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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 |
401 |
A*01:07 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
402 |
A*01:07 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
403 |
A*01:07 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
404 |
A*01:07 |  | South Korea pop 3 | | 0 |  | 485 | See |  |  | |
405 |
A*01:08 |  | Bulgaria | | 0 |  | 55 | See |  |  | |
406 |
A*01:08 |  | China North Han | | 0 |  | 105 | See |  |  | |
407 |
A*01:08 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
408 |
A*01:08 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
409 |
A*01:08 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
410 |
A*01:08 |  | South Korea pop 3 | | 0 |  | 485 | See |  |  | |
411 |
A*01:09 |  | Bulgaria | | 0 |  | 55 | See |  |  | |
412 |
A*01:09 |  | China North Han | | 0 |  | 105 | See |  |  | |
413 |
A*01:09 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
414 |
A*01:09 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
415 |
A*01:09 |  | Switzerland Aargau-Solothurn | | 0 |  | 1,838 | See |  |  | |
416 |
A*01:09 |  | Uganda Kampala pop 2 | | 0.0030 |  | 175 | See |  |  | |
417 |
A*01:10 |  | China North Han | | 0 |  | 105 | See |  |  | |
418 |
A*01:10 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
419 |
A*01:10 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
420 |
A*01:11N |  | China North Han | | 0 |  | 105 | See |  |  | |
421 |
A*01:11N |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
422 |
A*01:11N |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
423 |
A*01:11N |  | Switzerland Aargau-Solothurn | | 0 |  | 1,838 | See |  |  | |
424 |
A*01:12 |  | China North Han | | 0 |  | 105 | See |  |  | |
425 |
A*01:12 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
426 |
A*01:12 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
427 |
A*01:13 |  | China North Han | | 0 |  | 105 | See |  |  | |
428 |
A*01:13 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
429 |
A*01:13 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
430 |
A*01:14 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
431 |
A*01:15N |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
432 |
A*01:16N |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
433 |
A*01:16N |  | Switzerland Aargau-Solothurn | | 0 |  | 1,838 | See |  |  | |
434 |
A*01:16N |  | USA African American pop 4 | | 0 |  | 2,411 | See |  |  | |
435 |
A*01:16N |  | USA Asian pop 2 | | 0 |  | 1,772 | See |  |  | |
436 |
A*01:16N |  | USA Hispanic pop 2 | | 0 |  | 1,999 | See |  |  | |
437 |
A*01:17 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
438 |
A*01:18N |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
439 |
A*01:18N |  | Switzerland Aargau-Solothurn | | 0 |  | 1,838 | See |  |  | |
440 |
A*01:19 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
441 |
A*01:20 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
442 |
A*01:21 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
443 |
A*01:22N |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
444 |
A*01:23 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
445 |
A*01:23 |  | Uganda Kampala pop 2 | | 0.0030 |  | 175 | See |  |  | |
446 |
A*01:24 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
447 |
A*01:25 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
448 |
A*01:25 |  | Switzerland Aargau-Solothurn | | 0 |  | 1,838 | See |  |  | |
449 |
A*01:26 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
450 |
A*01:27N |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
451 |
A*01:28 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
452 |
A*01:29 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
453 |
A*01:30 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
454 |
A*01:31N |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
455 |
A*01:32 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
456 |
A*01:33 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
457 |
A*01:35 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
458 |
A*01:36 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
459 |
A*01:37 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
460 |
A*01:38 |  | Germany DKMS - Turkey minority | | 0.0001 |  | 4,856 | See |  |  | |
461 |
A*01:38 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
462 |
A*01:39 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
463 |
A*01:40 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
464 |
A*01:41 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
465 |
A*01:42 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
466 |
A*01:43 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
467 |
A*01:44 |  | Germany DKMS - Turkey minority | | 0.0001 |  | 4,856 | See |  |  | |
468 |
A*01:44 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
469 |
A*01:45 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
470 |
A*01:46 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
471 |
A*01:47 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
472 |
A*01:48 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
473 |
A*01:49 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
474 |
A*01:50 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
475 |
A*01:51 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
476 |
A*02 |  | Albania | | 0.3060 |  | 160 | See |  |  | |
477 |
A*02 |  | Albanian Kosovo | | 0.3250 |  | 120 | See |  |  | |
478 |
A*02 |  | Algeria pop 2 | 43.0 | 0.2450 |  | 106 | See |  |  | |
479 |
A*02 |  | Bangladesh Dhaka Bangalee | | 0.1450 |  | 141 | See |  |  | |
480 |
A*02 |  | Brazil Parana Afro Brazilian | 32.5 | 0.1620 |  | 77 | See |  |  | |
481 |
A*02 |  | Brazil Parana Cafuzo | 40.4 | 0.2020 |  | 319 | See |  |  | |
482 |
A*02 |  | Brazil Parana Caucasian | 46.5 | 0.2320 |  | 2,775 | See |  |  | |
483 |
A*02 |  | Brazil Parana Mixed race | 45.7 | 0.2280 |  | 186 | See |  |  | |
484 |
A*02 |  | Brazil Parana Oriental | 36.4 | 0.1820 |  | 33 | See |  |  | |
485 |
A*02 |  | Brazil Pernambuco Mixed | | 0.2870 |  | 101 | See |  |  | |
486 |
A*02 |  | Brazil South Ribeirao Preto | | 0.2170 |  | 184 | See |  |  | |
487 |
A*02 |  | Burkina Faso Mossi | | 0.2070 |  | 53 | See |  |  | |
488 |
A*02 |  | Cameroon Bakola Pygmy | 50.0 | 0.2928 |  | 50 | See |  |  | |
489 |
A*02 |  | Cameroon Bamileke 2 | | 0.0860 |  | 35 | See |  |  | |
490 |
A*02 |  | Cameroon Ewondo | | 0.1840 |  | 38 | See |  |  | |
491 |
A*02 |  | Cameroon Podokwo | | 0.2330 |  | 43 | See |  |  | |
492 |
A*02 |  | Cameroon Uldeme | | 0.3140 |  | 35 | See |  |  | |
493 |
A*02 |  | Central African Republic Mbenzele Pygmy | 37.0 | 0.2062 |  | 36 | See |  |  | |
494 |
A*02 |  | China Guangdong Province | 51.4 | 0.3028 |  | 403 | See |  |  | |
495 |
A*02 |  | China Jiangsu Province | | 0.2960 |  | 20,248 | See |  |  | |
496 |
A*02 |  | China North Han | | 0 |  | 105 | See |  |  | |
497 |
A*02 |  | China Shaanxi Province Han | | 0.2970 |  | 10,000 | See |  |  | |
498 |
A*02 |  | China Shandong Province Linqu County | 61.2 | 0.3771 |  | 139 | See |  |  | |
499 |
A*02 |  | China Wuhan | 52.9 | 0.3100 |  | 121 | See |  |  | |
500 |
A*02 |  | China Yunnan Province Han pop 2 | | 0.1930 |  | 129 | 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 401 to 500
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