		0.1765	34	19_49_N_90_32_W
497	A*01	Mexico Chiapas Rural		0.0248	121	16_47_N_92_41_W
498	A*01	Mexico Chiapas, Tuxtla Gutierrez		0.0566	52	16_45_N_93_6_W
499	A*01	Mexico Chihuahua Chihuahua City		0.0294	119	28_37_N_106_4_W
500	A*01	Mexico Chihuahua Rural		0.0586	236	27_18_N_107_53_W
501	A*01	Mexico Chihuahua, Ciudad Juarez		0.0417	106	31_41_N_106_25_W
502	A*01	Mexico Coahuila Rural		0.0872	216	26_59_N_102_3_W
503	A*01	Mexico Coahuila, Saltillo		0.0411	72	25_26_N_100_58_W
504	A*01	Mexico Coahuila, Torreon		0.0663	396	25_32_N_103_24_W
505	A*01	Mexico Colima Rural		0.0227	43	19_30_N_103_37_W
506	A*01	Mexico Colima, Colima city		0.0656	61	19_14_N_103_43_W
507	A*01	Mexico Durango Rural		0.0979	326	26_41_N_103_44_W
508	A*01	Mexico Durango, Durango city		0.0935	153	24_1_N_104_39_W
509	A*01	Mexico Guanajuato Rural		0.0644	162	20_54_N_100_44_W
510	A*01	Mexico Guanajuato, Guanajuato city		0.0455	22	21_2_N_101_15_W
511	A*01	Mexico Guanajuato, Leon		0.0769	78	21_7_N_101_41_W
512	A*01	Mexico Guerrero state		0.0208	144	17_36_N_99_57_W
513	A*01	Mexico Hidalgo Rural		0.0432	81	20_38_N_98_59_W
514	A*01	Mexico Hidalgo, Pachuca		0.0976	41	20_6_N_98_45_W
515	A*01	Mexico Huasteca Region Teenek		0.0000	55	22_1_N_100_9_W
516	A*01	Mexico Jalisco Rural		0.0614	585	20_52_N_103_50_W
517	A*01	Mexico Jalisco, Guadalajara city		0.0687	1,189	20_39_N_103_20_W
518	A*01	Mexico Jalisco, Tlajomulco		0.0500	30	20_28_N_103_26_W
519	A*01	Mexico Jalisco, Tlaquepaque		0.0513	39	20_37_N_103_19_W
520	A*01	Mexico Jalisco, Tonala		0.0857	35	20_37_N_103_14_W
521	A*01	Mexico Jalisco, Zapopan		0.0506	168	20_40_N_103_24_W
522	A*01	Mexico Mestizo	14.6	0.0850	41	19_24_N_99_9_W
523	A*01	Mexico Mestizo pop 4		0.0613	269	19_26_N_99_8_W
524	A*01	Mexico Mexico City Center		0.0714	152	19_25_N_99_7_W
525	A*01	Mexico Mexico City East		0.0250	79	19_17_N_99_2_W
526	A*01	Mexico Mexico City Mestizo		0.0830	121	19_25_N_99_8_W
527	A*01	Mexico Mexico City Mestizo pop 3		0.0430	256	19_25_N_99_8_W
528	A*01	Mexico Mexico City Metropolitan Area Rural		0.0362	150	19_7_N_98_45_W
529	A*01	Mexico Mexico City North		0.0226	751	19_29_N_99_7_W
530	A*01	Mexico Mexico City South		0.0288	52	19_18_N_99_12_W
531	A*01	Mexico Mexico City West		0.0303	33	19_21_N_99_17_W
532	A*01	Mexico Michoacan Rural		0.0774	348	19_38_N_101_38_W
533	A*01	Mexico Michoacan, Morelia		0.0464	150	19_42_N_101_11_W
534	A*01	Mexico Morelos Rural		0.0833	30	18_57_N_98_58_W
535	A*01	Mexico Morelos, Cuernavaca		0.0549	82	18_55_N_99_13_W
536	A*01	Mexico Nayarit Rural		0.0234	64	20_45_N_105_20_W
537	A*01	Mexico Nayarit, Tepic		0.0515	97	21_30_N_104_53_W
538	A*01	Mexico Nuevo Leon Rural		0.0273	439	25_37_N_100_14_W
539	A*01	Mexico Nuevo Leon, Monterrey city		0.0509	226	25_41_N_100_18_W
540	A*01	Mexico Oaxaca Rural		0.0308	485	15_49_N_96_19_W
541	A*01	Mexico Oaxaca, Oaxaca city		0.0397	151	17_4_N_96_43_W
542	A*01	Mexico Puebla Mestizo		0.0300	99	19_3_N_98_12_W
543	A*01	Mexico Puebla Rural		0.0330	833	19_57_N_97_38_W
544	A*01	Mexico Puebla, Puebla city		0.0321	1,994	19_2_N_98_12_W
545	A*01	Mexico Queretaro Rural		0.0465	43	20_35_N_100_23_W
546	A*01	Mexico Queretaro, Queretaro city		0.0222	45	20_34_N_100_22_W
547	A*01	Mexico Quintana Roo Rural		0.0600	50	20_12_N_87_25_W
548	A*01	Mexico Quintana Roo, Cancun		0.0104	48	21_9_N_86_51_W
549	A*01	Mexico San Luis Potosi Rural		0.0345	87	21_20_N_98_58_W
550	A*01	Mexico San Luis Potosi, San Luis Potosi city		0.0667	30	22_9_N_100_59_W
551	A*01	Mexico Sinaloa Capomos Mayo Yoremes		0.0083	60	26_25_N_108_30_W
552	A*01	Mexico Sinaloa Mestizo		0.0540	56	24_48_N_107_10_W
553	A*01	Mexico Sinaloa Rural		0.0492	183	25_21_N_107_33_W
554	A*01	Mexico Sinaloa, Culiacán		0.0388	103	24_48_N_107_23_W
555	A*01	Mexico Sonora Rural		0.0431	197	32_15_N_114_30_W
556	A*01	Mexico Sonora, Ciudad Obregón		0.0944	143	27_28_N_109_55_W
557	A*01	Mexico Sonora, Hermosillo		0.0354	99	29_4_N_110_57_W
558	A*01	Mexico Tabasco Rural		0.0458	142	18_23_N_93_12_W
559	A*01	Mexico Tabasco, Villahermosa		0.0671	82	17_59_N_92_56_W
560	A*01	Mexico Tamaulipas Rural		0.0952	125	22_23_N_97_56_W
561	A*01	Mexico Tamaulipas, Ciudad Victoria		0.0652	23	23_44_N_99_8_W
562	A*01	Mexico Tapachula, Chiapas Mestizo Population	13.9	0.0694	72	14_54_N_92_15_W
563	A*01	Mexico Tlaxcala Rural		0.0349	830	19_14_N_98_20_W
564	A*01	Mexico Tlaxcala, Tlaxcala city		0.0635	181	19_18_N_98_14_W
565	A*01	Mexico Veracruz Rural		0.0518	539	18_26_N_95_12_W
566	A*01	Mexico Veracruz, Coatzacoalcos		0.0357	55	18_8_N_94_27_W
567	A*01	Mexico Veracruz, Cordoba		0.0357	56	18_53_N_96_55_W
568	A*01	Mexico Veracruz, Orizaba		0.0667	60	18_51_N_97_6_W
569	A*01	Mexico Veracruz, Poza Rica		0.0556	45	20_31_N_97_27_W
570	A*01	Mexico Veracruz, Veracruz city		0.0436	171	19_10_N_96_8_W
571	A*01	Mexico Veracruz, Xalapa		0.0856	187	19_32_N_96_54_W
572	A*01	Mexico Yucatan Rural		0.0522	132	21_16_N_89_31_W
573	A*01	Mexico Yucatan, Merida		0.0410	192	20_58_N_89_35_W
574	A*01	Mexico Zacatecas Rural		0.0688	266	22_46_N_102_7_W
575	A*01	Mexico Zacatecas, Fresnillo		0.0905	103	23_10_N_102_52_W
576	A*01	Mexico Zacatecas, Zacatecas city		0.0476	84	22_46_N_102_34_W
577	A*01	Mongolia Hoton		0.0660	85	48_40_N_88_18_E
578	A*01	Mongolia Khalkha		0.0800	200	45_0_N_107_0_E
579	A*01	Mongolia Khalkha pop 2		0.0750	202	47_55_N_106_53_E
580	A*01	Mongolia Oold		0.1440	104	48_0_N_96_0_E
581	A*01	Mongolia Tarialan Khoton		0.0660	85	49_46_N_91_54_E
582	A*01	Mongolia Tsaatan		0.1250	144	51_0_N_101_0_E
583	A*01	Mongolia Ulaanbaatar Khalkha		0.0850	41	47_55_N_106_55_E
584	A*01	Morocco		0.1480	96	33_14_N_8_30_W
585	A*01	Morocco Casablanca		0.1100	100	33_32_N_7_35_W
586	A*01	Morocco pop 2		0.1000	110	32_4_N_6_51_W
587	A*01	Mozambique		0.0240	202	25_58_S_32_35_E
588	A*01	Myanmar Bamar	13.0	0.0761	46	17_54_N_96_31_E
589	A*01	Myanmar Chin	1.8	0.0091	55	17_54_N_96_31_E
590	A*01	Myanmar Kayah	1.8	0.0091	55	17_54_N_96_31_E
591	A*01	Myanmar Mon	9.4	0.0469	64	17_54_N_96_31_E
592	A*01	Myanmar Shan	5.6	0.0278	54	17_54_N_96_31_E
593	A*01	Nigeria	2.7	0.0155	258	6_27_N_3_23_E
594	A*01	Norway ethnic Norwegians	29.3	0.1592	4,510	61_0_N_10_0_E
595	A*01	Norway pop 2		0.1520	576	59_56_N_10_45_E
596	A*01	Norway Sami		0.0650	200	70_0_N_25_0_E
597	A*01	Pakistan Burusho		0.0540	92	36_0_N_73_0_E
598	A*01	Pakistan Mixed Sindhi		0.1330	101	24_0_N_68_0_E
599	A*01	Peru Arequipa Mestizo	8.1	0.0410	168	15_46_S_73_21_W
600	A*01	Peru Lamas City Lama		0.0120	83	6_25_S_76_32_W
601	A*01	Poland pop 3	29.2	0.1458	2,907	52_12_N_21_1_E
602	A*01	Portugal Aveiro		0.1080	5,933	40_34_N_8_27_W
603	A*01	Portugal Beja		0.0910	699	37_30_N_8_3_W
604	A*01	Portugal Braga		0.1200	1,971	41_37_N_8_20_W
605	A*01	Portugal Bragança		0.0900	301	41_48_N_6_50_W
606	A*01	Portugal Castelo Branco		0.1240	327	39_50_N_7_28_W
607	A*01	Portugal Center pop 2		0.1090	562	40_11_N_8_26_W
608	A*01	Portugal Coimbra		0.1200	5,057	40_13_N_8_3_W
609	A*01	Portugal Evora		0.0860	737	38_34_N_7_54_W
610	A*01	Portugal Faro		0.0970	1,242	37_2_N_7_55_W
611	A*01	Portugal Guarda		0.1060	797	40_49_N_7_33_W
612	A*01	Portugal Leiria		0.1260	1,847	39_45_N_8_56_W
613	A*01	Portugal Lisbon		0.1070	17,420	38_42_N_9_11_W
614	A*01	Portugal North pop 2		0.1121	1,801	41_9_N_8_37_W
615	A*01	Portugal Portalegre		0.1060	1,254	39_19_N_7_25_W
616	A*01	Portugal Porto		0.1110	7,937	41_9_N_8_38_W
617	A*01	Portugal Santarem		0.1070	3,865	39_14_N_8_41_W
618	A*01	Portugal Setubal		0.1040	5,268	38_32_N_8_53_W
619	A*01	Portugal South pop 2		0.1130	1,021	38_42_N_9_11_W
620	A*01	Portugal Viana do Castelo		0.0910	706	41_42_N_8_50_W
621	A*01	Portugal Vila Real		0.1090	1,167	41_17_N_7_48_W
622	A*01	Portugal Viseu		0.1150	2,915	40_40_N_7_55_W
623	A*01	Romania	23.0	0.1220	348	46_0_N_25_0_E
624	A*01	Romania pop 2		0.1200	6,936	44_25_N_26_6_E
625	A*01	Romania pop 3	24.1	0.1288	1,469	44_25_N_26_6_E
626	A*01	Russia Arkhangelsk Pomor		0.1400	63	64_0_N_64_0_E
627	A*01	Russia Chuvash		0.0730	82	58_0_N_43_0_E
628	A*01	Russia Moscow		0.1140	2,650	55_45_N_37_37_E
629	A*01	Russia Moscow Pop 2	22.3	0.1194	2,000	55_45_N_37_37_E
630	A*01	Russia Murmansk Saomi Mixed		0.0400	70	68_58_N_33_5_E
631	A*01	Russia Nenet Mixed		0.0310	55	67_5_N_52_0_E
632	A*01	Russia North Ossetian	22.8	0.1181	127	43_2_N_44_40_E
633	A*01	Russia Northwest	22.4		1,011	58_0_N_28_0_E
634	A*01	Russia Northwest pop 3	17.0	0.0950	100	59_58_N_30_18_E
635	A*01	Russia Northwest pop 2	22.9		346	63_0_N_41_0_E
636	A*01	Russia Sakhalin Island Nivkhi		0.0090	53	51_0_N_143_0_E
637	A*01	Russia Samara Region		0.1030	2,500	53_11_N_50_7_E
638	A*01	Russia South Ural Bashkir	17.8	0.0890	146	54_32_N_60_20_E
639	A*01	Russia South Ural Russian	31.9	0.1810	207	54_32_N_60_20_E
640	A*01	Russia South Ural Tatar	22.2	0.1180	135	54_32_N_60_20_E
641	A*01	Russia Tatars		0.1098	355	61_59_N_96_41_E
642	A*01	Russia Transbaikal Territory Buryats	20.0	0.1000	150	54_16_N_118_16_E
643	A*01	Russia, South Ural, Chelyabinsk region, Nagaybaks	18.8	0.0940	112	53_37_N_59_40_E
644	A*01	Rwanda		0.1360	280	1_57_S_30_4_E
645	A*01	Saudi Arabia	16.7		18	24_42_N_46_43_E
646	A*01	Saudi Arabia pop 2		0.0680	383	24_42_N_46_43_E
647	A*01	Saudi Arabia pop 4	13.6	0.0701	499	24_38_N_46_43_E
648	A*01	Scotland Orkney	33.0	0.1700	99	58_59_N_3_11_W
649	A*01	Serbia		0.1320	386	44_28_N_20_28_E
650	A*01	Serbia pop 3	26.3	0.1426	1,992	44_49_N_20_28_E
651	A*01	Serbia Vojvodina	25.2	0.1350	400	44_13_N_20_47_E
652	A*01	Slovakia pop 2		0.1438	4,234	48_6_N_17_7_E
653	A*01	Slovenia pop 3	17.7		130	46_3_N_14_30_E
654	A*01	South Africa Natal Zulu	8.0	0.0400	100	29_0_S_31_0_E
655	A*01	South Africa Tswana	2.5	0.0120	41	25_51_S_25_38_E
656	A*01	South Korea pop 1		0.0110	324	37_0_N_126_0_E
657	A*01	South Korea pop 8		0.0190	7,096	37_34_N_126_58_E
658	A*01	Spain Andalusia		0.1140	99	37_22_N_5_59_W
659	A*01	Spain Andalusia Romani		0.2790	99	37_22_N_5_59_W
660	A*01	Spain Arratia Valley Basque		0.1920	83	43_0_N_3_0_W
661	A*01	Spain Catalonia Girona		0.0860	88	41_59_N_2_49_E
662	A*01	Spain Gipuzkoa Basque		0.1350	100	43_10_N_2_10_W
663	A*01	Spain Ibiza		0.1380	88	39_0_N_1_3_E
664	A*01	Spain Majorca		0.1060	407	39_37_N_2_59_E
665	A*01	Spain Majorca and Minorca		0.0930	90	39_37_N_2_59_E
666	A*01	Spain Majorcans Jews		0.1590	103	39_37_N_2_57_E
667	A*01	Spain Minorca		0.0600	94	39_58_N_4_5_E
668	A*01	Spain Murcia		0.0950	173	37_59_N_1_7_W
669	A*01	Spain North Cabuerniga		0.0760	95	43_0_N_4_0_W
670	A*01	Spain North Cantabria		0.1080	83	43_0_N_3_0_W
671	A*01	Spain Pas Valley		0.0380	88	43_0_N_3_0_W
672	A*01	Spain Sevilla	21.2	0.1097	278	37_22_N_5_58_W
673	A*01	Spain, Castilla y Leon, Northwest,	19.4	0.1063	1,743	41_50_N_4_23_W
674	A*01	Sudan East Rashaida	14.8		27	14_0_N_35_0_E
675	A*01	Sudan Khartoum		0.0563	98	15_32_N_32_32_E
676	A*01	Sudan South Nuba	8.7		46	12_0_N_30_45_E
677	A*01	Sweden pop 4	26.1	0.1392	966	59_0_N_18_0_E
678	A*01	Sweden Stockholm	25.7	0.1360	1,347	59_21_N_18_4_E
679	A*01	Sweden Uppsala	24.3		300	59_51_N_17_38_E
680	A*01	Taiwan Aborigine		0.0000	111	25_5_N_121_5_E
681	A*01	Taiwan Chinese immigrants from Middle China		0.0090	211	25_5_N_121_5_E
682	A*01	Taiwan Chinese immigrants from North China		0.0300	152	25_5_N_121_5_E
683	A*01	Taiwan Chinese immigrants from South China		0.0150	172	25_5_N_121_5_E
684	A*01	Taiwan Hakka pop 2		0.0020	714	25_5_N_121_5_E
685	A*01	Taiwan Minnan and Hakka		0.0030	190	24_0_N_121_0_E
686	A*01	Taiwan Minnan pop 2		0.0030	7,137	25_5_N_121_5_E
687	A*01	Thailand Northeast		0.0240	66	15_13_N_104_51_E
688	A*01	Thailand Northeast pop 2		0.0160	400	16_27_N_103_10_E
689	A*01	Thailand pop 4		0.0220	16,807	13_45_N_100_29_E
690	A*01	Trinidad African		0.0600	67	10_25_N_61_15_W
691	A*01	Trinidad South Asian		0.1100	87	10_25_N_61_15_W
692	A*01	Tunisia Ghannouch		0.1100	82	33_3_N_10_20_E
693	A*01	Tunisia pop 3		0.0770	104	36_48_N_10_11_E
694	A*01	Turkey pop 2		0.0660	228	41_0_N_28_58_E
695	A*01	Turkey pop 5		0.1090	142	40_0_N_28_0_E
696	A*01	United Arab Emirates pop 2	12.1		373	24_0_N_53_0_E
697	A*01	United Kingdom Central		0.1910	135	54_0_N_4_0_W
698	A*01	United Kingdom East		0.1718	163	54_0_N_4_0_W
699	A*01	United Kingdom North		0.1770	330	54_0_N_4_0_W
700	A*01	United Kingdom Orkney		0.1829	88	54_0_N_4_0_W
701	A*01	United Kingdom pop 2		0.2050	101	51_30_N_0_7_W
702	A*01	United Kingdom South West		0.1687	163	54_0_N_4_0_W
703	A*01	USA OPTN African American		0.0550	1,510	38_53_N_77_2_W
704	A*01	USA OPTN Asian		0.0320	261	38_53_N_77_2_W
705	A*01	USA OPTN Caucasian		0.1570	8,525	38_53_N_77_2_W
706	A*01	USA OPTN Hispanic		0.0630	1,580	38_53_N_77_2_W
707	A*01	Venezuela Caracas Mestizo		0.0700	50	10_30_N_66_55_W
708	A*01	Venezuela Colonia Tovar		0.1221	86	10_15_N_67_10_W
709	A*01	Venezuela Valencia and Maracaibo Caracus Mixed		0.0420	96	10_10_N_68_0_W
710	A*01	Venezuela Zulia Maracaibo Mixed		0.0250	20	10_38_N_71_26_W
711	A*01	Venezuela Zulia San Jose de Heras Mixed		0.0250	20	10_36_N_72_0_W
712	A*01	Vietnam Hanoi		0.0300	50	21_2_N_105_51_E
713	A*01	Wales	37.4	0.2110	1,798	51_29_N_3_11_W
714	A*01	Wales pop 2	35.5	0.1970	39,979	51_29_N_3_11_W
715	A*01:01	Argentina Rosario Toba	15.1	0.0760	86	32_57_S_60_39_W
716	A*01:01	Armenia combined Regions		0.1250	100	40_11_N_44_31_E
717	A*01:01	Australia Cape York Peninsula Aborigine		0.0530	103	10_41_S_142_32_E
718	A*01:01	Australia Groote Eylandt Aborigine		0.0270	75	13_58_S_136_35_E
719	A*01:01	Australia New South Wales Caucasian		0.1870	134	33_0_S_146_0_E
720	A*01:01	Australia Yuendumu Aborigine		0.0080	191	22_15_S_131_47_E
721	A*01:01	Austria	27.0	0.1460	200	48_13_N_16_21_E
722	A*01:01	Azores Central Islands		0.0800	59	38_0_N_28_0_W
723	A*01:01	Azores Oriental Islands		0.1150	43	36_58_N_25_6_W
724	A*01:01	Azores Terceira Island		0.1090	130	38_44_N_27_19_W
725	A*01:01	Belgium	29.2	0.1550	99	50_50_N_4_21_E
726	A*01:01	Belgium pop 3	26.8	0.1445	31,412	51_2_N_4_28_E
727	A*01:01	Brazil Puyanawa	8.7	0.0430	150	10_52_S_53_5_W
728	A*01:01	Brazil Belo Horizonte Caucasian	13.7	0.0790	95	19_55_S_43_56_W
729	A*01:01	Brazil Mixed		0.0910	108	23_10_S_47_48_W
730	A*01:01	Brazil Vale do Ribeira Quilombos	0.0	0.0000	144	24_36_S_48_15_W
731	A*01:01	Bulgaria Romani		0.3330	13	42_44_N_23_20_E
732	A*01:01	Cameroon Baka Pygmy		0.0000	10	8_30_N_14_0_E
733	A*01:01	Cameroon Bamileke		0.0260	77	5_28_N_10_25_E
734	A*01:01	Cameroon Beti		0.0110	174	2_55_N_11_9_E
735	A*01:01	Cameroon Sawa		0.0000	13	4_50_N_10_0_E
736	A*01:01	Cameroon Yaounde		0.0110	92	3_52_N_11_31_E
737	A*01:01	Central African Republic Mbenzele Pygmy	0.0		36	3_32_N_16_4_E
738	A*01:01	Chile Mapuche		0.0615	66	37_48_S_73_23_W
739	A*01:01	Chile Santiago Mixed	11.0		70	33_26_S_70_39_W
740	A*01:01	China Beijing		0.0370	67	39_58_N_116_24_E
741	A*01:01	China Beijing Shijiazhuang Tianjian Han		0.0340	618	39_54_N_116_24_E
742	A*01:01	China Canton Han		0.0060	264	23_6_N_113_15_E
743	A*01:01	China Guangzhou		0.0100	102	23_6_N_113_15_E
744	A*01:01	China Guizhou Province Bouyei		0.0050	109	26_50_N_106_50_E
745	A*01:01	China Guizhou Province Miao pop 2		0.0000	85	26_50_N_106_50_E
746	A*01:01	China Guizhou Province Shui		0.0000	153	26_50_N_106_50_E
747	A*01:01	China Hubei Han	4.6	0.0229	3,732	30_45_N_114_15_E
748	A*01:01	China Inner Mongolia Region		0.0540	102	36_0_N_102_0_E
749	A*01:01	China Jiangsu Han		0.0370	3,238	32_54_N_119_48_E
750	A*01:01	China Jiangsu Province Han		0.0174	334	32_54_N_119_48_E
751	A*01:01	China Marrow Donor Registry		0.0200	600	39_54_N_116_23_E
752	A*01:01	China North Han		0.0000	105	39_54_N_116_24_E
753	A*01:01	China Qinghai Province Hui		0.0550	110	36_0_N_102_0_E
754	A*01:01	China Shanxi HIV negative		0.0910	22	36_5_N_111_50_E
755	A*01:01	China Sichuan HIV negative		0.0590	34	30_50_N_102_49_E
756	A*01:01	China South Han		0.0050	284	23_1_N_113_3_E
757	A*01:01	China Uyghur HIV negative		0.0260	19	36_34_N_103_49_E
758	A*01:01	China Yunnan Province Han		0.0150	101	24_2_N_105_3_E
759	A*01:01	Colombia Bogotá Cord Blood	12.0	0.0608	1,463	4_39_N_73_52_W
760	A*01:01	Colombia North Chimila Amerindians		0.0212	47	10_1_N_74_13_W
761	A*01:01	Colombia North Wiwa El Encanto		0.0192	52	10_10_N_73_53_W
762	A*01:01	Croatia		0.0970	150	45_0_N_15_0_E
763	A*01:01	Croatia pop 4		0.1244	4,000	45_48_N_15_57_E
764	A*01:01	Cuba Caucasian	15.7	0.0790	70	23_8_N_82_22_W
765	A*01:01	Cuba Mixed Race	14.3	0.0710	42	23_8_N_82_22_W
766	A*01:01	Czech Republic		0.1270	106	50_0_N_14_0_E
767	A*01:01	Czech Republic NMDR		0.1607	5,099	49_48_N_15_30_E
768	A*01:01	Ecuador Amerindians		0.0476	63	0_13_S_78_30_W
769	A*01:01	England North West	38.6	0.2080	298	53_0_N_3_0_W
770	A*01:01	Finland		0.0890	91	63_29_N_25_16_E
771	A*01:01	France French Bone Marrow Donor Registry		0.1301	42,623	48_51_N_2_21_E
772	A*01:01	France Southeast	27.7	0.1500	130	46_0_N_5_0_E
773	A*01:01	Gaza	33.3	0.1786	42	31_22_N_34_18_E
774	A*01:01	Georgia Svaneti Region Svan		0.0560	80	42_54_N_43_0_E
775	A*01:01	Georgia Tibilisi		0.0570	109	41_41_N_44_57_E
776	A*01:01	Georgia Tibilisi Kurd		0.0670	31	41_41_N_44_57_E
777	A*01:01	Germany DKMS - Austria minority		0.1561	1,698	48_31_N_9_3_E
778	A*01:01	Germany DKMS - Bosnia and Herzegovina minority		0.1513	1,028	48_31_N_9_3_E
779	A*01:01	Germany DKMS - China minority		0.0380	1,282	48_31_N_9_3_E
780	A*01:01	Germany DKMS - Croatia minority		0.1213	2,057	48_31_N_9_3_E
781	A*01:01	Germany DKMS - France minority		0.1376	1,406	48_31_N_9_3_E
782	A*01:01	Germany DKMS - German donors		0.1537	3,456,066	48_31_N_9_3_E
783	A*01:01	Germany DKMS - Greece minority		0.1038	1,894	48_31_N_9_3_E
784	A*01:01	Germany DKMS - Italy minority		0.1330	1,159	48_31_N_9_3_E
785	A*01:01	Germany DKMS - Netherlands minority		0.1681	1,374	48_31_N_9_3_E
786	A*01:01	Germany DKMS - Portugal minority		0.1158	1,176	48_31_N_9_3_E
787	A*01:01	Germany DKMS - Romania minority		0.1457	1,234	48_31_N_9_3_E
788	A*01:01	Germany DKMS - Spain minority		0.1189	1,107	48_31_N_9_3_E
789	A*01:01	Germany DKMS - Turkey minority		0.1032	4,856	48_31_N_9_3_E
790	A*01:01	Germany DKMS - United Kingdom minority		0.1803	1,043	48_31_N_9_3_E
791	A*01:01	Germany pop 6		0.1541	8,862	51_6_N_10_23_E
792	A*01:01	Germany pop 8		0.1507	39,689	48_24_N_9_58_E
793	A*01:01	Ghana Ga-Adangbe	4.6	0.0229	131	5_45_N_0_12_E
794	A*01:01	Greece pop 6		0.1136	242	37_58_N_23_43_E
795	A*01:01	Greece pop 8	25.3	0.1446	83	37_58_N_23_43_E
796	A*01:01	Guinea Bissau Balanta		0.0210	48	11_52_N_15_36_W
797	A*01:01	Guinea Bissau Bijago		0.0220	23	11_52_N_15_36_W
798	A*01:01	Guinea Bissau Fula		0.0810	31	11_52_N_15_36_W
799	A*01:01	Guinea Bissau Papel		0.0800	25	11_52_N_15_36_W
800	A*01:01	Hong Kong Chinese	2.1	0.0110	569	22_16_N_114_9_E
801	A*01:01	Hong Kong Chinese BMDR		0.0084	7,595	22_15_N_114_10_E
802	A*01:01	Hong Kong Chinese cord blood registry		0.0090	3,892	22_15_N_114_10_E
803	A*01:01	India Andhra Pradesh Golla		0.1250	111	17_5_N_78_5_E
804	A*01:01	India Central UCBB	25.9	0.1294	4,204	27_1_N_81_19_E
805	A*01:01	India Delhi pop 2	20.0	0.1050	90	28_36_N_77_13_E
806	A*01:01	India East UCBB	23.1	0.1267	2,403	22_25_N_88_18_E
807	A*01:01	India Khandesh Region Pawra		0.0500	50	20_54_N_74_46_E
808	A*01:01	India Mumbai Maratha		0.1230	91	18_58_N_72_49_E
809	A*01:01	India New Delhi		0.0380	71	28_37_N_77_13_E
810	A*01:01	India North pop 2		0.1150	72	25_0_N_74_0_E
811	A*01:01	India North UCBB	22.7	0.1234	5,849	28_36_N_77_11_E
812	A*01:01	India Northeast UCBB	11.1	0.0557	296	26_16_N_91_48_E
813	A*01:01	India South UCBB	29.7	0.1618	11,446	13_6_N_80_8_E
814	A*01:01	India Tamil Nadu		0.1593	2,492	11_32_N_78_52_E
815	A*01:01	India Tamil Nadu Nadar		0.0900	61	8_0_N_77_0_E
816	A*01:01	India West Bhil		0.0000	50	20_54_N_74_46_E
817	A*01:01	India West Coast Parsi		0.0100	50	18_0_N_74_0_E
818	A*01:01	India West UCBB	26.8	0.1466	5,829	19_11_N_73_3_E
819	A*01:01	Indonesia Java Western	5.1		236	7_30_S_111_15_E
820	A*01:01	Iran Gorgan		0.0310	64	36_50_N_54_43_E
821	A*01:01	Iran Kurd pop 2		0.0830	60	36_17_N_46_17_E
822	A*01:01	Iran Saqqez-Baneh Kurds		0.0833	60	36_14_N_46_16_E
823	A*01:01	Iran Tabriz Azeris		0.1186	97	38_4_N_46_17_E
824	A*01:01	Iran Yazd		0.0893	56	31_53_N_54_21_E
825	A*01:01	Ireland Northern	36.4	0.2020	1,000	54_40_N_6_45_W
826	A*01:01	Ireland South	44.8	0.2500	250	53_20_N_6_15_W
827	A*01:01	Israel Arab Druze		0.0400	101	31_8_N_35_2_E
828	A*01:01	Israel Arab pop 2		0.1642	12,301	32_8_N_34_52_E
829	A*01:01	Israel Argentina Jews		0.1296	4,307	32_8_N_34_52_E
830	A*01:01	Israel Ashkenazi and Non Ashkenazi Jews		0.1710	146	31_8_N_35_2_E
831	A*01:01	Israel Ashkenazi Jews pop 3		0.1078	4,625	32_8_N_34_52_E
832	A*01:01	Israel Bukhara Jews		0.0689	2,317	32_8_N_34_52_E
833	A*01:01	Israel Druze		0.0886	5,914	32_41_N_35_17_E
834	A*01:01	Israel Ethiopia Jews		0.0391	5,928	32_8_N_34_52_E
835	A*01:01	Israel Georgia Jews		0.0408	4,471	32_10_N_34_53_E
836	A*01:01	Israel Iran Jews		0.2180	8,153	32_8_N_34_52_E
837	A*01:01	Israel Iraq Jews		0.1829	13,270	32_10_N_34_52_E
838	A*01:01	Israel Kavkazi Jews		0.0635	2,840	32_10_N_34_52_E
839	A*01:01	Israel Libya Jews		0.1462	3,739	32_10_N_34_52_E
840	A*01:01	Israel Morocco Jews		0.1971	36,718	32_10_N_34_52_E
841	A*01:01	Israel Poland Jews		0.1294	13,871	32_10_N_34_52_E
842	A*01:01	Israel Tunisia Jews		0.1712	9,070	32_10_N_34_52_E
843	A*01:01	Israel USA Jews		0.1313	6,058	32_10_N_34_52_E
844	A*01:01	Israel USSR Jews		0.1205	45,681	32_9_N_34_50_E
845	A*01:01	Israel YemenJews		0.0199	15,542	32_9_N_34_50_E
846	A*01:01	Italy North pop 3	26.9	0.1540	97	43_0_N_12_0_E
847	A*01:01	Italy pop 5		0.1020	975	45_28_N_9_11_E
848	A*01:01	Japan Central		0.0090	371	35_41_N_139_46_E
849	A*01:01	Japan Hokkaido Ainu		0.0000	50	43_0_N_142_0_E
850	A*01:01	Japan pop 16		0.0040	18,604	35_41_N_139_46_E
851	A*01:01	Japan pop 3		0.0020	1,018	35_41_N_139_46_E
852	A*01:01	Japan pop 5		0.0180	117	35_41_N_139_46_E
853	A*01:01	Jordan Amman		0.0790	146	31_35_N_36_20_E
854	A*01:01	Kenya		0.0310	144	1_28_S_36_8_E
855	A*01:01	Kenya Luo		0.0740	265	0_0_S_34_6_E
856	A*01:01	Kenya Nandi		0.1180	240	0_0_S_35_0_E
857	A*01:01	Kenya, Nyanza Province, Luo tribe	9.0	0.0450	100	0_5_S_34_46_E
858	A*01:01	Kosovo	21.0	0.1048	124	42_34_N_21_0_E
859	A*01:01	Malaysia Champa	13.8	0.0690	29	2_30_N_112_30_E
860	A*01:01	Malaysia Kelantan	3.6	0.0180	28	2_30_N_112_30_E
861	A*01:01	Malaysia Mandailing	7.4	0.0370	27	2_30_N_112_30_E
862	A*01:01	Malaysia Patani	4.0	0.0200	25	2_30_N_112_30_E
863	A*01:01	Malaysia Peninsular Chinese	1.0	0.0052	194	3_9_N_101_57_E
864	A*01:01	Malaysia Peninsular Indian	28.8	0.1642	271	3_9_N_101_57_E
865	A*01:01	Malaysia Peninsular Malay	4.5	0.0231	951	3_9_N_101_57_E
866	A*01:01	Mali Bandiagara		0.0070	138	14_4_N_3_6_W
867	A*01:01	Mexico Chiapas Lacandon Mayans		0.0023	218	16_54_N_92_5_W
868	A*01:01	Mexico Chihuahua Chihuahua City Pop 2	18.2	0.0909	88	28_48_N_106_26_W
869	A*01:01	Mexico Mestizo	14.6	0.0850	41	19_24_N_99_9_W
870	A*01:01	Mexico Mexico City Mestizo pop 2		0.0363	234	19_26_N_99_8_W
871	A*01:01	Mexico Mexico City Mestizo population		0.0874	143	19_25_N_99_7_W
872	A*01:01	Mexico Mexico City Tlalpan	12.1	0.0606	330	19_25_N_99_8_W
873	A*01:01	Mexico Mixtec		0.0100	97	23_57_N_103_29_W
874	A*01:01	Mexico Oaxaca Jamiltepec Mixtec		0.0100	96	16_25_N_97_58_W
875	A*01:01	Mexico Oaxaca Mixe		0.0190	55	17_5_N_96_45_W
876	A*01:01	Mexico Oaxaca Mixtec		0.0000	103	17_5_N_96_45_W
877	A*01:01	Mexico Oaxaca Zapotec		0.0220	90	17_5_N_96_45_W
878	A*01:01	Mexico Veracruz Xalapa	3.6	0.0179	84	19_32_N_96_55_W
879	A*01:01	Netherlands Leiden		0.1750	1,305	52_9_N_4_31_E
880	A*01:01	Netherlands UMCU	37.5	0.1953	64	52_5_N_5_10_E
881	A*01:01	New Zealand Maori with Admixed History	12.4	0.0620	105	42_0_S_174_0_E
882	A*01:01	New Zealand Maori with Full Ancestry	8.7	0.0440	46	42_0_S_174_0_E
883	A*01:01	New Zealand Polynesians with Admixed History	11.1	0.0560	27	42_0_S_174_0_E
884	A*01:01	New Zealand Polynesians with Full Ancestry	9.5	0.0480	21	42_0_S_174_0_E
885	A*01:01	Nicaragua Managua	7.2	0.0374	339	12_7_N_86_10_W
886	A*01:01	Oman	14.4	0.0720	118	22_0_N_56_0_E
887	A*01:01	Pakistan Baloch		0.0300	66	30_0_N_67_0_E
888	A*01:01	Pakistan Brahui		0.0630	104	30_0_N_67_0_E
889	A*01:01	Pakistan Burusho		0.0540	92	36_0_N_73_0_E
890	A*01:01	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
891	A*01:01	Pakistan Karachi Parsi		0.0830	91	24_49_N_66_59_E
892	A*01:01	Pakistan Mixed Pathan		0.1280	100	32_0_N_70_0_E
893	A*01:01	Pakistan Mixed Sindhi		0.1330	101	24_0_N_68_0_E
894	A*01:01	Panama		0.0396	462	8_59_N_79_37_W
895	A*01:01	Peru Titikaka Lake Uro		0.0050	105	15_50_S_69_42_W
896	A*01:01	Peru Titikaka Lake Uros		0.0050	105	15_50_S_70_1_W
897	A*01:01	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
898	A*01:01	Poland		0.1330	200	51_7_N_17_2_E
899	A*01:01	Poland DKMS		0.1377	20,653	52_8_N_19_23_E
900	A*01:01	Portugal Azores Terceira Island	21.9	0.1096	130	38_43_N_27_13_W
901	A*01:01	Portugal Center		0.1300	50	39_0_N_8_0_W
902	A*01:01	Portugal North		0.0760	46	41_0_N_8_0_W
903	A*01:01	Portugal South		0.0510	49	37_0_N_8_0_W
904	A*01:01	Romania	23.0	0.1220	348	46_0_N_25_0_E
905	A*01:01	Russia Bering Island Aleuts		0.0962	104	55_0_N_166_15_E
906	A*01:01	Russia Karelia		0.1014	1,075	63_9_N_32_59_E
907	A*01:01	Russia Tuva pop 2		0.0660	169	51_30_N_95_5_E
908	A*01:01	Sao Tome Island Angolar		0.0470	32	0_20_N_6_44_E
909	A*01:01	Sao Tome Island Forro		0.0300	66	0_20_N_6_44_E
910	A*01:01	Saudi Arabia Guraiat and Hail	21.6	0.1150	213	27_31_N_41_41_E
911	A*01:01	Senegal Niokholo Mandenka		0.0270	165	12_35_N_12_9_W
912	A*01:01	Singapore Chinese	0.7	0.0030	149	1_24_N_103_45_E
913	A*01:01	Singapore Chinese Han		0.0120	94	1_17_N_103_51_E
914	A*01:01	Singapore Javaneses		0.0700	51	1_24_N_103_45_E
915	A*01:01	Singapore Riau Malay		0.0320	132	1_24_N_103_45_E
916	A*01:01	Singapore SGVP Chinese CHS		0.0160	96	1_21_N_103_50_E
917	A*01:01	Singapore SGVP Malay MAS		0.0420	89	1_21_N_103_50_E
918	A*01:01	Singapore SGVP. Indian INS		0.1550	86	1_21_N_103_50_E
919	A*01:01	Singapore Thai		0.0050	100	1_24_N_103_45_E
920	A*01:01	South Africa Cape Town Black	8.3	0.0476	84	34_7_S_18_22_E
921	A*01:01	South Africa Durban Black	9.1	0.0455	22	29_51_S_31_0_E
922	A*01:01	South Africa Johannesburg Black	5.0	0.0252	120	26_11_S_28_2_E
923	A*01:01	South Africa Natal Tamil		0.1700	51	30_0_S_31_0_E
924	A*01:01	South Africa Natal Zulu	8.0	0.0400	100	29_0_S_31_0_E
925	A*01:01	South Africa Worcester	13.0	0.0660	159	33_39_S_19_27_E
926	A*01:01	South Korea pop 10		0.0195	4,128	37_33_N_126_58_E
927	A*01:01	South Korea pop 3		0.0180	485	37_33_N_126_59_E
928	A*01:01	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	18.8	0.0985	4,335	41_22_N_2_11_E
929	A*01:01	Sri Lanka Colombo	21.1	0.1130	714	6_56_N_79_50_E
930	A*01:01	Sri Lanka Colombo Sinhalese		0.1090	101	6_56_N_79_50_E
931	A*01:01	Sudan Central Shaigiya Mixed	27.8		36	18_0_N_33_0_E
932	A*01:01	Sudan Mixed	10.0	0.0500	200	5_0_N_35_0_E
933	A*01:01	Sweden Northern Sami		0.0830	154	67_0_N_18_30_E
934	A*01:01	Sweden Southern Sami		0.0930	130	60_52_N_14_44_E
935	A*01:01	Switzerland Aargau-Solothurn		0.1165	1,838	46_48_N_8_13_E
936	A*01:01	Switzerland Basel		0.1204	1,888	47_33_N_7_34_E
937	A*01:01	Switzerland Bern		0.1278	3,545	46_57_N_7_27_E
938	A*01:01	Switzerland Geneva pop 2		0.1048	1,267	46_12_N_6_8_E
939	A*01:01	Switzerland Graubunden		0.0721	759	46_42_N_9_57_E
940	A*01:01	Switzerland Lausanne		0.1325	993	46_31_N_6_39_E
941	A*01:01	Switzerland Lugano		0.0744	1,169	46_0_N_8_55_E
942	A*01:01	Switzerland Luzern		0.0907	1,553	47_3_N_8_18_E
943	A*01:01	Switzerland Sion		0.1146	832	46_15_N_7_23_E
944	A*01:01	Switzerland St Gallen		0.1243	2,113	47_26_N_9_21_E
945	A*01:01	Switzerland Zurich		0.1002	4,875	47_22_N_8_32_E
946	A*01:01	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
947	A*01:01	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
948	A*01:01	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
949	A*01:01	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
950	A*01:01	Taiwan Han Chinese		0.0040	504	23_46_N_121_0_E
951	A*01:01	Taiwan Minnan pop 1	1.0	0.0050	102	24_0_N_121_0_E
952	A*01:01	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
953	A*01:01	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
954	A*01:01	Taiwan pop 2		0.0060	364	23_46_N_121_0_E
955	A*01:01	Taiwan pop 3		0.0120	212	23_46_N_121_0_E
956	A*01:01	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
957	A*01:01	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
958	A*01:01	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
959	A*01:01	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
960	A*01:01	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
961	A*01:01	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
962	A*01:01	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
963	A*01:01	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
964	A*01:01	Taiwan Tzu Chi Morrow Donor Registry		0.0069	46,682	25_2_N_121_38_E
965	A*01:01	Taiwan Tzu Chi Morrow Donor Registry Aborigine		0.0064	233	25_2_N_121_38_E
966	A*01:01	Taiwan Tzu Chi Cord Blood Bank		0.0090	710	23_46_N_121_0_E
967	A*01:01	Tanzania Maasai	17.9	0.0955	336	3_11_S_37_4_E
968	A*01:01	Thailand		0.0280	142	16_7_N_102_0_E
969	A*01:01	Tunisia	21.0	0.1120	100	35_0_N_9_0_E
970	A*01:01	Tunisia Gabes		0.1105	95	33_53_N_10_6_E
971	A*01:01	Uganda Kampala		0.0830	161	0_3_N_32_6_E
972	A*01:01	Uganda Kampala pop 2		0.0660	175	0_20_N_32_30_E
973	A*01:01	United Arab Emirates Abu Dhabi		0.0673	52	24_28_N_54_22_E
974	A*01:01	United Arab Emirates Pop 1		0.0750	570	24_42_N_54_82_E
975	A*01:01	USA African American		0.0560	252	30_0_N_95_0_W
976	A*01:01	USA African American Bethesda	14.8	0.0740	187	38_0_N_78_0_W
977	A*01:01	USA African American pop 2		0.0540	149	32_46_N_96_46_W
978	A*01:01	USA African American pop 4		0.0474	2,411	44_58_N_93_15_W
979	A*01:01	USA Alaska Yupik		0.0040	252	60_48_N_161_26_W
980	A*01:01	USA Arizona Gila River Amerindian		0.0040	492	33_4_N_111_44_W
981	A*01:01	USA Arizona Pima		0.0060	100	33_0_N_113_0_W
982	A*01:01	USA Asian		0.0150	358	30_0_N_95_0_W
983	A*01:01	USA Asian pop 2		0.0508	1,772	44_58_N_93_15_W
984	A*01:01	USA Caucasian Bethesda	28.7	0.1650	307	38_0_N_78_0_W
985	A*01:01	USA Caucasian pop 2		0.1510	265	30_0_N_95_0_W
986	A*01:01	USA Caucasian pop 3		0.1360	88	32_46_N_96_48_W
987	A*01:01	USA Caucasian pop 4		0.1682	1,070	38_53_N_77_2_W
988	A*01:01	USA Hawaii Okinawa		0.0050	106	21_0_N_157_0_W
989	A*01:01	USA Hispanic		0.0600	234	30_0_N_95_0_W
990	A*01:01	USA Hispanic pop 2		0.0670	1,999	44_58_N_93_15_W
991	A*01:01	USA NMDP African		0.0506	28,557	38_45_N_76_6_W
992	A*01:01	USA NMDP African American pop 2		0.0467	416,581	38_45_N_76_6_W
993	A*01:01	USA NMDP Alaska Native or Aleut		0.1050	1,376	38_45_N_76_6_W
994	A*01:01	USA NMDP American Indian South or Central America		0.1190	5,926	38_45_N_76_6_W
995	A*01:01	USA NMDP Caribean Black		0.0449	33,328	38_45_N_76_6_W
996	A*01:01	USA NMDP Caribean Hispanic		0.0666	115,374	38_45_N_76_6_W
997	A*01:01	USA NMDP Caribean Indian		0.0630	14,339	38_45_N_76_6_W
998	A*01:01	USA NMDP Chinese		0.0145	99,672	38_45_N_76_6_W
999	A*01:01	USA NMDP European Caucasian		0.1646	1,242,890	38_45_N_76_6_W
1,000	A*01:01	USA NMDP Filipino		0.0125	50,614	38_45_N_76_6_W
1,001	A*01:01	USA NMDP Hawaiian or other Pacific Islander		0.0447	11,499	38_45_N_76_6_W
1,002	A*01:01	USA NMDP Hispanic South or Central American		0.0726	146,714	38_45_N_76_6_W
1,003	A*01:01	USA NMDP Japanese		0.0100	24,582	38_45_N_76_6_W
1,004	A*01:01	USA NMDP Korean		0.0208	77,584	38_45_N_76_6_W
1,005	A*01:01	USA NMDP Mexican or Chicano		0.0735	261,235	38_45_N_76_6_W
1,006	A*01:01	USA NMDP Middle Eastern or North Coast of Africa		0.1350	70,890	38_45_N_76_6_W
1,007	A*01:01	USA NMDP North American Amerindian		0.1202	35,791	38_45_N_76_6_W
1,008	A*01:01	USA NMDP South Asian Indian		0.1545	185,391	38_45_N_76_6_W
1,009	A*01:01	USA NMDP Southeast Asian		0.1147	27,978	38_45_N_76_6_W
1,010	A*01:01	USA NMDP Vietnamese		0.0332	43,540	38_45_N_76_6_W
1,011	A*01:01	USA North American Native		0.0750	187	30_0_N_95_0_W
1,012	A*01:01	USA Philadelphia Caucasian	21.5	0.1190	141	39_56_N_75_10_W
1,013	A*01:01	USA San Antonio Caucasian		0.1506	222	29_23_N_98_33_W
1,014	A*01:01	USA San Diego	20.0	0.1060	496	32_50_N_117_15_W
1,015	A*01:01	USA San Francisco Caucasian		0.1600	220	37_46_N_122_25_W
1,016	A*01:01	USA South Dakota Lakota Sioux		0.0120	302	44_30_N_100_0_W
1,017	A*01:01	USA South Texas Hispanic		0.0600	194	29_0_N_98_0_W
1,018	A*01:01	Venezuela Perja Mountain Bari		0.0110	55	9_49_N_72_49_W
1,019	A*01:01	Vietnam Hanoi Kinh pop 2		0.0380	170	21_2_N_105_51_E
1,020	A*01:01	Zambia Lusaka		0.0350	44	15_25_S_28_17_E
1,021	A*01:01	Zimbabwe Harare Shona		0.0040	230	17_51_S_31_1_E
1,022	A*01:01:01	Brazil Barra Mansa Rio State Black		0.0822	73	22_32_S_44_10_W
1,023	A*01:01:01	Brazil Barra Mansa Rio State Caucasian		0.1173	405	22_32_S_44_10_W
1,024	A*01:01:01	Brazil Rio de Janeiro Black		0.0515	68	22_54_S_43_12_W
1,025	A*01:01:01	Brazil Rio de Janeiro Caucasian		0.0988	521	22_54_S_43_12_W
1,026	A*01:01:01	Brazil Rio de Janeiro Parda		0.0794	170	22_54_S_43_12_W
1,027	A*01:01:01	Bulgaria		0.0730	55	42_0_N_23_0_E
1,028	A*01:01:01	Cape Verde Northwestern Islands		0.0480	62	16_0_N_24_0_W
1,029	A*01:01:01	Cape Verde Southeastern Islands		0.0480	62	16_0_N_24_0_W
1,030	A*01:01:01	China Jingpo Minority	2.9	0.0145	105	25_0_N_102_0_E
1,031	A*01:01:01	China North Han		0.0710	105	39_54_N_116_24_E
1,032	A*01:01:01	China Tibet Region Tibetan		0.0220	158	29_39_N_91_7_E
1,033	A*01:01:01	China Zhejiang Han		0.0153	1,734	27_2_N_118_1_E
1,034	A*01:01:01	Costa Rica African -Caribbean (G)	8.8	0.0440	102	9_59_N_83_1_W
1,035	A*01:01:01	Costa Rica Amerindians (G)	4.8	0.0240	125	9_34_N_83_25_W
1,036	A*01:01:01	Costa Rica Central Valley Mestizo (G)	15.3	0.0790	221	9_55_N_84_5_W
1,037	A*01:01:01	Costa Rica Guanacaste Mestizo (G)	9.2	0.0460	110	10_16_N_85_37_W
1,038	A*01:01:01	England Blood Donors of Mixed Ethnicity	37.4	0.2062	519	52_21_N_1_10_W
1,039	A*01:01:01	Guinea Bissau		0.0460	65	12_0_N_15_0_W
1,040	A*01:01:01	Hong Kong Chinese HKBMDR HLA 11 loci	2.0	0.0103	5,266	22_25_N_114_17_E
1,041	A*01:01:01	India Andhra Pradesh Telugu Speaking	28.5	0.1479	186	13_4_N_80_14_E
1,042	A*01:01:01	India Karnataka Kannada Speaking	30.5	0.1580	174	12_57_N_77_36_E
1,043	A*01:01:01	India Kerala Malayalam speaking	16.3	0.0857	356	13_1_N_80_15_E
1,044	A*01:01:01	Iran Baloch		0.0170	100	29_29_N_60_52_E
1,045	A*01:01:01	Japan Okinawa Ryukyuan		0.0040	143	26_4_N_127_8_E
1,046	A*01:01:01	Latvia		0.0985	266	56_50_N_24_45_E
1,047	A*01:01:01	Madeira		0.0590	185	32_44_N_16_58_W
1,048	A*01:01:01	Mexico Guadalajara Mestizo pop 2		0.0680	103	20_40_N_103_21_W
1,049	A*01:01:01	Mexico Hidalgo Mezquital Valley/ Otomi	8.3	0.0556	72	20_28_N_99_13_W
1,050	A*01:01:01	Mongolia Buryat		0.1140	141	47_6_N_118_6_E
1,051	A*01:01:01	Morocco Atlantic Coast Chaouya		0.1280	98	30_22_N_7_22_W
1,052	A*01:01:01	Morocco Nador Metalsa pop 2		0.1370	73	35_10_N_2_56_W
1,053	A*01:01:01	Morocco Settat Chaouya	25.6	0.1280	98	33_0_N_7_36_W
1,054	A*01:01:01	Nicaragua Mestizo (G)	5.8	0.0290	155	12_40_N_85_13_W
1,055	A*01:01:01	Poland BMR	25.6	0.1372	23,595	52_0_N_19_22_E
1,056	A*01:01:01	Russia Belgorod region	19.0	0.0948	153	50_46_N_37_27_E
1,057	A*01:01:01	Russia Bashkortostan, Bashkirs	0.8	0.0083	120	54_0_N_55_0_E
1,058	A*01:01:01	Russia Bashkortostan, Tatars	0.5	0.0026	192	54_13_N_56_9_E
1,059	A*01:01:01	Russia Moscow Bashkir	21.0	0.1145	442	55_46_N_37_41_E
1,060	A*01:01:01	Russia Moscow Buryat	22.8	0.1242	149	55_46_N_37_41_E
1,061	A*01:01:01	Russia Moscow Kabardin	18.8	0.0996	834	55_46_N_37_41_E
1,062	A*01:01:01	Russia Moscow Kazakh	21.7	0.1120	299	55_46_N_37_41_E
1,063	A*01:01:01	Russia Moscow Mansi	15.8	0.0789	38	55_46_N_37_41_E
1,064	A*01:01:01	Russia Moscow Megrel	14.3	0.0789	230	55_46_N_37_41_E
1,065	A*01:01:01	Russia Moscow Russians	19.4	0.1030	13,147	55_46_N_37_41_E
1,066	A*01:01:01	Russia Moscow Similar genetically to Azerbaijan	14.1	0.0735	170	55_46_N_37_41_E
1,067	A*01:01:01	Russia Moscow Tatars	19.9	0.1041	2,512	55_46_N_37_41_E
1,068	A*01:01:01	Russia Moscow Tuvian	19.6	0.0982	112	55_46_N_37_41_E
1,069	A*01:01:01	Russia Moscow Uzbek	22.2	0.1187	198	55_46_N_37_41_E
1,070	A*01:01:01	Russia Moscow Yakut	30.6	0.1787	235	55_46_N_37_41_E
1,071	A*01:01:01	Russia Nizhny Novgorod, Russians	1.0	0.0050	1,510	56_19_N_43_59_E
1,072	A*01:01:01	Russian Federation Vologda Region	20.2	0.1050	119	59_13_N_39_54_E
1,073	A*01:01:01	Saudi Arabia Al-Baha (G)		0.0698	86	20_0_N_41_28_E
1,074	A*01:01:01	Saudi Arabia Al-Madinah (G)		0.0769	102	24_28_N_39_36_E
1,075	A*01:01:01	Saudi Arabia Jeddah (G)		0.0945	476	21_32_N_39_10_E
1,076	A*01:01:01	Saudi Arabia Makkah (G)		0.0930	344	21_25_N_39_49_E
1,077	A*01:01:01	Saudi Arabia pop 6 (G)		0.0712	28,927	23_52_N_45_4_E
1,078	A*01:01:01	Saudi Arabia Taif (G)		0.0577	104	21_16_N_40_24_E
1,079	A*01:01:01	South Africa Caucasians		0.2000	102	30_0_S_25_0_E
1,080	A*01:01:01	South African Black	5.6	0.0282	142	29_9_S_24_39_E
1,081	A*01:01:01	South African Indian population	24.0	0.1300	50	29_22_S_24_31_E
1,082	A*01:01:01	South African Mixed ancestry	24.0	0.1200	50	29_22_S_24_31_E
1,083	A*01:01:01	South Korea pop 3		0.0000	485	37_33_N_126_59_E
1,084	A*01:01:01	Spain, Canary Islands, Gran canaria island	20.0	0.1047	215	27_57_N_15_36_W
1,085	A*01:01:01	USA African American pop 3		0.0440	564	38_53_N_77_2_W
1,086	A*01:01:01	USA Arizona Gila River Pima		0.0009	3,000	33_4_N_111_43_W
1,087	A*01:01:01	USA Eastern European		0.1420	558	38_53_N_77_2_W
1,088	A*01:01:01	USA San Francisco Caucasian		0.1600	220	37_46_N_122_25_W
1,089	A*01:01:01	Vietnam Kinh	3.0	0.0149	101	14_5_N_108_27_E
1,090	A*01:01:01:01	Bolivia/Chile Aymara NA-DHS_13 (G)	5.0	0.0250	20	22_0_S_70_0_W
1,091	A*01:01:01:01	Canada Chipewyan NA-DHS_2 (G)	8.0	0.0400	25	59_36_N_107_20_W
1,092	A*01:01:01:01	Canada Cree NA-DHS_3 (G)	11.1	0.0556	18	50_20_N_102_30_W
1,093	A*01:01:01:01	Canada Ojibwa NA-DHS_4 (G)	20.0	0.1000	16	46_0_N_81_0_W
1,094	A*01:01:01:01	Chile Huilliche NA-DHS_14 (G)	10.0	0.0500	20	41_0_S_73_0_W
1,095	A*01:01:01:01	China Guangdong Province Meizhou Han		0.0150	100	24_27_N_116_7_E
1,096	A*01:01:01:01	Colombia Inga NA-DHS_11 (G)	6.7	0.0333	16	1_0_N_77_0_W
1,097	A*01:01:01:01	Colombia Wayuu NA-DHS_15 (G)	6.7	0.0333	15	11_0_N_73_0_W
1,098	A*01:01:01:01	Indonesia Java pop 2		0.0280	36	6_8_S_106_10_E
1,099	A*01:01:01:01	Indonesia Sundanese and Javanese		0.0250	201	6_8_S_106_10_E
1,100	A*01:01:01:01	Libya Cyrenaica		0.1140	118	32_7_N_20_4_E
1,101	A*01:01:01:01	Mexico Mixe NA-DHS_6 (G)	5.0	0.0250	20	17_0_N_96_0_W
1,102	A*01:01:01:01	Pakistan Mixed Punjabi	19.4	0.1015	389	31_0_N_72_0_E
1,103	A*01:01:01:01	Russia Bashkortostan, Bashkirs	16.7	0.0875	120	54_0_N_55_0_E
1,104	A*01:01:01:01	Russia Bashkortostan, Tatars	23.4	0.1276	192	54_13_N_56_9_E
1,105	A*01:01:01:01	Russia Nizhny Novgorod, Russians	22.6	0.1162	1,510	56_19_N_43_59_E
1,106	A*01:01:01:01	Saudi Arabia pop 5	4.4	0.0222	158	24_38_N_46_43_E
1,107	A*01:01:01:01	USA Italy Ancestry		0.1520	273	38_0_N_77_0_W
1,108	A*01:01:01:01	USA Mexican American Mestizo		0.0820	553	38_53_N_77_2_W
1,109	A*01:01:01:01	USA San Francisco Caucasian		0.1600	220	37_46_N_122_25_W
1,110	A*01:01:01:01	USA Spain Ancestry		0.0750	279	38_0_N_77_0_W
1,111	A*01:01:01:02N	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
1,112	A*01:01:01:04	Pakistan Mixed Punjabi	0.6	0.0026	389	31_0_N_72_0_E
1,113	A*01:01:02	Bulgaria		0.0000	55	42_0_N_23_0_E
1,114	A*01:01:02	China North Han		0.0000	105	39_54_N_116_24_E
1,115	A*01:01:02	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
1,116	A*01:01:02	Saudi Arabia pop 5	1.3	0.0063	158	24_38_N_46_43_E
1,117	A*01:01:02	South Korea pop 3		0.0000	485	37_33_N_126_59_E
1,118	A*01:01:03	Latvia		0.0038	266	56_50_N_24_45_E
1,119	A*01:01:03	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
1,120	A*01:01:03	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,121	A*01:01:09	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,122	A*01:01:11	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
1,123	A*01:01:12	Pakistan Mixed Punjabi	0.8	0.0039	389	31_0_N_72_0_E
1,124	A*01:01:13	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
1,125	A*01:01:13	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,126	A*01:01:14	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,127	A*01:01:26	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
1,128	A*01:01:27	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,129	A*01:01:33	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,130	A*01:01:33	Russia Nizhny Novgorod, Russians	0.1	0.0003	1,510	56_19_N_43_59_E
1,131	A*01:01:38L	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,132	A*01:01:41	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,133	A*01:01:44	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,134	A*01:01:44	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
1,135	A*01:01:58	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,136	A*01:01:69	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,137	A*01:01:76	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,138	A*01:01:76	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,139	A*01:01:78	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,140	A*01:01:81	Russia Moscow Yakut	0.4	0.0021	235	55_46_N_37_41_E
1,141	A*01:01:83	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,142	A*01:01:85	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,143	A*01:01:86	Pakistan Mixed Punjabi	1.8	0.0090	389	31_0_N_72_0_E
1,144	A*01:01:101	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,145	A*01:01:103	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,146	A*01:02	Argentina Rosario Toba	1.2	0.0060	86	32_57_S_60_39_W
1,147	A*01:02	Belgium pop 3	0.1	0.0006	31,412	51_2_N_4_28_E
1,148	A*01:02	Brazil Barra Mansa Rio State Caucasian		0.0012	405	22_32_S_44_10_W
1,149	A*01:02	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
1,150	A*01:02	Brazil Rio de Janeiro Caucasian		0.0010	521	22_54_S_43_12_W
1,151	A*01:02	Brazil Vale do Ribeira Quilombos	0.0	0.0000	144	24_36_S_48_15_W
1,152	A*01:02	Bulgaria		0.0000	55	42_0_N_23_0_E
1,153	A*01:02	Cameroon Baka Pygmy		0.0000	10	8_30_N_14_0_E
1,154	A*01:02	Cameroon Bamileke		0.0000	77	5_28_N_10_25_E
1,155	A*01:02	Cameroon Beti		0.0030	174	2_55_N_11_9_E
1,156	A*01:02	Cameroon Sawa		0.0000	13	4_50_N_10_0_E
1,157	A*01:02	Cape Verde Northwestern Islands		0.0000	62	16_0_N_24_0_W
1,158	A*01:02	Cape Verde Southeastern Islands		0.0160	62	16_0_N_24_0_W
1,159	A*01:02	Central African Republic Mbenzele Pygmy	0.0		36	3_32_N_16_4_E
1,160	A*01:02	China Jiangsu Province Han		0.0018	334	32_54_N_119_48_E
1,161	A*01:02	China North Han		0.0000	105	39_54_N_116_24_E
1,162	A*01:02	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
1,163	A*01:02	Colombia Bogotá Cord Blood	0.5	0.0027	1,463	4_39_N_73_52_W
1,164	A*01:02	Costa Rica African -Caribbean (G)	2.0	0.0100	102	9_59_N_83_1_W
1,165	A*01:02	Costa Rica Amerindians (G)	1.6	0.0080	125	9_34_N_83_25_W
1,166	A*01:02	Croatia		0.0000	150	45_0_N_15_0_E
1,167	A*01:02	Croatia pop 4		0.0001	4,000	45_48_N_15_57_E
1,168	A*01:02	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
1,169	A*01:02	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
1,170	A*01:02	Czech Republic		0.0000	106	50_0_N_14_0_E
1,171	A*01:02	Czech Republic NMDR		0.0001	5,099	49_48_N_15_30_E
1,172	A*01:02	France French Bone Marrow Donor Registry		0.0073	42,623	48_51_N_2_21_E
1,173	A*01:02	Germany DKMS - Croatia minority		0.0002	2,057	48_31_N_9_3_E
1,174	A*01:02	Germany DKMS - German donors		0.0001	3,456,066	48_31_N_9_3_E
1,175	A*01:02	Germany DKMS - Greece minority		0.0003	1,894	48_31_N_9_3_E
1,176	A*01:02	Germany DKMS - Italy minority		0.0022	1,159	48_31_N_9_3_E
1,177	A*01:02	Germany DKMS - Portugal minority		0.0013	1,176	48_31_N_9_3_E
1,178	A*01:02	Germany DKMS - Spain minority		0.0027	1,107	48_31_N_9_3_E
1,179	A*01:02	Germany DKMS - Turkey minority		0.0002	4,856	48_31_N_9_3_E
1,180	A*01:02	Germany DKMS - United Kingdom minority		0.0010	1,043	48_31_N_9_3_E
1,181	A*01:02	Germany pop 6		0.0002	8,862	51_6_N_10_23_E
1,182	A*01:02	Germany pop 8		0.0001	39,689	48_24_N_9_58_E
1,183	A*01:02	Guinea Bissau		0.0080	65	12_0_N_15_0_W
1,184	A*01:02	Guinea Bissau Balanta		0.0210	48	11_52_N_15_36_W
1,185	A*01:02	Guinea Bissau Bijago		0.0000	23	11_52_N_15_36_W
1,186	A*01:02	Guinea Bissau Fula		0.0000	31	11_52_N_15_36_W
1,187	A*01:02	Guinea Bissau Papel		0.0200	25	11_52_N_15_36_W
1,188	A*01:02	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
1,189	A*01:02	India Delhi pop 2	6.7	0.0330	90	28_36_N_77_13_E
1,190	A*01:02	Iran Gorgan		0.0470	64	36_50_N_54_43_E
1,191	A*01:02	Iran Kurd pop 2		0.0250	60	36_17_N_46_17_E
1,192	A*01:02	Iran Saqqez-Baneh Kurds		0.0250	60	36_14_N_46_16_E
1,193	A*01:02	Iran Tabriz Azeris		0.0619	97	38_4_N_46_17_E
1,194	A*01:02	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
1,195	A*01:02	Israel Arab pop 2		0.0000	12,301	32_8_N_34_52_E
1,196	A*01:02	Israel Argentina Jews		0.0001	4,307	32_8_N_34_52_E
1,197	A*01:02	Israel Bukhara Jews		0.0001	2,317	32_8_N_34_52_E
1,198	A*01:02	Israel Georgia Jews		0.0001	4,471	32_10_N_34_53_E
1,199	A*01:02	Israel Iran Jews		0.0000	8,153	32_8_N_34_52_E
1,200	A*01:02	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
1,201	A*01:02	Israel Morocco Jews		0.0001	36,718	32_10_N_34_52_E
1,202	A*01:02	Israel Poland Jews		0.0002	13,871	32_10_N_34_52_E
1,203	A*01:02	Israel Tunisia Jews		0.0000	9,070	32_10_N_34_52_E
1,204	A*01:02	Israel USA Jews		0.0002	6,058	32_10_N_34_52_E
1,205	A*01:02	Israel USSR Jews		0.0001	45,681	32_9_N_34_50_E
1,206	A*01:02	Israel YemenJews		0.0001	15,542	32_9_N_34_50_E
1,207	A*01:02	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
1,208	A*01:02	Italy pop 5		0.0030	975	45_28_N_9_11_E
1,209	A*01:02	Kenya Luo		0.0040	265	0_0_S_34_6_E
1,210	A*01:02	Kenya Nandi		0.0270	240	0_0_S_35_0_E
1,211	A*01:02	Madeira		0.0080	185	32_44_N_16_58_W
1,212	A*01:02	Mali Bandiagara		0.0110	138	14_4_N_3_6_W
1,213	A*01:02	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
1,214	A*01:02	Mexico Mexico City Mestizo pop 2		0.0043	234	19_26_N_99_8_W
1,215	A*01:02	Morocco Atlantic Coast Chaouya		0.0070	98	30_22_N_7_22_W
1,216	A*01:02	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
1,217	A*01:02	Morocco Settat Chaouya	1.4	0.0070	98	33_0_N_7_36_W
1,218	A*01:02	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,219	A*01:02	Nicaragua Managua	0.3	0.0015	339	12_7_N_86_10_W
1,220	A*01:02	Oman	0.0	0.0000	118	22_0_N_56_0_E
1,221	A*01:02	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
1,222	A*01:02	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
1,223	A*01:02	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
1,224	A*01:02	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
1,225	A*01:02	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
1,226	A*01:02	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
1,227	A*01:02	Panama		0.0045	462	8_59_N_79_37_W
1,228	A*01:02	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
1,229	A*01:02	Poland BMR	0.0	0.0001	23,595	52_0_N_19_22_E
1,230	A*01:02	Romania	0.0	0.0000	348	46_0_N_25_0_E
1,231	A*01:02	Russia Nizhny Novgorod, Russians	0.1	0.0003	1,510	56_19_N_43_59_E
1,232	A*01:02	Russia Tuva pop 2		0.0030	169	51_30_N_95_5_E
1,233	A*01:02	Saudi Arabia pop 5	1.3	0.0063	158	24_38_N_46_43_E
1,234	A*01:02	Saudi Arabia pop 6 (G)		0.0006	28,927	23_52_N_45_4_E
1,235	A*01:02	Senegal Niokholo Mandenka		0.0160	165	12_35_N_12_9_W
1,236	A*01:02	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
1,237	A*01:02	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
1,238	A*01:02	South Korea pop 10		0.0001	4,128	37_33_N_126_58_E
1,239	A*01:02	South Korea pop 3		0.0000	485	37_33_N_126_59_E
1,240	A*01:02	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.6	0.0029	4,335	41_22_N_2_11_E
1,241	A*01:02	Switzerland Aargau-Solothurn		0.0000	1,838	46_48_N_8_13_E
1,242	A*01:02	Switzerland Basel		0.0000	1,888	47_33_N_7_34_E
1,243	A*01:02	Switzerland Bern		0.0000	3,545	46_57_N_7_27_E
1,244	A*01:02	Switzerland Geneva pop 2		0.0000	1,267	46_12_N_6_8_E
1,245	A*01:02	Switzerland Graubunden		0.0000	759	46_42_N_9_57_E
1,246	A*01:02	Switzerland Lausanne		0.0000	993	46_31_N_6_39_E
1,247	A*01:02	Switzerland Lugano		0.0093	1,169	46_0_N_8_55_E
1,248	A*01:02	Switzerland Luzern		0.0000	1,553	47_3_N_8_18_E
1,249	A*01:02	Switzerland Sion		0.0000	832	46_15_N_7_23_E
1,250	A*01:02	Switzerland St Gallen		0.0000	2,113	47_26_N_9_21_E
1,251	A*01:02	Switzerland Zurich		0.0000	4,875	47_22_N_8_32_E
1,252	A*01:02	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
1,253	A*01:02	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
1,254	A*01:02	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
1,255	A*01:02	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
1,256	A*01:02	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
1,257	A*01:02	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
1,258	A*01:02	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
1,259	A*01:02	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
1,260	A*01:02	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
1,261	A*01:02	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
1,262	A*01:02	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
1,263	A*01:02	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
1,264	A*01:02	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
1,265	A*01:02	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
1,266	A*01:02	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
1,267	A*01:02	Tanzania Maasai	1.2	0.0060	336	3_11_S_37_4_E
1,268	A*01:02	Tunisia	2.0	0.0100	100	35_0_N_9_0_E
1,269	A*01:02	Uganda Kampala		0.0030	161	0_3_N_32_6_E
1,270	A*01:02	Uganda Kampala pop 2		0.0060	175	0_20_N_32_30_E
1,271	A*01:02	USA African American		0.0060	252	30_0_N_95_0_W
1,272	A*01:02	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
1,273	A*01:02	USA African American pop 3		0.0070	564	38_53_N_77_2_W
1,274	A*01:02	USA African American pop 4		0.0065	2,411	44_58_N_93_15_W
1,275	A*01:02	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
1,276	A*01:02	USA Asian		0.0000	358	30_0_N_95_0_W
1,277	A*01:02	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
1,278	A*01:02	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
1,279	A*01:02	USA Caucasian pop 2		0.0000	265	30_0_N_95_0_W
1,280	A*01:02	USA Caucasian pop 4		0.0009	1,070	38_53_N_77_2_W
1,281	A*01:02	USA Hispanic		0.0060	234	30_0_N_95_0_W
1,282	A*01:02	USA Hispanic pop 2		0.0030	1,999	44_58_N_93_15_W
1,283	A*01:02	USA NMDP African		0.0031	28,557	38_45_N_76_6_W
1,284	A*01:02	USA NMDP African American pop 2		0.0042	416,581	38_45_N_76_6_W
1,285	A*01:02	USA NMDP Caribean Black		0.0033	33,328	38_45_N_76_6_W
1,286	A*01:02	USA NMDP Caribean Hispanic		0.0021	115,374	38_45_N_76_6_W
1,287	A*01:02	USA NMDP Caribean Indian		0.0020	14,339	38_45_N_76_6_W
1,288	A*01:02	USA NMDP European Caucasian		0.0001	1,242,890	38_45_N_76_6_W
1,289	A*01:02	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
1,290	A*01:02	USA NMDP Hawaiian or other Pacific Islander		0.0001	11,499	38_45_N_76_6_W
1,291	A*01:02	USA NMDP Hispanic South or Central American		0.0014	146,714	38_45_N_76_6_W
1,292	A*01:02	USA NMDP Mexican or Chicano		0.0012	261,235	38_45_N_76_6_W
1,293	A*01:02	USA NMDP Middle Eastern or North Coast of Africa		0.0003	70,890	38_45_N_76_6_W
1,294	A*01:02	USA NMDP North American Amerindian		0.0002	35,791	38_45_N_76_6_W
1,295	A*01:02	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
1,296	A*01:02	USA NMDP Southeast Asian		0.0000	27,978	38_45_N_76_6_W
1,297	A*01:02	USA North American Native		0.0000	187	30_0_N_95_0_W
1,298	A*01:02	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
1,299	A*01:02	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
1,300	A*01:02	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
1,301	A*01:02	USA Spain Ancestry		0.0020	279	38_0_N_77_0_W
1,302	A*01:02	Zambia Lusaka		0.0000	44	15_25_S_28_17_E
1,303	A*01:03	Brazil Vale do Ribeira Quilombos	0.0	0.0000	144	24_36_S_48_15_W
1,304	A*01:03	Bulgaria		0.0000	55	42_0_N_23_0_E
1,305	A*01:03	China Beijing Shijiazhuang Tianjian Han		0.0020	618	39_54_N_116_24_E
1,306	A*01:03	China Guangdong Province Meizhou Han		0.0050	100	24_27_N_116_7_E
1,307	A*01:03	China Hubei Han	0.0	0.0001	3,732	30_45_N_114_15_E
1,308	A*01:03	China Jiangsu Province Han		0.0018	334	32_54_N_119_48_E
1,309	A*01:03	China North Han		0.0000	105	39_54_N_116_24_E
1,310	A*01:03	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
1,311	A*01:03	China Zhejiang Han		0.0003	1,734	27_2_N_118_1_E
1,312	A*01:03	Croatia		0.0000	150	45_0_N_15_0_E
1,313	A*01:03	Croatia pop 4		0.0001	4,000	45_48_N_15_57_E
1,314	A*01:03	Czech Republic		0.0000	106	50_0_N_14_0_E
1,315	A*01:03	Czech Republic NMDR		0.0002	5,099	49_48_N_15_30_E
1,316	A*01:03	Germany DKMS - Austria minority		0.0009	1,698	48_31_N_9_3_E
1,317	A*01:03	Germany DKMS - France minority		0.0007	1,406	48_31_N_9_3_E
1,318	A*01:03	Germany DKMS - German donors		0.0003	3,456,066	48_31_N_9_3_E
1,319	A*01:03	Germany DKMS - Greece minority		0.0008	1,894	48_31_N_9_3_E
1,320	A*01:03	Germany DKMS - Netherlands minority		0.0004	1,374	48_31_N_9_3_E
1,321	A*01:03	Germany DKMS - Portugal minority		0.0004	1,176	48_31_N_9_3_E
1,322	A*01:03	Germany DKMS - Spain minority		0.0005	1,107	48_31_N_9_3_E
1,323	A*01:03	Germany DKMS - Turkey minority		0.0009	4,856	48_31_N_9_3_E
1,324	A*01:03	Germany pop 6		0.0002	8,862	51_6_N_10_23_E
1,325	A*01:03	Germany pop 8		0.0005	39,689	48_24_N_9_58_E
1,326	A*01:03	Hong Kong Chinese BMDR		0.0003	7,595	22_15_N_114_10_E
1,327	A*01:03	India Central UCBB	0.0	0.0001	4,204	27_1_N_81_19_E
1,328	A*01:03	India South UCBB	0.0	0.0000	11,446	13_6_N_80_8_E
1,329	A*01:03	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
1,330	A*01:03	Iran Baloch		0.0060	100	29_29_N_60_52_E
1,331	A*01:03	Israel Arab pop 2		0.0002	12,301	32_8_N_34_52_E
1,332	A*01:03	Israel Argentina Jews		0.0001	4,307	32_8_N_34_52_E
1,333	A*01:03	Israel Ashkenazi and Non Ashkenazi Jews		0.0210	146	31_8_N_35_2_E
1,334	A*01:03	Israel Ashkenazi Jews pop 3		0.0004	4,625	32_8_N_34_52_E
1,335	A*01:03	Israel Ethiopia Jews		0.0705	5,928	32_8_N_34_52_E
1,336	A*01:03	Israel Georgia Jews		0.1185	4,471	32_10_N_34_53_E
1,337	A*01:03	Israel Iran Jews		0.0001	8,153	32_8_N_34_52_E
1,338	A*01:03	Israel Iraq Jews		0.0001	13,270	32_10_N_34_52_E
1,339	A*01:03	Israel Kavkazi Jews		0.0582	2,840	32_10_N_34_52_E
1,340	A*01:03	Israel Libya Jews		0.0004	3,739	32_10_N_34_52_E
1,341	A*01:03	Israel Morocco Jews		0.0001	36,718	32_10_N_34_52_E
1,342	A*01:03	Israel Poland Jews		0.0000	13,871	32_10_N_34_52_E
1,343	A*01:03	Israel Tunisia Jews		0.0002	9,070	32_10_N_34_52_E
1,344	A*01:03	Israel USSR Jews		0.0001	45,681	32_9_N_34_50_E
1,345	A*01:03	Israel YemenJews		0.0767	15,542	32_9_N_34_50_E
1,346	A*01:03	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
1,347	A*01:03	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
1,348	A*01:03	Kenya		0.0070	144	1_28_S_36_8_E
1,349	A*01:03	Kenya Luo		0.0000	265	0_0_S_34_6_E
1,350	A*01:03	Kenya Nandi		0.0170	240	0_0_S_35_0_E
1,351	A*01:03	Kenya, Nyanza Province, Luo tribe	1.0	0.0050	100	0_5_S_34_46_E
1,352	A*01:03	Malaysia Peninsular Indian	0.7	0.0037	271	3_9_N_101_57_E
1,353	A*01:03	Malaysia Peninsular Malay	0.1	0.0005	951	3_9_N_101_57_E
1,354	A*01:03	Mali Bandiagara		0.0000	138	14_4_N_3_6_W
1,355	A*01:03	Mexico Mexico City Mestizo pop 2		0.0021	234	19_26_N_99_8_W
1,356	A*01:03	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
1,357	A*01:03	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,358	A*01:03	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,359	A*01:03	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
1,360	A*01:03	Poland DKMS		0.0002	20,653	52_8_N_19_23_E
1,361	A*01:03	Russia Nizhny Novgorod, Russians	0.1	0.0003	1,510	56_19_N_43_59_E
1,362	A*01:03	Saudi Arabia Guraiat and Hail	1.4	0.0070	213	27_31_N_41_41_E
1,363	A*01:03	Saudi Arabia Jeddah (G)		0.0021	476	21_32_N_39_10_E
1,364	A*01:03	Saudi Arabia Makkah (G)		0.0087	344	21_25_N_39_49_E
1,365	A*01:03	Saudi Arabia pop 5	1.3	0.0063	158	24_38_N_46_43_E
1,366	A*01:03	Saudi Arabia pop 6 (G)		0.0061	28,927	23_52_N_45_4_E
1,367	A*01:03	Saudi Arabia Taif (G)		0.0096	104	21_16_N_40_24_E
1,368	A*01:03	South Africa Caucasians		0.0000	102	30_0_S_25_0_E
1,369	A*01:03	South Korea pop 3		0.0000	485	37_33_N_126_59_E
1,370	A*01:03	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.2	0.0008	4,335	41_22_N_2_11_E
1,371	A*01:03	Sudan Mixed	3.0	0.0150	200	5_0_N_35_0_E
1,372	A*01:03	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
1,373	A*01:03	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
1,374	A*01:03	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
1,375	A*01:03	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
1,376	A*01:03	Taiwan Han Chinese		0.0020	504	23_46_N_121_0_E
1,377	A*01:03	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
1,378	A*01:03	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
1,379	A*01:03	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
1,380	A*01:03	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
1,381	A*01:03	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
1,382	A*01:03	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
1,383	A*01:03	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
1,384	A*01:03	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
1,385	A*01:03	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
1,386	A*01:03	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
1,387	A*01:03	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
1,388	A*01:03	Tanzania Maasai	5.4	0.0269	336	3_11_S_37_4_E
1,389	A*01:03	Tunisia	0.0	0.0000	100	35_0_N_9_0_E
1,390	A*01:03	Uganda Kampala		0.0000	161	0_3_N_32_6_E
1,391	A*01:03	Uganda Kampala pop 2		0.0030	175	0_20_N_32_30_E
1,392	A*01:03	United Arab Emirates Abu Dhabi		0.0096	52	24_28_N_54_22_E
1,393	A*01:03	United Arab Emirates Pop 1		0.0140	570	24_42_N_54_82_E
1,394	A*01:03	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
1,395	A*01:03	USA African American pop 4		0.0002	2,411	44_58_N_93_15_W
1,396	A*01:03	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
1,397	A*01:03	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
1,398	A*01:03	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
1,399	A*01:03	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
1,400	A*01:03	USA NMDP African		0.0025	28,557	38_45_N_76_6_W
1,401	A*01:03	USA NMDP African American pop 2		0.0001	416,581	38_45_N_76_6_W
1,402	A*01:03	USA NMDP Caribean Black		0.0001	33,328	38_45_N_76_6_W
1,403	A*01:03	USA NMDP Caribean Hispanic		0.0001	115,374	38_45_N_76_6_W
1,404	A*01:03	USA NMDP Chinese		0.0001	99,672	38_45_N_76_6_W
1,405	A*01:03	USA NMDP European Caucasian		0.0001	1,242,890	38_45_N_76_6_W
1,406	A*01:03	USA NMDP Filipino		0.0002	50,614	38_45_N_76_6_W
1,407	A*01:03	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
1,408	A*01:03	USA NMDP Hispanic South or Central American		0.0001	146,714	38_45_N_76_6_W
1,409	A*01:03	USA NMDP Mexican or Chicano		0.0001	261,235	38_45_N_76_6_W
1,410	A*01:03	USA NMDP Middle Eastern or North Coast of Africa		0.0011	70,890	38_45_N_76_6_W
1,411	A*01:03	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
1,412	A*01:03	USA NMDP South Asian Indian		0.0001	185,391	38_45_N_76_6_W
1,413	A*01:03	USA NMDP Southeast Asian		0.0001	27,978	38_45_N_76_6_W
1,414	A*01:03	USA NMDP Vietnamese		0.0003	43,540	38_45_N_76_6_W
1,415	A*01:03	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
1,416	A*01:03	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
1,417	A*01:03	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
1,418	A*01:03	Zambia Lusaka		0.0000	44	15_25_S_28_17_E
1,419	A*01:03:01	Hong Kong Chinese HKBMDR HLA 11 loci	0.0	0.0001	5,266	22_25_N_114_17_E
1,420	A*01:03:01	Russia Belgorod region	0.7	0.0033	153	50_46_N_37_27_E
1,421	A*01:03:01	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
1,422	A*01:03:01	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,423	A*01:03:01	South African Black	0.7	0.0035	142	29_9_S_24_39_E
1,424	A*01:03:01:01	Pakistan Mixed Punjabi	0.6	0.0026	389	31_0_N_72_0_E
1,425	A*01:03:01:02	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
1,426	A*01:03:02	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,427	A*01:04	Belgium pop 3	0.0	0.0001	31,412	51_2_N_4_28_E
1,428	A*01:04N	Bulgaria		0.0000	55	42_0_N_23_0_E
1,429	A*01:04N	China North Han		0.0000	105	39_54_N_116_24_E
1,430	A*01:04N	Czech Republic		0.0000	106	50_0_N_14_0_E
1,431	A*01:04N	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
1,432	A*01:04N	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
1,433	A*01:04N	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
1,434	A*01:04N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,435	A*01:04N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,436	A*01:04	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
1,437	A*01:04N	South Korea pop 3		0.0000	485	37_33_N_126_59_E
1,438	A*01:04N	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
1,439	A*01:04N	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
1,440	A*01:04N	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
1,441	A*01:04N	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
1,442	A*01:04N	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
1,443	A*01:06	Bulgaria		0.0000	55	42_0_N_23_0_E
1,444	A*01:06	China North Han		0.0000	105	39_54_N_116_24_E
1,445	A*01:06	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
1,446	A*01:06	Czech Republic NMDR		0.0001	5,099	49_48_N_15_30_E
1,447	A*01:06	England North West	0.3	0.0020	298	53_0_N_3_0_W
1,448	A*01:06	Germany DKMS - German donors		0.0001	3,456,066	48_31_N_9_3_E
1,449	A*01:06	Germany DKMS - Turkey minority		0.0001	4,856	48_31_N_9_3_E
1,450	A*01:06	Germany pop 8		0.0001	39,689	48_24_N_9_58_E
1,451	A*01:06	India Khandesh Region Pawra		0.0100	50	20_54_N_74_46_E
1,452	A*01:06	India Mumbai Maratha		0.0000	91	18_58_N_72_49_E
1,453	A*01:06	India West Bhil		0.0000	50	20_54_N_74_46_E
1,454	A*01:06	India West Coast Parsi		0.0800	50	18_0_N_74_0_E
1,455	A*01:06	Israel Arab pop 2		0.0004	12,301	32_8_N_34_52_E
1,456	A*01:06	Israel Ashkenazi and Non Ashkenazi Jews		0.0040	146	31_8_N_35_2_E
1,457	A*01:06	Israel Bukhara Jews		0.0001	2,317	32_8_N_34_52_E
1,458	A*01:06	Israel Ethiopia Jews		0.0002	5,928	32_8_N_34_52_E
1,459	A*01:06	Israel Georgia Jews		0.0000	4,471	32_10_N_34_53_E
1,460	A*01:06	Israel Iran Jews		0.0001	8,153	32_8_N_34_52_E
1,461	A*01:06	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
1,462	A*01:06	Israel Kavkazi Jews		0.0001	2,840	32_10_N_34_52_E
1,463	A*01:06	Israel Morocco Jews		0.0000	36,718	32_10_N_34_52_E
1,464	A*01:06	Israel Tunisia Jews		0.0000	9,070	32_10_N_34_52_E
1,465	A*01:06	Israel YemenJews		0.0005	15,542	32_9_N_34_50_E
1,466	A*01:06	Kenya		0.0030	144	1_28_S_36_8_E
1,467	A*01:06	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
1,468	A*01:06	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,469	A*01:06	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,470	A*01:06	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
1,471	A*01:06	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
1,472	A*01:06	Poland DKMS		0.0001	20,653	52_8_N_19_23_E
1,473	A*01:06	Saudi Arabia pop 6 (G)		0.0001	28,927	23_52_N_45_4_E
1,474	A*01:06	South Korea pop 3		0.0000	485	37_33_N_126_59_E
1,475	A*01:06	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
1,476	A*01:06	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
1,477	A*01:06	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
1,478	A*01:06	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
1,479	A*01:06	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
1,480	A*01:06	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
1,481	A*01:06	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
1,482	A*01:06	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
1,483	A*01:06	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
1,484	A*01:06	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
1,485	A*01:06	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
1,486	A*01:06	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
1,487	A*01:06	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
1,488	A*01:06	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
1,489	A*01:06	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
1,490	A*01:06	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
1,491	A*01:06	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
1,492	A*01:06	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
1,493	A*01:06	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
1,494	A*01:06	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
1,495	A*01:06	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,496	A*01:06	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
1,497	A*01:06	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
1,498	A*01:06	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
1,499	A*01:06	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
1,500	A*01:06	USA NMDP Middle Eastern or North Coast of Africa		0.0001	70,890	38_45_N_76_6_W
1,501	A*01:06	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
1,502	A*01:06	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
1,503	A*01:06	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
1,504	A*01:06	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
1,505	A*01:07	Bulgaria		0.0000	55	42_0_N_23_0_E
1,506	A*01:07	China North Han		0.0000	105	39_54_N_116_24_E
1,507	A*01:07	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
1,508	A*01:07	Israel Bukhara Jews		0.0001	2,317	32_8_N_34_52_E
1,509	A*01:07	Israel Ethiopia Jews		0.0002	5,928	32_8_N_34_52_E
1,510	A*01:07	Israel Georgia Jews		0.0000	4,471	32_10_N_34_53_E
1,511	A*01:07	Israel Kavkazi Jews		0.0001	2,840	32_10_N_34_52_E
1,512	A*01:07	Israel Tunisia Jews		0.0000	9,070	32_10_N_34_52_E
1,513	A*01:07	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
1,514	A*01:07	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,515	A*01:07	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,516	A*01:07	South Korea pop 3		0.0000	485	37_33_N_126_59_E
1,517	A*01:07	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
1,518	A*01:07	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
1,519	A*01:07	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
1,520	A*01:07	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
1,521	A*01:07	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
1,522	A*01:08	Bulgaria		0.0000	55	42_0_N_23_0_E
1,523	A*01:08	China North Han		0.0000	105	39_54_N_116_24_E
1,524	A*01:08	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
1,525	A*01:08	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
1,526	A*01:08	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
1,527	A*01:08	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,528	A*01:08	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,529	A*01:08	South Korea pop 3		0.0000	485	37_33_N_126_59_E
1,530	A*01:08	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.0	0.0001	4,335	41_22_N_2_11_E
1,531	A*01:08	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
1,532	A*01:08	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
1,533	A*01:08	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,534	A*01:08	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
1,535	A*01:08	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
1,536	A*01:08	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
1,537	A*01:09	Bulgaria		0.0000	55	42_0_N_23_0_E
1,538	A*01:09	China North Han		0.0000	105	39_54_N_116_24_E
1,539	A*01:09	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
1,540	A*01:09	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,541	A*01:09	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
1,542	A*01:09	Kenya, Nyanza Province, Luo tribe	1.0	0.0050	100	0_5_S_34_46_E
1,543	A*01:09	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,544	A*01:09	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,545	A*01:09	Saudi Arabia pop 6 (G)		0.0000	28,927	23_52_N_45_4_E
1,546	A*01:09	Switzerland Aargau-Solothurn		0.0000	1,838	46_48_N_8_13_E
1,547	A*01:09	Switzerland Basel		0.0000	1,888	47_33_N_7_34_E
1,548	A*01:09	Switzerland Bern		0.0000	3,545	46_57_N_7_27_E
1,549	A*01:09	Switzerland Geneva pop 2		0.0000	1,267	46_12_N_6_8_E
1,550	A*01:09	Switzerland Graubunden		0.0040	759	46_42_N_9_57_E
1,551	A*01:09	Switzerland Lausanne		0.0000	993	46_31_N_6_39_E
1,552	A*01:09	Switzerland Lugano		0.0000	1,169	46_0_N_8_55_E
1,553	A*01:09	Switzerland Luzern		0.0000	1,553	47_3_N_8_18_E
1,554	A*01:09	Switzerland Sion		0.0000	832	46_15_N_7_23_E
1,555	A*01:09	Switzerland St Gallen		0.0000	2,113	47_26_N_9_21_E
1,556	A*01:09	Switzerland Zurich		0.0000	4,875	47_22_N_8_32_E
1,557	A*01:09	Tanzania Maasai	2.7	0.0134	336	3_11_S_37_4_E
1,558	A*01:09	Uganda Kampala pop 2		0.0030	175	0_20_N_32_30_E
1,559	A*01:09	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
1,560	A*01:09	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
1,561	A*01:09	USA NMDP African		0.0001	28,557	38_45_N_76_6_W
1,562	A*01:09	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
1,563	A*01:09	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
1,564	A*01:09	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
1,565	A*01:09:02	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
1,566	A*01:09:02	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
1,567	A*01:09:02	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
1,568	A*01:10	China North Han		0.0000	105	39_54_N_116_24_E
1,569	A*01:10	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
1,570	A*01:10	Israel Ashkenazi Jews pop 3		0.0003	4,625	32_8_N_34_52_E
1,571	A*01:10	Israel Bukhara Jews		0.0001	2,317	32_8_N_34_52_E
1,572	A*01:10	Israel Georgia Jews		0.0001	4,471	32_10_N_34_53_E
1,573	A*01:10	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
1,574	A*01:10	Israel Tunisia Jews		0.0000	9,070	32_10_N_34_52_E
1,575	A*01:10	Israel YemenJews		0.0001	15,542	32_9_N_34_50_E
1,576	A*01:10	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,577	A*01:10	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,578	A*01:10	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
1,579	A*01:10	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
1,580	A*01:10	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
1,581	A*01:11N	China North Han		0.0000	105	39_54_N_116_24_E
1,582	A*01:11N	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
1,583	A*01:11N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,584	A*01:11N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,585	A*01:11N	Switzerland Aargau-Solothurn		0.0000	1,838	46_48_N_8_13_E
1,586	A*01:11N	Switzerland Basel		0.0000	1,888	47_33_N_7_34_E
1,587	A*01:11N	Switzerland Bern		0.0000	3,545	46_57_N_7_27_E
1,588	A*01:11N	Switzerland Geneva pop 2		0.0000	1,267	46_12_N_6_8_E
1,589	A*01:11N	Switzerland Graubunden		0.0040	759	46_42_N_9_57_E
1,590	A*01:11N	Switzerland Lausanne		0.0000	993	46_31_N_6_39_E
1,591	A*01:11N	Switzerland Lugano		0.0000	1,169	46_0_N_8_55_E
1,592	A*01:11N	Switzerland Luzern		0.0000	1,553	47_3_N_8_18_E
1,593	A*01:11N	Switzerland Sion		0.0000	832	46_15_N_7_23_E
1,594	A*01:11N	Switzerland St Gallen		0.0000	2,113	47_26_N_9_21_E
1,595	A*01:11N	Switzerland Zurich		0.0000	4,875	47_22_N_8_32_E
1,596	A*01:12	China North Han		0.0000	105	39_54_N_116_24_E
1,597	A*01:12	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
1,598	A*01:12	India East UCBB	0.0	0.0002	2,403	22_25_N_88_18_E
1,599	A*01:12	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
1,600	A*01:12	Israel Bukhara Jews		0.0001	2,317	32_8_N_34_52_E
1,601	A*01:12	Israel Georgia Jews		0.0001	4,471	32_10_N_34_53_E
1,602	A*01:12	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
1,603	A*01:12	Israel Tunisia Jews		0.0000	9,070	32_10_N_34_52_E
1,604	A*01:12	Israel YemenJews		0.0001	15,542	32_9_N_34_50_E
1,605	A*01:12	Malaysia Peninsular Indian	0.4	0.0018	271	3_9_N_101_57_E
1,606	A*01:12	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,607	A*01:12	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,608	A*01:12	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
1,609	A*01:12	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
1,610	A*01:12	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
1,611	A*01:12	USA NMDP Southeast Asian		0.0000	27,978	38_45_N_76_6_W
1,612	A*01:12	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
1,613	A*01:13	China North Han		0.0000	105	39_54_N_116_24_E
1,614	A*01:13	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
1,615	A*01:13	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,616	A*01:13	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
1,617	A*01:13	Israel Bukhara Jews		0.0001	2,317	32_8_N_34_52_E
1,618	A*01:13	Israel Georgia Jews		0.0001	4,471	32_10_N_34_53_E
1,619	A*01:13	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
1,620	A*01:13	Israel Tunisia Jews		0.0000	9,070	32_10_N_34_52_E
1,621	A*01:13	Israel YemenJews		0.0001	15,542	32_9_N_34_50_E
1,622	A*01:13	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,623	A*01:13	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,624	A*01:13	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,625	A*01:13	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
1,626	A*01:14	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,627	A*01:14	Germany pop 8		0.0001	39,689	48_24_N_9_58_E
1,628	A*01:14	Israel Bukhara Jews		0.0001	2,317	32_8_N_34_52_E
1,629	A*01:14	Israel Ethiopia Jews		0.0002	5,928	32_8_N_34_52_E
1,630	A*01:14	Israel Georgia Jews		0.0000	4,471	32_10_N_34_53_E
1,631	A*01:14	Israel Kavkazi Jews		0.0001	2,840	32_10_N_34_52_E
1,632	A*01:14	Israel Tunisia Jews		0.0000	9,070	32_10_N_34_52_E
1,633	A*01:14	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,634	A*01:14	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,635	A*01:14	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,636	A*01:14	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
1,637	A*01:14	USA NMDP Southeast Asian		0.0000	27,978	38_45_N_76_6_W
1,638	A*01:15N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,639	A*01:15N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,640	A*01:15N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,641	A*01:15N	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,642	A*01:16N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,643	A*01:16N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,644	A*01:16N	Switzerland Aargau-Solothurn		0.0000	1,838	46_48_N_8_13_E
1,645	A*01:16N	Switzerland Basel		0.0000	1,888	47_33_N_7_34_E
1,646	A*01:16N	Switzerland Bern		0.0000	3,545	46_57_N_7_27_E
1,647	A*01:16N	Switzerland Geneva pop 2		0.0000	1,267	46_12_N_6_8_E
1,648	A*01:16N	Switzerland Graubunden		0.0040	759	46_42_N_9_57_E
1,649	A*01:16N	Switzerland Lausanne		0.0000	993	46_31_N_6_39_E
1,650	A*01:16N	Switzerland Lugano		0.0000	1,169	46_0_N_8_55_E
1,651	A*01:16N	Switzerland Luzern		0.0000	1,553	47_3_N_8_18_E
1,652	A*01:16N	Switzerland Sion		0.0000	832	46_15_N_7_23_E
1,653	A*01:16N	Switzerland St Gallen		0.0000	2,113	47_26_N_9_21_E
1,654	A*01:16N	Switzerland Zurich		0.0000	4,875	47_22_N_8_32_E
1,655	A*01:16N	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
1,656	A*01:16N	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
1,657	A*01:16N	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
1,658	A*01:17	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,659	A*01:17	Israel Arab pop 2		0.0003	12,301	32_8_N_34_52_E
1,660	A*01:17	Israel Argentina Jews		0.0006	4,307	32_8_N_34_52_E
1,661	A*01:17	Israel Ashkenazi Jews pop 3		0.0126	4,625	32_8_N_34_52_E
1,662	A*01:17	Israel Bukhara Jews		0.0117	2,317	32_8_N_34_52_E
1,663	A*01:17	Israel Druze		0.0003	5,914	32_41_N_35_17_E
1,664	A*01:17	Israel Ethiopia Jews		0.0014	5,928	32_8_N_34_52_E
1,665	A*01:17	Israel Georgia Jews		0.0100	4,471	32_10_N_34_53_E
1,666	A*01:17	Israel Iran Jews		0.0031	8,153	32_8_N_34_52_E
1,667	A*01:17	Israel Iraq Jews		0.0090	13,270	32_10_N_34_52_E
1,668	A*01:17	Israel Kavkazi Jews		0.0037	2,840	32_10_N_34_52_E
1,669	A*01:17	Israel Libya Jews		0.0029	3,739	32_10_N_34_52_E
1,670	A*01:17	Israel Morocco Jews		0.0031	36,718	32_10_N_34_52_E
1,671	A*01:17	Israel Poland Jews		0.0013	13,871	32_10_N_34_52_E
1,672	A*01:17	Israel Tunisia Jews		0.0032	9,070	32_10_N_34_52_E
1,673	A*01:17	Israel USA Jews		0.0005	6,058	32_10_N_34_52_E
1,674	A*01:17	Israel USSR Jews		0.0008	45,681	32_9_N_34_50_E
1,675	A*01:17	Israel YemenJews		0.0086	15,542	32_9_N_34_50_E
1,676	A*01:17	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,677	A*01:17	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,678	A*01:17	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
1,679	A*01:17	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
1,680	A*01:17	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,681	A*01:17	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
1,682	A*01:18N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,683	A*01:18N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,684	A*01:18N	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,685	A*01:18N	Switzerland Aargau-Solothurn		0.0000	1,838	46_48_N_8_13_E
1,686	A*01:18N	Switzerland Basel		0.0000	1,888	47_33_N_7_34_E
1,687	A*01:18N	Switzerland Bern		0.0000	3,545	46_57_N_7_27_E
1,688	A*01:18N	Switzerland Geneva pop 2		0.0000	1,267	46_12_N_6_8_E
1,689	A*01:18N	Switzerland Graubunden		0.0040	759	46_42_N_9_57_E
1,690	A*01:18N	Switzerland Lausanne		0.0000	993	46_31_N_6_39_E
1,691	A*01:18N	Switzerland Lugano		0.0000	1,169	46_0_N_8_55_E
1,692	A*01:18N	Switzerland Luzern		0.0000	1,553	47_3_N_8_18_E
1,693	A*01:18N	Switzerland Sion		0.0000	832	46_15_N_7_23_E
1,694	A*01:18N	Switzerland St Gallen		0.0000	2,113	47_26_N_9_21_E
1,695	A*01:18N	Switzerland Zurich		0.0000	4,875	47_22_N_8_32_E
1,696	A*01:19	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,697	A*01:19	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,698	A*01:19	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
1,699	A*01:19	USA NMDP Southeast Asian		0.0000	27,978	38_45_N_76_6_W
1,700	A*01:20	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
1,701	A*01:20	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,702	A*01:20	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,703	A*01:20	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
1,704	A*01:20	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
