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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 |
1,701 |
A*02:17 |  | Bulgaria | | 0.0090 |  | 55 | See |  |  | |
1,702 |
A*02:17 |  | Cape Verde Northwestern Islands | | 0 |  | 62 | See |  |  | |
1,703 |
A*02:17 |  | Cape Verde Southeastern Islands | | 0.0080 |  | 62 | See |  |  | |
1,704 |
A*02:17 |  | China Beijing Shijiazhuang Tianjian Han | | 0.0010 |  | 618 | See |  |  | |
1,705 |
A*02:17 |  | China North Han | | 0 |  | 105 | See |  |  | |
1,706 |
A*02:17 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
1,707 |
A*02:17 |  | Cuba Caucasian | 1.4 | 0.0070 |  | 70 | See |  |  | |
1,708 |
A*02:17 |  | Cuba Mixed Race | 0.0 | 0 |  | 42 | See |  |  | |
1,709 |
A*02:17 |  | Germany DKMS - Austria minority | | 0.0012 |  | 1,698 | See |  |  | |
1,710 |
A*02:17 |  | Germany DKMS - China minority | | 0.0004 |  | 1,282 | See |  |  | |
1,711 |
A*02:17 |  | Germany DKMS - Croatia minority | | 0.0002 |  | 2,057 | See |  |  | |
1,712 |
A*02:17 |  | Germany DKMS - France minority | | 0.0004 |  | 1,406 | See |  |  | |
1,713 |
A*02:17 |  | Germany DKMS - Greece minority | | 0.0042 |  | 1,894 | See |  |  | |
1,714 |
A*02:17 |  | Germany DKMS - Italy minority | | 0.0035 |  | 1,159 | See |  |  | |
1,715 |
A*02:17 |  | Germany DKMS - Netherlands minority | | 0.0007 |  | 1,374 | See |  |  | |
1,716 |
A*02:17 |  | Germany DKMS - Romania minority | | 0.0032 |  | 1,234 | See |  |  | |
1,717 |
A*02:17 |  | Germany DKMS - Spain minority | | 0.0009 |  | 1,107 | See |  |  | |
1,718 |
A*02:17 |  | Germany DKMS - Turkey minority | | 0.0056 |  | 4,856 | See |  |  | |
1,719 |
A*02:17 |  | Germany pop 6 | | 0.0010 |  | 8,862 | See |  |  | |
1,720 |
A*02:17 |  | Guinea Bissau | | 0 |  | 65 | See |  |  | |
1,721 |
A*02:17 |  | India Delhi pop 2 | 1.1 | 0.0050 |  | 90 | See |  |  | |
1,722 |
A*02:17 |  | Ireland Northern | 0.1 | 0.0010 |  | 1,000 | See |  |  | |
1,723 |
A*02:17 |  | Italy Bergamo | 1.0 | 0.0060 |  | 101 | See |  |  | |
1,724 |
A*02:17 |  | Italy North pop 3 | 0.0 | 0 |  | 97 | See |  |  | |
1,725 |
A*02:17 |  | Italy pop 5 | | 0.0060 |  | 975 | See |  |  | |
1,726 |
A*02:17 |  | Kenya Luo | | 0 |  | 265 | See |  |  | |
1,727 |
A*02:17 |  | Kenya Nandi | | 0 |  | 240 | See |  |  | |
1,728 |
A*02:17 |  | Mali Bandiagara | | 0.0040 |  | 138 | See |  |  | |
1,729 |
A*02:17 |  | Mexico Mestizo | 0.0 | 0 |  | 41 | See |  |  | |
1,730 |
A*02:17 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
1,731 |
A*02:17 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
1,732 |
A*02:17 |  | Oman | 0.0 | 0 |  | 118 | See |  |  | |
1,733 |
A*02:17 |  | Pakistan Baloch | | 0 |  | 66 | See |  |  | |
1,734 |
A*02:17 |  | Pakistan Brahui | | 0 |  | 104 | See |  |  | |
1,735 |
A*02:17 |  | Pakistan Burusho | | 0 |  | 92 | See |  |  | |
1,736 |
A*02:17 |  | Pakistan Kalash | | 0 |  | 69 | See |  |  | |
1,737 |
A*02:17 |  | Pakistan Karachi Parsi | | 0 |  | 91 | See |  |  | |
1,738 |
A*02:17 |  | Pakistan Mixed Pathan | | 0 |  | 100 | See |  |  | |
1,739 |
A*02:17 |  | Pakistan Mixed Sindhi | | 0 |  | 101 | See |  |  | |
1,740 |
A*02:17 |  | Poland DKMS | | 0.0017 |  | 20,653 | See |  |  | |
1,741 |
A*02:17 |  | Russia Tuva pop 2 | | 0.0030 |  | 169 | See |  |  | |
1,742 |
A*02:17 |  | Sao Tome Island Angolar | | 0 |  | 32 | See |  |  | |
1,743 |
A*02:17 |  | Sao Tome Island Forro | | 0.0080 |  | 66 | See |  |  | |
1,744 |
A*02:17 |  | Scotland Orkney | 0.0 | 0 |  | 99 | See |  |  | |
1,745 |
A*02:17 |  | Singapore Chinese | 0.0 | 0 |  | 149 | See |  |  | |
1,746 |
A*02:17 |  | South Africa Natal Zulu | 0.0 | 0 |  | 100 | See |  |  | |
1,747 |
A*02:17 |  | Uganda Kampala | | 0 |  | 161 | See |  |  | |
1,748 |
A*02:17 |  | USA African American pop 4 | | 0 |  | 2,411 | See |  |  | |
1,749 |
A*02:17 |  | USA Alaska Yupik | | 0 |  | 252 | See |  |  | |
1,750 |
