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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*11:02 |  | Brazil Belo Horizonte Caucasian | 0.0 | 0 |  | 95 | See |  |  | |
| 2 |
A*11:02 |  | Bulgaria | | 0 |  | 55 | See |  |  | |
| 3 |
A*11:02 |  | China Beijing Shijiazhuang Tianjian Han | | 0.0200 |  | 618 | See |  |  | |
| 4 |
A*11:02 |  | China Canton Han | | 0.0170 |  | 264 | See |  |  | |
| 5 |
A*11:02 |  | China Guangdong Province Meizhou Han | | 0.0460 |  | 100 | See |  |  | |
| 6 |
A*11:02 |  | China Guangxi Region Maonan | | 0.0650 |  | 108 | See |  |  | |
| 7 |
A*11:02 |  | China Guizhou Province Bouyei | | 0.0360 |  | 109 | See |  |  | |
| 8 |
A*11:02 |  | China Guizhou Province Miao pop 2 | | 0.0410 |  | 85 | See |  |  | |
| 9 |
A*11:02 |  | China Guizhou Province Shui | | 0.0520 |  | 153 | See |  |  | |
| 10 |
A*11:02 |  | China Hubei Han | 4.8 | 0.0238 |  | 3,732 | See |  |  |
|
| 11 |
A*11:02 |  | China Inner Mongolia Region | | 0.0100 |  | 102 | See |  |  | |
| 12 |
A*11:02 |  | China Jiangsu Han | | 0.0030 |  | 3,238 | See |  |  | |
| 13 |
A*11:02 |  | China Jiangsu Province Han | | 0.0053 |  | 334 | See |  |  | |
| 14 |
A*11:02 |  | China North Han | | 0 |  | 105 | See |  |  | |
| 15 |
A*11:02 |  | China Qinghai Province Hui | | 0.0090 |  | 110 | See |  |  | |
| 16 |
A*11:02 |  | China Sichuan HIV negative | | 0.0290 |  | 34 | See |  |  | |
| 17 |
A*11:02 |  | China Southwest Dai | | 0.0280 |  | 124 | See |  |  | |
| 18 |
A*11:02 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
| 19 |
A*11:02 |  | China Yunnan Province Han | | 0.0500 |  | 101 | See |  |  | |
| 20 |
A*11:02 |  | China Yunnan Province Jinuo | | 0.0090 |  | 109 | See |  |  | |
| 21 |
A*11:02 |  | China Yunnan Province Lisu | | 0.0060 |  | 111 | See |  |  | |
| 22 |
A*11:02 |  | China Yunnan Province Wa | | 0 |  | 119 | See |  |  | |
| 23 |
A*11:02 |  | Croatia | | 0 |  | 150 | See |  |  | |
| 24 |
A*11:02 |  | Cuba Caucasian | 0.0 | 0 |  | 70 | See |  |  | |
| 25 |
A*11:02 |  | Cuba Mixed Race | 0.0 | 0 |  | 42 | See |  |  | |
| 26 |
A*11:02 |  | Czech Republic | | 0 |  | 106 | See |  |  | |
| 27 |
A*11:02 |  | France French Bone Marrow Donor Registry | | 0.0050 |  | 42,623 | See |  |  | |
| 28 |
A*11:02 |  | Germany DKMS - China minority | | 0.0211 |  | 1,282 | See |  |  | |
| 29 |
A*11:02 |  | Germany DKMS - German donors | | 0.0000200 |  | 3,456,066 | See |  |  |
|
| 30 |
A*11:02 |  | Germany DKMS - Turkey minority | | 0.0004 |  | 4,856 | See |  |  | |
| 31 |
A*11:02 |  | Germany pop 8 | | 0.0000500 |  | 39,689 | See |  |  | |
| 32 |
A*11:02 |  | Hong Kong Chinese | 7.7 | 0.0400 |  | 569 | See |  |  | |