1,705	A*01:21	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,706	A*01:21	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,707	A*01:21	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,708	A*01:22N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,709	A*01:22N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,710	A*01:23	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,711	A*01:23	Israel Iran Jews		0.0000	8,153	32_8_N_34_52_E
1,712	A*01:23	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,713	A*01:23	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,714	A*01:23	Saudi Arabia pop 6 (G)		0.0001	28,927	23_52_N_45_4_E
1,715	A*01:23	South Africa Worcester	3.0	0.0160	159	33_39_S_19_27_E
1,716	A*01:23	Uganda Kampala pop 2		0.0030	175	0_20_N_32_30_E
1,717	A*01:23	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
1,718	A*01:23	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
1,719	A*01:23	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,720	A*01:23	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
1,721	A*01:24	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,722	A*01:24	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,723	A*01:24	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
1,724	A*01:24	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,725	A*01:24	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
1,726	A*01:24	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
1,727	A*01:25	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,728	A*01:25	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,729	A*01:25	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,730	A*01:25	Switzerland Aargau-Solothurn		0.0000	1,838	46_48_N_8_13_E
1,731	A*01:25	Switzerland Basel		0.0000	1,888	47_33_N_7_34_E
1,732	A*01:25	Switzerland Bern		0.0000	3,545	46_57_N_7_27_E
1,733	A*01:25	Switzerland Geneva pop 2		0.0000	1,267	46_12_N_6_8_E
1,734	A*01:25	Switzerland Graubunden		0.0040	759	46_42_N_9_57_E
1,735	A*01:25	Switzerland Lausanne		0.0000	993	46_31_N_6_39_E
1,736	A*01:25	Switzerland Lugano		0.0000	1,169	46_0_N_8_55_E
1,737	A*01:25	Switzerland Luzern		0.0000	1,553	47_3_N_8_18_E
1,738	A*01:25	Switzerland Sion		0.0000	832	46_15_N_7_23_E
1,739	A*01:25	Switzerland St Gallen		0.0000	2,113	47_26_N_9_21_E
1,740	A*01:25	Switzerland Zurich		0.0000	4,875	47_22_N_8_32_E
1,741	A*01:25	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
1,742	A*01:25	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,743	A*01:25	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
1,744	A*01:25	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
1,745	A*01:25	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
1,746	A*01:25	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
1,747	A*01:26	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,748	A*01:26	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
1,749	A*01:26	India South UCBB	0.0	0.0001	11,446	13_6_N_80_8_E
1,750	A*01:26	Malaysia Peninsular Indian	0.7	0.0037	271	3_9_N_101_57_E
1,751	A*01:26	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,752	A*01:26	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,753	A*01:26	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,754	A*01:27N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,755	A*01:27N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,756	A*01:27N	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,757	A*01:28	Israel Iran Jews		0.0000	8,153	32_8_N_34_52_E
1,758	A*01:28	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,759	A*01:28	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,760	A*01:28	Russia Bashkortostan, Tatars	0.5	0.0026	192	54_13_N_56_9_E
1,761	A*01:28	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
1,762	A*01:28	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
1,763	A*01:29	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,764	A*01:29	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,765	A*01:29	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,766	A*01:30	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,767	A*01:30	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,768	A*01:30	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,769	A*01:30	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
1,770	A*01:31N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,771	A*01:31N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,772	A*01:32	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,773	A*01:32	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,774	A*01:33	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,775	A*01:33	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
1,776	A*01:33	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
1,777	A*01:35	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,778	A*01:35	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,779	A*01:36	Israel Arab pop 2		0.0003	12,301	32_8_N_34_52_E
1,780	A*01:36	Israel Argentina Jews		0.0005	4,307	32_8_N_34_52_E
1,781	A*01:36	Israel Ashkenazi Jews pop 3		0.0126	4,625	32_8_N_34_52_E
1,782	A*01:36	Israel Bukhara Jews		0.0117	2,317	32_8_N_34_52_E
1,783	A*01:36	Israel Druze		0.0003	5,914	32_41_N_35_17_E
1,784	A*01:36	Israel Ethiopia Jews		0.0014	5,928	32_8_N_34_52_E
1,785	A*01:36	Israel Georgia Jews		0.0093	4,471	32_10_N_34_53_E
1,786	A*01:36	Israel Iran Jews		0.0031	8,153	32_8_N_34_52_E
1,787	A*01:36	Israel Iraq Jews		0.0090	13,270	32_10_N_34_52_E
1,788	A*01:36	Israel Kavkazi Jews		0.0037	2,840	32_10_N_34_52_E
1,789	A*01:36	Israel Libya Jews		0.0028	3,739	32_10_N_34_52_E
1,790	A*01:36	Israel Morocco Jews		0.0031	36,718	32_10_N_34_52_E
1,791	A*01:36	Israel Poland Jews		0.0013	13,871	32_10_N_34_52_E
1,792	A*01:36	Israel Tunisia Jews		0.0032	9,070	32_10_N_34_52_E
1,793	A*01:36	Israel USA Jews		0.0005	6,058	32_10_N_34_52_E
1,794	A*01:36	Israel USSR Jews		0.0008	45,681	32_9_N_34_50_E
1,795	A*01:36	Israel YemenJews		0.0083	15,542	32_9_N_34_50_E
1,796	A*01:36	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,797	A*01:36	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
1,798	A*01:37	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,799	A*01:37	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.0	0.0001	4,335	41_22_N_2_11_E
1,800	A*01:38	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,801	A*01:38	Germany DKMS - Turkey minority		0.0001	4,856	48_31_N_9_3_E
1,802	A*01:38	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,803	A*01:39	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,804	A*01:39	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,805	A*01:39	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,806	A*01:39	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.0	0.0001	4,335	41_22_N_2_11_E
1,807	A*01:40	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,808	A*01:40	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
1,809	A*01:40	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,810	A*01:41	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,811	A*01:41	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,812	A*01:42	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,813	A*01:42	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,814	A*01:43	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,815	A*01:43	India West UCBB	0.0	0.0002	5,829	19_11_N_73_3_E
1,816	A*01:43	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,817	A*01:43	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
1,818	A*01:43	Russia Moscow Russians	0.1	0.0005	13,147	55_46_N_37_41_E
1,819	A*01:43	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
1,820	A*01:43	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
1,821	A*01:44	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,822	A*01:44	Germany DKMS - Turkey minority		0.0001	4,856	48_31_N_9_3_E
1,823	A*01:44	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,824	A*01:44	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,825	A*01:45	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,826	A*01:46	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,827	A*01:47	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,828	A*01:48	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,829	A*01:49	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,830	A*01:49	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,831	A*01:50	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,832	A*01:51	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,833	A*01:51	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
1,834	A*01:51	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,835	A*01:54	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
1,836	A*01:57N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,837	A*01:61	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,838	A*01:63	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,839	A*01:65	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,840	A*01:66	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,841	A*01:66	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,842	A*01:66	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
1,843	A*01:67	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,844	A*01:68	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,845	A*01:69	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,846	A*01:69:01	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
1,847	A*01:69:01	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
1,848	A*01:69:01	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
1,849	A*01:72	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,850	A*01:74	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
1,851	A*01:76	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,852	A*01:77	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,853	A*01:83	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,854	A*01:85	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
1,855	A*01:86	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,856	A*01:86	Israel Tunisia Jews		0.0001	9,070	32_10_N_34_52_E
1,857	A*01:86	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
1,858	A*01:88	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,859	A*01:88:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,860	A*01:89	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,861	A*01:89	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,862	A*01:89	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,863	A*01:91	Saudi Arabia pop 6 (G)		0.0000	28,927	23_52_N_45_4_E
1,864	A*01:92	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,865	A*01:95	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,866	A*01:98	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,867	A*01:99	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,868	A*01:100	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,869	A*01:100	Latvia		0.0019	266	56_50_N_24_45_E
1,870	A*01:100	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
1,871	A*01:100	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
1,872	A*01:100	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
1,873	A*01:100	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
1,874	A*01:101	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,875	A*01:104	India South UCBB	0.0	0.0001	11,446	13_6_N_80_8_E
1,876	A*01:104	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
1,877	A*01:106	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,878	A*01:106	Saudi Arabia pop 6 (G)		0.0000	28,927	23_52_N_45_4_E
1,879	A*01:109	India Central UCBB	0.0	0.0001	4,204	27_1_N_81_19_E
1,880	A*01:109	India South UCBB	0.0	0.0000	11,446	13_6_N_80_8_E
1,881	A*01:109	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
1,882	A*01:109	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
1,883	A*01:110	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,884	A*01:113	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,885	A*01:115	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,886	A*01:116	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,887	A*01:117	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,888	A*01:118	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,889	A*01:118	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,890	A*01:123N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,891	A*01:124	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,892	A*01:126	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,893	A*01:128	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,894	A*01:131	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,895	A*01:134	Russian Federation Vologda Region	0.8	0.0042	119	59_13_N_39_54_E
1,896	A*01:135	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,897	A*01:136	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,898	A*01:136	India Tamil Nadu		0.0005	2,492	11_32_N_78_52_E
1,899	A*01:137	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
1,900	A*01:137	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
1,901	A*01:139	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,902	A*01:143	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
1,903	A*01:143	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,904	A*01:144	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,905	A*01:154	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,906	A*01:157	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,907	A*01:163	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,908	A*01:163	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,909	A*01:166	Latvia		0.0019	266	56_50_N_24_45_E
1,910	A*01:167	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,911	A*01:169	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,912	A*01:170	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,913	A*01:173	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,914	A*01:175	Russia Moscow Russians	0.1	0.0005	13,147	55_46_N_37_41_E
1,915	A*01:175	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
1,916	A*01:175	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,917	A*01:176	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
1,918	A*01:181	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,919	A*01:183	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,920	A*01:184	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,921	A*01:188	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
1,922	A*01:188	Russia Moscow Kabardin	0.2	0.0012	834	55_46_N_37_41_E
1,923	A*01:188	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
1,924	A*01:190	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,925	A*01:191	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,926	A*01:193	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,927	A*01:194	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,928	A*01:195	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,929	A*01:196	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,930	A*01:197	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,931	A*01:198	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,932	A*01:200	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,933	A*01:202	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,934	A*01:206	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
1,935	A*01:208Q	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,936	A*01:209	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
1,937	A*01:210:01	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
1,938	A*01:211	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,939	A*01:213	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
1,940	A*01:213	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,941	A*01:218	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,942	A*01:218	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
1,943	A*01:219	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,944	A*01:220	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,945	A*01:221	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
1,946	A*01:221	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,947	A*01:223	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,948	A*01:225	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,949	A*01:227	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,950	A*01:228Q	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,951	A*01:229	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,952	A*01:229	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
1,953	A*01:231	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,954	A*01:232	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,955	A*01:232:02	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
1,956	A*01:232:02	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,957	A*01:234	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,958	A*01:244	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,959	A*01:247N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,960	A*01:255	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
1,961	A*01:257	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,962	A*01:258N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,963	A*01:259	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,964	A*01:260	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,965	A*01:274	Russia Moscow Kabardin	0.4	0.0018	834	55_46_N_37_41_E
1,966	A*01:274	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
1,967	A*01:274	Russia Moscow Russians	0.1	0.0005	13,147	55_46_N_37_41_E
1,968	A*01:274	Russia Moscow Tatars	0.2	0.0008	2,512	55_46_N_37_41_E
1,969	A*01:274	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
1,970	A*01:274	Russia Moscow Yakut	0.4	0.0021	235	55_46_N_37_41_E
1,971	A*01:284	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
1,972	A*01:288	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,973	A*01:289	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
1,974	A*01:298	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
1,975	A*01:314	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
1,976	A*01:383	Latvia		0.0038	266	56_50_N_24_45_E
1,977	A*01:383	Russia Moscow Bashkir	0.9	0.0045	442	55_46_N_37_41_E
1,978	A*01:383	Russia Moscow Buryat	0.7	0.0034	149	55_46_N_37_41_E
1,979	A*01:383	Russia Moscow Kabardin	0.4	0.0018	834	55_46_N_37_41_E
1,980	A*01:383	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
1,981	A*01:383	Russia Moscow Russians	0.4	0.0022	13,147	55_46_N_37_41_E
1,982	A*01:383	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
1,983	A*01:383	Russia Moscow Tatars	0.4	0.0018	2,512	55_46_N_37_41_E
1,984	A*02	Albania		0.3060	160	41_0_N_20_0_E
1,985	A*02	Albania pop 2	56.3	0.3275	432	41_9_N_20_10_E
1,986	A*02	Albanian Kosovo		0.3250	120	41_50_N_20_1_E
1,987	A*02	Algeria pop 2	43.0		106	36_42_N_3_13_E
1,988	A*02	Argentina Buenos Aires	45.1	0.2600	466	34_38_S_58_28_W
1,989	A*02	Argentina Buenos Aires pop 2	47.2	0.2580	1,216	34_36_S_58_22_W
1,990	A*02	Argentina Chiriguano	45.3	0.2640	54	23_8_S_64_20_W
1,991	A*02	Argentina Corrientes	44.5	0.2640	155	27_30_S_58_49_W
1,992	A*02	Argentina Cuyo Region	43.2	0.2470	420	32_53_S_68_49_W
1,993	A*02	Argentina La Plata	47.0	0.2700	100	34_55_S_57_57_W
1,994	A*02	Argentina Rosario Toba	45.3	0.2850	86	32_57_S_60_39_W
1,995	A*02	Argentina Salta Wichi pop 2	47.4	0.2890	19	23_8_S_64_20_W
1,996	A*02	Armenia Ghegharkunik		0.1880	242	40_11_N_44_31_E
1,997	A*02	Armenia living in Iran		0.1880	85	40_11_N_44_31_E
1,998	A*02	Armenia living in Karabakh		0.1930	445	40_11_N_44_31_E
1,999	A*02	Armenia living in Lebanon		0.1850	368	40_11_N_44_31_E
2,000	A*02	Armenia living in USA		0.2000	233	40_11_N_44_31_E
2,001	A*02	Armenia Lori		0.1670	102	40_11_N_44_31_E
2,002	A*02	Armenia Shirak		0.2500	76	40_11_N_44_31_E
2,003	A*02	Armenia Syunik		0.1880	117	40_11_N_44_31_E
2,004	A*02	Armenia Yerevan		0.1800	445	40_11_N_44_31_E
2,005	A*02	Australia West Caucasian	46.5	0.2680	891	31_5_S_115_5_E
2,006	A*02	Austria	49.5	0.3120	200	48_13_N_16_21_E
2,007	A*02	Austria Cord Bloods		0.2996	1,368	47_4_N_15_26_E
2,008	A*02	Azerbaijan	37.7	0.2132	2,301	40_39_N_47_47_W
2,009	A*02	Azores		0.2720	1,050	37_44_N_25_40_W
2,010	A*02	Azores Terceira Island		0.3030	130	38_44_N_27_19_W
2,011	A*02	Bangladesh Dhaka Bangalee		0.1450	141	23_42_N_90_22_E
2,012	A*02	Bolivia La Paz Aymaras		0.6030	87	16_29_S_68_7_W
2,013	A*02	Bolivia Quechua		0.5072	69	16_30_S_68_9_W
2,014	A*02	Bosnia and Herzegovina		0.3470	134	43_52_N_18_25_E
2,015	A*02	Bosnia and Herzegovina pop 2	61.7	0.3750	120	44_32_N_18_41_E
2,016	A*02	Brazil Paraná Caucasian	47.7	0.2785	641	23_25_S_51_55_W
2,017	A*02	Brazil Curitiba-Parana Mixed		0.2418	264	25_25_S_49_16_W
2,018	A*02	Brazil Minas Gerais State Mixed	41.6	0.2360	1,000	19_49_S_43_57_W
2,019	A*02	Brazil Para Cord Blood Unit	47.9	0.2717	841	6_0_S_52_0_W
2,020	A*02	Brazil Para State	42.8	0.2380	5,000	6_0_S_52_0_W
2,021	A*02	Brazil Parana Afro Brazilian	32.5	0.1620	77	25_0_S_49_0_W
2,022	A*02	Brazil Parana Cafuzo	40.4	0.2020	319	25_0_S_49_0_W
2,023	A*02	Brazil Parana Caucasian	46.5	0.2320	2,775	25_0_S_49_0_W
2,024	A*02	Brazil Parana Japanese	47.0	0.2708	192	22_29_S_48_2_W
2,025	A*02	Brazil Parana Mixed race	45.7	0.2280	186	25_0_S_49_0_W
2,026	A*02	Brazil Parana Oriental	36.4	0.1820	33	25_0_S_49_0_W
2,027	A*02	Brazil Pernambuco Mixed		0.2870	101	8_3_S_34_54_W
2,028	A*02	Brazil Piaui Mixed		0.2550	21,943	2_44_S_40_25_W
2,029	A*02	Brazil REDOME Acre		0.2340	859	9_11_S_70_52_W
2,030	A*02	Brazil REDOME Alagoas		0.2505	25,349	9_57_S_36_55_W
2,031	A*02	Brazil REDOME Amapa		0.2550	17,864	1_0_N_52_0_W
2,032	A*02	Brazil REDOME Amazonas		0.2700	24,129	5_0_S_63_0_W
2,033	A*02	Brazil REDOME Bahia		0.2367	47,399	12_31_S_41_41_W
2,034	A*02	Brazil REDOME Ceara		0.2595	101,217	5_5_S_39_39_W
2,035	A*02	Brazil REDOME Espirito Santo		0.2535	88,485	20_19_S_40_20_W
2,036	A*02	Brazil REDOME Federal District		0.2604	29,549	15_46_S_47_56_W
2,037	A*02	Brazil REDOME Goias		0.2616	88,574	15_56_S_50_8_W
2,038	A*02	Brazil REDOME Maranhao		0.2526	10,180	2_31_S_44_18_W
2,039	A*02	Brazil REDOME Mato Grosso		0.2560	34,649	15_35_S_56_4_W
2,040	A*02	Brazil REDOME Mato Grosso do Sul		0.2533	95,667	20_26_S_54_38_W
2,041	A*02	Brazil REDOME Minas Gerais		0.2589	211,275	19_49_S_43_57_W
2,042	A*02	Brazil REDOME Para		0.2576	72,637	5_40_S_52_44_W
2,043	A*02	Brazil REDOME Paraiba		0.2562	43,868	7_10_S_36_50_W
2,044	A*02	Brazil REDOME Parana		0.2666	341,639	24_0_S_51_0_W
2,045	A*02	Brazil REDOME Pernambuco		0.2497	92,332	8_20_S_48_48_W
2,046	A*02	Brazil REDOME Piaui		0.2563	46,140	8_14_S_43_6_W
2,047	A*02	Brazil REDOME Rio de Janeiro		0.2482	139,322	22_54_S_43_11_W
2,048	A*02	Brazil REDOME Rio Grande do Norte		0.2546	46,603	5_44_S_36_33_W
2,049	A*02	Brazil REDOME Rio Grande do Sul		0.2745	241,329	30_0_S_53_0_W
2,050	A*02	Brazil REDOME Rondonia		0.2548	54,396	10_54_S_62_46_W
2,051	A*02	Brazil REDOME Roraima		0.2750	4,140	2_3_N_61_24_W
2,052	A*02	Brazil REDOME Santa Catarina		0.2804	106,673	27_15_S_50_20_W
2,053	A*02	Brazil REDOME Sao Paulo		0.2563	800,809	23_33_S_46_38_W
2,054	A*02	Brazil REDOME Sergipe		0.2471	7,321	10_35_S_37_23_W
2,055	A*02	Brazil REDOME Tocantins		0.2551	20,692	10_11_S_48_20_W
2,056	A*02	Brazil Rio Grande do Norte Mestizo		0.2540	12,973	5_47_S_35_12_W
2,057	A*02	Brazil Rio Grande do Sul Black		0.2230	248	30_0_S_53_0_W
2,058	A*02	Brazil Rio Grande do Sul Caucasoid		0.2820	4,428	30_0_S_53_0_W
2,059	A*02	Brazil Rio Grande do Sul Mixed		0.2600	324	30_0_S_53_0_W
2,060	A*02	Brazil Sao Paulo Bauru Mixed		0.2630	3,542	22_18_S_49_3_W
2,061	A*02	Brazil Sao Paulo Mixed	47.0	0.2720	239	23_0_S_48_0_W
2,062	A*02	Brazil South East Cord Blood		0.2539	11,409	14_14_S_51_55_W
2,063	A*02	Brazil South Ribeirao Preto		0.2170	184	21_11_S_47_49_W
2,064	A*02	Burkina Faso Mossi		0.2070	53	12_50_N_1_50_W
2,065	A*02	Cameroon Bakola Pygmy	50.0		50	4_3_N_9_42_E
2,066	A*02	Cameroon Bamileke 2		0.0860	35	5_15_N_10_20_E
2,067	A*02	Cameroon Ewondo		0.1840	38	3_55_N_11_24_E
2,068	A*02	Cameroon Podokwo		0.2330	43	10_37_N_14_20_E
2,069	A*02	Cameroon Uldeme		0.3140	35	10_37_N_14_20_E
2,070	A*02	Central African Republic Mbenzele Pygmy	37.0		36	3_32_N_16_4_E
2,071	A*02	Chile Santiago		0.2663	920	33_26_S_70_40_W
2,072	A*02	China Guangdong Province	51.4		403	23_20_N_113_30_E
2,073	A*02	China Harbin Korean		0.3070	201	45_45_N_126_38_E
2,074	A*02	China Harbin Manchu		0.3460	172	45_45_N_126_38_E
2,075	A*02	China Jiangsu Province		0.2960	20,248	33_0_N_120_0_E
2,076	A*02	China North Han		0.0000	105	39_54_N_116_24_E
2,077	A*02	China Shaanxi Province Han		0.2970	10,000	34_0_N_109_0_E
2,078	A*02	China Shandong Province	49.4	0.2880	7,418	36_30_N_118_0_E
2,079	A*02	China Shandong Province Linqu County	61.2		139	36_0_N_118_0_E
2,080	A*02	China Shanghai		0.3130	26,266	31_12_N_121_30_E
2,081	A*02	China Wuhan	52.9	0.3100	121	30_45_N_114_15_E
2,082	A*02	China Yunnan Province Han pop 2		0.1930	129	24_30_N_101_30_E
2,083	A*02	China Yunnan Province Naxi		0.2780	118	24_30_N_101_30_E
2,084	A*02	Colombia Barranquilla		0.2260	188	10_57_N_74_47_W
2,085	A*02	Colombia Jaidukama		0.3080	39	6_40_N_75_43_W
2,086	A*02	Colombia San Basilio de Palenque		0.3100	42	10_6_N_75_11_W
2,087	A*02	Colombia Wayu from Guajira Peninsula		0.3646	48	12_2_N_71_44_W
2,088	A*02	Croatia pop 2		0.3190	100	45_0_N_15_0_E
2,089	A*02	Croatia pop 3	48.3	0.2417	120	45_6_N_15_12_E
2,090	A*02	Cuba Mixed		0.3280	78	23_8_N_82_23_W
2,091	A*02	Cuba Mixed pop 2		0.1810	189	23_8_N_82_23_W
2,092	A*02	Cuba Mixed Race	35.7	0.2260	42	23_8_N_82_22_W
2,093	A*02	Czech Republic Kurd	40.5	0.2162	74	49_44_N_15_19_E
2,094	A*02	Czech Republic Romani pop 2	60.9	0.3913	46	49_44_N_15_19_E
2,095	A*02	Ecuador Amazonia Mixed Ancestry		0.3846	39	0_48_S_76_54_W
2,096	A*02	Ecuador Andes Mixed Ancestry		0.4102	824	1_28_S_78_49_W
2,097	A*02	Ecuador Coast Mixed Ancestry		0.2962	238	2_11_S_79_53_W
2,098	A*02	Ecuador Mixed Ancestry		0.3845	1,173	0_10_S_78_28_W
2,099	A*02	England Lancaster	48.1		545	54_2_N_2_48_W
2,100	A*02	England Leeds	49.9		5,024	53_47_N_1_32_W
2,101	A*02	England Liverpool	47.3		1,343	53_24_N_3_0_W
2,102	A*02	England London Ashkenazi Jews		0.2120	500	51_30_N_0_7_W
2,103	A*02	England Manchester	47.3		1,640	53_28_N_2_14_W
2,104	A*02	England Newcastle	48.5		2,739	54_58_N_1_36_W
2,105	A*02	England North West Mixed pop 2	41.7	0.2098	379	53_28_N_2_14_W
2,106	A*02	England Sheffield	49.1		4,755	53_23_N_1_28_W
2,107	A*02	France Bordeaux	51.8	0.2658	990	44_50_N_0_34_W
2,108	A*02	France Corsica Island		0.2640	100	42_9_N_9_5_E
2,109	A*02	France Grenoble	52.3	0.2649	1,000	45_12_N_5_43_E
2,110	A*02	France Grenoble, Nantes and Rennes		0.2910	6,094	47_13_N_1_33_W
2,111	A*02	France Lyon	50.5	0.2577	4,813	45_45_N_4_50_E
2,112	A*02	France Marseille	52.2	0.2650	1,000	43_17_N_5_22_E
2,113	A*02	France Reims		0.2300	102	49_15_N_4_2_E
2,114	A*02	France Rennes pop 3		0.2700	200	48_6_N_1_40_W
2,115	A*02	France Rennes pop 4	51.9	0.2633	1,000	48_6_N_1_40_W
2,116	A*02	France Southeast	42.3	0.2400	130	46_0_N_5_0_E
2,117	A*02	Gaza Palestinians		0.2030	165	31_31_N_34_27_E
2,118	A*02	Georgia Kurds		0.1550	30	41_43_N_44_47_E
2,119	A*02	Germany	49.9		11,407	50_6_N_8_40_E
2,120	A*02	Germany Essen	51.2	0.2820	174	51_27_N_7_0_E
2,121	A*02	Ghana Accre Asutuare Akan		0.2060	429	6_5_N_0_12_E
2,122	A*02	Greece Crete		0.2550	135	35_0_N_25_0_E
2,123	A*02	Greece North		0.2700	500	40_0_N_22_0_E
2,124	A*02	Greece pop 7		0.2386	11,250	37_58_N_23_43_E
2,125	A*02	Greece pop3	44.3		246	37_58_N_23_43_E
2,126	A*02	Guatemala Mayan		0.3630	132	14_8_N_91_5_W
2,127	A*02	Guatemala, Guatemala City Mixed Ancestry	49.6	0.2677	127	14_37_N_90_31_W
2,128	A*02	India Bikaner Non-Raikas	29.1		127	28_1_N_73_18_E
2,129	A*02	India Bikaner Raikas	36.1		143	28_1_N_73_18_E
2,130	A*02	India Jalpaiguri Toto	40.0		40	26_30_N_88_42_E
2,131	A*02	India Kerala Hindu Ezhava		0.2500	24	8_48_N_76_36_E
2,132	A*02	India Kerala Hindu Nair		0.1950	41	8_48_N_76_36_E
2,133	A*02	India Kerala Hindu Namboothiri		0.1500	40	11_14_N_75_44_E
2,134	A*02	India Kerala Hindu Pulaya		0.0630	16	11_48_N_75_19_E
2,135	A*02	India Kerala Malabar Muslim		0.1910	34	11_2_N_76_3_E
2,136	A*02	India Kerala Syrian Christian		0.1290	31	9_35_N_76_31_E
2,137	A*02	India North Gujarat	37.6	0.2101	338	23_13_N_72_41_E
2,138	A*02	India North pop 3		0.1690	587	28_38_N_77_9_E
2,139	A*02	India sub-Himalayan West Bengal Kami	28.5	0.1551	158	27_2_N_88_15_E
2,140	A*02	Iran pop 1		0.1560	64	32_35_N_54_16_E
2,141	A*02	Iran pop 4		0.1830	855	35_41_N_51_38_E
2,142	A*02	Iran Royan Cord Blood Bank	31.3		15,600	35_41_N_51_25_E
2,143	A*02	Iraq Arabs	26.9	0.1477	149	33_18_N_44_21_E
2,144	A*02	Iraq Erbil	35.5	0.1777	372	33_33_N_43_43_E
2,145	A*02	Iraq Kurdistan Region	31.1	0.1700	209	36_51_N_43_0_E
2,146	A*02	Ireland South pop 2		0.2762	17,624	53_20_N_6_15_W
2,147	A*02	Israel Ashkenazi Jews pop 2		0.2470	132	31_47_N_35_13_E
2,148	A*02	Israel Jews pop 2	33.1	0.1820	23,000	32_5_N_34_48_E
2,149	A*02	Israel Moroccan Jews		0.1690	113	31_47_N_35_13_E
2,150	A*02	Italy		0.2540	159,311	45_28_N_9_11_E
2,151	A*02	Italy East Sicily	50.0	0.3100	50	37_30_N_15_4_E
2,152	A*02	Italy Lombardy	46.0	0.2680	674	45_11_N_9_9_E
2,153	A*02	Italy North Pavia		0.2530	81	45_11_N_9_9_E
2,154	A*02	Italy North Pavia pop 2	45.1	0.2640	2,416	45_11_N_9_9_E
2,155	A*02	Italy North Pavia pop 3		0.2255	685	45_11_N_9_9_E
2,156	A*02	Italy pop 6	42.9	0.2364	184	41_53_N_12_29_E
2,157	A*02	Italy Rome	52.0	0.3000	100	41_54_N_12_27_E
2,158	A*02	Italy Sardinia pop3		0.2850	100	39_14_N_9_3_E
2,159	A*02	Italy South Campania Region	43.0		1,089	40_50_N_14_15_E
2,160	A*02	Japan Aichi	32.6	0.1710	50	35_11_N_136_54_E
2,161	A*02	Japan Hyogo	28.1	0.1550	32	34_0_N_135_0_E
2,162	A*02	Japan Hyogo Osaka and Nagano		0.2210	195	34_41_N_135_30_E
2,163	A*02	Japan Kyoto and Osaka		0.2270	165	35_1_N_135_46_E
2,164	A*02	Japan pop 11	32.2		62	35_41_N_139_46_E
2,165	A*02	Japan pop 2		0.2350	916	35_41_N_139_46_E
2,166	A*02	Jordan		0.2130	15,141	31_57_N_35_56_E
2,167	A*02	Macedonia		0.2902	286	41_32_N_21_42_E
2,168	A*02	Macedonia MBMDR - Albanian	43.8	0.2617	128	41_46_N_22_19_E
2,169	A*02	Macedonia MBMDR - Macedonian	51.3	0.3005	1,283	41_46_N_22_19_E
2,170	A*02	Macedonia MBMDR - Macedonian Muslims	56.6	0.3224	76	41_46_N_22_19_E
2,171	A*02	Macedonia pop 3		0.2560	172	42_0_N_22_0_E
2,172	A*02	Macedonia pop 4		0.3220	216	42_0_N_21_0_E
2,173	A*02	Madeira pop 2		0.2280	173	32_44_N_16_58_W
2,174	A*02	Madeira pop 3		0.2650	538	32_44_N_16_58_W
2,175	A*02	Malaysia Negeri Sembilan Minangkabau		0.1200	34	2_43_N_101_57_E
2,176	A*02	Malaysia Perak and Johor Banjar Bugis Jawa		0.1490	94	4_24_N_101_5_E
2,177	A*02	Malaysia Perak Rawa		0.1900	23	1_29_N_103_45_E
2,178	A*02	Malaysia pop 2		0.1880	62	3_8_N_101_42_E
2,179	A*02	Malaysia pop 3		0.1510	1,445	3_8_N_101_42_E
2,180	A*02	Malaysia Sabah Kadazan		0.0790	57	5_15_N_117_0_E
2,181	A*02	Malaysia Sarawak Bidayuh		0.1000	50	1_25_N_110_9_E
2,182	A*02	Malaysia Sarawak Iban		0.0880	51	2_18_N_111_49_E
2,183	A*02	Martinique		0.1600	100	14_40_N_61_0_W
2,184	A*02	Mexico Aguascalientes state		0.4000	95	22_1_N_102_21_W
2,185	A*02	Mexico Baja Californa, Mexicali		0.2750	100	32_37_N_115_27_W
2,186	A*02	Mexico Baja California Rural		0.2800	50	27_31_N_113_40_W
2,187	A*02	Mexico Baja California, La Paz		0.3467	75	24_8_N_110_18_W
2,188	A*02	Mexico Baja California, Tijuana		0.3000	25	32_30_N_117_2_W
2,189	A*02	Mexico Campeche Rural		0.2553	47	18_6_N_89_48_W
2,190	A*02	Mexico Campeche, Campeche city		0.2353	34	19_49_N_90_32_W
2,191	A*02	Mexico Chiapas Rural		0.3017	121	16_47_N_92_41_W
2,192	A*02	Mexico Chiapas, Tuxtla Gutierrez		0.3679	52	16_45_N_93_6_W
2,193	A*02	Mexico Chihuahua Chihuahua City		0.2815	119	28_37_N_106_4_W
2,194	A*02	Mexico Chihuahua Rural		0.2636	236	27_18_N_107_53_W
2,195	A*02	Mexico Chihuahua, Ciudad Juarez		0.3426	106	31_41_N_106_25_W
2,196	A*02	Mexico Coahuila Rural		0.3486	216	26_59_N_102_3_W
2,197	A*02	Mexico Coahuila, Saltillo		0.3630	72	25_26_N_100_58_W
2,198	A*02	Mexico Coahuila, Torreon		0.2963	396	25_32_N_103_24_W
2,199	A*02	Mexico Colima Rural		0.2841	43	19_30_N_103_37_W
2,200	A*02	Mexico Colima, Colima city		0.3279	61	19_14_N_103_43_W
2,201	A*02	Mexico Durango Rural		0.3073	326	26_41_N_103_44_W
2,202	A*02	Mexico Durango, Durango city		0.2419	153	24_1_N_104_39_W
2,203	A*02	Mexico Guanajuato Rural		0.2914	162	20_54_N_100_44_W
2,204	A*02	Mexico Guanajuato, Guanajuato city		0.2955	22	21_2_N_101_15_W
2,205	A*02	Mexico Guanajuato, Leon		0.3333	78	21_7_N_101_41_W
2,206	A*02	Mexico Guerrero state		0.3681	144	17_36_N_99_57_W
2,207	A*02	Mexico Hidalgo Rural		0.3889	81	20_38_N_98_59_W
2,208	A*02	Mexico Hidalgo, Pachuca		0.4268	41	20_6_N_98_45_W
2,209	A*02	Mexico Huasteca Region Teenek		0.3980	55	22_1_N_100_9_W
2,210	A*02	Mexico Jalisco Rural		0.2875	585	20_52_N_103_50_W
2,211	A*02	Mexico Jalisco, Guadalajara city		0.2768	1,189	20_39_N_103_20_W
2,212	A*02	Mexico Jalisco, Tlajomulco		0.3167	30	20_28_N_103_26_W
2,213	A*02	Mexico Jalisco, Tlaquepaque		0.3590	39	20_37_N_103_19_W
2,214	A*02	Mexico Jalisco, Tonala		0.3000	35	20_37_N_103_14_W
2,215	A*02	Mexico Jalisco, Zapopan		0.2887	168	20_40_N_103_24_W
2,216	A*02	Mexico Mestizo	65.8	0.4020	41	19_24_N_99_9_W
2,217	A*02	Mexico Mestizo pop 4		0.3086	269	19_26_N_99_8_W
2,218	A*02	Mexico Mexico City Center		0.3084	152	19_25_N_99_7_W
2,219	A*02	Mexico Mexico City East		0.4063	79	19_17_N_99_2_W
2,220	A*02	Mexico Mexico City Mestizo		0.3220	121	19_25_N_99_8_W
2,221	A*02	Mexico Mexico City Mestizo pop 3		0.3050	256	19_25_N_99_8_W
2,222	A*02	Mexico Mexico City Metropolitan Area Rural		0.3092	150	19_7_N_98_45_W
2,223	A*02	Mexico Mexico City North		0.4973	751	19_29_N_99_7_W
2,224	A*02	Mexico Mexico City South		0.3846	52	19_18_N_99_12_W
2,225	A*02	Mexico Mexico City West		0.2273	33	19_21_N_99_17_W
2,226	A*02	Mexico Michoacan Rural		0.3009	348	19_38_N_101_38_W
2,227	A*02	Mexico Michoacan Tarasco	58.4	0.3530	130	19_46_N_101_11_W
2,228	A*02	Mexico Michoacan, Morelia		0.2914	150	19_42_N_101_11_W
2,229	A*02	Mexico Morelos Rural		0.1667	30	18_57_N_98_58_W
2,230	A*02	Mexico Morelos, Cuernavaca		0.4207	82	18_55_N_99_13_W
2,231	A*02	Mexico Nayarit Rural		0.2344	64	20_45_N_105_20_W
2,232	A*02	Mexico Nayarit, Tepic		0.2732	97	21_30_N_104_53_W
2,233	A*02	Mexico Nuevo Leon Rural		0.3455	439	25_37_N_100_14_W
2,234	A*02	Mexico Nuevo Leon, Monterrey city		0.3473	226	25_41_N_100_18_W
2,235	A*02	Mexico Oaxaca Rural		0.3676	485	15_49_N_96_19_W
2,236	A*02	Mexico Oaxaca, Oaxaca city		0.4636	151	17_4_N_96_43_W
2,237	A*02	Mexico Puebla Mestizo		0.3330	99	19_3_N_98_12_W
2,238	A*02	Mexico Puebla Rural		0.3477	833	19_57_N_97_38_W
2,239	A*02	Mexico Puebla, Puebla city		0.3642	1,994	19_2_N_98_12_W
2,240	A*02	Mexico Queretaro Rural		0.3140	43	20_35_N_100_23_W
2,241	A*02	Mexico Queretaro, Queretaro city		0.3333	45	20_34_N_100_22_W
2,242	A*02	Mexico Quintana Roo Rural		0.1900	50	20_12_N_87_25_W
2,243	A*02	Mexico Quintana Roo, Cancun		0.2708	48	21_9_N_86_51_W
2,244	A*02	Mexico San Luis Potosi Rural		0.3621	87	21_20_N_98_58_W
2,245	A*02	Mexico San Luis Potosi, San Luis Potosi city		0.2000	30	22_9_N_100_59_W
2,246	A*02	Mexico San Vicente Tancuayalab Teenek/Huastecos		0.3962	53	21_43_N_98_35_W
2,247	A*02	Mexico Sinaloa Capomos Mayo Yoremes		0.4417	60	26_25_N_108_30_W
2,248	A*02	Mexico Sinaloa Mestizo		0.2770	56	24_48_N_107_10_W
2,249	A*02	Mexico Sinaloa Rural		0.3197	183	25_21_N_107_33_W
2,250	A*02	Mexico Sinaloa, Culiacán		0.3155	103	24_48_N_107_23_W
2,251	A*02	Mexico Sonora Rural		0.3756	197	32_15_N_114_30_W
2,252	A*02	Mexico Sonora, Ciudad Obregón		0.3007	143	27_28_N_109_55_W
2,253	A*02	Mexico Sonora, Hermosillo		0.3232	99	29_4_N_110_57_W
2,254	A*02	Mexico Tabasco Rural		0.3134	142	18_23_N_93_12_W
2,255	A*02	Mexico Tabasco, Villahermosa		0.2805	82	17_59_N_92_56_W
2,256	A*02	Mexico Tamaulipas Rural		0.2976	125	22_23_N_97_56_W
2,257	A*02	Mexico Tamaulipas, Ciudad Victoria		0.4348	23	23_44_N_99_8_W
2,258	A*02	Mexico Tapachula, Chiapas Mestizo Population	47.2	0.2639	72	14_54_N_92_15_W
2,259	A*02	Mexico Tlaxcala Rural		0.4169	830	19_14_N_98_20_W
2,260	A*02	Mexico Tlaxcala, Tlaxcala city		0.3702	181	19_18_N_98_14_W
2,261	A*02	Mexico Veracruz Rural		0.3762	539	18_26_N_95_12_W
2,262	A*02	Mexico Veracruz, Coatzacoalcos		0.2768	55	18_8_N_94_27_W
2,263	A*02	Mexico Veracruz, Cordoba		0.3839	56	18_53_N_96_55_W
2,264	A*02	Mexico Veracruz, Orizaba		0.2917	60	18_51_N_97_6_W
2,265	A*02	Mexico Veracruz, Poza Rica		0.3667	45	20_31_N_97_27_W
2,266	A*02	Mexico Veracruz, Veracruz city		0.3198	171	19_10_N_96_8_W
2,267	A*02	Mexico Veracruz, Xalapa		0.3930	187	19_32_N_96_54_W
2,268	A*02	Mexico Yucatan Rural		0.1866	132	21_16_N_89_31_W
2,269	A*02	Mexico Yucatan, Merida		0.2205	192	20_58_N_89_35_W
2,270	A*02	Mexico Zacatecas Rural		0.3271	266	22_46_N_102_7_W
2,271	A*02	Mexico Zacatecas, Fresnillo		0.2429	103	23_10_N_102_52_W
2,272	A*02	Mexico Zacatecas, Zacatecas city		0.2857	84	22_46_N_102_34_W
2,273	A*02	Mongolia Hoton		0.0940	85	48_40_N_88_18_E
2,274	A*02	Mongolia Khalkha		0.2650	200	45_0_N_107_0_E
2,275	A*02	Mongolia Khalkha pop 2		0.2790	202	47_55_N_106_53_E
2,276	A*02	Mongolia Oold		0.3270	104	48_0_N_96_0_E
2,277	A*02	Mongolia Tarialan Khoton		0.0940	85	49_46_N_91_54_E
2,278	A*02	Mongolia Tsaatan		0.2430	144	51_0_N_101_0_E
2,279	A*02	Mongolia Ulaanbaatar Khalkha		0.1920	41	47_55_N_106_55_E
2,280	A*02	Morocco		0.2620	96	33_14_N_8_30_W
2,281	A*02	Morocco Casablanca		0.2100	100	33_32_N_7_35_W
2,282	A*02	Morocco pop 2		0.1910	110	32_4_N_6_51_W
2,283	A*02	Mozambique		0.1510	202	25_58_S_32_35_E
2,284	A*02	Myanmar Bamar	32.6	0.1848	46	17_54_N_96_31_E
2,285	A*02	Myanmar Chin	38.2	0.2364	55	17_54_N_96_31_E
2,286	A*02	Myanmar Kachin	30.2	0.1825	63	17_54_N_96_31_E
2,287	A*02	Myanmar Kayah	12.7	0.0818	55	17_54_N_96_31_E
2,288	A*02	Myanmar Kayin	34.1	0.1818	44	17_54_N_96_31_E
2,289	A*02	Myanmar Mon	45.3	0.2500	64	17_54_N_96_31_E
2,290	A*02	Myanmar Rakhine	31.3	0.1563	48	17_54_N_96_31_E
2,291	A*02	Myanmar Shan	29.6	0.1481	54	17_54_N_96_31_E
2,292	A*02	Nigeria	31.4	0.1783	258	6_27_N_3_23_E
2,293	A*02	Norway ethnic Norwegians	55.1	0.3300	4,510	61_0_N_10_0_E
2,294	A*02	Norway pop 2		0.3430	576	59_56_N_10_45_E
2,295	A*02	Norway Sami		0.2730	200	70_0_N_25_0_E
2,296	A*02	Pakistan Burusho		0.1250	92	36_0_N_73_0_E
2,297	A*02	Pakistan Mixed Sindhi		0.1650	101	24_0_N_68_0_E
2,298	A*02	Peru Arequipa Mestizo	79.1	0.5450	168	15_46_S_73_21_W
2,299	A*02	Peru Lamas City Lama		0.6340	83	6_25_S_76_32_W
2,300	A*02	Philippines National Capital Region		0.0690	51	14_35_N_120_59_E
2,301	A*02	Poland pop 3	53.6	0.2708	2,907	52_12_N_21_1_E
2,302	A*02	Portugal Aveiro		0.2820	5,933	40_34_N_8_27_W
2,303	A*02	Portugal Beja		0.3030	699	37_30_N_8_3_W
2,304	A*02	Portugal Braga		0.2770	1,971	41_37_N_8_20_W
2,305	A*02	Portugal Bragança		0.2910	301	41_48_N_6_50_W
2,306	A*02	Portugal Castelo Branco		0.2710	327	39_50_N_7_28_W
2,307	A*02	Portugal Center pop 2		0.2700	562	40_11_N_8_26_W
2,308	A*02	Portugal Coimbra		0.2840	5,057	40_13_N_8_3_W
2,309	A*02	Portugal Evora		0.2780	737	38_34_N_7_54_W
2,310	A*02	Portugal Faro		0.2950	1,242	37_2_N_7_55_W
2,311	A*02	Portugal Guarda		0.2650	797	40_49_N_7_33_W
2,312	A*02	Portugal Leiria		0.2800	1,847	39_45_N_8_56_W
2,313	A*02	Portugal Lisbon		0.2790	17,420	38_42_N_9_11_W
2,314	A*02	Portugal North pop 2		0.2721	1,801	41_9_N_8_37_W
2,315	A*02	Portugal Portalegre		0.2700	1,254	39_19_N_7_25_W
2,316	A*02	Portugal Porto		0.2630	7,937	41_9_N_8_38_W
2,317	A*02	Portugal Santarem		0.2760	3,865	39_14_N_8_41_W
2,318	A*02	Portugal Setubal		0.2770	5,268	38_32_N_8_53_W
2,319	A*02	Portugal South pop 2		0.2750	1,021	38_42_N_9_11_W
2,320	A*02	Portugal Viana do Castelo		0.2680	706	41_42_N_8_50_W
2,321	A*02	Portugal Vila Real		0.2740	1,167	41_17_N_7_48_W
2,322	A*02	Portugal Viseu		0.2840	2,915	40_40_N_7_55_W
2,323	A*02	Romania	46.0	0.2610	348	46_0_N_25_0_E
2,324	A*02	Romania pop 2		0.2600	6,936	44_25_N_26_6_E
2,325	A*02	Romania pop 3	50.1	0.2832	1,469	44_25_N_26_6_E
2,326	A*02	Russia Belgorod region	1.3	0.0065	153	50_46_N_37_27_E
2,327	A*02	Russia Arkhangelsk Pomor		0.2600	63	64_0_N_64_0_E
2,328	A*02	Russia Chuvash		0.2500	82	58_0_N_43_0_E
2,329	A*02	Russia Moscow		0.2880	2,650	55_45_N_37_37_E
2,330	A*02	Russia Moscow Pop 2	50.0	0.2948	2,000	55_45_N_37_37_E
2,331	A*02	Russia Murmansk Saomi Mixed		0.2600	70	68_58_N_33_5_E
2,332	A*02	Russia Nenet Mixed		0.2610	55	67_5_N_52_0_E
2,333	A*02	Russia North Ossetian	44.1	0.2480	127	43_2_N_44_40_E
2,334	A*02	Russia Northwest	52.3		1,011	58_0_N_28_0_E
2,335	A*02	Russia Northwest pop 3	54.0	0.2950	100	59_58_N_30_18_E
2,336	A*02	Russia Northwest pop 2	51.2		346	63_0_N_41_0_E
2,337	A*02	Russia Sakhalin Island Nivkhi		0.2830	53	51_0_N_143_0_E
2,338	A*02	Russia Samara Region		0.2780	2,500	53_11_N_50_7_E
2,339	A*02	Russia South Ural Bashkir	58.2	0.3360	146	54_32_N_60_20_E
2,340	A*02	Russia South Ural Russian	51.7	0.2970	207	54_32_N_60_20_E
2,341	A*02	Russia South Ural Tatar	48.9	0.2740	135	54_32_N_60_20_E
2,342	A*02	Russia Tatars		0.2450	355	61_59_N_96_41_E
2,343	A*02	Russia Transbaikal Territory Buryats	43.0	0.2470	150	54_16_N_118_16_E
2,344	A*02	Russia, South Ural, Chelyabinsk region, Nagaybaks	36.6	0.2190	112	53_37_N_59_40_E
2,345	A*02	Rwanda		0.2180	280	1_57_S_30_4_E
2,346	A*02	Saudi Arabia	33.3		18	24_42_N_46_43_E
2,347	A*02	Saudi Arabia pop 2		0.3040	383	24_42_N_46_43_E
2,348	A*02	Saudi Arabia pop 4	49.3	0.2886	499	24_38_N_46_43_E
2,349	A*02	Scotland Orkney	54.0	0.3100	99	58_59_N_3_11_W
2,350	A*02	Serbia		0.2500	386	44_28_N_20_28_E
2,351	A*02	Serbia pop 3	51.0	0.2947	1,992	44_49_N_20_28_E
2,352	A*02	Serbia Vojvodina	51.2	0.3070	400	44_13_N_20_47_E
2,353	A*02	Slovakia pop 2		0.2399	4,234	48_6_N_17_7_E
2,354	A*02	Slovenia pop 3	45.4		130	46_3_N_14_30_E
2,355	A*02	South Africa Natal Zulu	23.0	0.1150	100	29_0_S_31_0_E
2,356	A*02	South Africa Tswana	24.4	0.1460	41	25_51_S_25_38_E
2,357	A*02	South Korea pop 1		0.2750	324	37_0_N_126_0_E
2,358	A*02	South Korea pop 8		0.2920	7,096	37_34_N_126_58_E
2,359	A*02	Spain Andalusia		0.2230	99	37_22_N_5_59_W
2,360	A*02	Spain Andalusia Romani		0.1620	99	37_22_N_5_59_W
2,361	A*02	Spain Arratia Valley Basque		0.1600	83	43_0_N_3_0_W
2,362	A*02	Spain Ibiza		0.2990	88	39_0_N_1_3_E
2,363	A*02	Spain Majorca		0.3120	407	39_37_N_2_59_E
2,364	A*02	Spain Majorca and Minorca		0.2640	90	39_37_N_2_59_E
2,365	A*02	Spain Majorcans Jews		0.1650	103	39_37_N_2_57_E
2,366	A*02	Spain Minorca		0.3480	94	39_58_N_4_5_E
2,367	A*02	Spain Murcia		0.2300	173	37_59_N_1_7_W
2,368	A*02	Spain North Cabuerniga		0.2050	95	43_0_N_4_0_W
2,369	A*02	Spain North Cantabria		0.2710	83	43_0_N_3_0_W
2,370	A*02	Spain Pas Valley		0.2930	88	43_0_N_3_0_W
2,371	A*02	Spain Sevilla	44.6	0.2626	278	37_22_N_5_58_W
2,372	A*02	Spain, Castilla y Leon, Northwest,	48.5	0.2718	1,743	41_50_N_4_23_W
2,373	A*02	Sudan Khartoum		0.3265	98	15_32_N_32_32_E
2,374	A*02	Sudan South Nuba	47.8		46	12_0_N_30_45_E
2,375	A*02	Sweden pop 4	55.2	0.3287	966	59_0_N_18_0_E
2,376	A*02	Sweden Stockholm	58.1	0.3590	1,347	59_21_N_18_4_E
2,377	A*02	Sweden Uppsala	57.6		300	59_51_N_17_38_E
2,378	A*02	Switzerland Aargau-Solothurn		0.3472	1,838	46_48_N_8_13_E
2,379	A*02	Switzerland Basel		0.2972	1,888	47_33_N_7_34_E
2,380	A*02	Switzerland Bern		0.2750	3,545	46_57_N_7_27_E
2,381	A*02	Switzerland Geneva pop 2		0.2476	1,267	46_12_N_6_8_E
2,382	A*02	Switzerland Graubunden		0.2921	759	46_42_N_9_57_E
2,383	A*02	Switzerland Lausanne		0.2594	993	46_31_N_6_39_E
2,384	A*02	Switzerland Lugano		0.2990	1,169	46_0_N_8_55_E
2,385	A*02	Switzerland Luzern		0.2848	1,553	47_3_N_8_18_E
2,386	A*02	Switzerland Sion		0.2411	832	46_15_N_7_23_E
2,387	A*02	Switzerland St Gallen		0.3300	2,113	47_26_N_9_21_E
2,388	A*02	Switzerland Zurich		0.2618	4,875	47_22_N_8_32_E
2,389	A*02	Taiwan Aborigine		0.1310	111	25_5_N_121_5_E
2,390	A*02	Taiwan Chinese immigrants from Middle China		0.3280	211	25_5_N_121_5_E
2,391	A*02	Taiwan Chinese immigrants from North China		0.2880	152	25_5_N_121_5_E
2,392	A*02	Taiwan Chinese immigrants from South China		0.3230	172	25_5_N_121_5_E
2,393	A*02	Taiwan Hakka pop 2		0.3160	714	25_5_N_121_5_E
2,394	A*02	Taiwan Minnan and Hakka		0.2960	190	24_0_N_121_0_E
2,395	A*02	Taiwan Minnan pop 2		0.3170	7,137	25_5_N_121_5_E
2,396	A*02	Thailand Northeast pop 2		0.2550	400	16_27_N_103_10_E
2,397	A*02	Thailand pop 4		0.2920	16,807	13_45_N_100_29_E
2,398	A*02	Trinidad African		0.1400	67	10_25_N_61_15_W
2,399	A*02	Trinidad South Asian		0.1100	87	10_25_N_61_15_W
2,400	A*02	Tunisia Ghannouch		0.1460	82	33_3_N_10_20_E
2,401	A*02	Tunisia pop 3		0.1810	104	36_48_N_10_11_E
2,402	A*02	Turkey pop 2		0.2190	228	41_0_N_28_58_E
2,403	A*02	Turkey pop 5		0.3020	142	40_0_N_28_0_E
2,404	A*02	United Arab Emirates pop 2	44.0		373	24_0_N_53_0_E
2,405	A*02	United Kingdom Central		0.2697	135	54_0_N_4_0_W
2,406	A*02	United Kingdom East		0.3037	163	54_0_N_4_0_W
2,407	A*02	United Kingdom North		0.2708	330	54_0_N_4_0_W
2,408	A*02	United Kingdom Orkney		0.2914	88	54_0_N_4_0_W
2,409	A*02	United Kingdom pop 2		0.2700	101	51_30_N_0_7_W
2,410	A*02	United Kingdom South West		0.3067	163	54_0_N_4_0_W
2,411	A*02	USA Caucasian	49.6		61,655	38_53_N_77_2_W
2,412	A*02	USA OPTN African American		0.1850	1,510	38_53_N_77_2_W
2,413	A*02	USA OPTN Asian		0.2700	261	38_53_N_77_2_W
2,414	A*02	USA OPTN Caucasian		0.2930	8,525	38_53_N_77_2_W
2,415	A*02	USA OPTN Hispanic		0.2780	1,580	38_53_N_77_2_W
2,416	A*02	Venezuela Caracas Mestizo		0.3400	50	10_30_N_66_55_W
2,417	A*02	Venezuela Colonia Tovar		0.2151	86	10_15_N_67_10_W
2,418	A*02	Venezuela Valencia and Maracaibo Caracus Mixed		0.2290	96	10_10_N_68_0_W
2,419	A*02	Venezuela Zulia Maracaibo Mixed		0.3000	20	10_38_N_71_26_W
2,420	A*02	Venezuela Zulia San Jose de Heras Mixed		0.2250	20	10_36_N_72_0_W
2,421	A*02	Vietnam Hanoi		0.2500	50	21_2_N_105_51_E
2,422	A*02	Wales	51.3	0.3020	1,798	51_29_N_3_11_W
2,423	A*02	Wales pop 2	49.8	0.2920	39,979	51_29_N_3_11_W
2,424	A*02:01	American Samoa		0.1300	51	14_18_S_170_42_W
2,425	A*02:01	Argentina Gran Chaco Eastern Toba	46.4	0.3040	135	26_11_S_58_11_W
2,426	A*02:01	Argentina Gran Chaco Mataco Wichi	40.9	0.2160	49	27_27_S_58_59_W
2,427	A*02:01	Argentina Gran Chaco Western Toba Pilaga	60.0	0.4000	19	27_27_S_58_59_W
2,428	A*02:01	Argentina Rosario Toba	34.9	0.1920	86	32_57_S_60_39_W
2,429	A*02:01	Armenia combined Regions		0.1550	100	40_11_N_44_31_E
2,430	A*02:01	Australia Cape York Peninsula Aborigine		0.1750	103	10_41_S_142_32_E
2,431	A*02:01	Australia Groote Eylandt Aborigine		0.1070	75	13_58_S_136_35_E
2,432	A*02:01	Australia Kimberly Aborigine		0.1110	41	15_46_S_128_44_E
2,433	A*02:01	Australia New South Wales Caucasian		0.2610	134	33_0_S_146_0_E
2,434	A*02:01	Australia Yuendumu Aborigine		0.1130	191	22_15_S_131_47_E
2,435	A*02:01	Austria	48.0	0.2940	200	48_13_N_16_21_E
2,436	A*02:01	Azores Central Islands		0.2590	59	38_0_N_28_0_W
2,437	A*02:01	Azores Oriental Islands		0.2560	43	36_58_N_25_6_W
2,438	A*02:01	Azores Terceira Island		0.2330	130	38_44_N_27_19_W
2,439	A*02:01	Belgium	50.0	0.2660	99	50_50_N_4_21_E
2,440	A*02:01	Belgium pop 3	45.1	0.2591	31,412	51_2_N_4_28_E
2,441	A*02:01	Brazil Puyanawa	54.0	0.2970	150	10_52_S_53_5_W
2,442	A*02:01	Brazil Belo Horizonte Caucasian	43.2	0.2370	95	19_55_S_43_56_W
2,443	A*02:01	Brazil Mixed		0.1920	108	23_10_S_47_48_W
2,444	A*02:01	Brazil Terena	38.3	0.2080	60	20_0_S_55_0_W
2,445	A*02:01	Brazil Vale do Ribeira Quilombos	0.1	0.0000	144	24_36_S_48_15_W
2,446	A*02:01	Bulgaria Romani		0.2080	13	42_44_N_23_20_E
2,447	A*02:01	Burkina Faso Fulani		0.0510	49	12_50_N_1_50_W
2,448	A*02:01	Burkina Faso Rimaibe		0.1380	47	12_50_N_1_50_W
2,449	A*02:01	Cameroon Baka Pygmy		0.0000	10	8_30_N_14_0_E
2,450	A*02:01	Cameroon Bamileke		0.0110	77	5_28_N_10_25_E
2,451	A*02:01	Cameroon Beti		0.1120	174	2_55_N_11_9_E
2,452	A*02:01	Cameroon Sawa		0.0380	13	4_50_N_10_0_E
2,453	A*02:01	Cameroon Yaounde		0.0710	92	3_52_N_11_31_E
2,454	A*02:01	Chile Easter Island		0.0950	21	27_7_S_109_22_W
2,455	A*02:01	Chile Mapuche		0.2238	66	37_48_S_73_23_W
2,456	A*02:01	Chile Santiago Mixed	30.0		70	33_26_S_70_39_W
2,457	A*02:01	China Beijing		0.1870	67	39_58_N_116_24_E
2,458	A*02:01	China Beijing Shijiazhuang Tianjian Han		0.1580	618	39_54_N_116_24_E
2,459	A*02:01	China Canton Han		0.1530	264	23_6_N_113_15_E
2,460	A*02:01	China Guangxi Region Maonan		0.0460	108	23_48_N_108_59_E
2,461	A*02:01	China Guangzhou		0.1280	102	23_6_N_113_15_E
2,462	A*02:01	China Guizhou Province Bouyei		0.0210	109	26_50_N_106_50_E
2,463	A*02:01	China Guizhou Province Miao pop 2		0.0240	85	26_50_N_106_50_E
2,464	A*02:01	China Guizhou Province Shui		0.0450	153	26_50_N_106_50_E
2,465	A*02:01	China Han HIV negative		0.0630	72	36_34_N_103_49_E
2,466	A*02:01	China Henan HIV negative		0.0630	16	34_0_N_113_40_E
2,467	A*02:01	China Hubei Han	20.3	0.1016	3,732	30_45_N_114_15_E
2,468	A*02:01	China Inner Mongolia Region		0.1270	102	36_0_N_102_0_E
2,469	A*02:01	China Jiangsu Han		0.1300	3,238	32_54_N_119_48_E
2,470	A*02:01	China Jiangsu Province Han		0.1289	334	32_54_N_119_48_E
2,471	A*02:01	China Marrow Donor Registry		0.1070	600	39_54_N_116_23_E
2,472	A*02:01	China North Han		0.0000	105	39_54_N_116_24_E
2,473	A*02:01	China Qinghai Province Hui		0.1730	110	36_0_N_102_0_E
2,474	A*02:01	China Shanxi HIV negative		0.2270	22	36_5_N_111_50_E
2,475	A*02:01	China Sichuan HIV negative		0.0880	34	30_50_N_102_49_E
2,476	A*02:01	China South Han		0.0530	284	23_1_N_113_3_E
2,477	A*02:01	China Southwest Dai		0.0160	124	22_0_N_100_48_E
2,478	A*02:01	China Tibet Region Tibetan		0.2180	158	29_39_N_91_7_E
2,479	A*02:01	China Uyghur HIV negative		0.2370	19	36_34_N_103_49_E
2,480	A*02:01	China Yunnan Bulang		0.0130	116	21_57_N_100_27_E
2,481	A*02:01	China Yunnan Hani		0.0330	150	21_59_N_100_49_E
2,482	A*02:01	China Yunnan Province Han		0.0100	101	24_2_N_105_3_E
2,483	A*02:01	China Yunnan Province Lisu		0.0850	111	24_30_N_101_30_E
2,484	A*02:01	China Yunnan Province Nu		0.0890	107	24_30_N_101_30_E
2,485	A*02:01	Colombia Bogotá Cord Blood	30.0	0.1613	1,463	4_39_N_73_52_W
2,486	A*02:01	Colombia North Chimila Amerindians		0.0638	47	10_1_N_74_13_W
2,487	A*02:01	Colombia North Wiwa El Encanto		0.1058	52	10_10_N_73_53_W
2,488	A*02:01	Croatia		0.2630	150	45_0_N_15_0_E
2,489	A*02:01	Croatia pop 4		0.2916	4,000	45_48_N_15_57_E
2,490	A*02:01	Cuba Caucasian	34.3	0.1790	70	23_8_N_82_22_W
2,491	A*02:01	Cuba Mixed Race	31.0	0.1790	42	23_8_N_82_22_W
2,492	A*02:01	Czech Republic		0.2740	106	50_0_N_14_0_E
2,493	A*02:01	Czech Republic NMDR		0.2431	5,099	49_48_N_15_30_E
2,494	A*02:01	Ecuador Amerindians		0.2540	63	0_13_S_78_30_W
2,495	A*02:01	Ecuador Cayapa		0.0900	183	1_3_N_79_12_W
2,496	A*02:01	England North West	50.7	0.2890	298	53_0_N_3_0_W
2,497	A*02:01	Finland		0.3440	91	63_29_N_25_16_E
2,498	A*02:01	France French Bone Marrow Donor Registry		0.2901	42,623	48_51_N_2_21_E
2,499	A*02:01	France Southeast	38.5	0.2160	130	46_0_N_5_0_E
2,500	A*02:01	Gaza	23.8	0.1190	42	31_22_N_34_18_E
2,501	A*02:01	Georgia Svaneti Region Svan		0.2130	80	42_54_N_43_0_E
2,502	A*02:01	Georgia Tibilisi		0.3100	109	41_41_N_44_57_E
2,503	A*02:01	Georgia Tibilisi Kurd		0.0830	31	41_41_N_44_57_E
2,504	A*02:01	Germany DKMS - Austria minority		0.2677	1,698	48_31_N_9_3_E
2,505	A*02:01	Germany DKMS - Bosnia and Herzegovina minority		0.3050	1,028	48_31_N_9_3_E
2,506	A*02:01	Germany DKMS - China minority		0.1226	1,282	48_31_N_9_3_E
2,507	A*02:01	Germany DKMS - Croatia minority		0.2842	2,057	48_31_N_9_3_E
2,508	A*02:01	Germany DKMS - France minority		0.2572	1,406	48_31_N_9_3_E
2,509	A*02:01	Germany DKMS - German donors		0.2839	3,456,066	48_31_N_9_3_E
2,510	A*02:01	Germany DKMS - Greece minority		0.2629	1,894	48_31_N_9_3_E
2,511	A*02:01	Germany DKMS - Italy minority		0.2252	1,159	48_31_N_9_3_E
2,512	A*02:01	Germany DKMS - Netherlands minority		0.2956	1,374	48_31_N_9_3_E
2,513	A*02:01	Germany DKMS - Portugal minority		0.2564	1,176	48_31_N_9_3_E
2,514	A*02:01	Germany DKMS - Romania minority		0.2763	1,234	48_31_N_9_3_E
2,515	A*02:01	Germany DKMS - Spain minority		0.2367	1,107	48_31_N_9_3_E
2,516	A*02:01	Germany DKMS - Turkey minority		0.1957	4,856	48_31_N_9_3_E
2,517	A*02:01	Germany DKMS - United Kingdom minority		0.2739	1,043	48_31_N_9_3_E
2,518	A*02:01	Germany pop 6		0.2888	8,862	51_6_N_10_23_E
2,519	A*02:01	Germany pop 8		0.2667	39,689	48_24_N_9_58_E
2,520	A*02:01	Ghana Ga-Adangbe	19.1	0.1069	131	5_45_N_0_12_E
2,521	A*02:01	Greece pop 6		0.2624	242	37_58_N_23_43_E
2,522	A*02:01	Greece pop 8	51.8	0.3072	83	37_58_N_23_43_E
2,523	A*02:01	Guinea Bissau Balanta		0.1350	48	11_52_N_15_36_W
2,524	A*02:01	Guinea Bissau Bijago		0.0870	23	11_52_N_15_36_W
2,525	A*02:01	Guinea Bissau Fula		0.1130	31	11_52_N_15_36_W
2,526	A*02:01	Guinea Bissau Papel		0.0400	25	11_52_N_15_36_W
2,527	A*02:01	Hong Kong Chinese	11.9	0.0620	569	22_16_N_114_9_E
2,528	A*02:01	Hong Kong Chinese BMDR		0.1891	7,595	22_15_N_114_10_E
2,529	A*02:01	Hong Kong Chinese cord blood registry		0.1867	3,892	22_15_N_114_10_E
2,530	A*02:01	India Andhra Pradesh Golla		0.0340	111	17_5_N_78_5_E
2,531	A*02:01	India Central UCBB	7.7	0.0384	4,204	27_1_N_81_19_E
2,532	A*02:01	India Delhi pop 2	3.3	0.0170	90	28_36_N_77_13_E
2,533	A*02:01	India East UCBB	7.3	0.0364	2,403	22_25_N_88_18_E
2,534	A*02:01	India Khandesh Region Pawra		0.0000	50	20_54_N_74_46_E
2,535	A*02:01	India Mumbai Maratha		0.0190	91	18_58_N_72_49_E
2,536	A*02:01	India New Delhi		0.0610	71	28_37_N_77_13_E
2,537	A*02:01	India North pop 2		0.0580	72	25_0_N_74_0_E
2,538	A*02:01	India North UCBB	9.1	0.0455	5,849	28_36_N_77_11_E
2,539	A*02:01	India Northeast UCBB	7.8	0.0389	296	26_16_N_91_48_E
2,540	A*02:01	India South UCBB	9.3	0.0467	11,446	13_6_N_80_8_E
2,541	A*02:01	India Tamil Nadu		0.0539	2,492	11_32_N_78_52_E
2,542	A*02:01	India West Bhil		0.0000	50	20_54_N_74_46_E
2,543	A*02:01	India West Coast Parsi		0.0000	50	18_0_N_74_0_E
2,544	A*02:01	India West UCBB	7.9	0.0395	5,829	19_11_N_73_3_E
2,545	A*02:01	Indonesia Java Western	12.7		236	7_30_S_111_15_E
2,546	A*02:01	Iran Gorgan		0.1090	64	36_50_N_54_43_E
2,547	A*02:01	Iran Kurd pop 2		0.1250	60	36_17_N_46_17_E
2,548	A*02:01	Iran Saqqez-Baneh Kurds		0.1250	60	36_14_N_46_16_E
2,549	A*02:01	Iran Tabriz Azeris		0.1340	97	38_4_N_46_17_E
2,550	A*02:01	Iran Yazd		0.1875	56	31_53_N_54_21_E
2,551	A*02:01	Ireland Northern	46.8	0.2740	1,000	54_40_N_6_45_W
2,552	A*02:01	Ireland South	43.6	0.2460	250	53_20_N_6_15_W
2,553	A*02:01	Israel Arab Druze		0.1550	101	31_8_N_35_2_E
2,554	A*02:01	Israel Arab pop 2		0.1498	12,301	32_8_N_34_52_E
2,555	A*02:01	Israel Argentina Jews		0.1839	4,307	32_8_N_34_52_E
2,556	A*02:01	Israel Ashkenazi and Non Ashkenazi Jews		0.1280	146	31_8_N_35_2_E
2,557	A*02:01	Israel Ashkenazi Jews pop 3		0.1539	4,625	32_8_N_34_52_E
2,558	A*02:01	Israel Bukhara Jews		0.0531	2,317	32_8_N_34_52_E
2,559	A*02:01	Israel Druze		0.0302	5,914	32_41_N_35_17_E
2,560	A*02:01	Israel Ethiopia Jews		0.0628	5,928	32_8_N_34_52_E
2,561	A*02:01	Israel Georgia Jews		0.1520	4,471	32_10_N_34_53_E
2,562	A*02:01	Israel Iran Jews		0.0126	8,153	32_8_N_34_52_E
2,563	A*02:01	Israel Iraq Jews		0.0192	13,270	32_10_N_34_52_E
2,564	A*02:01	Israel Kavkazi Jews		0.0998	2,840	32_10_N_34_52_E
2,565	A*02:01	Israel Libya Jews		0.2012	3,739	32_10_N_34_52_E
2,566	A*02:01	Israel Morocco Jews		0.0248	36,718	32_10_N_34_52_E
2,567	A*02:01	Israel Poland Jews		0.1839	13,871	32_10_N_34_52_E
2,568	A*02:01	Israel Tunisia Jews		0.0296	9,070	32_10_N_34_52_E
2,569	A*02:01	Israel USA Jews		0.1896	6,058	32_10_N_34_52_E
2,570	A*02:01	Israel USSR Jews		0.2168	45,681	32_9_N_34_50_E
2,571	A*02:01	Israel YemenJews		0.0271	15,542	32_9_N_34_50_E
2,572	A*02:01	Italy Bergamo	46.5	0.2800	101	45_42_N_9_40_E
2,573	A*02:01	Italy North Pavia		0.0060	81	45_11_N_9_9_E
2,574	A*02:01	Italy North pop 3	53.8	0.2890	97	43_0_N_12_0_E
2,575	A*02:01	Italy pop 5		0.2410	975	45_28_N_9_11_E
2,576	A*02:01	Japan Central		0.1150	371	35_41_N_139_46_E
2,577	A*02:01	Japan Hokkaido Ainu		0.2000	50	43_0_N_142_0_E
2,578	A*02:01	Japan pop 16		0.1162	18,604	35_41_N_139_46_E
2,579	A*02:01	Japan pop 3		0.1160	1,018	35_41_N_139_46_E
2,580	A*02:01	Japan pop 5		0.1060	117	35_41_N_139_46_E
2,581	A*02:01	Jordan Amman		0.1340	146	31_35_N_36_20_E
2,582	A*02:01	Kenya Luo		0.1150	265	0_0_S_34_6_E
2,583	A*02:01	Kenya Nandi		0.1180	240	0_0_S_35_0_E
2,584	A*02:01	Kenya, Nyanza Province, Luo tribe	27.0	0.1450	100	0_5_S_34_46_E
2,585	A*02:01	Kosovo	52.4	0.3186	124	42_34_N_21_0_E
2,586	A*02:01	Malaysia Champa	6.9	0.0340	29	2_30_N_112_30_E
2,587	A*02:01	Malaysia Jelebu Temuan		0.1460	25	2_56_N_102_5_E
2,588	A*02:01	Malaysia Kedah Baling Kensiu		0.2000	25	5_40_N_100_55_E
2,589	A*02:01	Malaysia Kedah Kensiu	57.0	0.4050	21	5_40_N_100_55_E
2,590	A*02:01	Malaysia Kelantan	3.6	0.0180	28	2_30_N_112_30_E
2,591	A*02:01	Malaysia Mandailing	55.6	0.2960	27	2_30_N_112_30_E
2,592	A*02:01	Malaysia Pahang Semai	10.5	0.0660	38	4_11_N_102_3_E
2,593	A*02:01	Malaysia Patani	16.0	0.1000	25	2_30_N_112_30_E
2,594	A*02:01	Malaysia Peninsular Chinese	25.3	0.1418	194	3_9_N_101_57_E
2,595	A*02:01	Malaysia Peninsular Indian	11.4	0.0627	271	3_9_N_101_57_E
2,596	A*02:01	Malaysia Peninsular Malay	11.6	0.0610	951	3_9_N_101_57_E
2,597	A*02:01	Malaysia Perak Grik Jehai		0.1400	25	5_25_N_101_8_E
2,598	A*02:01	Malaysia Sarawak Bau Bidayuh		0.0200	25	1_25_N_110_9_E
2,599	A*02:01	Mali Bandiagara		0.0830	138	14_4_N_3_6_W
2,600	A*02:01	Mexico Chiapas Lacandon Mayans		0.0940	218	16_54_N_92_5_W
2,601	A*02:01	Mexico Chichen Itza Maya (prehispanic)		0.0745	47	20_40_N_88_34_W
2,602	A*02:01	Mexico Chihuahua Chihuahua City Pop 2	46.6	0.2727	88	28_48_N_106_26_W
2,603	A*02:01	Mexico Mestizo	56.1	0.3410	41	19_24_N_99_9_W
2,604	A*02:01	Mexico Mexico City Mestizo pop 2		0.2286	234	19_26_N_99_8_W
2,605	A*02:01	Mexico Mexico City Mestizo population		0.2028	143	19_25_N_99_7_W
2,606	A*02:01	Mexico Mexico City Tlalpan	44.5	0.2591	330	19_25_N_99_8_W
2,607	A*02:01	Mexico Mixtec		0.2530	97	23_57_N_103_29_W
2,608	A*02:01	Mexico Oaxaca Jamiltepec Mixtec		0.2550	96	16_25_N_97_58_W
2,609	A*02:01	Mexico Oaxaca Mixe		0.3110	55	17_5_N_96_45_W
2,610	A*02:01	Mexico Oaxaca Mixtec		0.2840	103	17_5_N_96_45_W
2,611	A*02:01	Mexico Oaxaca Zapotec		0.2840	90	17_5_N_96_45_W
2,612	A*02:01	Mexico Sonora Seri		0.5450	34	28_57_N_112_34_W
2,613	A*02:01	Mexico Tixcacaltuyub Maya		0.0299	67	20_29_N_88_54_W
2,614	A*02:01	Mexico Veracruz Xalapa	47.6	0.2738	84	19_32_N_96_55_W
2,615	A*02:01	Netherlands Leiden		0.2920	1,305	52_9_N_4_31_E
2,616	A*02:01	Netherlands UMCU	45.3	0.2812	64	52_5_N_5_10_E
2,617	A*02:01	New Caledonia		0.0240	65	21_0_S_165_0_E
2,618	A*02:01	New Zealand Maori with Admixed History	16.2	0.0810	105	42_0_S_174_0_E
2,619	A*02:01	New Zealand Maori with Full Ancestry	6.5	0.0330	46	42_0_S_174_0_E
2,620	A*02:01	New Zealand Polynesians with Admixed History	22.2	0.1110	27	42_0_S_174_0_E
2,621	A*02:01	New Zealand Polynesians with Full Ancestry	19.0	0.0950	21	42_0_S_174_0_E
2,622	A*02:01	Nicaragua Managua	32.0	0.1766	339	12_7_N_86_10_W
2,623	A*02:01	Oman	39.8	0.2160	118	22_0_N_56_0_E
2,624	A*02:01	Pakistan Baloch		0.0950	66	30_0_N_67_0_E
2,625	A*02:01	Pakistan Brahui		0.1150	104	30_0_N_67_0_E
2,626	A*02:01	Pakistan Burusho		0.0540	92	36_0_N_73_0_E
2,627	A*02:01	Pakistan Kalash		0.1580	69	32_0_N_70_0_E
2,628	A*02:01	Pakistan Karachi Parsi		0.0280	91	24_49_N_66_59_E
2,629	A*02:01	Pakistan Mixed Pathan		0.0820	100	32_0_N_70_0_E
2,630	A*02:01	Pakistan Mixed Sindhi		0.0700	101	24_0_N_68_0_E
2,631	A*02:01	Panama		0.1493	462	8_59_N_79_37_W
2,632	A*02:01	Papua New Guinea East New Britain Rabaul		0.0000	60	4_12_S_152_11_E
2,633	A*02:01	Papua New Guinea Eastern Highlands Goroka Asaro		0.0000	57	6_5_S_145_23_E
2,634	A*02:01	Papua New Guinea Karimui Plateau Pawaia		0.0000	80	7_14_S_145_20_E
2,635	A*02:01	Papua New Guinea Madang		0.0000	65	5_13_S_145_48_E
2,636	A*02:01	Papua New Guinea Wanigela Keapara		0.0210	66	10_5_S_147_50_E
2,637	A*02:01	Papua New Guinea West Schrader Ranges Haruai		0.0000	55	5_5_S_144_10_E
2,638	A*02:01	Papua New Guinea Wosera Abelam		0.0000	131	3_48_S_143_2_E
2,639	A*02:01	Peru Titikaka Lake Uro		0.5000	105	15_50_S_69_42_W
2,640	A*02:01	Peru Titikaka Lake Uros		0.5000	105	15_50_S_70_1_W
2,641	A*02:01	Philippines Ivatan	48.0	0.3000	50	20_22_N_121_56_E
2,642	A*02:01	Poland		0.1930	200	51_7_N_17_2_E
2,643	A*02:01	Poland DKMS		0.2849	20,653	52_8_N_19_23_E
2,644	A*02:01	Portugal Azores Terceira Island	42.1	0.2325	130	38_43_N_27_13_W
2,645	A*02:01	Portugal Center		0.2600	50	39_0_N_8_0_W
2,646	A*02:01	Portugal North		0.2930	46	41_0_N_8_0_W
2,647	A*02:01	Portugal South		0.2350	49	37_0_N_8_0_W
2,648	A*02:01	Romania	43.7	0.2500	348	46_0_N_25_0_E
2,649	A*02:01	Russia Belgorod region	0.7	0.0033	153	50_46_N_37_27_E
2,650	A*02:01	Russia Bering Island Aleuts		0.2260	104	55_0_N_166_15_E
2,651	A*02:01	Russia Karelia		0.2963	1,075	63_9_N_32_59_E
2,652	A*02:01	Russia Tuva pop 2		0.1430	169	51_30_N_95_5_E
2,653	A*02:01	Sao Tome Island Angolar		0.0630	32	0_20_N_6_44_E
2,654	A*02:01	Sao Tome Island Forro		0.1280	66	0_20_N_6_44_E
2,655	A*02:01	Saudi Arabia Guraiat and Hail	33.3	0.1900	213	27_31_N_41_41_E
2,656	A*02:01	Senegal Niokholo Mandenka		0.0810	165	12_35_N_12_9_W
2,657	A*02:01	Singapore Chinese	19.5	0.1040	149	1_24_N_103_45_E
2,658	A*02:01	Singapore Chinese Han		0.0760	94	1_17_N_103_51_E
2,659	A*02:01	Singapore Javaneses		0.0400	51	1_24_N_103_45_E
2,660	A*02:01	Singapore Riau Malay		0.0650	132	1_24_N_103_45_E
2,661	A*02:01	Singapore SGVP Chinese CHS		0.1090	96	1_21_N_103_50_E
2,662	A*02:01	Singapore SGVP Malay MAS		0.1190	89	1_21_N_103_50_E
2,663	A*02:01	Singapore SGVP. Indian INS		0.0750	86	1_21_N_103_50_E
2,664	A*02:01	Singapore Thai		0.0310	100	1_24_N_103_45_E
2,665	A*02:01	South Africa Cape Town Black	11.9	0.0595	84	34_7_S_18_22_E
2,666	A*02:01	South Africa Durban Black	4.5	0.0227	22	29_51_S_31_0_E
2,667	A*02:01	South Africa Johannesburg Black	11.8	0.0588	120	26_11_S_28_2_E
2,668	A*02:01	South Africa Natal Tamil		0.0100	51	30_0_S_31_0_E
2,669	A*02:01	South Africa Natal Zulu	5.0	0.0250	100	29_0_S_31_0_E
2,670	A*02:01	South Africa Worcester	14.0	0.0750	159	33_39_S_19_27_E
2,671	A*02:01	South Korea pop 10		0.1548	4,128	37_33_N_126_58_E
2,672	A*02:01	South Korea pop 3		0.1650	485	37_33_N_126_59_E
2,673	A*02:01	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	41.7	0.2368	4,335	41_22_N_2_11_E
2,674	A*02:01	Spain Catalonia Girona		0.2990	88	41_59_N_2_49_E
2,675	A*02:01	Spain Gipuzkoa Basque		0.2700	100	43_10_N_2_10_W
2,676	A*02:01	Sri Lanka Colombo	6.6	0.0340	714	6_56_N_79_50_E
2,677	A*02:01	Sri Lanka Colombo Sinhalese		0.0490	101	6_56_N_79_50_E
2,678	A*02:01	Sudan Central Shaigiya Mixed	11.1		36	18_0_N_33_0_E
2,679	A*02:01	Sudan East Rashaida	18.5		27	14_0_N_35_0_E
2,680	A*02:01	Sudan Mixed	33.0	0.1730	200	5_0_N_35_0_E
2,681	A*02:01	Sweden Northern Sami		0.2360	154	67_0_N_18_30_E
2,682	A*02:01	Sweden Southern Sami		0.2600	130	60_52_N_14_44_E
2,683	A*02:01	Taiwan Ami	5.1	0.0260	98	23_0_N_121_0_E
2,684	A*02:01	Taiwan Atayal	13.2	0.0660	106	24_0_N_121_0_E
2,685	A*02:01	Taiwan Bunun	7.9	0.0400	101	23_46_N_121_0_E
2,686	A*02:01	Taiwan Hakka	10.9	0.0550	55	24_0_N_121_0_E
2,687	A*02:01	Taiwan Han Chinese		0.0920	504	23_46_N_121_0_E
2,688	A*02:01	Taiwan Minnan pop 1	17.6	0.0880	102	24_0_N_121_0_E
2,689	A*02:01	Taiwan Paiwan	7.8	0.0390	51	23_0_N_121_0_E
2,690	A*02:01	Taiwan Pazeh	10.9	0.0550	55	25_0_N_121_30_E
2,691	A*02:01	Taiwan pop 2		0.1290	364	23_46_N_121_0_E
2,692	A*02:01	Taiwan pop 3		0.0990	212	23_46_N_121_0_E
2,693	A*02:01	Taiwan Puyuma	4.0	0.0200	50	23_0_N_121_0_E
2,694	A*02:01	Taiwan Rukai	6.0	0.0300	50	23_0_N_121_0_E
2,695	A*02:01	Taiwan Saisiat	21.6	0.1080	51	24_0_N_121_0_E
2,696	A*02:01	Taiwan Siraya	7.8	0.0390	51	24_0_N_121_0_E
2,697	A*02:01	Taiwan Tao	4.0	0.0200	50	24_0_N_121_0_E
2,698	A*02:01	Taiwan Taroko	36.4	0.1910	55	24_0_N_121_0_E
2,699	A*02:01	Taiwan Thao	13.3	0.0670	30	24_0_N_121_0_E
2,700	A*02:01	Taiwan Tsou	5.9	0.0290	51	24_0_N_121_0_E
2,701	A*02:01	Taiwan Tzu Chi Morrow Donor Registry		0.1048	46,682	25_2_N_121_38_E
2,702	A*02:01	Taiwan Tzu Chi Morrow Donor Registry Aborigine		0.0987	233	25_2_N_121_38_E
2,703	A*02:01	Taiwan Tzu Chi Cord Blood Bank		0.1040	710	23_46_N_121_0_E
2,704	A*02:01	Tanzania Maasai	21.2	0.1180	336	3_11_S_37_4_E
2,705	A*02:01	Thailand		0.0180	142	16_7_N_102_0_E
2,706	A*02:01	Tunisia	32.0	0.1750	100	35_0_N_9_0_E
2,707	A*02:01	Tunisia Gabes		0.1579	95	33_53_N_10_6_E
2,708	A*02:01	Turkey pop 5		0.2570	142	40_0_N_28_0_E
2,709	A*02:01	Uganda Kampala		0.1840	161	0_3_N_32_6_E
2,710	A*02:01	Uganda Kampala pop 2		0.1140	175	0_20_N_32_30_E
2,711	A*02:01	United Arab Emirates Abu Dhabi		0.2019	52	24_28_N_54_22_E
2,712	A*02:01	United Arab Emirates Pop 1		0.1400	570	24_42_N_54_82_E
2,713	A*02:01	USA African American		0.1230	252	30_0_N_95_0_W
2,714	A*02:01	USA African American Bethesda	16.8	0.0840	187	38_0_N_78_0_W
2,715	A*02:01	USA African American pop 2		0.1070	149	32_46_N_96_46_W
2,716	A*02:01	USA African American pop 3		0.1210	564	38_53_N_77_2_W
2,717	A*02:01	USA African American pop 4		0.1246	2,411	44_58_N_93_15_W
2,718	A*02:01	USA Alaska Yupik		0.0240	252	60_48_N_161_26_W
2,719	A*02:01	USA Arizona Gila River Amerindian		0.4740	492	33_4_N_111_44_W
2,720	A*02:01	USA Arizona Pima		0.4360	100	33_0_N_113_0_W
2,721	A*02:01	USA Asian		0.0950	358	30_0_N_95_0_W
2,722	A*02:01	USA Asian pop 2		0.0946	1,772	44_58_N_93_15_W
2,723	A*02:01	USA Caucasian	0.0		61,655	38_53_N_77_2_W
2,724	A*02:01	USA Caucasian Bethesda	47.8	0.2610	307	38_0_N_78_0_W
2,725	A*02:01	USA Caucasian pop 2		0.2720	265	30_0_N_95_0_W
2,726	A*02:01	USA Caucasian pop 3		0.2220	88	32_46_N_96_48_W
2,727	A*02:01	USA Caucasian pop 4		0.2944	1,070	38_53_N_77_2_W
2,728	A*02:01	USA Hawaii Okinawa		0.0900	106	21_0_N_157_0_W
2,729	A*02:01	USA Hispanic		0.2310	234	30_0_N_95_0_W
2,730	A*02:01	USA Hispanic pop 2		0.1940	1,999	44_58_N_93_15_W
2,731	A*02:01	USA NMDP African		0.1146	28,557	38_45_N_76_6_W
2,732	A*02:01	USA NMDP African American pop 2		0.1235	416,581	38_45_N_76_6_W
2,733	A*02:01	USA NMDP Alaska Native or Aleut		0.1920	1,376	38_45_N_76_6_W
2,734	A*02:01	USA NMDP American Indian South or Central America		0.2650	5,926	38_45_N_76_6_W
2,735	A*02:01	USA NMDP Caribean Black		0.1107	33,328	38_45_N_76_6_W
2,736	A*02:01	USA NMDP Caribean Hispanic		0.1688	115,374	38_45_N_76_6_W
2,737	A*02:01	USA NMDP Caribean Indian		0.1450	14,339	38_45_N_76_6_W
2,738	A*02:01	USA NMDP Chinese		0.0946	99,672	38_45_N_76_6_W
2,739	A*02:01	USA NMDP European Caucasian		0.2755	1,242,890	38_45_N_76_6_W
2,740	A*02:01	USA NMDP Filipino		0.0671	50,614	38_45_N_76_6_W
2,741	A*02:01	USA NMDP Hawaiian or other Pacific Islander		0.1198	11,499	38_45_N_76_6_W
2,742	A*02:01	USA NMDP Hispanic South or Central American		0.2095	146,714	38_45_N_76_6_W
2,743	A*02:01	USA NMDP Japanese		0.1480	24,582	38_45_N_76_6_W
2,744	A*02:01	USA NMDP Korean		0.1857	77,584	38_45_N_76_6_W
2,745	A*02:01	USA NMDP Mexican or Chicano		0.2230	261,235	38_45_N_76_6_W
2,746	A*02:01	USA NMDP Middle Eastern or North Coast of Africa		0.1973	70,890	38_45_N_76_6_W
2,747	A*02:01	USA NMDP North American Amerindian		0.2776	35,791	38_45_N_76_6_W
2,748	A*02:01	USA NMDP South Asian Indian		0.0492	185,391	38_45_N_76_6_W
2,749	A*02:01	USA NMDP Southeast Asian		0.0578	27,978	38_45_N_76_6_W
2,750	A*02:01	USA NMDP Vietnamese		0.0349	43,540	38_45_N_76_6_W
2,751	A*02:01	USA North American Native		0.2170	187	30_0_N_95_0_W
2,752	A*02:01	USA Philadelphia Caucasian	40.7	0.2590	141	39_56_N_75_10_W
2,753	A*02:01	USA San Antonio Caucasian		0.2590	222	29_23_N_98_33_W
2,754	A*02:01	USA San Diego	37.4	0.2060	496	32_50_N_117_15_W
2,755	A*02:01	USA San Francisco Caucasian		0.2470	220	37_46_N_122_25_W
2,756	A*02:01	USA South Dakota Lakota Sioux		0.2970	302	44_30_N_100_0_W
2,757	A*02:01	USA South Texas Hispanic		0.2170	194	29_0_N_98_0_W
2,758	A*02:01	Venezuela Perja Mountain Bari		0.2160	55	9_49_N_72_49_W
2,759	A*02:01	Vietnam Hanoi Kinh pop 2		0.0210	170	21_2_N_105_51_E
2,760	A*02:01	Zambia Lusaka		0.1280	44	15_25_S_28_17_E
2,761	A*02:01	Zimbabwe Harare Shona		0.0910	230	17_51_S_31_1_E
2,762	A*02:01:01	Brazil Barra Mansa Rio State Black		0.1849	73	22_32_S_44_10_W
2,763	A*02:01:01	Brazil Barra Mansa Rio State Caucasian		0.2062	405	22_32_S_44_10_W
2,764	A*02:01:01	Brazil Rio de Janeiro Black		0.1029	68	22_54_S_43_12_W
2,765	A*02:01:01	Brazil Rio de Janeiro Caucasian		0.2207	521	22_54_S_43_12_W
2,766	A*02:01:01	Brazil Rio de Janeiro Parda		0.1824	170	22_54_S_43_12_W
2,767	A*02:01:01	Bulgaria		0.3000	55	42_0_N_23_0_E
2,768	A*02:01:01	Cape Verde Northwestern Islands		0.1490	62	16_0_N_24_0_W
2,769	A*02:01:01	Cape Verde Southeastern Islands		0.1210	62	16_0_N_24_0_W
2,770	A*02:01:01	China Guangdong Province Meizhou Han		0.0150	100	24_27_N_116_7_E
2,771	A*02:01:01	China Jingpo Minority	5.8	0.0291	105	25_0_N_102_0_E
2,772	A*02:01:01	China North Han		0.1900	105	39_54_N_116_24_E
2,773	A*02:01:01	China Zhejiang Han		0.1061	1,734	27_2_N_118_1_E
2,774	A*02:01:01	Costa Rica African -Caribbean (G)	16.6	0.0830	102	9_59_N_83_1_W
2,775	A*02:01:01	Costa Rica Amerindians (G)	18.6	0.0930	125	9_34_N_83_25_W
2,776	A*02:01:01	Costa Rica Central Valley Mestizo (G)	33.1	0.1770	221	9_55_N_84_5_W
2,777	A*02:01:01	Costa Rica Guanacaste Mestizo (G)	31.8	0.1680	110	10_16_N_85_37_W
2,778	A*02:01:01	Czech Republic		0.0050	106	50_0_N_14_0_E
2,779	A*02:01:01	England Blood Donors of Mixed Ethnicity	50.5	0.3035	519	52_21_N_1_10_W
2,780	A*02:01:01	Guinea Bissau		0.1670	65	12_0_N_15_0_W
2,781	A*02:01:01	Hong Kong Chinese HKBMDR HLA 11 loci	12.7	0.0653	5,266	22_25_N_114_17_E
2,782	A*02:01:01	India Andhra Pradesh Golla		0.0230	111	17_5_N_78_5_E
2,783	A*02:01:01	India Andhra Pradesh Telugu Speaking	6.5	0.0323	186	13_4_N_80_14_E
2,784	A*02:01:01	India Karnataka Kannada Speaking	8.0	0.0400	174	12_57_N_77_36_E
2,785	A*02:01:01	India Kerala Malayalam speaking	13.8	0.0730	356	13_1_N_80_15_E
2,786	A*02:01:01	India Tamil Nadu Nadar		0.1070	61	8_0_N_77_0_E
2,787	A*02:01:01	Iran Baloch		0.2020	100	29_29_N_60_52_E
2,788	A*02:01:01	Japan Okinawa Ryukyuan		0.1020	143	26_4_N_127_8_E
2,789	A*02:01:01	Kenya		0.0840	144	1_28_S_36_8_E
2,790	A*02:01:01	Latvia		0.2557	266	56_50_N_24_45_E
2,791	A*02:01:01	Madeira		0.2460	185	32_44_N_16_58_W
2,792	A*02:01:01	Mexico Chihuahua Tarahumara		0.2500	44	27_30_N_107_45_W
2,793	A*02:01:01	Mexico Guadalajara Mestizo pop 2		0.2230	103	20_40_N_103_21_W
2,794	A*02:01:01	Mexico Hidalgo Mezquital Valley/ Otomi	56.9	0.4028	72	20_28_N_99_13_W
2,795	A*02:01:01	Mongolia Buryat		0.1430	141	47_6_N_118_6_E
2,796	A*02:01:01	Morocco Atlantic Coast Chaouya		0.1620	98	30_22_N_7_22_W
2,797	A*02:01:01	Morocco Nador Metalsa pop 2		0.1780	73	35_10_N_2_56_W
2,798	A*02:01:01	Morocco Settat Chaouya	32.4	0.1620	98	33_0_N_7_36_W
2,799	A*02:01:01	Nicaragua Mestizo (G)	36.2	0.1940	155	12_40_N_85_13_W
2,800	A*02:01:01	Poland BMR	49.3	0.2848	23,595	52_0_N_19_22_E
2,801	A*02:01:01	Russia Belgorod region	51.0	0.2909	153	50_46_N_37_27_E
2,802	A*02:01:01	Russia Bashkortostan, Bashkirs	30.8	0.1833	120	54_0_N_55_0_E
2,803	A*02:01:01	Russia Bashkortostan, Tatars	1.0	0.0052	192	54_13_N_56_9_E
2,804	A*02:01:01	Russia Moscow Bashkir	43.0	0.2438	442	55_46_N_37_41_E
2,805	A*02:01:01	Russia Moscow Buryat	22.8	0.1242	149	55_46_N_37_41_E
2,806	A*02:01:01	Russia Moscow Kabardin	51.8	0.3127	834	55_46_N_37_41_E
2,807	A*02:01:01	Russia Moscow Kalmyk	26.7	0.1375	120	55_46_N_37_41_E
2,808	A*02:01:01	Russia Moscow Kazakh	30.4	0.1672	299	55_46_N_37_41_E
2,809	A*02:01:01	Russia Moscow Mansi	21.1	0.1447	38	55_46_N_37_41_E
2,810	A*02:01:01	Russia Moscow Megrel	32.2	0.1864	230	55_46_N_37_41_E
2,811	A*02:01:01	Russia Moscow Russians	43.6	0.2526	13,147	55_46_N_37_41_E
2,812	A*02:01:01	Russia Moscow Similar genetically to Azerbaijan	30.6	0.1588	170	55_46_N_37_41_E
2,813	A*02:01:01	Russia Moscow Tatars	37.3	0.2083	2,512	55_46_N_37_41_E
2,814	A*02:01:01	Russia Moscow Tuvian	22.3	0.1116	112	55_46_N_37_41_E
2,815	A*02:01:01	Russia Moscow Uzbek	31.8	0.1742	198	55_46_N_37_41_E
2,816	A*02:01:01	Russia Moscow Yakut	14.9	0.0745	235	55_46_N_37_41_E
2,817	A*02:01:01	Russia Nizhny Novgorod, Russians	2.1	0.0103	1,510	56_19_N_43_59_E
2,818	A*02:01:01	Russian Federation Vologda Region	47.1	0.2815	119	59_13_N_39_54_E
2,819	A*02:01:01	Saudi Arabia Al-Baha (G)		0.2791	86	20_0_N_41_28_E
2,820	A*02:01:01	Saudi Arabia Al-Madinah (G)		0.2404	102	24_28_N_39_36_E
2,821	A*02:01:01	Saudi Arabia Jeddah (G)		0.1807	476	21_32_N_39_10_E
2,822	A*02:01:01	Saudi Arabia Makkah (G)		0.1831	344	21_25_N_39_49_E
2,823	A*02:01:01	Saudi Arabia pop 6 (G)		0.2020	28,927	23_52_N_45_4_E
2,824	A*02:01:01	Saudi Arabia Taif (G)		0.1442	104	21_16_N_40_24_E
2,825	A*02:01:01	South Africa Caucasians		0.2600	102	30_0_S_25_0_E
2,826	A*02:01:01	South African Black	10.6	0.0599	142	29_9_S_24_39_E
2,827	A*02:01:01	South African Indian population	8.0	0.0400	50	29_22_S_24_31_E
2,828	A*02:01:01	South African Mixed ancestry	38.0	0.2100	50	29_22_S_24_31_E
2,829	A*02:01:01	South Korea pop 3		0.0000	485	37_33_N_126_59_E
2,830	A*02:01:01	Spain, Canary Islands, Gran canaria island	40.5	0.2372	215	27_57_N_15_36_W
2,831	A*02:01:01	USA Arizona Gila River Pima		0.4242	3,000	33_4_N_111_43_W
2,832	A*02:01:01	USA Caucasian	47.4		61,655	38_53_N_77_2_W
2,833	A*02:01:01	USA New Mexico Canoncito Navajo		0.3780	42	34_9_N_107_3_W
2,834	A*02:01:01	USA San Francisco Caucasian		0.2420	220	37_46_N_122_25_W
2,835	A*02:01:01	Vietnam Kinh	6.0	0.0297	101	14_5_N_108_27_E
2,836	A*02:01:01:01	Bolivia/Chile Aymara NA-DHS_13 (G)	85.0	0.5750	20	22_0_S_70_0_W
2,837	A*02:01:01:01	Bolivia/Peru Quechua NA-DHS_12 (G)	85.7	0.6667	21	14_0_S_74_0_W
2,838	A*02:01:01:01	Canada Chipewyan NA-DHS_2 (G)	76.0	0.4800	25	59_36_N_107_20_W
2,839	A*02:01:01:01	Canada Cree NA-DHS_3 (G)	61.1	0.3333	18	50_20_N_102_30_W
2,840	A*02:01:01:01	Canada Ojibwa NA-DHS_4 (G)	46.7	0.3000	16	46_0_N_81_0_W
2,841	A*02:01:01:01	Chile Huilliche NA-DHS_14 (G)	45.0	0.2250	20	41_0_S_73_0_W
2,842	A*02:01:01:01	China Yunnan Province Jinuo		0.0090	109	24_30_N_101_30_E
2,843	A*02:01:01:01	China Yunnan Province Wa		0.0080	119	24_30_N_101_30_E
2,844	A*02:01:01:01	Colombia Arhuaco NA-DHS_16 (G)	5.9	0.0294	17	11_0_N_73_0_W
2,845	A*02:01:01:01	Colombia Inga NA-DHS_11 (G)	13.3	0.0667	16	1_0_N_77_0_W
2,846	A*02:01:01:01	Colombia Waunana NA-DHS_20 (G)	5.0	0.0250	20	5_0_N_77_0_W
2,847	A*02:01:01:01	Colombia Wayuu NA-DHS_15 (G)	13.3	0.0667	15	11_0_N_73_0_W
2,848	A*02:01:01:01	Colombia/Brazil Ticuna Tarapaca NA-DHS_22 (G)	15.8	0.0789	19	4_0_S_70_0_W
2,849	A*02:01:01:01	Costa Rica Guaymi NA-DHS_10 (G)	5.6	0.0278	18	9_0_N_82_0_W
2,850	A*02:01:01:01	Guatemala Kaqchikel NA-DHS_8 (G)	47.4	0.2632	19	15_0_N_91_0_W
2,851	A*02:01:01:01	Indonesia Java pop 2		0.0420	36	6_8_S_106_10_E
2,852	A*02:01:01:01	Indonesia Sundanese and Javanese		0.0750	201	6_8_S_106_10_E
2,853	A*02:01:01:01	Libya Cyrenaica		0.1570	118	32_7_N_20_4_E
2,854	A*02:01:01:01	Mexico Mixe NA-DHS_6 (G)	55.0	0.3250	20	17_0_N_96_0_W
2,855	A*02:01:01:01	Mexico Mixtec NA-DHS_5 (G)	15.0	0.0750	20	17_0_N_97_0_W
2,856	A*02:01:01:01	Mexico Zapotec NA-DHS_7 (G)	55.0	0.4000	20	16_0_N_97_0_W
2,857	A*02:01:01:01	Pakistan Mixed Punjabi	12.2	0.0630	389	31_0_N_72_0_E
2,858	A*02:01:01:01	Paraguay/Argentina Ache NA-DHS_24 (G)	7.7	0.0385	13	24_0_S_56_0_W
2,859	A*02:01:01:01	Paraguay/Argentina Guarani NA-DHS_23 (G)	70.0	0.3500	10	23_0_S_56_0_W
2,860	A*02:01:01:01	Russia Bashkortostan, Bashkirs	19.2	0.1167	120	54_0_N_55_0_E
2,861	A*02:01:01:01	Russia Bashkortostan, Tatars	38.5	0.2135	192	54_13_N_56_9_E
2,862	A*02:01:01:01	Russia Nizhny Novgorod, Russians	46.1	0.2705	1,510	56_19_N_43_59_E
2,863	A*02:01:01:01	Russia Tundra Nentsi NA-DHS_1 (G)	62.5	0.3750	16	66_0_N_76_0_E
2,864	A*02:01:01:01	Saudi Arabia pop 5	21.5	0.1361	158	24_38_N_46_43_E
2,865	A*02:01:01:01	USA Eastern European		0.2750	558	38_53_N_77_2_W
2,866	A*02:01:01:01	USA Italy Ancestry		0.2600	273	38_0_N_77_0_W
2,867	A*02:01:01:01	USA Mexican American Mestizo		0.1970	553	38_53_N_77_2_W
2,868	A*02:01:01:01	USA San Francisco Caucasian		0.2420	220	37_46_N_122_25_W
2,869	A*02:01:01:01	USA Spain Ancestry		0.2150	279	38_0_N_77_0_W
2,870	A*02:01:01:04	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
2,871	A*02:01:01:05	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
2,872	A*02:01:02	Brazil Barra Mansa Rio State Caucasian		0.0012	405	22_32_S_44_10_W
2,873	A*02:01:02	Bulgaria		0.0000	55	42_0_N_23_0_E
2,874	A*02:01:02	China Guangdong Province Meizhou Han		0.0050	100	24_27_N_116_7_E
2,875	A*02:01:02	China North Han		0.0000	105	39_54_N_116_24_E
2,876	A*02:01:02	Hong Kong Chinese HKBMDR HLA 11 loci	0.0	0.0001	5,266	22_25_N_114_17_E
2,877	A*02:01:02	Kenya		0.0030	144	1_28_S_36_8_E
2,878	A*02:01:02	Malaysia Kedah Baling Kensiu		0.1000	25	5_40_N_100_55_E
2,879	A*02:01:02	Malaysia Perak Grik Jehai		0.1200	25	5_25_N_101_8_E
2,880	A*02:01:02	Mexico Guadalajara Mestizo pop 2		0.0100	103	20_40_N_103_21_W
2,881	A*02:01:02	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
2,882	A*02:01:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,883	A*02:01:02	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
2,884	A*02:01:02	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
2,885	A*02:01:02	Russia Moscow Russians	0.2	0.0008	13,147	55_46_N_37_41_E
2,886	A*02:01:02	Russia Moscow Tatars	0.2	0.0008	2,512	55_46_N_37_41_E
2,887	A*02:01:02	Saudi Arabia pop 5	0.6	0.0063	158	24_38_N_46_43_E
2,888	A*02:01:02	South Korea pop 3		0.0000	485	37_33_N_126_59_E
2,889	A*02:01:02	USA Caucasian	0.0		61,655	38_53_N_77_2_W
2,890	A*02:01:03	Bulgaria		0.0000	55	42_0_N_23_0_E
2,891	A*02:01:03	China North Han		0.0000	105	39_54_N_116_24_E
2,892	A*02:01:03	Japan Okinawa Ryukyuan		0.0140	143	26_4_N_127_8_E
2,893	A*02:01:03	Mongolia Buryat		0.0070	141	47_6_N_118_6_E
2,894	A*02:01:03	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
2,895	A*02:01:03	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,896	A*02:01:03	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
2,897	A*02:01:03	South Korea pop 3		0.0000	485	37_33_N_126_59_E
2,898	A*02:01:03	USA Caucasian	0.0		61,655	38_53_N_77_2_W
2,899	A*02:01:04	Bulgaria		0.0000	55	42_0_N_23_0_E
2,900	A*02:01:04	China North Han		0.0000	105	39_54_N_116_24_E
2,901	A*02:01:04	Costa Rica Guanacaste Mestizo (G)	1.0	0.0050	110	10_16_N_85_37_W
2,902	A*02:01:04	Madeira		0.0030	185	32_44_N_16_58_W
2,903	A*02:01:04	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
2,904	A*02:01:04	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,905	A*02:01:04	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
