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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*24:02:01:01 | | Singapore Chinese | 28.2 | 0.1540 | | 149 | See | | | |
402 |
A*24:02:01:01 | | Singapore Chinese Han | | 0.2150 | | 94 | See | | | |
403 |
A*24:02:01:01 | | Singapore Javaneses | | 0.1600 | | 51 | See | | | |
404 |
A*24:02:01:01 | | Singapore Riau Malay | | 0.1650 | | 132 | See | | | |
405 |
A*24:02:01:01 | | Singapore Thai | | 0.0820 | | 100 | See | | | |
406 |
A*24:02:01:01 | | South Africa Natal Tamil | | 0.1600 | | 51 | See | | | |
407 |
A*24:02:01:01 | | South Africa Natal Zulu | 2.0 | 0.0100 | | 100 | See | | | |
408 |
A*24:02:01:01 | | South Korea pop 3 | | 0 | | 485 | See | | | |
409 |
A*24:02:01:01 | | USA Alaska Yupik | | 0 | | 252 | See | | | |
410 |
A*24:02:01:01 | | USA Arizona Pima | | 0.3600 | | 100 | See | | | |
411 |
A*24:02:01:01 | | USA Eastern European | | 0.1020 | | 558 | See | | | |
412 |
A*24:02:01:01 | | USA Italy Ancestry | | 0.0840 | | 273 | See | | | |
413 |
A*24:02:01:01 | | USA New Mexico Canoncito Navajo | | 0.3050 | | 42 | See | | | |
414 |
A*24:02:01:01 | | USA San Francisco Caucasian | | 0.0750 | | 220 | See | | | |
415 |
A*24:02:01:01 | | USA Spain Ancestry | | 0.0020 | | 279 | See | | | |
416 |
A*24:02:01:01 | | Venezuela Perja Mountain Bari | | 0.6020 | | 55 | See | | | |
417 |
A*24:02:01:02L | | Brazil Belo Horizonte Caucasian | 0.0 | 0 | | 95 | See | | | |
418 |
A*24:02:01:02L | | Bulgaria | | 0 | | 55 | See | | | |
419 |
A*24:02:01:02L | | China North Han | | 0 | | 105 | See | | | |
420 |
A*24:02:01:02L | | Cuba Caucasian | 0.0 | 0 | | 70 | See | | | |
421 |
A*24:02:01:02L | | Cuba Mixed Race | 0.0 | 0 | | 42 | See | | | |
422 |
A*24:02:01:02L | | Czech Republic | | 0 | | 106 | See | | | |
423 |
A*24:02:01:02L | | France French Bone Marrow Donor Registry | | 0.0025 | | 42,623 | See | | | |
424 |
A*24:02:01:02L | | Hong Kong Chinese HKBMDR HLA 11 loci | 0.0 | 0.0000900 | | 5,266 | See | | |
|
425 |
A*24:02:01:02L | | India North pop 2 | | 0.0100 | | 72 | See | | | |
426 |
A*24:02:01:02L | | Ireland Northern | 0.0 | 0 | | 1,000 | See | | | |
427 |
A*24:02:01:02L | | Mexico Mestizo | 0.0 | 0 | | 41 | See | | | |
428 |
A*24:02:01:02L | | Morocco Nador Metalsa pop 2 | | 0 | | 73 | See | | | |
429 |
A*24:02:01:02L | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
430 |
A*24:02:01:02L | | Oman | 0.9 | 0.0040 | | 118 | See | | | |
431 |
A*24:02:01:02L | | Pakistan Baloch | | 0 | | 66 | See | | | |
432 |
A*24:02:01:02L | | Pakistan Brahui | | 0.0060 | | 104 | See | | | |
433 |
A*24:02:01:02L | | Pakistan Burusho | | 0 | | 92 | See | | | |
434 |
A*24:02:01:02L | | Pakistan Kalash | | 0.0170 | | 69 | See | | | |
435 |
A*24:02:01:02L | | Pakistan Karachi Parsi | | 0 | | 91 | See | | | |
436 |
A*24:02:01:02L | | Pakistan Mixed Pathan | | 0.0050 | | 100 | See | | | |
437 |
A*24:02:01:02L | | Pakistan Mixed Sindhi | | 0.0060 | | 101 | See | | | |
438 |
A*24:02:01:02L | | Singapore Chinese | 0.0 | 0 | | 149 | See | | | |
439 |
A*24:02:01:02L | | South Africa Natal Zulu | 0.0 | 0 | | 100 | See | | | |
440 |
A*24:02:01:02L | | South Korea pop 3 | | 0 | | 485 | See | | | |
441 |
A*24:02:01:02L | | USA Alaska Yupik | | 0 | | 252 | See | | | |
442 |
A*24:02:02 | | Brazil Rio de Janeiro Caucasian | | 0.0010 | | 521 | See | | |
|
443 |
A*24:02:02 | | Bulgaria | | 0 | | 55 | See | | | |
444 |
A*24:02:02 | | China North Han | | 0 | | 105 | See | | | |
445 |
A*24:02:02 | | Italy North pop 3 | 0.0 | 0 | | 97 | See | | | |
446 |
A*24:02:02 | | Morocco Nador Metalsa pop 2 | | 0 | | 73 | See | | | |
447 |
A*24:02:02 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
448 |
A*24:02:02 | | Saudi Arabia pop 5 | 1.3 | 0.0063 | | 158 | See | | | |
449 |
A*24:02:02 | | South Korea pop 3 | | 0 | | 485 | See | | | |
450 |
A*24:02:02 | | USA Alaska Yupik | | 0 | | 252 | See | | | |
