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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*80:01 |  | Belgium pop 3 | 0.1 | 0.0006 |  | 31,412 | See |  |  |
|
| 2 |
A*80:01 |  | Brazil Belo Horizonte Caucasian | 1.1 | 0.0050 |  | 95 | See |  |  | |
| 3 |
A*80:01 |  | Brazil Mixed | | 0.0050 |  | 108 | See |  |  | |
| 4 |
A*80:01 |  | Brazil Pernambuco Mixed | | 0.0050 |  | 101 | See |  |  | |
| 5 |
A*80:01 |  | Brazil Vale do Ribeira Quilombos | 0.0140 | 0 |  | 144 | See |  |  | |
| 6 |
A*80:01 |  | Bulgaria | | 0.0090 |  | 55 | See |  |  | |
| 7 |
A*80:01 |  | Burkina Faso Fulani | | 0.0100 |  | 49 | See |  |  | |
| 8 |
A*80:01 |  | Burkina Faso Mossi | | 0 |  | 53 | See |  |  | |
| 9 |
A*80:01 |  | Burkina Faso Rimaibe | | 0 |  | 47 | See |  |  | |
| 10 |
A*80:01 |  | Cameroon Baka Pygmy | | 0 |  | 10 | See |  |  | |
| 11 |
A*80:01 |  | Cameroon Bakola Pygmy | 2.0 | 0.0100 |  | 50 | See |  |  | |
| 12 |
A*80:01 |  | Cameroon Bamileke | | 0.0190 |  | 77 | See |  |  | |
| 13 |
A*80:01 |  | Cameroon Beti | | 0.0060 |  | 174 | See |  |  | |
| 14 |
A*80:01 |  | Cameroon Sawa | | 0 |  | 13 | See |  |  | |
| 15 |
A*80:01 |  | Cape Verde Northwestern Islands | | 0 |  | 62 | See |  |  | |
| 16 |
A*80:01 |  | Cape Verde Southeastern Islands | | 0.0080 |  | 62 | See |  |  | |
| 17 |
A*80:01 |  | Central African Republic Mbenzele Pygmy | 0.0 | 0 |  | 36 | See |  |  | |
| 18 |
A*80:01 |  | Chile Mapuche | | 0.0077 |  | 66 | See |  |  |
|
| 19 |
A*80:01 |  | China North Han | | 0 |  | 105 | See |  |  | |
| 20 |
A*80:01 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
| 21 |
A*80:01 |  | Colombia Bogotá Cord Blood | 0.1 | 0.0007 |  | 1,463 | See |  |  | |
| 22 |
A*80:01 |  | Croatia pop 4 | | 0.0007 |  | 4,000 | See |  |  | |
| 23 |
A*80:01 |  | Cuba Caucasian | 0.0 | 0 |  | 70 | See |  |  | |
| 24 |
A*80:01 |  | Cuba Mixed Race | 0.0 | 0 |  | 42 | See |  |  | |
| 25 |
A*80:01 |  | Czech Republic | | 0 |  | 106 | See |  |  | |
| 26 |
A*80:01 |  | Czech Republic NMDR | | 0.0002 |  | 5,099 | See |  |  | |
| 27 |
A*80:01 |  | Ecuador Amerindians | | 0.0079 |  | 63 | See |  |  |
|
| 28 |
A*80:01 |  | England Manchester | 0.1 | 0.0005 |  | 1,640 | See |  |  | |
| 29 |
A*80:01 |  | France Corsica Island | | 0 |  | 100 | See |  |  | |
| 30 |
A*80:01 |  | France Grenoble, Nantes and Rennes | | 0 |  | 6,094 | See |  |  | |
| 31 |
A*80:01 |  | France Rennes pop 3 | | 0 |  | 200 | See |  |  | |
| 32 |
A*80:01 |  | Germany DKMS - Croatia minority | | 0.0005 |  | 2,057 | See |  |  | |
| 33 |
A*80:01 |  | Germany DKMS - France minority | | 0.0011 |  | 1,406 | See |  |  | |