2,906	A*02:01:04	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
2,907	A*02:01:04	Russia Nizhny Novgorod, Russians	0.1	0.0003	1,510	56_19_N_43_59_E
2,908	A*02:01:04	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
2,909	A*02:01:04	Saudi Arabia pop 6 (G)		0.0003	28,927	23_52_N_45_4_E
2,910	A*02:01:04	South Korea pop 3		0.0000	485	37_33_N_126_59_E
2,911	A*02:01:04	USA Caucasian	0.0		61,655	38_53_N_77_2_W
2,912	A*02:01:04	USA Mexican American Mestizo		0.0010	553	38_53_N_77_2_W
2,913	A*02:01:04	USA San Francisco Caucasian		0.0020	220	37_46_N_122_25_W
2,914	A*02:01:05	Bulgaria		0.0000	55	42_0_N_23_0_E
2,915	A*02:01:05	China North Han		0.0000	105	39_54_N_116_24_E
2,916	A*02:01:05	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,917	A*02:01:05	Poland BMR	0.0	0.0001	23,595	52_0_N_19_22_E
2,918	A*02:01:05	Saudi Arabia pop 6 (G)		0.0001	28,927	23_52_N_45_4_E
2,919	A*02:01:05	South Korea pop 3		0.0000	485	37_33_N_126_59_E
2,920	A*02:01:05	USA Caucasian	0.0		61,655	38_53_N_77_2_W
2,921	A*02:01:06	Bulgaria		0.0000	55	42_0_N_23_0_E
2,922	A*02:01:06	China North Han		0.0000	105	39_54_N_116_24_E
2,923	A*02:01:06	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,924	A*02:01:06	South Korea pop 3		0.0000	485	37_33_N_126_59_E
2,925	A*02:01:06	USA Caucasian	0.0		61,655	38_53_N_77_2_W
2,926	A*02:01:07	Bulgaria		0.0000	55	42_0_N_23_0_E
2,927	A*02:01:07	China North Han		0.0000	105	39_54_N_116_24_E
2,928	A*02:01:07	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,929	A*02:01:07	USA Caucasian	0.0		61,655	38_53_N_77_2_W
2,930	A*02:01:08	Bulgaria		0.0000	55	42_0_N_23_0_E
2,931	A*02:01:08	China North Han		0.0000	105	39_54_N_116_24_E
2,932	A*02:01:08	India Kerala Malayalam speaking	0.3	0.0010	356	13_1_N_80_15_E
2,933	A*02:01:08	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,934	A*02:01:08	USA Caucasian	0.0		61,655	38_53_N_77_2_W
2,935	A*02:01:09	Brazil Barra Mansa Rio State Caucasian		0.0012	405	22_32_S_44_10_W
2,936	A*02:01:09	Bulgaria		0.0000	55	42_0_N_23_0_E
2,937	A*02:01:09	China North Han		0.0000	105	39_54_N_116_24_E
2,938	A*02:01:09	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,939	A*02:01:09	Pakistan Mixed Punjabi	1.0	0.0051	389	31_0_N_72_0_E
2,940	A*02:01:09	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
2,941	A*02:01:09	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
2,942	A*02:01:09	Russia Nizhny Novgorod, Russians	0.1	0.0007	1,510	56_19_N_43_59_E
2,943	A*02:01:09	Russian Federation Vologda Region	0.8	0.0042	119	59_13_N_39_54_E
2,944	A*02:01:09	Saudi Arabia Jeddah (G)		0.0021	476	21_32_N_39_10_E
2,945	A*02:01:09	Saudi Arabia pop 6 (G)		0.0003	28,927	23_52_N_45_4_E
2,946	A*02:01:09	USA Caucasian	0.0		61,655	38_53_N_77_2_W
2,947	A*02:01:10	China North Han		0.0000	105	39_54_N_116_24_E
2,948	A*02:01:10	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,949	A*02:01:10	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
2,950	A*02:01:10	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
2,951	A*02:01:10	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
2,952	A*02:01:11	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,953	A*02:01:11	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
2,954	A*02:01:12	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,955	A*02:01:13	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,956	A*02:01:14Q	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,957	A*02:01:15	Brazil Barra Mansa Rio State Caucasian		0.0025	405	22_32_S_44_10_W
2,958	A*02:01:15	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,959	A*02:01:17	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,960	A*02:01:18	Latvia		0.0095	266	56_50_N_24_45_E
2,961	A*02:01:18	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,962	A*02:01:18	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
2,963	A*02:01:19	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,964	A*02:01:21	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,965	A*02:01:22	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,966	A*02:01:23	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,967	A*02:01:23	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
2,968	A*02:01:24	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,969	A*02:01:24	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
2,970	A*02:01:25	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,971	A*02:01:26	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,972	A*02:01:27	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,973	A*02:01:28	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,974	A*02:01:28	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
2,975	A*02:01:29	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,976	A*02:01:30	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,977	A*02:01:30	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
2,978	A*02:01:31	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,979	A*02:01:32	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,980	A*02:01:32	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
2,981	A*02:01:33	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,982	A*02:01:34	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,983	A*02:01:35	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,984	A*02:01:35	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
2,985	A*02:01:36	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,986	A*02:01:36	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
2,987	A*02:01:36	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
2,988	A*02:01:36	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
2,989	A*02:01:37	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,990	A*02:01:38	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,991	A*02:01:39	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,992	A*02:01:39	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
2,993	A*02:01:40	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,994	A*02:01:41	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,995	A*02:01:42	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
2,996	A*02:01:52	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
2,997	A*02:01:52	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
2,998	A*02:01:54	Latvia		0.0019	266	56_50_N_24_45_E
2,999	A*02:01:54	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
3,000	A*02:01:55	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
3,001	A*02:01:57	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
3,002	A*02:01:61	Pakistan Mixed Punjabi	1.0	0.0051	389	31_0_N_72_0_E
3,003	A*02:01:61	USA Arizona Gila River Pima		0.0003	3,000	33_4_N_111_43_W
3,004	A*02:01:66	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
3,005	A*02:01:70	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
3,006	A*02:01:74	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
3,007	A*02:01:74	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
3,008	A*02:01:92	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
3,009	A*02:01:96	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
3,010	A*02:01:96	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
3,011	A*02:01:96	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
3,012	A*02:01:96	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
3,013	A*02:01:96	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
3,014	A*02:01:102	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
3,015	A*02:01:103	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
3,016	A*02:01:110	USA Arizona Gila River Pima		0.0003	3,000	33_4_N_111_43_W
3,017	A*02:01:111	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
3,018	A*02:01:121	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
3,019	A*02:01:124	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
3,020	A*02:01:126	Latvia		0.0019	266	56_50_N_24_45_E
3,021	A*02:01:141	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
3,022	A*02:01:151	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
3,023	A*02:01:153	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
3,024	A*02:01:166	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
3,025	A*02:02	Argentina Rosario Toba	7.0	0.0410	86	32_57_S_60_39_W
3,026	A*02:02	Azores Central Islands		0.0180	59	38_0_N_28_0_W
3,027	A*02:02	Azores Oriental Islands		0.0130	43	36_58_N_25_6_W
3,028	A*02:02	Azores Terceira Island		0.0270	130	38_44_N_27_19_W
3,029	A*02:02	Belgium	1.0	0.0050	99	50_50_N_4_21_E
3,030	A*02:02	Belgium pop 3	0.2	0.0010	31,412	51_2_N_4_28_E
3,031	A*02:02	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
3,032	A*02:02	Brazil Mixed		0.0400	108	23_10_S_47_48_W
3,033	A*02:02	Brazil Vale do Ribeira Quilombos	0.0	0.0000	144	24_36_S_48_15_W
3,034	A*02:02	Bulgaria		0.0000	55	42_0_N_23_0_E
3,035	A*02:02	Burkina Faso Fulani		0.0000	49	12_50_N_1_50_W
3,036	A*02:02	Burkina Faso Rimaibe		0.0530	47	12_50_N_1_50_W
3,037	A*02:02	Cameroon Baka Pygmy		0.0000	10	8_30_N_14_0_E
3,038	A*02:02	Cameroon Bamileke		0.1040	77	5_28_N_10_25_E
3,039	A*02:02	Cameroon Beti		0.0600	174	2_55_N_11_9_E
3,040	A*02:02	Cameroon Sawa		0.0000	13	4_50_N_10_0_E
3,041	A*02:02	Cameroon Yaounde		0.0820	92	3_52_N_11_31_E
3,042	A*02:02	Cape Verde Northwestern Islands		0.0080	62	16_0_N_24_0_W
3,043	A*02:02	Cape Verde Southeastern Islands		0.0240	62	16_0_N_24_0_W
3,044	A*02:02	Chile Mapuche		0.0077	66	37_48_S_73_23_W
3,045	A*02:02	China Hubei Han	0.0	0.0001	3,732	30_45_N_114_15_E
3,046	A*02:02	China Inner Mongolia Region		0.0000	102	36_0_N_102_0_E
3,047	A*02:02	China Jiangsu Province Han		0.0018	334	32_54_N_119_48_E
3,048	A*02:02	China North Han		0.0000	105	39_54_N_116_24_E
3,049	A*02:02	China Qinghai Province Hui		0.0140	110	36_0_N_102_0_E
3,050	A*02:02	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
3,051	A*02:02	Colombia Bogotá Cord Blood	0.8	0.0041	1,463	4_39_N_73_52_W
3,052	A*02:02	Costa Rica African -Caribbean (G)	11.8	0.0640	102	9_59_N_83_1_W
3,053	A*02:02	Costa Rica Central Valley Mestizo (G)	0.4	0.0020	221	9_55_N_84_5_W
3,054	A*02:02	Costa Rica Guanacaste Mestizo (G)	2.8	0.0140	110	10_16_N_85_37_W
3,055	A*02:02	Croatia		0.0000	150	45_0_N_15_0_E
3,056	A*02:02	Croatia pop 4		0.0004	4,000	45_48_N_15_57_E
3,057	A*02:02	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
3,058	A*02:02	Cuba Mixed Race	2.4	0.0120	42	23_8_N_82_22_W
3,059	A*02:02	Czech Republic		0.0050	106	50_0_N_14_0_E
3,060	A*02:02	Czech Republic NMDR		0.0012	5,099	49_48_N_15_30_E
3,061	A*02:02	France Southeast	0.8	0.0040	130	46_0_N_5_0_E
3,062	A*02:02	Gaza	2.4	0.0119	42	31_22_N_34_18_E
3,063	A*02:02	Georgia Svaneti Region Svan		0.0380	80	42_54_N_43_0_E
3,064	A*02:02	Georgia Tibilisi		0.0100	109	41_41_N_44_57_E
3,065	A*02:02	Germany DKMS - Austria minority		0.0006	1,698	48_31_N_9_3_E
3,066	A*02:02	Germany DKMS - Bosnia and Herzegovina minority		0.0015	1,028	48_31_N_9_3_E
3,067	A*02:02	Germany DKMS - Croatia minority		0.0002	2,057	48_31_N_9_3_E
3,068	A*02:02	Germany DKMS - France minority		0.0008	1,406	48_31_N_9_3_E
3,069	A*02:02	Germany DKMS - German donors		0.0006	3,456,066	48_31_N_9_3_E
3,070	A*02:02	Germany DKMS - Greece minority		0.0013	1,894	48_31_N_9_3_E
3,071	A*02:02	Germany DKMS - Italy minority		0.0030	1,159	48_31_N_9_3_E
3,072	A*02:02	Germany DKMS - Netherlands minority		0.0004	1,374	48_31_N_9_3_E
3,073	A*02:02	Germany DKMS - Portugal minority		0.0051	1,176	48_31_N_9_3_E
3,074	A*02:02	Germany DKMS - Romania minority		0.0016	1,234	48_31_N_9_3_E
3,075	A*02:02	Germany DKMS - Spain minority		0.0014	1,107	48_31_N_9_3_E
3,076	A*02:02	Germany DKMS - Turkey minority		0.0019	4,856	48_31_N_9_3_E
3,077	A*02:02	Germany DKMS - United Kingdom minority		0.0010	1,043	48_31_N_9_3_E
3,078	A*02:02	Germany pop 6		0.0005	8,862	51_6_N_10_23_E
3,079	A*02:02	Germany pop 8		0.0008	39,689	48_24_N_9_58_E
3,080	A*02:02	Ghana Ga-Adangbe	12.2	0.0649	131	5_45_N_0_12_E
3,081	A*02:02	Greece pop 6		0.0021	242	37_58_N_23_43_E
3,082	A*02:02	Guinea Bissau		0.0620	65	12_0_N_15_0_W
3,083	A*02:02	Guinea Bissau Balanta		0.0630	48	11_52_N_15_36_W
3,084	A*02:02	Guinea Bissau Bijago		0.0870	23	11_52_N_15_36_W
3,085	A*02:02	Guinea Bissau Fula		0.0650	31	11_52_N_15_36_W
3,086	A*02:02	Guinea Bissau Papel		0.0800	25	11_52_N_15_36_W
3,087	A*02:02	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
3,088	A*02:02	India Central UCBB	0.0	0.0001	4,204	27_1_N_81_19_E
3,089	A*02:02	India Delhi pop 2	3.3	0.0170	90	28_36_N_77_13_E
3,090	A*02:02	India North UCBB	0.0	0.0002	5,849	28_36_N_77_11_E
3,091	A*02:02	India South UCBB	0.0	0.0000	11,446	13_6_N_80_8_E
3,092	A*02:02	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
3,093	A*02:02	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
3,094	A*02:02	Israel Arab Druze		0.0200	101	31_8_N_35_2_E
3,095	A*02:02	Israel Arab pop 2		0.0040	12,301	32_8_N_34_52_E
3,096	A*02:02	Israel Argentina Jews		0.0009	4,307	32_8_N_34_52_E
3,097	A*02:02	Israel Ashkenazi and Non Ashkenazi Jews		0.0090	146	31_8_N_35_2_E
3,098	A*02:02	Israel Ashkenazi Jews pop 3		0.0017	4,625	32_8_N_34_52_E
3,099	A*02:02	Israel Bukhara Jews		0.0714	2,317	32_8_N_34_52_E
3,100	A*02:02	Israel Druze		0.1855	5,914	32_41_N_35_17_E
3,101	A*02:02	Israel Ethiopia Jews		0.1184	5,928	32_8_N_34_52_E
3,102	A*02:02	Israel Iran Jews		0.0372	8,153	32_8_N_34_52_E
3,103	A*02:02	Israel Iraq Jews		0.0340	13,270	32_10_N_34_52_E
3,104	A*02:02	Israel Kavkazi Jews		0.0004	2,840	32_10_N_34_52_E
3,105	A*02:02	Israel Libya Jews		0.0000	3,739	32_10_N_34_52_E
3,106	A*02:02	Israel Morocco Jews		0.1072	36,718	32_10_N_34_52_E
3,107	A*02:02	Israel Poland Jews		0.0006	13,871	32_10_N_34_52_E
3,108	A*02:02	Israel Tunisia Jews		0.1527	9,070	32_10_N_34_52_E
3,109	A*02:02	Israel USA Jews		0.0007	6,058	32_10_N_34_52_E
3,110	A*02:02	Israel USSR Jews		0.0006	45,681	32_9_N_34_50_E
3,111	A*02:02	Israel YemenJews		0.1040	15,542	32_9_N_34_50_E
3,112	A*02:02	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
3,113	A*02:02	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
3,114	A*02:02	Italy pop 5		0.0040	975	45_28_N_9_11_E
3,115	A*02:02	Kenya		0.0490	144	1_28_S_36_8_E
3,116	A*02:02	Kenya Luo		0.0300	265	0_0_S_34_6_E
3,117	A*02:02	Kenya Nandi		0.0660	240	0_0_S_35_0_E
3,118	A*02:02	Kenya, Nyanza Province, Luo tribe	4.0	0.0250	100	0_5_S_34_46_E
3,119	A*02:02	Libya Cyrenaica		0.0170	118	32_7_N_20_4_E
3,120	A*02:02	Madeira		0.0080	185	32_44_N_16_58_W
3,121	A*02:02	Malaysia Peninsular Chinese	4.6	0.0232	194	3_9_N_101_57_E
3,122	A*02:02	Malaysia Peninsular Indian	1.1	0.0055	271	3_9_N_101_57_E
3,123	A*02:02	Malaysia Peninsular Malay	6.9	0.0368	951	3_9_N_101_57_E
3,124	A*02:02	Mali Bandiagara		0.0760	138	14_4_N_3_6_W
3,125	A*02:02	Mexico Chihuahua Chihuahua City Pop 2	1.1	0.0057	88	28_48_N_106_26_W
3,126	A*02:02	Mexico Guadalajara Mestizo pop 2		0.0050	103	20_40_N_103_21_W
3,127	A*02:02	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
3,128	A*02:02	Mexico Mexico City Mestizo pop 2		0.0021	234	19_26_N_99_8_W
3,129	A*02:02	Mexico Mexico City Mestizo population		0.0035	143	19_25_N_99_7_W
3,130	A*02:02	Mexico Mexico City Tlalpan	4.8	0.0242	330	19_25_N_99_8_W
3,131	A*02:02	Mexico Veracruz Xalapa	3.6	0.0179	84	19_32_N_96_55_W
3,132	A*02:02	Morocco Atlantic Coast Chaouya		0.0070	98	30_22_N_7_22_W
3,133	A*02:02	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
3,134	A*02:02	Morocco Settat Chaouya	1.4	0.0070	98	33_0_N_7_36_W
3,135	A*02:02	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
3,136	A*02:02	Nicaragua Managua	0.6	0.0030	339	12_7_N_86_10_W
3,137	A*02:02	Nicaragua Mestizo (G)	0.6	0.0030	155	12_40_N_85_13_W
3,138	A*02:02	Oman	0.9	0.0040	118	22_0_N_56_0_E
3,139	A*02:02	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
3,140	A*02:02	Pakistan Brahui		0.0230	104	30_0_N_67_0_E
3,141	A*02:02	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
3,142	A*02:02	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
3,143	A*02:02	Pakistan Karachi Parsi		0.0170	91	24_49_N_66_59_E
3,144	A*02:02	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
3,145	A*02:02	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
3,146	A*02:02	Panama		0.0249	462	8_59_N_79_37_W
3,147	A*02:02	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
3,148	A*02:02	Poland DKMS		0.0003	20,653	52_8_N_19_23_E
3,149	A*02:02	Portugal Azores Terceira Island	5.3	0.0263	130	38_43_N_27_13_W
3,150	A*02:02	Portugal Center		0.0000	50	39_0_N_8_0_W
3,151	A*02:02	Portugal North		0.0110	46	41_0_N_8_0_W
3,152	A*02:02	Portugal South		0.0000	49	37_0_N_8_0_W
3,153	A*02:02	Romania	0.9	0.0040	348	46_0_N_25_0_E
3,154	A*02:02	Sao Tome Island Angolar		0.0630	32	0_20_N_6_44_E
3,155	A*02:02	Sao Tome Island Forro		0.0450	66	0_20_N_6_44_E
3,156	A*02:02	Saudi Arabia Guraiat and Hail	4.2	0.0240	213	27_31_N_41_41_E
3,157	A*02:02	Saudi Arabia pop 5	2.5	0.0158	158	24_38_N_46_43_E
3,158	A*02:02	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
3,159	A*02:02	Senegal Niokholo Mandenka		0.0910	165	12_35_N_12_9_W
3,160	A*02:02	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
3,161	A*02:02	Singapore SGVP Chinese CHS		0.0000	96	1_21_N_103_50_E
3,162	A*02:02	Singapore SGVP Malay MAS		0.0000	89	1_21_N_103_50_E
3,163	A*02:02	Singapore SGVP. Indian INS		0.0060	86	1_21_N_103_50_E
3,164	A*02:02	South Africa Cape Town Black	1.2	0.0060	84	34_7_S_18_22_E
3,165	A*02:02	South Africa Caucasians		0.0100	102	30_0_S_25_0_E
3,166	A*02:02	South Africa Johannesburg Black	0.8	0.0042	120	26_11_S_28_2_E
3,167	A*02:02	South Africa Natal Zulu	7.0	0.0350	100	29_0_S_31_0_E
3,168	A*02:02	South Africa Worcester	2.0	0.0090	159	33_39_S_19_27_E
3,169	A*02:02	South Korea pop 3		0.0000	485	37_33_N_126_59_E
3,170	A*02:02	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.5	0.0024	4,335	41_22_N_2_11_E
3,171	A*02:02	Sudan Central Shaigiya Mixed	2.7		36	18_0_N_33_0_E
3,172	A*02:02	Sudan Mixed	3.5	0.0180	200	5_0_N_35_0_E
3,173	A*02:02	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
3,174	A*02:02	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
3,175	A*02:02	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
3,176	A*02:02	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
3,177	A*02:02	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
3,178	A*02:02	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
3,179	A*02:02	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
3,180	A*02:02	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
3,181	A*02:02	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
3,182	A*02:02	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
3,183	A*02:02	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
3,184	A*02:02	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
3,185	A*02:02	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
3,186	A*02:02	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
3,187	A*02:02	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
3,188	A*02:02	Taiwan Tzu Chi Morrow Donor Registry		0.0001	46,682	25_2_N_121_38_E
3,189	A*02:02	Taiwan Tzu Chi Morrow Donor Registry Aborigine		0.0000	233	25_2_N_121_38_E
3,190	A*02:02	Tanzania Maasai	5.1	0.0254	336	3_11_S_37_4_E
3,191	A*02:02	Tunisia	2.0	0.0100	100	35_0_N_9_0_E
3,192	A*02:02	Turkey pop 5		0.0000	142	40_0_N_28_0_E
3,193	A*02:02	Uganda Kampala		0.0340	161	0_3_N_32_6_E
3,194	A*02:02	Uganda Kampala pop 2		0.0370	175	0_20_N_32_30_E
3,195	A*02:02	United Arab Emirates Pop 1		0.0110	570	24_42_N_54_82_E
3,196	A*02:02	USA African American		0.0340	252	30_0_N_95_0_W
3,197	A*02:02	USA African American Bethesda	7.9	0.0400	187	38_0_N_78_0_W
3,198	A*02:02	USA African American pop 2		0.0370	149	32_46_N_96_46_W
3,199	A*02:02	USA African American pop 3		0.0290	564	38_53_N_77_2_W
3,200	A*02:02	USA African American pop 4		0.0420	2,411	44_58_N_93_15_W
3,201	A*02:02	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
3,202	A*02:02	USA Asian		0.0030	358	30_0_N_95_0_W
3,203	A*02:02	USA Asian pop 2		0.0003	1,772	44_58_N_93_15_W
3,204	A*02:02	USA Caucasian	0.0		61,655	38_53_N_77_2_W
3,205	A*02:02	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
3,206	A*02:02	USA Caucasian pop 2		0.0000	265	30_0_N_95_0_W
3,207	A*02:02	USA Caucasian pop 3		0.0060	88	32_46_N_96_48_W
3,208	A*02:02	USA Caucasian pop 4		0.0009	1,070	38_53_N_77_2_W
3,209	A*02:02	USA Hispanic		0.0060	234	30_0_N_95_0_W
3,210	A*02:02	USA Hispanic pop 2		0.0068	1,999	44_58_N_93_15_W
3,211	A*02:02	USA Italy Ancestry		0.0020	273	38_0_N_77_0_W
3,212	A*02:02	USA Mexican American Mestizo		0.0050	553	38_53_N_77_2_W
3,213	A*02:02	USA NMDP African		0.0466	28,557	38_45_N_76_6_W
3,214	A*02:02	USA NMDP African American pop 2		0.0414	416,581	38_45_N_76_6_W
3,215	A*02:02	USA NMDP Alaska Native or Aleut		0.0020	1,376	38_45_N_76_6_W
3,216	A*02:02	USA NMDP American Indian South or Central America		0.0030	5,926	38_45_N_76_6_W
3,217	A*02:02	USA NMDP Caribean Black		0.0415	33,328	38_45_N_76_6_W
3,218	A*02:02	USA NMDP Caribean Hispanic		0.0184	115,374	38_45_N_76_6_W
3,219	A*02:02	USA NMDP Caribean Indian		0.0220	14,339	38_45_N_76_6_W
3,220	A*02:02	USA NMDP Chinese		0.0001	99,672	38_45_N_76_6_W
3,221	A*02:02	USA NMDP European Caucasian		0.0009	1,242,890	38_45_N_76_6_W
3,222	A*02:02	USA NMDP Filipino		0.0002	50,614	38_45_N_76_6_W
3,223	A*02:02	USA NMDP Hawaiian or other Pacific Islander		0.0013	11,499	38_45_N_76_6_W
3,224	A*02:02	USA NMDP Hispanic South or Central American		0.0076	146,714	38_45_N_76_6_W
3,225	A*02:02	USA NMDP Japanese		0.0000	24,582	38_45_N_76_6_W
3,226	A*02:02	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
3,227	A*02:02	USA NMDP Mexican or Chicano		0.0041	261,235	38_45_N_76_6_W
3,228	A*02:02	USA NMDP Middle Eastern or North Coast of Africa		0.0060	70,890	38_45_N_76_6_W
3,229	A*02:02	USA NMDP North American Amerindian		0.0014	35,791	38_45_N_76_6_W
3,230	A*02:02	USA NMDP South Asian Indian		0.0002	185,391	38_45_N_76_6_W
3,231	A*02:02	USA NMDP Southeast Asian		0.0003	27,978	38_45_N_76_6_W
3,232	A*02:02	USA NMDP Vietnamese		0.0003	43,540	38_45_N_76_6_W
3,233	A*02:02	USA North American Native		0.0080	187	30_0_N_95_0_W
3,234	A*02:02	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
3,235	A*02:02	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
3,236	A*02:02	USA San Diego	0.6	0.0030	496	32_50_N_117_15_W
3,237	A*02:02	USA South Dakota Lakota Sioux		0.0020	302	44_30_N_100_0_W
3,238	A*02:02	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
3,239	A*02:02	USA Spain Ancestry		0.0040	279	38_0_N_77_0_W
3,240	A*02:02	Zambia Lusaka		0.0230	44	15_25_S_28_17_E
3,241	A*02:02	Zimbabwe Harare Shona		0.0360	230	17_51_S_31_1_E
3,242	A*02:02:01	Brazil Barra Mansa Rio State Black		0.0342	73	22_32_S_44_10_W
3,243	A*02:02:01	Brazil Barra Mansa Rio State Caucasian		0.0074	405	22_32_S_44_10_W
3,244	A*02:02:01	Brazil Rio de Janeiro Black		0.0147	68	22_54_S_43_12_W
3,245	A*02:02:01	Brazil Rio de Janeiro Caucasian		0.0096	521	22_54_S_43_12_W
3,246	A*02:02:01	Brazil Rio de Janeiro Parda		0.0176	170	22_54_S_43_12_W
3,247	A*02:02:01	Russia Moscow Bashkir	0.9	0.0045	442	55_46_N_37_41_E
3,248	A*02:02:01	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
3,249	A*02:02:01	Russia Moscow Russians	0.2	0.0010	13,147	55_46_N_37_41_E
3,250	A*02:02:01	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
3,251	A*02:02:01	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
3,252	A*02:02:01	Russia Nizhny Novgorod, Russians	0.3	0.0013	1,510	56_19_N_43_59_E
3,253	A*02:02:01	Saudi Arabia Al-Baha (G)		0.0233	86	20_0_N_41_28_E
3,254	A*02:02:01	Saudi Arabia Jeddah (G)		0.0042	476	21_32_N_39_10_E
3,255	A*02:02:01	Saudi Arabia pop 6 (G)		0.0110	28,927	23_52_N_45_4_E
3,256	A*02:02:01	Saudi Arabia Taif (G)		0.0096	104	21_16_N_40_24_E
3,257	A*02:02:01	South African Black	5.6	0.0282	142	29_9_S_24_39_E
3,258	A*02:02:01	Spain, Canary Islands, Gran canaria island	1.4	0.0069	215	27_57_N_15_36_W
3,259	A*02:02:01:01	Poland BMR	0.1	0.0006	23,595	52_0_N_19_22_E
3,260	A*02:03	Australia New South Wales Caucasian		0.0000	134	33_0_S_146_0_E
3,261	A*02:03	Australia Yuendumu Aborigine		0.0000	191	22_15_S_131_47_E
3,262	A*02:03	Belgium pop 3	0.0	0.0002	31,412	51_2_N_4_28_E
3,263	A*02:03	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
3,264	A*02:03	Bulgaria		0.0000	55	42_0_N_23_0_E
3,265	A*02:03	Burkina Faso Fulani		0.0000	49	12_50_N_1_50_W
3,266	A*02:03	Burkina Faso Rimaibe		0.0110	47	12_50_N_1_50_W
3,267	A*02:03	China Beijing		0.0080	67	39_58_N_116_24_E
3,268	A*02:03	China Beijing Shijiazhuang Tianjian Han		0.0240	618	39_54_N_116_24_E
3,269	A*02:03	China Canton Han		0.1120	264	23_6_N_113_15_E
3,270	A*02:03	China Guangdong Province Meizhou Han		0.0100	100	24_27_N_116_7_E
3,271	A*02:03	China Guangxi Region Maonan		0.1760	108	23_48_N_108_59_E
3,272	A*02:03	China Guangzhou		0.0980	102	23_6_N_113_15_E
3,273	A*02:03	China Hubei Han	7.1	0.0356	3,732	30_45_N_114_15_E
3,274	A*02:03	China Jiangsu Han		0.0230	3,238	32_54_N_119_48_E
3,275	A*02:03	China Jiangsu Province Han		0.0279	334	32_54_N_119_48_E
3,276	A*02:03	China Marrow Donor Registry		0.0450	600	39_54_N_116_23_E
3,277	A*02:03	China North Han		0.0240	105	39_54_N_116_24_E
3,278	A*02:03	China Shanxi HIV negative		0.0230	22	36_5_N_111_50_E
3,279	A*02:03	China Sichuan HIV negative		0.0880	34	30_50_N_102_49_E
3,280	A*02:03	China South Han		0.1080	284	23_1_N_113_3_E
3,281	A*02:03	China Southwest Dai		0.1090	124	22_0_N_100_48_E
3,282	A*02:03	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
3,283	A*02:03	China Yunnan Bulang		0.0820	116	21_57_N_100_27_E
3,284	A*02:03	China Yunnan Hani		0.0430	150	21_59_N_100_49_E
3,285	A*02:03	China Yunnan Province Han		0.0050	101	24_2_N_105_3_E
3,286	A*02:03	Croatia		0.0000	150	45_0_N_15_0_E
3,287	A*02:03	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
3,288	A*02:03	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
3,289	A*02:03	Czech Republic		0.0000	106	50_0_N_14_0_E
3,290	A*02:03	Czech Republic NMDR		0.0002	5,099	49_48_N_15_30_E
3,291	A*02:03	Germany DKMS - Austria minority		0.0003	1,698	48_31_N_9_3_E
3,292	A*02:03	Germany DKMS - China minority		0.0335	1,282	48_31_N_9_3_E
3,293	A*02:03	Germany DKMS - France minority		0.0004	1,406	48_31_N_9_3_E
3,294	A*02:03	Germany DKMS - German donors		0.0001	3,456,066	48_31_N_9_3_E
3,295	A*02:03	Germany DKMS - Italy minority		0.0004	1,159	48_31_N_9_3_E
3,296	A*02:03	Germany DKMS - Turkey minority		0.0004	4,856	48_31_N_9_3_E
3,297	A*02:03	Germany pop 6		0.0001	8,862	51_6_N_10_23_E
3,298	A*02:03	Germany pop 8		0.0001	39,689	48_24_N_9_58_E
3,299	A*02:03	Hong Kong Chinese	14.7	0.0780	569	22_16_N_114_9_E
3,300	A*02:03	Hong Kong Chinese BMDR		0.0760	7,595	22_15_N_114_10_E
3,301	A*02:03	Hong Kong Chinese cord blood registry		0.0777	3,892	22_15_N_114_10_E
3,302	A*02:03	India Central UCBB	2.0	0.0099	4,204	27_1_N_81_19_E
3,303	A*02:03	India Delhi pop 2	1.1	0.0050	90	28_36_N_77_13_E
3,304	A*02:03	India East UCBB	5.1	0.0256	2,403	22_25_N_88_18_E
3,305	A*02:03	India Khandesh Region Pawra		0.0500	50	20_54_N_74_46_E
3,306	A*02:03	India Mumbai Maratha		0.0370	91	18_58_N_72_49_E
3,307	A*02:03	India North UCBB	1.0	0.0051	5,849	28_36_N_77_11_E
3,308	A*02:03	India Northeast UCBB	8.4	0.0422	296	26_16_N_91_48_E
3,309	A*02:03	India South UCBB	2.5	0.0124	11,446	13_6_N_80_8_E
3,310	A*02:03	India Tamil Nadu		0.0117	2,492	11_32_N_78_52_E
3,311	A*02:03	India West Bhil		0.0500	50	20_54_N_74_46_E
3,312	A*02:03	India West Coast Parsi		0.0000	50	18_0_N_74_0_E
3,313	A*02:03	India West UCBB	2.3	0.0112	5,829	19_11_N_73_3_E
3,314	A*02:03	Indonesia Java Western	7.2		236	7_30_S_111_15_E
3,315	A*02:03	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
3,316	A*02:03	Israel Iran Jews		0.0000	8,153	32_8_N_34_52_E
3,317	A*02:03	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
3,318	A*02:03	Israel Libya Jews		0.0000	3,739	32_10_N_34_52_E
3,319	A*02:03	Israel Morocco Jews		0.0000	36,718	32_10_N_34_52_E
3,320	A*02:03	Israel Poland Jews		0.0000	13,871	32_10_N_34_52_E
3,321	A*02:03	Israel USSR Jews		0.0000	45,681	32_9_N_34_50_E
3,322	A*02:03	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
3,323	A*02:03	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
3,324	A*02:03	Japan pop 16		0.0006	18,604	35_41_N_139_46_E
3,325	A*02:03	Japan pop 3		0.0010	1,018	35_41_N_139_46_E
3,326	A*02:03	Malaysia Champa	13.8	0.0690	29	2_30_N_112_30_E
3,327	A*02:03	Malaysia Jelebu Temuan		0.0210	25	2_56_N_102_5_E
3,328	A*02:03	Malaysia Kedah Baling Kensiu		0.0200	25	5_40_N_100_55_E
3,329	A*02:03	Malaysia Kedah Kensiu	9.5	0.0480	21	5_40_N_100_55_E
3,330	A*02:03	Malaysia Kelantan	3.6	0.0180	28	2_30_N_112_30_E
3,331	A*02:03	Malaysia Mandailing	18.5	0.0930	27	2_30_N_112_30_E
3,332	A*02:03	Malaysia Pahang Semai	23.7	0.1180	38	4_11_N_102_3_E
3,333	A*02:03	Malaysia Patani	16.0	0.0800	25	2_30_N_112_30_E
3,334	A*02:03	Malaysia Peninsular Chinese	12.4	0.0644	194	3_9_N_101_57_E
3,335	A*02:03	Malaysia Peninsular Indian	1.1	0.0055	271	3_9_N_101_57_E
3,336	A*02:03	Malaysia Peninsular Malay	4.3	0.0216	951	3_9_N_101_57_E
3,337	A*02:03	Malaysia Perak Grik Jehai		0.0400	25	5_25_N_101_8_E
3,338	A*02:03	Malaysia Sarawak Bau Bidayuh		0.0200	25	1_25_N_110_9_E
3,339	A*02:03	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
3,340	A*02:03	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
3,341	A*02:03	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
3,342	A*02:03	Netherlands Leiden		0.0010	1,305	52_9_N_4_31_E
3,343	A*02:03	Oman	0.0	0.0000	118	22_0_N_56_0_E
3,344	A*02:03	Pakistan Baloch		0.0080	66	30_0_N_67_0_E
3,345	A*02:03	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
3,346	A*02:03	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
3,347	A*02:03	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
3,348	A*02:03	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
3,349	A*02:03	Pakistan Mixed Pathan		0.0050	100	32_0_N_70_0_E
3,350	A*02:03	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
3,351	A*02:03	Panama		0.0011	462	8_59_N_79_37_W
3,352	A*02:03	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
3,353	A*02:03	Poland DKMS		0.0000	20,653	52_8_N_19_23_E
3,354	A*02:03	Romania	0.0	0.0000	348	46_0_N_25_0_E
3,355	A*02:03	Russia Tuva pop 2		0.0030	169	51_30_N_95_5_E
3,356	A*02:03	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
3,357	A*02:03	Singapore Chinese	12.8	0.0670	149	1_24_N_103_45_E
3,358	A*02:03	Singapore Chinese Han		0.0990	94	1_17_N_103_51_E
3,359	A*02:03	Singapore Javaneses		0.0900	51	1_24_N_103_45_E
3,360	A*02:03	Singapore Riau Malay		0.0520	132	1_24_N_103_45_E
3,361	A*02:03	Singapore SGVP Chinese CHS		0.0870	96	1_21_N_103_50_E
3,362	A*02:03	Singapore SGVP Malay MAS		0.0360	89	1_21_N_103_50_E
3,363	A*02:03	Singapore SGVP. Indian INS		0.0060	86	1_21_N_103_50_E
3,364	A*02:03	Singapore Thai		0.1330	100	1_24_N_103_45_E
3,365	A*02:03	South Africa Natal Tamil		0.0100	51	30_0_S_31_0_E
3,366	A*02:03	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
3,367	A*02:03	South Korea pop 10		0.0055	4,128	37_33_N_126_58_E
3,368	A*02:03	South Korea pop 3		0.0050	485	37_33_N_126_59_E
3,369	A*02:03	Sri Lanka Colombo	1.4	0.0070	714	6_56_N_79_50_E
3,370	A*02:03	Sri Lanka Colombo Sinhalese		0.0240	101	6_56_N_79_50_E
3,371	A*02:03	Sudan Central Shaigiya Mixed	2.7		36	18_0_N_33_0_E
3,372	A*02:03	Sudan East Rashaida	3.7		27	14_0_N_35_0_E
3,373	A*02:03	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
3,374	A*02:03	Taiwan Atayal	0.9	0.0050	106	24_0_N_121_0_E
3,375	A*02:03	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
3,376	A*02:03	Taiwan Hakka	5.5	0.0360	55	24_0_N_121_0_E
3,377	A*02:03	Taiwan Han Chinese		0.0640	504	23_46_N_121_0_E
3,378	A*02:03	Taiwan Minnan pop 1	12.7	0.0640	102	24_0_N_121_0_E
3,379	A*02:03	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
3,380	A*02:03	Taiwan Pazeh	9.1	0.0450	55	25_0_N_121_30_E
3,381	A*02:03	Taiwan pop 2		0.0560	364	23_46_N_121_0_E
3,382	A*02:03	Taiwan pop 3		0.0570	212	23_46_N_121_0_E
3,383	A*02:03	Taiwan Puyuma	12.0	0.0600	50	23_0_N_121_0_E
3,384	A*02:03	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
3,385	A*02:03	Taiwan Saisiat	2.0	0.0100	51	24_0_N_121_0_E
3,386	A*02:03	Taiwan Siraya	11.8	0.0590	51	24_0_N_121_0_E
3,387	A*02:03	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
3,388	A*02:03	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
3,389	A*02:03	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
3,390	A*02:03	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
3,391	A*02:03	Taiwan Tzu Chi Morrow Donor Registry		0.0736	46,682	25_2_N_121_38_E
3,392	A*02:03	Taiwan Tzu Chi Morrow Donor Registry Aborigine		0.0193	233	25_2_N_121_38_E
3,393	A*02:03	Taiwan Tzu Chi Cord Blood Bank		0.0730	710	23_46_N_121_0_E
3,394	A*02:03	Thailand		0.0770	142	16_7_N_102_0_E
3,395	A*02:03	Thailand Northeast		0.1220	66	15_13_N_104_51_E
3,396	A*02:03	Thailand Northeast pop 2		0.1060	400	16_27_N_103_10_E
3,397	A*02:03	Tunisia	0.0	0.0000	100	35_0_N_9_0_E
3,398	A*02:03	Turkey pop 5		0.0000	142	40_0_N_28_0_E
3,399	A*02:03	United Arab Emirates Pop 1		0.0020	570	24_42_N_54_82_E
3,400	A*02:03	USA African American		0.0000	252	30_0_N_95_0_W
3,401	A*02:03	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
3,402	A*02:03	USA African American pop 4		0.0002	2,411	44_58_N_93_15_W
3,403	A*02:03	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
3,404	A*02:03	USA Asian		0.0420	358	30_0_N_95_0_W
3,405	A*02:03	USA Asian pop 2		0.0316	1,772	44_58_N_93_15_W
3,406	A*02:03	USA Caucasian	0.0		61,655	38_53_N_77_2_W
3,407	A*02:03	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
3,408	A*02:03	USA Caucasian pop 2		0.0020	265	30_0_N_95_0_W
3,409	A*02:03	USA Caucasian pop 4		0.0009	1,070	38_53_N_77_2_W
3,410	A*02:03	USA Hispanic		0.0000	234	30_0_N_95_0_W
3,411	A*02:03	USA Hispanic pop 2		0.0003	1,999	44_58_N_93_15_W
3,412	A*02:03	USA NMDP African		0.0002	28,557	38_45_N_76_6_W
3,413	A*02:03	USA NMDP African American pop 2		0.0002	416,581	38_45_N_76_6_W
3,414	A*02:03	USA NMDP American Indian South or Central America		0.0010	5,926	38_45_N_76_6_W
3,415	A*02:03	USA NMDP Caribean Black		0.0003	33,328	38_45_N_76_6_W
3,416	A*02:03	USA NMDP Caribean Hispanic		0.0001	115,374	38_45_N_76_6_W
3,417	A*02:03	USA NMDP Caribean Indian		0.0020	14,339	38_45_N_76_6_W
3,418	A*02:03	USA NMDP Chinese		0.0774	99,672	38_45_N_76_6_W
3,419	A*02:03	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
3,420	A*02:03	USA NMDP Filipino		0.0149	50,614	38_45_N_76_6_W
3,421	A*02:03	USA NMDP Hawaiian or other Pacific Islander		0.0064	11,499	38_45_N_76_6_W
3,422	A*02:03	USA NMDP Hispanic South or Central American		0.0002	146,714	38_45_N_76_6_W
3,423	A*02:03	USA NMDP Japanese		0.0014	24,582	38_45_N_76_6_W
3,424	A*02:03	USA NMDP Korean		0.0063	77,584	38_45_N_76_6_W
3,425	A*02:03	USA NMDP Mexican or Chicano		0.0001	261,235	38_45_N_76_6_W
3,426	A*02:03	USA NMDP Middle Eastern or North Coast of Africa		0.0002	70,890	38_45_N_76_6_W
3,427	A*02:03	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
3,428	A*02:03	USA NMDP South Asian Indian		0.0110	185,391	38_45_N_76_6_W
3,429	A*02:03	USA NMDP Southeast Asian		0.0321	27,978	38_45_N_76_6_W
3,430	A*02:03	USA NMDP Vietnamese		0.0925	43,540	38_45_N_76_6_W
3,431	A*02:03	USA North American Native		0.0000	187	30_0_N_95_0_W
3,432	A*02:03	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
3,433	A*02:03	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
3,434	A*02:03	USA San Diego	1.0	0.0060	496	32_50_N_117_15_W
3,435	A*02:03	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
3,436	A*02:03	Vietnam Hanoi Kinh pop 2		0.0790	170	21_2_N_105_51_E
3,437	A*02:03:01	China Guizhou Province Bouyei		0.1030	109	26_50_N_106_50_E
3,438	A*02:03:01	China Guizhou Province Miao pop 2		0.1240	85	26_50_N_106_50_E
3,439	A*02:03:01	China Guizhou Province Shui		0.1120	153	26_50_N_106_50_E
3,440	A*02:03:01	China Inner Mongolia Region		0.0050	102	36_0_N_102_0_E
3,441	A*02:03:01	China Jingpo Minority	17.5	0.0971	105	25_0_N_102_0_E
3,442	A*02:03:01	China Qinghai Province Hui		0.0230	110	36_0_N_102_0_E
3,443	A*02:03:01	China Yunnan Province Jinuo		0.0780	109	24_30_N_101_30_E
3,444	A*02:03:01	China Yunnan Province Lisu		0.1520	111	24_30_N_101_30_E
3,445	A*02:03:01	China Yunnan Province Nu		0.0820	107	24_30_N_101_30_E
3,446	A*02:03:01	China Yunnan Province Wa		0.0550	119	24_30_N_101_30_E
3,447	A*02:03:01	China Zhejiang Han		0.0358	1,734	27_2_N_118_1_E
3,448	A*02:03:01	England Blood Donors of Mixed Ethnicity	0.2	0.0010	519	52_21_N_1_10_W
3,449	A*02:03:01	Hong Kong Chinese HKBMDR HLA 11 loci	16.0	0.0846	5,266	22_25_N_114_17_E
3,450	A*02:03:01	India Andhra Pradesh Telugu Speaking	4.8	0.0242	186	13_4_N_80_14_E
3,451	A*02:03:01	India Karnataka Kannada Speaking	2.9	0.0140	174	12_57_N_77_36_E
3,452	A*02:03:01	India Kerala Malayalam speaking	1.1	0.0060	356	13_1_N_80_15_E
3,453	A*02:03:01	Indonesia Java pop 2		0.0690	36	6_8_S_106_10_E
3,454	A*02:03:01	Indonesia Sundanese and Javanese		0.0370	201	6_8_S_106_10_E
3,455	A*02:03:01	Pakistan Mixed Punjabi	2.0	0.0061	389	31_0_N_72_0_E
3,456	A*02:03:01	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
3,457	A*02:03:01	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
3,458	A*02:03:01	Russia Moscow Buryat	0.7	0.0034	149	55_46_N_37_41_E
3,459	A*02:03:01	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
3,460	A*02:03:01	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
3,461	A*02:03:01	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
3,462	A*02:03:01	Russia Moscow Uzbek	2.0	0.0101	198	55_46_N_37_41_E
3,463	A*02:03:01	Saudi Arabia Al-Madinah (G)		0.0096	102	24_28_N_39_36_E
3,464	A*02:03:01	Saudi Arabia Jeddah (G)		0.0084	476	21_32_N_39_10_E
3,465	A*02:03:01	Saudi Arabia Makkah (G)		0.0204	344	21_25_N_39_49_E
3,466	A*02:03:01	Saudi Arabia pop 6 (G)		0.0017	28,927	23_52_N_45_4_E
3,467	A*02:03:01	South African Mixed ancestry	2.0	0.0100	50	29_22_S_24_31_E
3,468	A*02:03:01	Vietnam Kinh	15.0	0.0743	101	14_5_N_108_27_E
3,469	A*02:03:02	China North Han		0.0000	105	39_54_N_116_24_E
3,470	A*02:03:02	Hong Kong Chinese HKBMDR HLA 11 loci	0.0	0.0002	5,266	22_25_N_114_17_E
3,471	A*02:03:02	Vietnam Kinh	1.0	0.0050	101	14_5_N_108_27_E
3,472	A*02:04	Brazil Barra Mansa Rio State Black		0.0068	73	22_32_S_44_10_W
3,473	A*02:04	Brazil Barra Mansa Rio State Caucasian		0.0037	405	22_32_S_44_10_W
3,474	A*02:04	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
3,475	A*02:04	Brazil Mixed		0.0050	108	23_10_S_47_48_W
3,476	A*02:04	Brazil Rio de Janeiro Caucasian		0.0029	521	22_54_S_43_12_W
3,477	A*02:04	Brazil Rio de Janeiro Parda		0.0059	170	22_54_S_43_12_W
3,478	A*02:04	Bulgaria		0.0000	55	42_0_N_23_0_E
3,479	A*02:04	Cameroon Yaounde		0.0060	92	3_52_N_11_31_E
3,480	A*02:04	China Beijing Shijiazhuang Tianjian Han		0.0040	618	39_54_N_116_24_E
3,481	A*02:04	China North Han		0.0000	105	39_54_N_116_24_E
3,482	A*02:04	China Tibet Region Tibetan		0.0030	158	29_39_N_91_7_E
3,483	A*02:04	Colombia Bogotá Cord Blood	0.2	0.0010	1,463	4_39_N_73_52_W
3,484	A*02:04	Colombia North Chimila Amerindians		0.0425	47	10_1_N_74_13_W
3,485	A*02:04	Croatia		0.0000	150	45_0_N_15_0_E
3,486	A*02:04	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
3,487	A*02:04	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
3,488	A*02:04	Czech Republic		0.0000	106	50_0_N_14_0_E
3,489	A*02:04	Czech Republic NMDR		0.0001	5,099	49_48_N_15_30_E
3,490	A*02:04	Georgia Svaneti Region Svan		0.0190	80	42_54_N_43_0_E
3,491	A*02:04	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
3,492	A*02:04	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
3,493	A*02:04	Iran Baloch		0.0060	100	29_29_N_60_52_E
3,494	A*02:04	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
3,495	A*02:04	Israel Arab pop 2		0.0000	12,301	32_8_N_34_52_E
3,496	A*02:04	Israel Argentina Jews		0.0000	4,307	32_8_N_34_52_E
3,497	A*02:04	Israel USSR Jews		0.0001	45,681	32_9_N_34_50_E
3,498	A*02:04	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
3,499	A*02:04	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
3,500	A*02:04	Italy pop 5		0.0010	975	45_28_N_9_11_E
3,501	A*02:04	Japan Okinawa Ryukyuan		0.0140	143	26_4_N_127_8_E
3,502	A*02:04	Kenya		0.0070	144	1_28_S_36_8_E
3,503	A*02:04	Kenya Luo		0.0000	265	0_0_S_34_6_E
3,504	A*02:04	Kenya Nandi		0.0000	240	0_0_S_35_0_E
3,505	A*02:04	Mali Bandiagara		0.0000	138	14_4_N_3_6_W
3,506	A*02:04	Mexico Guadalajara Mestizo pop 2		0.0050	103	20_40_N_103_21_W
3,507	A*02:04	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
3,508	A*02:04	Mongolia Buryat		0.0040	141	47_6_N_118_6_E
3,509	A*02:04	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
3,510	A*02:04	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
3,511	A*02:04	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
3,512	A*02:04	Oman	0.0	0.0000	118	22_0_N_56_0_E
3,513	A*02:04	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
3,514	A*02:04	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
3,515	A*02:04	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
3,516	A*02:04	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
3,517	A*02:04	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
3,518	A*02:04	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
3,519	A*02:04	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
3,520	A*02:04	Panama		0.0011	462	8_59_N_79_37_W
3,521	A*02:04	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
3,522	A*02:04	Romania	0.0	0.0000	348	46_0_N_25_0_E
3,523	A*02:04	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
3,524	A*02:04	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
3,525	A*02:04	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
3,526	A*02:04	South Korea pop 3		0.0000	485	37_33_N_126_59_E
3,527	A*02:04	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.0	0.0001	4,335	41_22_N_2_11_E
3,528	A*02:04	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
3,529	A*02:04	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
3,530	A*02:04	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
3,531	A*02:04	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
3,532	A*02:04	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
3,533	A*02:04	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
3,534	A*02:04	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
3,535	A*02:04	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
3,536	A*02:04	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
3,537	A*02:04	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
3,538	A*02:04	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
3,539	A*02:04	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
3,540	A*02:04	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
3,541	A*02:04	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
3,542	A*02:04	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
3,543	A*02:04	Tunisia	0.0	0.0000	100	35_0_N_9_0_E
3,544	A*02:04	Tunisia Gabes		0.0526	95	33_53_N_10_6_E
3,545	A*02:04	Turkey pop 5		0.0000	142	40_0_N_28_0_E
3,546	A*02:04	Uganda Kampala		0.0000	161	0_3_N_32_6_E
3,547	A*02:04	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
3,548	A*02:04	USA African American pop 4		0.0002	2,411	44_58_N_93_15_W
3,549	A*02:04	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
3,550	A*02:04	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
3,551	A*02:04	USA Caucasian	0.7		61,655	38_53_N_77_2_W
3,552	A*02:04	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
3,553	A*02:04	USA Hispanic pop 2		0.0028	1,999	44_58_N_93_15_W
3,554	A*02:04	USA NMDP African		0.0003	28,557	38_45_N_76_6_W
3,555	A*02:04	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
3,556	A*02:04	USA NMDP Caribean Black		0.0006	33,328	38_45_N_76_6_W
3,557	A*02:04	USA NMDP Caribean Hispanic		0.0089	115,374	38_45_N_76_6_W
3,558	A*02:04	USA NMDP Caribean Indian		0.0080	14,339	38_45_N_76_6_W
3,559	A*02:04	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
3,560	A*02:04	USA NMDP Hispanic South or Central American		0.0016	146,714	38_45_N_76_6_W
3,561	A*02:04	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
3,562	A*02:04	USA NMDP Mexican or Chicano		0.0003	261,235	38_45_N_76_6_W
3,563	A*02:04	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
3,564	A*02:04	USA NMDP North American Amerindian		0.0001	35,791	38_45_N_76_6_W
3,565	A*02:04	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
3,566	A*02:04	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
3,567	A*02:04	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
3,568	A*02:04	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
3,569	A*02:04	USA Spain Ancestry		0.0070	279	38_0_N_77_0_W
3,570	A*02:04	Venezuela Sierra de Perija Yucpa		0.3140	73	9_0_N_72_0_W
3,571	A*02:04	Zambia Lusaka		0.0000	44	15_25_S_28_17_E
3,572	A*02:05	Armenia combined Regions		0.0050	100	40_11_N_44_31_E
3,573	A*02:05	Australia New South Wales Caucasian		0.0080	134	33_0_S_146_0_E
3,574	A*02:05	Australia Yuendumu Aborigine		0.0000	191	22_15_S_131_47_E
3,575	A*02:05	Austria	2.5	0.0120	200	48_13_N_16_21_E
3,576	A*02:05	Azores Central Islands		0.0090	59	38_0_N_28_0_W
3,577	A*02:05	Azores Oriental Islands		0.0130	43	36_58_N_25_6_W
3,578	A*02:05	Azores Terceira Island		0.0310	130	38_44_N_27_19_W
3,579	A*02:05	Belgium	1.0	0.0050	99	50_50_N_4_21_E
3,580	A*02:05	Belgium pop 3	1.8	0.0088	31,412	51_2_N_4_28_E
3,581	A*02:05	Brazil Puyanawa	1.3	0.0070	150	10_52_S_53_5_W
3,582	A*02:05	Brazil Belo Horizonte Caucasian	5.3	0.0260	95	19_55_S_43_56_W
3,583	A*02:05	Brazil Mixed		0.0150	108	23_10_S_47_48_W
3,584	A*02:05	Brazil Vale do Ribeira Quilombos	0.0	0.0000	144	24_36_S_48_15_W
3,585	A*02:05	Bulgaria		0.0270	55	42_0_N_23_0_E
3,586	A*02:05	Burkina Faso Fulani		0.0920	49	12_50_N_1_50_W
3,587	A*02:05	Burkina Faso Rimaibe		0.0430	47	12_50_N_1_50_W
3,588	A*02:05	Cameroon Baka Pygmy		0.0500	10	8_30_N_14_0_E
3,589	A*02:05	Cameroon Bamileke		0.0260	77	5_28_N_10_25_E
3,590	A*02:05	Cameroon Beti		0.0110	174	2_55_N_11_9_E
3,591	A*02:05	Cameroon Sawa		0.0380	13	4_50_N_10_0_E
3,592	A*02:05	Cameroon Yaounde		0.0220	92	3_52_N_11_31_E
3,593	A*02:05	Cape Verde Northwestern Islands		0.0000	62	16_0_N_24_0_W
3,594	A*02:05	Cape Verde Southeastern Islands		0.0080	62	16_0_N_24_0_W
3,595	A*02:05	China Beijing		0.0000	67	39_58_N_116_24_E
3,596	A*02:05	China Beijing Shijiazhuang Tianjian Han		0.0020	618	39_54_N_116_24_E
3,597	A*02:05	China Guangzhou		0.0050	102	23_6_N_113_15_E
3,598	A*02:05	China Hubei Han	0.5	0.0023	3,732	30_45_N_114_15_E
3,599	A*02:05	China Inner Mongolia Region		0.0050	102	36_0_N_102_0_E
3,600	A*02:05	China Jiangsu Han		0.0030	3,238	32_54_N_119_48_E
3,601	A*02:05	China Jiangsu Province Han		0.0035	334	32_54_N_119_48_E
3,602	A*02:05	China Marrow Donor Registry		0.0020	600	39_54_N_116_23_E
3,603	A*02:05	China North Han		0.0140	105	39_54_N_116_24_E
3,604	A*02:05	China Qinghai Province Hui		0.0090	110	36_0_N_102_0_E
3,605	A*02:05	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
3,606	A*02:05	China Uyghur HIV negative		0.0260	19	36_34_N_103_49_E
3,607	A*02:05	Colombia Bogotá Cord Blood	3.1	0.0167	1,463	4_39_N_73_52_W
3,608	A*02:05	Croatia		0.0070	150	45_0_N_15_0_E
3,609	A*02:05	Croatia pop 4		0.0079	4,000	45_48_N_15_57_E
3,610	A*02:05	Cuba Caucasian	4.3	0.0210	70	23_8_N_82_22_W
3,611	A*02:05	Cuba Mixed Race	4.8	0.0240	42	23_8_N_82_22_W
3,612	A*02:05	Czech Republic		0.0240	106	50_0_N_14_0_E
3,613	A*02:05	Czech Republic NMDR		0.0082	5,099	49_48_N_15_30_E
3,614	A*02:05	Ecuador Amerindians		0.0079	63	0_13_S_78_30_W
3,615	A*02:05	England North West	2.0	0.0100	298	53_0_N_3_0_W
3,616	A*02:05	France French Bone Marrow Donor Registry		0.0094	42,623	48_51_N_2_21_E
3,617	A*02:05	France Southeast	3.1	0.0160	130	46_0_N_5_0_E
3,618	A*02:05	Gaza	11.9	0.0595	42	31_22_N_34_18_E
3,619	A*02:05	Georgia Svaneti Region Svan		0.0060	80	42_54_N_43_0_E
3,620	A*02:05	Georgia Tibilisi		0.0140	109	41_41_N_44_57_E
3,621	A*02:05	Georgia Tibilisi Kurd		0.0500	31	41_41_N_44_57_E
3,622	A*02:05	Germany DKMS - Austria minority		0.0094	1,698	48_31_N_9_3_E
3,623	A*02:05	Germany DKMS - Bosnia and Herzegovina minority		0.0088	1,028	48_31_N_9_3_E
3,624	A*02:05	Germany DKMS - China minority		0.0043	1,282	48_31_N_9_3_E
3,625	A*02:05	Germany DKMS - Croatia minority		0.0071	2,057	48_31_N_9_3_E
3,626	A*02:05	Germany DKMS - France minority		0.0079	1,406	48_31_N_9_3_E
3,627	A*02:05	Germany DKMS - German donors		0.0070	3,456,066	48_31_N_9_3_E
3,628	A*02:05	Germany DKMS - Greece minority		0.0090	1,894	48_31_N_9_3_E
3,629	A*02:05	Germany DKMS - Italy minority		0.0224	1,159	48_31_N_9_3_E
3,630	A*02:05	Germany DKMS - Netherlands minority		0.0051	1,374	48_31_N_9_3_E
3,631	A*02:05	Germany DKMS - Portugal minority		0.0179	1,176	48_31_N_9_3_E
3,632	A*02:05	Germany DKMS - Romania minority		0.0073	1,234	48_31_N_9_3_E
3,633	A*02:05	Germany DKMS - Spain minority		0.0122	1,107	48_31_N_9_3_E
3,634	A*02:05	Germany DKMS - Turkey minority		0.0181	4,856	48_31_N_9_3_E
3,635	A*02:05	Germany DKMS - United Kingdom minority		0.0086	1,043	48_31_N_9_3_E
3,636	A*02:05	Germany pop 6		0.0064	8,862	51_6_N_10_23_E
3,637	A*02:05	Germany pop 8		0.0093	39,689	48_24_N_9_58_E
3,638	A*02:05	Ghana Ga-Adangbe	2.3	0.0153	131	5_45_N_0_12_E
3,639	A*02:05	Greece pop 6		0.0062	242	37_58_N_23_43_E
3,640	A*02:05	Greece pop 8	2.4	0.0121	83	37_58_N_23_43_E
3,641	A*02:05	Guinea Bissau		0.0000	65	12_0_N_15_0_W
3,642	A*02:05	Guinea Bissau Balanta		0.0100	48	11_52_N_15_36_W
3,643	A*02:05	Guinea Bissau Bijago		0.0220	23	11_52_N_15_36_W
3,644	A*02:05	Guinea Bissau Fula		0.0000	31	11_52_N_15_36_W
3,645	A*02:05	Guinea Bissau Papel		0.0000	25	11_52_N_15_36_W
3,646	A*02:05	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
3,647	A*02:05	Hong Kong Chinese BMDR		0.0011	7,595	22_15_N_114_10_E
3,648	A*02:05	Hong Kong Chinese cord blood registry		0.0010	3,892	22_15_N_114_10_E
3,649	A*02:05	India Central UCBB	2.2	0.0111	4,204	27_1_N_81_19_E
3,650	A*02:05	India Delhi pop 2	2.2	0.0110	90	28_36_N_77_13_E
3,651	A*02:05	India East UCBB	1.1	0.0056	2,403	22_25_N_88_18_E
3,652	A*02:05	India New Delhi		0.0300	71	28_37_N_77_13_E
3,653	A*02:05	India North pop 2		0.0100	72	25_0_N_74_0_E
3,654	A*02:05	India North UCBB	3.3	0.0163	5,849	28_36_N_77_11_E
3,655	A*02:05	India Northeast UCBB	0.7	0.0034	296	26_16_N_91_48_E
3,656	A*02:05	India South UCBB	0.7	0.0035	11,446	13_6_N_80_8_E
3,657	A*02:05	India Tamil Nadu		0.0032	2,492	11_32_N_78_52_E
3,658	A*02:05	India West UCBB	1.9	0.0094	5,829	19_11_N_73_3_E
3,659	A*02:05	Iran Baloch		0.0060	100	29_29_N_60_52_E
3,660	A*02:05	Ireland Northern	2.4	0.0120	1,000	54_40_N_6_45_W
3,661	A*02:05	Ireland South	0.8	0.0040	250	53_20_N_6_15_W
3,662	A*02:05	Israel Arab Druze		0.0400	101	31_8_N_35_2_E
3,663	A*02:05	Israel Arab pop 2		0.0329	12,301	32_8_N_34_52_E
3,664	A*02:05	Israel Argentina Jews		0.0057	4,307	32_8_N_34_52_E
3,665	A*02:05	Israel Ashkenazi and Non Ashkenazi Jews		0.0430	146	31_8_N_35_2_E
3,666	A*02:05	Israel Ashkenazi Jews pop 3		0.0110	4,625	32_8_N_34_52_E
3,667	A*02:05	Israel Bukhara Jews		0.0228	2,317	32_8_N_34_52_E
3,668	A*02:05	Israel Druze		0.0156	5,914	32_41_N_35_17_E
3,669	A*02:05	Israel Ethiopia Jews		0.0295	5,928	32_8_N_34_52_E
3,670	A*02:05	Israel Georgia Jews		0.0057	4,471	32_10_N_34_53_E
3,671	A*02:05	Israel Iran Jews		0.0016	8,153	32_8_N_34_52_E
3,672	A*02:05	Israel Iraq Jews		0.0040	13,270	32_10_N_34_52_E
3,673	A*02:05	Israel Kavkazi Jews		0.0083	2,840	32_10_N_34_52_E
3,674	A*02:05	Israel Libya Jews		0.0160	3,739	32_10_N_34_52_E
3,675	A*02:05	Israel Morocco Jews		0.0093	36,718	32_10_N_34_52_E
3,676	A*02:05	Israel Poland Jews		0.0135	13,871	32_10_N_34_52_E
3,677	A*02:05	Israel Tunisia Jews		0.0082	9,070	32_10_N_34_52_E
3,678	A*02:05	Israel USA Jews		0.0065	6,058	32_10_N_34_52_E
3,679	A*02:05	Israel USSR Jews		0.0054	45,681	32_9_N_34_50_E
3,680	A*02:05	Israel YemenJews		0.0092	15,542	32_9_N_34_50_E
3,681	A*02:05	Italy Bergamo	4.0	0.0210	101	45_42_N_9_40_E
3,682	A*02:05	Italy North pop 3	11.5	0.0580	97	43_0_N_12_0_E
3,683	A*02:05	Italy pop 5		0.0230	975	45_28_N_9_11_E
3,684	A*02:05	Japan pop 16		0.0001	18,604	35_41_N_139_46_E
3,685	A*02:05	Kenya		0.0140	144	1_28_S_36_8_E
3,686	A*02:05	Kenya Luo		0.0260	265	0_0_S_34_6_E
3,687	A*02:05	Kenya Nandi		0.0870	240	0_0_S_35_0_E
3,688	A*02:05	Kenya, Nyanza Province, Luo tribe	4.0	0.0200	100	0_5_S_34_46_E
3,689	A*02:05	Madeira		0.0220	185	32_44_N_16_58_W
3,690	A*02:05	Malaysia Peninsular Indian	1.5	0.0074	271	3_9_N_101_57_E
3,691	A*02:05	Malaysia Peninsular Malay	0.1	0.0005	951	3_9_N_101_57_E
3,692	A*02:05	Mali Bandiagara		0.0220	138	14_4_N_3_6_W
3,693	A*02:05	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
3,694	A*02:05	Mexico Mexico City Mestizo pop 2		0.0171	234	19_26_N_99_8_W
3,695	A*02:05	Mexico Mexico City Mestizo population		0.0035	143	19_25_N_99_7_W
3,696	A*02:05	Mexico Oaxaca Mixtec		0.0100	103	17_5_N_96_45_W
3,697	A*02:05	Mexico Oaxaca Zapotec		0.0000	90	17_5_N_96_45_W
3,698	A*02:05	Mongolia Buryat		0.0320	141	47_6_N_118_6_E
3,699	A*02:05	Morocco Atlantic Coast Chaouya		0.0140	98	30_22_N_7_22_W
3,700	A*02:05	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
3,701	A*02:05	Morocco Settat Chaouya	2.8	0.0140	98	33_0_N_7_36_W
3,702	A*02:05	Netherlands Leiden		0.0050	1,305	52_9_N_4_31_E
3,703	A*02:05	New Zealand Maori with Admixed History	3.8	0.0190	105	42_0_S_174_0_E
3,704	A*02:05	New Zealand Maori with Full Ancestry	2.2	0.0110	46	42_0_S_174_0_E
3,705	A*02:05	Nicaragua Managua	2.1	0.0105	339	12_7_N_86_10_W
3,706	A*02:05	Oman	4.2	0.0210	118	22_0_N_56_0_E
3,707	A*02:05	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
3,708	A*02:05	Pakistan Brahui		0.0060	104	30_0_N_67_0_E
3,709	A*02:05	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
3,710	A*02:05	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
3,711	A*02:05	Pakistan Karachi Parsi		0.0060	91	24_49_N_66_59_E
3,712	A*02:05	Pakistan Mixed Pathan		0.0050	100	32_0_N_70_0_E
3,713	A*02:05	Pakistan Mixed Sindhi		0.0250	101	24_0_N_68_0_E
3,714	A*02:05	Panama		0.0102	462	8_59_N_79_37_W
3,715	A*02:05	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
3,716	A*02:05	Poland		0.0100	200	51_7_N_17_2_E
3,717	A*02:05	Poland DKMS		0.0066	20,653	52_8_N_19_23_E
3,718	A*02:05	Portugal Azores Terceira Island	4.4	0.0263	130	38_43_N_27_13_W
3,719	A*02:05	Portugal Center		0.0100	50	39_0_N_8_0_W
3,720	A*02:05	Portugal North		0.0110	46	41_0_N_8_0_W
3,721	A*02:05	Portugal South		0.0100	49	37_0_N_8_0_W
3,722	A*02:05	Romania	0.3	0.0010	348	46_0_N_25_0_E
3,723	A*02:05	Russia Karelia		0.0017	1,075	63_9_N_32_59_E
3,724	A*02:05	Russia Tuva pop 2		0.0080	169	51_30_N_95_5_E
3,725	A*02:05	Sao Tome Island Angolar		0.0160	32	0_20_N_6_44_E
3,726	A*02:05	Sao Tome Island Forro		0.0380	66	0_20_N_6_44_E
3,727	A*02:05	Saudi Arabia Guraiat and Hail	12.2	0.0610	213	27_31_N_41_41_E
3,728	A*02:05	Senegal Niokholo Mandenka		0.0110	165	12_35_N_12_9_W
3,729	A*02:05	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
3,730	A*02:05	Singapore SGVP Chinese CHS		0.0000	96	1_21_N_103_50_E
3,731	A*02:05	Singapore SGVP Malay MAS		0.0060	89	1_21_N_103_50_E
3,732	A*02:05	Singapore SGVP. Indian INS		0.0060	86	1_21_N_103_50_E
3,733	A*02:05	South Africa Cape Town Black	4.8	0.0238	84	34_7_S_18_22_E
3,734	A*02:05	South Africa Caucasians		0.0050	102	30_0_S_25_0_E
3,735	A*02:05	South Africa Durban Black	18.2	0.1136	22	29_51_S_31_0_E
3,736	A*02:05	South Africa Johannesburg Black	11.8	0.0588	120	26_11_S_28_2_E
3,737	A*02:05	South Africa Natal Zulu	11.0	0.0550	100	29_0_S_31_0_E
3,738	A*02:05	South Africa Worcester	9.0	0.0500	159	33_39_S_19_27_E
3,739	A*02:05	South Korea pop 10		0.0009	4,128	37_33_N_126_58_E
3,740	A*02:05	South Korea pop 3		0.0000	485	37_33_N_126_59_E
3,741	A*02:05	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	2.9	0.0145	4,335	41_22_N_2_11_E
3,742	A*02:05	Spain Catalonia Girona		0.0230	88	41_59_N_2_49_E
3,743	A*02:05	Spain Gipuzkoa Basque		0.0100	100	43_10_N_2_10_W
3,744	A*02:05	Sri Lanka Colombo	0.8	0.0040	714	6_56_N_79_50_E
3,745	A*02:05	Sudan Central Shaigiya Mixed	38.8		36	18_0_N_33_0_E
3,746	A*02:05	Sudan East Rashaida	22.2		27	14_0_N_35_0_E
3,747	A*02:05	Sudan Mixed	13.0	0.0650	200	5_0_N_35_0_E
3,748	A*02:05	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
3,749	A*02:05	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
3,750	A*02:05	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
3,751	A*02:05	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
3,752	A*02:05	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
3,753	A*02:05	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
3,754	A*02:05	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
3,755	A*02:05	Taiwan pop 2		0.0020	364	23_46_N_121_0_E
3,756	A*02:05	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
3,757	A*02:05	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
3,758	A*02:05	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
3,759	A*02:05	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
3,760	A*02:05	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
3,761	A*02:05	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
3,762	A*02:05	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
3,763	A*02:05	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
3,764	A*02:05	Taiwan Tzu Chi Morrow Donor Registry		0.0006	46,682	25_2_N_121_38_E
3,765	A*02:05	Taiwan Tzu Chi Morrow Donor Registry Aborigine		0.0000	233	25_2_N_121_38_E
3,766	A*02:05	Taiwan Tzu Chi Cord Blood Bank		0.0000	710	23_46_N_121_0_E
3,767	A*02:05	Tanzania Maasai	10.4	0.0537	336	3_11_S_37_4_E
3,768	A*02:05	Thailand Northeast		0.0040	66	15_13_N_104_51_E
3,769	A*02:05	Thailand Northeast pop 2		0.0020	400	16_27_N_103_10_E
3,770	A*02:05	Tunisia	5.0	0.0250	100	35_0_N_9_0_E
3,771	A*02:05	Turkey pop 5		0.0350	142	40_0_N_28_0_E
3,772	A*02:05	Uganda Kampala		0.0220	161	0_3_N_32_6_E
3,773	A*02:05	Uganda Kampala pop 2		0.0140	175	0_20_N_32_30_E
3,774	A*02:05	United Arab Emirates Abu Dhabi		0.0385	52	24_28_N_54_22_E
3,775	A*02:05	United Arab Emirates Pop 1		0.0380	570	24_42_N_54_82_E
3,776	A*02:05	USA African American		0.0300	252	30_0_N_95_0_W
3,777	A*02:05	USA African American Bethesda	3.0	0.0150	187	38_0_N_78_0_W
3,778	A*02:05	USA African American pop 2		0.0170	149	32_46_N_96_46_W
3,779	A*02:05	USA African American pop 3		0.0220	564	38_53_N_77_2_W
3,780	A*02:05	USA African American pop 4		0.0187	2,411	44_58_N_93_15_W
3,781	A*02:05	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
3,782	A*02:05	USA Asian		0.0010	358	30_0_N_95_0_W
3,783	A*02:05	USA Asian pop 2		0.0034	1,772	44_58_N_93_15_W
3,784	A*02:05	USA Caucasian	0.7		61,655	38_53_N_77_2_W
3,785	A*02:05	USA Caucasian Bethesda	1.4	0.0070	307	38_0_N_78_0_W
3,786	A*02:05	USA Caucasian pop 2		0.0080	265	30_0_N_95_0_W
3,787	A*02:05	USA Caucasian pop 3		0.0110	88	32_46_N_96_48_W
3,788	A*02:05	USA Caucasian pop 4		0.0075	1,070	38_53_N_77_2_W
3,789	A*02:05	USA Eastern European		0.0160	558	38_53_N_77_2_W
3,790	A*02:05	USA Hispanic		0.0150	234	30_0_N_95_0_W
3,791	A*02:05	USA Hispanic pop 2		0.0148	1,999	44_58_N_93_15_W
3,792	A*02:05	USA Italy Ancestry		0.0160	273	38_0_N_77_0_W
3,793	A*02:05	USA Mexican American Mestizo		0.0140	553	38_53_N_77_2_W
3,794	A*02:05	USA NMDP African		0.0207	28,557	38_45_N_76_6_W
3,795	A*02:05	USA NMDP African American pop 2		0.0149	416,581	38_45_N_76_6_W
3,796	A*02:05	USA NMDP Alaska Native or Aleut		0.0050	1,376	38_45_N_76_6_W
3,797	A*02:05	USA NMDP American Indian South or Central America		0.0090	5,926	38_45_N_76_6_W
3,798	A*02:05	USA NMDP Caribean Black		0.0167	33,328	38_45_N_76_6_W
3,799	A*02:05	USA NMDP Caribean Hispanic		0.0223	115,374	38_45_N_76_6_W
3,800	A*02:05	USA NMDP Caribean Indian		0.0220	14,339	38_45_N_76_6_W
3,801	A*02:05	USA NMDP Chinese		0.0014	99,672	38_45_N_76_6_W
3,802	A*02:05	USA NMDP European Caucasian		0.0097	1,242,890	38_45_N_76_6_W
3,803	A*02:05	USA NMDP Filipino		0.0011	50,614	38_45_N_76_6_W
3,804	A*02:05	USA NMDP Hawaiian or other Pacific Islander		0.0032	11,499	38_45_N_76_6_W
3,805	A*02:05	USA NMDP Hispanic South or Central American		0.0148	146,714	38_45_N_76_6_W
3,806	A*02:05	USA NMDP Japanese		0.0006	24,582	38_45_N_76_6_W
3,807	A*02:05	USA NMDP Korean		0.0008	77,584	38_45_N_76_6_W
3,808	A*02:05	USA NMDP Mexican or Chicano		0.0142	261,235	38_45_N_76_6_W
3,809	A*02:05	USA NMDP Middle Eastern or North Coast of Africa		0.0212	70,890	38_45_N_76_6_W
3,810	A*02:05	USA NMDP North American Amerindian		0.0072	35,791	38_45_N_76_6_W
3,811	A*02:05	USA NMDP South Asian Indian		0.0100	185,391	38_45_N_76_6_W
3,812	A*02:05	USA NMDP Southeast Asian		0.0086	27,978	38_45_N_76_6_W
3,813	A*02:05	USA NMDP Vietnamese		0.0009	43,540	38_45_N_76_6_W
3,814	A*02:05	USA North American Native		0.0050	187	30_0_N_95_0_W
3,815	A*02:05	USA Philadelphia Caucasian	3.7	0.0190	141	39_56_N_75_10_W
3,816	A*02:05	USA San Antonio Caucasian		0.0120	222	29_23_N_98_33_W
3,817	A*02:05	USA San Diego	2.6	0.0130	496	32_50_N_117_15_W
3,818	A*02:05	USA San Francisco Caucasian		0.0090	220	37_46_N_122_25_W
3,819	A*02:05	USA South Texas Hispanic		0.0080	194	29_0_N_98_0_W
3,820	A*02:05	USA Spain Ancestry		0.0220	279	38_0_N_77_0_W
3,821	A*02:05	Zambia Lusaka		0.0000	44	15_25_S_28_17_E
3,822	A*02:05	Zimbabwe Harare Shona		0.0310	230	17_51_S_31_1_E
3,823	A*02:05:01	Brazil Barra Mansa Rio State Black		0.0068	73	22_32_S_44_10_W
3,824	A*02:05:01	Brazil Barra Mansa Rio State Caucasian		0.0148	405	22_32_S_44_10_W
3,825	A*02:05:01	Brazil Rio de Janeiro Black		0.0147	68	22_54_S_43_12_W
3,826	A*02:05:01	Brazil Rio de Janeiro Caucasian		0.0125	521	22_54_S_43_12_W
3,827	A*02:05:01	Brazil Rio de Janeiro Parda		0.0147	170	22_54_S_43_12_W
3,828	A*02:05:01	China Zhejiang Han		0.0014	1,734	27_2_N_118_1_E
3,829	A*02:05:01	Costa Rica African -Caribbean (G)	2.0	0.0100	102	9_59_N_83_1_W
3,830	A*02:05:01	Costa Rica Amerindians (G)	0.8	0.0040	125	9_34_N_83_25_W
3,831	A*02:05:01	Costa Rica Central Valley Mestizo (G)	1.4	0.0070	221	9_55_N_84_5_W
3,832	A*02:05:01	Costa Rica Guanacaste Mestizo (G)	5.4	0.0270	110	10_16_N_85_37_W
3,833	A*02:05:01	England Blood Donors of Mixed Ethnicity	1.4	0.0067	519	52_21_N_1_10_W
3,834	A*02:05:01	Hong Kong Chinese HKBMDR HLA 11 loci	0.1	0.0006	5,266	22_25_N_114_17_E
3,835	A*02:05:01	India Karnataka Kannada Speaking	0.6	0.0030	174	12_57_N_77_36_E
3,836	A*02:05:01	India Kerala Malayalam speaking	0.6	0.0030	356	13_1_N_80_15_E
3,837	A*02:05:01	Latvia		0.0057	266	56_50_N_24_45_E
3,838	A*02:05:01	Libya Cyrenaica		0.0510	118	32_7_N_20_4_E
3,839	A*02:05:01	Nicaragua Mestizo (G)	2.6	0.0130	155	12_40_N_85_13_W
3,840	A*02:05:01	Poland BMR	1.3	0.0065	23,595	52_0_N_19_22_E
3,841	A*02:05:01	Russia Belgorod region	1.3	0.0065	153	50_46_N_37_27_E
3,842	A*02:05:01	Russia Bashkortostan, Bashkirs	2.5	0.0125	120	54_0_N_55_0_E
3,843	A*02:05:01	Russia Bashkortostan, Tatars	1.6	0.0078	192	54_13_N_56_9_E
3,844	A*02:05:01	Russia Moscow Bashkir	0.9	0.0045	442	55_46_N_37_41_E
3,845	A*02:05:01	Russia Moscow Buryat	5.4	0.0268	149	55_46_N_37_41_E
3,846	A*02:05:01	Russia Moscow Kabardin	2.3	0.0114	834	55_46_N_37_41_E
3,847	A*02:05:01	Russia Moscow Kalmyk	9.2	0.0458	120	55_46_N_37_41_E
3,848	A*02:05:01	Russia Moscow Kazakh	3.7	0.0184	299	55_46_N_37_41_E
3,849	A*02:05:01	Russia Moscow Megrel	3.9	0.0197	230	55_46_N_37_41_E
3,850	A*02:05:01	Russia Moscow Russians	1.1	0.0058	13,147	55_46_N_37_41_E
3,851	A*02:05:01	Russia Moscow Similar genetically to Azerbaijan	4.7	0.0235	170	55_46_N_37_41_E
3,852	A*02:05:01	Russia Moscow Tatars	1.8	0.0096	2,512	55_46_N_37_41_E
3,853	A*02:05:01	Russia Moscow Tuvian	1.8	0.0089	112	55_46_N_37_41_E
3,854	A*02:05:01	Russia Moscow Uzbek	4.0	0.0202	198	55_46_N_37_41_E
3,855	A*02:05:01	Russia Moscow Yakut	2.6	0.0128	235	55_46_N_37_41_E
3,856	A*02:05:01	Russia Nizhny Novgorod, Russians	1.1	0.0053	1,510	56_19_N_43_59_E
3,857	A*02:05:01	Saudi Arabia Al-Baha (G)		0.0233	86	20_0_N_41_28_E
3,858	A*02:05:01	Saudi Arabia Al-Madinah (G)		0.0385	102	24_28_N_39_36_E
3,859	A*02:05:01	Saudi Arabia Jeddah (G)		0.0441	476	21_32_N_39_10_E
3,860	A*02:05:01	Saudi Arabia Makkah (G)		0.0407	344	21_25_N_39_49_E
3,861	A*02:05:01	Saudi Arabia pop 5	6.3	0.0380	158	24_38_N_46_43_E
3,862	A*02:05:01	Saudi Arabia pop 6 (G)		0.0443	28,927	23_52_N_45_4_E
3,863	A*02:05:01	Saudi Arabia Taif (G)		0.0289	104	21_16_N_40_24_E
3,864	A*02:05:01	South African Black	12.0	0.0634	142	29_9_S_24_39_E
3,865	A*02:05:01	South African Indian population	2.0	0.0100	50	29_22_S_24_31_E
3,866	A*02:05:01	Spain, Canary Islands, Gran canaria island	1.4	0.0069	215	27_57_N_15_36_W
3,867	A*02:05:01	USA Arizona Gila River Pima		0.0014	3,000	33_4_N_111_43_W
3,868	A*02:05:01	USA San Francisco Caucasian		0.0090	220	37_46_N_122_25_W
3,869	A*02:05:01:01	Pakistan Mixed Punjabi	3.6	0.0177	389	31_0_N_72_0_E
3,870	A*02:05:03	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
3,871	A*02:05:07	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
3,872	A*02:06	American Samoa		0.1300	51	14_18_S_170_42_W
3,873	A*02:06	Armenia combined Regions		0.0050	100	40_11_N_44_31_E
3,874	A*02:06	Australia New South Wales Caucasian		0.0000	134	33_0_S_146_0_E
3,875	A*02:06	Australia Yuendumu Aborigine		0.0000	191	22_15_S_131_47_E
3,876	A*02:06	Austria	0.5	0.0020	200	48_13_N_16_21_E
3,877	A*02:06	Azores Terceira Island		0.0040	130	38_44_N_27_19_W
3,878	A*02:06	Belgium pop 3	0.4	0.0018	31,412	51_2_N_4_28_E
3,879	A*02:06	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
3,880	A*02:06	Bulgaria		0.0000	55	42_0_N_23_0_E
3,881	A*02:06	Chile Santiago Mixed	1.0		70	33_26_S_70_39_W
3,882	A*02:06	China Beijing		0.0480	67	39_58_N_116_24_E
3,883	A*02:06	China Beijing Shijiazhuang Tianjian Han		0.0470	618	39_54_N_116_24_E
3,884	A*02:06	China Canton Han		0.0380	264	23_6_N_113_15_E
3,885	A*02:06	China Guangxi Region Maonan		0.0460	108	23_48_N_108_59_E
3,886	A*02:06	China Guangzhou		0.0400	102	23_6_N_113_15_E
3,887	A*02:06	China Han HIV negative		0.0630	72	36_34_N_103_49_E
3,888	A*02:06	China Henan HIV negative		0.0630	16	34_0_N_113_40_E
3,889	A*02:06	China Hubei Han	7.5	0.0376	3,732	30_45_N_114_15_E
3,890	A*02:06	China Jiangsu Han		0.0560	3,238	32_54_N_119_48_E
3,891	A*02:06	China Jiangsu Province Han		0.0436	334	32_54_N_119_48_E
3,892	A*02:06	China Marrow Donor Registry		0.0360	600	39_54_N_116_23_E
3,893	A*02:06	China North Han		0.0710	105	39_54_N_116_24_E
3,894	A*02:06	China Shanxi HIV negative		0.0450	22	36_5_N_111_50_E
3,895	A*02:06	China Sichuan HIV negative		0.0440	34	30_50_N_102_49_E
3,896	A*02:06	China South Han		0.0350	284	23_1_N_113_3_E
3,897	A*02:06	China Southwest Dai		0.0080	124	22_0_N_100_48_E
3,898	A*02:06	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
3,899	A*02:06	China Uyghur HIV negative		0.0530	19	36_34_N_103_49_E
3,900	A*02:06	China Yunnan Hani		0.0130	150	21_59_N_100_49_E
3,901	A*02:06	China Yunnan Province Han		0.0590	101	24_2_N_105_3_E
3,902	A*02:06	Colombia Bogotá Cord Blood	0.1	0.0007	1,463	4_39_N_73_52_W
3,903	A*02:06	Croatia		0.0030	150	45_0_N_15_0_E
3,904	A*02:06	Croatia pop 4		0.0016	4,000	45_48_N_15_57_E
3,905	A*02:06	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
3,906	A*02:06	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
3,907	A*02:06	Czech Republic		0.0050	106	50_0_N_14_0_E
3,908	A*02:06	Czech Republic NMDR		0.0021	5,099	49_48_N_15_30_E
3,909	A*02:06	Germany DKMS - Austria minority		0.0021	1,698	48_31_N_9_3_E
3,910	A*02:06	Germany DKMS - China minority		0.0543	1,282	48_31_N_9_3_E
3,911	A*02:06	Germany DKMS - Croatia minority		0.0015	2,057	48_31_N_9_3_E
3,912	A*02:06	Germany DKMS - France minority		0.0023	1,406	48_31_N_9_3_E
3,913	A*02:06	Germany DKMS - German donors		0.0019	3,456,066	48_31_N_9_3_E
3,914	A*02:06	Germany DKMS - Greece minority		0.0005	1,894	48_31_N_9_3_E
3,915	A*02:06	Germany DKMS - Italy minority		0.0004	1,159	48_31_N_9_3_E
3,916	A*02:06	Germany DKMS - Netherlands minority		0.0022	1,374	48_31_N_9_3_E
3,917	A*02:06	Germany DKMS - Portugal minority		0.0006	1,176	48_31_N_9_3_E
3,918	A*02:06	Germany DKMS - Romania minority		0.0012	1,234	48_31_N_9_3_E
3,919	A*02:06	Germany DKMS - Turkey minority		0.0058	4,856	48_31_N_9_3_E
3,920	A*02:06	Germany DKMS - United Kingdom minority		0.0010	1,043	48_31_N_9_3_E
3,921	A*02:06	Germany pop 6		0.0025	8,862	51_6_N_10_23_E
3,922	A*02:06	Germany pop 8		0.0021	39,689	48_24_N_9_58_E
3,923	A*02:06	Ghana Ga-Adangbe	0.8	0.0038	131	5_45_N_0_12_E
3,924	A*02:06	Guinea Bissau Balanta		0.0000	48	11_52_N_15_36_W
3,925	A*02:06	Guinea Bissau Bijago		0.0220	23	11_52_N_15_36_W
3,926	A*02:06	Guinea Bissau Fula		0.0000	31	11_52_N_15_36_W
3,927	A*02:06	Guinea Bissau Papel		0.0000	25	11_52_N_15_36_W
3,928	A*02:06	Hong Kong Chinese	9.3	0.0470	569	22_16_N_114_9_E
3,929	A*02:06	Hong Kong Chinese BMDR		0.0417	7,595	22_15_N_114_10_E
3,930	A*02:06	Hong Kong Chinese cord blood registry		0.0388	3,892	22_15_N_114_10_E
3,931	A*02:06	India Andhra Pradesh Golla		0.0280	111	17_5_N_78_5_E
3,932	A*02:06	India Central UCBB	4.7	0.0237	4,204	27_1_N_81_19_E
3,933	A*02:06	India Delhi pop 2	14.4	0.0770	90	28_36_N_77_13_E
3,934	A*02:06	India East UCBB	2.8	0.0142	2,403	22_25_N_88_18_E
3,935	A*02:06	India New Delhi		0.0300	71	28_37_N_77_13_E
3,936	A*02:06	India North pop 2		0.0290	72	25_0_N_74_0_E
3,937	A*02:06	India North UCBB	5.3	0.0262	5,849	28_36_N_77_11_E
3,938	A*02:06	India Northeast UCBB	3.7	0.0186	296	26_16_N_91_48_E
3,939	A*02:06	India South UCBB	3.3	0.0165	11,446	13_6_N_80_8_E
3,940	A*02:06	India Tamil Nadu		0.0156	2,492	11_32_N_78_52_E
3,941	A*02:06	India West UCBB	3.9	0.0193	5,829	19_11_N_73_3_E
3,942	A*02:06	Indonesia Java Western	6.8		236	7_30_S_111_15_E
3,943	A*02:06	Iran Baloch		0.0110	100	29_29_N_60_52_E
3,944	A*02:06	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
3,945	A*02:06	Israel Arab pop 2		0.0003	12,301	32_8_N_34_52_E
3,946	A*02:06	Israel Argentina Jews		0.0009	4,307	32_8_N_34_52_E
3,947	A*02:06	Israel Ashkenazi and Non Ashkenazi Jews		0.0040	146	31_8_N_35_2_E
3,948	A*02:06	Israel Ashkenazi Jews pop 3		0.0014	4,625	32_8_N_34_52_E
3,949	A*02:06	Israel Ethiopia Jews		0.0003	5,928	32_8_N_34_52_E
3,950	A*02:06	Israel Georgia Jews		0.0002	4,471	32_10_N_34_53_E
3,951	A*02:06	Israel Kavkazi Jews		0.0005	2,840	32_10_N_34_52_E
3,952	A*02:06	Israel Morocco Jews		0.0000	36,718	32_10_N_34_52_E
3,953	A*02:06	Israel Poland Jews		0.0019	13,871	32_10_N_34_52_E
3,954	A*02:06	Israel Tunisia Jews		0.0002	9,070	32_10_N_34_52_E
3,955	A*02:06	Israel USA Jews		0.0012	6,058	32_10_N_34_52_E
3,956	A*02:06	Israel USSR Jews		0.0014	45,681	32_9_N_34_50_E
3,957	A*02:06	Israel YemenJews		0.0001	15,542	32_9_N_34_50_E
3,958	A*02:06	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
3,959	A*02:06	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
3,960	A*02:06	Italy pop 5		0.0030	975	45_28_N_9_11_E
3,961	A*02:06	Japan Central		0.0770	371	35_41_N_139_46_E
3,962	A*02:06	Japan Hokkaido Ainu		0.2000	50	43_0_N_142_0_E
3,963	A*02:06	Japan Okinawa Ryukyuan		0.1830	143	26_4_N_127_8_E
3,964	A*02:06	Japan pop 16		0.0908	18,604	35_41_N_139_46_E
3,965	A*02:06	Japan pop 3		0.0870	1,018	35_41_N_139_46_E
3,966	A*02:06	Japan pop 5		0.0840	117	35_41_N_139_46_E
3,967	A*02:06	Kenya Luo		0.0000	265	0_0_S_34_6_E
3,968	A*02:06	Kenya Nandi		0.0000	240	0_0_S_35_0_E
3,969	A*02:06	Kosovo	1.6	0.0081	124	42_34_N_21_0_E
3,970	A*02:06	Malaysia Champa	6.9	0.0340	29	2_30_N_112_30_E
3,971	A*02:06	Malaysia Mandailing	3.7	0.0190	27	2_30_N_112_30_E
3,972	A*02:06	Malaysia Pahang Semai	2.6	0.0130	38	4_11_N_102_3_E
3,973	A*02:06	Malaysia Patani	4.0	0.0200	25	2_30_N_112_30_E
3,974	A*02:06	Malaysia Peninsular Chinese	8.3	0.0412	194	3_9_N_101_57_E
3,975	A*02:06	Malaysia Peninsular Indian	4.1	0.0203	271	3_9_N_101_57_E
3,976	A*02:06	Malaysia Peninsular Malay	3.5	0.0174	951	3_9_N_101_57_E
3,977	A*02:06	Malaysia Perak Grik Jehai		0.0200	25	5_25_N_101_8_E
3,978	A*02:06	Mali Bandiagara		0.0000	138	14_4_N_3_6_W
3,979	A*02:06	Mexico Chiapas Lacandon Mayans		0.1399	218	16_54_N_92_5_W
3,980	A*02:06	Mexico Chichen Itza Maya (prehispanic)		0.0638	47	20_40_N_88_34_W
3,981	A*02:06	Mexico Chihuahua Tarahumara		0.1250	44	27_30_N_107_45_W
3,982	A*02:06	Mexico Mestizo	12.2	0.0610	41	19_24_N_99_9_W
3,983	A*02:06	Mexico Mexico City Mestizo pop 2		0.0962	234	19_26_N_99_8_W
3,984	A*02:06	Mexico Mexico City Mestizo population		0.0594	143	19_25_N_99_7_W
3,985	A*02:06	Mexico Mixtec		0.1650	97	23_57_N_103_29_W
3,986	A*02:06	Mexico Oaxaca Jamiltepec Mixtec		0.1670	96	16_25_N_97_58_W
3,987	A*02:06	Mexico Oaxaca Mixe		0.3490	55	17_5_N_96_45_W
3,988	A*02:06	Mexico Oaxaca Mixtec		0.2160	103	17_5_N_96_45_W
3,989	A*02:06	Mexico Oaxaca Zapotec		0.2610	90	17_5_N_96_45_W
3,990	A*02:06	Mexico Tixcacaltuyub Maya		0.0224	67	20_29_N_88_54_W
3,991	A*02:06	Mongolia Buryat		0.0430	141	47_6_N_118_6_E
3,992	A*02:06	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
3,993	A*02:06	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
3,994	A*02:06	Netherlands Leiden		0.0030	1,305	52_9_N_4_31_E
3,995	A*02:06	New Caledonia		0.0000	65	21_0_S_165_0_E
3,996	A*02:06	New Zealand Maori with Admixed History	41.0	0.2240	105	42_0_S_174_0_E
3,997	A*02:06	New Zealand Maori with Full Ancestry	52.2	0.2940	46	42_0_S_174_0_E
3,998	A*02:06	New Zealand Polynesians with Admixed History	22.2	0.1110	27	42_0_S_174_0_E
3,999	A*02:06	New Zealand Polynesians with Full Ancestry	19.0	0.0950	21	42_0_S_174_0_E
4,000	A*02:06	Nicaragua Managua	7.5	0.0374	339	12_7_N_86_10_W
4,001	A*02:06	Oman	0.0	0.0000	118	22_0_N_56_0_E
4,002	A*02:06	Pakistan Baloch		0.0160	66	30_0_N_67_0_E
4,003	A*02:06	Pakistan Brahui		0.0230	104	30_0_N_67_0_E
4,004	A*02:06	Pakistan Burusho		0.0600	92	36_0_N_73_0_E
4,005	A*02:06	Pakistan Kalash		0.2160	69	32_0_N_70_0_E
4,006	A*02:06	Pakistan Karachi Parsi		0.0110	91	24_49_N_66_59_E
4,007	A*02:06	Pakistan Mixed Pathan		0.0310	100	32_0_N_70_0_E
4,008	A*02:06	Pakistan Mixed Sindhi		0.0320	101	24_0_N_68_0_E
4,009	A*02:06	Panama		0.0147	462	8_59_N_79_37_W
4,010	A*02:06	Papua New Guinea East New Britain Rabaul		0.0000	60	4_12_S_152_11_E
4,011	A*02:06	Papua New Guinea Eastern Highlands Goroka Asaro		0.0260	57	6_5_S_145_23_E
4,012	A*02:06	Papua New Guinea Karimui Plateau Pawaia		0.0000	80	7_14_S_145_20_E
4,013	A*02:06	Papua New Guinea Madang		0.0000	65	5_13_S_145_48_E
4,014	A*02:06	Papua New Guinea Wanigela Keapara		0.0210	66	10_5_S_147_50_E
4,015	A*02:06	Papua New Guinea West Schrader Ranges Haruai		0.0000	55	5_5_S_144_10_E
4,016	A*02:06	Papua New Guinea Wosera Abelam		0.0000	131	3_48_S_143_2_E
4,017	A*02:06	Philippines Ivatan	8.0	0.0400	50	20_22_N_121_56_E
4,018	A*02:06	Poland DKMS		0.0022	20,653	52_8_N_19_23_E
4,019	A*02:06	Portugal Azores Terceira Island	0.9	0.0044	130	38_43_N_27_13_W
4,020	A*02:06	Romania	0.9	0.0040	348	46_0_N_25_0_E
4,021	A*02:06	Russia Bering Island Aleuts		0.0144	104	55_0_N_166_15_E
4,022	A*02:06	Russia Karelia		0.0051	1,075	63_9_N_32_59_E
4,023	A*02:06	Russia Tuva pop 2		0.0690	169	51_30_N_95_5_E
4,024	A*02:06	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
4,025	A*02:06	Senegal Niokholo Mandenka		0.0050	165	12_35_N_12_9_W
4,026	A*02:06	Singapore Chinese	7.4	0.0400	149	1_24_N_103_45_E
4,027	A*02:06	Singapore Chinese Han		0.0520	94	1_17_N_103_51_E
4,028	A*02:06	Singapore Javaneses		0.0200	51	1_24_N_103_45_E
4,029	A*02:06	Singapore Riau Malay		0.0200	132	1_24_N_103_45_E
4,030	A*02:06	Singapore SGVP Chinese CHS		0.0220	96	1_21_N_103_50_E
4,031	A*02:06	Singapore SGVP Malay MAS		0.0120	89	1_21_N_103_50_E
4,032	A*02:06	Singapore SGVP. Indian INS		0.0060	86	1_21_N_103_50_E
4,033	A*02:06	Singapore Thai		0.0200	100	1_24_N_103_45_E
4,034	A*02:06	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
4,035	A*02:06	South Africa Worcester	1.0	0.0060	159	33_39_S_19_27_E
4,036	A*02:06	South Korea pop 10		0.0866	4,128	37_33_N_126_58_E
4,037	A*02:06	South Korea pop 3		0.0710	485	37_33_N_126_59_E
4,038	A*02:06	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.2	0.0008	4,335	41_22_N_2_11_E
4,039	A*02:06	Sri Lanka Colombo	4.1	0.0210	714	6_56_N_79_50_E
4,040	A*02:06	Sri Lanka Colombo Sinhalese		0.0240	101	6_56_N_79_50_E
4,041	A*02:06	Sweden Northern Sami		0.0140	154	67_0_N_18_30_E
4,042	A*02:06	Sweden Southern Sami		0.0120	130	60_52_N_14_44_E
4,043	A*02:06	Taiwan Ami	3.1	0.0150	98	23_0_N_121_0_E
4,044	A*02:06	Taiwan Atayal	17.0	0.0940	106	24_0_N_121_0_E
4,045	A*02:06	Taiwan Bunun	13.9	0.0740	101	23_46_N_121_0_E
4,046	A*02:06	Taiwan Hakka	5.5	0.0270	55	24_0_N_121_0_E
4,047	A*02:06	Taiwan Han Chinese		0.0270	504	23_46_N_121_0_E
4,048	A*02:06	Taiwan Minnan pop 1	5.9	0.0290	102	24_0_N_121_0_E
4,049	A*02:06	Taiwan Paiwan	5.9	0.0290	51	23_0_N_121_0_E
4,050	A*02:06	Taiwan Pazeh	7.3	0.0360	55	25_0_N_121_30_E
4,051	A*02:06	Taiwan pop 2		0.0260	364	23_46_N_121_0_E
4,052	A*02:06	Taiwan pop 3		0.0240	212	23_46_N_121_0_E
4,053	A*02:06	Taiwan Puyuma	24.0	0.1200	50	23_0_N_121_0_E
4,054	A*02:06	Taiwan Rukai	6.0	0.0300	50	23_0_N_121_0_E
4,055	A*02:06	Taiwan Saisiat	3.9	0.0200	51	24_0_N_121_0_E
4,056	A*02:06	Taiwan Siraya	3.9	0.0200	51	24_0_N_121_0_E
4,057	A*02:06	Taiwan Taroko	10.9	0.0550	55	24_0_N_121_0_E
4,058	A*02:06	Taiwan Thao	6.7	0.0330	30	24_0_N_121_0_E
4,059	A*02:06	Taiwan Tzu Chi Morrow Donor Registry		0.0322	46,682	25_2_N_121_38_E
4,060	A*02:06	Taiwan Tzu Chi Morrow Donor Registry Aborigine		0.0558	233	25_2_N_121_38_E
4,061	A*02:06	Taiwan Tzu Chi Cord Blood Bank		0.0310	710	23_46_N_121_0_E
4,062	A*02:06	Thailand		0.0250	142	16_7_N_102_0_E
4,063	A*02:06	Tunisia	0.0	0.0000	100	35_0_N_9_0_E
4,064	A*02:06	Turkey pop 5		0.0000	142	40_0_N_28_0_E
4,065	A*02:06	Uganda Kampala		0.0030	161	0_3_N_32_6_E
4,066	A*02:06	United Arab Emirates Abu Dhabi		0.0096	52	24_28_N_54_22_E
4,067	A*02:06	United Arab Emirates Pop 1		0.0040	570	24_42_N_54_82_E
4,068	A*02:06	USA African American		0.0000	252	30_0_N_95_0_W
4,069	A*02:06	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
4,070	A*02:06	USA African American pop 4		0.0002	2,411	44_58_N_93_15_W
4,071	A*02:06	USA Alaska Yupik		0.1650	252	60_48_N_161_26_W
4,072	A*02:06	USA Arizona Gila River Amerindian		0.0690	492	33_4_N_111_44_W
4,073	A*02:06	USA Arizona Pima		0.0760	100	33_0_N_113_0_W
4,074	A*02:06	USA Asian		0.0500	358	30_0_N_95_0_W
4,075	A*02:06	USA Asian pop 2		0.0483	1,772	44_58_N_93_15_W
4,076	A*02:06	USA Caucasian	0.7		61,655	38_53_N_77_2_W
4,077	A*02:06	USA Caucasian Bethesda	1.4	0.0070	307	38_0_N_78_0_W
4,078	A*02:06	USA Caucasian pop 2		0.0020	265	30_0_N_95_0_W
4,079	A*02:06	USA Caucasian pop 4		0.0037	1,070	38_53_N_77_2_W
4,080	A*02:06	USA Eastern European		0.0040	558	38_53_N_77_2_W
4,081	A*02:06	USA Hawaii Okinawa		0.1430	106	21_0_N_157_0_W
4,082	A*02:06	USA Hispanic		0.0320	234	30_0_N_95_0_W
4,083	A*02:06	USA Hispanic pop 2		0.0392	1,999	44_58_N_93_15_W
4,084	A*02:06	USA New Mexico Canoncito Navajo		0.0490	42	34_9_N_107_3_W
