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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 |
59,501 |
DRB1*15:02 | | Macedonia pop 2 | | 0.0120 | | 80 | See | | | |
59,502 |
DRB1*15:02 | | Macedonia pop 3 | | 0.0260 | | 172 | See | | | |
59,503 |
DRB1*15:02 | | Madeira pop 2 | | 0.0200 | | 173 | See | | | |
59,504 |
DRB1*15:02 | | Malaysia | | 0.1360 | | 74 | See | | | |
59,505 |
DRB1*15:02 | | Malaysia Jelebu Temuan | | 0.0830 | | 25 | See | | | |
59,506 |
DRB1*15:02 | | Malaysia Kedah Baling Kensiu | | 0.1400 | | 25 | See | | | |
59,507 |
DRB1*15:02 | | Malaysia Perak Grik Jehai | | 0.3000 | | 25 | See | | | |
59,508 |
DRB1*15:02 | | Malaysia Sabah Kadazan | | 0.5000 | | 57 | See | | | |
59,509 |
DRB1*15:02 | | Malaysia Sarawak Bau Bidayuh | | 0.1600 | | 25 | See | | | |
59,510 |
DRB1*15:02 | | Malaysia Sarawak Bidayuh | | 0.1100 | | 50 | See | | | |
59,511 |
DRB1*15:02 | | Malaysia Sarawak Iban | | 0.1860 | | 51 | See | | | |
59,512 |
DRB1*15:02 | | Martinique | | 0.0200 | | 100 | See | | | |
59,513 |
DRB1*15:02 | | Mexico Guanajuato and Jalisco Mestizo | 2.0 | 0.0100 | | 101 | See | | | |
59,514 |
DRB1*15:02 | | Mexico Highlands Mestizos | 1.9 | 0.0090 | | 160 | See | | | |
59,515 |
DRB1*15:02 | | Mexico Mexico City Mestizo pop 2 | | 0.0107 | | 234 | See | | | |
59,516 |
DRB1*15:02 | | Mexico Nuevo Leon Mestizo | 2.5 | 0.0120 | | 40 | See | | | |
59,517 |
DRB1*15:02 | | Mexico Oaxaca Mixtec | | 0.0050 | | 103 | See | | | |
59,518 |
DRB1*15:02 | | Mexico Oaxaca Zapotec | | 0 | | 90 | See | | | |
59,519 |
DRB1*15:02 | | Mongolia Khalkha | | 0.0350 | | 200 | See | | | |
59,520 |
DRB1*15:02 | | Mongolia Oold | | 0.0390 | | 104 | See | | | |
59,521 |
DRB1*15:02 | | Mongolia Tsaatan | | 0.0420 | | 144 | See | | | |
59,522 |
DRB1*15:02 | | Morocco | | 0.0150 | | 96 | See | | | |
59,523 |
DRB1*15:02 | | Nauru | | 0.2390 | | 67 | See | | | |
59,524 |
DRB1*15:02 | | New Caledonia | | 0.1150 | | 65 | See | | | |
59,525 |
DRB1*15:02 | | Niue | | 0.0140 | | 70 | See | | | |
59,526 |
DRB1*15:02 | | Papua New Guinea Abam | | 0.2700 | | 63 | See | | | |
59,527 |
DRB1*15:02 | | Papua New Guinea Dorogori | | 0.1780 | | 59 | See | | | |
59,528 |
DRB1*15:02 | | Papua New Guinea East New Britain Rabaul | | 0.1000 | | 60 | See | | | |
59,529 |
DRB1*15:02 | | Papua New Guinea Eastern Highlands Goroka Asaro | | 0.1320 | | 57 | See | | | |
59,530 |
DRB1*15:02 | | Papua New Guinea Highland | | 0.1210 | | 94 | See | | | |
59,531 |
DRB1*15:02 | | Papua New Guinea Iamega | | 0.1770 | | 79 | See | | | |
59,532 |
DRB1*15:02 | | Papua New Guinea Kapal | | 0.1770 | | 65 | See | | | |
59,533 |