| 34 |
A*80:01 |  | Germany DKMS - German donors | | 0.0002 |  | 3,456,066 | See |  |  |
|
| 35 |
A*80:01 |  | Germany DKMS - Greece minority | | 0.0003 |  | 1,894 | See |  |  | |
| 36 |
A*80:01 |  | Germany DKMS - Italy minority | | 0.0004 |  | 1,159 | See |  |  | |
| 37 |
A*80:01 |  | Germany DKMS - Netherlands minority | | 0.0004 |  | 1,374 | See |  |  | |
| 38 |
A*80:01 |  | Germany DKMS - Portugal minority | | 0.0009 |  | 1,176 | See |  |  | |
| 39 |
A*80:01 |  | Germany DKMS - Romania minority | | 0.0004 |  | 1,234 | See |  |  | |
| 40 |
A*80:01 |  | Germany DKMS - Spain minority | | 0.0005 |  | 1,107 | See |  |  | |
| 41 |
A*80:01 |  | Germany DKMS - Turkey minority | | 0.0002 |  | 4,856 | See |  |  | |
| 42 |
A*80:01 |  | Germany DKMS - United Kingdom minority | | 0.0010 |  | 1,043 | See |  |  | |
| 43 |
A*80:01 |  | Germany pop 8 | | 0.0002 |  | 39,689 | See |  |  | |
| 44 |
A*80:01 |  | Ghana Ga-Adangbe | 7.6 | 0.0382 |  | 131 | See |  |  | |
| 45 |
A*80:01 |  | Guatemala Mayan | | 0.0040 |  | 132 | See |  |  | |
| 46 |
A*80:01 |  | Guinea Bissau | | 0.0080 |  | 65 | See |  |  | |
| 47 |
A*80:01 |  | Guinea Bissau Balanta | | 0.0210 |  | 48 | See |  |  | |
| 48 |
A*80:01 |  | Guinea Bissau Bijago | | 0 |  | 23 | See |  |  | |
| 49 |
A*80:01 |  | Guinea Bissau Fula | | 0 |  | 31 | See |  |  | |
| 50 |
A*80:01 |  | Guinea Bissau Papel | | 0 |  | 25 | See |  |  | |
| 51 |
A*80:01 |  | Hong Kong Chinese | 0.0 | 0 |  | 569 | See |  |  | |
| 52 |
A*80:01 |  | Ireland Northern | 0.0 | 0 |  | 1,000 | See |  |  | |
| 53 |
A*80:01 |  | Israel Arab pop 2 | | 0.0005 |  | 12,301 | See |  |  |
|
| 54 |
A*80:01 |  | Israel Argentina Jews | | 0.0007 |  | 4,307 | See |  |  |
|
| 55 |
A*80:01 |  | Israel Ashkenazi Jews pop 3 | | 0.0003 |  | 4,625 | See |  |  |
|
| 56 |
A*80:01 |  | Israel Druze | | 0.0002 |  | 5,914 | See |  |  |
|
| 57 |
A*80:01 |  | Israel Ethiopia Jews | | 0.0002 |  | 5,928 | See |  |  |
|
| 58 |
A*80:01 |  | Israel Iraq Jews | | 0.0000380 |  | 13,270 | See |  |  |
|
| 59 |
A*80:01 |  | Israel Libya Jews | | 0.0008 |  | 3,739 | See |  |  |
|
| 60 |
A*80:01 |  | Israel Morocco Jews | | 0.0009 |  | 36,718 | See |  |  |
|
| 61 |
A*80:01 |  | Israel Poland Jews | | 0.0002 |  | 13,871 | See |  |  |
|
| 62 |
A*80:01 |  | Israel Tunisia Jews | | 0.0013 |  | 9,070 | See |  |  |
|
| 63 |
A*80:01 |  | Israel USA Jews | | 0.0003 |  | 6,058 | See |  |  |
|
| 64 |
A*80:01 |  | Israel USSR Jews | | 0.0000040 |  | 45,681 | See |  |  |
|
| 65 |
A*80:01 |  | Israel YemenJews | | 0.0000110 |  | 15,542 | See |  |  |
|
| 66 |
A*80:01 |  | Italy Sardinia pop3 | | 0 |  | 100 | See |  |  | |
