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 | | Brazil Puyanawa | 54.0 | 0.2970 | | 150 | See | | |
|
18 |
A*02:01 | | Brazil Belo Horizonte Caucasian | 43.2 | 0.2370 | | 95 | See | | | |
19 |
A*02:01 | | Brazil Mixed | | 0.1920 | | 108 | See | | | |
20 |
A*02:01 | | Brazil Terena | 38.3 | 0.2080 | | 60 | See | | | |
21 |
A*02:01 | | Brazil Vale do Ribeira Quilombos | 0.1 | 0 | | 144 | See | | | |
22 |
A*02:01 | | Bulgaria Romani | | 0.2080 | | 13 | See | | | |
23 |
A*02:01 | | Burkina Faso Fulani | | 0.0510 | | 49 | See | | | |
24 |
A*02:01 | | Burkina Faso Rimaibe | | 0.1380 | | 47 | See | | | |
25 |
A*02:01 | | Cameroon Baka Pygmy | | 0 | | 10 | See | | | |
26 |
A*02:01 | | Cameroon Bamileke | | 0.0110 | | 77 | See | | | |
27 |
A*02:01 | | Cameroon Beti | | 0.1120 | | 174 | See | | | |
28 |
A*02:01 | | Cameroon Sawa | | 0.0380 | | 13 | See | | | |
29 |
A*02:01 | | Cameroon Yaounde | | 0.0710 | | 92 | See | | | |
30 |
A*02:01 | | Chile Easter Island | | 0.0950 | | 21 | See | | | |
31 |
A*02:01 | | Chile Mapuche | | 0.2238 | | 66 | See | | |
|
32 |
A*02:01 | | Chile Santiago Mixed | 30.0 | 0.1633 | | 70 | See | | | |
33 |
A*02:01 | | China Beijing | | 0.1870 | | 67 | See | | | |
34 |
A*02:01 | | China Beijing Shijiazhuang Tianjian Han | | 0.1580 | | 618 | See | | | |
35 |
A*02:01 | | China Canton Han | | 0.1530 | | 264 | See | | | |
36 |
A*02:01 | | China Guangxi Region Maonan | | 0.0460 | | 108 | See | | | |
37 |
A*02:01 | | China Guangzhou | | 0.1280 | | 102 | See | | | |
38 |
A*02:01 | | China Guizhou Province Bouyei | | 0.0210 | | 109 | See | | | |
39 |
A*02:01 | | China Guizhou Province Miao pop 2 | | 0.0240 | | 85 | See | | | |
40 |
A*02:01 | | China Guizhou Province Shui | | 0.0450 | | 153 | See | | | |
41 |
A*02:01 | | China Han HIV negative | | 0.0630 | | 72 | See | | | |
42 |
A*02:01 | | China Henan HIV negative | | 0.0630 | | 16 | See | | | |
43 |
A*02:01 | | China Hubei Han | 20.3 | 0.1016 | | 3,732 | See | | |
|
44 |
A*02:01 | | China Inner Mongolia Region | | 0.1270 | | 102 | See | | | |
45 |
A*02:01 | | China Jiangsu Han | | 0.1300 | | 3,238 | See | | | |
46 |
A*02:01 | | China Jiangsu Province Han | | 0.1289 | | 334 | See | | | |
47 |
A*02:01 | | China North Han | | 0 | | 105 | See | | | |
48 |
A*02:01 | | China Qinghai Province Hui | | 0.1730 | | 110 | See | | | |
49 |
A*02:01 | | China Shanxi HIV negative | | 0.2270 | | 22 | See | | | |
50 |
A*02:01 | | China Sichuan HIV negative | | 0.0880 | | 34 | See | | | |
51 |
A*02:01 | | China South Han | | 0.0530 | | 284 | See | | | |
52 |
A*02:01 | | China Southwest Dai | | 0.0160 | | 124 | See | | | |
53 |
A*02:01 | | China Tibet Region Tibetan | | 0.2180 | | 158 | See | | | |
54 |
A*02:01 | | China Uyghur HIV negative | | 0.2370 | | 19 | See | | | |
55 |
A*02:01 | | China Yunnan Bulang | | 0.0130 | | 116 | See | | | |
56 |
A*02:01 | | China Yunnan Hani | | 0.0330 | | 150 | See | | | |
57 |
A*02:01 | | China Yunnan Province Han | | 0.0100 | | 101 | See | | | |
58 |
A*02:01 | | China Yunnan Province Lisu | | 0.0850 | | 111 | See | | | |
59 |
A*02:01 | | China Yunnan Province Nu | | 0.0890 | | 107 | See | | | |