DRB1*15:02 | | Papua New Guinea Kuru | | 0.2170 | | 46 | See | | | |
59,534 |
DRB1*15:02 | | Papua New Guinea Lowland | | 0.2470 | | 47 | See | | | |
59,535 |
DRB1*15:02 | | Papua New Guinea Lowland Wosera | | 0.1290 | | 79 | See | | | |
59,536 |
DRB1*15:02 | | Papua New Guinea Madang | | 0.2000 | | 65 | See | | | |
59,537 |
DRB1*15:02 | | Papua New Guinea Podare | | 0.2330 | | 43 | See | | | |
59,538 |
DRB1*15:02 | | Papua New Guinea Rual | | 0.2000 | | 50 | See | | | |
59,539 |
DRB1*15:02 | | Papua New Guinea South Gidra | | 0.2240 | | 192 | See | | | |
59,540 |
DRB1*15:02 | | Papua New Guinea Ume | | 0.2440 | | 90 | See | | | |
59,541 |
DRB1*15:02 | | Papua New Guinea Wipim | | 0.3550 | | 55 | See | | | |
59,542 |
DRB1*15:02 | | Papua New Guinea Woigi | | 0.3460 | | 26 | See | | | |
59,543 |
DRB1*15:02 | | Papua New Guinea Wonie | | 0.2160 | | 51 | See | | | |
59,544 |
DRB1*15:02 | | Papua New Guinea Wuroi | | 0.3380 | | 34 | See | | | |
59,545 |
DRB1*15:02 | | Philippines | | 0.4850 | | 34 | See | | | |
59,546 |
DRB1*15:02 | | Poland | | 0.0100 | | 200 | See | | | |
59,547 |
DRB1*15:02 | | Poland DKMS | | 0.0073 | | 20,653 | See | | | |
59,548 |
DRB1*15:02 | | Portugal Center | | 0.0100 | | 50 | See | | | |
59,549 |
DRB1*15:02 | | Portugal North | | 0 | | 46 | See | | | |
59,550 |
DRB1*15:02 | | Portugal South | | 0 | | 49 | See | | | |
59,551 |
DRB1*15:02 | | Russia Sakhalin Island Nivkhi | | 0.0090 | | 53 | See | | | |
59,552 |
DRB1*15:02 | | Russia Siberia Irkutsk Tofalar | | 0.0120 | | 43 | See | | | |
59,553 |
DRB1*15:02 | | Russia Siberia Khabarovsk Evenki | | 0 | | 25 | See | | | |
59,554 |
DRB1*15:02 | | Russia Siberia Khanty Mansi | | 0.0150 | | 68 | See | | | |
59,555 |
DRB1*15:02 | | Russia Siberia Kushun Buryat | | 0 | | 25 | See | | | |
59,556 |
DRB1*15:02 | | Russia Siberia Negidal | | 0 | | 35 | See | | | |
59,557 |
DRB1*15:02 | | Russia Siberia Ulchi | | 0 | | 73 | See | | | |
59,558 |
DRB1*15:02 | | Russia Tuva | | 0.0330 | | 190 | See | | | |
59,559 |
DRB1*15:02 | | Russia Tuva pop 2 | | 0.0290 | | 169 | See | | | |
59,560 |
DRB1*15:02 | | Russia Tuva pop3 | | 0.0110 | | 44 | See | | | |
59,561 |
DRB1*15:02 | | Russia Tuva Todja | | 0 | | 22 | See | | | |
59,562 |
DRB1*15:02 | | Samoa | | 0.0520 | | 29 | See | | | |
59,563 |
DRB1*15:02 | | Slovenia | 1.0 | 0.0050 | | 100 | See | | | |
59,564 |
DRB1*15:02 | | Slovenia pop 2 | 3.6 | 0.0181 | | 140 | See | | | |
59,565 |
DRB1*15:02 | | South Korea pop 2 | 4.3 | 0.0220 | | 207 | See | | | |
59,566 |
DRB1*15:02 | | South Korea pop 3 | | 0.0330 | | 485 | See | | | |
59,567 |
