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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*02:03 |  | Australia New South Wales Caucasian | | 0 |  | 134 | See |  |  | |
| 2 |
A*02:03 |  | Australia Yuendumu Aborigine | | 0 |  | 191 | See |  |  | |
| 3 |
A*02:03 |  | Belgium pop 3 | 0.0325 | 0.0002 |  | 31,412 | See |  |  |
|
| 4 |
A*02:03 |  | Brazil Belo Horizonte Caucasian | 0.0 | 0 |  | 95 | See |  |  | |
| 5 |
A*02:03 |  | Bulgaria | | 0 |  | 55 | See |  |  | |
| 6 |
A*02:03 |  | Burkina Faso Fulani | | 0 |  | 49 | See |  |  | |
| 7 |
A*02:03 |  | Burkina Faso Rimaibe | | 0.0110 |  | 47 | See |  |  | |
| 8 |
A*02:03 |  | China Beijing | | 0.0080 |  | 67 | See |  |  | |
| 9 |
A*02:03 |  | China Beijing Shijiazhuang Tianjian Han | | 0.0240 |  | 618 | See |  |  | |
| 10 |
A*02:03 |  | China Canton Han | | 0.1120 |  | 264 | See |  |  | |
| 11 |
A*02:03 |  | China Guangdong Province Meizhou Han | | 0.0100 |  | 100 | See |  |  | |
| 12 |
A*02:03 |  | China Guangxi Region Maonan | | 0.1760 |  | 108 | See |  |  | |
| 13 |
A*02:03 |  | China Guangzhou | | 0.0980 |  | 102 | See |  |  | |
| 14 |
A*02:03 |  | China Hubei Han | 7.1 | 0.0356 |  | 3,732 | See |  |  |
|
| 15 |
A*02:03 |  | China Jiangsu Han | | 0.0230 |  | 3,238 | See |  |  | |
| 16 |
A*02:03 |  | China Jiangsu Province Han | | 0.0279 |  | 334 | See |  |  | |
| 17 |
A*02:03 |  | China North Han | | 0.0240 |  | 105 | See |  |  | |
| 18 |
A*02:03 |  | China Shanxi HIV negative | | 0.0230 |  | 22 | See |  |  | |
| 19 |
A*02:03 |  | China Sichuan HIV negative | | 0.0880 |  | 34 | See |  |  | |
| 20 |
A*02:03 |  | China South Han | | 0.1080 |  | 284 | See |  |  | |
| 21 |
A*02:03 |  | China Southwest Dai | | 0.1090 |  | 124 | See |  |  | |
| 22 |
A*02:03 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
| 23 |
A*02:03 |  | China Yunnan Bulang | | 0.0820 |  | 116 | See |  |  | |
| 24 |
A*02:03 |  | China Yunnan Hani | | 0.0430 |  | 150 | See |  |  | |
| 25 |
A*02:03 |  | China Yunnan Province Han | | 0.0050 |  | 101 | See |  |  | |
| 26 |
A*02:03 |  | Croatia | | 0 |  | 150 | See |  |  | |
| 27 |
A*02:03 |  | Cuba Caucasian | 0.0 | 0 |  | 70 | See |  |  | |
| 28 |
A*02:03 |  | Cuba Mixed Race | 0.0 | 0 |  | 42 | See |  |  | |
| 29 |
A*02:03 |  | Czech Republic | | 0 |  | 106 | See |  |  | |
| 30 |
A*02:03 |  | Czech Republic NMDR | | 0.0002 |  | 5,099 | See |  |  | |
| 31 |
A*02:03 |  | Germany DKMS - Austria minority | | 0.0003 |  | 1,698 | See |  |  | |
| 32 |
A*02:03 |  | Germany DKMS - China minority | | 0.0335 |  | 1,282 | See |  |  | |
| 33 |
A*02:03 |  | Germany DKMS - France minority | | 0.0004 |  | 1,406 | See |  |  | |
