Line |
Allele |
Population |
% of individuals
that have the allele |
Allele
Frequency
(in_decimals) |
Sample
Size |
IMGT/HLA¹
Database |
Distribution² |
Haplotype³
Association |
Notesª |
1 |
A*02:01 |  | American Samoa | | 0.1300 |  | 51 | See |  |  | |
2 |
A*02:01 |  | Argentina Gran Chaco Eastern Toba | 46.4 | 0.3040 |  | 135 | See |  |  | |
3 |
A*02:01 |  | Argentina Gran Chaco Mataco Wichi | 40.9 | 0.2160 |  | 49 | See |  |  | |
4 |
A*02:01 |  | Argentina Gran Chaco Western Toba Pilaga | 60.0 | 0.4000 |  | 19 | See |  |  | |
5 |
A*02:01 |  | Argentina Rosario Toba | 34.9 | 0.1920 |  | 86 | See |  |  | |
6 |
A*02:01 |  | Armenia combined Regions | | 0.1550 |  | 100 | See |  |  | |
7 |
A*02:01 |  | Australia Cape York Peninsula Aborigine | | 0.1750 |  | 103 | See |  |  | |
8 |
A*02:01 |  | Australia Groote Eylandt Aborigine | | 0.1070 |  | 75 | See |  |  | |
9 |
A*02:01 |  | Australia Kimberly Aborigine | | 0.1110 |  | 41 | See |  |  | |
10 |
A*02:01 |  | Australia New South Wales Caucasian | | 0.2610 |  | 134 | See |  |  | |
11 |
A*02:01 |  | Australia Yuendumu Aborigine | | 0.1130 |  | 191 | See |  |  | |
12 |
A*02:01 |  | Austria | 48.0 | 0.2940 |  | 200 | See |  |  | |
13 |
A*02:01 |  | Azores Central Islands | | 0.2590 |  | 59 | See |  |  | |
14 |
A*02:01 |  | Azores Oriental Islands | | 0.2560 |  | 43 | See |  |  | |
15 |
A*02:01 |  | Azores Terceira Island | | 0.2330 |  | 130 | See |  |  | |
16 |
A*02:01 |  | Belgium | 50.0 | 0.2660 |  | 99 | See |  |  | |
17 |
A*02:01 |  | Belgium | 45.1 | 0.2591 |  | 31,412 | See |  |  |
|
18 |
A*02:01 |  | Brazil Puyanawa | 54.0 | 0.2970 |  | 150 | See |  |  |
|
19 |
A*02:01 |  | Brazil Belo Horizonte Caucasian | 43.2 | 0.2370 |  | 95 | See |  |  | |
20 |
A*02:01 |  | Brazil Mixed | | 0.1920 |  | 108 | See |  |  | |
21 |
A*02:01 |  | Brazil Terena | 38.3 | 0.2080 |  | 60 | See |  |  | |
22 |
A*02:01 |  | Brazil Vale do Ribeira Quilombos | 0.1 | 0 |  | 144 | See |  |  | |
23 |
A*02:01 |  | Bulgaria Romani | | 0.2080 |  | 13 | See |  |  | |
24 |
A*02:01 |  | Burkina Faso Fulani | | 0.0510 |  | 49 | See |  |  | |
25 |
A*02:01 |  | Burkina Faso Rimaibe | | 0.1380 |  | 47 | See |  |  | |
26 |
A*02:01 |  | Cameroon Baka Pygmy | | 0 |  | 10 | See |  |  | |
27 |
A*02:01 |  | Cameroon Bamileke | | 0.0110 |  | 77 | See |  |  | |
28 |
A*02:01 |  | Cameroon Beti | | 0.1120 |  | 174 | See |  |  | |
29 |
A*02:01 |  | Cameroon Sawa | | 0.0380 |  | 13 | See |  |  | |
30 |
A*02:01 |  | Cameroon Yaounde | | 0.0710 |  | 92 | See |  |  | |
31 |
A*02:01 |  | Chile Easter Island | | 0.0950 |  | 21 | See |  |  | |
32 |
A*02:01 |  | Chile Mapuche | | 0.2238 |  | 66 | See |  |  |
|
33 |
A*02:01 |  | Chile Santiago Mixed | 30.0 | 0.1633 |  | 70 | See |  |  | |
34 |
A*02:01 |  | China Beijing | | 0.1870 |  | 67 | See |  |  | |
35 |
A*02:01 |  | China Beijing Shijiazhuang Tianjian Han | | 0.1580 |  | 618 | See |  |  | |
36 |
A*02:01 |  | China Canton Han | | 0.1530 |  | 264 | See |  |  | |
37 |