4,085	A*02:06	USA NMDP African		0.0003	28,557	38_45_N_76_6_W
4,086	A*02:06	USA NMDP African American pop 2		0.0007	416,581	38_45_N_76_6_W
4,087	A*02:06	USA NMDP Alaska Native or Aleut		0.1030	1,376	38_45_N_76_6_W
4,088	A*02:06	USA NMDP American Indian South or Central America		0.0200	5,926	38_45_N_76_6_W
4,089	A*02:06	USA NMDP Caribean Black		0.0003	33,328	38_45_N_76_6_W
4,090	A*02:06	USA NMDP Caribean Hispanic		0.0024	115,374	38_45_N_76_6_W
4,091	A*02:06	USA NMDP Caribean Indian		0.0040	14,339	38_45_N_76_6_W
4,092	A*02:06	USA NMDP Chinese		0.0349	99,672	38_45_N_76_6_W
4,093	A*02:06	USA NMDP European Caucasian		0.0018	1,242,890	38_45_N_76_6_W
4,094	A*02:06	USA NMDP Filipino		0.0278	50,614	38_45_N_76_6_W
4,095	A*02:06	USA NMDP Hawaiian or other Pacific Islander		0.0847	11,499	38_45_N_76_6_W
4,096	A*02:06	USA NMDP Hispanic South or Central American		0.0198	146,714	38_45_N_76_6_W
4,097	A*02:06	USA NMDP Japanese		0.0748	24,582	38_45_N_76_6_W
4,098	A*02:06	USA NMDP Korean		0.0747	77,584	38_45_N_76_6_W
4,099	A*02:06	USA NMDP Mexican or Chicano		0.0504	261,235	38_45_N_76_6_W
4,100	A*02:06	USA NMDP Middle Eastern or North Coast of Africa		0.0020	70,890	38_45_N_76_6_W
4,101	A*02:06	USA NMDP North American Amerindian		0.0275	35,791	38_45_N_76_6_W
4,102	A*02:06	USA NMDP South Asian Indian		0.0175	185,391	38_45_N_76_6_W
4,103	A*02:06	USA NMDP Southeast Asian		0.0189	27,978	38_45_N_76_6_W
4,104	A*02:06	USA NMDP Vietnamese		0.0406	43,540	38_45_N_76_6_W
4,105	A*02:06	USA North American Native		0.0720	187	30_0_N_95_0_W
4,106	A*02:06	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
4,107	A*02:06	USA San Antonio Caucasian		0.0060	222	29_23_N_98_33_W
4,108	A*02:06	USA San Diego	3.5	0.0180	496	32_50_N_117_15_W
4,109	A*02:06	USA South Dakota Lakota Sioux		0.1050	302	44_30_N_100_0_W
4,110	A*02:06	USA South Texas Hispanic		0.0730	194	29_0_N_98_0_W
4,111	A*02:06	Vietnam Hanoi Kinh pop 2		0.0470	170	21_2_N_105_51_E
4,112	A*02:06	Zambia Lusaka		0.0120	44	15_25_S_28_17_E
4,113	A*02:06:01	Brazil Rio de Janeiro Caucasian		0.0019	521	22_54_S_43_12_W
4,114	A*02:06:01	Brazil Rio de Janeiro Parda		0.0029	170	22_54_S_43_12_W
4,115	A*02:06:01	Canada Chipewyan NA-DHS_2 (G)	16.0	0.1000	25	59_36_N_107_20_W
4,116	A*02:06:01	Canada Cree NA-DHS_3 (G)	27.8	0.1667	18	50_20_N_102_30_W
4,117	A*02:06:01	Canada Ojibwa NA-DHS_4 (G)	26.7	0.1333	16	46_0_N_81_0_W
4,118	A*02:06:01	Chile Easter Island		0.0480	21	27_7_S_109_22_W
4,119	A*02:06:01	China Guizhou Province Bouyei		0.0310	109	26_50_N_106_50_E
4,120	A*02:06:01	China Guizhou Province Miao pop 2		0.0000	85	26_50_N_106_50_E
4,121	A*02:06:01	China Guizhou Province Shui		0.0260	153	26_50_N_106_50_E
4,122	A*02:06:01	China Inner Mongolia Region		0.0250	102	36_0_N_102_0_E
4,123	A*02:06:01	China Jingpo Minority	4.9	0.0240	105	25_0_N_102_0_E
4,124	A*02:06:01	China Qinghai Province Hui		0.0500	110	36_0_N_102_0_E
4,125	A*02:06:01	China Tibet Region Tibetan		0.1010	158	29_39_N_91_7_E
4,126	A*02:06:01	China Yunnan Province Jinuo		0.0090	109	24_30_N_101_30_E
4,127	A*02:06:01	China Yunnan Province Lisu		0.0340	111	24_30_N_101_30_E
4,128	A*02:06:01	China Yunnan Province Wa		0.0000	119	24_30_N_101_30_E
4,129	A*02:06:01	China Zhejiang Han		0.0433	1,734	27_2_N_118_1_E
4,130	A*02:06:01	Colombia Zenu NA-DHS_18 (G)	16.7	0.0833	16	9_0_N_75_0_W
4,131	A*02:06:01	Costa Rica Amerindians (G)	8.8	0.0440	125	9_34_N_83_25_W
4,132	A*02:06:01	Costa Rica Cabecar NA-DHS_9 (G)	21.1	0.1053	19	10_0_N_84_0_W
4,133	A*02:06:01	Costa Rica Central Valley Mestizo (G)	5.8	0.0290	221	9_55_N_84_5_W
4,134	A*02:06:01	Costa Rica Guanacaste Mestizo (G)	6.4	0.0320	110	10_16_N_85_37_W
4,135	A*02:06:01	England Blood Donors of Mixed Ethnicity	0.6	0.0029	519	52_21_N_1_10_W
4,136	A*02:06:01	Hong Kong Chinese HKBMDR HLA 11 loci	8.0	0.0406	5,266	22_25_N_114_17_E
4,137	A*02:06:01	India Andhra Pradesh Telugu Speaking	2.7	0.0134	186	13_4_N_80_14_E
4,138	A*02:06:01	India Karnataka Kannada Speaking	1.1	0.0060	174	12_57_N_77_36_E
4,139	A*02:06:01	India Kerala Malayalam speaking	1.4	0.0084	356	13_1_N_80_15_E
4,140	A*02:06:01	Indonesia Java pop 2		0.0280	36	6_8_S_106_10_E
4,141	A*02:06:01	Indonesia Sundanese and Javanese		0.0350	201	6_8_S_106_10_E
4,142	A*02:06:01	Latvia		0.0019	266	56_50_N_24_45_E
4,143	A*02:06:01	Mexico Mixe NA-DHS_6 (G)	60.0	0.3750	20	17_0_N_96_0_W
4,144	A*02:06:01	Mexico Mixtec NA-DHS_5 (G)	70.0	0.4750	20	17_0_N_97_0_W
4,145	A*02:06:01	Mexico Zapotec NA-DHS_7 (G)	45.0	0.3000	20	16_0_N_97_0_W
4,146	A*02:06:01	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,147	A*02:06:01	Nicaragua Mestizo (G)	7.8	0.0390	155	12_40_N_85_13_W
4,148	A*02:06:01	Poland BMR	0.3	0.0015	23,595	52_0_N_19_22_E
4,149	A*02:06:01	Russia Belgorod region	2.0	0.0098	153	50_46_N_37_27_E
4,150	A*02:06:01	Russia Bashkortostan, Bashkirs	7.5	0.0375	120	54_0_N_55_0_E
4,151	A*02:06:01	Russia Bashkortostan, Tatars	4.2	0.0208	192	54_13_N_56_9_E
4,152	A*02:06:01	Russia Moscow Bashkir	5.9	0.0295	442	55_46_N_37_41_E
4,153	A*02:06:01	Russia Moscow Buryat	10.7	0.0570	149	55_46_N_37_41_E
4,154	A*02:06:01	Russia Moscow Kabardin	1.2	0.0066	834	55_46_N_37_41_E
4,155	A*02:06:01	Russia Moscow Kalmyk	14.2	0.0708	120	55_46_N_37_41_E
4,156	A*02:06:01	Russia Moscow Kazakh	8.0	0.0418	299	55_46_N_37_41_E
4,157	A*02:06:01	Russia Moscow Mansi	13.2	0.0658	38	55_46_N_37_41_E
4,158	A*02:06:01	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
4,159	A*02:06:01	Russia Moscow Russians	0.6	0.0032	13,147	55_46_N_37_41_E
4,160	A*02:06:01	Russia Moscow Similar genetically to Azerbaijan	1.8	0.0088	170	55_46_N_37_41_E
4,161	A*02:06:01	Russia Moscow Tatars	4.7	0.0239	2,512	55_46_N_37_41_E
4,162	A*02:06:01	Russia Moscow Tuvian	11.6	0.0580	112	55_46_N_37_41_E
4,163	A*02:06:01	Russia Moscow Uzbek	6.1	0.0328	198	55_46_N_37_41_E
4,164	A*02:06:01	Russia Moscow Yakut	18.3	0.0957	235	55_46_N_37_41_E
4,165	A*02:06:01	Russia Nizhny Novgorod, Russians	0.4	0.0020	1,510	56_19_N_43_59_E
4,166	A*02:06:01	Russia Tundra Nentsi NA-DHS_1 (G)	25.0	0.1563	16	66_0_N_76_0_E
4,167	A*02:06:01	Saudi Arabia Al-Baha (G)		0.0116	86	20_0_N_41_28_E
4,168	A*02:06:01	Saudi Arabia Al-Madinah (G)		0.0096	102	24_28_N_39_36_E
4,169	A*02:06:01	Saudi Arabia Jeddah (G)		0.0042	476	21_32_N_39_10_E
4,170	A*02:06:01	Saudi Arabia Makkah (G)		0.0029	344	21_25_N_39_49_E
4,171	A*02:06:01	Saudi Arabia pop 6 (G)		0.0020	28,927	23_52_N_45_4_E
4,172	A*02:06:01	South African Indian population	6.0	0.0300	50	29_22_S_24_31_E
4,173	A*02:06:01	USA Arizona Gila River Pima		0.0574	3,000	33_4_N_111_43_W
4,174	A*02:06:01	USA Italy Ancestry		0.0020	273	38_0_N_77_0_W
4,175	A*02:06:01	USA Mexican American Mestizo		0.0670	553	38_53_N_77_2_W
4,176	A*02:06:01	USA Spain Ancestry		0.0380	279	38_0_N_77_0_W
4,177	A*02:06:01	Vietnam Kinh	6.0	0.0297	101	14_5_N_108_27_E
4,178	A*02:06:01:01	Pakistan Mixed Punjabi	6.3	0.0321	389	31_0_N_72_0_E
4,179	A*02:06:02	China North Han		0.0000	105	39_54_N_116_24_E
4,180	A*02:06:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,181	A*02:06:03	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,182	A*02:06:04	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,183	A*02:06:05	Brazil Rio de Janeiro Caucasian		0.0010	521	22_54_S_43_12_W
4,184	A*02:06:05	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,185	A*02:06:06	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,186	A*02:06:07	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,187	A*02:06:08	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,188	A*02:06:09	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,189	A*02:06:09	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
4,190	A*02:06:09	Russia Moscow Russians	0.1	0.0005	13,147	55_46_N_37_41_E
4,191	A*02:06:09	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
4,192	A*02:06:22	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
4,193	A*02:06:23	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
4,194	A*02:06:24	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
4,195	A*02:06:25	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
4,196	A*02:07	Australia New South Wales Caucasian		0.0080	134	33_0_S_146_0_E
4,197	A*02:07	Australia Yuendumu Aborigine		0.0000	191	22_15_S_131_47_E
4,198	A*02:07	Belgium pop 3	0.0	0.0002	31,412	51_2_N_4_28_E
4,199	A*02:07	Bolivia/Peru Quechua NA-DHS_12 (G)	4.8	0.0238	21	14_0_S_74_0_W
4,200	A*02:07	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
4,201	A*02:07	Bulgaria		0.0000	55	42_0_N_23_0_E
4,202	A*02:07	Burkina Faso Fulani		0.0100	49	12_50_N_1_50_W
4,203	A*02:07	Burkina Faso Rimaibe		0.0000	47	12_50_N_1_50_W
4,204	A*02:07	China Beijing		0.0670	67	39_58_N_116_24_E
4,205	A*02:07	China Beijing Shijiazhuang Tianjian Han		0.0770	618	39_54_N_116_24_E
4,206	A*02:07	China Guangxi Region Maonan		0.1340	108	23_48_N_108_59_E
4,207	A*02:07	China Guangzhou		0.0930	102	23_6_N_113_15_E
4,208	A*02:07	China Guizhou Province Bouyei		0.2270	109	26_50_N_106_50_E
4,209	A*02:07	China Guizhou Province Miao pop 2		0.1650	85	26_50_N_106_50_E
4,210	A*02:07	China Guizhou Province Shui		0.1750	153	26_50_N_106_50_E
4,211	A*02:07	China Han HIV negative		0.0310	72	36_34_N_103_49_E
4,212	A*02:07	China Henan HIV negative		0.0310	16	34_0_N_113_40_E
4,213	A*02:07	China Hubei Han	25.6	0.1281	3,732	30_45_N_114_15_E
4,214	A*02:07	China Inner Mongolia Region		0.0250	102	36_0_N_102_0_E
4,215	A*02:07	China Jiangsu Han		0.0670	3,238	32_54_N_119_48_E
4,216	A*02:07	China Jiangsu Province Han		0.0610	334	32_54_N_119_48_E
4,217	A*02:07	China Jingpo Minority	3.9	0.0190	105	25_0_N_102_0_E
4,218	A*02:07	China Marrow Donor Registry		0.1240	600	39_54_N_116_23_E
4,219	A*02:07	China North Han		0.0430	105	39_54_N_116_24_E
4,220	A*02:07	China Qinghai Province Hui		0.0360	110	36_0_N_102_0_E
4,221	A*02:07	China Shanxi HIV negative		0.0680	22	36_5_N_111_50_E
4,222	A*02:07	China Sichuan HIV negative		0.0880	34	30_50_N_102_49_E
4,223	A*02:07	China South Han		0.0940	284	23_1_N_113_3_E
4,224	A*02:07	China Southwest Dai		0.1850	124	22_0_N_100_48_E
4,225	A*02:07	China Tibet Region Tibetan		0.0160	158	29_39_N_91_7_E
4,226	A*02:07	China Uyghur HIV negative		0.0260	19	36_34_N_103_49_E
4,227	A*02:07	China Yunnan Bulang		0.0040	116	21_57_N_100_27_E
4,228	A*02:07	China Yunnan Hani		0.1070	150	21_59_N_100_49_E
4,229	A*02:07	China Yunnan Province Han		0.1830	101	24_2_N_105_3_E
4,230	A*02:07	China Yunnan Province Jinuo		0.1880	109	24_30_N_101_30_E
4,231	A*02:07	China Yunnan Province Lisu		0.0220	111	24_30_N_101_30_E
4,232	A*02:07	China Yunnan Province Nu		0.0820	107	24_30_N_101_30_E
4,233	A*02:07	China Yunnan Province Wa		0.0080	119	24_30_N_101_30_E
4,234	A*02:07	Colombia Bogotá Cord Blood	0.1	0.0003	1,463	4_39_N_73_52_W
4,235	A*02:07	Croatia		0.0000	150	45_0_N_15_0_E
4,236	A*02:07	Cuba Caucasian	1.4	0.0070	70	23_8_N_82_22_W
4,237	A*02:07	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
4,238	A*02:07	Czech Republic		0.0000	106	50_0_N_14_0_E
4,239	A*02:07	Czech Republic NMDR		0.0004	5,099	49_48_N_15_30_E
4,240	A*02:07	Germany DKMS - China minority		0.0827	1,282	48_31_N_9_3_E
4,241	A*02:07	Germany DKMS - France minority		0.0004	1,406	48_31_N_9_3_E
4,242	A*02:07	Germany DKMS - German donors		0.0002	3,456,066	48_31_N_9_3_E
4,243	A*02:07	Germany DKMS - Greece minority		0.0016	1,894	48_31_N_9_3_E
4,244	A*02:07	Germany DKMS - Romania minority		0.0004	1,234	48_31_N_9_3_E
4,245	A*02:07	Germany DKMS - Turkey minority		0.0021	4,856	48_31_N_9_3_E
4,246	A*02:07	Germany pop 6		0.0001	8,862	51_6_N_10_23_E
4,247	A*02:07	Germany pop 8		0.0004	39,689	48_24_N_9_58_E
4,248	A*02:07	Hong Kong Chinese	24.0	0.1310	569	22_16_N_114_9_E
4,249	A*02:07	India East UCBB	0.0	0.0002	2,403	22_25_N_88_18_E
4,250	A*02:07	India Northeast UCBB	1.7	0.0085	296	26_16_N_91_48_E
4,251	A*02:07	India South UCBB	0.0	0.0000	11,446	13_6_N_80_8_E
4,252	A*02:07	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
4,253	A*02:07	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
4,254	A*02:07	Israel Kavkazi Jews		0.0002	2,840	32_10_N_34_52_E
4,255	A*02:07	Israel USSR Jews		0.0001	45,681	32_9_N_34_50_E
4,256	A*02:07	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
4,257	A*02:07	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
4,258	A*02:07	Italy pop 5		0.0010	975	45_28_N_9_11_E
4,259	A*02:07	Japan Central		0.0220	371	35_41_N_139_46_E
4,260	A*02:07	Japan Hokkaido Ainu		0.0100	50	43_0_N_142_0_E
4,261	A*02:07	Japan Okinawa Ryukyuan		0.0070	143	26_4_N_127_8_E
4,262	A*02:07	Japan pop 16		0.0346	18,604	35_41_N_139_46_E
4,263	A*02:07	Japan pop 3		0.0340	1,018	35_41_N_139_46_E
4,264	A*02:07	Japan pop 5		0.0400	117	35_41_N_139_46_E
4,265	A*02:07	Malaysia Champa	10.3	0.0520	29	2_30_N_112_30_E
4,266	A*02:07	Malaysia Jelebu Temuan		0.0420	25	2_56_N_102_5_E
4,267	A*02:07	Malaysia Kelantan	17.9	0.0890	28	2_30_N_112_30_E
4,268	A*02:07	Malaysia Peninsular Chinese	6.2	0.0309	194	3_9_N_101_57_E
4,269	A*02:07	Malaysia Peninsular Malay	0.8	0.0042	951	3_9_N_101_57_E
4,270	A*02:07	Malaysia Perak Grik Jehai		0.0400	25	5_25_N_101_8_E
4,271	A*02:07	Malaysia Sarawak Bau Bidayuh		0.0200	25	1_25_N_110_9_E
4,272	A*02:07	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
4,273	A*02:07	Mongolia Buryat		0.0210	141	47_6_N_118_6_E
4,274	A*02:07	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
4,275	A*02:07	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,276	A*02:07	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
4,277	A*02:07	Oman	0.0	0.0000	118	22_0_N_56_0_E
4,278	A*02:07	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
4,279	A*02:07	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
4,280	A*02:07	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
4,281	A*02:07	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
4,282	A*02:07	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
4,283	A*02:07	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
4,284	A*02:07	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
4,285	A*02:07	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
4,286	A*02:07	Poland DKMS		0.0004	20,653	52_8_N_19_23_E
4,287	A*02:07	Romania	0.0	0.0000	348	46_0_N_25_0_E
4,288	A*02:07	Russia Karelia		0.0005	1,075	63_9_N_32_59_E
4,289	A*02:07	Russia Tuva pop 2		0.0160	169	51_30_N_95_5_E
4,290	A*02:07	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
4,291	A*02:07	Singapore Chinese	24.2	0.1310	149	1_24_N_103_45_E
4,292	A*02:07	Singapore Chinese Han		0.1160	94	1_17_N_103_51_E
4,293	A*02:07	Singapore Javaneses		0.0100	51	1_24_N_103_45_E
4,294	A*02:07	Singapore Riau Malay		0.0200	132	1_24_N_103_45_E
4,295	A*02:07	Singapore SGVP Chinese CHS		0.0650	96	1_21_N_103_50_E
4,296	A*02:07	Singapore SGVP Malay MAS		0.0120	89	1_21_N_103_50_E
4,297	A*02:07	Singapore SGVP. Indian INS		0.0000	86	1_21_N_103_50_E
4,298	A*02:07	Singapore Thai		0.1220	100	1_24_N_103_45_E
4,299	A*02:07	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
4,300	A*02:07	South Korea pop 10		0.0340	4,128	37_33_N_126_58_E
4,301	A*02:07	South Korea pop 3		0.0300	485	37_33_N_126_59_E
4,302	A*02:07	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
4,303	A*02:07	Taiwan Atayal	0.9	0.0050	106	24_0_N_121_0_E
4,304	A*02:07	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
4,305	A*02:07	Taiwan Hakka	16.4	0.0820	55	24_0_N_121_0_E
4,306	A*02:07	Taiwan Han Chinese		0.1120	504	23_46_N_121_0_E
4,307	A*02:07	Taiwan Minnan pop 1	19.6	0.1130	102	24_0_N_121_0_E
4,308	A*02:07	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
4,309	A*02:07	Taiwan Pazeh	12.7	0.0640	55	25_0_N_121_30_E
4,310	A*02:07	Taiwan pop 2		0.1380	364	23_46_N_121_0_E
4,311	A*02:07	Taiwan pop 3		0.1160	212	23_46_N_121_0_E
4,312	A*02:07	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
4,313	A*02:07	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
4,314	A*02:07	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
4,315	A*02:07	Taiwan Siraya	11.8	0.0690	51	24_0_N_121_0_E
4,316	A*02:07	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
4,317	A*02:07	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
4,318	A*02:07	Taiwan Thao	3.3	0.0170	30	24_0_N_121_0_E
4,319	A*02:07	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
4,320	A*02:07	Taiwan Tzu Chi Morrow Donor Registry		0.1174	46,682	25_2_N_121_38_E
4,321	A*02:07	Taiwan Tzu Chi Morrow Donor Registry Aborigine		0.0343	233	25_2_N_121_38_E
4,322	A*02:07	Taiwan Tzu Chi Cord Blood Bank		0.0990	710	23_46_N_121_0_E
4,323	A*02:07	Thailand		0.1090	142	16_7_N_102_0_E
4,324	A*02:07	Thailand Northeast		0.1570	66	15_13_N_104_51_E
4,325	A*02:07	Thailand Northeast pop 2		0.1440	400	16_27_N_103_10_E
4,326	A*02:07	Turkey pop 5		0.0000	142	40_0_N_28_0_E
4,327	A*02:07	USA African American		0.0000	252	30_0_N_95_0_W
4,328	A*02:07	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
4,329	A*02:07	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
4,330	A*02:07	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
4,331	A*02:07	USA Asian		0.0660	358	30_0_N_95_0_W
4,332	A*02:07	USA Asian pop 2		0.0438	1,772	44_58_N_93_15_W
4,333	A*02:07	USA Caucasian	0.0		61,655	38_53_N_77_2_W
4,334	A*02:07	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
4,335	A*02:07	USA Caucasian pop 2		0.0000	265	30_0_N_95_0_W
4,336	A*02:07	USA Hawaii Okinawa		0.0140	106	21_0_N_157_0_W
4,337	A*02:07	USA Hispanic		0.0000	234	30_0_N_95_0_W
4,338	A*02:07	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
4,339	A*02:07	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
4,340	A*02:07	USA NMDP Alaska Native or Aleut		0.0010	1,376	38_45_N_76_6_W
4,341	A*02:07	USA NMDP Caribean Black		0.0001	33,328	38_45_N_76_6_W
4,342	A*02:07	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
4,343	A*02:07	USA NMDP Chinese		0.0948	99,672	38_45_N_76_6_W
4,344	A*02:07	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
4,345	A*02:07	USA NMDP Filipino		0.0174	50,614	38_45_N_76_6_W
4,346	A*02:07	USA NMDP Hawaiian or other Pacific Islander		0.0083	11,499	38_45_N_76_6_W
4,347	A*02:07	USA NMDP Hispanic South or Central American		0.0001	146,714	38_45_N_76_6_W
4,348	A*02:07	USA NMDP Japanese		0.0200	24,582	38_45_N_76_6_W
4,349	A*02:07	USA NMDP Korean		0.0279	77,584	38_45_N_76_6_W
4,350	A*02:07	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
4,351	A*02:07	USA NMDP Middle Eastern or North Coast of Africa		0.0001	70,890	38_45_N_76_6_W
4,352	A*02:07	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
4,353	A*02:07	USA NMDP South Asian Indian		0.0006	185,391	38_45_N_76_6_W
4,354	A*02:07	USA NMDP Southeast Asian		0.0220	27,978	38_45_N_76_6_W
4,355	A*02:07	USA NMDP Vietnamese		0.0800	43,540	38_45_N_76_6_W
4,356	A*02:07	USA North American Native		0.0000	187	30_0_N_95_0_W
4,357	A*02:07	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
4,358	A*02:07	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
4,359	A*02:07	USA San Diego	2.9	0.0150	496	32_50_N_117_15_W
4,360	A*02:07	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
4,361	A*02:07	Vietnam Hanoi Kinh pop 2		0.0850	170	21_2_N_105_51_E
4,362	A*02:07:01	Brazil Rio de Janeiro Caucasian		0.0010	521	22_54_S_43_12_W
4,363	A*02:07:01	China Zhejiang Han		0.0975	1,734	27_2_N_118_1_E
4,364	A*02:07:01	England Blood Donors of Mixed Ethnicity	0.2	0.0010	519	52_21_N_1_10_W
4,365	A*02:07:01	Hong Kong Chinese HKBMDR HLA 11 loci	22.1	0.1189	5,266	22_25_N_114_17_E
4,366	A*02:07:01	Poland BMR	0.1	0.0005	23,595	52_0_N_19_22_E
4,367	A*02:07:01	Russia Bashkortostan, Bashkirs	0.8	0.0042	120	54_0_N_55_0_E
4,368	A*02:07:01	Russia Bashkortostan, Tatars	1.0	0.0052	192	54_13_N_56_9_E
4,369	A*02:07:01	Russia Moscow Bashkir	1.1	0.0057	442	55_46_N_37_41_E
4,370	A*02:07:01	Russia Moscow Buryat	0.7	0.0034	149	55_46_N_37_41_E
4,371	A*02:07:01	Russia Moscow Kabardin	0.4	0.0018	834	55_46_N_37_41_E
4,372	A*02:07:01	Russia Moscow Kalmyk	1.7	0.0083	120	55_46_N_37_41_E
4,373	A*02:07:01	Russia Moscow Kazakh	1.7	0.0084	299	55_46_N_37_41_E
4,374	A*02:07:01	Russia Moscow Russians	0.3	0.0014	13,147	55_46_N_37_41_E
4,375	A*02:07:01	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
4,376	A*02:07:01	Russia Moscow Tatars	0.6	0.0032	2,512	55_46_N_37_41_E
4,377	A*02:07:01	Russia Moscow Tuvian	0.9	0.0045	112	55_46_N_37_41_E
4,378	A*02:07:01	Russia Moscow Uzbek	6.1	0.0328	198	55_46_N_37_41_E
4,379	A*02:07:01	Russia Nizhny Novgorod, Russians	0.5	0.0027	1,510	56_19_N_43_59_E
4,380	A*02:07:01	Saudi Arabia Jeddah (G)		0.0021	476	21_32_N_39_10_E
4,381	A*02:07:01	Saudi Arabia pop 6 (G)		0.0003	28,927	23_52_N_45_4_E
4,382	A*02:07:01	Vietnam Kinh	18.0	0.0990	101	14_5_N_108_27_E
4,383	A*02:08	Armenia combined Regions		0.0100	100	40_11_N_44_31_E
4,384	A*02:08	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
4,385	A*02:08	Bulgaria		0.0000	55	42_0_N_23_0_E
4,386	A*02:08	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
4,387	A*02:08	China North Han		0.0000	105	39_54_N_116_24_E
4,388	A*02:08	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
4,389	A*02:08	Croatia		0.0000	150	45_0_N_15_0_E
4,390	A*02:08	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
4,391	A*02:08	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
4,392	A*02:08	Czech Republic		0.0000	106	50_0_N_14_0_E
4,393	A*02:08	Czech Republic NMDR		0.0002	5,099	49_48_N_15_30_E
4,394	A*02:08	Gaza	2.4	0.0119	42	31_22_N_34_18_E
4,395	A*02:08	Georgia Svaneti Region Svan		0.0130	80	42_54_N_43_0_E
4,396	A*02:08	Germany DKMS - France minority		0.0004	1,406	48_31_N_9_3_E
4,397	A*02:08	Germany DKMS - German donors		0.0002	3,456,066	48_31_N_9_3_E
4,398	A*02:08	Germany DKMS - Romania minority		0.0004	1,234	48_31_N_9_3_E
4,399	A*02:08	Germany DKMS - Turkey minority		0.0011	4,856	48_31_N_9_3_E
4,400	A*02:08	Germany DKMS - United Kingdom minority		0.0005	1,043	48_31_N_9_3_E
4,401	A*02:08	Germany pop 6		0.0002	8,862	51_6_N_10_23_E
4,402	A*02:08	Germany pop 8		0.0004	39,689	48_24_N_9_58_E
4,403	A*02:08	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
4,404	A*02:08	India Central UCBB	0.2	0.0012	4,204	27_1_N_81_19_E
4,405	A*02:08	India East UCBB	0.0	0.0002	2,403	22_25_N_88_18_E
4,406	A*02:08	India North UCBB	0.4	0.0020	5,849	28_36_N_77_11_E
4,407	A*02:08	India West UCBB	0.1	0.0003	5,829	19_11_N_73_3_E
4,408	A*02:08	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
4,409	A*02:08	Israel Arab pop 2		0.0007	12,301	32_8_N_34_52_E
4,410	A*02:08	Israel Argentina Jews		0.0000	4,307	32_8_N_34_52_E
4,411	A*02:08	Israel Georgia Jews		0.0001	4,471	32_10_N_34_53_E
4,412	A*02:08	Israel Iraq Jews		0.0001	13,270	32_10_N_34_52_E
4,413	A*02:08	Israel USSR Jews		0.0000	45,681	32_9_N_34_50_E
4,414	A*02:08	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
4,415	A*02:08	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
4,416	A*02:08	Madeira		0.0050	185	32_44_N_16_58_W
4,417	A*02:08	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
4,418	A*02:08	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
4,419	A*02:08	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,420	A*02:08	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
4,421	A*02:08	Oman	0.9	0.0040	118	22_0_N_56_0_E
4,422	A*02:08	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
4,423	A*02:08	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
4,424	A*02:08	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
4,425	A*02:08	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
4,426	A*02:08	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
4,427	A*02:08	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
4,428	A*02:08	Pakistan Mixed Sindhi		0.0130	101	24_0_N_68_0_E
4,429	A*02:08	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
4,430	A*02:08	Poland BMR	0.0	0.0001	23,595	52_0_N_19_22_E
4,431	A*02:08	Poland DKMS		0.0002	20,653	52_8_N_19_23_E
4,432	A*02:08	Romania	0.0	0.0000	348	46_0_N_25_0_E
4,433	A*02:08	Russia Bashkortostan, Tatars	0.5	0.0026	192	54_13_N_56_9_E
4,434	A*02:08	Russia Nizhny Novgorod, Russians	0.1	0.0003	1,510	56_19_N_43_59_E
4,435	A*02:08	Saudi Arabia pop 6 (G)		0.0004	28,927	23_52_N_45_4_E
4,436	A*02:08	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
4,437	A*02:08	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
4,438	A*02:08	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
4,439	A*02:08	South Korea pop 3		0.0000	485	37_33_N_126_59_E
4,440	A*02:08	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
4,441	A*02:08	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
4,442	A*02:08	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
4,443	A*02:08	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
4,444	A*02:08	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
4,445	A*02:08	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
4,446	A*02:08	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
4,447	A*02:08	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
4,448	A*02:08	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
4,449	A*02:08	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
4,450	A*02:08	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
4,451	A*02:08	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
4,452	A*02:08	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
4,453	A*02:08	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
4,454	A*02:08	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
4,455	A*02:08	Thailand Northeast pop 2		0.0010	400	16_27_N_103_10_E
4,456	A*02:08	Turkey pop 5		0.0000	142	40_0_N_28_0_E
4,457	A*02:08	United Arab Emirates Pop 1		0.0010	570	24_42_N_54_82_E
4,458	A*02:08	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
4,459	A*02:08	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
4,460	A*02:08	USA Caucasian	0.0		61,655	38_53_N_77_2_W
4,461	A*02:08	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
4,462	A*02:08	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
4,463	A*02:08	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
4,464	A*02:08	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
4,465	A*02:08	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
4,466	A*02:08	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
4,467	A*02:08	USA NMDP Middle Eastern or North Coast of Africa		0.0001	70,890	38_45_N_76_6_W
4,468	A*02:08	USA NMDP South Asian Indian		0.0002	185,391	38_45_N_76_6_W
4,469	A*02:08	USA NMDP Southeast Asian		0.0001	27,978	38_45_N_76_6_W
4,470	A*02:08	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
4,471	A*02:08	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
4,472	A*02:08	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
4,473	A*02:08:01	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
4,474	A*02:08:01	Russia Moscow Similar genetically to Azerbaijan	2.4	0.0118	170	55_46_N_37_41_E
4,475	A*02:08:01	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
4,476	A*02:09	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
4,477	A*02:09	Bulgaria		0.0000	55	42_0_N_23_0_E
4,478	A*02:09	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
4,479	A*02:09	China Hubei Han	0.1	0.0003	3,732	30_45_N_114_15_E
4,480	A*02:09	China North Han		0.0000	105	39_54_N_116_24_E
4,481	A*02:09	China Zhejiang Han		0.0003	1,734	27_2_N_118_1_E
4,482	A*02:09	Croatia		0.0000	150	45_0_N_15_0_E
4,483	A*02:09	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
4,484	A*02:09	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
4,485	A*02:09	Czech Republic		0.0000	106	50_0_N_14_0_E
4,486	A*02:09	Czech Republic NMDR		0.0004	5,099	49_48_N_15_30_E
4,487	A*02:09	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
4,488	A*02:09	India Andhra Pradesh Telugu Speaking	1.1	0.0054	186	13_4_N_80_14_E
4,489	A*02:09	India Central UCBB	0.2	0.0008	4,204	27_1_N_81_19_E
4,490	A*02:09	India East UCBB	0.4	0.0021	2,403	22_25_N_88_18_E
4,491	A*02:09	India Karnataka Kannada Speaking	0.6	0.0030	174	12_57_N_77_36_E
4,492	A*02:09	India Kerala Malayalam speaking	0.6	0.0030	356	13_1_N_80_15_E
4,493	A*02:09	India North pop 2		0.0100	72	25_0_N_74_0_E
4,494	A*02:09	India North UCBB	0.2	0.0011	5,849	28_36_N_77_11_E
4,495	A*02:09	India Northeast UCBB	0.3	0.0017	296	26_16_N_91_48_E
4,496	A*02:09	India South UCBB	1.2	0.0061	11,446	13_6_N_80_8_E
4,497	A*02:09	India West UCBB	0.4	0.0021	5,829	19_11_N_73_3_E
4,498	A*02:09	Iran Baloch		0.0110	100	29_29_N_60_52_E
4,499	A*02:09	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
4,500	A*02:09	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
4,501	A*02:09	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
4,502	A*02:09	Italy pop 5		0.0010	975	45_28_N_9_11_E
4,503	A*02:09	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
4,504	A*02:09	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
4,505	A*02:09	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,506	A*02:09	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
4,507	A*02:09	Oman	0.0	0.0000	118	22_0_N_56_0_E
4,508	A*02:09	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
4,509	A*02:09	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
4,510	A*02:09	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
4,511	A*02:09	Pakistan Kalash		0.0250	69	32_0_N_70_0_E
4,512	A*02:09	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
4,513	A*02:09	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
4,514	A*02:09	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
4,515	A*02:09	Poland BMR	0.1	0.0006	23,595	52_0_N_19_22_E
4,516	A*02:09	Romania	0.0	0.0000	348	46_0_N_25_0_E
4,517	A*02:09	Russia Nizhny Novgorod, Russians	0.1	0.0007	1,510	56_19_N_43_59_E
4,518	A*02:09	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
4,519	A*02:09	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
4,520	A*02:09	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
4,521	A*02:09	South Korea pop 3		0.0000	485	37_33_N_126_59_E
4,522	A*02:09	Turkey pop 5		0.0000	142	40_0_N_28_0_E
4,523	A*02:09	United Arab Emirates Pop 1		0.0010	570	24_42_N_54_82_E
4,524	A*02:09	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
4,525	A*02:09	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
4,526	A*02:09	USA Caucasian	0.0		61,655	38_53_N_77_2_W
4,527	A*02:09	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
4,528	A*02:09	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
4,529	A*02:09	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
4,530	A*02:09	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
4,531	A*02:09:01	Hong Kong Chinese HKBMDR HLA 11 loci	0.1	0.0003	5,266	22_25_N_114_17_E
4,532	A*02:09:01	Russia Moscow Kabardin	0.5	0.0024	834	55_46_N_37_41_E
4,533	A*02:09:01	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
4,534	A*02:09:01	Russia Moscow Russians	0.1	0.0006	13,147	55_46_N_37_41_E
4,535	A*02:09:01:01	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
4,536	A*02:09:01:02	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
4,537	A*02:10	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
4,538	A*02:10	Bulgaria		0.0000	55	42_0_N_23_0_E
4,539	A*02:10	China Beijing		0.0000	67	39_58_N_116_24_E
4,540	A*02:10	China Beijing Shijiazhuang Tianjian Han		0.0030	618	39_54_N_116_24_E
4,541	A*02:10	China Canton Han		0.0020	264	23_6_N_113_15_E
4,542	A*02:10	China Guangzhou		0.0050	102	23_6_N_113_15_E
4,543	A*02:10	China Hubei Han	0.3	0.0015	3,732	30_45_N_114_15_E
4,544	A*02:10	China Inner Mongolia Region		0.0000	102	36_0_N_102_0_E
4,545	A*02:10	China Jiangsu Han		0.0040	3,238	32_54_N_119_48_E
4,546	A*02:10	China North Han		0.0000	105	39_54_N_116_24_E
4,547	A*02:10	China Qinghai Province Hui		0.0140	110	36_0_N_102_0_E
4,548	A*02:10	China Shanxi HIV negative		0.0230	22	36_5_N_111_50_E
4,549	A*02:10	China South Han		0.0020	284	23_1_N_113_3_E
4,550	A*02:10	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
4,551	A*02:10	China Zhejiang Han		0.0026	1,734	27_2_N_118_1_E
4,552	A*02:10	Croatia		0.0000	150	45_0_N_15_0_E
4,553	A*02:10	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
4,554	A*02:10	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
4,555	A*02:10	Czech Republic		0.0000	106	50_0_N_14_0_E
4,556	A*02:10	Germany DKMS - China minority		0.0039	1,282	48_31_N_9_3_E
4,557	A*02:10	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
4,558	A*02:10	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
4,559	A*02:10	Hong Kong Chinese BMDR		0.0003	7,595	22_15_N_114_10_E
4,560	A*02:10	Hong Kong Chinese cord blood registry		0.0003	3,892	22_15_N_114_10_E
4,561	A*02:10	Hong Kong Chinese HKBMDR HLA 11 loci	0.2	0.0008	5,266	22_25_N_114_17_E
4,562	A*02:10	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
4,563	A*02:10	Israel Libya Jews		0.0000	3,739	32_10_N_34_52_E
4,564	A*02:10	Israel Morocco Jews		0.0000	36,718	32_10_N_34_52_E
4,565	A*02:10	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
4,566	A*02:10	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
4,567	A*02:10	Japan Central		0.0070	371	35_41_N_139_46_E
4,568	A*02:10	Japan pop 16		0.0039	18,604	35_41_N_139_46_E
4,569	A*02:10	Japan pop 3		0.0040	1,018	35_41_N_139_46_E
4,570	A*02:10	Malaysia Peninsular Malay	0.2	0.0011	951	3_9_N_101_57_E
4,571	A*02:10	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
4,572	A*02:10	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
4,573	A*02:10	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,574	A*02:10	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
4,575	A*02:10	Oman	0.0	0.0000	118	22_0_N_56_0_E
4,576	A*02:10	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
4,577	A*02:10	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
4,578	A*02:10	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
4,579	A*02:10	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
4,580	A*02:10	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
4,581	A*02:10	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
4,582	A*02:10	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
4,583	A*02:10	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
4,584	A*02:10	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
4,585	A*02:10	Romania	0.0	0.0000	348	46_0_N_25_0_E
4,586	A*02:10	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
4,587	A*02:10	Russia Tuva pop 2		0.0030	169	51_30_N_95_5_E
4,588	A*02:10	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
4,589	A*02:10	Singapore Chinese	0.7	0.0030	149	1_24_N_103_45_E
4,590	A*02:10	South Africa Natal Tamil		0.0200	51	30_0_S_31_0_E
4,591	A*02:10	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
4,592	A*02:10	South Korea pop 10		0.0030	4,128	37_33_N_126_58_E
4,593	A*02:10	South Korea pop 3		0.0060	485	37_33_N_126_59_E
4,594	A*02:10	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
4,595	A*02:10	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
4,596	A*02:10	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
4,597	A*02:10	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
4,598	A*02:10	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
4,599	A*02:10	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
4,600	A*02:10	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
4,601	A*02:10	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
4,602	A*02:10	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
4,603	A*02:10	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
4,604	A*02:10	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
4,605	A*02:10	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
4,606	A*02:10	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
4,607	A*02:10	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
4,608	A*02:10	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
4,609	A*02:10	Taiwan Tzu Chi Morrow Donor Registry		0.0008	46,682	25_2_N_121_38_E
4,610	A*02:10	Taiwan Tzu Chi Morrow Donor Registry Aborigine		0.0000	233	25_2_N_121_38_E
4,611	A*02:10	Turkey pop 5		0.0000	142	40_0_N_28_0_E
4,612	A*02:10	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
4,613	A*02:10	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
4,614	A*02:10	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
4,615	A*02:10	USA Arizona Pima		0.0060	100	33_0_N_113_0_W
4,616	A*02:10	USA Asian pop 2		0.0009	1,772	44_58_N_93_15_W
4,617	A*02:10	USA Caucasian	0.0		61,655	38_53_N_77_2_W
4,618	A*02:10	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
4,619	A*02:10	USA Hawaii Okinawa		0.0050	106	21_0_N_157_0_W
4,620	A*02:10	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
4,621	A*02:10	USA NMDP Chinese		0.0011	99,672	38_45_N_76_6_W
4,622	A*02:10	USA NMDP Filipino		0.0002	50,614	38_45_N_76_6_W
4,623	A*02:10	USA NMDP Hawaiian or other Pacific Islander		0.0002	11,499	38_45_N_76_6_W
4,624	A*02:10	USA NMDP Korean		0.0025	77,584	38_45_N_76_6_W
4,625	A*02:10	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
4,626	A*02:10	USA NMDP Southeast Asian		0.0001	27,978	38_45_N_76_6_W
4,627	A*02:10	USA NMDP Vietnamese		0.0001	43,540	38_45_N_76_6_W
4,628	A*02:10	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
4,629	A*02:10	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
4,630	A*02:10	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
4,631	A*02:11	Belgium pop 3	0.0	0.0001	31,412	51_2_N_4_28_E
4,632	A*02:11	Bolivia/Peru Quechua NA-DHS_12 (G)	4.8	0.0238	21	14_0_S_74_0_W
4,633	A*02:11	Brazil Puyanawa	0.7	0.0030	150	10_52_S_53_5_W
4,634	A*02:11	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
4,635	A*02:11	Brazil Mixed		0.0150	108	23_10_S_47_48_W
4,636	A*02:11	Brazil Vale do Ribeira Quilombos	0.0	0.0000	144	24_36_S_48_15_W
4,637	A*02:11	Bulgaria		0.0090	55	42_0_N_23_0_E
4,638	A*02:11	Bulgaria Romani		0.0830	13	42_44_N_23_20_E
4,639	A*02:11	Burkina Faso Fulani		0.0000	49	12_50_N_1_50_W
4,640	A*02:11	Burkina Faso Mossi		0.0000	53	12_50_N_1_50_W
4,641	A*02:11	Burkina Faso Rimaibe		0.0000	47	12_50_N_1_50_W
4,642	A*02:11	Cameroon Yaounde		0.0060	92	3_52_N_11_31_E
4,643	A*02:11	Chile Santiago Mixed	2.0		70	33_26_S_70_39_W
4,644	A*02:11	China Beijing Shijiazhuang Tianjian Han		0.0020	618	39_54_N_116_24_E
4,645	A*02:11	China Inner Mongolia Region		0.0000	102	36_0_N_102_0_E
4,646	A*02:11	China North Han		0.0050	105	39_54_N_116_24_E
4,647	A*02:11	China Qinghai Province Hui		0.0000	110	36_0_N_102_0_E
4,648	A*02:11	China Tibet Region Tibetan		0.0030	158	29_39_N_91_7_E
4,649	A*02:11	Colombia Bogotá Cord Blood	1.0	0.0051	1,463	4_39_N_73_52_W
4,650	A*02:11	Colombia Embera NA-DHS_19 (G)	7.1	0.0357	14	7_0_N_76_0_W
4,651	A*02:11	Colombia Inga NA-DHS_11 (G)	40.0	0.2333	16	1_0_N_77_0_W
4,652	A*02:11	Colombia Waunana NA-DHS_20 (G)	50.0	0.2750	20	5_0_N_77_0_W
4,653	A*02:11	Colombia/Brazil Ticuna Arara NA-DHS_21 (G)	6.7	0.0333	17	4_0_S_70_0_W
4,654	A*02:11	Colombia/Brazil Ticuna Tarapaca NA-DHS_22 (G)	21.1	0.1053	19	4_0_S_70_0_W
4,655	A*02:11	Croatia		0.0000	150	45_0_N_15_0_E
4,656	A*02:11	Croatia pop 4		0.0001	4,000	45_48_N_15_57_E
4,657	A*02:11	Cuba Caucasian	1.4	0.0070	70	23_8_N_82_22_W
4,658	A*02:11	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
4,659	A*02:11	Czech Republic		0.0000	106	50_0_N_14_0_E
4,660	A*02:11	Czech Republic NMDR		0.0003	5,099	49_48_N_15_30_E
4,661	A*02:11	Ecuador Amerindians		0.0238	63	0_13_S_78_30_W
4,662	A*02:11	Ecuador Cayapa		0.0480	183	1_3_N_79_12_W
4,663	A*02:11	Gaza	2.4	0.0119	42	31_22_N_34_18_E
4,664	A*02:11	Germany DKMS - Bosnia and Herzegovina minority		0.0010	1,028	48_31_N_9_3_E
4,665	A*02:11	Germany DKMS - China minority		0.0008	1,282	48_31_N_9_3_E
4,666	A*02:11	Germany DKMS - Croatia minority		0.0002	2,057	48_31_N_9_3_E
4,667	A*02:11	Germany DKMS - German donors		0.0001	3,456,066	48_31_N_9_3_E
4,668	A*02:11	Germany DKMS - Greece minority		0.0042	1,894	48_31_N_9_3_E
4,669	A*02:11	Germany DKMS - Italy minority		0.0004	1,159	48_31_N_9_3_E
4,670	A*02:11	Germany DKMS - Portugal minority		0.0004	1,176	48_31_N_9_3_E
4,671	A*02:11	Germany DKMS - Romania minority		0.0016	1,234	48_31_N_9_3_E
4,672	A*02:11	Germany DKMS - Spain minority		0.0009	1,107	48_31_N_9_3_E
4,673	A*02:11	Germany DKMS - Turkey minority		0.0028	4,856	48_31_N_9_3_E
4,674	A*02:11	Germany DKMS - United Kingdom minority		0.0005	1,043	48_31_N_9_3_E
4,675	A*02:11	Germany pop 8		0.0003	39,689	48_24_N_9_58_E
4,676	A*02:11	Greece pop 6		0.0021	242	37_58_N_23_43_E
4,677	A*02:11	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
4,678	A*02:11	Hong Kong Chinese BMDR		0.0003	7,595	22_15_N_114_10_E
4,679	A*02:11	Hong Kong Chinese cord blood registry		0.0004	3,892	22_15_N_114_10_E
4,680	A*02:11	India Andhra Pradesh Golla		0.0970	111	17_5_N_78_5_E
4,681	A*02:11	India Central UCBB	13.5	0.0677	4,204	27_1_N_81_19_E
4,682	A*02:11	India Delhi pop 2	3.3	0.0170	90	28_36_N_77_13_E
4,683	A*02:11	India East UCBB	13.3	0.0666	2,403	22_25_N_88_18_E
4,684	A*02:11	India Khandesh Region Pawra		0.1600	50	20_54_N_74_46_E
4,685	A*02:11	India Mumbai Maratha		0.0860	91	18_58_N_72_49_E
4,686	A*02:11	India New Delhi		0.0680	71	28_37_N_77_13_E
4,687	A*02:11	India North pop 2		0.0670	72	25_0_N_74_0_E
4,688	A*02:11	India North UCBB	12.2	0.0610	5,849	28_36_N_77_11_E
4,689	A*02:11	India Northeast UCBB	7.1	0.0355	296	26_16_N_91_48_E
4,690	A*02:11	India South UCBB	19.2	0.0959	11,446	13_6_N_80_8_E
4,691	A*02:11	India Tamil Nadu		0.0780	2,492	11_32_N_78_52_E
4,692	A*02:11	India West Bhil		0.0400	50	20_54_N_74_46_E
4,693	A*02:11	India West Coast Parsi		0.0000	50	18_0_N_74_0_E
4,694	A*02:11	India West UCBB	11.8	0.0592	5,829	19_11_N_73_3_E
4,695	A*02:11	Indonesia Java pop 2		0.0140	36	6_8_S_106_10_E
4,696	A*02:11	Indonesia Sundanese and Javanese		0.0000	201	6_8_S_106_10_E
4,697	A*02:11	Iran Baloch		0.0110	100	29_29_N_60_52_E
4,698	A*02:11	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
4,699	A*02:11	Israel Arab Druze		0.0050	101	31_8_N_35_2_E
4,700	A*02:11	Israel Arab pop 2		0.0008	12,301	32_8_N_34_52_E
4,701	A*02:11	Israel Argentina Jews		0.0005	4,307	32_8_N_34_52_E
4,702	A*02:11	Israel Ashkenazi and Non Ashkenazi Jews		0.0040	146	31_8_N_35_2_E
4,703	A*02:11	Israel Druze		0.0006	5,914	32_41_N_35_17_E
4,704	A*02:11	Israel Iran Jews		0.0009	8,153	32_8_N_34_52_E
4,705	A*02:11	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
4,706	A*02:11	Israel Kavkazi Jews		0.0002	2,840	32_10_N_34_52_E
4,707	A*02:11	Israel USA Jews		0.0001	6,058	32_10_N_34_52_E
4,708	A*02:11	Israel USSR Jews		0.0001	45,681	32_9_N_34_50_E
4,709	A*02:11	Israel YemenJews		0.0001	15,542	32_9_N_34_50_E
4,710	A*02:11	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
4,711	A*02:11	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
4,712	A*02:11	Kenya Luo		0.0000	265	0_0_S_34_6_E
4,713	A*02:11	Kenya Nandi		0.0000	240	0_0_S_35_0_E
4,714	A*02:11	Malaysia Peninsular Indian	17.7	0.0886	271	3_9_N_101_57_E
4,715	A*02:11	Malaysia Peninsular Malay	1.6	0.0079	951	3_9_N_101_57_E
4,716	A*02:11	Mali Bandiagara		0.0000	138	14_4_N_3_6_W
4,717	A*02:11	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
4,718	A*02:11	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
4,719	A*02:11	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,720	A*02:11	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
4,721	A*02:11	Oman	0.0	0.0000	118	22_0_N_56_0_E
4,722	A*02:11	Pakistan Baloch		0.0160	66	30_0_N_67_0_E
4,723	A*02:11	Pakistan Brahui		0.0230	104	30_0_N_67_0_E
4,724	A*02:11	Pakistan Burusho		0.0110	92	36_0_N_73_0_E
4,725	A*02:11	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
4,726	A*02:11	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
4,727	A*02:11	Pakistan Mixed Pathan		0.0200	100	32_0_N_70_0_E
4,728	A*02:11	Pakistan Mixed Sindhi		0.0250	101	24_0_N_68_0_E
4,729	A*02:11	Panama		0.0091	462	8_59_N_79_37_W
4,730	A*02:11	Paraguay/Argentina Ache NA-DHS_24 (G)	46.2	0.2692	13	24_0_S_56_0_W
4,731	A*02:11	Paraguay/Argentina Guarani NA-DHS_23 (G)	20.0	0.1000	10	23_0_S_56_0_W
4,732	A*02:11	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
4,733	A*02:11	Poland DKMS		0.0000	20,653	52_8_N_19_23_E
4,734	A*02:11	Romania	0.0	0.0000	348	46_0_N_25_0_E
4,735	A*02:11	Russia Karelia		0.0005	1,075	63_9_N_32_59_E
4,736	A*02:11	Russia Tuva pop 2		0.0050	169	51_30_N_95_5_E
4,737	A*02:11	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
4,738	A*02:11	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
4,739	A*02:11	Singapore Javaneses		0.0200	51	1_24_N_103_45_E
4,740	A*02:11	Singapore Riau Malay		0.0120	132	1_24_N_103_45_E
4,741	A*02:11	Singapore SGVP Chinese CHS		0.0000	96	1_21_N_103_50_E
4,742	A*02:11	Singapore SGVP Malay MAS		0.0000	89	1_21_N_103_50_E
4,743	A*02:11	Singapore SGVP. Indian INS		0.0250	86	1_21_N_103_50_E
4,744	A*02:11	Singapore Thai		0.0050	100	1_24_N_103_45_E
4,745	A*02:11	South Africa Natal Tamil		0.1300	51	30_0_S_31_0_E
4,746	A*02:11	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
4,747	A*02:11	South Africa Worcester	1.0	0.0060	159	33_39_S_19_27_E
4,748	A*02:11	South Korea pop 3		0.0000	485	37_33_N_126_59_E
4,749	A*02:11	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.1	0.0006	4,335	41_22_N_2_11_E
4,750	A*02:11	Sri Lanka Colombo	12.9	0.0660	714	6_56_N_79_50_E
4,751	A*02:11	Sri Lanka Colombo Sinhalese		0.0740	101	6_56_N_79_50_E
4,752	A*02:11	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
4,753	A*02:11	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
4,754	A*02:11	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
4,755	A*02:11	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
4,756	A*02:11	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
4,757	A*02:11	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
4,758	A*02:11	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
4,759	A*02:11	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
4,760	A*02:11	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
4,761	A*02:11	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
4,762	A*02:11	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
4,763	A*02:11	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
4,764	A*02:11	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
4,765	A*02:11	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
4,766	A*02:11	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
4,767	A*02:11	Thailand		0.0040	142	16_7_N_102_0_E
4,768	A*02:11	Turkey pop 5		0.0030	142	40_0_N_28_0_E
4,769	A*02:11	Uganda Kampala		0.0000	161	0_3_N_32_6_E
4,770	A*02:11	United Arab Emirates Pop 1		0.0180	570	24_42_N_54_82_E
4,771	A*02:11	USA African American		0.0000	252	30_0_N_95_0_W
4,772	A*02:11	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
4,773	A*02:11	USA African American pop 3		0.0010	564	38_53_N_77_2_W
4,774	A*02:11	USA African American pop 4		0.0002	2,411	44_58_N_93_15_W
4,775	A*02:11	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
4,776	A*02:11	USA Asian		0.0000	358	30_0_N_95_0_W
4,777	A*02:11	USA Asian pop 2		0.0116	1,772	44_58_N_93_15_W
4,778	A*02:11	USA Caucasian	0.0		61,655	38_53_N_77_2_W
4,779	A*02:11	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
4,780	A*02:11	USA Caucasian pop 2		0.0000	265	30_0_N_95_0_W
4,781	A*02:11	USA Hispanic		0.0040	234	30_0_N_95_0_W
4,782	A*02:11	USA Hispanic pop 2		0.0033	1,999	44_58_N_93_15_W
4,783	A*02:11	USA NMDP African		0.0001	28,557	38_45_N_76_6_W
4,784	A*02:11	USA NMDP African American pop 2		0.0002	416,581	38_45_N_76_6_W
4,785	A*02:11	USA NMDP American Indian South or Central America		0.0030	5,926	38_45_N_76_6_W
4,786	A*02:11	USA NMDP Caribean Black		0.0019	33,328	38_45_N_76_6_W
4,787	A*02:11	USA NMDP Caribean Hispanic		0.0059	115,374	38_45_N_76_6_W
4,788	A*02:11	USA NMDP Caribean Indian		0.0110	14,339	38_45_N_76_6_W
4,789	A*02:11	USA NMDP Chinese		0.0002	99,672	38_45_N_76_6_W
4,790	A*02:11	USA NMDP European Caucasian		0.0001	1,242,890	38_45_N_76_6_W
4,791	A*02:11	USA NMDP Filipino		0.0008	50,614	38_45_N_76_6_W
4,792	A*02:11	USA NMDP Hawaiian or other Pacific Islander		0.0030	11,499	38_45_N_76_6_W
4,793	A*02:11	USA NMDP Hispanic South or Central American		0.0081	146,714	38_45_N_76_6_W
4,794	A*02:11	USA NMDP Korean		0.0002	77,584	38_45_N_76_6_W
4,795	A*02:11	USA NMDP Mexican or Chicano		0.0007	261,235	38_45_N_76_6_W
4,796	A*02:11	USA NMDP Middle Eastern or North Coast of Africa		0.0012	70,890	38_45_N_76_6_W
4,797	A*02:11	USA NMDP North American Amerindian		0.0003	35,791	38_45_N_76_6_W
4,798	A*02:11	USA NMDP South Asian Indian		0.0500	185,391	38_45_N_76_6_W
4,799	A*02:11	USA NMDP Southeast Asian		0.0336	27,978	38_45_N_76_6_W
4,800	A*02:11	USA NMDP Vietnamese		0.0011	43,540	38_45_N_76_6_W
4,801	A*02:11	USA North American Native		0.0000	187	30_0_N_95_0_W
4,802	A*02:11	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
4,803	A*02:11	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
4,804	A*02:11	USA San Diego	0.3	0.0020	496	32_50_N_117_15_W
4,805	A*02:11	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
4,806	A*02:11	USA Spain Ancestry		0.0070	279	38_0_N_77_0_W
4,807	A*02:11	Zambia Lusaka		0.0000	44	15_25_S_28_17_E
4,808	A*02:11:01	Brazil Barra Mansa Rio State Caucasian		0.0037	405	22_32_S_44_10_W
4,809	A*02:11:01	Brazil Rio de Janeiro Caucasian		0.0029	521	22_54_S_43_12_W
4,810	A*02:11:01	Brazil Rio de Janeiro Parda		0.0059	170	22_54_S_43_12_W
4,811	A*02:11:01	Costa Rica Amerindians (G)	0.8	0.0040	125	9_34_N_83_25_W
4,812	A*02:11:01	Costa Rica Central Valley Mestizo (G)	0.4	0.0020	221	9_55_N_84_5_W
4,813	A*02:11:01	Costa Rica Guanacaste Mestizo (G)	2.8	0.0140	110	10_16_N_85_37_W
4,814	A*02:11:01	England Blood Donors of Mixed Ethnicity	0.8	0.0039	519	52_21_N_1_10_W
4,815	A*02:11:01	Hong Kong Chinese HKBMDR HLA 11 loci	0.1	0.0004	5,266	22_25_N_114_17_E
4,816	A*02:11:01	India Andhra Pradesh Telugu Speaking	19.9	0.1048	186	13_4_N_80_14_E
4,817	A*02:11:01	India Karnataka Kannada Speaking	16.1	0.0800	174	12_57_N_77_36_E
4,818	A*02:11:01	India Kerala Malayalam speaking	9.8	0.0520	356	13_1_N_80_15_E
4,819	A*02:11:01	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
4,820	A*02:11:01	Russia Bashkortostan, Bashkirs	0.8	0.0042	120	54_0_N_55_0_E
4,821	A*02:11:01	Russia Bashkortostan, Tatars	0.5	0.0026	192	54_13_N_56_9_E
4,822	A*02:11:01	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
4,823	A*02:11:01	Russia Moscow Buryat	0.7	0.0034	149	55_46_N_37_41_E
4,824	A*02:11:01	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
4,825	A*02:11:01	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
4,826	A*02:11:01	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
4,827	A*02:11:01	Russia Moscow Tuvian	1.8	0.0089	112	55_46_N_37_41_E
4,828	A*02:11:01	Russia Moscow Uzbek	2.5	0.0126	198	55_46_N_37_41_E
4,829	A*02:11:01	Saudi Arabia Al-Madinah (G)		0.0096	102	24_28_N_39_36_E
4,830	A*02:11:01	Saudi Arabia Jeddah (G)		0.0021	476	21_32_N_39_10_E
4,831	A*02:11:01	Saudi Arabia Makkah (G)		0.0116	344	21_25_N_39_49_E
4,832	A*02:11:01	Saudi Arabia pop 6 (G)		0.0037	28,927	23_52_N_45_4_E
4,833	A*02:11:01	South African Indian population	8.0	0.0400	50	29_22_S_24_31_E
4,834	A*02:11:01	South African Mixed ancestry	4.0	0.0200	50	29_22_S_24_31_E
4,835	A*02:11:01:01	Pakistan Mixed Punjabi	8.8	0.0450	389	31_0_N_72_0_E
4,836	A*02:12	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
4,837	A*02:12	Bulgaria		0.0000	55	42_0_N_23_0_E
4,838	A*02:12	Burkina Faso Fulani		0.0000	49	12_50_N_1_50_W
4,839	A*02:12	Burkina Faso Mossi		0.0000	53	12_50_N_1_50_W
4,840	A*02:12	Burkina Faso Rimaibe		0.0000	47	12_50_N_1_50_W
4,841	A*02:12	China Beijing Shijiazhuang Tianjian Han		0.0030	618	39_54_N_116_24_E
4,842	A*02:12	China Guangdong Province Meizhou Han		0.0050	100	24_27_N_116_7_E
4,843	A*02:12	China Inner Mongolia Region		0.0050	102	36_0_N_102_0_E
4,844	A*02:12	China North Han		0.0000	105	39_54_N_116_24_E
4,845	A*02:12	China Qinghai Province Hui		0.0000	110	36_0_N_102_0_E
4,846	A*02:12	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
4,847	A*02:12	Croatia		0.0000	150	45_0_N_15_0_E
4,848	A*02:12	Croatia pop 4		0.0001	4,000	45_48_N_15_57_E
4,849	A*02:12	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
4,850	A*02:12	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
4,851	A*02:12	Czech Republic		0.0000	106	50_0_N_14_0_E
4,852	A*02:12	Ecuador Cayapa		0.6000	183	1_3_N_79_12_W
4,853	A*02:12	Germany DKMS - China minority		0.0008	1,282	48_31_N_9_3_E
4,854	A*02:12	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
4,855	A*02:12	Germany DKMS - Turkey minority		0.0003	4,856	48_31_N_9_3_E
4,856	A*02:12	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
4,857	A*02:12	Iran Baloch		0.0060	100	29_29_N_60_52_E
4,858	A*02:12	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
4,859	A*02:12	Israel USSR Jews		0.0001	45,681	32_9_N_34_50_E
4,860	A*02:12	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
4,861	A*02:12	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
4,862	A*02:12	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
4,863	A*02:12	Mongolia Buryat		0.0040	141	47_6_N_118_6_E
4,864	A*02:12	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
4,865	A*02:12	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,866	A*02:12	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
4,867	A*02:12	New Zealand Maori with Admixed History	2.9	0.0140	105	42_0_S_174_0_E
4,868	A*02:12	New Zealand Maori with Full Ancestry	2.2	0.0110	46	42_0_S_174_0_E
4,869	A*02:12	Oman	0.0	0.0000	118	22_0_N_56_0_E
4,870	A*02:12	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
4,871	A*02:12	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
4,872	A*02:12	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
4,873	A*02:12	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
4,874	A*02:12	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
4,875	A*02:12	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
4,876	A*02:12	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
4,877	A*02:12	Panama		0.0023	462	8_59_N_79_37_W
4,878	A*02:12	Paraguay/Argentina Guarani NA-DHS_23 (G)	10.0	0.0500	10	23_0_S_56_0_W
4,879	A*02:12	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
4,880	A*02:12	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
4,881	A*02:12	Poland DKMS		0.0001	20,653	52_8_N_19_23_E
4,882	A*02:12	Romania	0.0	0.0000	348	46_0_N_25_0_E
4,883	A*02:12	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
4,884	A*02:12	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
4,885	A*02:12	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
4,886	A*02:12	South Korea pop 3		0.0000	485	37_33_N_126_59_E
4,887	A*02:12	Sudan Central Shaigiya Mixed	2.7		36	18_0_N_33_0_E
4,888	A*02:12	Sudan East Rashaida	3.7		27	14_0_N_35_0_E
4,889	A*02:12	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
4,890	A*02:12	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
4,891	A*02:12	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
4,892	A*02:12	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
4,893	A*02:12	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
4,894	A*02:12	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
4,895	A*02:12	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
4,896	A*02:12	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
4,897	A*02:12	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
4,898	A*02:12	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
4,899	A*02:12	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
4,900	A*02:12	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
4,901	A*02:12	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
4,902	A*02:12	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
4,903	A*02:12	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
4,904	A*02:12	Turkey pop 5		0.0000	142	40_0_N_28_0_E
4,905	A*02:12	USA African American		0.0000	252	30_0_N_95_0_W
4,906	A*02:12	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
4,907	A*02:12	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
4,908	A*02:12	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
4,909	A*02:12	USA Asian		0.0010	358	30_0_N_95_0_W
4,910	A*02:12	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
4,911	A*02:12	USA Caucasian	0.0		61,655	38_53_N_77_2_W
4,912	A*02:12	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
4,913	A*02:12	USA Caucasian pop 2		0.0000	265	30_0_N_95_0_W
4,914	A*02:12	USA Hispanic		0.0040	234	30_0_N_95_0_W
4,915	A*02:12	USA Hispanic pop 2		0.0003	1,999	44_58_N_93_15_W
4,916	A*02:12	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
4,917	A*02:12	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
4,918	A*02:12	USA NMDP Hawaiian or other Pacific Islander		0.0002	11,499	38_45_N_76_6_W
4,919	A*02:12	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
4,920	A*02:12	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
4,921	A*02:12	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
4,922	A*02:12	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
4,923	A*02:12	USA North American Native		0.0000	187	30_0_N_95_0_W
4,924	A*02:12	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
4,925	A*02:12	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
4,926	A*02:12	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
4,927	A*02:12	Venezuela Sierra de Perija Yucpa		0.0120	73	9_0_N_72_0_W
4,928	A*02:13	Bolivia/Peru Quechua NA-DHS_12 (G)	4.8	0.0238	21	14_0_S_74_0_W
4,929	A*02:13	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
4,930	A*02:13	Bulgaria		0.0000	55	42_0_N_23_0_E
4,931	A*02:13	Burkina Faso Fulani		0.0000	49	12_50_N_1_50_W
4,932	A*02:13	Burkina Faso Mossi		0.0000	53	12_50_N_1_50_W
4,933	A*02:13	Burkina Faso Rimaibe		0.0000	47	12_50_N_1_50_W
4,934	A*02:13	China Guangdong Province Meizhou Han		0.0050	100	24_27_N_116_7_E
4,935	A*02:13	China North Han		0.0000	105	39_54_N_116_24_E
4,936	A*02:13	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
4,937	A*02:13	Colombia Bogotá Cord Blood	4.0	0.0198	1,463	4_39_N_73_52_W
4,938	A*02:13	Colombia Embera NA-DHS_19 (G)	21.4	0.1071	14	7_0_N_76_0_W
4,939	A*02:13	Colombia Inga NA-DHS_11 (G)	26.7	0.1333	16	1_0_N_77_0_W
4,940	A*02:13	Colombia Waunana NA-DHS_20 (G)	10.0	0.0500	20	5_0_N_77_0_W
4,941	A*02:13	Colombia Wayuu NA-DHS_15 (G)	6.7	0.0333	15	11_0_N_73_0_W
4,942	A*02:13	Colombia/Brazil Ticuna Arara NA-DHS_21 (G)	13.3	0.0667	17	4_0_S_70_0_W
4,943	A*02:13	Croatia		0.0000	150	45_0_N_15_0_E
4,944	A*02:13	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
4,945	A*02:13	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
4,946	A*02:13	Czech Republic		0.0000	106	50_0_N_14_0_E
4,947	A*02:13	Ecuador Amerindians		0.0079	63	0_13_S_78_30_W
4,948	A*02:13	Ecuador Cayapa		0.0240	183	1_3_N_79_12_W
4,949	A*02:13	England Blood Donors of Mixed Ethnicity	0.2	0.0010	519	52_21_N_1_10_W
4,950	A*02:13	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
4,951	A*02:13	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
4,952	A*02:13	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
4,953	A*02:13	Hong Kong Chinese cord blood registry		0.0001	3,892	22_15_N_114_10_E
4,954	A*02:13	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
4,955	A*02:13	Israel Ashkenazi and Non Ashkenazi Jews		0.0040	146	31_8_N_35_2_E
4,956	A*02:13	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
4,957	A*02:13	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
4,958	A*02:13	Japan pop 16		0.0001	18,604	35_41_N_139_46_E
4,959	A*02:13	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
4,960	A*02:13	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
4,961	A*02:13	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
4,962	A*02:13	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
4,963	A*02:13	Oman	0.0	0.0000	118	22_0_N_56_0_E
4,964	A*02:13	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
4,965	A*02:13	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
4,966	A*02:13	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
4,967	A*02:13	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
4,968	A*02:13	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
4,969	A*02:13	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
4,970	A*02:13	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
4,971	A*02:13	Panama		0.0034	462	8_59_N_79_37_W
4,972	A*02:13	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
4,973	A*02:13	Romania	0.0	0.0000	348	46_0_N_25_0_E
4,974	A*02:13	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
4,975	A*02:13	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
4,976	A*02:13	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
4,977	A*02:13	South Korea pop 3		0.0000	485	37_33_N_126_59_E
4,978	A*02:13	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.1	0.0002	4,335	41_22_N_2_11_E
4,979	A*02:13	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
4,980	A*02:13	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
4,981	A*02:13	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
4,982	A*02:13	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
4,983	A*02:13	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
4,984	A*02:13	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
4,985	A*02:13	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
4,986	A*02:13	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
4,987	A*02:13	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
4,988	A*02:13	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
4,989	A*02:13	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
4,990	A*02:13	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
4,991	A*02:13	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
4,992	A*02:13	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
4,993	A*02:13	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
4,994	A*02:13	Turkey pop 5		0.0000	142	40_0_N_28_0_E
4,995	A*02:13	USA African American		0.0000	252	30_0_N_95_0_W
4,996	A*02:13	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
4,997	A*02:13	USA African American pop 4		0.0002	2,411	44_58_N_93_15_W
4,998	A*02:13	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
4,999	A*02:13	USA Arizona Gila River Pima		0.0005	3,000	33_4_N_111_43_W
5,000	A*02:13	USA Asian		0.0000	358	30_0_N_95_0_W
5,001	A*02:13	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
5,002	A*02:13	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,003	A*02:13	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
5,004	A*02:13	USA Caucasian pop 2		0.0000	265	30_0_N_95_0_W
5,005	A*02:13	USA Hispanic		0.0020	234	30_0_N_95_0_W
5,006	A*02:13	USA Hispanic pop 2		0.0008	1,999	44_58_N_93_15_W
5,007	A*02:13	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
5,008	A*02:13	USA NMDP Caribean Black		0.0000	33,328	38_45_N_76_6_W
5,009	A*02:13	USA NMDP Caribean Hispanic		0.0001	115,374	38_45_N_76_6_W
5,010	A*02:13	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
5,011	A*02:13	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
5,012	A*02:13	USA NMDP Hispanic South or Central American		0.0009	146,714	38_45_N_76_6_W
5,013	A*02:13	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
5,014	A*02:13	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
5,015	A*02:13	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
5,016	A*02:13	USA North American Native		0.0000	187	30_0_N_95_0_W
5,017	A*02:13	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
5,018	A*02:13	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
5,019	A*02:13	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,020	A*02:13	USA Spain Ancestry		0.0020	279	38_0_N_77_0_W
5,021	A*02:13	Venezuela Sierra de Perija Yucpa		0.0120	73	9_0_N_72_0_W
5,022	A*02:14	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,023	A*02:14	Bulgaria		0.0000	55	42_0_N_23_0_E
5,024	A*02:14	Cameroon Yaounde		0.0060	92	3_52_N_11_31_E
5,025	A*02:14	China North Han		0.0000	105	39_54_N_116_24_E
5,026	A*02:14	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
5,027	A*02:14	Colombia North Wiwa El Encanto		0.0096	52	10_10_N_73_53_W
5,028	A*02:14	Croatia		0.0000	150	45_0_N_15_0_E
5,029	A*02:14	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
5,030	A*02:14	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,031	A*02:14	Czech Republic		0.0000	106	50_0_N_14_0_E
5,032	A*02:14	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
5,033	A*02:14	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
5,034	A*02:14	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,035	A*02:14	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
5,036	A*02:14	Israel Morocco Jews		0.0000	36,718	32_10_N_34_52_E
5,037	A*02:14	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
5,038	A*02:14	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,039	A*02:14	Kenya		0.0030	144	1_28_S_36_8_E
5,040	A*02:14	Kenya Luo		0.0150	265	0_0_S_34_6_E
5,041	A*02:14	Kenya Nandi		0.0080	240	0_0_S_35_0_E
5,042	A*02:14	Kenya, Nyanza Province, Luo tribe	2.0	0.0100	100	0_5_S_34_46_E
5,043	A*02:14	Mali Bandiagara		0.0000	138	14_4_N_3_6_W
5,044	A*02:14	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,045	A*02:14	Mongolia Buryat		0.0070	141	47_6_N_118_6_E
5,046	A*02:14	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,047	A*02:14	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,048	A*02:14	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
5,049	A*02:14	Oman	0.9	0.0040	118	22_0_N_56_0_E
5,050	A*02:14	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,051	A*02:14	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,052	A*02:14	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,053	A*02:14	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,054	A*02:14	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,055	A*02:14	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
5,056	A*02:14	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,057	A*02:14	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
5,058	A*02:14	Romania	0.0	0.0000	348	46_0_N_25_0_E
5,059	A*02:14	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
5,060	A*02:14	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
5,061	A*02:14	Saudi Arabia Makkah (G)		0.0029	344	21_25_N_39_49_E
5,062	A*02:14	Saudi Arabia pop 6 (G)		0.0005	28,927	23_52_N_45_4_E
5,063	A*02:14	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
5,064	A*02:14	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,065	A*02:14	South Africa Caucasians		0.0000	102	30_0_S_25_0_E
5,066	A*02:14	South Africa Johannesburg Black	0.8	0.0042	120	26_11_S_28_2_E
5,067	A*02:14	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,068	A*02:14	South African Black	0.7	0.0035	142	29_9_S_24_39_E
5,069	A*02:14	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,070	A*02:14	Sudan Mixed	0.5	0.0030	200	5_0_N_35_0_E
5,071	A*02:14	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
5,072	A*02:14	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
5,073	A*02:14	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
5,074	A*02:14	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
5,075	A*02:14	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
5,076	A*02:14	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
5,077	A*02:14	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
5,078	A*02:14	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
5,079	A*02:14	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
5,080	A*02:14	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
5,081	A*02:14	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
5,082	A*02:14	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
5,083	A*02:14	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
5,084	A*02:14	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
5,085	A*02:14	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
5,086	A*02:14	Tanzania Maasai	1.2	0.0060	336	3_11_S_37_4_E
5,087	A*02:14	Turkey pop 5		0.0000	142	40_0_N_28_0_E
5,088	A*02:14	Uganda Kampala		0.0060	161	0_3_N_32_6_E
5,089	A*02:14	Uganda Kampala pop 2		0.0200	175	0_20_N_32_30_E
5,090	A*02:14	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
5,091	A*02:14	USA African American pop 4		0.0002	2,411	44_58_N_93_15_W
5,092	A*02:14	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
5,093	A*02:14	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
5,094	A*02:14	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,095	A*02:14	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
5,096	A*02:14	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
5,097	A*02:14	USA NMDP African		0.0003	28,557	38_45_N_76_6_W
5,098	A*02:14	USA NMDP African American pop 2		0.0001	416,581	38_45_N_76_6_W
5,099	A*02:14	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
5,100	A*02:14	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
5,101	A*02:14	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
5,102	A*02:14	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
5,103	A*02:14	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
5,104	A*02:14	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
5,105	A*02:14	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
5,106	A*02:14	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,107	A*02:14	Zambia Lusaka		0.0120	44	15_25_S_28_17_E
5,108	A*02:15N	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,109	A*02:15N	Bulgaria		0.0000	55	42_0_N_23_0_E
5,110	A*02:15N	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
5,111	A*02:15N	China North Han		0.0000	105	39_54_N_116_24_E
5,112	A*02:15N	Croatia		0.0000	150	45_0_N_15_0_E
5,113	A*02:15N	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
5,114	A*02:15N	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,115	A*02:15N	Czech Republic		0.0000	106	50_0_N_14_0_E
5,116	A*02:15N	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,117	A*02:15N	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
5,118	A*02:15N	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
5,119	A*02:15N	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,120	A*02:15N	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,121	A*02:15N	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,122	A*02:15N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,123	A*02:15N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
5,124	A*02:15N	Oman	0.0	0.0000	118	22_0_N_56_0_E
5,125	A*02:15N	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,126	A*02:15N	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,127	A*02:15N	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,128	A*02:15N	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,129	A*02:15N	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,130	A*02:15N	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
5,131	A*02:15N	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,132	A*02:15N	Romania	0.0	0.0000	348	46_0_N_25_0_E
5,133	A*02:15N	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
5,134	A*02:15N	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,135	A*02:15N	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,136	A*02:15N	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,137	A*02:15N	Turkey pop 5		0.0000	142	40_0_N_28_0_E
5,138	A*02:15N	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
5,139	A*02:15N	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
5,140	A*02:15N	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,141	A*02:15N	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
5,142	A*02:15N	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
5,143	A*02:15N	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,144	A*02:16	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,145	A*02:16	Bulgaria		0.0000	55	42_0_N_23_0_E
5,146	A*02:16	China North Han		0.0000	105	39_54_N_116_24_E
5,147	A*02:16	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
5,148	A*02:16	Croatia		0.0000	150	45_0_N_15_0_E
5,149	A*02:16	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
5,150	A*02:16	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,151	A*02:16	Czech Republic		0.0000	106	50_0_N_14_0_E
5,152	A*02:16	Georgia Svaneti Region Svan		0.0060	80	42_54_N_43_0_E
5,153	A*02:16	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
5,154	A*02:16	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,155	A*02:16	India Andhra Pradesh Golla		0.0060	111	17_5_N_78_5_E
5,156	A*02:16	India Karnataka Kannada Speaking	1.1	0.0060	174	12_57_N_77_36_E
5,157	A*02:16	India Kerala Malayalam speaking	0.8	0.0042	356	13_1_N_80_15_E
5,158	A*02:16	India Tamil Nadu		0.0057	2,492	11_32_N_78_52_E
5,159	A*02:16	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
5,160	A*02:16	Israel Argentina Jews		0.0008	4,307	32_8_N_34_52_E
5,161	A*02:16	Israel Bukhara Jews		0.0035	2,317	32_8_N_34_52_E
5,162	A*02:16	Israel Druze		0.0003	5,914	32_41_N_35_17_E
5,163	A*02:16	Israel Ethiopia Jews		0.0013	5,928	32_8_N_34_52_E
5,164	A*02:16	Israel Georgia Jews		0.0183	4,471	32_10_N_34_53_E
5,165	A*02:16	Israel Iran Jews		0.0001	8,153	32_8_N_34_52_E
5,166	A*02:16	Israel Kavkazi Jews		0.0018	2,840	32_10_N_34_52_E
5,167	A*02:16	Israel Libya Jews		0.0000	3,739	32_10_N_34_52_E
5,168	A*02:16	Israel USA Jews		0.0005	6,058	32_10_N_34_52_E
5,169	A*02:16	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
5,170	A*02:16	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,171	A*02:16	Malaysia Peninsular Indian	0.7	0.0037	271	3_9_N_101_57_E
5,172	A*02:16	Malaysia Peninsular Malay	0.2	0.0011	951	3_9_N_101_57_E
5,173	A*02:16	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,174	A*02:16	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,175	A*02:16	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,176	A*02:16	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
5,177	A*02:16	Oman	0.0	0.0000	118	22_0_N_56_0_E
5,178	A*02:16	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,179	A*02:16	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,180	A*02:16	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,181	A*02:16	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,182	A*02:16	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,183	A*02:16	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
5,184	A*02:16	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,185	A*02:16	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
5,186	A*02:16	Romania	0.0	0.0000	348	46_0_N_25_0_E
5,187	A*02:16	Saudi Arabia pop 6 (G)		0.0002	28,927	23_52_N_45_4_E
5,188	A*02:16	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
5,189	A*02:16	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,190	A*02:16	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,191	A*02:16	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,192	A*02:16	Sri Lanka Colombo	1.1	0.0060	714	6_56_N_79_50_E
5,193	A*02:16	Sri Lanka Colombo Sinhalese		0.0090	101	6_56_N_79_50_E
5,194	A*02:16	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
5,195	A*02:16	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
5,196	A*02:16	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
5,197	A*02:16	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
5,198	A*02:16	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
5,199	A*02:16	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
5,200	A*02:16	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
5,201	A*02:16	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
5,202	A*02:16	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
5,203	A*02:16	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
5,204	A*02:16	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
5,205	A*02:16	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
5,206	A*02:16	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
5,207	A*02:16	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
5,208	A*02:16	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
5,209	A*02:16	Turkey pop 5		0.0000	142	40_0_N_28_0_E
5,210	A*02:16	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
5,211	A*02:16	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
5,212	A*02:16	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
5,213	A*02:16	USA Asian pop 2		0.0011	1,772	44_58_N_93_15_W
5,214	A*02:16	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,215	A*02:16	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
5,216	A*02:16	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
5,217	A*02:16	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
5,218	A*02:16	USA NMDP Filipino		0.0002	50,614	38_45_N_76_6_W
5,219	A*02:16	USA NMDP Hawaiian or other Pacific Islander		0.0003	11,499	38_45_N_76_6_W
5,220	A*02:16	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
5,221	A*02:16	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
5,222	A*02:16	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
5,223	A*02:16	USA NMDP South Asian Indian		0.0030	185,391	38_45_N_76_6_W
5,224	A*02:16	USA NMDP Southeast Asian		0.0018	27,978	38_45_N_76_6_W
5,225	A*02:16	USA NMDP Vietnamese		0.0001	43,540	38_45_N_76_6_W
5,226	A*02:16	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
5,227	A*02:16	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
5,228	A*02:16	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,229	A*02:17	Belgium pop 3	0.3	0.0013	31,412	51_2_N_4_28_E
5,230	A*02:17	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,231	A*02:17	Brazil Mixed		0.0050	108	23_10_S_47_48_W
5,232	A*02:17	Bulgaria		0.0090	55	42_0_N_23_0_E
5,233	A*02:17	Cape Verde Northwestern Islands		0.0000	62	16_0_N_24_0_W
5,234	A*02:17	Cape Verde Southeastern Islands		0.0080	62	16_0_N_24_0_W
5,235	A*02:17	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
5,236	A*02:17	China North Han		0.0000	105	39_54_N_116_24_E
5,237	A*02:17	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
5,238	A*02:17	Colombia Bogotá Cord Blood	0.8	0.0041	1,463	4_39_N_73_52_W
5,239	A*02:17	Croatia		0.0030	150	45_0_N_15_0_E
5,240	A*02:17	Cuba Caucasian	1.4	0.0070	70	23_8_N_82_22_W
5,241	A*02:17	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,242	A*02:17	Czech Republic		0.0000	106	50_0_N_14_0_E
5,243	A*02:17	Czech Republic NMDR		0.0019	5,099	49_48_N_15_30_E
5,244	A*02:17	Georgia Tibilisi		0.0050	109	41_41_N_44_57_E
5,245	A*02:17	Germany DKMS - Austria minority		0.0012	1,698	48_31_N_9_3_E
5,246	A*02:17	Germany DKMS - China minority		0.0004	1,282	48_31_N_9_3_E
5,247	A*02:17	Germany DKMS - Croatia minority		0.0002	2,057	48_31_N_9_3_E
5,248	A*02:17	Germany DKMS - France minority		0.0004	1,406	48_31_N_9_3_E
5,249	A*02:17	Germany DKMS - German donors		0.0010	3,456,066	48_31_N_9_3_E
5,250	A*02:17	Germany DKMS - Greece minority		0.0042	1,894	48_31_N_9_3_E
5,251	A*02:17	Germany DKMS - Italy minority		0.0035	1,159	48_31_N_9_3_E
5,252	A*02:17	Germany DKMS - Netherlands minority		0.0007	1,374	48_31_N_9_3_E
5,253	A*02:17	Germany DKMS - Romania minority		0.0032	1,234	48_31_N_9_3_E