| 34 |
A*02:03 |  | Germany DKMS - German donors | | 0.0000540 |  | 3,456,066 | See |  |  |
|
| 35 |
A*02:03 |  | Germany DKMS - Italy minority | | 0.0004 |  | 1,159 | See |  |  | |
| 36 |
A*02:03 |  | Germany DKMS - Turkey minority | | 0.0004 |  | 4,856 | See |  |  | |
| 37 |
A*02:03 |  | Germany pop 6 | | 0.0001 |  | 8,862 | See |  |  | |
| 38 |
A*02:03 |  | Germany pop 8 | | 0.0001 |  | 39,689 | See |  |  | |
| 39 |
A*02:03 |  | Hong Kong Chinese | 14.7 | 0.0780 |  | 569 | See |  |  | |
| 40 |
A*02:03 |  | Hong Kong Chinese BMDR | | 0.0760 |  | 7,595 | See |  |  |
|
| 41 |
A*02:03 |  | Hong Kong Chinese cord blood registry | | 0.0777 |  | 3,892 | See |  |  |
|
| 42 |
A*02:03 |  | India Central UCBB | 2.0 | 0.0099 |  | 4,204 | See |  |  |
|
| 43 |
A*02:03 |  | India Delhi pop 2 | 1.1 | 0.0050 |  | 90 | See |  |  | |
| 44 |
A*02:03 |  | India East UCBB | 5.1 | 0.0256 |  | 2,403 | See |  |  |
|
| 45 |
A*02:03 |  | India Khandesh Region Pawra | | 0.0500 |  | 50 | See |  |  | |
| 46 |
A*02:03 |  | India Mumbai Maratha | | 0.0370 |  | 91 | See |  |  | |
| 47 |
A*02:03 |  | India North UCBB | 1.0 | 0.0051 |  | 5,849 | See |  |  |
|
| 48 |
A*02:03 |  | India Northeast UCBB | 8.4 | 0.0422 |  | 296 | See |  |  |
|
| 49 |
A*02:03 |  | India South UCBB | 2.5 | 0.0124 |  | 11,446 | See |  |  |
|
| 50 |
A*02:03 |  | India Tamil Nadu | | 0.0117 |  | 2,492 | See |  |  |
|
| 51 |
A*02:03 |  | India West Bhil | | 0.0500 |  | 50 | See |  |  | |
| 52 |
A*02:03 |  | India West Coast Parsi | | 0 |  | 50 | See |  |  | |
| 53 |
A*02:03 |  | India West UCBB | 2.3 | 0.0112 |  | 5,829 | See |  |  |
|
| 54 |
A*02:03 |  | Indonesia Java Western | 7.2 | 0.0370 |  | 236 | See |  |  | |
| 55 |
A*02:03 |  | Ireland Northern | 0.0 | 0 |  | 1,000 | See |  |  | |
| 56 |
A*02:03 |  | Israel Iran Jews | | 0.0000150 |  | 8,153 | See |  |  |
|
| 57 |
A*02:03 |  | Israel Iraq Jews | | 0.0000090 |  | 13,270 | See |  |  |
|
| 58 |
A*02:03 |  | Israel Libya Jews | | 0.0000270 |  | 3,739 | See |  |  |
|
| 59 |
A*02:03 |  | Israel Morocco Jews | | 0.0000030 |  | 36,718 | See |  |  |
|
| 60 |
A*02:03 |  | Israel Poland Jews | | 0.0000360 |  | 13,871 | See |  |  |
|
| 61 |
A*02:03 |  | Israel USSR Jews | | 0.0000110 |  | 45,681 | See |  |  |
|
| 62 |
A*02:03 |  | Italy Bergamo | 0.0 | 0 |  | 101 | See |  |  | |
| 63 |
A*02:03 |  | Italy North pop 3 | 0.0 | 0 |  | 97 | See |  |  | |
| 64 |
A*02:03 |  | Japan pop 16 | | 0.0006 |  | 18,604 | See |  |  | |
| 65 |
A*02:03 |  | Japan pop 3 | | 0.0010 |  | 1,018 | See |  |  | |
| 66 |
A*02:03 |  | Malaysia Champa | 13.8 | 0.0690 |  | 29 | See |  |  |
|
| 67 |