451 |
A*24:02:03Q | | Bulgaria | | 0 | | 55 | See | | |
|
452 |
A*24:02:03Q | | China North Han | | 0 | | 105 | See | | |
|
453 |
A*24:02:03Q | | Italy North pop 3 | 0.0 | 0 | | 97 | See | | |
|
454 |
A*24:02:03Q | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | |
|
455 |
A*24:02:04 | | Bulgaria | | 0 | | 55 | See | | | |
456 |
A*24:02:04 | | China North Han | | 0 | | 105 | See | | | |
457 |
A*24:02:04 | | Italy North pop 3 | 0.0 | 0 | | 97 | See | | | |
458 |
A*24:02:04 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
459 |
A*24:02:04 | | Pakistan Mixed Punjabi | 1.8 | 0.0090 | | 389 | See | | |
|
460 |
A*24:02:04 | | Saudi Arabia pop 6 (G) | | 0.0002 (*) | | 28,927 | See | | | |
461 |
A*24:02:05 | | China North Han | | 0 | | 105 | See | | | |
462 |
A*24:02:05 | | India Karnataka Kannada Speaking | 0.6 | 0.0030 | | 174 | See | | |
|
463 |
A*24:02:05 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
464 |
A*24:02:05 | | Pakistan Mixed Punjabi | 5.3 | 0.0270 | | 389 | See | | |
|
465 |
A*24:02:05 | | Saudi Arabia pop 6 (G) | | 0.0008 (*) | | 28,927 | See | | | |
466 |
A*24:02:06 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
467 |
A*24:02:07 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
468 |
A*24:02:08 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
469 |
A*24:02:08 | | Pakistan Mixed Punjabi | 1.2 | 0.0064 | | 389 | See | | |
|
470 |
A*24:02:09 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
471 |
A*24:02:10 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
472 |
A*24:02:10 | | Poland BMR | 0.0042 | 0.0000210 | | 23,595 | See | | |
|
473 |
A*24:02:11 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
474 |
A*24:02:11 | | Pakistan Mixed Punjabi | 0.2 | 0.0013 | | 389 | See | | |
|
475 |
A*24:02:12 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
476 |
A*24:02:13 | | India Andhra Pradesh Telugu Speaking | 2.7 | 0.0134 | | 186 | See | | |
|
477 |
A*24:02:13 | | India Kerala Malayalam speaking | 0.3 | 0.0010 | | 356 | See | | |
|
478 |
A*24:02:13 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
479 |
A*24:02:13 | | Poland BMR | 0.0127 | 0.0000640 | | 23,595 | See | | |
|
480 |
A*24:02:13 | | Russia Bashkortostan, Bashkirs | 0.8 | 0.0042 | | 120 | See | | |
|
481 |
A*24:02:13 | | Russia Nizhny Novgorod, Russians | 0.1 | 0.0007 | | 1,510 | See | | |
|
482 |
A*24:02:13 | | Vietnam Kinh | 1.0 | 0.0050 | | 101 | See | | |
|
483 |
A*24:02:14 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
484 |
A*24:02:15 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
485 |
A*24:02:16 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
486 |
A*24:02:17 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
487 |
A*24:02:18 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
488 |
A*24:02:19 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
489 |
A*24:02:19 | | Poland BMR | 0.0042 | 0.0000210 | | 23,595 | See | | |
|
490 |
A*24:02:19 | | Saudi Arabia pop 6 (G) | | 0.0000170 (*) | | 28,927 | See | | | |
491 |
A*24:02:20 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
492 |
A*24:02:21 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
493 |
A*24:02:22 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
494 |
A*24:02:23 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
495 |
A*24:02:23 | | Pakistan Mixed Punjabi | 0.2 | 0.0013 | | 389 | See | | |
|
496 |
A*24:02:24 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
497 |
A*24:02:25 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
498 |
A*24:02:26 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
499 |
A*24:02:27 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
500 |
A*24:02:28 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | 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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