5,254	A*02:17	Germany DKMS - Spain minority		0.0009	1,107	48_31_N_9_3_E
5,255	A*02:17	Germany DKMS - Turkey minority		0.0056	4,856	48_31_N_9_3_E
5,256	A*02:17	Germany pop 6		0.0010	8,862	51_6_N_10_23_E
5,257	A*02:17	Germany pop 8		0.0013	39,689	48_24_N_9_58_E
5,258	A*02:17	Guinea Bissau		0.0000	65	12_0_N_15_0_W
5,259	A*02:17	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,260	A*02:17	India Delhi pop 2	1.1	0.0050	90	28_36_N_77_13_E
5,261	A*02:17	Ireland Northern	0.1	0.0010	1,000	54_40_N_6_45_W
5,262	A*02:17	Israel Arab pop 2		0.0028	12,301	32_8_N_34_52_E
5,263	A*02:17	Israel Argentina Jews		0.0007	4,307	32_8_N_34_52_E
5,264	A*02:17	Israel Ashkenazi and Non Ashkenazi Jews		0.0090	146	31_8_N_35_2_E
5,265	A*02:17	Israel Ashkenazi Jews pop 3		0.0241	4,625	32_8_N_34_52_E
5,266	A*02:17	Israel Bukhara Jews		0.0062	2,317	32_8_N_34_52_E
5,267	A*02:17	Israel Druze		0.0004	5,914	32_41_N_35_17_E
5,268	A*02:17	Israel Ethiopia Jews		0.0024	5,928	32_8_N_34_52_E
5,269	A*02:17	Israel Iran Jews		0.0541	8,153	32_8_N_34_52_E
5,270	A*02:17	Israel Iraq Jews		0.0717	13,270	32_10_N_34_52_E
5,271	A*02:17	Israel Kavkazi Jews		0.0018	2,840	32_10_N_34_52_E
5,272	A*02:17	Israel Libya Jews		0.0064	3,739	32_10_N_34_52_E
5,273	A*02:17	Israel Morocco Jews		0.0038	36,718	32_10_N_34_52_E
5,274	A*02:17	Israel Poland Jews		0.0028	13,871	32_10_N_34_52_E
5,275	A*02:17	Israel Tunisia Jews		0.0057	9,070	32_10_N_34_52_E
5,276	A*02:17	Israel USA Jews		0.0005	6,058	32_10_N_34_52_E
5,277	A*02:17	Israel USSR Jews		0.0028	45,681	32_9_N_34_50_E
5,278	A*02:17	Israel YemenJews		0.0582	15,542	32_9_N_34_50_E
5,279	A*02:17	Italy Bergamo	1.0	0.0060	101	45_42_N_9_40_E
5,280	A*02:17	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,281	A*02:17	Italy pop 5		0.0060	975	45_28_N_9_11_E
5,282	A*02:17	Kenya Luo		0.0000	265	0_0_S_34_6_E
5,283	A*02:17	Kenya Nandi		0.0000	240	0_0_S_35_0_E
5,284	A*02:17	Mali Bandiagara		0.0040	138	14_4_N_3_6_W
5,285	A*02:17	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,286	A*02:17	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,287	A*02:17	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,288	A*02:17	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
5,289	A*02:17	Nicaragua Managua	0.3	0.0015	339	12_7_N_86_10_W
5,290	A*02:17	Oman	0.0	0.0000	118	22_0_N_56_0_E
5,291	A*02:17	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,292	A*02:17	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,293	A*02:17	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,294	A*02:17	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,295	A*02:17	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,296	A*02:17	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
5,297	A*02:17	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,298	A*02:17	Panama		0.0079	462	8_59_N_79_37_W
5,299	A*02:17	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
5,300	A*02:17	Poland DKMS		0.0017	20,653	52_8_N_19_23_E
5,301	A*02:17	Romania	0.3	0.0010	348	46_0_N_25_0_E
5,302	A*02:17	Russia Karelia		0.0005	1,075	63_9_N_32_59_E
5,303	A*02:17	Russia Tuva pop 2		0.0030	169	51_30_N_95_5_E
5,304	A*02:17	Sao Tome Island Angolar		0.0000	32	0_20_N_6_44_E
5,305	A*02:17	Sao Tome Island Forro		0.0080	66	0_20_N_6_44_E
5,306	A*02:17	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
5,307	A*02:17	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,308	A*02:17	South Africa Caucasians		0.0050	102	30_0_S_25_0_E
5,309	A*02:17	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,310	A*02:17	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.3	0.0013	4,335	41_22_N_2_11_E
5,311	A*02:17	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
5,312	A*02:17	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
5,313	A*02:17	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
5,314	A*02:17	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
5,315	A*02:17	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
5,316	A*02:17	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
5,317	A*02:17	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
5,318	A*02:17	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
5,319	A*02:17	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
5,320	A*02:17	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
5,321	A*02:17	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
5,322	A*02:17	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
5,323	A*02:17	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
5,324	A*02:17	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
5,325	A*02:17	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
5,326	A*02:17	Turkey pop 5		0.0070	142	40_0_N_28_0_E
5,327	A*02:17	Uganda Kampala		0.0000	161	0_3_N_32_6_E
5,328	A*02:17	United Arab Emirates Pop 1		0.0040	570	24_42_N_54_82_E
5,329	A*02:17	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
5,330	A*02:17	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
5,331	A*02:17	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
5,332	A*02:17	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
5,333	A*02:17	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,334	A*02:17	USA Caucasian Bethesda	0.8	0.0040	307	38_0_N_78_0_W
5,335	A*02:17	USA Caucasian pop 4		0.0009	1,070	38_53_N_77_2_W
5,336	A*02:17	USA Eastern European		0.0010	558	38_53_N_77_2_W
5,337	A*02:17	USA Hispanic pop 2		0.0035	1,999	44_58_N_93_15_W
5,338	A*02:17	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
5,339	A*02:17	USA NMDP African American pop 2		0.0002	416,581	38_45_N_76_6_W
5,340	A*02:17	USA NMDP Caribean Black		0.0002	33,328	38_45_N_76_6_W
5,341	A*02:17	USA NMDP Caribean Hispanic		0.0098	115,374	38_45_N_76_6_W
5,342	A*02:17	USA NMDP Caribean Indian		0.0110	14,339	38_45_N_76_6_W
5,343	A*02:17	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
5,344	A*02:17	USA NMDP European Caucasian		0.0004	1,242,890	38_45_N_76_6_W
5,345	A*02:17	USA NMDP Filipino		0.0001	50,614	38_45_N_76_6_W
5,346	A*02:17	USA NMDP Hawaiian or other Pacific Islander		0.0001	11,499	38_45_N_76_6_W
5,347	A*02:17	USA NMDP Hispanic South or Central American		0.0019	146,714	38_45_N_76_6_W
5,348	A*02:17	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
5,349	A*02:17	USA NMDP Mexican or Chicano		0.0005	261,235	38_45_N_76_6_W
5,350	A*02:17	USA NMDP Middle Eastern or North Coast of Africa		0.0022	70,890	38_45_N_76_6_W
5,351	A*02:17	USA NMDP North American Amerindian		0.0003	35,791	38_45_N_76_6_W
5,352	A*02:17	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
5,353	A*02:17	USA NMDP Southeast Asian		0.0001	27,978	38_45_N_76_6_W
5,354	A*02:17	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
5,355	A*02:17	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
5,356	A*02:17	USA San Antonio Caucasian		0.0030	222	29_23_N_98_33_W
5,357	A*02:17	USA San Francisco Caucasian		0.0020	220	37_46_N_122_25_W
5,358	A*02:17	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,359	A*02:17	Venezuela Perja Mountain Bari		0.0800	55	9_49_N_72_49_W
5,360	A*02:17	Zambia Lusaka		0.0000	44	15_25_S_28_17_E
5,361	A*02:17	Zimbabwe Harare Shona		0.0020	230	17_51_S_31_1_E
5,362	A*02:17:01	China North Han		0.0000	105	39_54_N_116_24_E
5,363	A*02:17:01	Mongolia Buryat		0.0040	141	47_6_N_118_6_E
5,364	A*02:17:01	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,365	A*02:17:01	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,366	A*02:17:01	Saudi Arabia pop 5	1.3	0.0063	158	24_38_N_46_43_E
5,367	A*02:17:01	Saudi Arabia pop 6 (G)		0.0008	28,927	23_52_N_45_4_E
5,368	A*02:17:01	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,369	A*02:17:01	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,370	A*02:17:01	USA San Francisco Caucasian		0.0020	220	37_46_N_122_25_W
5,371	A*02:17:01	USA Spain Ancestry		0.0070	279	38_0_N_77_0_W
5,372	A*02:17:02	China North Han		0.0000	105	39_54_N_116_24_E
5,373	A*02:17:02	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,374	A*02:17:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,375	A*02:17:02	Poland BMR	0.2	0.0011	23,595	52_0_N_19_22_E
5,376	A*02:17:02	Russia Bashkortostan, Bashkirs	0.8	0.0042	120	54_0_N_55_0_E
5,377	A*02:17:02	Russia Moscow Kabardin	0.2	0.0012	834	55_46_N_37_41_E
5,378	A*02:17:02	Russia Moscow Megrel	1.7	0.0088	230	55_46_N_37_41_E
5,379	A*02:17:02	Russia Moscow Russians	0.3	0.0013	13,147	55_46_N_37_41_E
5,380	A*02:17:02	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
5,381	A*02:17:02	Russia Moscow Tuvian	0.9	0.0045	112	55_46_N_37_41_E
5,382	A*02:17:02	Russia Moscow Yakut	0.4	0.0021	235	55_46_N_37_41_E
5,383	A*02:17:02	Russia Nizhny Novgorod, Russians	0.1	0.0007	1,510	56_19_N_43_59_E
5,384	A*02:17:02	Russian Federation Vologda Region	0.8	0.0042	119	59_13_N_39_54_E
5,385	A*02:17:02	Saudi Arabia Makkah (G)		0.0029	344	21_25_N_39_49_E
5,386	A*02:17:02	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,387	A*02:17:02	USA Arizona Gila River Pima		0.0041	3,000	33_4_N_111_43_W
5,388	A*02:17:02	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,389	A*02:18	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,390	A*02:18	Bulgaria		0.0000	55	42_0_N_23_0_E
5,391	A*02:18	China North Han		0.0000	105	39_54_N_116_24_E
5,392	A*02:18	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
5,393	A*02:18	Croatia		0.0000	150	45_0_N_15_0_E
5,394	A*02:18	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
5,395	A*02:18	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,396	A*02:18	Czech Republic		0.0000	106	50_0_N_14_0_E
5,397	A*02:18	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,398	A*02:18	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
5,399	A*02:18	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
5,400	A*02:18	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,401	A*02:18	Japan Central		0.0010	371	35_41_N_139_46_E
5,402	A*02:18	Japan pop 16		0.0008	18,604	35_41_N_139_46_E
5,403	A*02:18	Japan pop 3		0.0010	1,018	35_41_N_139_46_E
5,404	A*02:18	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,405	A*02:18	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,406	A*02:18	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,407	A*02:18	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
5,408	A*02:18	Oman	0.0	0.0000	118	22_0_N_56_0_E
5,409	A*02:18	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,410	A*02:18	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,411	A*02:18	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,412	A*02:18	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,413	A*02:18	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,414	A*02:18	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
5,415	A*02:18	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,416	A*02:18	Romania	0.0	0.0000	348	46_0_N_25_0_E
5,417	A*02:18	Scotland Orkney	0.0	0.0000	99	58_59_N_3_11_W
5,418	A*02:18	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,419	A*02:18	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,420	A*02:18	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,421	A*02:18	Turkey pop 5		0.0000	142	40_0_N_28_0_E
5,422	A*02:18	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
5,423	A*02:18	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
5,424	A*02:18	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,425	A*02:18	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
5,426	A*02:18	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
5,427	A*02:18	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
5,428	A*02:18	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,429	A*02:19	Argentina Chiriguano	3.8	0.0280	54	23_8_S_64_20_W
5,430	A*02:19	Argentina Gran Chaco Eastern Toba	17.9	0.0950	135	26_11_S_58_11_W
5,431	A*02:19	Argentina Gran Chaco Mataco Wichi	15.9	0.0800	49	27_27_S_58_59_W
5,432	A*02:19	Argentina Gran Chaco Western Toba Pilaga	0.0	0.0000	19	27_27_S_58_59_W
5,433	A*02:19	Argentina Rosario Toba	9.3	0.0520	86	32_57_S_60_39_W
5,434	A*02:19	Argentina Salta Wichi pop 2	10.5	0.0790	19	23_8_S_64_20_W
5,435	A*02:19	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,436	A*02:19	Brazil Terena	3.3	0.0170	60	20_0_S_55_0_W
5,437	A*02:19	Bulgaria		0.0000	55	42_0_N_23_0_E
5,438	A*02:19	China North Han		0.0000	105	39_54_N_116_24_E
5,439	A*02:19	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
5,440	A*02:19	China Yunnan Province Lisu		0.0060	111	24_30_N_101_30_E
5,441	A*02:19	Croatia		0.0000	150	45_0_N_15_0_E
5,442	A*02:19	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
5,443	A*02:19	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,444	A*02:19	Czech Republic		0.0000	106	50_0_N_14_0_E
5,445	A*02:19	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
5,446	A*02:19	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,447	A*02:19	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
5,448	A*02:19	Israel Argentina Jews		0.0000	4,307	32_8_N_34_52_E
5,449	A*02:19	Israel Ashkenazi and Non Ashkenazi Jews		0.0040	146	31_8_N_35_2_E
5,450	A*02:19	Israel Morocco Jews		0.0000	36,718	32_10_N_34_52_E
5,451	A*02:19	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
5,452	A*02:19	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,453	A*02:19	Japan Okinawa Ryukyuan		0.0070	143	26_4_N_127_8_E
5,454	A*02:19	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,455	A*02:19	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,456	A*02:19	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,457	A*02:19	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
5,458	A*02:19	Oman	0.0	0.0000	118	22_0_N_56_0_E
5,459	A*02:19	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,460	A*02:19	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,461	A*02:19	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,462	A*02:19	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,463	A*02:19	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,464	A*02:19	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
5,465	A*02:19	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,466	A*02:19	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
5,467	A*02:19	Romania	0.0	0.0000	348	46_0_N_25_0_E
5,468	A*02:19	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
5,469	A*02:19	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
5,470	A*02:19	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
5,471	A*02:19	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,472	A*02:19	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,473	A*02:19	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,474	A*02:19	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
5,475	A*02:19	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
5,476	A*02:19	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
5,477	A*02:19	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
5,478	A*02:19	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
5,479	A*02:19	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
5,480	A*02:19	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
5,481	A*02:19	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
5,482	A*02:19	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
5,483	A*02:19	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
5,484	A*02:19	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
5,485	A*02:19	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
5,486	A*02:19	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
5,487	A*02:19	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
5,488	A*02:19	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
5,489	A*02:19	Turkey pop 5		0.0000	142	40_0_N_28_0_E
5,490	A*02:19	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
5,491	A*02:19	USA African American pop 2		0.0000	149	32_46_N_96_46_W
5,492	A*02:19	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
5,493	A*02:19	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
5,494	A*02:19	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
5,495	A*02:19	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,496	A*02:19	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
5,497	A*02:19	USA Caucasian pop 3		0.0000	88	32_46_N_96_48_W
5,498	A*02:19	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
5,499	A*02:19	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
5,500	A*02:19	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
5,501	A*02:19	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
5,502	A*02:19	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
5,503	A*02:19	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
5,504	A*02:19	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,505	A*02:20	Belgium pop 3	0.1	0.0003	31,412	51_2_N_4_28_E
5,506	A*02:20	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,507	A*02:20	Brazil Mixed		0.0050	108	23_10_S_47_48_W
5,508	A*02:20	China Beijing Shijiazhuang Tianjian Han		0.0020	618	39_54_N_116_24_E
5,509	A*02:20	China North Han		0.0000	105	39_54_N_116_24_E
5,510	A*02:20	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
5,511	A*02:20	China Uyghur HIV negative		0.0260	19	36_34_N_103_49_E
5,512	A*02:20	Croatia		0.0000	150	45_0_N_15_0_E
5,513	A*02:20	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
5,514	A*02:20	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,515	A*02:20	Czech Republic		0.0000	106	50_0_N_14_0_E
5,516	A*02:20	Ecuador Amerindians		0.0079	63	0_13_S_78_30_W
5,517	A*02:20	Georgia Svaneti Region Svan		0.0060	80	42_54_N_43_0_E
5,518	A*02:20	Georgia Tibilisi		0.0050	109	41_41_N_44_57_E
5,519	A*02:20	Germany DKMS - German donors		0.0001	3,456,066	48_31_N_9_3_E
5,520	A*02:20	Germany DKMS - Greece minority		0.0008	1,894	48_31_N_9_3_E
5,521	A*02:20	Germany DKMS - Netherlands minority		0.0004	1,374	48_31_N_9_3_E
5,522	A*02:20	Germany DKMS - Portugal minority		0.0030	1,176	48_31_N_9_3_E
5,523	A*02:20	Germany DKMS - Spain minority		0.0005	1,107	48_31_N_9_3_E
5,524	A*02:20	Germany DKMS - Turkey minority		0.0007	4,856	48_31_N_9_3_E
5,525	A*02:20	Germany pop 6		0.0001	8,862	51_6_N_10_23_E
5,526	A*02:20	Germany pop 8		0.0001	39,689	48_24_N_9_58_E
5,527	A*02:20	Guinea Bissau Balanta		0.0100	48	11_52_N_15_36_W
5,528	A*02:20	Guinea Bissau Bijago		0.0000	23	11_52_N_15_36_W
5,529	A*02:20	Guinea Bissau Fula		0.0000	31	11_52_N_15_36_W
5,530	A*02:20	Guinea Bissau Papel		0.0000	25	11_52_N_15_36_W
5,531	A*02:20	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,532	A*02:20	India Andhra Pradesh Golla		0.0060	111	17_5_N_78_5_E
5,533	A*02:20	India Central UCBB	0.5	0.0023	4,204	27_1_N_81_19_E
5,534	A*02:20	India East UCBB	0.2	0.0010	2,403	22_25_N_88_18_E
5,535	A*02:20	India North UCBB	0.9	0.0043	5,849	28_36_N_77_11_E
5,536	A*02:20	India Northeast UCBB	0.3	0.0017	296	26_16_N_91_48_E
5,537	A*02:20	India South UCBB	0.1	0.0004	11,446	13_6_N_80_8_E
5,538	A*02:20	India West UCBB	0.4	0.0021	5,829	19_11_N_73_3_E
5,539	A*02:20	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
5,540	A*02:20	Israel USA Jews		0.0001	6,058	32_10_N_34_52_E
5,541	A*02:20	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
5,542	A*02:20	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,543	A*02:20	Italy pop 5		0.0010	975	45_28_N_9_11_E
5,544	A*02:20	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,545	A*02:20	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,546	A*02:20	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,547	A*02:20	Netherlands Leiden		0.0030	1,305	52_9_N_4_31_E
5,548	A*02:20	Oman	0.0	0.0000	118	22_0_N_56_0_E
5,549	A*02:20	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,550	A*02:20	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,551	A*02:20	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,552	A*02:20	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,553	A*02:20	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,554	A*02:20	Pakistan Mixed Pathan		0.0050	100	32_0_N_70_0_E
5,555	A*02:20	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,556	A*02:20	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
5,557	A*02:20	Portugal Center		0.0100	50	39_0_N_8_0_W
5,558	A*02:20	Portugal North		0.0000	46	41_0_N_8_0_W
5,559	A*02:20	Portugal South		0.0000	49	37_0_N_8_0_W
5,560	A*02:20	Romania	0.0	0.0000	348	46_0_N_25_0_E
5,561	A*02:20	Saudi Arabia pop 6 (G)		0.0002	28,927	23_52_N_45_4_E
5,562	A*02:20	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,563	A*02:20	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,564	A*02:20	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.2	0.0008	4,335	41_22_N_2_11_E
5,565	A*02:20	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
5,566	A*02:20	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
5,567	A*02:20	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
5,568	A*02:20	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
5,569	A*02:20	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
5,570	A*02:20	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
5,571	A*02:20	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
5,572	A*02:20	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
5,573	A*02:20	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
5,574	A*02:20	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
5,575	A*02:20	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
5,576	A*02:20	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
5,577	A*02:20	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
5,578	A*02:20	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
5,579	A*02:20	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
5,580	A*02:20	Turkey pop 5		0.0000	142	40_0_N_28_0_E
5,581	A*02:20	United Arab Emirates Pop 1		0.0010	570	24_42_N_54_82_E
5,582	A*02:20	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
5,583	A*02:20	USA African American pop 4		0.0002	2,411	44_58_N_93_15_W
5,584	A*02:20	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
5,585	A*02:20	USA Asian pop 2		0.0006	1,772	44_58_N_93_15_W
5,586	A*02:20	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,587	A*02:20	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
5,588	A*02:20	USA Caucasian pop 4		0.0000	1,070	38_53_N_77_2_W
5,589	A*02:20	USA Hispanic pop 2		0.0015	1,999	44_58_N_93_15_W
5,590	A*02:20	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
5,591	A*02:20	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
5,592	A*02:20	USA NMDP Caribean Black		0.0000	33,328	38_45_N_76_6_W
5,593	A*02:20	USA NMDP Caribean Hispanic		0.0027	115,374	38_45_N_76_6_W
5,594	A*02:20	USA NMDP Caribean Indian		0.0020	14,339	38_45_N_76_6_W
5,595	A*02:20	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
5,596	A*02:20	USA NMDP European Caucasian		0.0001	1,242,890	38_45_N_76_6_W
5,597	A*02:20	USA NMDP Filipino		0.0001	50,614	38_45_N_76_6_W
5,598	A*02:20	USA NMDP Hawaiian or other Pacific Islander		0.0001	11,499	38_45_N_76_6_W
5,599	A*02:20	USA NMDP Hispanic South or Central American		0.0003	146,714	38_45_N_76_6_W
5,600	A*02:20	USA NMDP Japanese		0.0001	24,582	38_45_N_76_6_W
5,601	A*02:20	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
5,602	A*02:20	USA NMDP Mexican or Chicano		0.0002	261,235	38_45_N_76_6_W
5,603	A*02:20	USA NMDP Middle Eastern or North Coast of Africa		0.0002	70,890	38_45_N_76_6_W
5,604	A*02:20	USA NMDP North American Amerindian		0.0001	35,791	38_45_N_76_6_W
5,605	A*02:20	USA NMDP South Asian Indian		0.0011	185,391	38_45_N_76_6_W
5,606	A*02:20	USA NMDP Southeast Asian		0.0009	27,978	38_45_N_76_6_W
5,607	A*02:20	USA NMDP Vietnamese		0.0001	43,540	38_45_N_76_6_W
5,608	A*02:20	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
5,609	A*02:20	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
5,610	A*02:20	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,611	A*02:20:01	Brazil Barra Mansa Rio State Black		0.0068	73	22_32_S_44_10_W
5,612	A*02:20:01	Brazil Barra Mansa Rio State Caucasian		0.0012	405	22_32_S_44_10_W
5,613	A*02:20:01	Brazil Rio de Janeiro Caucasian		0.0019	521	22_54_S_43_12_W
5,614	A*02:20:01	Bulgaria		0.0000	55	42_0_N_23_0_E
5,615	A*02:20:01	China North Han		0.0000	105	39_54_N_116_24_E
5,616	A*02:20:01	England North West	0.3	0.0020	298	53_0_N_3_0_W
5,617	A*02:20:01	Libya Cyrenaica		0.0040	118	32_7_N_20_4_E
5,618	A*02:20:01	Mexico Guadalajara Mestizo pop 2		0.0100	103	20_40_N_103_21_W
5,619	A*02:20:01	Mongolia Buryat		0.0040	141	47_6_N_118_6_E
5,620	A*02:20:01	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,621	A*02:20:01	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,622	A*02:20:01	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
5,623	A*02:20:01	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
5,624	A*02:20:01	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
5,625	A*02:20:01	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
5,626	A*02:20:01	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,627	A*02:20:01	USA African American pop 3		0.0010	564	38_53_N_77_2_W
5,628	A*02:20:01	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,629	A*02:20:01	USA Mexican American Mestizo		0.0020	553	38_53_N_77_2_W
5,630	A*02:20:01	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
5,631	A*02:20:02	Bulgaria		0.0000	55	42_0_N_23_0_E
5,632	A*02:20:02	China North Han		0.0000	105	39_54_N_116_24_E
5,633	A*02:20:02	Mexico Guadalajara Mestizo pop 2		0.0050	103	20_40_N_103_21_W
5,634	A*02:20:02	Mongolia Buryat		0.0040	141	47_6_N_118_6_E
5,635	A*02:20:02	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,636	A*02:20:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,637	A*02:20:02	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,638	A*02:20:02	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,639	A*02:21	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,640	A*02:21	Bulgaria		0.0000	55	42_0_N_23_0_E
5,641	A*02:21	China Beijing Shijiazhuang Tianjian Han		0.0020	618	39_54_N_116_24_E
5,642	A*02:21	China North Han		0.0000	105	39_54_N_116_24_E
5,643	A*02:21	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
5,644	A*02:21	Croatia		0.0000	150	45_0_N_15_0_E
5,645	A*02:21	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
5,646	A*02:21	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,647	A*02:21	Czech Republic		0.0000	106	50_0_N_14_0_E
5,648	A*02:21	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,649	A*02:21	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
5,650	A*02:21	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
5,651	A*02:21	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,652	A*02:21	Mexico Guadalajara Mestizo pop 2		0.0050	103	20_40_N_103_21_W
5,653	A*02:21	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,654	A*02:21	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,655	A*02:21	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,656	A*02:21	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
5,657	A*02:21	Oman	0.0	0.0000	118	22_0_N_56_0_E
5,658	A*02:21	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,659	A*02:21	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,660	A*02:21	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,661	A*02:21	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,662	A*02:21	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,663	A*02:21	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
5,664	A*02:21	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,665	A*02:21	Romania	0.0	0.0000	348	46_0_N_25_0_E
5,666	A*02:21	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,667	A*02:21	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,668	A*02:21	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,669	A*02:21	Turkey pop 5		0.0000	142	40_0_N_28_0_E
5,670	A*02:21	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
5,671	A*02:21	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
5,672	A*02:21	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,673	A*02:21	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
5,674	A*02:21	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
5,675	A*02:21	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
5,676	A*02:21	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
5,677	A*02:21	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,678	A*02:22	Bolivia/Chile Aymara NA-DHS_13 (G)	10.0	0.0500	20	22_0_S_70_0_W
5,679	A*02:22	Bolivia/Peru Quechua NA-DHS_12 (G)	4.8	0.0238	21	14_0_S_74_0_W
5,680	A*02:22	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,681	A*02:22	Brazil Terena	25.0	0.1500	60	20_0_S_55_0_W
5,682	A*02:22	Bulgaria		0.0000	55	42_0_N_23_0_E
5,683	A*02:22	Chile Santiago Mixed	1.0		70	33_26_S_70_39_W
5,684	A*02:22	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
5,685	A*02:22	China Guangdong Province Meizhou Han		0.0050	100	24_27_N_116_7_E
5,686	A*02:22	China Inner Mongolia Region		0.0000	102	36_0_N_102_0_E
5,687	A*02:22	China North Han		0.0000	105	39_54_N_116_24_E
5,688	A*02:22	China Qinghai Province Hui		0.0050	110	36_0_N_102_0_E
5,689	A*02:22	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
5,690	A*02:22	Colombia Arhuaco NA-DHS_16 (G)	23.5	0.1176	17	11_0_N_73_0_W
5,691	A*02:22	Colombia Bogotá Cord Blood	6.4	0.0321	1,463	4_39_N_73_52_W
5,692	A*02:22	Colombia Wayuu NA-DHS_15 (G)	33.3	0.1667	15	11_0_N_73_0_W
5,693	A*02:22	Colombia Zenu NA-DHS_18 (G)	33.3	0.1667	16	9_0_N_75_0_W
5,694	A*02:22	Costa Rica Guaymi NA-DHS_10 (G)	83.3	0.4444	18	9_0_N_82_0_W
5,695	A*02:22	Croatia		0.0000	150	45_0_N_15_0_E
5,696	A*02:22	Croatia pop 4		0.0002	4,000	45_48_N_15_57_E
5,697	A*02:22	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
5,698	A*02:22	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,699	A*02:22	Czech Republic		0.0000	106	50_0_N_14_0_E
5,700	A*02:22	Germany DKMS - Croatia minority		0.0007	2,057	48_31_N_9_3_E
5,701	A*02:22	Germany DKMS - German donors		0.0002	3,456,066	48_31_N_9_3_E
5,702	A*02:22	Germany DKMS - Portugal minority		0.0004	1,176	48_31_N_9_3_E
5,703	A*02:22	Germany DKMS - Spain minority		0.0005	1,107	48_31_N_9_3_E
5,704	A*02:22	Germany DKMS - Turkey minority		0.0002	4,856	48_31_N_9_3_E
5,705	A*02:22	Germany pop 6		0.0001	8,862	51_6_N_10_23_E
5,706	A*02:22	Germany pop 8		0.0001	39,689	48_24_N_9_58_E
5,707	A*02:22	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,708	A*02:22	India Khandesh Region Pawra		0.0000	50	20_54_N_74_46_E
5,709	A*02:22	India Mumbai Maratha		0.0000	91	18_58_N_72_49_E
5,710	A*02:22	India New Delhi		0.0080	71	28_37_N_77_13_E
5,711	A*02:22	India West Bhil		0.0800	50	20_54_N_74_46_E
5,712	A*02:22	India West Coast Parsi		0.0000	50	18_0_N_74_0_E
5,713	A*02:22	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
5,714	A*02:22	Israel Argentina Jews		0.0000	4,307	32_8_N_34_52_E
5,715	A*02:22	Israel Ashkenazi and Non Ashkenazi Jews		0.0130	146	31_8_N_35_2_E
5,716	A*02:22	Israel Ethiopia Jews		0.0004	5,928	32_8_N_34_52_E
5,717	A*02:22	Israel Libya Jews		0.0000	3,739	32_10_N_34_52_E
5,718	A*02:22	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
5,719	A*02:22	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,720	A*02:22	Italy pop 5		0.0010	975	45_28_N_9_11_E
5,721	A*02:22	Malaysia Peninsular Indian	0.4	0.0018	271	3_9_N_101_57_E
5,722	A*02:22	Malaysia Peninsular Malay	0.3	0.0016	951	3_9_N_101_57_E
5,723	A*02:22	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,724	A*02:22	Mongolia Buryat		0.0070	141	47_6_N_118_6_E
5,725	A*02:22	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,726	A*02:22	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,727	A*02:22	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
5,728	A*02:22	Nicaragua Managua	1.2	0.0060	339	12_7_N_86_10_W
5,729	A*02:22	Oman	0.0	0.0000	118	22_0_N_56_0_E
5,730	A*02:22	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,731	A*02:22	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,732	A*02:22	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,733	A*02:22	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,734	A*02:22	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,735	A*02:22	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
5,736	A*02:22	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,737	A*02:22	Panama		0.0068	462	8_59_N_79_37_W
5,738	A*02:22	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
5,739	A*02:22	Poland DKMS		0.0003	20,653	52_8_N_19_23_E
5,740	A*02:22	Romania	0.0	0.0000	348	46_0_N_25_0_E
5,741	A*02:22	Saudi Arabia Guraiat and Hail	0.5	0.0020	213	27_31_N_41_41_E
5,742	A*02:22	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,743	A*02:22	Singapore Thai		0.0050	100	1_24_N_103_45_E
5,744	A*02:22	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,745	A*02:22	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,746	A*02:22	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.2	0.0008	4,335	41_22_N_2_11_E
5,747	A*02:22	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
5,748	A*02:22	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
5,749	A*02:22	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
5,750	A*02:22	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
5,751	A*02:22	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
5,752	A*02:22	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
5,753	A*02:22	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
5,754	A*02:22	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
5,755	A*02:22	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
5,756	A*02:22	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
5,757	A*02:22	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
5,758	A*02:22	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
5,759	A*02:22	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
5,760	A*02:22	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
5,761	A*02:22	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
5,762	A*02:22	Tanzania Maasai	0.3	0.0015	336	3_11_S_37_4_E
5,763	A*02:22	Turkey pop 5		0.0000	142	40_0_N_28_0_E
5,764	A*02:22	United Arab Emirates Pop 1		0.0030	570	24_42_N_54_82_E
5,765	A*02:22	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
5,766	A*02:22	USA African American pop 2		0.0000	149	32_46_N_96_46_W
5,767	A*02:22	USA African American pop 3		0.0010	564	38_53_N_77_2_W
5,768	A*02:22	USA African American pop 4		0.0002	2,411	44_58_N_93_15_W
5,769	A*02:22	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
5,770	A*02:22	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
5,771	A*02:22	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,772	A*02:22	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
5,773	A*02:22	USA Caucasian pop 3		0.0000	88	32_46_N_96_48_W
5,774	A*02:22	USA Caucasian pop 4		0.0019	1,070	38_53_N_77_2_W
5,775	A*02:22	USA Hispanic pop 2		0.0020	1,999	44_58_N_93_15_W
5,776	A*02:22	USA Mexican American Mestizo		0.0010	553	38_53_N_77_2_W
5,777	A*02:22	USA NMDP African		0.0008	28,557	38_45_N_76_6_W
5,778	A*02:22	USA NMDP African American pop 2		0.0001	416,581	38_45_N_76_6_W
5,779	A*02:22	USA NMDP Black South or Central American		0.0845	4,889	38_45_N_76_6_W
5,780	A*02:22	USA NMDP Caribean Black		0.0002	33,328	38_45_N_76_6_W
5,781	A*02:22	USA NMDP Caribean Hispanic		0.0014	115,374	38_45_N_76_6_W
5,782	A*02:22	USA NMDP Caribean Indian		0.0010	14,339	38_45_N_76_6_W
5,783	A*02:22	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
5,784	A*02:22	USA NMDP European Caucasian		0.0001	1,242,890	38_45_N_76_6_W
5,785	A*02:22	USA NMDP Hawaiian or other Pacific Islander		0.0001	11,499	38_45_N_76_6_W
5,786	A*02:22	USA NMDP Hispanic South or Central American		0.0056	146,714	38_45_N_76_6_W
5,787	A*02:22	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
5,788	A*02:22	USA NMDP Mexican or Chicano		0.0002	261,235	38_45_N_76_6_W
5,789	A*02:22	USA NMDP Middle Eastern or North Coast of Africa		0.0002	70,890	38_45_N_76_6_W
5,790	A*02:22	USA NMDP North American Amerindian		0.0001	35,791	38_45_N_76_6_W
5,791	A*02:22	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
5,792	A*02:22	USA NMDP Southeast Asian		0.0000	27,978	38_45_N_76_6_W
5,793	A*02:22	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
5,794	A*02:22	USA San Antonio Caucasian		0.0030	222	29_23_N_98_33_W
5,795	A*02:22	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,796	A*02:22	USA Spain Ancestry		0.0020	279	38_0_N_77_0_W
5,797	A*02:22	Venezuela Perja Mountain Bari		0.0910	55	9_49_N_72_49_W
5,798	A*02:22:01	Brazil Barra Mansa Rio State Caucasian		0.0012	405	22_32_S_44_10_W
5,799	A*02:22:01	Costa Rica Amerindians (G)	5.6	0.0280	125	9_34_N_83_25_W
5,800	A*02:22:01	Costa Rica Central Valley Mestizo (G)	1.4	0.0070	221	9_55_N_84_5_W
5,801	A*02:22:01	Costa Rica Guanacaste Mestizo (G)	3.6	0.0180	110	10_16_N_85_37_W
5,802	A*02:22:01	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,803	A*02:22:01	Nicaragua Mestizo (G)	1.4	0.0070	155	12_40_N_85_13_W
5,804	A*02:22:01	Poland BMR	0.0	0.0002	23,595	52_0_N_19_22_E
5,805	A*02:22:01	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
5,806	A*02:22:01	Russia Moscow Russians	0.1	0.0005	13,147	55_46_N_37_41_E
5,807	A*02:22:01	Russia Moscow Tatars	0.2	0.0008	2,512	55_46_N_37_41_E
5,808	A*02:22:01	Saudi Arabia Al-Baha (G)		0.0116	86	20_0_N_41_28_E
5,809	A*02:22:01	Saudi Arabia Jeddah (G)		0.0021	476	21_32_N_39_10_E
5,810	A*02:22:01	Saudi Arabia Makkah (G)		0.0058	344	21_25_N_39_49_E
5,811	A*02:22:01	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
5,812	A*02:22:01	Saudi Arabia pop 6 (G)		0.0022	28,927	23_52_N_45_4_E
5,813	A*02:22:01	Saudi Arabia Taif (G)		0.0096	104	21_16_N_40_24_E
5,814	A*02:22:01	USA Arizona Gila River Pima		0.0009	3,000	33_4_N_111_43_W
5,815	A*02:22:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,816	A*02:24	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,817	A*02:24	Brazil Mixed		0.0400	108	23_10_S_47_48_W
5,818	A*02:24	Bulgaria		0.0000	55	42_0_N_23_0_E
5,819	A*02:24	China North Han		0.0000	105	39_54_N_116_24_E
5,820	A*02:24	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
5,821	A*02:24	Croatia		0.0030	150	45_0_N_15_0_E
5,822	A*02:24	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
5,823	A*02:24	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,824	A*02:24	England North West	0.3	0.0020	298	53_0_N_3_0_W
5,825	A*02:24	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
5,826	A*02:24	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
5,827	A*02:24	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,828	A*02:24	India Khandesh Region Pawra		0.0000	50	20_54_N_74_46_E
5,829	A*02:24	India Mumbai Maratha		0.0000	91	18_58_N_72_49_E
5,830	A*02:24	India West Bhil		0.0500	50	20_54_N_74_46_E
5,831	A*02:24	India West Coast Parsi		0.0000	50	18_0_N_74_0_E
5,832	A*02:24	Iran Baloch		0.0060	100	29_29_N_60_52_E
5,833	A*02:24	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
5,834	A*02:24	Israel Ashkenazi and Non Ashkenazi Jews		0.0130	146	31_8_N_35_2_E
5,835	A*02:24	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
5,836	A*02:24	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,837	A*02:24	Kenya		0.0030	144	1_28_S_36_8_E
5,838	A*02:24	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,839	A*02:24	Mexico Mexico City Mestizo pop 2		0.0021	234	19_26_N_99_8_W
5,840	A*02:24	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,841	A*02:24	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,842	A*02:24	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
5,843	A*02:24	Oman	0.0	0.0000	118	22_0_N_56_0_E
5,844	A*02:24	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,845	A*02:24	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,846	A*02:24	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,847	A*02:24	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,848	A*02:24	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,849	A*02:24	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
5,850	A*02:24	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,851	A*02:24	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
5,852	A*02:24	Romania	0.0	0.0000	348	46_0_N_25_0_E
5,853	A*02:24	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,854	A*02:24	Singapore Riau Malay		0.0040	132	1_24_N_103_45_E
5,855	A*02:24	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,856	A*02:24	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,857	A*02:24	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.0	0.0001	4,335	41_22_N_2_11_E
5,858	A*02:24	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
5,859	A*02:24	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
5,860	A*02:24	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
5,861	A*02:24	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
5,862	A*02:24	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
5,863	A*02:24	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
5,864	A*02:24	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
5,865	A*02:24	Taiwan pop 3		0.0020	212	23_46_N_121_0_E
5,866	A*02:24	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
5,867	A*02:24	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
5,868	A*02:24	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
5,869	A*02:24	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
5,870	A*02:24	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
5,871	A*02:24	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
5,872	A*02:24	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
5,873	A*02:24	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
5,874	A*02:24	Turkey pop 5		0.0000	142	40_0_N_28_0_E
5,875	A*02:24	United Arab Emirates Pop 1		0.0010	570	24_42_N_54_82_E
5,876	A*02:24	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
5,877	A*02:24	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
5,878	A*02:24	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
5,879	A*02:24	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
5,880	A*02:24	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,881	A*02:24	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
5,882	A*02:24	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
5,883	A*02:24	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
5,884	A*02:24	USA NMDP European Caucasian		0.0001	1,242,890	38_45_N_76_6_W
5,885	A*02:24	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
5,886	A*02:24	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
5,887	A*02:24	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
5,888	A*02:24	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
5,889	A*02:24	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
5,890	A*02:24	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
5,891	A*02:24	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
5,892	A*02:24	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,893	A*02:25	Azores Terceira Island		0.0040	130	38_44_N_27_19_W
5,894	A*02:25	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,895	A*02:25	Bulgaria		0.0000	55	42_0_N_23_0_E
5,896	A*02:25	China North Han		0.0000	105	39_54_N_116_24_E
5,897	A*02:25	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
5,898	A*02:25	Croatia		0.0000	150	45_0_N_15_0_E
5,899	A*02:25	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
5,900	A*02:25	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,901	A*02:25	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
5,902	A*02:25	Ghana Ga-Adangbe	0.8	0.0038	131	5_45_N_0_12_E
5,903	A*02:25	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,904	A*02:25	Iran Baloch		0.0060	100	29_29_N_60_52_E
5,905	A*02:25	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
5,906	A*02:25	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
5,907	A*02:25	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,908	A*02:25	Kenya Luo		0.0040	265	0_0_S_34_6_E
5,909	A*02:25	Kenya Nandi		0.0000	240	0_0_S_35_0_E
5,910	A*02:25	Malaysia Peninsular Malay	0.3	0.0016	951	3_9_N_101_57_E
5,911	A*02:25	Mali Bandiagara		0.0000	138	14_4_N_3_6_W
5,912	A*02:25	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,913	A*02:25	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,914	A*02:25	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,915	A*02:25	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
5,916	A*02:25	Oman	0.0	0.0000	118	22_0_N_56_0_E
5,917	A*02:25	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,918	A*02:25	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,919	A*02:25	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,920	A*02:25	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,921	A*02:25	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,922	A*02:25	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
5,923	A*02:25	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,924	A*02:25	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
5,925	A*02:25	Portugal Azores Terceira Island	0.9	0.0044	130	38_43_N_27_13_W
5,926	A*02:25	Romania	0.0	0.0000	348	46_0_N_25_0_E
5,927	A*02:25	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,928	A*02:25	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,929	A*02:25	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,930	A*02:25	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
5,931	A*02:25	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
5,932	A*02:25	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
5,933	A*02:25	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
5,934	A*02:25	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
5,935	A*02:25	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
5,936	A*02:25	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
5,937	A*02:25	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
5,938	A*02:25	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
5,939	A*02:25	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
5,940	A*02:25	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
5,941	A*02:25	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
5,942	A*02:25	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
5,943	A*02:25	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
5,944	A*02:25	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
5,945	A*02:25	Turkey pop 5		0.0000	142	40_0_N_28_0_E
5,946	A*02:25	Uganda Kampala		0.0000	161	0_3_N_32_6_E
5,947	A*02:25	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
5,948	A*02:25	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
5,949	A*02:25	USA Caucasian	0.0		61,655	38_53_N_77_2_W
5,950	A*02:25	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
5,951	A*02:25	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
5,952	A*02:25	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
5,953	A*02:25	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
5,954	A*02:25	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
5,955	A*02:25	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
5,956	A*02:25	Zambia Lusaka		0.0000	44	15_25_S_28_17_E
5,957	A*02:26	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
5,958	A*02:26	Bulgaria		0.0000	55	42_0_N_23_0_E
5,959	A*02:26	China Beijing Shijiazhuang Tianjian Han		0.0020	618	39_54_N_116_24_E
5,960	A*02:26	China North Han		0.0000	105	39_54_N_116_24_E
5,961	A*02:26	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
5,962	A*02:26	Croatia		0.0030	150	45_0_N_15_0_E
5,963	A*02:26	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
5,964	A*02:26	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
5,965	A*02:26	Germany DKMS - Austria minority		0.0006	1,698	48_31_N_9_3_E
5,966	A*02:26	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
5,967	A*02:26	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
5,968	A*02:26	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
5,969	A*02:26	Israel Ashkenazi and Non Ashkenazi Jews		0.0040	146	31_8_N_35_2_E
5,970	A*02:26	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
5,971	A*02:26	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
5,972	A*02:26	Kenya		0.0030	144	1_28_S_36_8_E
5,973	A*02:26	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
5,974	A*02:26	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
5,975	A*02:26	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
5,976	A*02:26	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
5,977	A*02:26	Oman	0.0	0.0000	118	22_0_N_56_0_E
5,978	A*02:26	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
5,979	A*02:26	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
5,980	A*02:26	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
5,981	A*02:26	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
5,982	A*02:26	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
5,983	A*02:26	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
5,984	A*02:26	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
5,985	A*02:26	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
5,986	A*02:26	Romania	0.0	0.0000	348	46_0_N_25_0_E
5,987	A*02:26	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
5,988	A*02:26	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
5,989	A*02:26	South Korea pop 3		0.0000	485	37_33_N_126_59_E
5,990	A*02:26	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
5,991	A*02:26	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
5,992	A*02:26	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
5,993	A*02:26	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
5,994	A*02:26	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
5,995	A*02:26	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
5,996	A*02:26	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
5,997	A*02:26	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
5,998	A*02:26	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
5,999	A*02:26	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,000	A*02:26	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,001	A*02:26	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,002	A*02:26	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,003	A*02:26	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,004	A*02:26	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,005	A*02:26	Turkey pop 5		0.0000	142	40_0_N_28_0_E
6,006	A*02:26	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,007	A*02:26	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
6,008	A*02:26	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,009	A*02:26	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,010	A*02:26	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,011	A*02:26	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,012	A*02:26	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,013	A*02:26	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,014	A*02:27	Bulgaria		0.0000	55	42_0_N_23_0_E
6,015	A*02:27	China Guangdong Province Meizhou Han		0.0050	100	24_27_N_116_7_E
6,016	A*02:27	China North Han		0.0000	105	39_54_N_116_24_E
6,017	A*02:27	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,018	A*02:27	Croatia		0.0000	150	45_0_N_15_0_E
6,019	A*02:27	Germany DKMS - Austria minority		0.0003	1,698	48_31_N_9_3_E
6,020	A*02:27	Germany DKMS - German donors		0.0001	3,456,066	48_31_N_9_3_E
6,021	A*02:27	Germany DKMS - Greece minority		0.0003	1,894	48_31_N_9_3_E
6,022	A*02:27	Germany DKMS - Romania minority		0.0004	1,234	48_31_N_9_3_E
6,023	A*02:27	Germany pop 6		0.0002	8,862	51_6_N_10_23_E
6,024	A*02:27	Germany pop 8		0.0001	39,689	48_24_N_9_58_E
6,025	A*02:27	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
6,026	A*02:27	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
6,027	A*02:27	Malaysia Peninsular Chinese	0.5	0.0026	194	3_9_N_101_57_E
6,028	A*02:27	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,029	A*02:27	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,030	A*02:27	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,031	A*02:27	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,032	A*02:27	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
6,033	A*02:27	Poland DKMS		0.0000	20,653	52_8_N_19_23_E
6,034	A*02:27	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
6,035	A*02:27	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,036	A*02:27	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,037	A*02:27	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,038	A*02:27	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,039	A*02:27	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,040	A*02:27	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,041	A*02:27	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,042	A*02:27	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,043	A*02:27	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,044	A*02:27	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,045	A*02:27	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,046	A*02:27	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,047	A*02:27	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,048	A*02:27	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,049	A*02:27	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,050	A*02:27	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,051	A*02:27	Turkey pop 5		0.0000	142	40_0_N_28_0_E
6,052	A*02:27	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,053	A*02:27	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
6,054	A*02:27	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,055	A*02:27	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,056	A*02:27	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,057	A*02:27	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
6,058	A*02:27	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
6,059	A*02:27	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,060	A*02:27	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,061	A*02:27	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,062	A*02:28	Bulgaria		0.0000	55	42_0_N_23_0_E
6,063	A*02:28	China North Han		0.0000	105	39_54_N_116_24_E
6,064	A*02:28	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,065	A*02:28	China Zhejiang Han		0.0003	1,734	27_2_N_118_1_E
6,066	A*02:28	Croatia		0.0000	150	45_0_N_15_0_E
6,067	A*02:28	Germany DKMS - China minority		0.0004	1,282	48_31_N_9_3_E
6,068	A*02:28	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
6,069	A*02:28	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
6,070	A*02:28	Japan Okinawa Ryukyuan		0.0040	143	26_4_N_127_8_E
6,071	A*02:28	Japan pop 16		0.0001	18,604	35_41_N_139_46_E
6,072	A*02:28	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,073	A*02:28	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,074	A*02:28	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,075	A*02:28	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,076	A*02:28	Turkey pop 5		0.0000	142	40_0_N_28_0_E
6,077	A*02:28	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,078	A*02:28	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
6,079	A*02:28	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,080	A*02:28	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,081	A*02:28	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,082	A*02:28	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,083	A*02:28	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,084	A*02:28	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,085	A*02:29	Bulgaria		0.0000	55	42_0_N_23_0_E
6,086	A*02:29	China North Han		0.0000	105	39_54_N_116_24_E
6,087	A*02:29	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,088	A*02:29	Croatia		0.0000	150	45_0_N_15_0_E
6,089	A*02:29	Germany DKMS - German donors		0.0001	3,456,066	48_31_N_9_3_E
6,090	A*02:29	Germany pop 6		0.0001	8,862	51_6_N_10_23_E
6,091	A*02:29	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
6,092	A*02:29	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
6,093	A*02:29	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,094	A*02:29	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,095	A*02:29	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,096	A*02:29	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,097	A*02:29	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,098	A*02:29	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,099	A*02:29	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,100	A*02:29	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,101	A*02:29	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,102	A*02:29	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,103	A*02:29	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,104	A*02:29	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,105	A*02:29	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,106	A*02:29	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,107	A*02:29	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,108	A*02:29	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,109	A*02:29	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,110	A*02:29	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,111	A*02:29	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,112	A*02:29	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,113	A*02:29	Turkey pop 5		0.0000	142	40_0_N_28_0_E
6,114	A*02:29	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,115	A*02:29	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
6,116	A*02:29	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,117	A*02:29	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,118	A*02:29	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,119	A*02:29	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,120	A*02:29	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,121	A*02:29	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,122	A*02:29	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,123	A*02:30	Bulgaria		0.0000	55	42_0_N_23_0_E
6,124	A*02:30	China Guangdong Province Meizhou Han		0.0050	100	24_27_N_116_7_E
6,125	A*02:30	China North Han		0.0000	105	39_54_N_116_24_E
6,126	A*02:30	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,127	A*02:30	Croatia		0.0000	150	45_0_N_15_0_E
6,128	A*02:30	Czech Republic NMDR		0.0010	5,099	49_48_N_15_30_E
6,129	A*02:30	Germany DKMS - Bosnia and Herzegovina minority		0.0015	1,028	48_31_N_9_3_E
6,130	A*02:30	Germany DKMS - German donors		0.0006	3,456,066	48_31_N_9_3_E
6,131	A*02:30	Germany DKMS - Romania minority		0.0008	1,234	48_31_N_9_3_E
6,132	A*02:30	Germany DKMS - United Kingdom minority		0.0005	1,043	48_31_N_9_3_E
6,133	A*02:30	Germany pop 6		0.0002	8,862	51_6_N_10_23_E
6,134	A*02:30	Germany pop 8		0.0005	39,689	48_24_N_9_58_E
6,135	A*02:30	Israel Ashkenazi Jews pop 3		0.0008	4,625	32_8_N_34_52_E
6,136	A*02:30	Israel Morocco Jews		0.0000	36,718	32_10_N_34_52_E
6,137	A*02:30	Israel Poland Jews		0.0001	13,871	32_10_N_34_52_E
6,138	A*02:30	Israel USSR Jews		0.0001	45,681	32_9_N_34_50_E
6,139	A*02:30	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
6,140	A*02:30	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
6,141	A*02:30	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,142	A*02:30	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,143	A*02:30	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,144	A*02:30	Poland DKMS		0.0004	20,653	52_8_N_19_23_E
6,145	A*02:30	Russia Karelia		0.0005	1,075	63_9_N_32_59_E
6,146	A*02:30	Russia Nizhny Novgorod, Russians	0.1	0.0003	1,510	56_19_N_43_59_E
6,147	A*02:30	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,148	A*02:30	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,149	A*02:30	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
6,150	A*02:30	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
6,151	A*02:30	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
6,152	A*02:30	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,153	A*02:30	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,154	A*02:30	USA Hispanic pop 2		0.0003	1,999	44_58_N_93_15_W
6,155	A*02:30	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
6,156	A*02:30	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
6,157	A*02:30	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,158	A*02:30	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
6,159	A*02:30	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,160	A*02:30	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
6,161	A*02:30	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,162	A*02:30	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,163	A*02:30	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,164	A*02:30:01	Poland BMR	0.1	0.0004	23,595	52_0_N_19_22_E
6,165	A*02:30:01	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
6,166	A*02:31	Bulgaria		0.0000	55	42_0_N_23_0_E
6,167	A*02:31	China North Han		0.0000	105	39_54_N_116_24_E
6,168	A*02:31	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,169	A*02:31	Croatia		0.0000	150	45_0_N_15_0_E
6,170	A*02:31	Israel Argentina Jews		0.0000	4,307	32_8_N_34_52_E
6,171	A*02:31	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,172	A*02:31	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,173	A*02:31	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,174	A*02:31	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
6,175	A*02:31	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
6,176	A*02:31	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,177	A*02:31	United Arab Emirates Pop 1		0.0010	570	24_42_N_54_82_E
6,178	A*02:31	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,179	A*02:31	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,180	A*02:31	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,181	A*02:31	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
6,182	A*02:31	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,183	A*02:31	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,184	A*02:31	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,185	A*02:31	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,186	A*02:32N	Bulgaria		0.0000	55	42_0_N_23_0_E
6,187	A*02:32N	China North Han		0.0000	105	39_54_N_116_24_E
6,188	A*02:32N	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,189	A*02:32N	Croatia		0.0000	150	45_0_N_15_0_E
6,190	A*02:32N	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,191	A*02:32N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,192	A*02:32N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,193	A*02:32N	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,194	A*02:32N	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,195	A*02:32N	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,196	A*02:32N	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,197	A*02:32N	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,198	A*02:32N	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,199	A*02:32N	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,200	A*02:32N	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,201	A*02:32N	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,202	A*02:32N	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,203	A*02:32N	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,204	A*02:32N	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,205	A*02:32N	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,206	A*02:32N	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,207	A*02:32N	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,208	A*02:32N	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,209	A*02:32N	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,210	A*02:32N	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,211	A*02:32N	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,212	A*02:32N	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,213	A*02:32N	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,214	A*02:32N	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,215	A*02:33	Bulgaria		0.0000	55	42_0_N_23_0_E
6,216	A*02:33	China North Han		0.0000	105	39_54_N_116_24_E
6,217	A*02:33	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,218	A*02:33	Costa Rica Guanacaste Mestizo (G)	1.0	0.0050	110	10_16_N_85_37_W
6,219	A*02:33	Croatia		0.0000	150	45_0_N_15_0_E
6,220	A*02:33	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,221	A*02:33	Mongolia Buryat		0.0040	141	47_6_N_118_6_E
6,222	A*02:33	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,223	A*02:33	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,224	A*02:33	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,225	A*02:33	Nicaragua Managua	1.2	0.0060	339	12_7_N_86_10_W
6,226	A*02:33	Nicaragua Mestizo (G)	1.4	0.0070	155	12_40_N_85_13_W
6,227	A*02:33	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,228	A*02:33	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,229	A*02:33	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,230	A*02:33	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,231	A*02:33	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,232	A*02:33	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,233	A*02:33	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,234	A*02:33	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,235	A*02:33	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,236	A*02:33	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,237	A*02:33	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,238	A*02:33	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,239	A*02:33	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,240	A*02:33	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,241	A*02:33	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,242	A*02:33	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,243	A*02:33	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,244	A*02:33	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,245	A*02:33	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,246	A*02:33	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,247	A*02:33	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
6,248	A*02:33	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,249	A*02:33	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
6,250	A*02:33	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,251	A*02:33	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,252	A*02:33	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,253	A*02:33	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,254	A*02:34	Bulgaria		0.0000	55	42_0_N_23_0_E
6,255	A*02:34	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
6,256	A*02:34	China North Han		0.0000	105	39_54_N_116_24_E
6,257	A*02:34	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,258	A*02:34	Croatia		0.0000	150	45_0_N_15_0_E
6,259	A*02:34	Croatia pop 4		0.0002	4,000	45_48_N_15_57_E
6,260	A*02:34	Georgia Tibilisi		0.0050	109	41_41_N_44_57_E
6,261	A*02:34	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,262	A*02:34	Iran Gorgan		0.0390	64	36_50_N_54_43_E
6,263	A*02:34	Kenya		0.0070	144	1_28_S_36_8_E
6,264	A*02:34	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,265	A*02:34	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,266	A*02:34	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,267	A*02:34	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,268	A*02:34	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,269	A*02:34	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,270	A*02:34	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,271	A*02:34	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,272	A*02:34	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,273	A*02:34	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,274	A*02:34	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,275	A*02:34	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,276	A*02:34	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,277	A*02:34	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,278	A*02:34	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,279	A*02:34	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,280	A*02:34	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,281	A*02:34	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,282	A*02:34	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,283	A*02:34	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,284	A*02:34	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,285	A*02:34	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,286	A*02:34	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,287	A*02:34	USA Caucasian pop 4		0.0000	1,070	38_53_N_77_2_W
6,288	A*02:34	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,289	A*02:34	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,290	A*02:34	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,291	A*02:34	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
6,292	A*02:34	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,293	A*02:34	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,294	A*02:34	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,295	A*02:35	Bulgaria		0.0000	55	42_0_N_23_0_E
6,296	A*02:35	China North Han		0.0000	105	39_54_N_116_24_E
6,297	A*02:35	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,298	A*02:35	Croatia		0.0000	150	45_0_N_15_0_E
6,299	A*02:35	Croatia pop 4		0.0002	4,000	45_48_N_15_57_E
6,300	A*02:35	Czech Republic NMDR		0.0001	5,099	49_48_N_15_30_E
6,301	A*02:35	Germany DKMS - France minority		0.0005	1,406	48_31_N_9_3_E
6,302	A*02:35	Germany DKMS - German donors		0.0002	3,456,066	48_31_N_9_3_E
6,303	A*02:35	Germany DKMS - Netherlands minority		0.0004	1,374	48_31_N_9_3_E
6,304	A*02:35	Germany pop 6		0.0002	8,862	51_6_N_10_23_E
6,305	A*02:35	Germany pop 8		0.0002	39,689	48_24_N_9_58_E
6,306	A*02:35	India South UCBB	0.0	0.0000	11,446	13_6_N_80_8_E
6,307	A*02:35	India Tamil Nadu		0.0002	2,492	11_32_N_78_52_E
6,308	A*02:35	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
6,309	A*02:35	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,310	A*02:35	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,311	A*02:35	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,312	A*02:35	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,313	A*02:35	Poland DKMS		0.0003	20,653	52_8_N_19_23_E
6,314	A*02:35	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,315	A*02:35	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,316	A*02:35	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,317	A*02:35	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,318	A*02:35	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,319	A*02:35	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,320	A*02:35	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,321	A*02:35	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,322	A*02:35	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,323	A*02:35	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,324	A*02:35	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,325	A*02:35	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,326	A*02:35	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,327	A*02:35	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,328	A*02:35	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,329	A*02:35	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,330	A*02:35	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,331	A*02:35	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,332	A*02:35	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,333	A*02:35	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
6,334	A*02:35	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
6,335	A*02:35	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,336	A*02:35	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,337	A*02:35	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
6,338	A*02:35	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,339	A*02:35	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
6,340	A*02:35	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
6,341	A*02:35	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
6,342	A*02:35	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
6,343	A*02:35	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,344	A*02:35	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,345	A*02:35	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,346	A*02:35:01	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,347	A*02:35:01	Poland BMR	0.1	0.0007	23,595	52_0_N_19_22_E
6,348	A*02:35:01	Russia Moscow Russians	0.1	0.0005	13,147	55_46_N_37_41_E
6,349	A*02:35:01	Saudi Arabia pop 5	1.3	0.0063	158	24_38_N_46_43_E
6,350	A*02:35:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,351	A*02:35:03	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,352	A*02:36	Bulgaria		0.0000	55	42_0_N_23_0_E
6,353	A*02:36	China Inner Mongolia Region		0.0050	102	36_0_N_102_0_E
6,354	A*02:36	China North Han		0.0000	105	39_54_N_116_24_E
6,355	A*02:36	China Qinghai Province Hui		0.0000	110	36_0_N_102_0_E
6,356	A*02:36	China Tibet Region Tibetan		0.0030	158	29_39_N_91_7_E
6,357	A*02:36	Croatia		0.0000	150	45_0_N_15_0_E
6,358	A*02:36	Germany DKMS - France minority		0.0004	1,406	48_31_N_9_3_E
6,359	A*02:36	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,360	A*02:36	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
6,361	A*02:36	India Khandesh Region Pawra		0.0000	50	20_54_N_74_46_E
6,362	A*02:36	India Mumbai Maratha		0.0120	91	18_58_N_72_49_E
6,363	A*02:36	India West Bhil		0.0000	50	20_54_N_74_46_E
6,364	A*02:36	India West Coast Parsi		0.0000	50	18_0_N_74_0_E
6,365	A*02:36	Iran Baloch		0.0060	100	29_29_N_60_52_E
6,366	A*02:36	Israel Argentina Jews		0.0005	4,307	32_8_N_34_52_E
6,367	A*02:36	Israel Ashkenazi and Non Ashkenazi Jews		0.0040	146	31_8_N_35_2_E
6,368	A*02:36	Israel Bukhara Jews		0.0027	2,317	32_8_N_34_52_E
6,369	A*02:36	Israel Druze		0.0003	5,914	32_41_N_35_17_E
6,370	A*02:36	Israel Ethiopia Jews		0.0013	5,928	32_8_N_34_52_E
6,371	A*02:36	Israel Kavkazi Jews		0.0018	2,840	32_10_N_34_52_E
6,372	A*02:36	Israel USA Jews		0.0005	6,058	32_10_N_34_52_E
6,373	A*02:36	Kenya		0.0030	144	1_28_S_36_8_E
6,374	A*02:36	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,375	A*02:36	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,376	A*02:36	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,377	A*02:36	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
6,378	A*02:36	Pakistan Brahui		0.0000	104	30_0_N_67_0_E
6,379	A*02:36	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
6,380	A*02:36	Pakistan Kalash		0.0000	69	32_0_N_70_0_E
6,381	A*02:36	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
6,382	A*02:36	Pakistan Mixed Pathan		0.0000	100	32_0_N_70_0_E
6,383	A*02:36	Pakistan Mixed Sindhi		0.0000	101	24_0_N_68_0_E
6,384	A*02:36	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,385	A*02:36	Russia Tuva pop 2		0.0050	169	51_30_N_95_5_E
6,386	A*02:36	Saudi Arabia pop 5	1.3	0.0063	158	24_38_N_46_43_E
6,387	A*02:36	Singapore Riau Malay		0.0040	132	1_24_N_103_45_E
6,388	A*02:36	South Korea pop 10		0.0001	4,128	37_33_N_126_58_E
6,389	A*02:36	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,390	A*02:36	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,391	A*02:36	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,392	A*02:36	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,393	A*02:36	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,394	A*02:36	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,395	A*02:36	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,396	A*02:36	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,397	A*02:36	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,398	A*02:36	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,399	A*02:36	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,400	A*02:36	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,401	A*02:36	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,402	A*02:36	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,403	A*02:36	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,404	A*02:36	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,405	A*02:36	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,406	A*02:36	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,407	A*02:36	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,408	A*02:36	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
6,409	A*02:36	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,410	A*02:36	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,411	A*02:36	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
6,412	A*02:36	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
6,413	A*02:36	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,414	A*02:36	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,415	A*02:36	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,416	A*02:37	Bulgaria		0.0000	55	42_0_N_23_0_E
6,417	A*02:37	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
6,418	A*02:37	China Guizhou Province Bouyei		0.0000	109	26_50_N_106_50_E
6,419	A*02:37	China Guizhou Province Miao pop 2		0.0000	85	26_50_N_106_50_E
6,420	A*02:37	China Guizhou Province Shui		0.0040	153	26_50_N_106_50_E
6,421	A*02:37	China Inner Mongolia Region		0.0000	102	36_0_N_102_0_E
6,422	A*02:37	China North Han		0.0000	105	39_54_N_116_24_E
6,423	A*02:37	China Qinghai Province Hui		0.0050	110	36_0_N_102_0_E
6,424	A*02:37	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,425	A*02:37	Croatia		0.0000	150	45_0_N_15_0_E
6,426	A*02:37	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,427	A*02:37	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,428	A*02:37	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,429	A*02:37	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,430	A*02:37	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,431	A*02:37	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