| 67 |
A*80:01 |  | Kenya Luo | | 0 |  | 265 | See |  |  | |
| 68 |
A*80:01 |  | Kenya Nandi | | 0 |  | 240 | See |  |  | |
| 69 |
A*80:01 |  | Mali Bandiagara | | 0.0150 |  | 138 | See |  |  | |
| 70 |
A*80:01 |  | Martinique | | 0.0100 |  | 100 | See |  |  | |
| 71 |
A*80:01 |  | Mexico Mestizo | 0.0 | 0 |  | 41 | See |  |  | |
| 72 |
A*80:01 |  | Mexico Mexico City Tlalpan | 0.6 | 0.0030 |  | 330 | See |  |  |
|
| 73 |
A*80:01 |  | Mexico Tixcacaltuyub Maya | | 0.0075 |  | 67 | See |  |  |
|
| 74 |
A*80:01 |  | Mongolia Buryat | | 0.0040 |  | 141 | See |  |  | |
| 75 |
A*80:01 |  | Morocco Atlantic Coast Chaouya | | 0.0270 |  | 98 | See |  |  | |
| 76 |
A*80:01 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
| 77 |
A*80:01 |  | Morocco Settat Chaouya | 5.4 | 0.0270 |  | 98 | See |  |  | |
| 78 |
A*80:01 |  | Oman | 0.0 | 0 |  | 118 | See |  |  | |
| 79 |
A*80:01 |  | Pakistan Baloch | | 0 |  | 66 | See |  |  | |
| 80 |
A*80:01 |  | Pakistan Brahui | | 0 |  | 104 | See |  |  | |
| 81 |
A*80:01 |  | Pakistan Burusho | | 0 |  | 92 | See |  |  | |
| 82 |
A*80:01 |  | Pakistan Kalash | | 0 |  | 69 | See |  |  | |
| 83 |
A*80:01 |  | Pakistan Mixed Pathan | | 0 |  | 100 | See |  |  | |
| 84 |
A*80:01 |  | Pakistan Mixed Sindhi | | 0 |  | 101 | See |  |  | |
| 85 |
A*80:01 |  | Panama | | 0.0023 |  | 462 | See |  |  |
|
| 86 |
A*80:01 |  | Philippines Ivatan | 0.0 | 0 |  | 50 | See |  |  | |
| 87 |
A*80:01 |  | Poland DKMS | | 0.0000700 |  | 20,653 | See |  |  | |
| 88 |
A*80:01 |  | Romania | 0.0 | 0 |  | 348 | See |  |  | |
| 89 |
A*80:01 |  | Rwanda | | 0.0020 |  | 280 | See |  |  | |
| 90 |
A*80:01 |  | Sao Tome Island Angolar | | 0 |  | 32 | See |  |  | |
| 91 |
A*80:01 |  | Sao Tome Island Forro | | 0.0080 |  | 66 | See |  |  | |
| 92 |
A*80:01 |  | Scotland Orkney | 0.0 | 0 |  | 99 | See |  |  | |
| 93 |
A*80:01 |  | Senegal Niokholo Mandenka | | 0.0380 |  | 165 | See |  |  | |
| 94 |
A*80:01 |  | Singapore Chinese | 0.0 | 0 |  | 149 | See |  |  | |
| 95 |
A*80:01 |  | South Africa Cape Town Black | 1.2 | 0.0060 |  | 84 | See |  |  | |
| 96 |
A*80:01 |  | South Africa Caucasians | | 0 |  | 102 | See |  |  | |
| 97 |
A*80:01 |  | South Africa Durban Black | 4.5 | 0.0227 |  | 22 | See |  |  | |
| 98 |
A*80:01 |  | South Africa Johannesburg Black | 1.7 | 0.0084 |  | 120 | See |  |  | |
| 99 |
A*80:01 |  | South Africa Natal Zulu | 0.0 | 0 |  | 100 | See |  |  | |
| 100 |
A*80:01 |  | South Africa Worcester | 1.0 | 0.0030 |  | 159 | 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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