A*02:01 |  | China Guangxi Region Maonan | | 0.0460 |  | 108 | See |  |  | |
38 |
A*02:01 |  | China Guangzhou | | 0.1280 |  | 102 | See |  |  | |
39 |
A*02:01 |  | China Guizhou Province Bouyei | | 0.0210 |  | 109 | See |  |  | |
40 |
A*02:01 |  | China Guizhou Province Miao pop 2 | | 0.0240 |  | 85 | See |  |  | |
41 |
A*02:01 |  | China Guizhou Province Shui | | 0.0450 |  | 153 | See |  |  | |
42 |
A*02:01 |  | China Han HIV negative | | 0.0630 |  | 72 | See |  |  | |
43 |
A*02:01 |  | China Henan HIV negative | | 0.0630 |  | 16 | See |  |  | |
44 |
A*02:01 |  | China Hubei Han | 20.3 | 0.1016 |  | 3,732 | See |  |  |
|
45 |
A*02:01 |  | China Inner Mongolia Region | | 0.1270 |  | 102 | See |  |  | |
46 |
A*02:01 |  | China Jiangsu Han | | 0.1300 |  | 3,238 | See |  |  | |
47 |
A*02:01 |  | China Jiangsu Province Han | | 0.1289 |  | 334 | See |  |  | |
48 |
A*02:01 |  | China North Han | | 0 |  | 105 | See |  |  | |
49 |
A*02:01 |  | China Qinghai Province Hui | | 0.1730 |  | 110 | See |  |  | |
50 |
A*02:01 |  | China Shanxi HIV negative | | 0.2270 |  | 22 | See |  |  | |
51 |
A*02:01 |  | China Sichuan HIV negative | | 0.0880 |  | 34 | See |  |  | |
52 |
A*02:01 |  | China South Han | | 0.0530 |  | 284 | See |  |  | |
53 |
A*02:01 |  | China Southwest Dai | | 0.0160 |  | 124 | See |  |  | |
54 |
A*02:01 |  | China Tibet Region Tibetan | | 0.2180 |  | 158 | See |  |  | |
55 |
A*02:01 |  | China Uyghur HIV negative | | 0.2370 |  | 19 | See |  |  | |
56 |
A*02:01 |  | China Yunnan Bulang | | 0.0130 |  | 116 | See |  |  | |
57 |
A*02:01 |  | China Yunnan Hani | | 0.0330 |  | 150 | See |  |  | |
58 |
A*02:01 |  | China Yunnan Province Han | | 0.0100 |  | 101 | See |  |  | |
59 |
A*02:01 |  | China Yunnan Province Lisu | | 0.0850 |  | 111 | See |  |  | |
60 |
A*02:01 |  | China Yunnan Province Nu | | 0.0890 |  | 107 | See |  |  | |
61 |
A*02:01 |  | Colombia Bogotá Cord Blood | 30.0 | 0.1613 |  | 1,463 | See |  |  | |
62 |
A*02:01 |  | Colombia North Chimila Amerindians | | 0.0638 |  | 47 | See |  |  |
|
63 |
A*02:01 |  | Colombia North Wiwa El Encanto | | 0.1058 |  | 52 | See |  |  |
|
64 |
A*02:01 |  | Croatia | | 0.2630 |  | 150 | See |  |  | |
65 |
A*02:01 |  | Croatia pop 4 | | 0.2916 |  | 4,000 | See |  |  | |
66 |
A*02:01 |  | Cuba Caucasian | 34.3 | 0.1790 |  | 70 | See |  |  | |
67 |
A*02:01 |  | Cuba Mixed Race | 31.0 | 0.1790 |  | 42 | See |  |  | |
68 |
A*02:01 |  | Czech Republic | | 0.2740 |  | 106 | See |  |  | |
69 |
A*02:01 |  | Czech Republic NMDR | | 0.2431 |  | 5,099 | See |  |  | |
70 |
A*02:01 |  | Ecuador Amerindians | | 0.2540 |  | 63 | See |  |  |
|
71 |
A*02:01 |  | Ecuador Cayapa | | 0.0900 |  | 183 | See |  |  | |
72 |
A*02:01 |  | England North West | 50.7 | 0.2890 |  | 298 | See |  |  | |
73 |
A*02:01 |  | Finland | | 0.3440 |  | 91 | See |  |  | |
74 |
A*02:01 |  | France French Bone Marrow Donor Registry | | 0.2901 |  | 42,623 | See |  |  | |
75 |