DRB1*15:02 | | South Korea pop 4 | 9.5 | 0.0486 | | 211 | See | | | |
59,568 |
DRB1*15:02 | | South Korea pop 6 | | 0.0320 | | 800 | See | | | |
59,569 |
DRB1*15:02 | | Spain Barcelona | 2.4 | 0.0120 | | 941 | See | | | |
59,570 |
DRB1*15:02 | | Spain Malaga | 0.0 | 0 | | 160 | See | | | |
59,571 |
DRB1*15:02 | | Spain Malaga Romani | 20.0 | 0.1055 | | 80 | See | | | |
59,572 |
DRB1*15:02 | | Spain Murcia | | 0.0200 | | 173 | See | | | |
59,573 |
DRB1*15:02 | | Switzerland Aargau-Solothurn | | 0.0052 | | 1,838 | See | | | |
59,574 |
DRB1*15:02 | | Taiwan Han Chinese | | 0.0230 | | 504 | See | | | |
59,575 |
DRB1*15:02 | | Taiwan pop 2 | | 0.0230 | | 364 | See | | | |
59,576 |
DRB1*15:02 | | Taiwan Tzu Chi Cord Blood Bank | | 0.0230 | | 710 | See | | | |
59,577 |
DRB1*15:02 | | Thailand | | 0.1200 | | 142 | See | | | |
59,578 |
DRB1*15:02 | | Thailand Bangkok pop 2 | | 0.0700 | | 50 | See | | | |
59,579 |
DRB1*15:02 | | Thailand Bangkok pop 3 | | 0.1230 | | 187 | See | | | |
59,580 |
DRB1*15:02 | | Thailand Kamphaeng Phet | | 0.1650 | | 97 | See | | | |
59,581 |
DRB1*15:02 | | Thailand North Dai Lue | | 0.1930 | | 96 | See | | | |
59,582 |
DRB1*15:02 | | Thailand Northeast | | 0.1960 | | 66 | See | | | |
59,583 |
DRB1*15:02 | | Thailand Northeast pop 2 | | 0.1830 | | 400 | See | | | |
59,584 |
DRB1*15:02 | | Tunisia | 6.0 | 0.0300 | | 100 | See | | | |
59,585 |
DRB1*15:02 | | Tunisia Gabes Arab | | 0.0210 | | 96 | See | | | |
59,586 |
DRB1*15:02 | | Tunisia Ghannouch | | 0.0430 | | 82 | See | | | |
59,587 |
DRB1*15:02 | | Tunisia Jerba Berber | | 0 | | 55 | See | | | |
59,588 |
DRB1*15:02 | | Tunisia Matmata Berber | | 0.0060 | | 81 | See | | | |
59,589 |
DRB1*15:02 | | USA African American pop 4 | | 0.0017 | | 2,411 | See | | | |
59,590 |
DRB1*15:02 | | USA Alaska Yupik | | 0 | | 252 | See | | | |
59,591 |
DRB1*15:02 | | USA Asian pop 2 | | 0.0809 | | 1,772 | See | | | |
59,592 |
DRB1*15:02 | | USA Caucasian Houston | 2.0 | 0.0100 | | 194 | See | | | |
59,593 |
DRB1*15:02 | | USA Caucasian Pittsburgh | 3.4 | 0.0170 | | 177 | See | | | |
59,594 |
DRB1*15:02 | | USA Caucasian pop 4 | | 0.0077 | | 1,070 | See | | | |
59,595 |
DRB1*15:02 | | USA Caucasian pop 5 | | 0.0090 | | 268 | See | | | |
59,596 |
DRB1*15:02 | | USA Eastern European | | 0.0090 | | 558 | See | | | |
59,597 |
DRB1*15:02 | | USA Hawaii | | 0.1280 | | 39 | See | | | |
59,598 |
DRB1*15:02 | | USA Hispanic pop 2 | | 0.0133 | | 1,999 | See | | | |
59,599 |
DRB1*15:02 | | USA Mexican American Mestizo pop 3 | | 0.0040 | | 116 | See | | | |
59,600 |
DRB1*15:02 | | USA Philippines | | 0.3860 | | 105 | 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.