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Line |
Allele |
Population |
% of individuals
that have the allele |
Allele
Frequency
(in_decimals) |
Sample
Size |
IMGT/HLA¹
Database |
Distribution² |
Haplotype³
Association |
Notesª |
1 |
A*01:01 | | Argentina Rosario Toba | 15.1 | 0.0760 | | 86 | See | | | |
2 |
A*01:01 | | Armenia combined Regions | | 0.1250 | | 100 | See | | | |
3 |
A*01:01 | | Australia Cape York Peninsula Aborigine | | 0.0530 | | 103 | See | | | |
4 |
A*01:01 | | Australia Groote Eylandt Aborigine | | 0.0270 | | 75 | See | | | |
5 |
A*01:01 | | Australia New South Wales Caucasian | | 0.1870 | | 134 | See | | | |
6 |
A*01:01 | | Australia Yuendumu Aborigine | | 0.0080 | | 191 | See | | | |
7 |
A*01:01 | | Austria | 27.0 | 0.1460 | | 200 | See | | | |
8 |
A*01:01 | | Azores Central Islands | | 0.0800 | | 59 | See | | | |
9 |
A*01:01 | | Azores Oriental Islands | | 0.1150 | | 43 | See | | | |
10 |
A*01:01 | | Azores Terceira Island | | 0.1090 | | 130 | See | | | |
11 |
A*01:01 | | Belgium | 29.2 | 0.1550 | | 99 | See | | | |
12 |
A*01:01 | | Belgium | 26.8 | 0.1445 | | 31,412 | See | | |
|
13 |
A*01:01 | | Brazil Puyanawa | 8.7 | 0.0430 | | 150 | See | | |
|
14 |
A*01:01 | | Brazil Belo Horizonte Caucasian | 13.7 | 0.0790 | | 95 | See | | | |
15 |
A*01:01 | | Brazil Mixed | | 0.0910 | | 108 | See | | | |
16 |
A*01:01 | | Brazil Vale do Ribeira Quilombos | 0.0420 | 0 | | 144 | See | | | |
17 |
A*01:01 | | Bulgaria Romani | | 0.3330 | | 13 | See | | | |
18 |
A*01:01 | | Cameroon Baka Pygmy | | 0 | | 10 | See | | | |
19 |
A*01:01 | | Cameroon Bamileke | | 0.0260 | | 77 | See | | | |
20 |
A*01:01 | | Cameroon Beti | | 0.0110 | | 174 | See | | | |
21 |
A*01:01 | | Cameroon Sawa | | 0 | | 13 | See | | | |
22 |
A*01:01 | | Cameroon Yaounde | | 0.0110 | | 92 | See | | | |
23 |
A*01:01 | | Central African Republic Mbenzele Pygmy | 0.0 | 0 | | 36 | See | | | |
24 |
A*01:01 | | Chile Mapuche | | 0.0615 | | 66 | See | | |
|
25 |
A*01:01 | | Chile Santiago Mixed | 11.0 | 0.0566 | | 70 | See | | | |
26 |
A*01:01 | | China Beijing | | 0.0370 | | 67 | See | | | |
27 |
A*01:01 | | China Beijing Shijiazhuang Tianjian Han | | 0.0340 | | 618 | See | | | |
28 |
A*01:01 | | China Canton Han | | 0.0060 | | 264 | See | | | |
29 |
A*01:01 | | China Guangzhou | | 0.0100 | | 102 | See | | | |
30 |
A*01:01 | | China Guizhou Province Bouyei | | 0.0050 | | 109 | See | | | |
31 |
A*01:01 | | China Guizhou Province Miao pop 2 | | 0 | | 85 | See | | | |
32 |
A*01:01 | | China Guizhou Province Shui | | 0 | | 153 | See | | | |
33 |
A*01:01 | | China Hubei Han | 4.6 | 0.0229 | | 3,732 | See | | |
|
34 |
A*01:01 | | China Inner Mongolia Region | | 0.0540 | | 102 | See | | | |
35 |