6,432	A*02:37	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,433	A*02:37	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,434	A*02:37	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,435	A*02:37	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,436	A*02:37	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,437	A*02:37	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,438	A*02:37	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,439	A*02:37	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,440	A*02:37	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,441	A*02:37	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,442	A*02:37	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,443	A*02:37	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,444	A*02:37	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,445	A*02:37	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,446	A*02:37	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,447	A*02:37	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,448	A*02:37	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,449	A*02:37	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,450	A*02:37	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,451	A*02:37	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,452	A*02:37	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
6,453	A*02:37	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,454	A*02:37	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,455	A*02:37	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,456	A*02:38	Bulgaria		0.0000	55	42_0_N_23_0_E
6,457	A*02:38	China Guangdong Province Meizhou Han		0.0050	100	24_27_N_116_7_E
6,458	A*02:38	China Guizhou Province Bouyei		0.0000	109	26_50_N_106_50_E
6,459	A*02:38	China Guizhou Province Miao pop 2		0.0060	85	26_50_N_106_50_E
6,460	A*02:38	China Guizhou Province Shui		0.0000	153	26_50_N_106_50_E
6,461	A*02:38	China North Han		0.0000	105	39_54_N_116_24_E
6,462	A*02:38	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,463	A*02:38	Croatia		0.0000	150	45_0_N_15_0_E
6,464	A*02:38	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,465	A*02:38	Germany DKMS - United Kingdom minority		0.0005	1,043	48_31_N_9_3_E
6,466	A*02:38	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
6,467	A*02:38	Malaysia Peninsular Malay	0.1	0.0005	951	3_9_N_101_57_E
6,468	A*02:38	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,469	A*02:38	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,470	A*02:38	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,471	A*02:38	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,472	A*02:38	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,473	A*02:38	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,474	A*02:38	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,475	A*02:38	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,476	A*02:38	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,477	A*02:38	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,478	A*02:38	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,479	A*02:38	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,480	A*02:38	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,481	A*02:38	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,482	A*02:38	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,483	A*02:38	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,484	A*02:38	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,485	A*02:38	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,486	A*02:38	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,487	A*02:38	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,488	A*02:38	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,489	A*02:38	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,490	A*02:38	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,491	A*02:38	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
6,492	A*02:38	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,493	A*02:38	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,494	A*02:38	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,495	A*02:38	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
6,496	A*02:38	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,497	A*02:38	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,498	A*02:38	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,499	A*02:39	Bulgaria		0.0000	55	42_0_N_23_0_E
6,500	A*02:39	China North Han		0.0000	105	39_54_N_116_24_E
6,501	A*02:39	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,502	A*02:39	Croatia		0.0000	150	45_0_N_15_0_E
6,503	A*02:39	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,504	A*02:39	Malaysia Peninsular Malay	0.1	0.0005	951	3_9_N_101_57_E
6,505	A*02:39	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,506	A*02:39	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,507	A*02:39	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,508	A*02:39	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,509	A*02:39	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,510	A*02:39	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,511	A*02:39	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,512	A*02:39	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,513	A*02:39	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,514	A*02:39	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,515	A*02:39	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,516	A*02:39	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,517	A*02:39	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,518	A*02:39	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,519	A*02:39	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,520	A*02:39	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,521	A*02:39	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,522	A*02:39	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,523	A*02:39	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,524	A*02:39	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,525	A*02:39	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,526	A*02:39	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,527	A*02:39	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,528	A*02:39	USA NMDP Hispanic South or Central American		0.0001	146,714	38_45_N_76_6_W
6,529	A*02:39	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,530	A*02:39	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,531	A*02:39	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,532	A*02:40	Bulgaria		0.0000	55	42_0_N_23_0_E
6,533	A*02:40	China North Han		0.0000	105	39_54_N_116_24_E
6,534	A*02:40	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,535	A*02:40	Croatia		0.0000	150	45_0_N_15_0_E
6,536	A*02:40	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,537	A*02:40	Italy pop 5		0.0010	975	45_28_N_9_11_E
6,538	A*02:40	Mexico Chihuahua Tarahumara		0.0110	44	27_30_N_107_45_W
6,539	A*02:40	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,540	A*02:40	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,541	A*02:40	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,542	A*02:40	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,543	A*02:40	Russia Tuva pop 2		0.0030	169	51_30_N_95_5_E
6,544	A*02:40	Saudi Arabia pop 5	1.3	0.0063	158	24_38_N_46_43_E
6,545	A*02:40	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,546	A*02:40	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,547	A*02:40	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,548	A*02:40	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,549	A*02:40	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,550	A*02:40	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,551	A*02:40	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,552	A*02:40	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,553	A*02:40	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,554	A*02:40	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,555	A*02:40	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,556	A*02:40	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,557	A*02:40	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,558	A*02:40	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,559	A*02:40	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,560	A*02:40	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,561	A*02:40	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,562	A*02:40	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,563	A*02:40	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,564	A*02:40	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
6,565	A*02:40	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,566	A*02:40	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
6,567	A*02:40	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,568	A*02:40	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
6,569	A*02:40	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,570	A*02:40	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,571	A*02:40	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,572	A*02:40:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
6,573	A*02:41	Bulgaria		0.0000	55	42_0_N_23_0_E
6,574	A*02:41	China North Han		0.0000	105	39_54_N_116_24_E
6,575	A*02:41	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,576	A*02:41	Croatia		0.0000	150	45_0_N_15_0_E
6,577	A*02:41	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,578	A*02:41	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,579	A*02:41	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,580	A*02:41	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
6,581	A*02:41	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,582	A*02:41	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
6,583	A*02:41	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
6,584	A*02:41	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
6,585	A*02:41	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
6,586	A*02:41	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
6,587	A*02:41	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
6,588	A*02:41	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
6,589	A*02:41	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
6,590	A*02:41	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
6,591	A*02:41	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
6,592	A*02:41	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
6,593	A*02:41	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
6,594	A*02:41	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
6,595	A*02:41	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
6,596	A*02:41	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
6,597	A*02:41	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,598	A*02:41	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,599	A*02:41	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,600	A*02:41	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,601	A*02:41	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,602	A*02:41	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,603	A*02:42	Bulgaria		0.0000	55	42_0_N_23_0_E
6,604	A*02:42	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
6,605	A*02:42	China North Han		0.0000	105	39_54_N_116_24_E
6,606	A*02:42	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,607	A*02:42	Croatia		0.0000	150	45_0_N_15_0_E
6,608	A*02:42	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,609	A*02:42	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,610	A*02:42	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,611	A*02:42	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,612	A*02:42	Saudi Arabia pop 6 (G)		0.0000	28,927	23_52_N_45_4_E
6,613	A*02:42	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,614	A*02:42	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,615	A*02:42	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
6,616	A*02:42	USA Asian pop 2		0.0003	1,772	44_58_N_93_15_W
6,617	A*02:42	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,618	A*02:42	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,619	A*02:42	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
6,620	A*02:42	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,621	A*02:42	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
6,622	A*02:42	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,623	A*02:42	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,624	A*02:42	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,625	A*02:43N	Bulgaria		0.0000	55	42_0_N_23_0_E
6,626	A*02:43N	China North Han		0.0000	105	39_54_N_116_24_E
6,627	A*02:43N	Croatia		0.0000	150	45_0_N_15_0_E
6,628	A*02:43N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,629	A*02:43N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,630	A*02:43N	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,631	A*02:43N	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,632	A*02:43N	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,633	A*02:43N	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,634	A*02:43N	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,635	A*02:43N	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,636	A*02:44	Bulgaria		0.0000	55	42_0_N_23_0_E
6,637	A*02:44	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
6,638	A*02:44	China Inner Mongolia Region		0.0050	102	36_0_N_102_0_E
6,639	A*02:44	China North Han		0.0000	105	39_54_N_116_24_E
6,640	A*02:44	China Qinghai Province Hui		0.0000	110	36_0_N_102_0_E
6,641	A*02:44	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,642	A*02:44	Croatia		0.0000	150	45_0_N_15_0_E
6,643	A*02:44	Libya Cyrenaica		0.0040	118	32_7_N_20_4_E
6,644	A*02:44	Mongolia Buryat		0.0070	141	47_6_N_118_6_E
6,645	A*02:44	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,646	A*02:44	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,647	A*02:44	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,648	A*02:44	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,649	A*02:44	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,650	A*02:44	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,651	A*02:44	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,652	A*02:44	USA Italy Ancestry		0.0020	273	38_0_N_77_0_W
6,653	A*02:44	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
6,654	A*02:44	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,655	A*02:44	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
6,656	A*02:44	USA NMDP Mexican or Chicano		0.0001	261,235	38_45_N_76_6_W
6,657	A*02:44	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
6,658	A*02:44	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,659	A*02:44	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,660	A*02:44	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,661	A*02:44	USA Spain Ancestry		0.0860	279	38_0_N_77_0_W
6,662	A*02:45	Bulgaria		0.0000	55	42_0_N_23_0_E
6,663	A*02:45	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
6,664	A*02:45	China North Han		0.0000	105	39_54_N_116_24_E
6,665	A*02:45	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,666	A*02:45	Colombia Bogotá Cord Blood	0.1	0.0003	1,463	4_39_N_73_52_W
6,667	A*02:45	Croatia		0.0000	150	45_0_N_15_0_E
6,668	A*02:45	Croatia pop 4		0.0001	4,000	45_48_N_15_57_E
6,669	A*02:45	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,670	A*02:45	Japan Okinawa Ryukyuan		0.0040	143	26_4_N_127_8_E
6,671	A*02:45	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,672	A*02:45	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,673	A*02:45	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,674	A*02:45	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
6,675	A*02:45	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,676	A*02:45	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,677	A*02:45	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,678	A*02:45	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,679	A*02:45	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
6,680	A*02:45	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,681	A*02:45	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,682	A*02:45	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,683	A*02:45	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,684	A*02:45	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,685	A*02:46	Bulgaria		0.0000	55	42_0_N_23_0_E
6,686	A*02:46	China North Han		0.0000	105	39_54_N_116_24_E
6,687	A*02:46	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,688	A*02:46	Colombia North Chimila Amerindians		0.0106	47	10_1_N_74_13_W
6,689	A*02:46	Croatia		0.0000	150	45_0_N_15_0_E
6,690	A*02:46	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,691	A*02:46	Israel Iran Jews		0.0000	8,153	32_8_N_34_52_E
6,692	A*02:46	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
6,693	A*02:46	Japan Okinawa Ryukyuan		0.0070	143	26_4_N_127_8_E
6,694	A*02:46	Mongolia Buryat		0.0210	141	47_6_N_118_6_E
6,695	A*02:46	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
6,696	A*02:46	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,697	A*02:46	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,698	A*02:46	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,699	A*02:46	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,700	A*02:46	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,701	A*02:46	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,702	A*02:46	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,703	A*02:46	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,704	A*02:46	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
6,705	A*02:46	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,706	A*02:46	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,707	A*02:46	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,708	A*02:47	Bulgaria		0.0000	55	42_0_N_23_0_E
6,709	A*02:47	China North Han		0.0000	105	39_54_N_116_24_E
6,710	A*02:47	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,711	A*02:47	Croatia		0.0000	150	45_0_N_15_0_E
6,712	A*02:47	Israel Iran Jews		0.0000	8,153	32_8_N_34_52_E
6,713	A*02:47	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
6,714	A*02:47	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,715	A*02:47	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,716	A*02:47	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,717	A*02:47	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,718	A*02:47	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,719	A*02:47	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,720	A*02:47	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
6,721	A*02:47	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
6,722	A*02:47	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,723	A*02:47	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,724	A*02:47	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,725	A*02:47	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,726	A*02:48	Bulgaria		0.0000	55	42_0_N_23_0_E
6,727	A*02:48	China Hubei Han	0.1	0.0003	3,732	30_45_N_114_15_E
6,728	A*02:48	China North Han		0.0000	105	39_54_N_116_24_E
6,729	A*02:48	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,730	A*02:48	Croatia		0.0000	150	45_0_N_15_0_E
6,731	A*02:48	Germany DKMS - China minority		0.0004	1,282	48_31_N_9_3_E
6,732	A*02:48	Iran Gorgan		0.0080	64	36_50_N_54_43_E
6,733	A*02:48	Iran Kurd pop 2		0.0170	60	36_17_N_46_17_E
6,734	A*02:48	Iran Saqqez-Baneh Kurds		0.0167	60	36_14_N_46_16_E
6,735	A*02:48	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,736	A*02:48	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,737	A*02:48	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,738	A*02:48	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,739	A*02:48	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,740	A*02:48	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,741	A*02:48	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,742	A*02:48	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
6,743	A*02:48	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,744	A*02:48	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,745	A*02:48	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,746	A*02:49	Belgium pop 3	0.0	0.0001	31,412	51_2_N_4_28_E
6,747	A*02:49	Bulgaria		0.0000	55	42_0_N_23_0_E
6,748	A*02:49	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
6,749	A*02:49	China North Han		0.0000	105	39_54_N_116_24_E
6,750	A*02:49	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,751	A*02:49	Croatia		0.0000	150	45_0_N_15_0_E
6,752	A*02:49	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,753	A*02:49	Israel Iran Jews		0.0000	8,153	32_8_N_34_52_E
6,754	A*02:49	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
6,755	A*02:49	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,756	A*02:49	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,757	A*02:49	Poland DKMS		0.0000	20,653	52_8_N_19_23_E
6,758	A*02:49	South Korea pop 3		0.0000	485	37_33_N_126_59_E
6,759	A*02:49	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,760	A*02:49	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,761	A*02:49	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,762	A*02:49	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
6,763	A*02:49	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,764	A*02:49	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,765	A*02:49	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
6,766	A*02:49	USA NMDP Southeast Asian		0.0000	27,978	38_45_N_76_6_W
6,767	A*02:49	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,768	A*02:49	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,769	A*02:49	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,770	A*02:50	Bulgaria		0.0000	55	42_0_N_23_0_E
6,771	A*02:50	China North Han		0.0000	105	39_54_N_116_24_E
6,772	A*02:50	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,773	A*02:50	Croatia		0.0000	150	45_0_N_15_0_E
6,774	A*02:50	Iran Gorgan		0.0700	64	36_50_N_54_43_E
6,775	A*02:50	Iran Kurd pop 2		0.0580	60	36_17_N_46_17_E
6,776	A*02:50	Iran Saqqez-Baneh Kurds		0.0583	60	36_14_N_46_16_E
6,777	A*02:50	Iran Tabriz Azeris		0.0412	97	38_4_N_46_17_E
6,778	A*02:50	Israel Argentina Jews		0.0001	4,307	32_8_N_34_52_E
6,779	A*02:50	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,780	A*02:50	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,781	A*02:50	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
6,782	A*02:50	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,783	A*02:50	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,784	A*02:50	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,785	A*02:50	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,786	A*02:50	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,787	A*02:50	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,788	A*02:51	Bulgaria		0.0000	55	42_0_N_23_0_E
6,789	A*02:51	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
6,790	A*02:51	China North Han		0.0000	105	39_54_N_116_24_E
6,791	A*02:51	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,792	A*02:51	Croatia		0.0000	150	45_0_N_15_0_E
6,793	A*02:51	Mexico Chihuahua Tarahumara		0.0110	44	27_30_N_107_45_W
6,794	A*02:51	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,795	A*02:51	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,796	A*02:51	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,797	A*02:51	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,798	A*02:51	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,799	A*02:51	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
6,800	A*02:51	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
6,801	A*02:51	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,802	A*02:51	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
6,803	A*02:51	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,804	A*02:51	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,805	A*02:51	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,806	A*02:52	Brazil Barra Mansa Rio State Caucasian		0.0012	405	22_32_S_44_10_W
6,807	A*02:52	Bulgaria		0.0000	55	42_0_N_23_0_E
6,808	A*02:52	China North Han		0.0000	105	39_54_N_116_24_E
6,809	A*02:52	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,810	A*02:52	Croatia		0.0000	150	45_0_N_15_0_E
6,811	A*02:52	Iran Gorgan		0.0230	64	36_50_N_54_43_E
6,812	A*02:52	Iran Kurd pop 2		0.0670	60	36_17_N_46_17_E
6,813	A*02:52	Iran Saqqez-Baneh Kurds		0.0667	60	36_14_N_46_16_E
6,814	A*02:52	Iran Tabriz Azeris		0.0052	97	38_4_N_46_17_E
6,815	A*02:52	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,816	A*02:52	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,817	A*02:52	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.0	0.0001	4,335	41_22_N_2_11_E
6,818	A*02:52	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,819	A*02:52	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,820	A*02:52	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,821	A*02:52	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,822	A*02:52	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
6,823	A*02:52	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,824	A*02:52	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,825	A*02:52	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,826	A*02:52	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,827	A*02:53N	China Hubei Han	0.0	0.0001	3,732	30_45_N_114_15_E
6,828	A*02:53N	China Jiangsu Han		0.0010	3,238	32_54_N_119_48_E
6,829	A*02:53N	China North Han		0.0000	105	39_54_N_116_24_E
6,830	A*02:53N	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,831	A*02:53N	China Zhejiang Han		0.0006	1,734	27_2_N_118_1_E
6,832	A*02:53N	Croatia		0.0000	150	45_0_N_15_0_E
6,833	A*02:53N	Germany DKMS - China minority		0.0004	1,282	48_31_N_9_3_E
6,834	A*02:53N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,835	A*02:53N	Hong Kong Chinese HKBMDR HLA 11 loci	0.0	0.0001	5,266	22_25_N_114_17_E
6,836	A*02:53N	Japan pop 16		0.0001	18,604	35_41_N_139_46_E
6,837	A*02:53N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,838	A*02:53N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,839	A*02:53N	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
6,840	A*02:53N	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
6,841	A*02:53N	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,842	A*02:53N	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
6,843	A*02:53N	USA Asian pop 2		0.0003	1,772	44_58_N_93_15_W
6,844	A*02:53N	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,845	A*02:53N	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,846	A*02:53N	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
6,847	A*02:53N	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,848	A*02:53N	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
6,849	A*02:53N	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,850	A*02:53N	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,851	A*02:54	Bulgaria		0.0000	55	42_0_N_23_0_E
6,852	A*02:54	China Guizhou Province Bouyei		0.0000	109	26_50_N_106_50_E
6,853	A*02:54	China Guizhou Province Miao pop 2		0.0060	85	26_50_N_106_50_E
6,854	A*02:54	China Guizhou Province Shui		0.0000	153	26_50_N_106_50_E
6,855	A*02:54	China North Han		0.0000	105	39_54_N_116_24_E
6,856	A*02:54	China Tibet Region Tibetan		0.0030	158	29_39_N_91_7_E
6,857	A*02:54	Croatia		0.0000	150	45_0_N_15_0_E
6,858	A*02:54	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,859	A*02:54	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,860	A*02:54	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,861	A*02:54	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,862	A*02:54	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,863	A*02:54	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
6,864	A*02:54	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,865	A*02:54	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,866	A*02:54	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,867	A*02:54	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,868	A*02:55	Bulgaria		0.0000	55	42_0_N_23_0_E
6,869	A*02:55	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
6,870	A*02:55	China North Han		0.0000	105	39_54_N_116_24_E
6,871	A*02:55	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,872	A*02:55	Croatia		0.0000	150	45_0_N_15_0_E
6,873	A*02:55	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,874	A*02:55	Germany DKMS - Italy minority		0.0004	1,159	48_31_N_9_3_E
6,875	A*02:55	Italy pop 5		0.0010	975	45_28_N_9_11_E
6,876	A*02:55	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,877	A*02:55	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,878	A*02:55	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,879	A*02:55	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,880	A*02:55	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,881	A*02:55	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,882	A*02:55	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,883	A*02:55	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,884	A*02:55	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,885	A*02:56	Bulgaria		0.0000	55	42_0_N_23_0_E
6,886	A*02:56	China North Han		0.0000	105	39_54_N_116_24_E
6,887	A*02:56	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,888	A*02:56	Croatia		0.0000	150	45_0_N_15_0_E
6,889	A*02:56	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,890	A*02:56	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,891	A*02:56	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,892	A*02:56	Poland DKMS		0.0000	20,653	52_8_N_19_23_E
6,893	A*02:56	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,894	A*02:56	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,895	A*02:56	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,896	A*02:56	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,897	A*02:56	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
6,898	A*02:56	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,899	A*02:56	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,900	A*02:56	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,901	A*02:56:01	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,902	A*02:56:01	Poland BMR	0.0	0.0001	23,595	52_0_N_19_22_E
6,903	A*02:56:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,904	A*02:57	Bulgaria		0.0000	55	42_0_N_23_0_E
6,905	A*02:57	China North Han		0.0000	105	39_54_N_116_24_E
6,906	A*02:57	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,907	A*02:57	Croatia		0.0000	150	45_0_N_15_0_E
6,908	A*02:57	Malaysia Peninsular Indian	0.4	0.0018	271	3_9_N_101_57_E
6,909	A*02:57	Mexico Chihuahua Tarahumara		0.0110	44	27_30_N_107_45_W
6,910	A*02:57	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,911	A*02:57	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,912	A*02:57	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,913	A*02:57	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,914	A*02:57	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,915	A*02:57	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
6,916	A*02:57	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,917	A*02:57	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
6,918	A*02:57	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,919	A*02:57	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,920	A*02:57	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,921	A*02:58	Bulgaria		0.0000	55	42_0_N_23_0_E
6,922	A*02:58	China Inner Mongolia Region		0.0000	102	36_0_N_102_0_E
6,923	A*02:58	China North Han		0.0000	105	39_54_N_116_24_E
6,924	A*02:58	China Qinghai Province Hui		0.0050	110	36_0_N_102_0_E
6,925	A*02:58	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,926	A*02:58	Colombia North Chimila Amerindians		0.0106	47	10_1_N_74_13_W
6,927	A*02:58	Croatia		0.0000	150	45_0_N_15_0_E
6,928	A*02:58	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,929	A*02:58	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,930	A*02:58	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,931	A*02:58	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
6,932	A*02:58	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,933	A*02:58	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,934	A*02:58	USA Mexican American Mestizo		0.0010	553	38_53_N_77_2_W
6,935	A*02:58	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
6,936	A*02:58	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
6,937	A*02:58	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,938	A*02:58	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
6,939	A*02:58	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,940	A*02:58	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
6,941	A*02:58	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
6,942	A*02:59	Bulgaria		0.0000	55	42_0_N_23_0_E
6,943	A*02:59	China North Han		0.0000	105	39_54_N_116_24_E
6,944	A*02:59	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,945	A*02:59	Croatia		0.0000	150	45_0_N_15_0_E
6,946	A*02:59	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,947	A*02:59	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,948	A*02:59	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,949	A*02:59	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,950	A*02:59	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,951	A*02:59	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
6,952	A*02:59	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,953	A*02:60	Bulgaria		0.0000	55	42_0_N_23_0_E
6,954	A*02:60	China North Han		0.0000	105	39_54_N_116_24_E
6,955	A*02:60	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,956	A*02:60	Croatia		0.0000	150	45_0_N_15_0_E
6,957	A*02:60	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
6,958	A*02:60	Germany DKMS - Greece minority		0.0003	1,894	48_31_N_9_3_E
6,959	A*02:60	Germany DKMS - Spain minority		0.0005	1,107	48_31_N_9_3_E
6,960	A*02:60	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
6,961	A*02:60	Malaysia Peninsular Malay	0.2	0.0011	951	3_9_N_101_57_E
6,962	A*02:60	Mexico Mexico City Mestizo population		0.0035	143	19_25_N_99_7_W
6,963	A*02:60	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,964	A*02:60	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,965	A*02:60	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,966	A*02:60	USA African American pop 3		0.0020	564	38_53_N_77_2_W
6,967	A*02:60	USA African American pop 4		0.0006	2,411	44_58_N_93_15_W
6,968	A*02:60	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
6,969	A*02:60	USA Caucasian	0.0		61,655	38_53_N_77_2_W
6,970	A*02:60	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,971	A*02:60	USA Hispanic pop 2		0.0003	1,999	44_58_N_93_15_W
6,972	A*02:60	USA NMDP African		0.0011	28,557	38_45_N_76_6_W
6,973	A*02:60	USA NMDP African American pop 2		0.0005	416,581	38_45_N_76_6_W
6,974	A*02:60	USA NMDP Black South or Central American		0.0007	4,889	38_45_N_76_6_W
6,975	A*02:60	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
6,976	A*02:60	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
6,977	A*02:60	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
6,978	A*02:60	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
6,979	A*02:60	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
6,980	A*02:60	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,981	A*02:61	China North Han		0.0000	105	39_54_N_116_24_E
6,982	A*02:61	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,983	A*02:61	Iran Gorgan		0.0080	64	36_50_N_54_43_E
6,984	A*02:61	Iran Kurd pop 2		0.0170	60	36_17_N_46_17_E
6,985	A*02:61	Iran Saqqez-Baneh Kurds		0.0167	60	36_14_N_46_16_E
6,986	A*02:61	Malaysia Peninsular Indian	0.4	0.0018	271	3_9_N_101_57_E
6,987	A*02:61	Malaysia Peninsular Malay	0.2	0.0011	951	3_9_N_101_57_E
6,988	A*02:61	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,989	A*02:61	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,990	A*02:61	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,991	A*02:61	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
6,992	A*02:61	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
6,993	A*02:61	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
6,994	A*02:62	China North Han		0.0000	105	39_54_N_116_24_E
6,995	A*02:62	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
6,996	A*02:62	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
6,997	A*02:62	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
6,998	A*02:62	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
6,999	A*02:62	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
7,000	A*02:62	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,001	A*02:62	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,002	A*02:62	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,003	A*02:63	China North Han		0.0000	105	39_54_N_116_24_E
7,004	A*02:63	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,005	A*02:63	Israel Morocco Jews		0.0000	36,718	32_10_N_34_52_E
7,006	A*02:63	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,007	A*02:63	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,008	A*02:63	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
7,009	A*02:63	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
7,010	A*02:63	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
7,011	A*02:63	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,012	A*02:63	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
7,013	A*02:63	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,014	A*02:63	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,015	A*02:63	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,016	A*02:64	Bolivia/Peru Quechua NA-DHS_12 (G)	4.8	0.0238	21	14_0_S_74_0_W
7,017	A*02:64	China North Han		0.0000	105	39_54_N_116_24_E
7,018	A*02:64	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,019	A*02:64	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,020	A*02:64	Germany DKMS - Turkey minority		0.0001	4,856	48_31_N_9_3_E
7,021	A*02:64	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,022	A*02:64	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,023	A*02:64	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,024	A*02:64	Russia Bashkortostan, Tatars	0.5	0.0026	192	54_13_N_56_9_E
7,025	A*02:64	Russia Karelia		0.0005	1,075	63_9_N_32_59_E
7,026	A*02:64	Saudi Arabia pop 6 (G)		0.0000	28,927	23_52_N_45_4_E
7,027	A*02:64	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
7,028	A*02:64	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
7,029	A*02:64	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
7,030	A*02:64	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,031	A*02:64	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,032	A*02:64	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,033	A*02:64	USA NMDP Hispanic South or Central American		0.0001	146,714	38_45_N_76_6_W
7,034	A*02:64	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,035	A*02:64	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,036	A*02:64	USA NMDP Southeast Asian		0.0001	27,978	38_45_N_76_6_W
7,037	A*02:64	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,038	A*02:64	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,039	A*02:64:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,040	A*02:65	China North Han		0.0000	105	39_54_N_116_24_E
7,041	A*02:65	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,042	A*02:65	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,043	A*02:65	Malaysia Peninsular Malay	0.1	0.0005	951	3_9_N_101_57_E
7,044	A*02:65	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,045	A*02:65	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,046	A*02:65	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,047	A*02:65	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
7,048	A*02:65	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
7,049	A*02:65	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
7,050	A*02:65	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,051	A*02:65	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,052	A*02:66	China North Han		0.0000	105	39_54_N_116_24_E
7,053	A*02:66	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,054	A*02:66	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,055	A*02:66	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
7,056	A*02:66	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
7,057	A*02:66	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
7,058	A*02:66	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,059	A*02:67	Austria	0.5	0.0020	200	48_13_N_16_21_E
7,060	A*02:67	China North Han		0.0000	105	39_54_N_116_24_E
7,061	A*02:67	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,062	A*02:67	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,063	A*02:67	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,064	A*02:67	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,065	A*02:67	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
7,066	A*02:67	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
7,067	A*02:67	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
7,068	A*02:67	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,069	A*02:68	China North Han		0.0000	105	39_54_N_116_24_E
7,070	A*02:68	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,071	A*02:68	Malaysia Peninsular Chinese	0.5	0.0026	194	3_9_N_101_57_E
7,072	A*02:68	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,073	A*02:68	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,074	A*02:68	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
7,075	A*02:68	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
7,076	A*02:68	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,077	A*02:68	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,078	A*02:69	China North Han		0.0000	105	39_54_N_116_24_E
7,079	A*02:69	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,080	A*02:69	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,081	A*02:69	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,082	A*02:70	China North Han		0.0000	105	39_54_N_116_24_E
7,083	A*02:70	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,084	A*02:70	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,085	A*02:70	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,086	A*02:70	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,087	A*02:70	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,088	A*02:71	China North Han		0.0000	105	39_54_N_116_24_E
7,089	A*02:71	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,090	A*02:71	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,091	A*02:71	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,092	A*02:71	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,093	A*02:72	China North Han		0.0000	105	39_54_N_116_24_E
7,094	A*02:72	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,095	A*02:72	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,096	A*02:72	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,097	A*02:72	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,098	A*02:73	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,099	A*02:73	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,100	A*02:73	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,101	A*02:73	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,102	A*02:73	USA NMDP Black South or Central American		0.0001	4,889	38_45_N_76_6_W
7,103	A*02:73	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,104	A*02:74	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,105	A*02:74	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,106	A*02:74	Malaysia Peninsular Indian	0.4	0.0018	271	3_9_N_101_57_E
7,107	A*02:74	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,108	A*02:74	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,109	A*02:74	USA African American pop 4		0.0002	2,411	44_58_N_93_15_W
7,110	A*02:74	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
7,111	A*02:74	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
7,112	A*02:74	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,113	A*02:74	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,114	A*02:74	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,115	A*02:74	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,116	A*02:74:01	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,117	A*02:74:02	China North Han		0.0000	105	39_54_N_116_24_E
7,118	A*02:74:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,119	A*02:74:02	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,120	A*02:75	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,121	A*02:75	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,122	A*02:76	China North Han		0.0000	105	39_54_N_116_24_E
7,123	A*02:76	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,124	A*02:76	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,125	A*02:76	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,126	A*02:76	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,127	A*02:76	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
7,128	A*02:76	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,129	A*02:76:02	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
7,130	A*02:76:02	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,131	A*02:76:02	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,132	A*02:77	China North Han		0.0000	105	39_54_N_116_24_E
7,133	A*02:77	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,134	A*02:77	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,135	A*02:77	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,136	A*02:77	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,137	A*02:77	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,138	A*02:77	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,139	A*02:78	China North Han		0.0000	105	39_54_N_116_24_E
7,140	A*02:78	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
7,141	A*02:78	Malaysia Peninsular Malay	0.1	0.0005	951	3_9_N_101_57_E
7,142	A*02:78	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,143	A*02:78	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,144	A*02:78	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
7,145	A*02:79	Malaysia Peninsular Malay	0.1	0.0005	951	3_9_N_101_57_E
7,146	A*02:79	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,147	A*02:79	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,148	A*02:79	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,149	A*02:80	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,150	A*02:80	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,151	A*02:80	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,152	A*02:81	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,153	A*02:81	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,154	A*02:81	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,155	A*02:81	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,156	A*02:82N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,157	A*02:82N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,158	A*02:83N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,159	A*02:83N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,160	A*02:84	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,161	A*02:84	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,162	A*02:84	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,163	A*02:84	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
7,164	A*02:84	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,165	A*02:84	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,166	A*02:84	USA NMDP North American Amerindian		0.0001	35,791	38_45_N_76_6_W
7,167	A*02:84	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
7,168	A*02:85	Germany DKMS - France minority		0.0004	1,406	48_31_N_9_3_E
7,169	A*02:85	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,170	A*02:85	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,171	A*02:85	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,172	A*02:85	Saudi Arabia Jeddah (G)		0.0021	476	21_32_N_39_10_E
7,173	A*02:85	Saudi Arabia pop 6 (G)		0.0008	28,927	23_52_N_45_4_E
7,174	A*02:85	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.1	0.0002	4,335	41_22_N_2_11_E
7,175	A*02:85	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
7,176	A*02:85	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,177	A*02:85	USA NMDP Middle Eastern or North Coast of Africa		0.0002	70,890	38_45_N_76_6_W
7,178	A*02:86	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,179	A*02:86	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,180	A*02:86	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,181	A*02:86	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
7,182	A*02:86	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
7,183	A*02:86	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,184	A*02:86	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,185	A*02:86	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,186	A*02:86	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,187	A*02:86	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,188	A*02:87	Colombia North Chimila Amerindians		0.0106	47	10_1_N_74_13_W
7,189	A*02:87	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,190	A*02:87	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,191	A*02:87	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,192	A*02:87	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,193	A*02:87	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
7,194	A*02:87	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,195	A*02:87	USA NMDP Hispanic South or Central American		0.0001	146,714	38_45_N_76_6_W
7,196	A*02:88N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,197	A*02:88N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,198	A*02:88N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,199	A*02:88N	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,200	A*02:89	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,201	A*02:89	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,202	A*02:90	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,203	A*02:90	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,204	A*02:90	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,205	A*02:90	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
7,206	A*02:90	Saudi Arabia Guraiat and Hail	0.5	0.0020	213	27_31_N_41_41_E
7,207	A*02:90	Taiwan pop 2		0.0010	364	23_46_N_121_0_E
7,208	A*02:90	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,209	A*02:90	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,210	A*02:91	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,211	A*02:91	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,212	A*02:92	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,213	A*02:92	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,214	A*02:92	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,215	A*02:92	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,216	A*02:93	Colombia North Chimila Amerindians		0.0106	47	10_1_N_74_13_W
7,217	A*02:93	Germany DKMS - China minority		0.0008	1,282	48_31_N_9_3_E
7,218	A*02:93	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,219	A*02:93	Germany DKMS - Turkey minority		0.0001	4,856	48_31_N_9_3_E
7,220	A*02:93	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,221	A*02:93	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,222	A*02:93	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,223	A*02:93	Saudi Arabia pop 6 (G)		0.0004	28,927	23_52_N_45_4_E
7,224	A*02:93	United Arab Emirates Pop 1		0.0020	570	24_42_N_54_82_E
7,225	A*02:93	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,226	A*02:93	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,227	A*02:93	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,228	A*02:93:01	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
7,229	A*02:93:02	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
7,230	A*02:94N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,231	A*02:94N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,232	A*02:94N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,233	A*02:94N	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,234	A*02:95	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,235	A*02:95	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,236	A*02:95	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,237	A*02:95	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,238	A*02:95	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,239	A*02:95	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,240	A*02:96	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,241	A*02:96	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,242	A*02:96	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,243	A*02:96	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,244	A*02:97	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,245	A*02:97	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,246	A*02:97:01	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,247	A*02:97:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,248	A*02:99	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,249	A*02:99	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,250	A*02:101	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,251	A*02:101	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,252	A*02:101	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,253	A*02:101	Poland DKMS		0.0000	20,653	52_8_N_19_23_E
7,254	A*02:101	United Arab Emirates Pop 1		0.0010	570	24_42_N_54_82_E
7,255	A*02:101	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,256	A*02:101	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,257	A*02:101:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,258	A*02:101:02	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,259	A*02:102	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,260	A*02:102	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,261	A*02:102	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,262	A*02:102	Russia Moscow Kazakh	0.7	0.0033	299	55_46_N_37_41_E
7,263	A*02:102	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,264	A*02:102	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
7,265	A*02:102	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
7,266	A*02:102	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
7,267	A*02:102	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,268	A*02:103	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,269	A*02:103	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,270	A*02:103	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,271	A*02:104	Germany pop 8		0.0001	39,689	48_24_N_9_58_E
7,272	A*02:104	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,273	A*02:104	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,274	A*02:105	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,275	A*02:105	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,276	A*02:105	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,277	A*02:105	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,278	A*02:105	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,279	A*02:105	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,280	A*02:106	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,281	A*02:106	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,282	A*02:106	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,283	A*02:107	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,284	A*02:107	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,285	A*02:107	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,286	A*02:107	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,287	A*02:107	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,288	A*02:108	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,289	A*02:108	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,290	A*02:108	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,291	A*02:108	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,292	A*02:108	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
7,293	A*02:109	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,294	A*02:109	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,295	A*02:109	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,296	A*02:110	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,297	A*02:110	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,298	A*02:110	Latvia		0.0019	266	56_50_N_24_45_E
7,299	A*02:110	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,300	A*02:110	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,301	A*02:110	Russia Moscow Kabardin	0.2	0.0012	834	55_46_N_37_41_E
7,302	A*02:110	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
7,303	A*02:110	USA NMDP Caribean Hispanic		0.0001	115,374	38_45_N_76_6_W
7,304	A*02:110	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,305	A*02:110	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,306	A*02:110	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,307	A*02:110	USA NMDP Middle Eastern or North Coast of Africa		0.0001	70,890	38_45_N_76_6_W
7,308	A*02:111	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,309	A*02:111	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,310	A*02:112	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,311	A*02:112	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,312	A*02:112	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
7,313	A*02:112	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,314	A*02:113N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,315	A*02:113N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,316	A*02:114	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,317	A*02:114	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,318	A*02:114	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,319	A*02:114	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,320	A*02:114	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,321	A*02:114	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,322	A*02:115	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,323	A*02:115	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,324	A*02:115	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
7,325	A*02:116	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,326	A*02:116	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,327	A*02:117	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,328	A*02:117	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,329	A*02:118	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,330	A*02:118	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,331	A*02:118	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,332	A*02:118	Russia Karelia		0.0005	1,075	63_9_N_32_59_E
7,333	A*02:118	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,334	A*02:119	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,335	A*02:119	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,336	A*02:119	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,337	A*02:119	New Zealand Maori with Admixed History	1.0	0.0050	105	42_0_S_174_0_E
7,338	A*02:119	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,339	A*02:119	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,340	A*02:120	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,341	A*02:120	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,342	A*02:120	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,343	A*02:121	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,344	A*02:121	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,345	A*02:121	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,346	A*02:121	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,347	A*02:121	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,348	A*02:121	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,349	A*02:121	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,350	A*02:122	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,351	A*02:122	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,352	A*02:122	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
7,353	A*02:122	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,354	A*02:122	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
7,355	A*02:122	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,356	A*02:122	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,357	A*02:123	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,358	A*02:123	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,359	A*02:123	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,360	A*02:123	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
7,361	A*02:124	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,362	A*02:124	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,363	A*02:125N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,364	A*02:125N	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,365	A*02:125N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,366	A*02:125N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,367	A*02:125N	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
7,368	A*02:126	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,369	A*02:126	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,370	A*02:127	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,371	A*02:127	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,372	A*02:128	India North UCBB	0.0	0.0001	5,849	28_36_N_77_11_E
7,373	A*02:128	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,374	A*02:128	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,375	A*02:128	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,376	A*02:128	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
7,377	A*02:129	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,378	A*02:129	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,379	A*02:129	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,380	A*02:130	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,381	A*02:130	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,382	A*02:130	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,383	A*02:130	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,384	A*02:130	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
7,385	A*02:131	India Andhra Pradesh Telugu Speaking	0.5	0.0027	186	13_4_N_80_14_E
7,386	A*02:131	India Central UCBB	0.2	0.0012	4,204	27_1_N_81_19_E
7,387	A*02:131	India East UCBB	0.1	0.0006	2,403	22_25_N_88_18_E
7,388	A*02:131	India Karnataka Kannada Speaking	0.6	0.0030	174	12_57_N_77_36_E
7,389	A*02:131	India Kerala Malayalam speaking	0.3	0.0010	356	13_1_N_80_15_E
7,390	A*02:131	India North UCBB	0.2	0.0008	5,849	28_36_N_77_11_E
7,391	A*02:131	India South UCBB	1.1	0.0055	11,446	13_6_N_80_8_E
7,392	A*02:131	India West UCBB	0.5	0.0025	5,829	19_11_N_73_3_E
7,393	A*02:131	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,394	A*02:131	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,395	A*02:132	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,396	A*02:132	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,397	A*02:133	Germany DKMS - Austria minority		0.0003	1,698	48_31_N_9_3_E
7,398	A*02:133	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,399	A*02:133	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,400	A*02:133	Latvia		0.0019	266	56_50_N_24_45_E
7,401	A*02:133	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,402	A*02:133	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,403	A*02:133	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,404	A*02:134	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,405	A*02:134	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,406	A*02:135	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,407	A*02:135	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,408	A*02:135	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,409	A*02:135	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
7,410	A*02:135	Russia Moscow Russians	0.1	0.0005	13,147	55_46_N_37_41_E
7,411	A*02:135	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,412	A*02:135	Tanzania Maasai	0.3	0.0030	336	3_11_S_37_4_E
7,413	A*02:136	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,414	A*02:136	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,415	A*02:136	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
7,416	A*02:136	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,417	A*02:136	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,418	A*02:137	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,419	A*02:137	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,420	A*02:137	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,421	A*02:138	Czech Republic NMDR		0.0001	5,099	49_48_N_15_30_E
7,422	A*02:138	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,423	A*02:138	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,424	A*02:138	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,425	A*02:138	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,426	A*02:138	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,427	A*02:139	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,428	A*02:139	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,429	A*02:139	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,430	A*02:140	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,431	A*02:140	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,432	A*02:141	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,433	A*02:141	Germany DKMS - Turkey minority		0.0001	4,856	48_31_N_9_3_E
7,434	A*02:141	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,435	A*02:141	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,436	A*02:141	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
7,437	A*02:142	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,438	A*02:142	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,439	A*02:143	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,440	A*02:143	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,441	A*02:144	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,442	A*02:144	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,443	A*02:145	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,444	A*02:145	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,445	A*02:145	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,446	A*02:145	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
7,447	A*02:146	Germany DKMS - Turkey minority		0.0001	4,856	48_31_N_9_3_E
7,448	A*02:146	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,449	A*02:146	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,450	A*02:147	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,451	A*02:147	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,452	A*02:147	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,453	A*02:147	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,454	A*02:148	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,455	A*02:148	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,456	A*02:148	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
7,457	A*02:149	Czech Republic NMDR		0.0001	5,099	49_48_N_15_30_E
7,458	A*02:149	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,459	A*02:149	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,460	A*02:149	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,461	A*02:149	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,462	A*02:149	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,463	A*02:150	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,464	A*02:150	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,465	A*02:151	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,466	A*02:151	Germany pop 8		0.0001	39,689	48_24_N_9_58_E
7,467	A*02:151	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,468	A*02:151	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,469	A*02:151	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,470	A*02:152	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,471	A*02:152	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,472	A*02:152	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,473	A*02:152	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
7,474	A*02:152	Sri Lanka Colombo	0.1	0.0010	714	6_56_N_79_50_E
7,475	A*02:152	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,476	A*02:152	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,477	A*02:153	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,478	A*02:153	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,479	A*02:153	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,480	A*02:153	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,481	A*02:153	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,482	A*02:154	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,483	A*02:154	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,484	A*02:154	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,485	A*02:154	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,486	A*02:154	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
7,487	A*02:155	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,488	A*02:155	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,489	A*02:155	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,490	A*02:156	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,491	A*02:156	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,492	A*02:156	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
7,493	A*02:156	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
7,494	A*02:156	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
7,495	A*02:156	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,496	A*02:156	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,497	A*02:157	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,498	A*02:157	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,499	A*02:157	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,500	A*02:157	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,501	A*02:157:02	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
7,502	A*02:158	Czech Republic NMDR		0.0001	5,099	49_48_N_15_30_E
7,503	A*02:158	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,504	A*02:158	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,505	A*02:158	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,506	A*02:159	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,507	A*02:159	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,508	A*02:159	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
7,509	A*02:160	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,510	A*02:160	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,511	A*02:160	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,512	A*02:160	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,513	A*02:160	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,514	A*02:160	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,515	A*02:161	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,516	A*02:161	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,517	A*02:161	India South UCBB	0.0	0.0000	11,446	13_6_N_80_8_E
7,518	A*02:161	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,519	A*02:161	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
7,520	A*02:161	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
7,521	A*02:161	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,522	A*02:161	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,523	A*02:161	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,524	A*02:161	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,525	A*02:162	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,526	A*02:162	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,527	A*02:162	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,528	A*02:162	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,529	A*02:162	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,530	A*02:162	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,531	A*02:163	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,532	A*02:163	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,533	A*02:163	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,534	A*02:163	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,535	A*02:163	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,536	A*02:164	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,537	A*02:164	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,538	A*02:164	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,539	A*02:164	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,540	A*02:165	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,541	A*02:165	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,542	A*02:165	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
7,543	A*02:165	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,544	A*02:165	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,545	A*02:165	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,546	A*02:166	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,547	A*02:166	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,548	A*02:166	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,549	A*02:166	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,550	A*02:166	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,551	A*02:167	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,552	A*02:167	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,553	A*02:167	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,554	A*02:167	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,555	A*02:168	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,556	A*02:168	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,557	A*02:168	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,558	A*02:168	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,559	A*02:168	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
7,560	A*02:169	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,561	A*02:169	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,562	A*02:169	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,563	A*02:169	USA NMDP Southeast Asian		0.0000	27,978	38_45_N_76_6_W
7,564	A*02:170	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,565	A*02:171	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,566	A*02:171	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,567	A*02:171:01	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,568	A*02:171:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,569	A*02:172	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,570	A*02:172	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,571	A*02:173	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,572	A*02:173	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,573	A*02:174	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,574	A*02:174	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,575	A*02:175	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,576	A*02:175	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,577	A*02:176	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,578	A*02:176	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,579	A*02:177	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,580	A*02:177	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
7,581	A*02:177	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,582	A*02:178	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,583	A*02:179	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,584	A*02:179	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,585	A*02:179	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
7,586	A*02:180	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,587	A*02:180	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,588	A*02:181	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,589	A*02:181	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,590	A*02:182	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,591	A*02:182	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,592	A*02:183	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,593	A*02:183	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,594	A*02:183	USA NMDP Chinese		0.0001	99,672	38_45_N_76_6_W
7,595	A*02:183	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,596	A*02:183	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,597	A*02:183	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,598	A*02:184	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,599	A*02:184	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,600	A*02:185	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,601	A*02:186	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,602	A*02:186	United Arab Emirates Pop 1		0.0010	570	24_42_N_54_82_E
7,603	A*02:186	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,604	A*02:186	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,605	A*02:187	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,606	A*02:188	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,607	A*02:188	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
7,608	A*02:188	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,609	A*02:189	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,610	A*02:189	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,611	A*02:189	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,612	A*02:190	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,613	A*02:191	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,614	A*02:191	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,615	A*02:192	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,616	A*02:192	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,617	A*02:193	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,618	A*02:193	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,619	A*02:193	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,620	A*02:193	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,621	A*02:193	USA NMDP Hispanic South or Central American		0.0001	146,714	38_45_N_76_6_W