A*02:03 |  | Malaysia Jelebu Temuan | | 0.0210 |  | 25 | See |  |  | |
| 68 |
A*02:03 |  | Malaysia Kedah Baling Kensiu | | 0.0200 |  | 25 | See |  |  | |
| 69 |
A*02:03 |  | Malaysia Kedah Kensiu | 9.5 | 0.0480 |  | 21 | See |  |  |
|
| 70 |
A*02:03 |  | Malaysia Kelantan | 3.6 | 0.0180 |  | 28 | See |  |  |
|
| 71 |
A*02:03 |  | Malaysia Mandailing | 18.5 | 0.0930 |  | 27 | See |  |  |
|
| 72 |
A*02:03 |  | Malaysia Pahang Semai | 23.7 | 0.1180 |  | 38 | See |  |  |
|
| 73 |
A*02:03 |  | Malaysia Patani | 16.0 | 0.0800 |  | 25 | See |  |  |
|
| 74 |
A*02:03 |  | Malaysia Peninsular Chinese | 12.4 | 0.0644 |  | 194 | See |  |  |
|
| 75 |
A*02:03 |  | Malaysia Peninsular Indian | 1.1 | 0.0055 |  | 271 | See |  |  |
|
| 76 |
A*02:03 |  | Malaysia Peninsular Malay | 4.3 | 0.0216 |  | 951 | See |  |  |
|
| 77 |
A*02:03 |  | Malaysia Perak Grik Jehai | | 0.0400 |  | 25 | See |  |  | |
| 78 |
A*02:03 |  | Malaysia Sarawak Bau Bidayuh | | 0.0200 |  | 25 | See |  |  | |
| 79 |
A*02:03 |  | Mexico Mestizo | 0.0 | 0 |  | 41 | See |  |  | |
| 80 |
A*02:03 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
| 81 |
A*02:03 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
| 82 |
A*02:03 |  | Netherlands Leiden | | 0.0010 |  | 1,305 | See |  |  | |
| 83 |
A*02:03 |  | Oman | 0.0 | 0 |  | 118 | See |  |  | |
| 84 |
A*02:03 |  | Pakistan Baloch | | 0.0080 |  | 66 | See |  |  | |
| 85 |
A*02:03 |  | Pakistan Brahui | | 0 |  | 104 | See |  |  | |
| 86 |
A*02:03 |  | Pakistan Burusho | | 0 |  | 92 | See |  |  | |
| 87 |
A*02:03 |  | Pakistan Kalash | | 0 |  | 69 | See |  |  | |
| 88 |
A*02:03 |  | Pakistan Karachi Parsi | | 0 |  | 91 | See |  |  | |
| 89 |
A*02:03 |  | Pakistan Mixed Pathan | | 0.0050 |  | 100 | See |  |  | |
| 90 |
A*02:03 |  | Pakistan Mixed Sindhi | | 0 |  | 101 | See |  |  | |
| 91 |
A*02:03 |  | Panama | | 0.0011 |  | 462 | See |  |  |
|
| 92 |
A*02:03 |  | Philippines Ivatan | 0.0 | 0 |  | 50 | See |  |  | |
| 93 |
A*02:03 |  | Poland DKMS | | 0.0000200 |  | 20,653 | See |  |  | |
| 94 |
A*02:03 |  | Romania | 0.0 | 0 |  | 348 | See |  |  | |
| 95 |
A*02:03 |  | Russia Tuva pop 2 | | 0.0030 |  | 169 | See |  |  | |
| 96 |
A*02:03 |  | Scotland Orkney | 0.0 | 0 |  | 99 | See |  |  | |
| 97 |
A*02:03 |  | Singapore Chinese | 12.8 | 0.0670 |  | 149 | See |  |  | |
| 98 |
A*02:03 |  | Singapore Chinese Han | | 0.0990 |  | 94 | See |  |  | |
| 99 |
A*02:03 |  | Singapore Javaneses | | 0.0900 |  | 51 | See |  |  | |
| 100 |
A*02:03 |  | Singapore Riau Malay | | 0.0520 |  | 132 | 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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