A*01:01 | | China Jiangsu Han | | 0.0370 | | 3,238 | See | | | |
36 |
A*01:01 | | China Jiangsu Province Han | | 0.0174 | | 334 | See | | | |
37 |
A*01:01 | | China North Han | | 0 | | 105 | See | | | |
38 |
A*01:01 | | China Qinghai Province Hui | | 0.0550 | | 110 | See | | | |
39 |
A*01:01 | | China Shanxi HIV negative | | 0.0910 | | 22 | See | | | |
40 |
A*01:01 | | China Sichuan HIV negative | | 0.0590 | | 34 | See | | | |
41 |
A*01:01 | | China South Han | | 0.0050 | | 284 | See | | | |
42 |
A*01:01 | | China Uyghur HIV negative | | 0.0260 | | 19 | See | | | |
43 |
A*01:01 | | China Yunnan Province Han | | 0.0150 | | 101 | See | | | |
44 |
A*01:01 | | Colombia Bogotá Cord Blood | 12.0 | 0.0608 | | 1,463 | See | | | |
45 |
A*01:01 | | Colombia North Chimila Amerindians | | 0.0212 | | 47 | See | | |
|
46 |
A*01:01 | | Colombia North Wiwa El Encanto | | 0.0192 | | 52 | See | | |
|
47 |
A*01:01 | | Croatia | | 0.0970 | | 150 | See | | | |
48 |
A*01:01 | | Croatia pop 4 | | 0.1244 | | 4,000 | See | | | |
49 |
A*01:01 | | Cuba Caucasian | 15.7 | 0.0790 | | 70 | See | | | |
50 |
A*01:01 | | Cuba Mixed Race | 14.3 | 0.0710 | | 42 | See | | | |
51 |
A*01:01 | | Czech Republic | | 0.1270 | | 106 | See | | | |
52 |
A*01:01 | | Czech Republic NMDR | | 0.1607 | | 5,099 | See | | | |
53 |
A*01:01 | | Ecuador Amerindians | | 0.0476 | | 63 | See | | |
|
54 |
A*01:01 | | England North West | 38.6 | 0.2080 | | 298 | See | | | |
55 |
A*01:01 | | Finland | | 0.0890 | | 91 | See | | | |
56 |
A*01:01 | | France French Bone Marrow Donor Registry | | 0.1301 | | 42,623 | See | | | |
57 |
A*01:01 | | France Southeast | 27.7 | 0.1500 | | 130 | See | | | |
58 |
A*01:01 | | Gaza | 33.3 | 0.1786 | | 42 | See | | |
|
59 |
A*01:01 | | Georgia Svaneti Region Svan | | 0.0560 | | 80 | See | | | |
60 |
A*01:01 | | Georgia Tibilisi | | 0.0570 | | 109 | See | | | |
61 |
A*01:01 | | Georgia Tibilisi Kurd | | 0.0670 | | 31 | See | | | |
62 |
A*01:01 | | Germany DKMS - Austria minority | | 0.1561 | | 1,698 | See | | | |
63 |
A*01:01 | | Germany DKMS - Bosnia and Herzegovina minority | | 0.1513 | | 1,028 | See | | | |
64 |
A*01:01 | | Germany DKMS - China minority | | 0.0380 | | 1,282 | See | | | |
65 |
A*01:01 | | Germany DKMS - Croatia minority | | 0.1213 | | 2,057 | See | | | |
66 |
A*01:01 | | Germany DKMS - France minority | | 0.1376 | | 1,406 | See | | | |
67 |
A*01:01 | | Germany DKMS - German donors | | 0.1537 | | 3,456,066 | See | | |
|
68 |
A*01:01 | | Germany DKMS - Greece minority | | 0.1038 | | 1,894 | See | | | |
69 |
A*01:01 | | Germany DKMS - Italy minority | | 0.1330 | | 1,159 | See | | | |
70 |
A*01:01 | | Germany DKMS - Netherlands minority | | 0.1681 | | 1,374 | See | | | |
71 |