7,622	A*02:194	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,623	A*02:194	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,624	A*02:194	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,625	A*02:194	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,626	A*02:194	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,627	A*02:195	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,628	A*02:195	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,629	A*02:195	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,630	A*02:195	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,631	A*02:196	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,632	A*02:196	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,633	A*02:197	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,634	A*02:197	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,635	A*02:197	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,636	A*02:197:02	Latvia		0.0019	266	56_50_N_24_45_E
7,637	A*02:197:02	USA Arizona Gila River Pima		0.0521	3,000	33_4_N_111_43_W
7,638	A*02:198	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,639	A*02:198	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,640	A*02:199	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,641	A*02:199	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
7,642	A*02:199	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,643	A*02:200	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,644	A*02:200	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,645	A*02:201	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,646	A*02:202	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,647	A*02:204	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,648	A*02:205	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,649	A*02:206	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,650	A*02:209	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,651	A*02:209	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,652	A*02:210	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,653	A*02:210	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,654	A*02:210	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,655	A*02:211	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,656	A*02:211	Mexico Mexico City Mestizo pop 2		0.0021	234	19_26_N_99_8_W
7,657	A*02:211	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,658	A*02:211	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,659	A*02:211	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
7,660	A*02:211:02	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,661	A*02:211:02	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
7,662	A*02:212	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,663	A*02:212	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,664	A*02:213	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,665	A*02:213	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,666	A*02:213	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,667	A*02:213	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,668	A*02:214	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,669	A*02:215	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,670	A*02:216	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,671	A*02:217	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,672	A*02:218	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,673	A*02:220	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,674	A*02:221	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,675	A*02:221	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,676	A*02:222N	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,677	A*02:223N	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,678	A*02:224	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,679	A*02:224	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,680	A*02:224	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,681	A*02:226N	Germany DKMS - China minority		0.0004	1,282	48_31_N_9_3_E
7,682	A*02:226N	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,683	A*02:227N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,684	A*02:227N	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,685	A*02:229	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
7,686	A*02:230	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
7,687	A*02:233	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,688	A*02:234	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,689	A*02:235	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,690	A*02:237	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,691	A*02:238	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,692	A*02:238	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,693	A*02:240	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
7,694	A*02:240	USA NMDP Black South or Central American		0.0272	4,889	38_45_N_76_6_W
7,695	A*02:243:03	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
7,696	A*02:245	Saudi Arabia pop 5	1.3	0.0063	158	24_38_N_46_43_E
7,697	A*02:245	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,698	A*02:245	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,699	A*02:245	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,700	A*02:246	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,701	A*02:246	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
7,702	A*02:246	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,703	A*02:251	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,704	A*02:251	South Korea pop 10		0.0001	4,128	37_33_N_126_58_E
7,705	A*02:251	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,706	A*02:252	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,707	A*02:252	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,708	A*02:254	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,709	A*02:254	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,710	A*02:255	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
7,711	A*02:255	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,712	A*02:263	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,713	A*02:264	China Hubei Han	0.0	0.0001	3,732	30_45_N_114_15_E
7,714	A*02:264	Hong Kong Chinese HKBMDR HLA 11 loci	0.0	0.0002	5,266	22_25_N_114_17_E
7,715	A*02:264	Malaysia Pahang Semai	7.9	0.0390	38	4_11_N_102_3_E
7,716	A*02:267	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,717	A*02:268	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,718	A*02:268	USA NMDP Caribean Hispanic		0.0001	115,374	38_45_N_76_6_W
7,719	A*02:268	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,720	A*02:268	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,721	A*02:268	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,722	A*02:268	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,723	A*02:269	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,724	A*02:270	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,725	A*02:270	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,726	A*02:270	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
7,727	A*02:271	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
7,728	A*02:271	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
7,729	A*02:271	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
7,730	A*02:272	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
7,731	A*02:273	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,732	A*02:273	India South UCBB	0.0	0.0001	11,446	13_6_N_80_8_E
7,733	A*02:273	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
7,734	A*02:274	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
7,735	A*02:275	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
7,736	A*02:276	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,737	A*02:276	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
7,738	A*02:277	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
7,739	A*02:278	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
7,740	A*02:279	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,741	A*02:279	USA NMDP Hawaiian or other Pacific Islander		0.0000	11,499	38_45_N_76_6_W
7,742	A*02:279	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
7,743	A*02:279	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
7,744	A*02:280	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,745	A*02:281	Hong Kong Chinese HKBMDR HLA 11 loci	0.0	0.0002	5,266	22_25_N_114_17_E
7,746	A*02:281	USA NMDP Alaska Native or Aleut		0.0010	1,376	38_45_N_76_6_W
7,747	A*02:282	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
7,748	A*02:282	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,749	A*02:283	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,750	A*02:286	Saudi Arabia Jeddah (G)		0.0021	476	21_32_N_39_10_E
7,751	A*02:287	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,752	A*02:288	Latvia		0.0019	266	56_50_N_24_45_E
7,753	A*02:289	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,754	A*02:289	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,755	A*02:289	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,756	A*02:289:02	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
7,757	A*02:292	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,758	A*02:292	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
7,759	A*02:293Q	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,760	A*02:294	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
7,761	A*02:294	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,762	A*02:294	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,763	A*02:298	India East UCBB	0.0	0.0002	2,403	22_25_N_88_18_E
7,764	A*02:299	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
7,765	A*02:300	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
7,766	A*02:300	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
7,767	A*02:301N	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,768	A*02:302	USA NMDP Black South or Central American		0.0001	4,889	38_45_N_76_6_W
7,769	A*02:303	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,770	A*02:303	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,771	A*02:303	USA NMDP Hawaiian or other Pacific Islander		0.0001	11,499	38_45_N_76_6_W
7,772	A*02:303	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,773	A*02:303	USA NMDP Vietnamese		0.0000	43,540	38_45_N_76_6_W
7,774	A*02:304	USA NMDP Korean		0.0000	77,584	38_45_N_76_6_W
7,775	A*02:306	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,776	A*02:307	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,777	A*02:307	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
7,778	A*02:311	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,779	A*02:311	USA NMDP Filipino		0.0000	50,614	38_45_N_76_6_W
7,780	A*02:312	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,781	A*02:313	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,782	A*02:317	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,783	A*02:317	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,784	A*02:317	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,785	A*02:318	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,786	A*02:321N	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
7,787	A*02:325	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,788	A*02:325	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,789	A*02:327	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,790	A*02:332	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,791	A*02:332	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,792	A*02:337	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,793	A*02:338	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,794	A*02:338	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,795	A*02:339	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,796	A*02:341	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,797	A*02:342	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,798	A*02:343	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,799	A*02:346	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,800	A*02:346	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,801	A*02:350N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,802	A*02:351	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,803	A*02:359	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,804	A*02:363	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,805	A*02:364	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,806	A*02:366N	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,807	A*02:367	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,808	A*02:367	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
7,809	A*02:367	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,810	A*02:372	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,811	A*02:379	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,812	A*02:381	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
7,813	A*02:382	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,814	A*02:382	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,815	A*02:384	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
7,816	A*02:384	Russia Moscow Russians	0.1	0.0007	13,147	55_46_N_37_41_E
7,817	A*02:384	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
7,818	A*02:390	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,819	A*02:391	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,820	A*02:391	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,821	A*02:392	Saudi Arabia pop 6 (G)		0.0000	28,927	23_52_N_45_4_E
7,822	A*02:393	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,823	A*02:394	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.0	0.0001	4,335	41_22_N_2_11_E
7,824	A*02:399	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,825	A*02:405	Russia Moscow Yakut	0.4	0.0021	235	55_46_N_37_41_E
7,826	A*02:406	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,827	A*02:406	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,828	A*02:406	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,829	A*02:406	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
7,830	A*02:407	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,831	A*02:407	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,832	A*02:417	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
7,833	A*02:423	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,834	A*02:430	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,835	A*02:433	Saudi Arabia pop 6 (G)		0.0002	28,927	23_52_N_45_4_E
7,836	A*02:434	Saudi Arabia pop 6 (G)		0.0001	28,927	23_52_N_45_4_E
7,837	A*02:435	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,838	A*02:436	India South UCBB	0.0	0.0000	11,446	13_6_N_80_8_E
7,839	A*02:436	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
7,840	A*02:441	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,841	A*02:445	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,842	A*02:447	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,843	A*02:448	Kenya, Nyanza Province, Luo tribe	1.0	0.0050	100	0_5_S_34_46_E
7,844	A*02:456	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,845	A*02:462	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,846	A*02:464	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,847	A*02:467	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,848	A*02:472	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,849	A*02:475	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
7,850	A*02:482	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,851	A*02:484	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,852	A*02:488	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,853	A*02:491	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,854	A*02:508	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,855	A*02:509	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,856	A*02:512	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
7,857	A*02:512	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
7,858	A*02:514N	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,859	A*02:521	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,860	A*02:523	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,861	A*02:523	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,862	A*02:524	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,863	A*02:524:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,864	A*02:526	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,865	A*02:528	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,866	A*02:530	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
7,867	A*02:530	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
7,868	A*02:530	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
7,869	A*02:535	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,870	A*02:536	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,871	A*02:545	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,872	A*02:548	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
7,873	A*02:553	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,874	A*02:554	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,875	A*02:561	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,876	A*02:564	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,877	A*02:565	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,878	A*02:571	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,879	A*02:573	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,880	A*02:578	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,881	A*02:585	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
7,882	A*02:586	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,883	A*02:587	India Central UCBB	0.0	0.0001	4,204	27_1_N_81_19_E
7,884	A*02:590	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,885	A*02:591:01	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,886	A*02:595	India East UCBB	0.1	0.0006	2,403	22_25_N_88_18_E
7,887	A*02:596	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,888	A*02:599	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,889	A*02:600	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,890	A*02:605Q	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,891	A*02:605Q	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
7,892	A*02:610:02	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,893	A*02:614	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
7,894	A*02:614	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
7,895	A*02:614	Russia Moscow Kazakh	0.7	0.0033	299	55_46_N_37_41_E
7,896	A*02:614	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
7,897	A*02:614	Russia Moscow Russians	0.1	0.0006	13,147	55_46_N_37_41_E
7,898	A*02:614	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,899	A*02:614	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
7,900	A*02:618Q	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,901	A*02:620	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,902	A*02:620	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,903	A*02:623	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,904	A*02:623	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
7,905	A*02:623	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
7,906	A*02:623	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,907	A*02:627	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,908	A*02:629	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
7,909	A*02:629	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,910	A*02:638	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,911	A*02:640	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,912	A*02:642	Mexico Mexico City Mestizo population		0.0035	143	19_25_N_99_7_W
7,913	A*02:644	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
7,914	A*02:644	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,915	A*02:645	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,916	A*02:646	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,917	A*02:647	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,918	A*02:648	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,919	A*02:650	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,920	A*02:650	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,921	A*02:656	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,922	A*02:656	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,923	A*02:657	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,924	A*02:659	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,925	A*02:660	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,926	A*02:661	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,927	A*02:666	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,928	A*02:669	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
7,929	A*02:673	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,930	A*02:674	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,931	A*02:680	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
7,932	A*02:685	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,933	A*02:686	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
7,934	A*02:687	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,935	A*02:695	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,936	A*02:699	Russia Moscow Russians	0.1	0.0004	13,147	55_46_N_37_41_E
7,937	A*02:699	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
7,938	A*02:703	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,939	A*02:704	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
7,940	A*02:704	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,941	A*02:707	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,942	A*02:713	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,943	A*02:729	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,944	A*02:729	Latvia		0.0019	266	56_50_N_24_45_E
7,945	A*02:730	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,946	A*02:732	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,947	A*02:744	India Central UCBB	0.1	0.0002	4,204	27_1_N_81_19_E
7,948	A*02:744	India South UCBB	0.0	0.0001	11,446	13_6_N_80_8_E
7,949	A*02:744	India West UCBB	0.0	0.0002	5,829	19_11_N_73_3_E
7,950	A*02:747	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,951	A*02:748N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,952	A*02:749	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,953	A*02:756	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,954	A*02:768	India South UCBB	0.0	0.0000	11,446	13_6_N_80_8_E
7,955	A*02:770	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,956	A*02:772	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,957	A*02:783	Latvia		0.0455	266	56_50_N_24_45_E
7,958	A*02:783	Russia Moscow Kabardin	0.2	0.0012	834	55_46_N_37_41_E
7,959	A*02:783	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
7,960	A*02:783	Russia Moscow Russians	0.1	0.0006	13,147	55_46_N_37_41_E
7,961	A*02:783	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
7,962	A*02:783	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,963	A*02:796N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,964	A*02:798	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,965	A*02:802	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,966	A*02:804	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
7,967	A*02:804	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
7,968	A*02:804	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
7,969	A*02:812	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,970	A*02:816	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,971	A*02:821	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
7,972	A*02:829	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,973	A*02:844	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,974	A*02:848	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,975	A*02:861	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,976	A*02:870	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
7,977	A*02:873	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
7,978	A*02:875	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
7,979	A*02:884:01	Latvia		0.0057	266	56_50_N_24_45_E
7,980	A*02:884:01	Russia Moscow Bashkir	0.7	0.0034	442	55_46_N_37_41_E
7,981	A*02:884:01	Russia Moscow Buryat	0.7	0.0034	149	55_46_N_37_41_E
7,982	A*02:884:01	Russia Moscow Kabardin	0.4	0.0018	834	55_46_N_37_41_E
7,983	A*02:884:01	Russia Moscow Russians	0.6	0.0028	13,147	55_46_N_37_41_E
7,984	A*02:884:01	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
7,985	A*02:884:01	Russia Moscow Tatars	0.4	0.0018	2,512	55_46_N_37_41_E
7,986	A*03	Albania		0.0910	160	41_0_N_20_0_E
7,987	A*03	Albania pop 2	18.5	0.0972	432	41_9_N_20_10_E
7,988	A*03	Albanian Kosovo		0.0958	120	41_50_N_20_1_E
7,989	A*03	Algeria pop 2	16.0		106	36_42_N_3_13_E
7,990	A*03	Argentina Buenos Aires	17.0	0.0880	466	34_38_S_58_28_W
7,991	A*03	Argentina Buenos Aires pop 2	17.0	0.0870	1,216	34_36_S_58_22_W
7,992	A*03	Argentina Chiriguano	0.0	0.0000	54	23_8_S_64_20_W
7,993	A*03	Argentina Corrientes	12.3	0.0640	155	27_30_S_58_49_W
7,994	A*03	Argentina Cuyo Region	14.0	0.0730	420	32_53_S_68_49_W
7,995	A*03	Argentina La Plata	19.0	0.0980	100	34_55_S_57_57_W
7,996	A*03	Argentina Rosario Toba	3.5	0.0170	86	32_57_S_60_39_W
7,997	A*03	Argentina Salta Wichi pop 2	0.0	0.0000	19	23_8_S_64_20_W
7,998	A*03	Armenia Ghegharkunik		0.1820	242	40_11_N_44_31_E
7,999	A*03	Armenia living in Iran		0.1290	85	40_11_N_44_31_E
8,000	A*03	Armenia living in Karabakh		0.1440	445	40_11_N_44_31_E
8,001	A*03	Armenia living in Lebanon		0.1400	368	40_11_N_44_31_E
8,002	A*03	Armenia living in USA		0.1370	233	40_11_N_44_31_E
8,003	A*03	Armenia Lori		0.1620	102	40_11_N_44_31_E
8,004	A*03	Armenia Shirak		0.1180	76	40_11_N_44_31_E
8,005	A*03	Armenia Syunik		0.1370	117	40_11_N_44_31_E
8,006	A*03	Armenia Yerevan		0.1580	445	40_11_N_44_31_E
8,007	A*03	Australia West Caucasian	22.1	0.1170	891	31_5_S_115_5_E
8,008	A*03	Austria	28.5	0.1520	200	48_13_N_16_21_E
8,009	A*03	Austria Cord Bloods		0.1526	1,368	47_4_N_15_26_E
8,010	A*03	Azerbaijan	22.5	0.1211	2,301	40_39_N_47_47_W
8,011	A*03	Azores		0.0970	1,050	37_44_N_25_40_W
8,012	A*03	Azores Terceira Island		0.1170	130	38_44_N_27_19_W
8,013	A*03	Bangladesh Dhaka Bangalee		0.0350	141	23_42_N_90_22_E
8,014	A*03	Bolivia La Paz Aymaras		0.0060	87	16_29_S_68_7_W
8,015	A*03	Bolivia Quechua		0.0217	69	16_30_S_68_9_W
8,016	A*03	Bosnia and Herzegovina		0.1567	134	43_52_N_18_25_E
8,017	A*03	Bosnia and Herzegovina pop 2	20.8	0.1042	120	44_32_N_18_41_E
8,018	A*03	Brazil Paraná Caucasian	18.7	0.1014	641	23_25_S_51_55_W
8,019	A*03	Brazil Curitiba-Parana Mixed		0.1107	264	25_25_S_49_16_W
8,020	A*03	Brazil Minas Gerais State Mixed	18.5	0.0980	1,000	19_49_S_43_57_W
8,021	A*03	Brazil Para Cord Blood Unit	13.1	0.0666	841	6_0_S_52_0_W
8,022	A*03	Brazil Para State	14.4	0.0682	5,000	6_0_S_52_0_W
8,023	A*03	Brazil Parana Afro Brazilian	15.6	0.0780	77	25_0_S_49_0_W
8,024	A*03	Brazil Parana Cafuzo	19.1	0.0960	319	25_0_S_49_0_W
8,025	A*03	Brazil Parana Caucasian	18.9	0.0950	2,775	25_0_S_49_0_W
8,026	A*03	Brazil Parana Japanese	7.0	0.0370	192	22_29_S_48_2_W
8,027	A*03	Brazil Parana Mixed race	18.3	0.0910	186	25_0_S_49_0_W
8,028	A*03	Brazil Parana Oriental	9.1	0.0450	33	25_0_S_49_0_W
8,029	A*03	Brazil Pernambuco Mixed		0.0840	101	8_3_S_34_54_W
8,030	A*03	Brazil Piaui Mixed		0.0790	21,943	2_44_S_40_25_W
8,031	A*03	Brazil REDOME Acre		0.0862	859	9_11_S_70_52_W
8,032	A*03	Brazil REDOME Alagoas		0.0891	25,349	9_57_S_36_55_W
8,033	A*03	Brazil REDOME Amapa		0.0633	17,864	1_0_N_52_0_W
8,034	A*03	Brazil REDOME Amazonas		0.0622	24,129	5_0_S_63_0_W
8,035	A*03	Brazil REDOME Bahia		0.0888	47,399	12_31_S_41_41_W
8,036	A*03	Brazil REDOME Ceara		0.0813	101,217	5_5_S_39_39_W
8,037	A*03	Brazil REDOME Espirito Santo		0.0948	88,485	20_19_S_40_20_W
8,038	A*03	Brazil REDOME Federal District		0.0870	29,549	15_46_S_47_56_W
8,039	A*03	Brazil REDOME Goias		0.0863	88,574	15_56_S_50_8_W
8,040	A*03	Brazil REDOME Maranhao		0.0760	10,180	2_31_S_44_18_W
8,041	A*03	Brazil REDOME Mato Grosso		0.0883	34,649	15_35_S_56_4_W
8,042	A*03	Brazil REDOME Mato Grosso do Sul		0.0868	95,667	20_26_S_54_38_W
8,043	A*03	Brazil REDOME Minas Gerais		0.0882	211,275	19_49_S_43_57_W
8,044	A*03	Brazil REDOME Para		0.0690	72,637	5_40_S_52_44_W
8,045	A*03	Brazil REDOME Paraiba		0.0783	43,868	7_10_S_36_50_W
8,046	A*03	Brazil REDOME Parana		0.1015	341,639	24_0_S_51_0_W
8,047	A*03	Brazil REDOME Pernambuco		0.0830	92,332	8_20_S_48_48_W
8,048	A*03	Brazil REDOME Piaui		0.0805	46,140	8_14_S_43_6_W
8,049	A*03	Brazil REDOME Rio de Janeiro		0.0898	139,322	22_54_S_43_11_W
8,050	A*03	Brazil REDOME Rio Grande do Norte		0.0778	46,603	5_44_S_36_33_W
8,051	A*03	Brazil REDOME Rio Grande do Sul		0.1098	241,329	30_0_S_53_0_W
8,052	A*03	Brazil REDOME Rondonia		0.0890	54,396	10_54_S_62_46_W
8,053	A*03	Brazil REDOME Roraima		0.0664	4,140	2_3_N_61_24_W
8,054	A*03	Brazil REDOME Santa Catarina		0.1126	106,673	27_15_S_50_20_W
8,055	A*03	Brazil REDOME Sao Paulo		0.0938	800,809	23_33_S_46_38_W
8,056	A*03	Brazil REDOME Sergipe		0.0911	7,321	10_35_S_37_23_W
8,057	A*03	Brazil REDOME Tocantins		0.0845	20,692	10_11_S_48_20_W
8,058	A*03	Brazil Rio Grande do Norte Mestizo		0.0770	12,973	5_47_S_35_12_W
8,059	A*03	Brazil Rio Grande do Sul Black		0.0640	248	30_0_S_53_0_W
8,060	A*03	Brazil Rio Grande do Sul Caucasoid		0.1070	4,428	30_0_S_53_0_W
8,061	A*03	Brazil Rio Grande do Sul Mixed		0.0920	324	30_0_S_53_0_W
8,062	A*03	Brazil Sao Paulo Bauru Mixed		0.0940	3,542	22_18_S_49_3_W
8,063	A*03	Brazil Sao Paulo Mixed	11.0	0.0570	239	23_0_S_48_0_W
8,064	A*03	Brazil South East Cord Blood		0.0913	11,409	14_14_S_51_55_W
8,065	A*03	Brazil South Ribeirao Preto		0.0740	184	21_11_S_47_49_W
8,066	A*03	Burkina Faso Fulani		0.0200	49	12_50_N_1_50_W
8,067	A*03	Burkina Faso Mossi		0.0280	53	12_50_N_1_50_W
8,068	A*03	Burkina Faso Rimaibe		0.0210	47	12_50_N_1_50_W
8,069	A*03	Cameroon Bakola Pygmy	8.0		50	4_3_N_9_42_E
8,070	A*03	Cameroon Bamileke 2		0.0570	35	5_15_N_10_20_E
8,071	A*03	Cameroon Ewondo		0.0400	38	3_55_N_11_24_E
8,072	A*03	Cameroon Podokwo		0.0350	43	10_37_N_14_20_E
8,073	A*03	Cameroon Uldeme		0.1140	35	10_37_N_14_20_E
8,074	A*03	Central African Republic Mbenzele Pygmy	3.0		36	3_32_N_16_4_E
8,075	A*03	Chile Santiago		0.0701	920	33_26_S_70_40_W
8,076	A*03	China Guangdong Province	4.2		403	23_20_N_113_30_E
8,077	A*03	China Harbin Korean		0.0220	201	45_45_N_126_38_E
8,078	A*03	China Harbin Manchu		0.0440	172	45_45_N_126_38_E
8,079	A*03	China Jiangsu Province		0.0240	20,248	33_0_N_120_0_E
8,080	A*03	China North Han		0.0000	105	39_54_N_116_24_E
8,081	A*03	China Shaanxi Province Han		0.0470	10,000	34_0_N_109_0_E
8,082	A*03	China Shandong Province	8.3	0.0430	7,418	36_30_N_118_0_E
8,083	A*03	China Shandong Province Linqu County	6.5		139	36_0_N_118_0_E
8,084	A*03	China Shanghai		0.0190	26,266	31_12_N_121_30_E
8,085	A*03	China Wuhan	7.4	0.0370	121	30_45_N_114_15_E
8,086	A*03	China Yunnan Province Han pop 2		0.0250	129	24_30_N_101_30_E
8,087	A*03	China Yunnan Province Naxi		0.0280	118	24_30_N_101_30_E
8,088	A*03	Colombia Barranquilla		0.0610	188	10_57_N_74_47_W
8,089	A*03	Colombia San Basilio de Palenque		0.0710	42	10_6_N_75_11_W
8,090	A*03	Croatia pop 2		0.0980	100	45_0_N_15_0_E
8,091	A*03	Croatia pop 3	31.7	0.1583	120	45_6_N_15_12_E
8,092	A*03	Cuba Mixed		0.0560	78	23_8_N_82_23_W
8,093	A*03	Cuba Mixed pop 2		0.0800	189	23_8_N_82_23_W
8,094	A*03	Cuba Mixed Race	16.7	0.0830	42	23_8_N_82_22_W
8,095	A*03	Czech Republic Kurd	14.9	0.0811	74	49_44_N_15_19_E
8,096	A*03	Czech Republic Romani pop 2	8.7	0.0544	46	49_44_N_15_19_E
8,097	A*03	Ecuador Amazonia Mixed Ancestry		0.0256	39	0_48_S_76_54_W
8,098	A*03	Ecuador Andes Mixed Ancestry		0.0479	824	1_28_S_78_49_W
8,099	A*03	Ecuador Coast Mixed Ancestry		0.0399	238	2_11_S_79_53_W
8,100	A*03	Ecuador Mixed Ancestry		0.0456	1,173	0_10_S_78_28_W
8,101	A*03	England Lancaster	24.6		545	54_2_N_2_48_W
8,102	A*03	England Leeds	26.2		5,024	53_47_N_1_32_W
8,103	A*03	England Liverpool	27.2		1,343	53_24_N_3_0_W
8,104	A*03	England London Ashkenazi Jews		0.1000	500	51_30_N_0_7_W
8,105	A*03	England Manchester	28.0		1,640	53_28_N_2_14_W
8,106	A*03	England Newcastle	26.3		2,739	54_58_N_1_36_W
8,107	A*03	England North West Mixed pop 2	20.1	0.1003	379	53_28_N_2_14_W
8,108	A*03	England Sheffield	27.7		4,755	53_23_N_1_28_W
8,109	A*03	France Bordeaux	25.4	0.1268	990	44_50_N_0_34_W
8,110	A*03	France Corsica Island		0.0670	100	42_9_N_9_5_E
8,111	A*03	France Grenoble	25.1	0.1255	1,000	45_12_N_5_43_E
8,112	A*03	France Grenoble, Nantes and Rennes		0.1320	6,094	47_13_N_1_33_W
8,113	A*03	France Lyon	24.4	0.1224	4,813	45_45_N_4_50_E
8,114	A*03	France Marseille	25.9	0.1298	1,000	43_17_N_5_22_E
8,115	A*03	France Reims		0.1520	102	49_15_N_4_2_E
8,116	A*03	France Rennes pop 3		0.1100	200	48_6_N_1_40_W
8,117	A*03	France Rennes pop 4	29.2	0.1465	1,000	48_6_N_1_40_W
8,118	A*03	France Southeast	24.6	0.1320	130	46_0_N_5_0_E
8,119	A*03	Gaza Palestinians		0.1060	165	31_31_N_34_27_E
8,120	A*03	Georgia Kurds		0.1720	30	41_43_N_44_47_E
8,121	A*03	Germany	28.6		11,407	50_6_N_8_40_E
8,122	A*03	Germany Essen	28.7	0.1540	174	51_27_N_7_0_E
8,123	A*03	Ghana Accre Asutuare Akan		0.1180	429	6_5_N_0_12_E
8,124	A*03	Greece Crete		0.0750	135	35_0_N_25_0_E
8,125	A*03	Greece North		0.0800	500	40_0_N_22_0_E
8,126	A*03	Greece pop 7		0.0913	11,250	37_58_N_23_43_E
8,127	A*03	Greece pop3	16.7		246	37_58_N_23_43_E
8,128	A*03	Guatemala Mayan		0.0190	132	14_8_N_91_5_W
8,129	A*03	Guatemala, Guatemala City Mixed Ancestry	6.3	0.0354	127	14_37_N_90_31_W
8,130	A*03	India Bikaner Non-Raikas	15.5		127	28_1_N_73_18_E
8,131	A*03	India Bikaner Raikas	5.0		143	28_1_N_73_18_E
8,132	A*03	India Jalpaiguri Toto	15.0		40	26_30_N_88_42_E
8,133	A*03	India Kerala Hindu Ezhava		0.0830	24	8_48_N_76_36_E
8,134	A*03	India Kerala Hindu Nair		0.1460	41	8_48_N_76_36_E
8,135	A*03	India Kerala Hindu Namboothiri		0.0380	40	11_14_N_75_44_E
8,136	A*03	India Kerala Hindu Pulaya		0.0630	16	11_48_N_75_19_E
8,137	A*03	India Kerala Malabar Muslim		0.0880	34	11_2_N_76_3_E
8,138	A*03	India Kerala Syrian Christian		0.1130	31	9_35_N_76_31_E
8,139	A*03	India North Gujarat	13.9	0.0740	338	23_13_N_72_41_E
8,140	A*03	India North pop 3		0.0770	587	28_38_N_77_9_E
8,141	A*03	India sub-Himalayan West Bengal Kami	1.9	0.0095	158	27_2_N_88_15_E
8,142	A*03	Iran pop 1		0.1410	64	32_35_N_54_16_E
8,143	A*03	Iran pop 4		0.1175	855	35_41_N_51_38_E
8,144	A*03	Iran Royan Cord Blood Bank	20.5		15,600	35_41_N_51_25_E
8,145	A*03	Iraq Arabs	29.5	0.1678	149	33_18_N_44_21_E
8,146	A*03	Iraq Erbil	27.7	0.1386	372	33_33_N_43_43_E
8,147	A*03	Iraq Kurdistan Region	27.3	0.1510	209	36_51_N_43_0_E
8,148	A*03	Ireland South pop 2		0.1346	17,624	53_20_N_6_15_W
8,149	A*03	Israel Ashkenazi Jews pop 2		0.0570	132	31_47_N_35_13_E
8,150	A*03	Israel Jews pop 2	19.6	0.1040	23,000	32_5_N_34_48_E
8,151	A*03	Israel Moroccan Jews		0.1280	113	31_47_N_35_13_E
8,152	A*03	Italy		0.1140	159,311	45_28_N_9_11_E
8,153	A*03	Italy Bergamo	18.8	0.0960	101	45_42_N_9_40_E
8,154	A*03	Italy East Sicily	12.0	0.0600	50	37_30_N_15_4_E
8,155	A*03	Italy Lombardy	21.8	0.1180	674	45_11_N_9_9_E
8,156	A*03	Italy North Pavia		0.1300	81	45_11_N_9_9_E
8,157	A*03	Italy North Pavia pop 2	20.9	0.1110	2,416	45_11_N_9_9_E
8,158	A*03	Italy North Pavia pop 3		0.1153	685	45_11_N_9_9_E
8,159	A*03	Italy pop 6	22.3	0.1141	184	41_53_N_12_29_E
8,160	A*03	Italy Rome	20.0	0.1050	100	41_54_N_12_27_E
8,161	A*03	Italy Sardinia pop3		0.0360	100	39_14_N_9_3_E
8,162	A*03	Italy South Campania Region	22.7		1,089	40_50_N_14_15_E
8,163	A*03	Japan Hyogo	3.1	0.0160	32	34_0_N_135_0_E
8,164	A*03	Japan Hyogo Osaka and Nagano		0.0100	195	34_41_N_135_30_E
8,165	A*03	Japan pop 2		0.0060	916	35_41_N_139_46_E
8,166	A*03	Jordan		0.0880	15,141	31_57_N_35_56_E
8,167	A*03	Macedonia		0.1014	286	41_32_N_21_42_E
8,168	A*03	Macedonia MBMDR - Albanian	21.9	0.1133	128	41_46_N_22_19_E
8,169	A*03	Macedonia MBMDR - Macedonian	19.5	0.1025	1,283	41_46_N_22_19_E
8,170	A*03	Macedonia MBMDR - Macedonian Muslims	5.3	0.0263	76	41_46_N_22_19_E
8,171	A*03	Macedonia pop 3		0.1190	172	42_0_N_22_0_E
8,172	A*03	Macedonia pop 4		0.1120	216	42_0_N_21_0_E
8,173	A*03	Madeira pop 2		0.0660	173	32_44_N_16_58_W
8,174	A*03	Madeira pop 3		0.0920	538	32_44_N_16_58_W
8,175	A*03	Malaysia Negeri Sembilan Minangkabau		0.0300	34	2_43_N_101_57_E
8,176	A*03	Malaysia Perak and Johor Banjar Bugis Jawa		0.1060	94	4_24_N_101_5_E
8,177	A*03	Malaysia Perak Rawa		0.0200	23	1_29_N_103_45_E
8,178	A*03	Malaysia pop 2		0.0240	62	3_8_N_101_42_E
8,179	A*03	Malaysia pop 3		0.0190	1,445	3_8_N_101_42_E
8,180	A*03	Malaysia Sarawak Bidayuh		0.0100	50	1_25_N_110_9_E
8,181	A*03	Malaysia Sarawak Iban		0.0100	51	2_18_N_111_49_E
8,182	A*03	Martinique		0.0800	100	14_40_N_61_0_W
8,183	A*03	Mexico Aguascalientes state		0.0368	95	22_1_N_102_21_W
8,184	A*03	Mexico Baja Californa, Mexicali		0.0550	100	32_37_N_115_27_W
8,185	A*03	Mexico Baja California Rural		0.0800	50	27_31_N_113_40_W
8,186	A*03	Mexico Baja California, La Paz		0.0400	75	24_8_N_110_18_W
8,187	A*03	Mexico Baja California, Tijuana		0.1400	25	32_30_N_117_2_W
8,188	A*03	Mexico Campeche Rural		0.0532	47	18_6_N_89_48_W
8,189	A*03	Mexico Campeche, Campeche city		0.0441	34	19_49_N_90_32_W
8,190	A*03	Mexico Chiapas Rural		0.0372	121	16_47_N_92_41_W
8,191	A*03	Mexico Chiapas, Tuxtla Gutierrez		0.0755	52	16_45_N_93_6_W
8,192	A*03	Mexico Chihuahua Chihuahua City		0.1008	119	28_37_N_106_4_W
8,193	A*03	Mexico Chihuahua Rural		0.1046	236	27_18_N_107_53_W
8,194	A*03	Mexico Chihuahua, Ciudad Juarez		0.0231	106	31_41_N_106_25_W
8,195	A*03	Mexico Coahuila Rural		0.0550	216	26_59_N_102_3_W
8,196	A*03	Mexico Coahuila, Saltillo		0.0548	72	25_26_N_100_58_W
8,197	A*03	Mexico Coahuila, Torreon		0.0663	396	25_32_N_103_24_W
8,198	A*03	Mexico Colima Rural		0.0795	43	19_30_N_103_37_W
8,199	A*03	Mexico Colima, Colima city		0.0246	61	19_14_N_103_43_W
8,200	A*03	Mexico Durango Rural		0.0352	326	26_41_N_103_44_W
8,201	A*03	Mexico Durango, Durango city		0.0548	153	24_1_N_104_39_W
8,202	A*03	Mexico Guanajuato Rural		0.0399	162	20_54_N_100_44_W
8,203	A*03	Mexico Guanajuato, Guanajuato city		0.0000	22	21_2_N_101_15_W
8,204	A*03	Mexico Guanajuato, Leon		0.0449	78	21_7_N_101_41_W
8,205	A*03	Mexico Guerrero state		0.0347	144	17_36_N_99_57_W
8,206	A*03	Mexico Hidalgo Rural		0.0432	81	20_38_N_98_59_W
8,207	A*03	Mexico Hidalgo, Pachuca		0.0122	41	20_6_N_98_45_W
8,208	A*03	Mexico Huasteca Region Teenek		0.0000	55	22_1_N_100_9_W
8,209	A*03	Mexico Jalisco Rural		0.0614	585	20_52_N_103_50_W
8,210	A*03	Mexico Jalisco, Guadalajara city		0.0540	1,189	20_39_N_103_20_W
8,211	A*03	Mexico Jalisco, Tlajomulco		0.0833	30	20_28_N_103_26_W
8,212	A*03	Mexico Jalisco, Tlaquepaque		0.0513	39	20_37_N_103_19_W
8,213	A*03	Mexico Jalisco, Tonala		0.0571	35	20_37_N_103_14_W
8,214	A*03	Mexico Jalisco, Zapopan		0.0387	168	20_40_N_103_24_W
8,215	A*03	Mexico Mestizo	4.9	0.0240	41	19_24_N_99_9_W
8,216	A*03	Mexico Mestizo pop 4		0.0520	269	19_26_N_99_8_W
8,217	A*03	Mexico Mexico City Center		0.0422	152	19_25_N_99_7_W
8,218	A*03	Mexico Mexico City East		0.0313	79	19_17_N_99_2_W
8,219	A*03	Mexico Mexico City Mestizo		0.0410	121	19_25_N_99_8_W
8,220	A*03	Mexico Mexico City Mestizo pop 3		0.0350	256	19_25_N_99_8_W
8,221	A*03	Mexico Mexico City Metropolitan Area Rural		0.0395	150	19_7_N_98_45_W
8,222	A*03	Mexico Mexico City North		0.0325	751	19_29_N_99_7_W
8,223	A*03	Mexico Mexico City South		0.0096	52	19_18_N_99_12_W
8,224	A*03	Mexico Mexico City West		0.0000	33	19_21_N_99_17_W
8,225	A*03	Mexico Michoacan Rural		0.0372	348	19_38_N_101_38_W
8,226	A*03	Mexico Michoacan, Morelia		0.0563	150	19_42_N_101_11_W
8,227	A*03	Mexico Morelos Rural		0.0500	30	18_57_N_98_58_W
8,228	A*03	Mexico Morelos, Cuernavaca		0.0183	82	18_55_N_99_13_W
8,229	A*03	Mexico Nayarit Rural		0.0234	64	20_45_N_105_20_W
8,230	A*03	Mexico Nayarit, Tepic		0.0464	97	21_30_N_104_53_W
8,231	A*03	Mexico Nuevo Leon Rural		0.0682	439	25_37_N_100_14_W
8,232	A*03	Mexico Nuevo Leon, Monterrey city		0.0752	226	25_41_N_100_18_W
8,233	A*03	Mexico Oaxaca Rural		0.0226	485	15_49_N_96_19_W
8,234	A*03	Mexico Oaxaca, Oaxaca city		0.0265	151	17_4_N_96_43_W
8,235	A*03	Mexico Puebla Mestizo		0.0100	99	19_3_N_98_12_W
8,236	A*03	Mexico Puebla Rural		0.0264	833	19_57_N_97_38_W
8,237	A*03	Mexico Puebla, Puebla city		0.0303	1,994	19_2_N_98_12_W
8,238	A*03	Mexico Queretaro Rural		0.0814	43	20_35_N_100_23_W
8,239	A*03	Mexico Queretaro, Queretaro city		0.0111	45	20_34_N_100_22_W
8,240	A*03	Mexico Quintana Roo Rural		0.0400	50	20_12_N_87_25_W
8,241	A*03	Mexico Quintana Roo, Cancun		0.0313	48	21_9_N_86_51_W
8,242	A*03	Mexico San Luis Potosi Rural		0.0230	87	21_20_N_98_58_W
8,243	A*03	Mexico San Luis Potosi, San Luis Potosi city		0.0667	30	22_9_N_100_59_W
8,244	A*03	Mexico Sinaloa Capomos Mayo Yoremes		0.0167	60	26_25_N_108_30_W
8,245	A*03	Mexico Sinaloa Mestizo		0.0540	56	24_48_N_107_10_W
8,246	A*03	Mexico Sinaloa Rural		0.0546	183	25_21_N_107_33_W
8,247	A*03	Mexico Sinaloa, Culiacán		0.0631	103	24_48_N_107_23_W
8,248	A*03	Mexico Sonora Rural		0.0660	197	32_15_N_114_30_W
8,249	A*03	Mexico Sonora, Ciudad Obregón		0.0629	143	27_28_N_109_55_W
8,250	A*03	Mexico Sonora, Hermosillo		0.0606	99	29_4_N_110_57_W
8,251	A*03	Mexico Tabasco Rural		0.0423	142	18_23_N_93_12_W
8,252	A*03	Mexico Tabasco, Villahermosa		0.0244	82	17_59_N_92_56_W
8,253	A*03	Mexico Tamaulipas Rural		0.0278	125	22_23_N_97_56_W
8,254	A*03	Mexico Tamaulipas, Ciudad Victoria		0.0000	23	23_44_N_99_8_W
8,255	A*03	Mexico Tapachula, Chiapas Mestizo Population	4.2	0.0208	72	14_54_N_92_15_W
8,256	A*03	Mexico Tlaxcala Rural		0.0223	830	19_14_N_98_20_W
8,257	A*03	Mexico Tlaxcala, Tlaxcala city		0.0193	181	19_18_N_98_14_W
8,258	A*03	Mexico Veracruz Rural		0.0323	539	18_26_N_95_12_W
8,259	A*03	Mexico Veracruz, Coatzacoalcos		0.0089	55	18_8_N_94_27_W
8,260	A*03	Mexico Veracruz, Cordoba		0.0179	56	18_53_N_96_55_W
8,261	A*03	Mexico Veracruz, Orizaba		0.0500	60	18_51_N_97_6_W
8,262	A*03	Mexico Veracruz, Poza Rica		0.0111	45	20_31_N_97_27_W
8,263	A*03	Mexico Veracruz, Veracruz city		0.0291	171	19_10_N_96_8_W
8,264	A*03	Mexico Veracruz, Xalapa		0.0401	187	19_32_N_96_54_W
8,265	A*03	Mexico Yucatan Rural		0.0373	132	21_16_N_89_31_W
8,266	A*03	Mexico Yucatan, Merida		0.0282	192	20_58_N_89_35_W
8,267	A*03	Mexico Zacatecas Rural		0.0558	266	22_46_N_102_7_W
8,268	A*03	Mexico Zacatecas, Fresnillo		0.0571	103	23_10_N_102_52_W
8,269	A*03	Mexico Zacatecas, Zacatecas city		0.1012	84	22_46_N_102_34_W
8,270	A*03	Mongolia Hoton		0.0790	85	48_40_N_88_18_E
8,271	A*03	Mongolia Khalkha		0.0600	200	45_0_N_107_0_E
8,272	A*03	Mongolia Khalkha pop 2		0.0300	202	47_55_N_106_53_E
8,273	A*03	Mongolia Oold		0.0580	104	48_0_N_96_0_E
8,274	A*03	Mongolia Tarialan Khoton		0.0790	85	49_46_N_91_54_E
8,275	A*03	Mongolia Tsaatan		0.0690	144	51_0_N_101_0_E
8,276	A*03	Mongolia Ulaanbaatar Khalkha		0.0380	41	47_55_N_106_55_E
8,277	A*03	Morocco		0.0670	96	33_14_N_8_30_W
8,278	A*03	Morocco Casablanca		0.1000	100	33_32_N_7_35_W
8,279	A*03	Morocco pop 2		0.1000	110	32_4_N_6_51_W
8,280	A*03	Mozambique		0.0570	202	25_58_S_32_35_E
8,281	A*03	Myanmar Bamar	2.2	0.0109	46	17_54_N_96_31_E
8,282	A*03	Myanmar Kachin	1.6	0.0079	63	17_54_N_96_31_E
8,283	A*03	Myanmar Kayin	4.5	0.0227	44	17_54_N_96_31_E
8,284	A*03	Myanmar Mon	4.7	0.0234	64	17_54_N_96_31_E
8,285	A*03	Myanmar Rakhine	2.1	0.0104	48	17_54_N_96_31_E
8,286	A*03	Nigeria	17.8	0.0911	258	6_27_N_3_23_E
8,287	A*03	Norway ethnic Norwegians	28.6	0.1565	4,510	61_0_N_10_0_E
8,288	A*03	Norway pop 2		0.1550	576	59_56_N_10_45_E
8,289	A*03	Norway Sami		0.2890	200	70_0_N_25_0_E
8,290	A*03	Pakistan Burusho		0.1300	92	36_0_N_73_0_E
8,291	A*03	Pakistan Mixed Sindhi		0.0570	101	24_0_N_68_0_E
8,292	A*03	Peru Lamas City Lama		0.0120	83	6_25_S_76_32_W
8,293	A*03	Philippines National Capital Region		0.0200	51	14_35_N_120_59_E
8,294	A*03	Poland pop 3	26.6	0.1333	2,907	52_12_N_21_1_E
8,295	A*03	Portugal Aveiro		0.1150	5,933	40_34_N_8_27_W
8,296	A*03	Portugal Beja		0.0810	699	37_30_N_8_3_W
8,297	A*03	Portugal Braga		0.1010	1,971	41_37_N_8_20_W
8,298	A*03	Portugal Bragança		0.0810	301	41_48_N_6_50_W
8,299	A*03	Portugal Castelo Branco		0.0960	327	39_50_N_7_28_W
8,300	A*03	Portugal Center pop 2		0.0930	562	40_11_N_8_26_W
8,301	A*03	Portugal Coimbra		0.1010	5,057	40_13_N_8_3_W
8,302	A*03	Portugal Evora		0.1090	737	38_34_N_7_54_W
8,303	A*03	Portugal Faro		0.0940	1,242	37_2_N_7_55_W
8,304	A*03	Portugal Guarda		0.1190	797	40_49_N_7_33_W
8,305	A*03	Portugal Leiria		0.0970	1,847	39_45_N_8_56_W
8,306	A*03	Portugal Lisbon		0.0990	17,420	38_42_N_9_11_W
8,307	A*03	Portugal North pop 2		0.1077	1,801	41_9_N_8_37_W
8,308	A*03	Portugal Portalegre		0.1160	1,254	39_19_N_7_25_W
8,309	A*03	Portugal Porto		0.1100	7,937	41_9_N_8_38_W
8,310	A*03	Portugal Santarem		0.1070	3,865	39_14_N_8_41_W
8,311	A*03	Portugal Setubal		0.1010	5,268	38_32_N_8_53_W
8,312	A*03	Portugal South pop 2		0.1040	1,021	38_42_N_9_11_W
8,313	A*03	Portugal Viana do Castelo		0.0930	706	41_42_N_8_50_W
8,314	A*03	Portugal Vila Real		0.1080	1,167	41_17_N_7_48_W
8,315	A*03	Portugal Viseu		0.1060	2,915	40_40_N_7_55_W
8,316	A*03	Romania	21.8	0.1160	348	46_0_N_25_0_E
8,317	A*03	Romania pop 2		0.0900	6,936	44_25_N_26_6_E
8,318	A*03	Romania pop 3	19.3	0.0991	1,469	44_25_N_26_6_E
8,319	A*03	Russia Arkhangelsk Pomor		0.1600	63	64_0_N_64_0_E
8,320	A*03	Russia Chuvash		0.1580	82	58_0_N_43_0_E
8,321	A*03	Russia Moscow		0.1340	2,650	55_45_N_37_37_E
8,322	A*03	Russia Moscow Pop 2	25.5	0.1384	2,000	55_45_N_37_37_E
8,323	A*03	Russia Murmansk Saomi Mixed		0.1800	70	68_58_N_33_5_E
8,324	A*03	Russia Nenet Mixed		0.1720	55	67_5_N_52_0_E
8,325	A*03	Russia North Ossetian	22.8	0.1260	127	43_2_N_44_40_E
8,326	A*03	Russia Northwest	29.6		1,011	58_0_N_28_0_E
8,327	A*03	Russia Northwest pop 3	24.0	0.1500	100	59_58_N_30_18_E
8,328	A*03	Russia Northwest pop 2	32.7		346	63_0_N_41_0_E
8,329	A*03	Russia Sakhalin Island Nivkhi		0.0280	53	51_0_N_143_0_E
8,330	A*03	Russia Samara Region		0.1520	2,500	53_11_N_50_7_E
8,331	A*03	Russia South Ural Bashkir	16.4	0.0860	146	54_32_N_60_20_E
8,332	A*03	Russia South Ural Russian	29.0	0.1590	207	54_32_N_60_20_E
8,333	A*03	Russia South Ural Tatar	28.9	0.1590	135	54_32_N_60_20_E
8,334	A*03	Russia Tatars		0.1521	355	61_59_N_96_41_E
8,335	A*03	Russia Transbaikal Territory Buryats	8.0	0.0400	150	54_16_N_118_16_E
8,336	A*03	Russia, South Ural, Chelyabinsk region, Nagaybaks	22.3	0.1210	112	53_37_N_59_40_E
8,337	A*03	Rwanda		0.0650	280	1_57_S_30_4_E
8,338	A*03	Saudi Arabia	16.7		18	24_42_N_46_43_E
8,339	A*03	Saudi Arabia pop 2		0.0630	383	24_42_N_46_43_E
8,340	A*03	Saudi Arabia pop 4	13.0	0.0721	499	24_38_N_46_43_E
8,341	A*03	Scotland Orkney	17.0	0.0950	99	58_59_N_3_11_W
8,342	A*03	Serbia		0.1110	386	44_28_N_20_28_E
8,343	A*03	Serbia pop 3	21.1	0.1132	1,992	44_49_N_20_28_E
8,344	A*03	Serbia Vojvodina	19.2	0.1010	400	44_13_N_20_47_E
8,345	A*03	Slovakia pop 2		0.1284	4,234	48_6_N_17_7_E
8,346	A*03	Slovenia pop 3	27.7		130	46_3_N_14_30_E
8,347	A*03	South Africa Natal Zulu	12.0	0.0600	100	29_0_S_31_0_E
8,348	A*03	South Africa Tswana	17.1	0.0850	41	25_51_S_25_38_E
8,349	A*03	South Korea pop 1		0.0200	324	37_0_N_126_0_E
8,350	A*03	South Korea pop 8		0.0200	7,096	37_34_N_126_58_E
8,351	A*03	Spain Andalusia		0.2280	99	37_22_N_5_59_W
8,352	A*03	Spain Arratia Valley Basque		0.1070	83	43_0_N_3_0_W
8,353	A*03	Spain Catalonia Girona		0.1260	88	41_59_N_2_49_E
8,354	A*03	Spain Gipuzkoa Basque		0.0980	100	43_10_N_2_10_W
8,355	A*03	Spain Ibiza		0.0860	88	39_0_N_1_3_E
8,356	A*03	Spain Majorca		0.1100	407	39_37_N_2_59_E
8,357	A*03	Spain Majorca and Minorca		0.0830	90	39_37_N_2_59_E
8,358	A*03	Spain Majorcans Jews		0.1650	103	39_37_N_2_57_E
8,359	A*03	Spain Minorca		0.0980	94	39_58_N_4_5_E
8,360	A*03	Spain Murcia		0.0870	173	37_59_N_1_7_W
8,361	A*03	Spain North Cabuerniga		0.1280	95	43_0_N_4_0_W
8,362	A*03	Spain North Cantabria		0.0780	83	43_0_N_3_0_W
8,363	A*03	Spain Pas Valley		0.1410	88	43_0_N_3_0_W
8,364	A*03	Spain Sevilla	20.1	0.1025	278	37_22_N_5_58_W
8,365	A*03	Spain, Castilla y Leon, Northwest,	17.5	0.1015	1,743	41_50_N_4_23_W
8,366	A*03	Sri Lanka Colombo Sinhalese		0.0350	101	6_56_N_79_50_E
8,367	A*03	Sudan Central Shaigiya Mixed	16.6		36	18_0_N_33_0_E
8,368	A*03	Sudan East Rashaida	18.5		27	14_0_N_35_0_E
8,369	A*03	Sudan Khartoum		0.0918	98	15_32_N_32_32_E
8,370	A*03	Sudan South Nuba	8.7		46	12_0_N_30_45_E
8,371	A*03	Sweden pop 4	30.2	0.1677	966	59_0_N_18_0_E
8,372	A*03	Sweden Stockholm	32.1	0.1750	1,347	59_21_N_18_4_E
8,373	A*03	Sweden Uppsala	19.7		300	59_51_N_17_38_E
8,374	A*03	Switzerland Aargau-Solothurn		0.1004	1,838	46_48_N_8_13_E
8,375	A*03	Switzerland Basel		0.1133	1,888	47_33_N_7_34_E
8,376	A*03	Switzerland Bern		0.1438	3,545	46_57_N_7_27_E
8,377	A*03	Switzerland Geneva pop 2		0.1048	1,267	46_12_N_6_8_E
8,378	A*03	Switzerland Graubunden		0.1224	759	46_42_N_9_57_E
8,379	A*03	Switzerland Lausanne		0.1229	993	46_31_N_6_39_E
8,380	A*03	Switzerland Lugano		0.1191	1,169	46_0_N_8_55_E
8,381	A*03	Switzerland Luzern		0.1295	1,553	47_3_N_8_18_E
8,382	A*03	Switzerland Sion		0.1139	832	46_15_N_7_23_E
8,383	A*03	Switzerland St Gallen		0.0893	2,113	47_26_N_9_21_E
8,384	A*03	Switzerland Zurich		0.1291	4,875	47_22_N_8_32_E
8,385	A*03	Taiwan Aborigine		0.0000	111	25_5_N_121_5_E
8,386	A*03	Taiwan Chinese immigrants from Middle China		0.0170	211	25_5_N_121_5_E
8,387	A*03	Taiwan Chinese immigrants from North China		0.0430	152	25_5_N_121_5_E
8,388	A*03	Taiwan Chinese immigrants from South China		0.0030	172	25_5_N_121_5_E
8,389	A*03	Taiwan Hakka pop 2		0.0030	714	25_5_N_121_5_E
8,390	A*03	Taiwan Minnan pop 2		0.0030	7,137	25_5_N_121_5_E
8,391	A*03	Thailand Northeast		0.0040	66	15_13_N_104_51_E
8,392	A*03	Thailand Northeast pop 2		0.0040	400	16_27_N_103_10_E
8,393	A*03	Thailand pop 4		0.0090	16,807	13_45_N_100_29_E
8,394	A*03	Trinidad African		0.0900	67	10_25_N_61_15_W
8,395	A*03	Trinidad South Asian		0.1000	87	10_25_N_61_15_W
8,396	A*03	Tunisia Ghannouch		0.0180	82	33_3_N_10_20_E
8,397	A*03	Tunisia pop 3		0.0820	104	36_48_N_10_11_E
8,398	A*03	Turkey pop 2		0.1090	228	41_0_N_28_58_E
8,399	A*03	Turkey pop 5		0.0780	142	40_0_N_28_0_E
8,400	A*03	United Arab Emirates pop 2	17.4		373	24_0_N_53_0_E
8,401	A*03	United Kingdom Central		0.1573	135	54_0_N_4_0_W
8,402	A*03	United Kingdom East		0.1748	163	54_0_N_4_0_W
8,403	A*03	United Kingdom North		0.1513	330	54_0_N_4_0_W
8,404	A*03	United Kingdom Orkney		0.0914	88	54_0_N_4_0_W
8,405	A*03	United Kingdom pop 2		0.1400	101	51_30_N_0_7_W
8,406	A*03	United Kingdom South West		0.1564	163	54_0_N_4_0_W
8,407	A*03	USA OPTN African American		0.0800	1,510	38_53_N_77_2_W
8,408	A*03	USA OPTN Asian		0.0260	261	38_53_N_77_2_W
8,409	A*03	USA OPTN Caucasian		0.1390	8,525	38_53_N_77_2_W
8,410	A*03	USA OPTN Hispanic		0.0740	1,580	38_53_N_77_2_W
8,411	A*03	Venezuela Caracas Mestizo		0.0100	50	10_30_N_66_55_W
8,412	A*03	Venezuela Colonia Tovar		0.1047	86	10_15_N_67_10_W
8,413	A*03	Venezuela Valencia and Maracaibo Caracus Mixed		0.0830	96	10_10_N_68_0_W
8,414	A*03	Venezuela Zulia Maracaibo Mixed		0.0750	20	10_38_N_71_26_W
8,415	A*03	Venezuela Zulia San Jose de Heras Mixed		0.0750	20	10_36_N_72_0_W
8,416	A*03	Vietnam Hanoi		0.0100	50	21_2_N_105_51_E
8,417	A*03	Wales	24.9	0.1330	1,798	51_29_N_3_11_W
8,418	A*03	Wales pop 2	25.9	0.1390	39,979	51_29_N_3_11_W
8,419	A*03:01	American Samoa		0.0200	51	14_18_S_170_42_W
8,420	A*03:01	Argentina Rosario Toba	3.5	0.0170	86	32_57_S_60_39_W
8,421	A*03:01	Armenia combined Regions		0.1050	100	40_11_N_44_31_E
8,422	A*03:01	Australia Cape York Peninsula Aborigine		0.0440	103	10_41_S_142_32_E
8,423	A*03:01	Australia Kimberly Aborigine		0.0140	41	15_46_S_128_44_E
8,424	A*03:01	Australia New South Wales Caucasian		0.1380	134	33_0_S_146_0_E
8,425	A*03:01	Australia Yuendumu Aborigine		0.0030	191	22_15_S_131_47_E
8,426	A*03:01	Austria	28.0	0.1520	200	48_13_N_16_21_E
8,427	A*03:01	Azores Central Islands		0.1250	59	38_0_N_28_0_W
8,428	A*03:01	Azores Oriental Islands		0.0640	43	36_58_N_25_6_W
8,429	A*03:01	Azores Terceira Island		0.1050	130	38_44_N_27_19_W
8,430	A*03:01	Belgium	30.2	0.1710	99	50_50_N_4_21_E
8,431	A*03:01	Belgium pop 3	30.1	0.1638	31,412	51_2_N_4_28_E
8,432	A*03:01	Brazil Puyanawa	10.7	0.0530	150	10_52_S_53_5_W
8,433	A*03:01	Brazil Belo Horizonte Caucasian	17.9	0.1000	95	19_55_S_43_56_W
8,434	A*03:01	Brazil Mixed		0.0510	108	23_10_S_47_48_W
8,435	A*03:01	Brazil Vale do Ribeira Quilombos	0.1	0.0000	144	24_36_S_48_15_W
8,436	A*03:01	Cameroon Baka Pygmy		0.0500	10	8_30_N_14_0_E
8,437	A*03:01	Cameroon Bamileke		0.0780	77	5_28_N_10_25_E
8,438	A*03:01	Cameroon Beti		0.0860	174	2_55_N_11_9_E
8,439	A*03:01	Cameroon Sawa		0.0380	13	4_50_N_10_0_E
8,440	A*03:01	Chile Mapuche		0.0846	66	37_48_S_73_23_W
8,441	A*03:01	Chile Santiago Mixed	13.0		70	33_26_S_70_39_W
8,442	A*03:01	China Beijing		0.0450	67	39_58_N_116_24_E
8,443	A*03:01	China Beijing Shijiazhuang Tianjian Han		0.0290	618	39_54_N_116_24_E
8,444	A*03:01	China Canton Han		0.0170	264	23_6_N_113_15_E
8,445	A*03:01	China Guangzhou		0.0290	102	23_6_N_113_15_E
8,446	A*03:01	China Han HIV negative		0.0940	72	36_34_N_103_49_E
8,447	A*03:01	China Henan HIV negative		0.0940	16	34_0_N_113_40_E
8,448	A*03:01	China Hubei Han	3.5	0.0173	3,732	30_45_N_114_15_E
8,449	A*03:01	China Jiangsu Han		0.0290	3,238	32_54_N_119_48_E
8,450	A*03:01	China Jiangsu Province Han		0.0297	334	32_54_N_119_48_E
8,451	A*03:01	China Marrow Donor Registry		0.0130	600	39_54_N_116_23_E
8,452	A*03:01	China North Han		0.0000	105	39_54_N_116_24_E
8,453	A*03:01	China Shanxi HIV negative		0.0910	22	36_5_N_111_50_E
8,454	A*03:01	China Sichuan HIV negative		0.0290	34	30_50_N_102_49_E
8,455	A*03:01	China South Han		0.0160	284	23_1_N_113_3_E
8,456	A*03:01	China Southwest Dai		0.0040	124	22_0_N_100_48_E
8,457	A*03:01	China Uyghur HIV negative		0.1050	19	36_34_N_103_49_E
8,458	A*03:01	China Yunnan Province Han		0.0150	101	24_2_N_105_3_E
8,459	A*03:01	China Yunnan Province Lisu		0.0060	111	24_30_N_101_30_E
8,460	A*03:01	China Yunnan Province Nu		0.0250	107	24_30_N_101_30_E
8,461	A*03:01	Colombia Bogotá Cord Blood	11.8	0.0612	1,463	4_39_N_73_52_W
8,462	A*03:01	Colombia North Wiwa El Encanto		0.0288	52	10_10_N_73_53_W
8,463	A*03:01	Croatia		0.1100	150	45_0_N_15_0_E
8,464	A*03:01	Croatia pop 4		0.1184	4,000	45_48_N_15_57_E
8,465	A*03:01	Cuba Caucasian	15.7	0.0860	70	23_8_N_82_22_W
8,466	A*03:01	Cuba Mixed Race	16.7	0.0830	42	23_8_N_82_22_W
8,467	A*03:01	Czech Republic		0.1890	106	50_0_N_14_0_E
8,468	A*03:01	Czech Republic NMDR		0.1424	5,099	49_48_N_15_30_E
8,469	A*03:01	Ecuador Amerindians		0.0794	63	0_13_S_78_30_W
8,470	A*03:01	England North West	25.5	0.1380	298	53_0_N_3_0_W
8,471	A*03:01	Finland		0.2500	91	63_29_N_25_16_E
8,472	A*03:01	France French Bone Marrow Donor Registry		0.1226	42,623	48_51_N_2_21_E
8,473	A*03:01	France Southeast	23.8	0.1270	130	46_0_N_5_0_E
8,474	A*03:01	Gaza	11.9	0.0714	42	31_22_N_34_18_E
8,475	A*03:01	Georgia Svaneti Region Svan		0.1380	80	42_54_N_43_0_E
8,476	A*03:01	Georgia Tibilisi		0.1380	109	41_41_N_44_57_E
8,477	A*03:01	Germany DKMS - Austria minority		0.1376	1,698	48_31_N_9_3_E
8,478	A*03:01	Germany DKMS - Bosnia and Herzegovina minority		0.1094	1,028	48_31_N_9_3_E
8,479	A*03:01	Germany DKMS - China minority		0.0301	1,282	48_31_N_9_3_E
8,480	A*03:01	Germany DKMS - Croatia minority		0.1198	2,057	48_31_N_9_3_E
8,481	A*03:01	Germany DKMS - France minority		0.1465	1,406	48_31_N_9_3_E
8,482	A*03:01	Germany DKMS - German donors		0.1510	3,456,066	48_31_N_9_3_E
8,483	A*03:01	Germany DKMS - Greece minority		0.0882	1,894	48_31_N_9_3_E
8,484	A*03:01	Germany DKMS - Italy minority		0.1123	1,159	48_31_N_9_3_E
8,485	A*03:01	Germany DKMS - Netherlands minority		0.1521	1,374	48_31_N_9_3_E
8,486	A*03:01	Germany DKMS - Portugal minority		0.1111	1,176	48_31_N_9_3_E
8,487	A*03:01	Germany DKMS - Romania minority		0.1175	1,234	48_31_N_9_3_E
8,488	A*03:01	Germany DKMS - Spain minority		0.1147	1,107	48_31_N_9_3_E
8,489	A*03:01	Germany DKMS - Turkey minority		0.0943	4,856	48_31_N_9_3_E
8,490	A*03:01	Germany DKMS - United Kingdom minority		0.1366	1,043	48_31_N_9_3_E
8,491	A*03:01	Germany pop 6		0.1588	8,862	51_6_N_10_23_E
8,492	A*03:01	Germany pop 8		0.1474	39,689	48_24_N_9_58_E
8,493	A*03:01	Ghana Ga-Adangbe	19.1	0.0954	131	5_45_N_0_12_E
8,494	A*03:01	Greece pop 6		0.0868	242	37_58_N_23_43_E
8,495	A*03:01	Greece pop 8	15.7	0.0843	83	37_58_N_23_43_E
8,496	A*03:01	Guinea Bissau Balanta		0.0210	48	11_52_N_15_36_W
8,497	A*03:01	Guinea Bissau Bijago		0.0650	23	11_52_N_15_36_W
8,498	A*03:01	Guinea Bissau Fula		0.0160	31	11_52_N_15_36_W
8,499	A*03:01	Guinea Bissau Papel		0.0200	25	11_52_N_15_36_W
8,500	A*03:01	Hong Kong Chinese	1.6	0.0080	569	22_16_N_114_9_E
8,501	A*03:01	Hong Kong Chinese BMDR		0.0103	7,595	22_15_N_114_10_E
8,502	A*03:01	Hong Kong Chinese cord blood registry		0.0109	3,892	22_15_N_114_10_E
8,503	A*03:01	India Andhra Pradesh Golla		0.0800	111	17_5_N_78_5_E
8,504	A*03:01	India Central UCBB	15.0	0.0748	4,204	27_1_N_81_19_E
8,505	A*03:01	India Delhi pop 2	13.3	0.0720	90	28_36_N_77_13_E
8,506	A*03:01	India East UCBB	13.3	0.0664	2,403	22_25_N_88_18_E
8,507	A*03:01	India New Delhi		0.0980	71	28_37_N_77_13_E
8,508	A*03:01	India North pop 2		0.0580	72	25_0_N_74_0_E
8,509	A*03:01	India North UCBB	16.2	0.0811	5,849	28_36_N_77_11_E
8,510	A*03:01	India Northeast UCBB	9.1	0.0456	296	26_16_N_91_48_E
8,511	A*03:01	India South UCBB	11.5	0.0573	11,446	13_6_N_80_8_E
8,512	A*03:01	India Tamil Nadu		0.0676	2,492	11_32_N_78_52_E
8,513	A*03:01	India West UCBB	12.1	0.0607	5,829	19_11_N_73_3_E
8,514	A*03:01	Indonesia Java Western	4.7		236	7_30_S_111_15_E
8,515	A*03:01	Iran Gorgan		0.0700	64	36_50_N_54_43_E
8,516	A*03:01	Iran Kurd pop 2		0.1080	60	36_17_N_46_17_E
8,517	A*03:01	Iran Saqqez-Baneh Kurds		0.1166	60	36_14_N_46_16_E
8,518	A*03:01	Iran Tabriz Azeris		0.0979	97	38_4_N_46_17_E
8,519	A*03:01	Iran Yazd		0.1071	56	31_53_N_54_21_E
8,520	A*03:01	Ireland Northern	26.3	0.1430	1,000	54_40_N_6_45_W
8,521	A*03:01	Ireland South	21.6	0.1160	250	53_20_N_6_15_W
8,522	A*03:01	Israel Arab Druze		0.0600	101	31_8_N_35_2_E
8,523	A*03:01	Israel Arab pop 2		0.0471	12,301	32_8_N_34_52_E
8,524	A*03:01	Israel Argentina Jews		0.0132	4,307	32_8_N_34_52_E
8,525	A*03:01	Israel Ashkenazi and Non Ashkenazi Jews		0.0340	146	31_8_N_35_2_E
8,526	A*03:01	Israel Ashkenazi Jews pop 3		0.0284	4,625	32_8_N_34_52_E
8,527	A*03:01	Israel Bukhara Jews		0.0217	2,317	32_8_N_34_52_E
8,528	A*03:01	Israel Druze		0.0088	5,914	32_41_N_35_17_E
8,529	A*03:01	Israel Ethiopia Jews		0.0699	5,928	32_8_N_34_52_E
8,530	A*03:01	Israel Georgia Jews		0.0279	4,471	32_10_N_34_53_E
8,531	A*03:01	Israel Iran Jews		0.0118	8,153	32_8_N_34_52_E
8,532	A*03:01	Israel Iraq Jews		0.0159	13,270	32_10_N_34_52_E
8,533	A*03:01	Israel Kavkazi Jews		0.0356	2,840	32_10_N_34_52_E
8,534	A*03:01	Israel Libya Jews		0.0188	3,739	32_10_N_34_52_E
8,535	A*03:01	Israel Morocco Jews		0.0307	36,718	32_10_N_34_52_E
8,536	A*03:01	Israel Poland Jews		0.0249	13,871	32_10_N_34_52_E
8,537	A*03:01	Israel Tunisia Jews		0.0140	9,070	32_10_N_34_52_E
8,538	A*03:01	Israel USA Jews		0.0155	6,058	32_10_N_34_52_E
8,539	A*03:01	Israel USSR Jews		0.0294	45,681	32_9_N_34_50_E
8,540	A*03:01	Israel YemenJews		0.0096	15,542	32_9_N_34_50_E
8,541	A*03:01	Italy North pop 3	19.2	0.0970	97	43_0_N_12_0_E
8,542	A*03:01	Italy pop 5		0.1020	975	45_28_N_9_11_E
8,543	A*03:01	Japan Central		0.0040	371	35_41_N_139_46_E
8,544	A*03:01	Japan pop 16		0.0040	18,604	35_41_N_139_46_E
8,545	A*03:01	Japan pop 3		0.0020	1,018	35_41_N_139_46_E
8,546	A*03:01	Jordan Amman		0.0520	146	31_35_N_36_20_E
8,547	A*03:01	Kenya Luo		0.0360	265	0_0_S_34_6_E
8,548	A*03:01	Kenya Nandi		0.0310	240	0_0_S_35_0_E
8,549	A*03:01	Kenya, Nyanza Province, Luo tribe	3.0	0.0150	100	0_5_S_34_46_E
8,550	A*03:01	Kosovo	19.4	0.1048	124	42_34_N_21_0_E
8,551	A*03:01	Malaysia Kedah Kensiu	9.5	0.0480	21	5_40_N_100_55_E
8,552	A*03:01	Malaysia Kelantan	7.1	0.0360	28	2_30_N_112_30_E
8,553	A*03:01	Malaysia Patani	4.0	0.0200	25	2_30_N_112_30_E
8,554	A*03:01	Malaysia Peninsular Chinese	0.5	0.0026	194	3_9_N_101_57_E
8,555	A*03:01	Malaysia Peninsular Indian	10.0	0.0498	271	3_9_N_101_57_E
8,556	A*03:01	Malaysia Peninsular Malay	2.1	0.0105	951	3_9_N_101_57_E
8,557	A*03:01	Mali Bandiagara		0.0440	138	14_4_N_3_6_W
8,558	A*03:01	Mexico Chiapas Lacandon Mayans		0.0069	218	16_54_N_92_5_W
8,559	A*03:01	Mexico Chihuahua Chihuahua City Pop 2	11.4	0.0568	88	28_48_N_106_26_W
8,560	A*03:01	Mexico Mestizo	4.9	0.0240	41	19_24_N_99_9_W
8,561	A*03:01	Mexico Mexico City Mestizo pop 2		0.0321	234	19_26_N_99_8_W
8,562	A*03:01	Mexico Mexico City Mestizo population		0.0524	143	19_25_N_99_7_W
8,563	A*03:01	Mexico Mexico City Tlalpan	8.8	0.0439	330	19_25_N_99_8_W
8,564	A*03:01	Mexico Oaxaca Mixtec		0.0000	103	17_5_N_96_45_W
8,565	A*03:01	Mexico Oaxaca Zapotec		0.0070	90	17_5_N_96_45_W
8,566	A*03:01	Mexico Tixcacaltuyub Maya		0.0075	67	20_29_N_88_54_W
8,567	A*03:01	Mexico Veracruz Xalapa	10.7	0.0536	84	19_32_N_96_55_W
8,568	A*03:01	Netherlands Leiden		0.1470	1,305	52_9_N_4_31_E
8,569	A*03:01	Netherlands UMCU	29.7	0.1875	64	52_5_N_5_10_E
8,570	A*03:01	New Caledonia		0.0120	65	21_0_S_165_0_E
8,571	A*03:01	New Zealand Maori with Admixed History	5.7	0.0290	105	42_0_S_174_0_E
8,572	A*03:01	Nicaragua Managua	13.2	0.0674	339	12_7_N_86_10_W
8,573	A*03:01	Oman	9.3	0.0510	118	22_0_N_56_0_E
8,574	A*03:01	Pakistan Baloch		0.0630	66	30_0_N_67_0_E
8,575	A*03:01	Pakistan Brahui		0.0460	104	30_0_N_67_0_E
8,576	A*03:01	Pakistan Burusho		0.1300	92	36_0_N_73_0_E
8,577	A*03:01	Pakistan Kalash		0.0750	69	32_0_N_70_0_E
8,578	A*03:01	Pakistan Karachi Parsi		0.0110	91	24_49_N_66_59_E
8,579	A*03:01	Pakistan Mixed Pathan		0.0870	100	32_0_N_70_0_E
8,580	A*03:01	Pakistan Mixed Sindhi		0.0440	101	24_0_N_68_0_E
8,581	A*03:01	Panama		0.0453	462	8_59_N_79_37_W
8,582	A*03:01	Papua New Guinea East New Britain Rabaul		0.0000	60	4_12_S_152_11_E
8,583	A*03:01	Papua New Guinea Eastern Highlands Goroka Asaro		0.0000	57	6_5_S_145_23_E
8,584	A*03:01	Papua New Guinea Karimui Plateau Pawaia		0.0000	80	7_14_S_145_20_E
8,585	A*03:01	Papua New Guinea Madang		0.0000	65	5_13_S_145_48_E
8,586	A*03:01	Papua New Guinea Wanigela Keapara		0.0000	66	10_5_S_147_50_E
8,587	A*03:01	Papua New Guinea West Schrader Ranges Haruai		0.0000	55	5_5_S_144_10_E
8,588	A*03:01	Papua New Guinea Wosera Abelam		0.0000	131	3_48_S_143_2_E
8,589	A*03:01	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
8,590	A*03:01	Poland		0.1230	200	51_7_N_17_2_E
8,591	A*03:01	Poland DKMS		0.1315	20,653	52_8_N_19_23_E
8,592	A*03:01	Portugal Azores Terceira Island	21.9	0.1096	130	38_43_N_27_13_W
8,593	A*03:01	Portugal Center		0.0900	50	39_0_N_8_0_W
8,594	A*03:01	Portugal North		0.0650	46	41_0_N_8_0_W
8,595	A*03:01	Portugal South		0.0820	49	37_0_N_8_0_W
8,596	A*03:01	Romania	21.8	0.1160	348	46_0_N_25_0_E
8,597	A*03:01	Russia Belgorod region	0.7	0.0033	153	50_46_N_37_27_E
8,598	A*03:01	Russia Bering Island Aleuts		0.1298	104	55_0_N_166_15_E
8,599	A*03:01	Russia Karelia		0.1642	1,075	63_9_N_32_59_E
8,600	A*03:01	Russia Tuva pop 2		0.0690	169	51_30_N_95_5_E
8,601	A*03:01	Russian Federation Vologda Region	3.4	0.0168	119	59_13_N_39_54_E
8,602	A*03:01	Sao Tome Island Angolar		0.0310	32	0_20_N_6_44_E
8,603	A*03:01	Sao Tome Island Forro		0.0600	66	0_20_N_6_44_E
8,604	A*03:01	Saudi Arabia Guraiat and Hail	14.1	0.0750	213	27_31_N_41_41_E
8,605	A*03:01	Senegal Niokholo Mandenka		0.0380	165	12_35_N_12_9_W
8,606	A*03:01	Singapore Chinese	1.3	0.0070	149	1_24_N_103_45_E
8,607	A*03:01	Singapore Chinese Han		0.0060	94	1_17_N_103_51_E
8,608	A*03:01	Singapore Javaneses		0.0200	51	1_24_N_103_45_E
8,609	A*03:01	Singapore SGVP Chinese CHS		0.0000	96	1_21_N_103_50_E
8,610	A*03:01	Singapore SGVP Malay MAS		0.0060	89	1_21_N_103_50_E
8,611	A*03:01	Singapore SGVP. Indian INS		0.0620	86	1_21_N_103_50_E
8,612	A*03:01	Singapore Thai		0.0050	100	1_24_N_103_45_E
8,613	A*03:01	South Africa Cape Town Black	6.0	0.0298	84	34_7_S_18_22_E
8,614	A*03:01	South Africa Durban Black	13.6	0.0909	22	29_51_S_31_0_E
8,615	A*03:01	South Africa Johannesburg Black	10.1	0.0546	120	26_11_S_28_2_E
8,616	A*03:01	South Africa Natal Tamil		0.0400	51	30_0_S_31_0_E
8,617	A*03:01	South Africa Natal Zulu	12.0	0.0600	100	29_0_S_31_0_E
8,618	A*03:01	South Africa Worcester	18.0	0.0910	159	33_39_S_19_27_E
8,619	A*03:01	South Korea pop 10		0.0176	4,128	37_33_N_126_58_E
8,620	A*03:01	South Korea pop 3		0.0180	485	37_33_N_126_59_E
8,621	A*03:01	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	19.5	0.1024	4,335	41_22_N_2_11_E
8,622	A*03:01	Spain Andalusia Romani		0.0190	99	37_22_N_5_59_W
8,623	A*03:01	Sri Lanka Colombo	10.8	0.0540	714	6_56_N_79_50_E
8,624	A*03:01	Sudan Mixed	9.5	0.0480	200	5_0_N_35_0_E
8,625	A*03:01	Sweden Northern Sami		0.3130	154	67_0_N_18_30_E
8,626	A*03:01	Sweden Southern Sami		0.2480	130	60_52_N_14_44_E
8,627	A*03:01	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
8,628	A*03:01	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
8,629	A*03:01	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
8,630	A*03:01	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
8,631	A*03:01	Taiwan Han Chinese		0.0060	504	23_46_N_121_0_E
8,632	A*03:01	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
8,633	A*03:01	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
8,634	A*03:01	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
8,635	A*03:01	Taiwan pop 2		0.0020	364	23_46_N_121_0_E
8,636	A*03:01	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
8,637	A*03:01	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
8,638	A*03:01	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
8,639	A*03:01	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
8,640	A*03:01	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
8,641	A*03:01	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
8,642	A*03:01	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
8,643	A*03:01	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
8,644	A*03:01	Taiwan Tzu Chi Morrow Donor Registry		0.0052	46,682	25_2_N_121_38_E
8,645	A*03:01	Taiwan Tzu Chi Morrow Donor Registry Aborigine		0.0021	233	25_2_N_121_38_E
8,646	A*03:01	Taiwan Tzu Chi Cord Blood Bank		0.0050	710	23_46_N_121_0_E
8,647	A*03:01	Tanzania Maasai	9.6	0.0493	336	3_11_S_37_4_E
8,648	A*03:01	Thailand		0.0070	142	16_7_N_102_0_E
8,649	A*03:01	Tunisia	13.0	0.0670	100	35_0_N_9_0_E
8,650	A*03:01	Tunisia Gabes		0.0736	95	33_53_N_10_6_E
8,651	A*03:01	Uganda Kampala		0.0550	161	0_3_N_32_6_E
8,652	A*03:01	Uganda Kampala pop 2		0.0430	175	0_20_N_32_30_E
8,653	A*03:01	United Arab Emirates Abu Dhabi		0.0865	52	24_28_N_54_22_E
8,654	A*03:01	United Arab Emirates Pop 1		0.0690	570	24_42_N_54_82_E
8,655	A*03:01	USA African American		0.0990	252	30_0_N_95_0_W
8,656	A*03:01	USA African American Bethesda	23.8	0.1190	187	38_0_N_78_0_W
8,657	A*03:01	USA African American pop 2		0.0940	149	32_46_N_96_46_W
8,658	A*03:01	USA African American pop 4		0.0813	2,411	44_58_N_93_15_W
8,659	A*03:01	USA Alaska Yupik		0.0060	252	60_48_N_161_26_W
8,660	A*03:01	USA Arizona Gila River Amerindian		0.0020	492	33_4_N_111_44_W
8,661	A*03:01	USA Asian		0.0100	358	30_0_N_95_0_W
8,662	A*03:01	USA Asian pop 2		0.0260	1,772	44_58_N_93_15_W
8,663	A*03:01	USA Caucasian Bethesda	20.6	0.1070	307	38_0_N_78_0_W
8,664	A*03:01	USA Caucasian pop 2		0.1260	265	30_0_N_95_0_W
8,665	A*03:01	USA Caucasian pop 3		0.1080	88	32_46_N_96_48_W
8,666	A*03:01	USA Caucasian pop 4		0.1383	1,070	38_53_N_77_2_W
8,667	A*03:01	USA Hispanic		0.0730	234	30_0_N_95_0_W
8,668	A*03:01	USA Hispanic pop 2		0.0791	1,999	44_58_N_93_15_W
8,669	A*03:01	USA NMDP African		0.0783	28,557	38_45_N_76_6_W
8,670	A*03:01	USA NMDP African American pop 2		0.0839	416,581	38_45_N_76_6_W
8,671	A*03:01	USA NMDP Alaska Native or Aleut		0.0770	1,376	38_45_N_76_6_W
8,672	A*03:01	USA NMDP American Indian South or Central America		0.1060	5,926	38_45_N_76_6_W
8,673	A*03:01	USA NMDP Caribean Black		0.0783	33,328	38_45_N_76_6_W
8,674	A*03:01	USA NMDP Caribean Hispanic		0.0863	115,374	38_45_N_76_6_W
8,675	A*03:01	USA NMDP Caribean Indian		0.0810	14,339	38_45_N_76_6_W
8,676	A*03:01	USA NMDP Chinese		0.0140	99,672	38_45_N_76_6_W
8,677	A*03:01	USA NMDP European Caucasian		0.1399	1,242,890	38_45_N_76_6_W
8,678	A*03:01	USA NMDP Filipino		0.0131	50,614	38_45_N_76_6_W
8,679	A*03:01	USA NMDP Hawaiian or other Pacific Islander		0.0323	11,499	38_45_N_76_6_W
8,680	A*03:01	USA NMDP Hispanic South or Central American		0.0738	146,714	38_45_N_76_6_W
8,681	A*03:01	USA NMDP Japanese		0.0090	24,582	38_45_N_76_6_W
8,682	A*03:01	USA NMDP Korean		0.0191	77,584	38_45_N_76_6_W
8,683	A*03:01	USA NMDP Mexican or Chicano		0.0815	261,235	38_45_N_76_6_W
8,684	A*03:01	USA NMDP Middle Eastern or North Coast of Africa		0.1010	70,890	38_45_N_76_6_W
8,685	A*03:01	USA NMDP North American Amerindian		0.1044	35,791	38_45_N_76_6_W
8,686	A*03:01	USA NMDP South Asian Indian		0.0636	185,391	38_45_N_76_6_W
8,687	A*03:01	USA NMDP Southeast Asian		0.0499	27,978	38_45_N_76_6_W
8,688	A*03:01	USA NMDP Vietnamese		0.0097	43,540	38_45_N_76_6_W
8,689	A*03:01	USA North American Native		0.0670	187	30_0_N_95_0_W
8,690	A*03:01	USA Philadelphia Caucasian	21.5	0.0540	141	39_56_N_75_10_W
8,691	A*03:01	USA San Antonio Caucasian		0.1416	222	29_23_N_98_33_W
8,692	A*03:01	USA San Diego	18.1	0.0920	496	32_50_N_117_15_W
8,693	A*03:01	USA San Francisco Caucasian		0.1480	220	37_46_N_122_25_W
8,694	A*03:01	USA South Dakota Lakota Sioux		0.0170	302	44_30_N_100_0_W
8,695	A*03:01	USA South Texas Hispanic		0.0600	194	29_0_N_98_0_W
8,696	A*03:01	Zambia Lusaka		0.0580	44	15_25_S_28_17_E
8,697	A*03:01	Zimbabwe Harare Shona		0.0400	230	17_51_S_31_1_E
8,698	A*03:01:01	Brazil Barra Mansa Rio State Black		0.0753	73	22_32_S_44_10_W
8,699	A*03:01:01	Brazil Barra Mansa Rio State Caucasian		0.0827	405	22_32_S_44_10_W
8,700	A*03:01:01	Brazil Rio de Janeiro Black		0.0956	68	22_54_S_43_12_W
8,701	A*03:01:01	Brazil Rio de Janeiro Caucasian		0.0806	521	22_54_S_43_12_W
8,702	A*03:01:01	Brazil Rio de Janeiro Parda		0.1118	170	22_54_S_43_12_W
8,703	A*03:01:01	Bulgaria		0.0460	55	42_0_N_23_0_E
8,704	A*03:01:01	Cameroon Yaounde		0.0770	92	3_52_N_11_31_E
8,705	A*03:01:01	Cape Verde Northwestern Islands		0.1130	62	16_0_N_24_0_W
8,706	A*03:01:01	Cape Verde Southeastern Islands		0.0650	62	16_0_N_24_0_W
8,707	A*03:01:01	China Guizhou Province Bouyei		0.0000	109	26_50_N_106_50_E
8,708	A*03:01:01	China Guizhou Province Miao pop 2		0.0060	85	26_50_N_106_50_E
8,709	A*03:01:01	China Guizhou Province Shui		0.0000	153	26_50_N_106_50_E
8,710	A*03:01:01	China Inner Mongolia Region		0.0440	102	36_0_N_102_0_E
8,711	A*03:01:01	China Jingpo Minority	1.9	0.0100	105	25_0_N_102_0_E
8,712	A*03:01:01	China North Han		0.0290	105	39_54_N_116_24_E
8,713	A*03:01:01	China Qinghai Province Hui		0.0410	110	36_0_N_102_0_E
8,714	A*03:01:01	China Tibet Region Tibetan		0.0250	158	29_39_N_91_7_E
8,715	A*03:01:01	China Zhejiang Han		0.0150	1,734	27_2_N_118_1_E
8,716	A*03:01:01	Costa Rica African -Caribbean (G)	16.6	0.0880	102	9_59_N_83_1_W
8,717	A*03:01:01	Costa Rica Amerindians (G)	2.4	0.0160	125	9_34_N_83_25_W
8,718	A*03:01:01	Costa Rica Central Valley Mestizo (G)	22.2	0.1180	221	9_55_N_84_5_W
8,719	A*03:01:01	Costa Rica Guanacaste Mestizo (G)	8.2	0.0410	110	10_16_N_85_37_W
8,720	A*03:01:01	England Blood Donors of Mixed Ethnicity	24.9	0.1320	519	52_21_N_1_10_W
8,721	A*03:01:01	Guinea Bissau		0.0620	65	12_0_N_15_0_W
8,722	A*03:01:01	Hong Kong Chinese HKBMDR HLA 11 loci	1.8	0.0091	5,266	22_25_N_114_17_E
8,723	A*03:01:01	India Andhra Pradesh Telugu Speaking	10.8	0.0538	186	13_4_N_80_14_E
8,724	A*03:01:01	India Karnataka Kannada Speaking	12.6	0.0630	174	12_57_N_77_36_E
8,725	A*03:01:01	India Kerala Malayalam speaking	12.9	0.0690	356	13_1_N_80_15_E
8,726	A*03:01:01	India Khandesh Region Pawra		0.0000	50	20_54_N_74_46_E
8,727	A*03:01:01	India Mumbai Maratha		0.0930	91	18_58_N_72_49_E
8,728	A*03:01:01	India Tamil Nadu Nadar		0.2050	61	8_0_N_77_0_E
8,729	A*03:01:01	India West Bhil		0.0400	50	20_54_N_74_46_E
8,730	A*03:01:01	India West Coast Parsi		0.0000	50	18_0_N_74_0_E
8,731	A*03:01:01	Iran Baloch		0.0560	100	29_29_N_60_52_E
8,732	A*03:01:01	Japan Okinawa Ryukyuan		0.0040	143	26_4_N_127_8_E
8,733	A*03:01:01	Kenya		0.0520	144	1_28_S_36_8_E
8,734	A*03:01:01	Latvia		0.1553	266	56_50_N_24_45_E
8,735	A*03:01:01	Madeira		0.0700	185	32_44_N_16_58_W
8,736	A*03:01:01	Mexico Chihuahua Tarahumara		0.0110	44	27_30_N_107_45_W
8,737	A*03:01:01	Mexico Hidalgo Mezquital Valley/ Otomi	1.4	0.0069	72	20_28_N_99_13_W
8,738	A*03:01:01	Mongolia Buryat		0.0290	141	47_6_N_118_6_E
8,739	A*03:01:01	Morocco Atlantic Coast Chaouya		0.0470	98	30_22_N_7_22_W
8,740	A*03:01:01	Morocco Nador Metalsa pop 2		0.0340	73	35_10_N_2_56_W
8,741	A*03:01:01	Morocco Settat Chaouya	9.4	0.0470	98	33_0_N_7_36_W
8,742	A*03:01:01	Nicaragua Mestizo (G)	11.0	0.0550	155	12_40_N_85_13_W
8,743	A*03:01:01	Poland BMR	24.8	0.1323	23,595	52_0_N_19_22_E
8,744	A*03:01:01	Russia Belgorod region	20.9	0.1111	153	50_46_N_37_27_E
8,745	A*03:01:01	Russia Moscow Bashkir	15.2	0.0816	442	55_46_N_37_41_E
8,746	A*03:01:01	Russia Moscow Buryat	9.4	0.0503	149	55_46_N_37_41_E
8,747	A*03:01:01	Russia Moscow Kabardin	16.1	0.0846	834	55_46_N_37_41_E
8,748	A*03:01:01	Russia Moscow Kalmyk	14.2	0.0708	120	55_46_N_37_41_E
8,749	A*03:01:01	Russia Moscow Kazakh	15.1	0.0786	299	55_46_N_37_41_E
8,750	A*03:01:01	Russia Moscow Mansi	31.6	0.1711	38	55_46_N_37_41_E
8,751	A*03:01:01	Russia Moscow Megrel	11.3	0.0614	230	55_46_N_37_41_E
8,752	A*03:01:01	Russia Moscow Russians	24.6	0.1327	13,147	55_46_N_37_41_E
8,753	A*03:01:01	Russia Moscow Similar genetically to Azerbaijan	12.4	0.0618	170	55_46_N_37_41_E
8,754	A*03:01:01	Russia Moscow Tatars	23.0	0.1225	2,512	55_46_N_37_41_E
8,755	A*03:01:01	Russia Moscow Tuvian	8.9	0.0446	112	55_46_N_37_41_E
8,756	A*03:01:01	Russia Moscow Uzbek	17.7	0.0909	198	55_46_N_37_41_E
8,757	A*03:01:01	Russia Moscow Yakut	10.2	0.0511	235	55_46_N_37_41_E
8,758	A*03:01:01	Russia Nizhny Novgorod, Russians	0.8	0.0040	1,510	56_19_N_43_59_E
8,759	A*03:01:01	Russian Federation Vologda Region	36.1	0.1891	119	59_13_N_39_54_E
8,760	A*03:01:01	Saudi Arabia Al-Baha (G)		0.0581	86	20_0_N_41_28_E
8,761	A*03:01:01	Saudi Arabia Al-Madinah (G)		0.0962	102	24_28_N_39_36_E
8,762	A*03:01:01	Saudi Arabia Jeddah (G)		0.0525	476	21_32_N_39_10_E
8,763	A*03:01:01	Saudi Arabia Makkah (G)		0.0407	344	21_25_N_39_49_E
8,764	A*03:01:01	Saudi Arabia pop 6 (G)		0.0590	28,927	23_52_N_45_4_E
8,765	A*03:01:01	Saudi Arabia Taif (G)		0.1442	104	21_16_N_40_24_E
8,766	A*03:01:01	South Africa Caucasians		0.1200	102	30_0_S_25_0_E
8,767	A*03:01:01	South African Black	12.7	0.0634	142	29_9_S_24_39_E
8,768	A*03:01:01	South African Indian population	6.0	0.0300	50	29_22_S_24_31_E
8,769	A*03:01:01	South African Mixed ancestry	10.0	0.0600	50	29_22_S_24_31_E
8,770	A*03:01:01	South Korea pop 3		0.0000	485	37_33_N_126_59_E
8,771	A*03:01:01	Spain, Canary Islands, Gran canaria island	14.0	0.0698	215	27_57_N_15_36_W
8,772	A*03:01:01	USA African American pop 3		0.0860	564	38_53_N_77_2_W
8,773	A*03:01:01	USA Arizona Gila River Pima		0.0011	3,000	33_4_N_111_43_W
8,774	A*03:01:01	USA San Francisco Caucasian		0.1480	220	37_46_N_122_25_W
8,775	A*03:01:01	Vietnam Kinh	1.0	0.0050	101	14_5_N_108_27_E
8,776	A*03:01:01:01	Canada Chipewyan NA-DHS_2 (G)	8.0	0.0400	25	59_36_N_107_20_W
8,777	A*03:01:01:01	Canada Cree NA-DHS_3 (G)	5.6	0.0278	18	50_20_N_102_30_W
8,778	A*03:01:01:01	Canada Ojibwa NA-DHS_4 (G)	6.7	0.0333	16	46_0_N_81_0_W
8,779	A*03:01:01:01	Chile Huilliche NA-DHS_14 (G)	10.0	0.0500	20	41_0_S_73_0_W
8,780	A*03:01:01:01	China North Han		0.0000	105	39_54_N_116_24_E
8,781	A*03:01:01:01	Guatemala Kaqchikel NA-DHS_8 (G)	5.3	0.0263	19	15_0_N_91_0_W
8,782	A*03:01:01:01	Indonesia Java pop 2		0.0140	36	6_8_S_106_10_E
8,783	A*03:01:01:01	Indonesia Sundanese and Javanese		0.0250	201	6_8_S_106_10_E
8,784	A*03:01:01:01	Libya Cyrenaica		0.0930	118	32_7_N_20_4_E
8,785	A*03:01:01:01	Mexico Guadalajara Mestizo pop 2		0.0290	103	20_40_N_103_21_W
8,786	A*03:01:01:01	Mexico Zapotec NA-DHS_7 (G)	5.0	0.0500	20	16_0_N_97_0_W
8,787	A*03:01:01:01	Pakistan Mixed Punjabi	6.7	0.0335	389	31_0_N_72_0_E
8,788	A*03:01:01:01	Russia Bashkortostan, Bashkirs	17.5	0.0917	120	54_0_N_55_0_E
8,789	A*03:01:01:01	Russia Bashkortostan, Tatars	17.7	0.0938	192	54_13_N_56_9_E
8,790	A*03:01:01:01	Russia Nizhny Novgorod, Russians	24.0	0.1298	1,510	56_19_N_43_59_E
8,791	A*03:01:01:01	Russia Tundra Nentsi NA-DHS_1 (G)	12.5	0.0625	16	66_0_N_76_0_E
8,792	A*03:01:01:01	Saudi Arabia pop 5	5.1	0.0253	158	24_38_N_46_43_E
8,793	A*03:01:01:01	USA Eastern European		0.1290	558	38_53_N_77_2_W
8,794	A*03:01:01:01	USA Italy Ancestry		0.1140	273	38_0_N_77_0_W
8,795	A*03:01:01:01	USA Mexican American Mestizo		0.0680	553	38_53_N_77_2_W
8,796	A*03:01:01:01	USA San Francisco Caucasian		0.1480	220	37_46_N_122_25_W
8,797	A*03:01:01:01	USA Spain Ancestry		0.0020	279	38_0_N_77_0_W
8,798	A*03:01:01:02N	China North Han		0.0000	105	39_54_N_116_24_E
8,799	A*03:01:01:02N	France French Bone Marrow Donor Registry		0.0084	42,623	48_51_N_2_21_E
8,800	A*03:01:01:03	Russia Nizhny Novgorod, Russians	0.3	0.0013	1,510	56_19_N_43_59_E
8,801	A*03:01:01:05	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
8,802	A*03:01:02	Bulgaria		0.0000	55	42_0_N_23_0_E
8,803	A*03:01:02	Cameroon Yaounde		0.0060	92	3_52_N_11_31_E
8,804	A*03:01:02	China North Han		0.0000	105	39_54_N_116_24_E
8,805	A*03:01:02	Georgia Tibilisi Kurd		0.0500	31	41_41_N_44_57_E
8,806	A*03:01:02	India Khandesh Region Pawra		0.0000	50	20_54_N_74_46_E
8,807	A*03:01:02	India Mumbai Maratha		0.0000	91	18_58_N_72_49_E
8,808	A*03:01:02	India West Bhil		0.0500	50	20_54_N_74_46_E
8,809	A*03:01:02	India West Coast Parsi		0.0100	50	18_0_N_74_0_E
8,810	A*03:01:02	Mexico Guadalajara Mestizo pop 2		0.0050	103	20_40_N_103_21_W
8,811	A*03:01:02	Mexico Hidalgo Mezquital Valley/ Otomi	1.4	0.0069	72	20_28_N_99_13_W
8,812	A*03:01:02	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
8,813	A*03:01:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,814	A*03:01:02	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
8,815	A*03:01:02	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,816	A*03:01:02	South Korea pop 3		0.0000	485	37_33_N_126_59_E