60 |
A*02:01 | | Colombia Bogotá Cord Blood | 30.0 | 0.1613 | | 1,463 | See | | | |
61 |
A*02:01 | | Colombia North Chimila Amerindians | | 0.0638 | | 47 | See | | |
|
62 |
A*02:01 | | Colombia North Wiwa El Encanto | | 0.1058 | | 52 | See | | |
|
63 |
A*02:01 | | Croatia | | 0.2630 | | 150 | See | | | |
64 |
A*02:01 | | Croatia pop 4 | | 0.2916 | | 4,000 | See | | | |
65 |
A*02:01 | | Cuba Caucasian | 34.3 | 0.1790 | | 70 | See | | | |
66 |
A*02:01 | | Cuba Mixed Race | 31.0 | 0.1790 | | 42 | See | | | |
67 |
A*02:01 | | Czech Republic | | 0.2740 | | 106 | See | | | |
68 |
A*02:01 | | Czech Republic NMDR | | 0.2431 | | 5,099 | See | | | |
69 |
A*02:01 | | Ecuador Amerindians | | 0.2540 | | 63 | See | | |
|
70 |
A*02:01 | | Ecuador Cayapa | | 0.0900 | | 183 | See | | | |
71 |
A*02:01 | | England North West | 50.7 | 0.2890 | | 298 | See | | | |
72 |
A*02:01 | | Finland | | 0.3440 | | 91 | See | | | |
73 |
A*02:01 | | France French Bone Marrow Donor Registry | | 0.2901 | | 42,623 | See | | | |
74 |
A*02:01 | | France Southeast | 38.5 | 0.2160 | | 130 | See | | | |
75 |
A*02:01 | | Gaza | 23.8 | 0.1190 | | 42 | See | | |
|
76 |
A*02:01 | | Georgia Svaneti Region Svan | | 0.2130 | | 80 | See | | | |
77 |
A*02:01 | | Georgia Tibilisi | | 0.3100 | | 109 | See | | | |
78 |
A*02:01 | | Georgia Tibilisi Kurd | | 0.0830 | | 31 | See | | | |
79 |
A*02:01 | | Germany DKMS - Austria minority | | 0.2677 | | 1,698 | See | | | |
80 |
A*02:01 | | Germany DKMS - Bosnia and Herzegovina minority | | 0.3050 | | 1,028 | See | | | |
81 |
A*02:01 | | Germany DKMS - China minority | | 0.1226 | | 1,282 | See | | | |
82 |
A*02:01 | | Germany DKMS - Croatia minority | | 0.2842 | | 2,057 | See | | | |
83 |
A*02:01 | | Germany DKMS - France minority | | 0.2572 | | 1,406 | See | | | |
84 |
A*02:01 | | Germany DKMS - German donors | | 0.2839 | | 3,456,066 | See | | |
|
85 |
A*02:01 | | Germany DKMS - Greece minority | | 0.2629 | | 1,894 | See | | | |
86 |
A*02:01 | | Germany DKMS - Italy minority | | 0.2252 | | 1,159 | See | | | |
87 |
A*02:01 | | Germany DKMS - Netherlands minority | | 0.2956 | | 1,374 | See | | | |
88 |
A*02:01 | | Germany DKMS - Portugal minority | | 0.2564 | | 1,176 | See | | | |
89 |
A*02:01 | | Germany DKMS - Romania minority | | 0.2763 | | 1,234 | See | | | |
90 |
A*02:01 | | Germany DKMS - Spain minority | | 0.2367 | | 1,107 | See | | | |
91 |
A*02:01 | | Germany DKMS - Turkey minority | | 0.1957 | | 4,856 | See | | | |
92 |
A*02:01 | | Germany DKMS - United Kingdom minority | | 0.2739 | | 1,043 | See | | | |
93 |
A*02:01 | | Germany pop 6 | | 0.2888 | | 8,862 | See | | | |
94 |
A*02:01 | | Germany pop 8 | | 0.2667 | | 39,689 | See | | | |
95 |
A*02:01 | | Ghana Ga-Adangbe | 19.1 | 0.1069 | | 131 | See | | | |
96 |
A*02:01 | | Greece pop 6 | | 0.2624 | | 242 | See | | | |
97 |
A*02:01 | | Greece pop 8 | 51.8 | 0.3072 | | 83 | See | | | |
98 |
A*02:01 | | Guinea Bissau Balanta | | 0.1350 | | 48 | See | | | |
99 |
A*02:01 | | Guinea Bissau Bijago | | 0.0870 | | 23 | See | | | |
100 |
A*02:01 | | Guinea Bissau Fula | | 0.1130 | | 31 | 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.