A*02:17 |  | USA Asian pop 2 | | 0 |  | 1,772 | See |  |  | |
1,751 |
A*02:17 |  | USA Caucasian pop 4 | | 0.0009 |  | 1,070 | See |  |  | |
1,752 |
A*02:17 |  | USA Eastern European | | 0.0010 |  | 558 | See |  |  | |
1,753 |
A*02:17 |  | USA Hispanic pop 2 | | 0.0035 |  | 1,999 | See |  |  | |
1,754 |
A*02:17 |  | USA San Francisco Caucasian | | 0.0020 |  | 220 | See |  |  | |
1,755 |
A*02:17 |  | Zambia Lusaka | | 0 |  | 44 | See |  |  | |
1,756 |
A*02:17:01 |  | China North Han | | 0 |  | 105 | See |  |  | |
1,757 |
A*02:17:01 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
1,758 |
A*02:17:01 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
1,759 |
A*02:17:01 |  | Saudi Arabia pop 5 | 1.3 | 0.0063 |  | 158 | See |  |  | |
1,760 |
A*02:17:01 |  | South Korea pop 3 | | 0 |  | 485 | See |  |  | |
1,761 |
A*02:17:01 |  | USA San Francisco Caucasian | | 0.0020 |  | 220 | See |  |  | |
1,762 |
A*02:17:02 |  | China North Han | | 0 |  | 105 | See |  |  | |
1,763 |
A*02:17:02 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
1,764 |
A*02:17:02 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
1,765 |
A*02:17:02 |  | South Korea pop 3 | | 0 |  | 485 | See |  |  | |
1,766 |
A*02:18 |  | Brazil Belo Horizonte Caucasian | 0.0 | 0 |  | 95 | See |  |  | |
1,767 |
A*02:18 |  | Bulgaria | | 0 |  | 55 | See |  |  | |
1,768 |
A*02:18 |  | China North Han | | 0 |  | 105 | See |  |  | |
1,769 |
A*02:18 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
1,770 |
A*02:18 |  | Cuba Caucasian | 0.0 | 0 |  | 70 | See |  |  | |
1,771 |
A*02:18 |  | Cuba Mixed Race | 0.0 | 0 |  | 42 | See |  |  | |
1,772 |
A*02:18 |  | Ireland Northern | 0.0 | 0 |  | 1,000 | See |  |  | |
1,773 |
A*02:18 |  | Italy Bergamo | 0.0 | 0 |  | 101 | See |  |  | |
1,774 |
A*02:18 |  | Italy North pop 3 | 0.0 | 0 |  | 97 | See |  |  | |
1,775 |
A*02:18 |  | Japan Central | | 0.0010 |  | 371 | See |  |  | |
1,776 |
A*02:18 |  | Japan pop 3 | | 0.0010 |  | 1,018 | See |  |  | |
1,777 |
A*02:18 |  | Mexico Mestizo | 0.0 | 0 |  | 41 | See |  |  | |
1,778 |
A*02:18 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
1,779 |
A*02:18 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
1,780 |
A*02:18 |  | Oman | 0.0 | 0 |  | 118 | See |  |  | |
1,781 |
A*02:18 |  | Pakistan Baloch | | 0 |  | 66 | See |  |  | |
1,782 |
A*02:18 |  | Pakistan Brahui | | 0 |  | 104 | See |  |  | |
1,783 |
A*02:18 |  | Pakistan Burusho | | 0 |  | 92 | See |  |  | |
1,784 |
A*02:18 |  | Pakistan Kalash | | 0 |  | 69 | See |  |  | |
1,785 |
A*02:18 |  | Pakistan Karachi Parsi | | 0 |  | 91 | See |  |  | |
1,786 |
A*02:18 |  | Pakistan Mixed Pathan | | 0 |  | 100 | See |  |  | |
1,787 |
A*02:18 |  | Pakistan Mixed Sindhi | | 0 |  | 101 | See |  |  | |
1,788 |
A*02:18 |  | Scotland Orkney | 0.0 | 0 |  | 99 | See |  |  | |
1,789 |
A*02:18 |  | Singapore Chinese | 0.0 | 0 |  | 149 | See |  |  | |
1,790 |
A*02:18 |  | South Africa Natal Zulu | 0.0 | 0 |  | 100 | See |  |  | |
1,791 |
A*02:18 |  | South Korea pop 3 | | 0 |  | 485 | See |  |  | |
1,792 |
A*02:18 |  | USA Alaska Yupik | | 0 |  | 252 | See |  |  | |
1,793 |
A*02:19 |  | Argentina Gran Chaco Eastern Toba | 17.9 | 0.0950 |  | 135 | See |  |  | |
1,794 |
A*02:19 |  | Argentina Gran Chaco Mataco Wichi | 15.9 | 0.0800 |  | 49 | See |  |  | |
1,795 |
A*02:19 |  | Argentina Gran Chaco Western Toba Pilaga | 0.0 | 0 |  | 19 | See |  |  | |
1,796 |
A*02:19 |  | Brazil Belo Horizonte Caucasian | 0.0 | 0 |  | 95 | See |  |  | |
1,797 |
A*02:19 |  | Brazil Terena | 3.3 | 0.0170 |  | 60 | See |  |  | |
1,798 |
A*02:19 |  | Bulgaria | | 0 |  | 55 | See |  |  | |
1,799 |
A*02:19 |  | China North Han | | 0 |  | 105 | See |  |  | |
1,800 |
A*02:19 |  | China Tibet Region Tibetan | | 0 |  | 158 | 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.
Displaying 1,701 to 1,800
(from 60,683) records |
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