8,817	A*03:01:02	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
8,818	A*03:01:02	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
8,819	A*03:01:02	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
8,820	A*03:01:02	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
8,821	A*03:01:02	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
8,822	A*03:01:02	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
8,823	A*03:01:02	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
8,824	A*03:01:02	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
8,825	A*03:01:02	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
8,826	A*03:01:02	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
8,827	A*03:01:02	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
8,828	A*03:01:02	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
8,829	A*03:01:02	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
8,830	A*03:01:02	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
8,831	A*03:01:02	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
8,832	A*03:01:03	Bulgaria		0.0000	55	42_0_N_23_0_E
8,833	A*03:01:03	Cape Verde Northwestern Islands		0.0080	62	16_0_N_24_0_W
8,834	A*03:01:03	Cape Verde Southeastern Islands		0.0000	62	16_0_N_24_0_W
8,835	A*03:01:03	China North Han		0.0000	105	39_54_N_116_24_E
8,836	A*03:01:03	Guinea Bissau		0.0000	65	12_0_N_15_0_W
8,837	A*03:01:03	Japan Okinawa Ryukyuan		0.0040	143	26_4_N_127_8_E
8,838	A*03:01:03	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
8,839	A*03:01:03	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,840	A*03:01:03	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
8,841	A*03:01:03	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,842	A*03:01:03	South Korea pop 3		0.0000	485	37_33_N_126_59_E
8,843	A*03:01:04	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,844	A*03:01:04	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
8,845	A*03:01:04	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
8,846	A*03:01:04	USA NMDP Black South or Central American		0.0875	4,889	38_45_N_76_6_W
8,847	A*03:01:05	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,848	A*03:01:06	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,849	A*03:01:07	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,850	A*03:01:08	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,851	A*03:01:09	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,852	A*03:01:09	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,853	A*03:01:10	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,854	A*03:01:11	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,855	A*03:01:11	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,856	A*03:01:12	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,857	A*03:01:12	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
8,858	A*03:01:12	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
8,859	A*03:01:13	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,860	A*03:01:14	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,861	A*03:01:14	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
8,862	A*03:01:14	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
8,863	A*03:01:14	Russia Belgorod region	0.7	0.0033	153	50_46_N_37_27_E
8,864	A*03:01:14	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,865	A*03:01:14	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
8,866	A*03:01:15	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,867	A*03:01:15	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,868	A*03:01:16	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,869	A*03:01:17	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
8,870	A*03:01:18	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
8,871	A*03:01:23	USA NMDP Black South or Central American		0.0001	4,889	38_45_N_76_6_W
8,872	A*03:01:30	Pakistan Mixed Punjabi	0.2	0.0013	389	31_0_N_72_0_E
8,873	A*03:01:34	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
8,874	A*03:01:34	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
8,875	A*03:01:39	Latvia		0.0019	266	56_50_N_24_45_E
8,876	A*03:01:39	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,877	A*03:01:39	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
8,878	A*03:01:39	USA NMDP Black South or Central American		0.0001	4,889	38_45_N_76_6_W
8,879	A*03:01:42	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,880	A*03:01:50	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,881	A*03:01:58	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,882	A*03:01:58	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
8,883	A*03:01:59	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
8,884	A*03:01:71	Latvia		0.0019	266	56_50_N_24_45_E
8,885	A*03:01:71	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
8,886	A*03:01:71	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
8,887	A*03:01:74	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
8,888	A*03:01:74	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
8,889	A*03:01:77	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,890	A*03:01:83	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,891	A*03:01:84	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
8,892	A*03:01:96Q	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
8,893	A*03:01:96Q	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
8,894	A*03:02	Armenia combined Regions		0.0500	100	40_11_N_44_31_E
8,895	A*03:02	Austria	0.5	0.0020	200	48_13_N_16_21_E
8,896	A*03:02	Azores Terceira Island		0.0120	130	38_44_N_27_19_W
8,897	A*03:02	Belgium pop 3	0.5	0.0024	31,412	51_2_N_4_28_E
8,898	A*03:02	Brazil Belo Horizonte Caucasian	1.1	0.0050	95	19_55_S_43_56_W
8,899	A*03:02	Bulgaria		0.0000	55	42_0_N_23_0_E
8,900	A*03:02	Chile Santiago Mixed	1.0		70	33_26_S_70_39_W
8,901	A*03:02	China Beijing Shijiazhuang Tianjian Han		0.0020	618	39_54_N_116_24_E
8,902	A*03:02	China Hubei Han	0.4	0.0017	3,732	30_45_N_114_15_E
8,903	A*03:02	China Inner Mongolia Region		0.0050	102	36_0_N_102_0_E
8,904	A*03:02	China Jiangsu Han		0.0020	3,238	32_54_N_119_48_E
8,905	A*03:02	China Marrow Donor Registry		0.0020	600	39_54_N_116_23_E
8,906	A*03:02	China North Han		0.0000	105	39_54_N_116_24_E
8,907	A*03:02	China Qinghai Province Hui		0.0000	110	36_0_N_102_0_E
8,908	A*03:02	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
8,909	A*03:02	Colombia Bogotá Cord Blood	0.8	0.0038	1,463	4_39_N_73_52_W
8,910	A*03:02	Croatia		0.0000	150	45_0_N_15_0_E
8,911	A*03:02	Croatia pop 4		0.0051	4,000	45_48_N_15_57_E
8,912	A*03:02	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
8,913	A*03:02	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
8,914	A*03:02	Czech Republic		0.0000	106	50_0_N_14_0_E
8,915	A*03:02	Czech Republic NMDR		0.0032	5,099	49_48_N_15_30_E
8,916	A*03:02	Ecuador Amerindians		0.0079	63	0_13_S_78_30_W
8,917	A*03:02	England North West	0.7	0.0030	298	53_0_N_3_0_W
8,918	A*03:02	France French Bone Marrow Donor Registry		0.0021	42,623	48_51_N_2_21_E
8,919	A*03:02	France Southeast	0.8	0.0040	130	46_0_N_5_0_E
8,920	A*03:02	Georgia Svaneti Region Svan		0.0060	80	42_54_N_43_0_E
8,921	A*03:02	Georgia Tibilisi		0.0140	109	41_41_N_44_57_E
8,922	A*03:02	Georgia Tibilisi Kurd		0.0830	31	41_41_N_44_57_E
8,923	A*03:02	Germany DKMS - Austria minority		0.0038	1,698	48_31_N_9_3_E
8,924	A*03:02	Germany DKMS - Bosnia and Herzegovina minority		0.0010	1,028	48_31_N_9_3_E
8,925	A*03:02	Germany DKMS - China minority		0.0031	1,282	48_31_N_9_3_E
8,926	A*03:02	Germany DKMS - Croatia minority		0.0039	2,057	48_31_N_9_3_E
8,927	A*03:02	Germany DKMS - France minority		0.0036	1,406	48_31_N_9_3_E
8,928	A*03:02	Germany DKMS - German donors		0.0020	3,456,066	48_31_N_9_3_E
8,929	A*03:02	Germany DKMS - Greece minority		0.0071	1,894	48_31_N_9_3_E
8,930	A*03:02	Germany DKMS - Italy minority		0.0039	1,159	48_31_N_9_3_E
8,931	A*03:02	Germany DKMS - Netherlands minority		0.0033	1,374	48_31_N_9_3_E
8,932	A*03:02	Germany DKMS - Portugal minority		0.0030	1,176	48_31_N_9_3_E
8,933	A*03:02	Germany DKMS - Romania minority		0.0061	1,234	48_31_N_9_3_E
8,934	A*03:02	Germany DKMS - Spain minority		0.0036	1,107	48_31_N_9_3_E
8,935	A*03:02	Germany DKMS - Turkey minority		0.0282	4,856	48_31_N_9_3_E
8,936	A*03:02	Germany DKMS - United Kingdom minority		0.0014	1,043	48_31_N_9_3_E
8,937	A*03:02	Germany pop 6		0.0024	8,862	51_6_N_10_23_E
8,938	A*03:02	Germany pop 8		0.0039	39,689	48_24_N_9_58_E
8,939	A*03:02	Greece pop 6		0.0083	242	37_58_N_23_43_E
8,940	A*03:02	Hong Kong Chinese	0.0	0.0000	569	22_16_N_114_9_E
8,941	A*03:02	Hong Kong Chinese BMDR		0.0001	7,595	22_15_N_114_10_E
8,942	A*03:02	India Andhra Pradesh Golla		0.0170	111	17_5_N_78_5_E
8,943	A*03:02	India Central UCBB	1.6	0.0081	4,204	27_1_N_81_19_E
8,944	A*03:02	India Delhi pop 2	4.4	0.0220	90	28_36_N_77_13_E
8,945	A*03:02	India East UCBB	1.3	0.0067	2,403	22_25_N_88_18_E
8,946	A*03:02	India Khandesh Region Pawra		0.0000	50	20_54_N_74_46_E
8,947	A*03:02	India Mumbai Maratha		0.0000	91	18_58_N_72_49_E
8,948	A*03:02	India New Delhi		0.0230	71	28_37_N_77_13_E
8,949	A*03:02	India North pop 2		0.0100	72	25_0_N_74_0_E
8,950	A*03:02	India North UCBB	2.5	0.0123	5,849	28_36_N_77_11_E
8,951	A*03:02	India Northeast UCBB	1.4	0.0068	296	26_16_N_91_48_E
8,952	A*03:02	India South UCBB	0.9	0.0045	11,446	13_6_N_80_8_E
8,953	A*03:02	India Tamil Nadu		0.0047	2,492	11_32_N_78_52_E
8,954	A*03:02	India West Bhil		0.0000	50	20_54_N_74_46_E
8,955	A*03:02	India West Coast Parsi		0.0200	50	18_0_N_74_0_E
8,956	A*03:02	India West UCBB	2.2	0.0111	5,829	19_11_N_73_3_E
8,957	A*03:02	Iran Baloch		0.0060	100	29_29_N_60_52_E
8,958	A*03:02	Iran Gorgan		0.0160	64	36_50_N_54_43_E
8,959	A*03:02	Iran Kurd pop 2		0.0080	60	36_17_N_46_17_E
8,960	A*03:02	Iran Saqqez-Baneh Kurds		0.0083	60	36_14_N_46_16_E
8,961	A*03:02	Iran Tabriz Azeris		0.0155	97	38_4_N_46_17_E
8,962	A*03:02	Ireland Northern	0.3	0.0020	1,000	54_40_N_6_45_W
8,963	A*03:02	Israel Arab Druze		0.0250	101	31_8_N_35_2_E
8,964	A*03:02	Israel Arab pop 2		0.0414	12,301	32_8_N_34_52_E
8,965	A*03:02	Israel Argentina Jews		0.0797	4,307	32_8_N_34_52_E
8,966	A*03:02	Israel Ashkenazi and Non Ashkenazi Jews		0.0170	146	31_8_N_35_2_E
8,967	A*03:02	Israel Ashkenazi Jews pop 3		0.0680	4,625	32_8_N_34_52_E
8,968	A*03:02	Israel Bukhara Jews		0.1306	2,317	32_8_N_34_52_E
8,969	A*03:02	Israel Druze		0.0419	5,914	32_41_N_35_17_E
8,970	A*03:02	Israel Ethiopia Jews		0.0730	5,928	32_8_N_34_52_E
8,971	A*03:02	Israel Georgia Jews		0.1931	4,471	32_10_N_34_53_E
8,972	A*03:02	Israel Iran Jews		0.0776	8,153	32_8_N_34_52_E
8,973	A*03:02	Israel Iraq Jews		0.1075	13,270	32_10_N_34_52_E
8,974	A*03:02	Israel Kavkazi Jews		0.0748	2,840	32_10_N_34_52_E
8,975	A*03:02	Israel Libya Jews		0.0779	3,739	32_10_N_34_52_E
8,976	A*03:02	Israel Morocco Jews		0.0997	36,718	32_10_N_34_52_E
8,977	A*03:02	Israel Poland Jews		0.0633	13,871	32_10_N_34_52_E
8,978	A*03:02	Israel Tunisia Jews		0.0661	9,070	32_10_N_34_52_E
8,979	A*03:02	Israel USA Jews		0.0799	6,058	32_10_N_34_52_E
8,980	A*03:02	Israel USSR Jews		0.0863	45,681	32_9_N_34_50_E
8,981	A*03:02	Israel YemenJews		0.0380	15,542	32_9_N_34_50_E
8,982	A*03:02	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
8,983	A*03:02	Italy pop 5		0.0150	975	45_28_N_9_11_E
8,984	A*03:02	Japan Central		0.0010	371	35_41_N_139_46_E
8,985	A*03:02	Japan pop 16		0.0009	18,604	35_41_N_139_46_E
8,986	A*03:02	Japan pop 3		0.0010	1,018	35_41_N_139_46_E
8,987	A*03:02	Kenya Luo		0.0000	265	0_0_S_34_6_E
8,988	A*03:02	Kenya Nandi		0.0000	240	0_0_S_35_0_E
8,989	A*03:02	Kosovo	0.8	0.0040	124	42_34_N_21_0_E
8,990	A*03:02	Libya Cyrenaica		0.0040	118	32_7_N_20_4_E
8,991	A*03:02	Madeira		0.0030	185	32_44_N_16_58_W
8,992	A*03:02	Malaysia Mandailing	3.7	0.0190	27	2_30_N_112_30_E
8,993	A*03:02	Malaysia Peninsular Indian	1.5	0.0074	271	3_9_N_101_57_E
8,994	A*03:02	Malaysia Peninsular Malay	0.4	0.0021	951	3_9_N_101_57_E
8,995	A*03:02	Mali Bandiagara		0.0000	138	14_4_N_3_6_W
8,996	A*03:02	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
8,997	A*03:02	Mexico Mexico City Mestizo pop 2		0.0064	234	19_26_N_99_8_W
8,998	A*03:02	Mongolia Buryat		0.0070	141	47_6_N_118_6_E
8,999	A*03:02	Morocco Atlantic Coast Chaouya		0.0070	98	30_22_N_7_22_W
9,000	A*03:02	Morocco Nador Metalsa pop 2		0.0410	73	35_10_N_2_56_W
9,001	A*03:02	Morocco Settat Chaouya	1.4	0.0070	98	33_0_N_7_36_W
9,002	A*03:02	Netherlands Leiden		0.0030	1,305	52_9_N_4_31_E
9,003	A*03:02	Oman	2.5	0.0130	118	22_0_N_56_0_E
9,004	A*03:02	Pakistan Baloch		0.0160	66	30_0_N_67_0_E
9,005	A*03:02	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
9,006	A*03:02	Pakistan Kalash		0.0170	69	32_0_N_70_0_E
9,007	A*03:02	Pakistan Karachi Parsi		0.0280	91	24_49_N_66_59_E
9,008	A*03:02	Pakistan Mixed Pathan		0.0100	100	32_0_N_70_0_E
9,009	A*03:02	Pakistan Mixed Sindhi		0.0130	101	24_0_N_68_0_E
9,010	A*03:02	Panama		0.0023	462	8_59_N_79_37_W
9,011	A*03:02	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
9,012	A*03:02	Poland		0.0030	200	51_7_N_17_2_E
9,013	A*03:02	Poland DKMS		0.0034	20,653	52_8_N_19_23_E
9,014	A*03:02	Portugal Azores Terceira Island	1.8	0.0088	130	38_43_N_27_13_W
9,015	A*03:02	Portugal Center		0.0200	50	39_0_N_8_0_W
9,016	A*03:02	Portugal North		0.0000	46	41_0_N_8_0_W
9,017	A*03:02	Portugal South		0.0000	49	37_0_N_8_0_W
9,018	A*03:02	Romania	0.0	0.0000	348	46_0_N_25_0_E
9,019	A*03:02	Russia Karelia		0.0014	1,075	63_9_N_32_59_E
9,020	A*03:02	Russia Tuva pop 2		0.0030	169	51_30_N_95_5_E
9,021	A*03:02	Saudi Arabia Guraiat and Hail	2.8	0.0140	213	27_31_N_41_41_E
9,022	A*03:02	Saudi Arabia pop 5	1.9	0.0095	158	24_38_N_46_43_E
9,023	A*03:02	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
9,024	A*03:02	South Africa Natal Tamil		0.0100	51	30_0_S_31_0_E
9,025	A*03:02	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
9,026	A*03:02	South Korea pop 10		0.0029	4,128	37_33_N_126_58_E
9,027	A*03:02	South Korea pop 3		0.0020	485	37_33_N_126_59_E
9,028	A*03:02	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.7	0.0036	4,335	41_22_N_2_11_E
9,029	A*03:02	Sudan Mixed	4.5	0.0230	200	5_0_N_35_0_E
9,030	A*03:02	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
9,031	A*03:02	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
9,032	A*03:02	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
9,033	A*03:02	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
9,034	A*03:02	Taiwan Han Chinese		0.0010	504	23_46_N_121_0_E
9,035	A*03:02	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
9,036	A*03:02	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
9,037	A*03:02	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
9,038	A*03:02	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
9,039	A*03:02	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
9,040	A*03:02	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
9,041	A*03:02	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
9,042	A*03:02	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
9,043	A*03:02	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
9,044	A*03:02	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
9,045	A*03:02	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
9,046	A*03:02	Taiwan Tzu Chi Morrow Donor Registry		0.0002	46,682	25_2_N_121_38_E
9,047	A*03:02	Taiwan Tzu Chi Morrow Donor Registry Aborigine		0.0000	233	25_2_N_121_38_E
9,048	A*03:02	Tunisia	1.0	0.0050	100	35_0_N_9_0_E
9,049	A*03:02	Uganda Kampala		0.0030	161	0_3_N_32_6_E
9,050	A*03:02	United Arab Emirates Abu Dhabi		0.0096	52	24_28_N_54_22_E
9,051	A*03:02	United Arab Emirates Pop 1		0.0150	570	24_42_N_54_82_E
9,052	A*03:02	USA African American		0.0000	252	30_0_N_95_0_W
9,053	A*03:02	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,054	A*03:02	USA African American pop 4		0.0008	2,411	44_58_N_93_15_W
9,055	A*03:02	USA Alaska Yupik		0.0020	252	60_48_N_161_26_W
9,056	A*03:02	USA Asian		0.0000	358	30_0_N_95_0_W
9,057	A*03:02	USA Asian pop 2		0.0054	1,772	44_58_N_93_15_W
9,058	A*03:02	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,059	A*03:02	USA Caucasian pop 2		0.0060	265	30_0_N_95_0_W
9,060	A*03:02	USA Caucasian pop 4		0.0019	1,070	38_53_N_77_2_W
9,061	A*03:02	USA Eastern European		0.0060	558	38_53_N_77_2_W
9,062	A*03:02	USA Hispanic		0.0060	234	30_0_N_95_0_W
9,063	A*03:02	USA Hispanic pop 2		0.0018	1,999	44_58_N_93_15_W
9,064	A*03:02	USA Mexican American Mestizo		0.0010	553	38_53_N_77_2_W
9,065	A*03:02	USA NMDP African		0.0016	28,557	38_45_N_76_6_W
9,066	A*03:02	USA NMDP African American pop 2		0.0004	416,581	38_45_N_76_6_W
9,067	A*03:02	USA NMDP American Indian South or Central America		0.0010	5,926	38_45_N_76_6_W
9,068	A*03:02	USA NMDP Caribean Black		0.0004	33,328	38_45_N_76_6_W
9,069	A*03:02	USA NMDP Caribean Hispanic		0.0012	115,374	38_45_N_76_6_W
9,070	A*03:02	USA NMDP Caribean Indian		0.0020	14,339	38_45_N_76_6_W
9,071	A*03:02	USA NMDP Chinese		0.0006	99,672	38_45_N_76_6_W
9,072	A*03:02	USA NMDP European Caucasian		0.0024	1,242,890	38_45_N_76_6_W
9,073	A*03:02	USA NMDP Filipino		0.0004	50,614	38_45_N_76_6_W
9,074	A*03:02	USA NMDP Hawaiian or other Pacific Islander		0.0017	11,499	38_45_N_76_6_W
9,075	A*03:02	USA NMDP Hispanic South or Central American		0.0025	146,714	38_45_N_76_6_W
9,076	A*03:02	USA NMDP Japanese		0.0010	24,582	38_45_N_76_6_W
9,077	A*03:02	USA NMDP Korean		0.0026	77,584	38_45_N_76_6_W
9,078	A*03:02	USA NMDP Mexican or Chicano		0.0016	261,235	38_45_N_76_6_W
9,079	A*03:02	USA NMDP Middle Eastern or North Coast of Africa		0.0175	70,890	38_45_N_76_6_W
9,080	A*03:02	USA NMDP North American Amerindian		0.0015	35,791	38_45_N_76_6_W
9,081	A*03:02	USA NMDP South Asian Indian		0.0086	185,391	38_45_N_76_6_W
9,082	A*03:02	USA NMDP Southeast Asian		0.0080	27,978	38_45_N_76_6_W
9,083	A*03:02	USA NMDP Vietnamese		0.0020	43,540	38_45_N_76_6_W
9,084	A*03:02	USA North American Native		0.0030	187	30_0_N_95_0_W
9,085	A*03:02	USA Philadelphia Caucasian	0.7	0.0040	141	39_56_N_75_10_W
9,086	A*03:02	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
9,087	A*03:02	USA San Diego	0.3	0.0020	496	32_50_N_117_15_W
9,088	A*03:02	USA South Texas Hispanic		0.0080	194	29_0_N_98_0_W
9,089	A*03:02	USA Spain Ancestry		0.0520	279	38_0_N_77_0_W
9,090	A*03:02	Vietnam Hanoi Kinh pop 2		0.0030	170	21_2_N_105_51_E
9,091	A*03:02	Zambia Lusaka		0.0000	44	15_25_S_28_17_E
9,092	A*03:02:01	Brazil Barra Mansa Rio State Black		0.0068	73	22_32_S_44_10_W
9,093	A*03:02:01	Brazil Barra Mansa Rio State Caucasian		0.0025	405	22_32_S_44_10_W
9,094	A*03:02:01	Brazil Rio de Janeiro Black		0.0074	68	22_54_S_43_12_W
9,095	A*03:02:01	Brazil Rio de Janeiro Caucasian		0.0019	521	22_54_S_43_12_W
9,096	A*03:02:01	Brazil Rio de Janeiro Parda		0.0059	170	22_54_S_43_12_W
9,097	A*03:02:01	China Zhejiang Han		0.0009	1,734	27_2_N_118_1_E
9,098	A*03:02:01	Costa Rica Central Valley Mestizo (G)	1.0	0.0050	221	9_55_N_84_5_W
9,099	A*03:02:01	England Blood Donors of Mixed Ethnicity	0.2	0.0010	519	52_21_N_1_10_W
9,100	A*03:02:01	India Andhra Pradesh Telugu Speaking	0.5	0.0027	186	13_4_N_80_14_E
9,101	A*03:02:01	India Kerala Malayalam speaking	0.3	0.0010	356	13_1_N_80_15_E
9,102	A*03:02:01	Poland BMR	0.5	0.0026	23,595	52_0_N_19_22_E
9,103	A*03:02:01	Russia Bashkortostan, Bashkirs	0.8	0.0042	120	54_0_N_55_0_E
9,104	A*03:02:01	Russia Moscow Bashkir	0.7	0.0034	442	55_46_N_37_41_E
9,105	A*03:02:01	Russia Moscow Buryat	2.7	0.0134	149	55_46_N_37_41_E
9,106	A*03:02:01	Russia Moscow Kabardin	4.2	0.0216	834	55_46_N_37_41_E
9,107	A*03:02:01	Russia Moscow Kalmyk	2.5	0.0125	120	55_46_N_37_41_E
9,108	A*03:02:01	Russia Moscow Kazakh	2.0	0.0100	299	55_46_N_37_41_E
9,109	A*03:02:01	Russia Moscow Mansi	2.6	0.0132	38	55_46_N_37_41_E
9,110	A*03:02:01	Russia Moscow Megrel	7.4	0.0417	230	55_46_N_37_41_E
9,111	A*03:02:01	Russia Moscow Russians	0.5	0.0027	13,147	55_46_N_37_41_E
9,112	A*03:02:01	Russia Moscow Similar genetically to Azerbaijan	6.5	0.0324	170	55_46_N_37_41_E
9,113	A*03:02:01	Russia Moscow Tatars	0.9	0.0046	2,512	55_46_N_37_41_E
9,114	A*03:02:01	Russia Moscow Uzbek	3.5	0.0177	198	55_46_N_37_41_E
9,115	A*03:02:01	Russia Nizhny Novgorod, Russians	0.5	0.0023	1,510	56_19_N_43_59_E
9,116	A*03:02:01	Saudi Arabia Al-Madinah (G)		0.0096	102	24_28_N_39_36_E
9,117	A*03:02:01	Saudi Arabia Jeddah (G)		0.0147	476	21_32_N_39_10_E
9,118	A*03:02:01	Saudi Arabia Makkah (G)		0.0174	344	21_25_N_39_49_E
9,119	A*03:02:01	Saudi Arabia pop 6 (G)		0.0157	28,927	23_52_N_45_4_E
9,120	A*03:02:01	Saudi Arabia Taif (G)		0.0192	104	21_16_N_40_24_E
9,121	A*03:02:01	South African Indian population	2.0	0.0100	50	29_22_S_24_31_E
9,122	A*03:02:01	Vietnam Kinh	2.0	0.0099	101	14_5_N_108_27_E
9,123	A*03:03N	Bulgaria		0.0000	55	42_0_N_23_0_E
9,124	A*03:03N	China North Han		0.0000	105	39_54_N_116_24_E
9,125	A*03:03N	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,126	A*03:03N	Croatia		0.0000	150	45_0_N_15_0_E
9,127	A*03:03N	Czech Republic		0.0000	106	50_0_N_14_0_E
9,128	A*03:03N	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
9,129	A*03:03N	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
9,130	A*03:03N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,131	A*03:03N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,132	A*03:03N	South Korea pop 3		0.0000	485	37_33_N_126_59_E
9,133	A*03:03N	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,134	A*03:03N	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
9,135	A*03:03N	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,136	A*03:03N	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,137	A*03:03N	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
9,138	A*03:03N	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
9,139	A*03:04	Bulgaria		0.0000	55	42_0_N_23_0_E
9,140	A*03:04	China North Han		0.0000	105	39_54_N_116_24_E
9,141	A*03:04	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,142	A*03:04	Croatia		0.0000	150	45_0_N_15_0_E
9,143	A*03:04	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,144	A*03:04	Israel Arab pop 2		0.0000	12,301	32_8_N_34_52_E
9,145	A*03:04	Israel Argentina Jews		0.0007	4,307	32_8_N_34_52_E
9,146	A*03:04	Israel Ashkenazi Jews pop 3		0.0000	4,625	32_8_N_34_52_E
9,147	A*03:04	Israel Bukhara Jews		0.0004	2,317	32_8_N_34_52_E
9,148	A*03:04	Israel Druze		0.0001	5,914	32_41_N_35_17_E
9,149	A*03:04	Israel Ethiopia Jews		0.0003	5,928	32_8_N_34_52_E
9,150	A*03:04	Israel Georgia Jews		0.0001	4,471	32_10_N_34_53_E
9,151	A*03:04	Israel Iran Jews		0.0001	8,153	32_8_N_34_52_E
9,152	A*03:04	Israel Iraq Jews		0.0007	13,270	32_10_N_34_52_E
9,153	A*03:04	Israel Kavkazi Jews		0.0004	2,840	32_10_N_34_52_E
9,154	A*03:04	Israel Libya Jews		0.0001	3,739	32_10_N_34_52_E
9,155	A*03:04	Israel Morocco Jews		0.0009	36,718	32_10_N_34_52_E
9,156	A*03:04	Israel Poland Jews		0.0004	13,871	32_10_N_34_52_E
9,157	A*03:04	Israel Tunisia Jews		0.0001	9,070	32_10_N_34_52_E
9,158	A*03:04	Israel USA Jews		0.0012	6,058	32_10_N_34_52_E
9,159	A*03:04	Israel USSR Jews		0.0008	45,681	32_9_N_34_50_E
9,160	A*03:04	Israel YemenJews		0.0002	15,542	32_9_N_34_50_E
9,161	A*03:04	Italy Bergamo	0.0	0.0000	101	45_42_N_9_40_E
9,162	A*03:04	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
9,163	A*03:04	Malaysia Peninsular Malay	0.1	0.0005	951	3_9_N_101_57_E
9,164	A*03:04	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
9,165	A*03:04	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,166	A*03:04	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,167	A*03:04	South Korea pop 3		0.0000	485	37_33_N_126_59_E
9,168	A*03:04	Sudan Mixed	0.0	0.0000	200	5_0_N_35_0_E
9,169	A*03:04	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,170	A*03:04	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
9,171	A*03:04	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,172	A*03:04	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
9,173	A*03:04	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,174	A*03:04	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
9,175	A*03:04	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,176	A*03:04	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
9,177	A*03:04	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
9,178	A*03:05	Bulgaria		0.0000	55	42_0_N_23_0_E
9,179	A*03:05	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
9,180	A*03:05	China North Han		0.0000	105	39_54_N_116_24_E
9,181	A*03:05	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,182	A*03:05	Croatia		0.0000	150	45_0_N_15_0_E
9,183	A*03:05	Croatia pop 4		0.0006	4,000	45_48_N_15_57_E
9,184	A*03:05	Germany DKMS - German donors		0.0001	3,456,066	48_31_N_9_3_E
9,185	A*03:05	Germany DKMS - Greece minority		0.0003	1,894	48_31_N_9_3_E
9,186	A*03:05	Germany pop 6		0.0001	8,862	51_6_N_10_23_E
9,187	A*03:05	Germany pop 8		0.0001	39,689	48_24_N_9_58_E
9,188	A*03:05	India North UCBB	0.0	0.0001	5,849	28_36_N_77_11_E
9,189	A*03:05	Israel Ethiopia Jews		0.0000	5,928	32_8_N_34_52_E
9,190	A*03:05	Italy pop 5		0.0030	975	45_28_N_9_11_E
9,191	A*03:05	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
9,192	A*03:05	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,193	A*03:05	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,194	A*03:05	Philippines Ivatan	0.0	0.0000	50	20_22_N_121_56_E
9,195	A*03:05	South Korea pop 3		0.0000	485	37_33_N_126_59_E
9,196	A*03:05	Sudan Mixed	0.0	0.0000	200	5_0_N_35_0_E
9,197	A*03:05	Taiwan Ami	0.0	0.0000	98	23_0_N_121_0_E
9,198	A*03:05	Taiwan Atayal	0.0	0.0000	106	24_0_N_121_0_E
9,199	A*03:05	Taiwan Bunun	0.0	0.0000	101	23_46_N_121_0_E
9,200	A*03:05	Taiwan Hakka	0.0	0.0000	55	24_0_N_121_0_E
9,201	A*03:05	Taiwan Minnan pop 1	0.0	0.0000	102	24_0_N_121_0_E
9,202	A*03:05	Taiwan Paiwan	0.0	0.0000	51	23_0_N_121_0_E
9,203	A*03:05	Taiwan Pazeh	0.0	0.0000	55	25_0_N_121_30_E
9,204	A*03:05	Taiwan Puyuma	0.0	0.0000	50	23_0_N_121_0_E
9,205	A*03:05	Taiwan Rukai	0.0	0.0000	50	23_0_N_121_0_E
9,206	A*03:05	Taiwan Saisiat	0.0	0.0000	51	24_0_N_121_0_E
9,207	A*03:05	Taiwan Siraya	0.0	0.0000	51	24_0_N_121_0_E
9,208	A*03:05	Taiwan Tao	0.0	0.0000	50	24_0_N_121_0_E
9,209	A*03:05	Taiwan Taroko	0.0	0.0000	55	24_0_N_121_0_E
9,210	A*03:05	Taiwan Thao	0.0	0.0000	30	24_0_N_121_0_E
9,211	A*03:05	Taiwan Tsou	0.0	0.0000	51	24_0_N_121_0_E
9,212	A*03:05	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,213	A*03:05	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
9,214	A*03:05	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
9,215	A*03:05	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,216	A*03:05	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
9,217	A*03:05	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
9,218	A*03:05	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
9,219	A*03:05	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
9,220	A*03:05	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,221	A*03:05	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
9,222	A*03:05	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
9,223	A*03:05	USA NMDP Middle Eastern or North Coast of Africa		0.0001	70,890	38_45_N_76_6_W
9,224	A*03:05	USA NMDP North American Amerindian		0.0001	35,791	38_45_N_76_6_W
9,225	A*03:05	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
9,226	A*03:05	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,227	A*03:05	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
9,228	A*03:05	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
9,229	A*03:05:01	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,230	A*03:06	Bulgaria		0.0000	55	42_0_N_23_0_E
9,231	A*03:06	China North Han		0.0000	105	39_54_N_116_24_E
9,232	A*03:06	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,233	A*03:06	Croatia		0.0000	150	45_0_N_15_0_E
9,234	A*03:06	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,235	A*03:06	Israel Ethiopia Jews		0.0000	5,928	32_8_N_34_52_E
9,236	A*03:06	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
9,237	A*03:06	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,238	A*03:06	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,239	A*03:06	South Korea pop 3		0.0000	485	37_33_N_126_59_E
9,240	A*03:06	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,241	A*03:06	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,242	A*03:06	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
9,243	A*03:06	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,244	A*03:06	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
9,245	A*03:06	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,246	A*03:06	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
9,247	A*03:06	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
9,248	A*03:07	Bulgaria		0.0000	55	42_0_N_23_0_E
9,249	A*03:07	China Inner Mongolia Region		0.0050	102	36_0_N_102_0_E
9,250	A*03:07	China North Han		0.0000	105	39_54_N_116_24_E
9,251	A*03:07	China Qinghai Province Hui		0.0000	110	36_0_N_102_0_E
9,252	A*03:07	China Tibet Region Tibetan		0.0030	158	29_39_N_91_7_E
9,253	A*03:07	Croatia		0.0000	150	45_0_N_15_0_E
9,254	A*03:07	Germany DKMS - China minority		0.0004	1,282	48_31_N_9_3_E
9,255	A*03:07	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,256	A*03:07	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
9,257	A*03:07	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,258	A*03:07	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,259	A*03:07	Saudi Arabia pop 5	1.3	0.0063	158	24_38_N_46_43_E
9,260	A*03:07	South Korea pop 3		0.0000	485	37_33_N_126_59_E
9,261	A*03:07	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,262	A*03:07	USA African American pop 4		0.0000	2,411	44_58_N_93_15_W
9,263	A*03:07	USA Asian pop 2		0.0000	1,772	44_58_N_93_15_W
9,264	A*03:07	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,265	A*03:07	USA Hispanic pop 2		0.0000	1,999	44_58_N_93_15_W
9,266	A*03:07	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
9,267	A*03:07	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
9,268	A*03:07	USA NMDP Caribean Hispanic		0.0001	115,374	38_45_N_76_6_W
9,269	A*03:07	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,270	A*03:07	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
9,271	A*03:07	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
9,272	A*03:07	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
9,273	A*03:07	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,274	A*03:07	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
9,275	A*03:07	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
9,276	A*03:07:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,277	A*03:08	Bulgaria		0.0000	55	42_0_N_23_0_E
9,278	A*03:08	China Beijing Shijiazhuang Tianjian Han		0.0020	618	39_54_N_116_24_E
9,279	A*03:08	China Inner Mongolia Region		0.0100	102	36_0_N_102_0_E
9,280	A*03:08	China North Han		0.0100	105	39_54_N_116_24_E
9,281	A*03:08	China Qinghai Province Hui		0.0000	110	36_0_N_102_0_E
9,282	A*03:08	China Tibet Region Tibetan		0.0060	158	29_39_N_91_7_E
9,283	A*03:08	Croatia		0.0000	150	45_0_N_15_0_E
9,284	A*03:08	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,285	A*03:08	Germany pop 6		0.0001	8,862	51_6_N_10_23_E
9,286	A*03:08	India Tamil Nadu		0.0003	2,492	11_32_N_78_52_E
9,287	A*03:08	Mexico Guadalajara Mestizo pop 2		0.0100	103	20_40_N_103_21_W
9,288	A*03:08	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
9,289	A*03:08	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,290	A*03:08	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,291	A*03:08	Saudi Arabia pop 6 (G)		0.0000	28,927	23_52_N_45_4_E
9,292	A*03:08	South Korea pop 3		0.0000	485	37_33_N_126_59_E
9,293	A*03:08	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,294	A*03:08	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,295	A*03:08	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
9,296	A*03:08	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
9,297	A*03:08	USA NMDP Caribean Hispanic		0.0000	115,374	38_45_N_76_6_W
9,298	A*03:08	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,299	A*03:08	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
9,300	A*03:08	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
9,301	A*03:08	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,302	A*03:08	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
9,303	A*03:08	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
9,304	A*03:09	Bulgaria		0.0000	55	42_0_N_23_0_E
9,305	A*03:09	China North Han		0.0000	105	39_54_N_116_24_E
9,306	A*03:09	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,307	A*03:09	Croatia		0.0000	150	45_0_N_15_0_E
9,308	A*03:09	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
9,309	A*03:09	Israel Morocco Jews		0.0001	36,718	32_10_N_34_52_E
9,310	A*03:09	Israel USA Jews		0.0001	6,058	32_10_N_34_52_E
9,311	A*03:09	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,312	A*03:09	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,313	A*03:09	Saudi Arabia pop 6 (G)		0.0000	28,927	23_52_N_45_4_E
9,314	A*03:09	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,315	A*03:09	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,316	A*03:09	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,317	A*03:09	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
9,318	A*03:09	USA NMDP North American Amerindian		0.0000	35,791	38_45_N_76_6_W
9,319	A*03:09	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,320	A*03:09	USA San Antonio Caucasian		0.0000	222	29_23_N_98_33_W
9,321	A*03:09	USA South Texas Hispanic		0.0000	194	29_0_N_98_0_W
9,322	A*03:10	Bulgaria		0.0000	55	42_0_N_23_0_E
9,323	A*03:10	China Beijing Shijiazhuang Tianjian Han		0.0010	618	39_54_N_116_24_E
9,324	A*03:10	China North Han		0.0000	105	39_54_N_116_24_E
9,325	A*03:10	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,326	A*03:10	Croatia		0.0000	150	45_0_N_15_0_E
9,327	A*03:10	India South UCBB	0.0	0.0000	11,446	13_6_N_80_8_E
9,328	A*03:10	Malaysia Peninsular Indian	0.4	0.0018	271	3_9_N_101_57_E
9,329	A*03:10	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,330	A*03:10	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,331	A*03:10	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,332	A*03:10	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,333	A*03:10	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
9,334	A*03:10	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,335	A*03:11N	China North Han		0.0000	105	39_54_N_116_24_E
9,336	A*03:11N	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,337	A*03:11N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,338	A*03:11N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,339	A*03:11N	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,340	A*03:11N	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,341	A*03:12	China North Han		0.0000	105	39_54_N_116_24_E
9,342	A*03:12	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,343	A*03:12	Germany DKMS - Greece minority		0.0003	1,894	48_31_N_9_3_E
9,344	A*03:12	India Tamil Nadu		0.0004	2,492	11_32_N_78_52_E
9,345	A*03:12	Libya Cyrenaica		0.0040	118	32_7_N_20_4_E
9,346	A*03:12	Mexico Guadalajara Mestizo pop 2		0.0050	103	20_40_N_103_21_W
9,347	A*03:12	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,348	A*03:12	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,349	A*03:12	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
9,350	A*03:12	Russia Moscow Kabardin	0.5	0.0024	834	55_46_N_37_41_E
9,351	A*03:12	Russia Moscow Kalmyk	0.8	0.0042	120	55_46_N_37_41_E
9,352	A*03:12	Russia Moscow Mansi	2.6	0.0132	38	55_46_N_37_41_E
9,353	A*03:12	Russia Moscow Megrel	0.9	0.0044	230	55_46_N_37_41_E
9,354	A*03:12	Russia Moscow Russians	0.7	0.0034	13,147	55_46_N_37_41_E
9,355	A*03:12	Russia Moscow Tatars	0.6	0.0030	2,512	55_46_N_37_41_E
9,356	A*03:12	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
9,357	A*03:12	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,358	A*03:12	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,359	A*03:12	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,360	A*03:12	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,361	A*03:13	China North Han		0.0000	105	39_54_N_116_24_E
9,362	A*03:13	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,363	A*03:13	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,364	A*03:13	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,365	A*03:13	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,366	A*03:13	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,367	A*03:13	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
9,368	A*03:13	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
9,369	A*03:13	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
9,370	A*03:13	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,371	A*03:14	China North Han		0.0000	105	39_54_N_116_24_E
9,372	A*03:14	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,373	A*03:14	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,374	A*03:14	Israel Iraq Jews		0.0000	13,270	32_10_N_34_52_E
9,375	A*03:14	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,376	A*03:14	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,377	A*03:14	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,378	A*03:14	USA African American Bethesda	0.0	0.0000	187	38_0_N_78_0_W
9,379	A*03:14	USA Caucasian Bethesda	0.0	0.0000	307	38_0_N_78_0_W
9,380	A*03:14	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,381	A*03:14	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,382	A*03:15	China North Han		0.0000	105	39_54_N_116_24_E
9,383	A*03:15	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,384	A*03:15	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,385	A*03:15	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,386	A*03:15	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,387	A*03:15	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,388	A*03:15	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,389	A*03:16	China North Han		0.0000	105	39_54_N_116_24_E
9,390	A*03:16	China Tibet Region Tibetan		0.0000	158	29_39_N_91_7_E
9,391	A*03:16	Croatia pop 4		0.0001	4,000	45_48_N_15_57_E
9,392	A*03:16	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,393	A*03:16	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,394	A*03:16	USA NMDP Chinese		0.0000	99,672	38_45_N_76_6_W
9,395	A*03:16	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,396	A*03:16	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
9,397	A*03:16	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
9,398	A*03:16	USA Philadelphia Caucasian	0.0	0.0000	141	39_56_N_75_10_W
9,399	A*03:17	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,400	A*03:17	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,401	A*03:17	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,402	A*03:18	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,403	A*03:18	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,404	A*03:18	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,405	A*03:18	Russia Moscow Megrel	0.4	0.0022	230	55_46_N_37_41_E
9,406	A*03:18	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,407	A*03:18	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
9,408	A*03:18	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
9,409	A*03:18	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,410	A*03:18	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
9,411	A*03:19	Israel USA Jews		0.0001	6,058	32_10_N_34_52_E
9,412	A*03:19	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,413	A*03:19	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,414	A*03:20	Czech Republic NMDR		0.0005	5,099	49_48_N_15_30_E
9,415	A*03:20	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,416	A*03:20	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,417	A*03:20	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,418	A*03:20	Russia Nizhny Novgorod, Russians	0.2	0.0010	1,510	56_19_N_43_59_E
9,419	A*03:20	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	0.0	0.0001	4,335	41_22_N_2_11_E
9,420	A*03:21N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,421	A*03:21N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,422	A*03:22	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,423	A*03:22	Germany DKMS - United Kingdom minority		0.0005	1,043	48_31_N_9_3_E
9,424	A*03:22	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,425	A*03:22	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,426	A*03:22	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,427	A*03:22:01	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
9,428	A*03:22:01	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
9,429	A*03:23	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,430	A*03:23	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,431	A*03:23	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,432	A*03:23	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,433	A*03:24	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,434	A*03:24	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,435	A*03:24	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
9,436	A*03:24	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
9,437	A*03:24	Russia Moscow Russians	0.1	0.0007	13,147	55_46_N_37_41_E
9,438	A*03:24	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
9,439	A*03:24	USA NMDP African		0.0000	28,557	38_45_N_76_6_W
9,440	A*03:24	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
9,441	A*03:25	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,442	A*03:25	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,443	A*03:25	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,444	A*03:25	USA Eastern European		0.0010	558	38_53_N_77_2_W
9,445	A*03:25	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,446	A*03:26	India East UCBB	0.1	0.0004	2,403	22_25_N_88_18_E
9,447	A*03:26	India North UCBB	0.0	0.0001	5,849	28_36_N_77_11_E
9,448	A*03:26	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,449	A*03:26	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,450	A*03:26	Poland BMR	0.0	0.0001	23,595	52_0_N_19_22_E
9,451	A*03:26	Tanzania Maasai	0.3	0.0015	336	3_11_S_37_4_E
9,452	A*03:27	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,453	A*03:27	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,454	A*03:27	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,455	A*03:27	USA NMDP Black South or Central American		0.0608	4,889	38_45_N_76_6_W
9,456	A*03:28	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,457	A*03:28	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,458	A*03:28	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,459	A*03:29	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,460	A*03:29	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,461	A*03:29	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,462	A*03:29	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,463	A*03:30	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,464	A*03:30	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,465	A*03:30	USA NMDP Hispanic South or Central American		0.0000	146,714	38_45_N_76_6_W
9,466	A*03:31	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,467	A*03:31	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,468	A*03:31	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,469	A*03:32	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,470	A*03:32	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,471	A*03:32	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
9,472	A*03:33	Czech Republic NMDR		0.0002	5,099	49_48_N_15_30_E
9,473	A*03:33	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,474	A*03:33	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,475	A*03:33	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,476	A*03:33	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
9,477	A*03:33	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,478	A*03:34	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,479	A*03:34	Malaysia Peninsular Indian	0.4	0.0018	271	3_9_N_101_57_E
9,480	A*03:34	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,481	A*03:34	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,482	A*03:34	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,483	A*03:35	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,484	A*03:35	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,485	A*03:35	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
9,486	A*03:36N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,487	A*03:36N	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,488	A*03:37	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,489	A*03:37	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,490	A*03:38	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,491	A*03:38	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,492	A*03:38	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,493	A*03:38	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,494	A*03:39	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,495	A*03:39	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,496	A*03:40	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,497	A*03:40	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,498	A*03:41	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,499	A*03:41	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,500	A*03:42	Israel Arab pop 2		0.0000	12,301	32_8_N_34_52_E
9,501	A*03:42	Israel Ashkenazi Jews pop 3		0.0000	4,625	32_8_N_34_52_E
9,502	A*03:42	Israel Druze		0.0001	5,914	32_41_N_35_17_E
9,503	A*03:42	Israel Ethiopia Jews		0.0002	5,928	32_8_N_34_52_E
9,504	A*03:42	Israel Georgia Jews		0.0001	4,471	32_10_N_34_53_E
9,505	A*03:42	Israel Iran Jews		0.0001	8,153	32_8_N_34_52_E
9,506	A*03:42	Israel Kavkazi Jews		0.0002	2,840	32_10_N_34_52_E
9,507	A*03:42	Israel Libya Jews		0.0001	3,739	32_10_N_34_52_E
9,508	A*03:42	Israel Tunisia Jews		0.0001	9,070	32_10_N_34_52_E
9,509	A*03:42	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,510	A*03:42	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,511	A*03:42	Russia Moscow Tatars	0.1	0.0004	2,512	55_46_N_37_41_E
9,512	A*03:43	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,513	A*03:43	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,514	A*03:43	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,515	A*03:44	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,516	A*03:44	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,517	A*03:44	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,518	A*03:45	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,519	A*03:45	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,520	A*03:46	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,521	A*03:46	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,522	A*03:46	Netherlands Leiden		0.0000	1,305	52_9_N_4_31_E
9,523	A*03:46	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,524	A*03:46	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,525	A*03:47	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,526	A*03:47	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,527	A*03:47	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,528	A*03:48	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,529	A*03:48	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
9,530	A*03:48	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,531	A*03:48	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,532	A*03:48	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,533	A*03:49	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,534	A*03:49	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,535	A*03:49	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,536	A*03:49	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,537	A*03:50	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,538	A*03:50	Israel Arab pop 2		0.0000	12,301	32_8_N_34_52_E
9,539	A*03:50	Israel Ashkenazi Jews pop 3		0.0000	4,625	32_8_N_34_52_E
9,540	A*03:50	Israel Druze		0.0001	5,914	32_41_N_35_17_E
9,541	A*03:50	Israel Ethiopia Jews		0.0002	5,928	32_8_N_34_52_E
9,542	A*03:50	Israel Georgia Jews		0.0001	4,471	32_10_N_34_53_E
9,543	A*03:50	Israel Iran Jews		0.0001	8,153	32_8_N_34_52_E
9,544	A*03:50	Israel Kavkazi Jews		0.0002	2,840	32_10_N_34_52_E
9,545	A*03:50	Israel Libya Jews		0.0001	3,739	32_10_N_34_52_E
9,546	A*03:50	Israel Tunisia Jews		0.0001	9,070	32_10_N_34_52_E
9,547	A*03:50	Malaysia Peninsular Indian	0.4	0.0018	271	3_9_N_101_57_E
9,548	A*03:50	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,549	A*03:50	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,550	A*03:51	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,551	A*03:51	USA NMDP African American pop 2		0.0000	416,581	38_45_N_76_6_W
9,552	A*03:51:02	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,553	A*03:52	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,554	A*03:52	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,555	A*03:53	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,556	A*03:53	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,557	A*03:53	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
9,558	A*03:54	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,559	A*03:54	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,560	A*03:55	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,561	A*03:56	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,562	A*03:56	India South UCBB	0.0	0.0000	11,446	13_6_N_80_8_E
9,563	A*03:56	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
9,564	A*03:56	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,565	A*03:56	Russia Nizhny Novgorod, Russians	0.1	0.0003	1,510	56_19_N_43_59_E
9,566	A*03:56	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
9,567	A*03:57	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,568	A*03:57	India North UCBB	0.0	0.0001	5,849	28_36_N_77_11_E
9,569	A*03:57	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,570	A*03:58	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,571	A*03:58	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,572	A*03:59	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,573	A*03:59	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,574	A*03:59	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
9,575	A*03:60	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,576	A*03:61	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,577	A*03:62	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,578	A*03:63	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,579	A*03:63	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,580	A*03:63	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
9,581	A*03:63	Russia Moscow Russians	0.2	0.0008	13,147	55_46_N_37_41_E
9,582	A*03:63	Russia Moscow Tatars	0.2	0.0008	2,512	55_46_N_37_41_E
9,583	A*03:63	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
9,584	A*03:64	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,585	A*03:64	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,586	A*03:65	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,587	A*03:65	Germany DKMS - Portugal minority		0.0004	1,176	48_31_N_9_3_E
9,588	A*03:65	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,589	A*03:66	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,590	A*03:66	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,591	A*03:67	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,592	A*03:68N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,593	A*03:69N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,594	A*03:69N	Germany DKMS - Portugal minority		0.0004	1,176	48_31_N_9_3_E
9,595	A*03:69N	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,596	A*03:69N	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
9,597	A*03:69N	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,598	A*03:70	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,599	A*03:71	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,600	A*03:72	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,601	A*03:72	Latvia		0.0019	266	56_50_N_24_45_E
9,602	A*03:72	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,603	A*03:72	Russia Moscow Bashkir	0.5	0.0023	442	55_46_N_37_41_E
9,604	A*03:72	Russia Moscow Kabardin	0.5	0.0024	834	55_46_N_37_41_E
9,605	A*03:72	Russia Moscow Megrel	0.9	0.0044	230	55_46_N_37_41_E
9,606	A*03:72	Russia Moscow Russians	0.4	0.0021	13,147	55_46_N_37_41_E
9,607	A*03:72	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
9,608	A*03:72	Russia Moscow Tatars	0.6	0.0030	2,512	55_46_N_37_41_E
9,609	A*03:72	Russia Moscow Tuvian	0.9	0.0045	112	55_46_N_37_41_E
9,610	A*03:72	Russia Moscow Yakut	0.9	0.0043	235	55_46_N_37_41_E
9,611	A*03:73	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,612	A*03:73	USA NMDP South Asian Indian		0.0000	185,391	38_45_N_76_6_W
9,613	A*03:74	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,614	A*03:74	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
9,615	A*03:74	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
9,616	A*03:74	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,617	A*03:77	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,618	A*03:79	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,619	A*03:79	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,620	A*03:81	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,621	A*03:81	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
9,622	A*03:81	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,623	A*03:81	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
9,624	A*03:86	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,625	A*03:86	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,626	A*03:86	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
9,627	A*03:87	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,628	A*03:88	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
9,629	A*03:88	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,630	A*03:88	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,631	A*03:89	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,632	A*03:89	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
9,633	A*03:89	USA San Diego	0.3	0.0020	496	32_50_N_117_15_W
9,634	A*03:89:01	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
9,635	A*03:89:02	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,636	A*03:92	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,637	A*03:93	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,638	A*03:95	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,639	A*03:97	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,640	A*03:97	Germany DKMS - Italy minority		0.0005	1,159	48_31_N_9_3_E
9,641	A*03:97	Germany DKMS - Romania minority		0.0004	1,234	48_31_N_9_3_E
9,642	A*03:97	Germany DKMS - Turkey minority		0.0002	4,856	48_31_N_9_3_E
9,643	A*03:97	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,644	A*03:97	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
9,645	A*03:98	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
9,646	A*03:100	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,647	A*03:101	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,648	A*03:102	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,649	A*03:102	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,650	A*03:103	Saudi Arabia pop 5	0.6	0.0032	158	24_38_N_46_43_E
9,651	A*03:103	USA NMDP European Caucasian		0.0000	1,242,890	38_45_N_76_6_W
9,652	A*03:104	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,653	A*03:105	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,654	A*03:105	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,655	A*03:105	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
9,656	A*03:106	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,657	A*03:106	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
9,658	A*03:106	USA NMDP Mexican or Chicano		0.0000	261,235	38_45_N_76_6_W
9,659	A*03:107	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,660	A*03:107	Germany pop 8		0.0000	39,689	48_24_N_9_58_E
9,661	A*03:108	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,662	A*03:108	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,663	A*03:108	USA NMDP Middle Eastern or North Coast of Africa		0.0000	70,890	38_45_N_76_6_W
9,664	A*03:111	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,665	A*03:114	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,666	A*03:114	USA NMDP Black South or Central American		0.0018	4,889	38_45_N_76_6_W
9,667	A*03:117	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,668	A*03:119	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,669	A*03:120	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,670	A*03:120	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
9,671	A*03:120	USA NMDP Black South or Central American		0.0010	4,889	38_45_N_76_6_W
9,672	A*03:122	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,673	A*03:122	Russia Moscow Tatars	0.0	0.0004	2,512	55_46_N_37_41_E
9,674	A*03:123	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,675	A*03:123	Russia Karelia		0.0005	1,075	63_9_N_32_59_E
9,676	A*03:124	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,677	A*03:126	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,678	A*03:128	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,679	A*03:128	USA NMDP Black South or Central American		0.0001	4,889	38_45_N_76_6_W
9,680	A*03:135	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,681	A*03:136	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
9,682	A*03:136	Russia Moscow Russians	0.0	0.0002	13,147	55_46_N_37_41_E
9,683	A*03:139	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,684	A*03:140	USA NMDP Black South or Central American		0.0002	4,889	38_45_N_76_6_W
9,685	A*03:144	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,686	A*03:146	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
9,687	A*03:149	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,688	A*03:152	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,689	A*03:154	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,690	A*03:154	India Central UCBB	0.0	0.0001	4,204	27_1_N_81_19_E
9,691	A*03:154:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,692	A*03:157	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,693	A*03:157:02	Russia Moscow Similar genetically to Azerbaijan	0.6	0.0029	170	55_46_N_37_41_E
9,694	A*03:158	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,695	A*03:159	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
9,696	A*03:161N	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
9,697	A*03:163	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,698	A*03:166	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,699	A*03:167	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
9,700	A*03:177	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,701	A*03:182	India North UCBB	0.0	0.0001	5,849	28_36_N_77_11_E
9,702	A*03:184	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,703	A*03:188	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,704	A*03:195	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,705	A*03:197N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,706	A*03:198	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,707	A*03:202	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,708	A*03:202	Saudi Arabia pop 6 (G)		0.0000	28,927	23_52_N_45_4_E
9,709	A*03:203	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,710	A*03:204	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,711	A*03:205	Russia Moscow Bashkir	0.5	0.0023	442	55_46_N_37_41_E
9,712	A*03:205	Russia Moscow Russians	0.1	0.0003	13,147	55_46_N_37_41_E
9,713	A*03:205	Russia Moscow Tatars	0.1	0.0006	2,512	55_46_N_37_41_E
9,714	A*03:205	Russia Moscow Uzbek	0.5	0.0025	198	55_46_N_37_41_E
9,715	A*03:210	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,716	A*03:214	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,717	A*03:215	Latvia		0.0038	266	56_50_N_24_45_E
9,718	A*03:215	Russia Moscow Bashkir	0.2	0.0011	442	55_46_N_37_41_E
9,719	A*03:215	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,720	A*03:219	India Central UCBB	0.0	0.0001	4,204	27_1_N_81_19_E
9,721	A*03:221	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,722	A*03:224	India Tamil Nadu		0.0002	2,492	11_32_N_78_52_E
9,723	A*03:227	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,724	A*03:230	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,725	A*03:238	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,726	A*03:239	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,727	A*03:240	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,728	A*03:243	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,729	A*03:245	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,730	A*03:246	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,731	A*03:246	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,732	A*03:251	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,733	A*03:255	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,734	A*03:258	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,735	A*03:261	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,736	A*03:261	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
9,737	A*03:264	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,738	A*03:266N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,739	A*03:267	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,740	A*03:268	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,741	A*03:269N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,742	A*03:270	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,743	A*03:271	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,744	A*03:279N	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W
9,745	A*03:280:01	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,746	A*03:295	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,747	A*03:298	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,748	A*03:298	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,749	A*03:299	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,750	A*03:304	India West UCBB	0.0	0.0001	5,829	19_11_N_73_3_E
9,751	A*03:312	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
9,752	A*03:312	Russia Moscow Kazakh	0.3	0.0017	299	55_46_N_37_41_E
9,753	A*03:312	Russia Moscow Russians	0.1	0.0006	13,147	55_46_N_37_41_E
9,754	A*03:312	Russia Moscow Tatars	0.2	0.0010	2,512	55_46_N_37_41_E
9,755	A*03:317	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,756	A*03:320	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,757	A*03:323N	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,758	A*03:324	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,759	A*03:326	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,760	A*03:327	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,761	A*03:328	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,762	A*03:331	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,763	A*03:332	Russia Moscow Kabardin	0.1	0.0006	834	55_46_N_37_41_E
9,764	A*03:332	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,765	A*03:343	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,766	A*03:343	Russia Moscow Tatars	0.0	0.0002	2,512	55_46_N_37_41_E
9,767	A*03:347	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,768	A*03:348	Germany DKMS - German donors		0.0000	3,456,066	48_31_N_9_3_E
9,769	A*03:353	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,770	A*03:365	Russia Moscow Russians	0.0	0.0000	13,147	55_46_N_37_41_E
9,771	A*03:369	Russia Moscow Russians	0.0	0.0001	13,147	55_46_N_37_41_E
9,772	A*11	Albania		0.0750	160	41_0_N_20_0_E
9,773	A*11	Albania pop 2	13.4	0.0694	432	41_9_N_20_10_E
9,774	A*11	Albanian Kosovo		0.0542	120	41_50_N_20_1_E
9,775	A*11	Algeria pop 2	12.0		106	36_42_N_3_13_E
9,776	A*11	Argentina Buenos Aires	12.7	0.0640	466	34_38_S_58_28_W
9,777	A*11	Argentina Buenos Aires pop 2	11.1	0.0570	1,216	34_36_S_58_22_W
9,778	A*11	Argentina Chiriguano	1.9	0.0090	54	23_8_S_64_20_W
9,779	A*11	Argentina Corrientes	13.5	0.0680	155	27_30_S_58_49_W
9,780	A*11	Argentina Cuyo Region	11.2	0.0580	420	32_53_S_68_49_W
9,781	A*11	Argentina La Plata	9.0	0.0450	100	34_55_S_57_57_W
9,782	A*11	Argentina Rosario Toba	4.7	0.0230	86	32_57_S_60_39_W
9,783	A*11	Argentina Salta Wichi pop 2	0.0	0.0000	19	23_8_S_64_20_W
9,784	A*11	Armenia Ghegharkunik		0.0620	242	40_11_N_44_31_E
9,785	A*11	Armenia living in Iran		0.0820	85	40_11_N_44_31_E
9,786	A*11	Armenia living in Karabakh		0.0620	445	40_11_N_44_31_E
9,787	A*11	Armenia living in Lebanon		0.0820	368	40_11_N_44_31_E
9,788	A*11	Armenia living in USA		0.0540	233	40_11_N_44_31_E
9,789	A*11	Armenia Lori		0.0640	102	40_11_N_44_31_E
9,790	A*11	Armenia Shirak		0.0920	76	40_11_N_44_31_E
9,791	A*11	Armenia Syunik		0.0850	117	40_11_N_44_31_E
9,792	A*11	Armenia Yerevan		0.0680	445	40_11_N_44_31_E
9,793	A*11	Australia West Caucasian	11.9	0.0610	891	31_5_S_115_5_E
9,794	A*11	Austria	6.5	0.0320	200	48_13_N_16_21_E
9,795	A*11	Austria Cord Bloods		0.0533	1,368	47_4_N_15_26_E
9,796	A*11	Azerbaijan	18.9	0.1039	2,301	40_39_N_47_47_W
9,797	A*11	Azores		0.0580	1,050	37_44_N_25_40_W
9,798	A*11	Azores Terceira Island		0.0700	130	38_44_N_27_19_W
9,799	A*11	Bangladesh Dhaka Bangalee		0.1560	141	23_42_N_90_22_E
9,800	A*11	Bolivia La Paz Aymaras		0.0060	87	16_29_S_68_7_W
9,801	A*11	Bolivia Quechua		0.0145	69	16_30_S_68_9_W
9,802	A*11	Bosnia and Herzegovina		0.0485	134	43_52_N_18_25_E
9,803	A*11	Bosnia and Herzegovina pop 2	10.0	0.0500	120	44_32_N_18_41_E
9,804	A*11	Brazil Paraná Caucasian	11.4	0.0601	641	23_25_S_51_55_W
9,805	A*11	Brazil Curitiba-Parana Mixed		0.0615	264	25_25_S_49_16_W
9,806	A*11	Brazil Minas Gerais State Mixed	10.7	0.0550	1,000	19_49_S_43_57_W
9,807	A*11	Brazil Para Cord Blood Unit	6.2	0.0321	841	6_0_S_52_0_W
9,808	A*11	Brazil Para State	8.9	0.0390	5,000	6_0_S_52_0_W
9,809	A*11	Brazil Parana Afro Brazilian	2.6	0.0130	77	25_0_S_49_0_W
9,810	A*11	Brazil Parana Cafuzo	10.3	0.0520	319	25_0_S_49_0_W
9,811	A*11	Brazil Parana Caucasian	10.7	0.0530	2,775	25_0_S_49_0_W
9,812	A*11	Brazil Parana Japanese	14.0	0.0760	192	22_29_S_48_2_W
9,813	A*11	Brazil Parana Mixed race	8.6	0.0430	186	25_0_S_49_0_W
9,814	A*11	Brazil Parana Oriental	24.2	0.1210	33	25_0_S_49_0_W
9,815	A*11	Brazil Pernambuco Mixed		0.0250	101	8_3_S_34_54_W
9,816	A*11	Brazil Piaui Mixed		0.0470	21,943	2_44_S_40_25_W
9,817	A*11	Brazil REDOME Acre		0.0489	859	9_11_S_70_52_W
9,818	A*11	Brazil REDOME Alagoas		0.0533	25,349	9_57_S_36_55_W
9,819	A*11	Brazil REDOME Amapa		0.0401	17,864	1_0_N_52_0_W
9,820	A*11	Brazil REDOME Amazonas		0.0402	24,129	5_0_S_63_0_W
9,821	A*11	Brazil REDOME Bahia		0.0459	47,399	12_31_S_41_41_W
9,822	A*11	Brazil REDOME Ceara		0.0506	101,217	5_5_S_39_39_W
9,823	A*11	Brazil REDOME Espirito Santo		0.0471	88,485	20_19_S_40_20_W
9,824	A*11	Brazil REDOME Federal District		0.0545	29,549	15_46_S_47_56_W
9,825	A*11	Brazil REDOME Goias		0.0550	88,574	15_56_S_50_8_W
9,826	A*11	Brazil REDOME Maranhao		0.0419	10,180	2_31_S_44_18_W
9,827	A*11	Brazil REDOME Mato Grosso		0.0503	34,649	15_35_S_56_4_W
9,828	A*11	Brazil REDOME Mato Grosso do Sul		0.0527	95,667	20_26_S_54_38_W
9,829	A*11	Brazil REDOME Minas Gerais		0.0544	211,275	19_49_S_43_57_W
9,830	A*11	Brazil REDOME Para		0.0424	72,637	5_40_S_52_44_W
9,831	A*11	Brazil REDOME Paraiba		0.0493	43,868	7_10_S_36_50_W
9,832	A*11	Brazil REDOME Parana		0.0542	341,639	24_0_S_51_0_W
9,833	A*11	Brazil REDOME Pernambuco		0.0540	92,332	8_20_S_48_48_W
9,834	A*11	Brazil REDOME Piaui		0.0442	46,140	8_14_S_43_6_W
9,835	A*11	Brazil REDOME Rio de Janeiro		0.0504	139,322	22_54_S_43_11_W
9,836	A*11	Brazil REDOME Rio Grande do Norte		0.0485	46,603	5_44_S_36_33_W
9,837	A*11	Brazil REDOME Rio Grande do Sul		0.0530	241,329	30_0_S_53_0_W
9,838	A*11	Brazil REDOME Rondonia		0.0536	54,396	10_54_S_62_46_W
9,839	A*11	Brazil REDOME Roraima		0.0471	4,140	2_3_N_61_24_W
9,840	A*11	Brazil REDOME Santa Catarina		0.0528	106,673	27_15_S_50_20_W
9,841	A*11	Brazil REDOME Sao Paulo		0.0563	800,809	23_33_S_46_38_W
9,842	A*11	Brazil REDOME Sergipe		0.0490	7,321	10_35_S_37_23_W
9,843	A*11	Brazil REDOME Tocantins		0.0486	20,692	10_11_S_48_20_W
9,844	A*11	Brazil Rio Grande do Norte Mestizo		0.0480	12,973	5_47_S_35_12_W
9,845	A*11	Brazil Rio Grande do Sul Black		0.0240	248	30_0_S_53_0_W
9,846	A*11	Brazil Rio Grande do Sul Caucasoid		0.0530	4,428	30_0_S_53_0_W
9,847	A*11	Brazil Rio Grande do Sul Mixed		0.0500	324	30_0_S_53_0_W
9,848	A*11	Brazil Sao Paulo Bauru Mixed		0.0550	3,542	22_18_S_49_3_W
9,849	A*11	Brazil Sao Paulo Mixed	14.0	0.0730	239	23_0_S_48_0_W
9,850	A*11	Brazil South East Cord Blood		0.0536	11,409	14_14_S_51_55_W
9,851	A*11	Brazil South Ribeirao Preto		0.0910	184	21_11_S_47_49_W
9,852	A*11	Burkina Faso Fulani		0.0000	49	12_50_N_1_50_W
9,853	A*11	Burkina Faso Mossi		0.0000	53	12_50_N_1_50_W
9,854	A*11	Burkina Faso Rimaibe		0.0000	47	12_50_N_1_50_W
9,855	A*11	Chile Santiago		0.0418	920	33_26_S_70_40_W
9,856	A*11	China Guangdong Province	51.4		403	23_20_N_113_30_E
9,857	A*11	China Harbin Korean		0.1210	201	45_45_N_126_38_E
9,858	A*11	China Harbin Manchu		0.1620	172	45_45_N_126_38_E
9,859	A*11	China Jiangsu Province		0.1860	20,248	33_0_N_120_0_E
9,860	A*11	China North Han		0.0000	105	39_54_N_116_24_E
9,861	A*11	China Shaanxi Province Han		0.1870	10,000	34_0_N_109_0_E
9,862	A*11	China Shandong Province	25.9	0.1400	7,418	36_30_N_118_0_E
9,863	A*11	China Shandong Province Linqu County	36.7		139	36_0_N_118_0_E
9,864	A*11	China Shanghai		0.2260	26,266	31_12_N_121_30_E
9,865	A*11	China Wuhan	49.6	0.2930	121	30_45_N_114_15_E
9,866	A*11	China Yunnan Province Han pop 2		0.1230	129	24_30_N_101_30_E
9,867	A*11	China Yunnan Province Naxi		0.3800	118	24_30_N_101_30_E
9,868	A*11	Colombia Barranquilla		0.0265	188	10_57_N_74_47_W
9,869	A*11	Croatia pop 2		0.0490	100	45_0_N_15_0_E
9,870	A*11	Croatia pop 3	12.5	0.0625	120	45_6_N_15_12_E
9,871	A*11	Cuba Mixed		0.0800	78	23_8_N_82_23_W
9,872	A*11	Cuba Mixed pop 2		0.0270	189	23_8_N_82_23_W
9,873	A*11	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
9,874	A*11	Czech Republic Kurd	6.8	0.0338	74	49_44_N_15_19_E
9,875	A*11	Czech Republic Romani pop 2	13.0	0.0652	46	49_44_N_15_19_E
9,876	A*11	Ecuador Amazonia Mixed Ancestry		0.0256	39	0_48_S_76_54_W
9,877	A*11	Ecuador Andes Mixed Ancestry		0.0225	824	1_28_S_78_49_W
9,878	A*11	Ecuador Coast Mixed Ancestry		0.0399	238	2_11_S_79_53_W
9,879	A*11	Ecuador Mixed Ancestry		0.0251	1,173	0_10_S_78_28_W
9,880	A*11	England Lancaster	10.1		545	54_2_N_2_48_W
9,881	A*11	England Leeds	12.0		5,024	53_47_N_1_32_W
9,882	A*11	England Liverpool	11.8		1,343	53_24_N_3_0_W
9,883	A*11	England London Ashkenazi Jews		0.0570	500	51_30_N_0_7_W
9,884	A*11	England Manchester	11.0		1,640	53_28_N_2_14_W
9,885	A*11	England Newcastle	11.1		2,739	54_58_N_1_36_W
9,886	A*11	England North West Mixed pop 2	13.7	0.0686	379	53_28_N_2_14_W
9,887	A*11	England Sheffield	10.7		4,755	53_23_N_1_28_W
9,888	A*11	France Bordeaux	10.2	0.0510	990	44_50_N_0_34_W
9,889	A*11	France Corsica Island		0.1110	100	42_9_N_9_5_E
9,890	A*11	France Grenoble	11.6	0.0580	1,000	45_12_N_5_43_E
9,891	A*11	France Grenoble, Nantes and Rennes		0.0610	6,094	47_13_N_1_33_W
9,892	A*11	France Lyon	12.1	0.0610	4,813	45_45_N_4_50_E
9,893	A*11	France Marseille	10.3	0.0514	1,000	43_17_N_5_22_E
9,894	A*11	France Reims		0.0690	102	49_15_N_4_2_E
9,895	A*11	France Rennes pop 3		0.0800	200	48_6_N_1_40_W
9,896	A*11	France Rennes pop 4	13.6	0.0679	1,000	48_6_N_1_40_W
9,897	A*11	France Southeast	8.5	0.0430	130	46_0_N_5_0_E
9,898	A*11	Gaza Palestinians		0.0390	165	31_31_N_34_27_E
9,899	A*11	Georgia Kurds		0.1030	30	41_43_N_44_47_E
9,900	A*11	Germany	10.0		11,407	50_6_N_8_40_E
9,901	A*11	Germany Essen	8.6	0.0440	174	51_27_N_7_0_E
9,902	A*11	Greece Crete		0.0400	135	35_0_N_25_0_E
9,903	A*11	Greece North		0.0800	500	40_0_N_22_0_E
9,904	A*11	Greece pop 7		0.0753	11,250	37_58_N_23_43_E
9,905	A*11	Greece pop3	11.0		246	37_58_N_23_43_E
9,906	A*11	Guatemala Mayan		0.0150	132	14_8_N_91_5_W
9,907	A*11	Guatemala, Guatemala City Mixed Ancestry	10.2	0.0512	127	14_37_N_90_31_W
9,908	A*11	India Bikaner Non-Raikas	36.9		127	28_1_N_73_18_E
9,909	A*11	India Bikaner Raikas	21.8		143	28_1_N_73_18_E
9,910	A*11	India Jalpaiguri Toto	17.5		40	26_30_N_88_42_E
9,911	A*11	India Kerala Hindu Ezhava		0.1040	24	8_48_N_76_36_E
9,912	A*11	India Kerala Hindu Nair		0.0850	41	8_48_N_76_36_E
9,913	A*11	India Kerala Hindu Namboothiri		0.0750	40	11_14_N_75_44_E
9,914	A*11	India Kerala Hindu Pulaya		0.5310	16	11_48_N_75_19_E
9,915	A*11	India Kerala Malabar Muslim		0.1470	34	11_2_N_76_3_E
9,916	A*11	India Kerala Syrian Christian		0.1130	31	9_35_N_76_31_E
9,917	A*11	India North Gujarat	23.7	0.1257	338	23_13_N_72_41_E
9,918	A*11	India North pop 3		0.1720	587	28_38_N_77_9_E
9,919	A*11	India sub-Himalayan West Bengal Kami	55.1	0.3101	158	27_2_N_88_15_E
9,920	A*11	Iran pop 1		0.0390	64	32_35_N_54_16_E
9,921	A*11	Iran pop 4		0.1012	855	35_41_N_51_38_E
9,922	A*11	Iran Royan Cord Blood Bank	17.3		15,600	35_41_N_51_25_E
9,923	A*11	Iraq Arabs	9.4	0.0470	149	33_18_N_44_21_E
9,924	A*11	Iraq Erbil	19.1	0.0956	372	33_33_N_43_43_E
9,925	A*11	Iraq Kurdistan Region	18.7	0.0960	209	36_51_N_43_0_E
9,926	A*11	Ireland South pop 2		0.0687	17,624	53_20_N_6_15_W
9,927	A*11	Israel Ashkenazi Jews pop 2		0.0400	132	31_47_N_35_13_E
9,928	A*11	Israel Jews pop 2	10.4	0.0540	23,000	32_5_N_34_48_E
9,929	A*11	Israel Moroccan Jews		0.0640	113	31_47_N_35_13_E
9,930	A*11	Italy		0.0600	159,311	45_28_N_9_11_E
9,931	A*11	Italy Bergamo	8.9	0.0480	101	45_42_N_9_40_E
9,932	A*11	Italy East Sicily	16.0	0.0800	50	37_30_N_15_4_E
9,933	A*11	Italy Lombardy	10.7	0.0530	674	45_11_N_9_9_E
9,934	A*11	Italy North Pavia		0.0560	81	45_11_N_9_9_E
9,935	A*11	Italy North Pavia pop 2	11.3	0.0590	2,416	45_11_N_9_9_E
9,936	A*11	Italy North Pavia pop 3		0.0672	685	45_11_N_9_9_E
9,937	A*11	Italy pop 6	13.0	0.0652	184	41_53_N_12_29_E
9,938	A*11	Italy Rome	8.0	0.0400	100	41_54_N_12_27_E
9,939	A*11	Italy Sardinia pop3		0.0770	100	39_14_N_9_3_E
9,940	A*11	Japan Aichi	21.7	0.1270	50	35_11_N_136_54_E
9,941	A*11	Japan Hyogo	18.8	0.0940	32	34_0_N_135_0_E
9,942	A*11	Japan Hyogo Osaka and Nagano		0.0920	195	34_41_N_135_30_E
9,943	A*11	Japan Kyoto and Osaka		0.0910	165	35_1_N_135_46_E
9,944	A*11	Japan pop 11	22.6		62	35_41_N_139_46_E
9,945	A*11	Japan pop 2		0.0850	916	35_41_N_139_46_E
9,946	A*11	Jordan		0.0410	15,141	31_57_N_35_56_E
9,947	A*11	Macedonia		0.0804	286	41_32_N_21_42_E
9,948	A*11	Macedonia MBMDR - Albanian	6.3	0.0352	128	41_46_N_22_19_E
9,949	A*11	Macedonia MBMDR - Macedonian	13.7	0.0701	1,283	41_46_N_22_19_E
9,950	A*11	Macedonia MBMDR - Macedonian Muslims	10.5	0.0526	76	41_46_N_22_19_E
9,951	A*11	Macedonia pop 3		0.0760	172	42_0_N_22_0_E
9,952	A*11	Macedonia pop 4		0.0490	216	42_0_N_21_0_E
9,953	A*11	Madeira pop 2		0.0350	173	32_44_N_16_58_W
9,954	A*11	Madeira pop 3		0.0470	538	32_44_N_16_58_W
9,955	A*11	Malaysia Negeri Sembilan Minangkabau		0.2800	34	2_43_N_101_57_E
9,956	A*11	Malaysia Perak and Johor Banjar Bugis Jawa		0.1330	94	4_24_N_101_5_E
9,957	A*11	Malaysia Perak Rawa		0.1500	23	1_29_N_103_45_E
9,958	A*11	Malaysia pop 2		0.2280	62	3_8_N_101_42_E
9,959	A*11	Malaysia pop 3		0.2090	1,445	3_8_N_101_42_E
9,960	A*11	Malaysia Sabah Kadazan		0.1750	57	5_15_N_117_0_E
9,961	A*11	Malaysia Sarawak Bidayuh		0.2100	50	1_25_N_110_9_E
9,962	A*11	Malaysia Sarawak Iban		0.2250	51	2_18_N_111_49_E
9,963	A*11	Martinique		0.0100	100	14_40_N_61_0_W
9,964	A*11	Mexico Aguascalientes state		0.0421	95	22_1_N_102_21_W
9,965	A*11	Mexico Baja Californa, Mexicali		0.0450	100	32_37_N_115_27_W
9,966	A*11	Mexico Baja California Rural		0.1200	50	27_31_N_113_40_W
9,967	A*11	Mexico Baja California, La Paz		0.0533	75	24_8_N_110_18_W
9,968	A*11	Mexico Baja California, Tijuana		0.0200	25	32_30_N_117_2_W
9,969	A*11	Mexico Campeche Rural		0.0213	47	18_6_N_89_48_W
9,970	A*11	Mexico Campeche, Campeche city		0.0000	34	19_49_N_90_32_W
9,971	A*11	Mexico Chiapas Rural		0.0207	121	16_47_N_92_41_W
9,972	A*11	Mexico Chiapas, Tuxtla Gutierrez		0.0283	52	16_45_N_93_6_W
9,973	A*11	Mexico Chihuahua Chihuahua City		0.0504	119	28_37_N_106_4_W
9,974	A*11	Mexico Chihuahua Rural		0.0565	236	27_18_N_107_53_W
9,975	A*11	Mexico Chihuahua, Ciudad Juarez		0.0278	106	31_41_N_106_25_W
9,976	A*11	Mexico Coahuila Rural		0.0275	216	26_59_N_102_3_W
9,977	A*11	Mexico Coahuila, Saltillo		0.0068	72	25_26_N_100_58_W
9,978	A*11	Mexico Coahuila, Torreon		0.0350	396	25_32_N_103_24_W
9,979	A*11	Mexico Colima Rural		0.0341	43	19_30_N_103_37_W
9,980	A*11	Mexico Colima, Colima city		0.0328	61	19_14_N_103_43_W
9,981	A*11	Mexico Durango Rural		0.0352	326	26_41_N_103_44_W
9,982	A*11	Mexico Durango, Durango city		0.0742	153	24_1_N_104_39_W
9,983	A*11	Mexico Guanajuato Rural		0.0337	162	20_54_N_100_44_W
9,984	A*11	Mexico Guanajuato, Guanajuato city		0.0455	22	21_2_N_101_15_W
9,985	A*11	Mexico Guanajuato, Leon		0.0577	78	21_7_N_101_41_W
9,986	A*11	Mexico Guerrero state		0.0208	144	17_36_N_99_57_W
9,987	A*11	Mexico Hidalgo Rural		0.0494	81	20_38_N_98_59_W
9,988	A*11	Mexico Hidalgo, Pachuca		0.0000	41	20_6_N_98_45_W
9,989	A*11	Mexico Huasteca Region Teenek		0.0000	55	22_1_N_100_9_W
9,990	A*11	Mexico Jalisco Rural		0.0580	585	20_52_N_103_50_W
9,991	A*11	Mexico Jalisco, Guadalajara city		0.0494	1,189	20_39_N_103_20_W
9,992	A*11	Mexico Jalisco, Tlajomulco		0.0500	30	20_28_N_103_26_W
9,993	A*11	Mexico Jalisco, Tlaquepaque		0.0385	39	20_37_N_103_19_W
9,994	A*11	Mexico Jalisco, Tonala		0.0286	35	20_37_N_103_14_W
9,995	A*11	Mexico Jalisco, Zapopan		0.0536	168	20_40_N_103_24_W
9,996	A*11	Mexico Mestizo	7.3	0.0370	41	19_24_N_99_9_W
9,997	A*11	Mexico Mestizo pop 4		0.0558	269	19_26_N_99_8_W
9,998	A*11	Mexico Mexico City Center		0.0487	152	19_25_N_99_7_W
9,999	A*11	Mexico Mexico City East		0.0000	79	19_17_N_99_2_W
10,000	A*11	Mexico Mexico City Mestizo		0.0496	121	19_25_N_99_8_W

The search is limited to 10000 records.

©2026 Allele Frequencies Website.

Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools
Gonzalez-Galarza FF, McCabe A, Santos EJ, Jones J, Takeshita LY, Ortega-Rivera ND, Del Cid-Pavon GM, Ramsbottom K, Ghattaoraya GS, Alfirevic A, Middleton D and Jones AR Nucleic Acid Research 2020, 48:D783-8.
Liverpool, U.K.

support@allelefrequencies.net