A*01:01 | | Germany DKMS - Portugal minority | | 0.1158 | | 1,176 | See | | | |
72 |
A*01:01 | | Germany DKMS - Romania minority | | 0.1457 | | 1,234 | See | | | |
73 |
A*01:01 | | Germany DKMS - Spain minority | | 0.1189 | | 1,107 | See | | | |
74 |
A*01:01 | | Germany DKMS - Turkey minority | | 0.1032 | | 4,856 | See | | | |
75 |
A*01:01 | | Germany DKMS - United Kingdom minority | | 0.1803 | | 1,043 | See | | | |
76 |
A*01:01 | | Germany pop 6 | | 0.1541 | | 8,862 | See | | | |
77 |
A*01:01 | | Germany pop 8 | | 0.1507 | | 39,689 | See | | | |
78 |
A*01:01 | | Ghana Ga-Adangbe | 4.6 | 0.0229 | | 131 | See | | | |
79 |
A*01:01 | | Greece pop 6 | | 0.1136 | | 242 | See | | | |
80 |
A*01:01 | | Greece pop 8 | 25.3 | 0.1446 | | 83 | See | | | |
81 |
A*01:01 | | Guinea Bissau Balanta | | 0.0210 | | 48 | See | | | |
82 |
A*01:01 | | Guinea Bissau Bijago | | 0.0220 | | 23 | See | | | |
83 |
A*01:01 | | Guinea Bissau Fula | | 0.0810 | | 31 | See | | | |
84 |
A*01:01 | | Guinea Bissau Papel | | 0.0800 | | 25 | See | | | |
85 |
A*01:01 | | Hong Kong Chinese | 2.1 | 0.0110 | | 569 | See | | | |
86 |
A*01:01 | | Hong Kong Chinese BMDR | | 0.0084 | | 7,595 | See | | |
|
87 |
A*01:01 | | Hong Kong Chinese cord blood registry | | 0.0090 | | 3,892 | See | | |
|
88 |
A*01:01 | | India Andhra Pradesh Golla | | 0.1250 | | 111 | See | | | |
89 |
A*01:01 | | India Central UCBB | 25.9 | 0.1294 | | 4,204 | See | | |
|
90 |
A*01:01 | | India Delhi pop 2 | 20.0 | 0.1050 | | 90 | See | | | |
91 |
A*01:01 | | India East UCBB | 23.1 | 0.1267 | | 2,403 | See | | |
|
92 |
A*01:01 | | India Khandesh Region Pawra | | 0.0500 | | 50 | See | | | |
93 |
A*01:01 | | India Mumbai Maratha | | 0.1230 | | 91 | See | | | |
94 |
A*01:01 | | India New Delhi | | 0.0380 | | 71 | See | | | |
95 |
A*01:01 | | India North pop 2 | | 0.1150 | | 72 | See | | | |
96 |
A*01:01 | | India North UCBB | 22.7 | 0.1234 | | 5,849 | See | | |
|
97 |
A*01:01 | | India Northeast UCBB | 11.1 | 0.0557 | | 296 | See | | |
|
98 |
A*01:01 | | India South UCBB | 29.7 | 0.1618 | | 11,446 | See | | |
|
99 |
A*01:01 | | India Tamil Nadu | | 0.1593 | | 2,492 | See | | |
|
100 |
A*01:01 | | India Tamil Nadu Nadar | | 0.0900 | | 61 | See | | | |
Notes:
* Allele Frequency: Total number of copies of the allele in the population sample (Alleles / 2n) in decimal format.
Important: 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).
* Allele Frequencies shown in
green were calculated from Phenotype Frequencies assuming Hardy-Weinberg proportions.
AF = 1-square_root(1-PF)
PF = 1-(1-AF)
2
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
G group.
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.
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