| 33 |
A*11:02 |  | Hong Kong Chinese BMDR | | 0.0377 |  | 7,595 | See |  |  |
|
| 34 |
A*11:02 |  | Hong Kong Chinese cord blood registry | | 0.0372 |  | 3,892 | See |  |  |
|
| 35 |
A*11:02 |  | India Khandesh Region Pawra | | 0 |  | 50 | See |  |  | |
| 36 |
A*11:02 |  | India Mumbai Maratha | | 0 |  | 91 | See |  |  | |
| 37 |
A*11:02 |  | India West Bhil | | 0 |  | 50 | See |  |  | |
| 38 |
A*11:02 |  | India West Coast Parsi | | 0.0100 |  | 50 | See |  |  | |
| 39 |
A*11:02 |  | Ireland Northern | 0.0 | 0 |  | 1,000 | See |  |  | |
| 40 |
A*11:02 |  | Israel Poland Jews | | 0.0000120 |  | 13,871 | See |  |  |
|
| 41 |
A*11:02 |  | Israel YemenJews | | 0.0000320 |  | 15,542 | See |  |  |
|
| 42 |
A*11:02 |  | Italy North pop 3 | 3.8 | 0.0190 |  | 97 | See |  |  | |
| 43 |
A*11:02 |  | Japan Central | | 0.0010 |  | 371 | See |  |  | |
| 44 |
A*11:02 |  | Japan pop 16 | | 0.0022 |  | 18,604 | See |  |  | |
| 45 |
A*11:02 |  | Japan pop 3 | | 0.0020 |  | 1,018 | See |  |  | |
| 46 |
A*11:02 |  | Malaysia Champa | 3.5 | 0.0170 |  | 29 | See |  |  |
|
| 47 |
A*11:02 |  | Malaysia Peninsular Chinese | 1.6 | 0.0077 |  | 194 | See |  |  |
|
| 48 |
A*11:02 |  | Malaysia Peninsular Malay | 0.2 | 0.0011 |  | 951 | See |  |  |
|
| 49 |
A*11:02 |  | Mexico Mestizo | 0.0 | 0 |  | 41 | See |  |  | |
| 50 |
A*11:02 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
| 51 |
A*11:02 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
| 52 |
A*11:02 |  | Netherlands Leiden | | 0 |  | 1,305 | See |  |  | |
| 53 |
A*11:02 |  | Oman | 0.0 | 0 |  | 118 | See |  |  | |
| 54 |
A*11:02 |  | Pakistan Baloch | | 0 |  | 66 | See |  |  | |
| 55 |
A*11:02 |  | Pakistan Brahui | | 0 |  | 104 | See |  |  | |
| 56 |
A*11:02 |  | Pakistan Burusho | | 0 |  | 92 | See |  |  | |
| 57 |
A*11:02 |  | Pakistan Kalash | | 0 |  | 69 | See |  |  | |
| 58 |
A*11:02 |  | Pakistan Karachi Parsi | | 0 |  | 91 | See |  |  | |
| 59 |
A*11:02 |  | Pakistan Mixed Pathan | | 0 |  | 100 | See |  |  | |
| 60 |
A*11:02 |  | Pakistan Mixed Sindhi | | 0 |  | 101 | See |  |  | |
| 61 |
A*11:02 |  | Philippines Ivatan | 10.0 | 0.0500 |  | 50 | See |  |  | |
| 62 |
A*11:02 |  | Poland DKMS | | 0.0000200 |  | 20,653 | See |  |  | |
| 63 |
A*11:02 |  | Romania | 0.0 | 0 |  | 348 | See |  |  | |
| 64 |
A*11:02 |  | Singapore Chinese | 6.0 | 0.0300 |  | 149 | See |  |  | |
| 65 |
A*11:02 |  | Singapore SGVP Chinese CHS | | 0.0380 |  | 96 | See |  |  |
|
| 66 |
A*11:02 |  | Singapore SGVP Malay MAS | | 0.0120 |  | 89 | See |  |  |
|
| 67 |
A*11:02 |  | Singapore SGVP. Indian INS | | 0 |  | 86 | See |  |  |