A*02:01 |  | France Southeast | 38.5 | 0.2160 |  | 130 | See |  |  | |
76 |
A*02:01 |  | Gaza | 23.8 | 0.1190 |  | 42 | See |  |  |
|
77 |
A*02:01 |  | Georgia Svaneti Region Svan | | 0.2130 |  | 80 | See |  |  | |
78 |
A*02:01 |  | Georgia Tibilisi | | 0.3100 |  | 109 | See |  |  | |
79 |
A*02:01 |  | Georgia Tibilisi Kurd | | 0.0830 |  | 31 | See |  |  | |
80 |
A*02:01 |  | Germany DKMS - Austria minority | | 0.2677 |  | 1,698 | See |  |  | |
81 |
A*02:01 |  | Germany DKMS - Bosnia and Herzegovina minority | | 0.3050 |  | 1,028 | See |  |  | |
82 |
A*02:01 |  | Germany DKMS - China minority | | 0.1226 |  | 1,282 | See |  |  | |
83 |
A*02:01 |  | Germany DKMS - Croatia minority | | 0.2842 |  | 2,057 | See |  |  | |
84 |
A*02:01 |  | Germany DKMS - France minority | | 0.2572 |  | 1,406 | See |  |  | |
85 |
A*02:01 |  | Germany DKMS - German donors | | 0.2839 |  | 3,456,066 | See |  |  |
|
86 |
A*02:01 |  | Germany DKMS - Greece minority | | 0.2629 |  | 1,894 | See |  |  | |
87 |
A*02:01 |  | Germany DKMS - Italy minority | | 0.2252 |  | 1,159 | See |  |  | |
88 |
A*02:01 |  | Germany DKMS - Netherlands minority | | 0.2956 |  | 1,374 | See |  |  | |
89 |
A*02:01 |  | Germany DKMS - Portugal minority | | 0.2564 |  | 1,176 | See |  |  | |
90 |
A*02:01 |  | Germany DKMS - Romania minority | | 0.2763 |  | 1,234 | See |  |  | |
91 |
A*02:01 |  | Germany DKMS - Spain minority | | 0.2367 |  | 1,107 | See |  |  | |
92 |
A*02:01 |  | Germany DKMS - Turkey minority | | 0.1957 |  | 4,856 | See |  |  | |
93 |
A*02:01 |  | Germany DKMS - United Kingdom minority | | 0.2739 |  | 1,043 | See |  |  | |
94 |
A*02:01 |  | Germany pop 6 | | 0.2888 |  | 8,862 | See |  |  | |
95 |
A*02:01 |  | Germany pop 8 | | 0.2667 |  | 39,689 | See |  |  | |
96 |
A*02:01 |  | Ghana Ga-Adangbe | 19.1 | 0.1069 |  | 131 | See |  |  | |
97 |
A*02:01 |  | Greece pop 6 | | 0.2624 |  | 242 | See |  |  | |
98 |
A*02:01 |  | Greece pop 8 | 51.8 | 0.3072 |  | 83 | See |  |  | |
99 |
A*02:01 |  | Guinea Bissau Balanta | | 0.1350 |  | 48 | See |  |  | |
100 |
A*02:01 |  | Guinea Bissau Bijago | | 0.0870 |  | 23 | See |  |  | |
* Allele Frequency: Total number of copies of the allele in the population sample (Alleles / 2n) in decimal format.
: This field has been expanded to four decimals to better represent frequencies of large datasets (e.g. where sample size > 1000 individuals)
* % of individuals that have the allele: Percentage of individuals who have the allele in the population (Individuals / n).
were calculated from Phenotype Frequencies assuming Hardy-Weinberg proportions.
AF = Allele Frequency; PF = Phenotype Frequency, i.e. (%) of the individuals carrying the allele.
* Allele Frequencies marked with (*) were calculated from all alleles in the corresponding
¹ IMGT/HLA Database - For more details of the allele.
² Distribution - Graphical distribution of the allele.
³ Haplotype Association - Find HLA haplotypes with this allele.
ª Notes - See notes for ambiguous combinations of alleles.