|
| 68 |
A*11:02 |  | South Africa Natal Zulu | 0.0 | 0 |  | 100 | See |  |  | |
| 69 |
A*11:02 |  | South Korea pop 10 | | 0.0023 |  | 4,128 | See |  |  | |
| 70 |
A*11:02 |  | South Korea pop 3 | | 0 |  | 485 | See |  |  | |
| 71 |
A*11:02 |  | Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, | 0.0200 | 0.0001 |  | 4,335 | See |  |  |
|
| 72 |
A*11:02 |  | Taiwan Ami | 16.3 | 0.0870 |  | 98 | See |  |  | |
| 73 |
A*11:02 |  | Taiwan Atayal | 10.4 | 0.0520 |  | 106 | See |  |  | |
| 74 |
A*11:02 |  | Taiwan Bunun | 2.0 | 0.0100 |  | 101 | See |  |  | |
| 75 |
A*11:02 |  | Taiwan Hakka | 9.1 | 0.0450 |  | 55 | See |  |  | |
| 76 |
A*11:02 |  | Taiwan Minnan pop 1 | 7.8 | 0.0390 |  | 102 | See |  |  | |
| 77 |
A*11:02 |  | Taiwan Paiwan | 0.0 | 0 |  | 51 | See |  |  | |
| 78 |
A*11:02 |  | Taiwan Pazeh | 21.8 | 0.1090 |  | 55 | See |  |  | |
| 79 |
A*11:02 |  | Taiwan pop 2 | | 0.0260 |  | 364 | See |  |  | |
| 80 |
A*11:02 |  | Taiwan pop 3 | | 0.0380 |  | 212 | See |  |  | |
| 81 |
A*11:02 |  | Taiwan Puyuma | 14.0 | 0.0700 |  | 50 | See |  |  | |
| 82 |
A*11:02 |  | Taiwan Rukai | 0.0 | 0 |  | 50 | See |  |  | |
| 83 |
A*11:02 |  | Taiwan Saisiat | 23.5 | 0.1270 |  | 51 | See |  |  | |
| 84 |
A*11:02 |  | Taiwan Siraya | 13.7 | 0.0690 |  | 51 | See |  |  | |
| 85 |
A*11:02 |  | Taiwan Tao | 6.0 | 0.0300 |  | 50 | See |  |  | |
| 86 |
A*11:02 |  | Taiwan Taroko | 3.6 | 0.0180 |  | 55 | See |  |  | |
| 87 |
A*11:02 |  | Taiwan Thao | 0.0 | 0 |  | 30 | See |  |  | |
| 88 |
A*11:02 |  | Taiwan Tsou | 5.9 | 0.0290 |  | 51 | See |  |  | |
| 89 |
A*11:02 |  | Taiwan Tzu Chi Cord Blood Bank | | 0.0590 |  | 710 | See |  |  | |
| 90 |
A*11:02 |  | Thailand | | 0.0350 |  | 142 | See |  |  | |
| 91 |
A*11:02 |  | USA African American Bethesda | 0.0 | 0 |  | 187 | See |  |  | |
| 92 |
A*11:02 |  | USA African American pop 4 | | 0 |  | 2,411 | See |  |  | |
| 93 |
A*11:02 |  | USA Alaska Yupik | | 0 |  | 252 | See |  |  | |
| 94 |
A*11:02 |  | USA Asian pop 2 | | 0.0153 |  | 1,772 | See |  |  | |
| 95 |
A*11:02 |  | USA Caucasian Bethesda | 0.0 | 0 |  | 307 | See |  |  | |
| 96 |
A*11:02 |  | USA Hispanic pop 2 | | 0 |  | 1,999 | See |  |  | |
| 97 |
A*11:02 |  | USA NMDP African American pop 2 | | 0.0000050 |  | 416,581 | See |  |  | |
| 98 |
A*11:02 |  | USA NMDP Caribean Hispanic | | 0.0000150 |  | 115,374 | See |  |  | |
| 99 |
A*11:02 |  | USA NMDP Chinese | | 0.0263 |  | 99,672 | See |  |  | |
| 100 |
A*11:02 |  | USA NMDP European Caucasian | | 0.0000056 |  | 1,242,890 | 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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