Line |
Allele |
Population |
% of individuals that have the allele/gene |
Allele Frequency (3 decimals) |
Sample Size |
IPD-KIR Database¹ |
Distribution² |
Notes³ |
2,501 | 3DL1*042 |  | USA California Hispanic pop 2 KIR | | | 0.000 | | 156 | See |  | |
2,502 | 3DL1*042 |  | Venezuela Bari KIR | | | 0.000 | | 80 | See |  | |
2,503 | 3DL1*042 |  | Venezuela Warao KIR | | | 0.000 | | 89 | See |  | |
2,504 | 3DL1*042 |  | Venezuela Yucpa KIR | | | 0.000 | | 61 | See |  | |
2,505 | 3DL1*042 |  | Zimbabwe KIR | | | 0.011 |  | 93 | See |  | |
2,506 | 3DL1*043 |  | Ghana Prampram KIR | | | 0.000 | | 154 | See |  | |
2,507 | 3DL1*043 |  | India Madurai Piramalai Kallar KIR | | | 0.000 | | 107 | See |  | |
2,508 | 3DL1*043 |  | India Madurai Yadhav KIR | | | 0.004 |  | 117 | See |  | |
2,509 | 3DL1*043 |  | India Sourashtran KIR | | | 0.000 | | 104 | See |  | |
2,510 | 3DL1*043 |  | Nigeria KIR | | | 0.000 | | 164 | See |  | |
2,511 | 3DL1*043 |  | Spain Majorca KIR | | | 0.000 | | 63 | See |  | |
2,512 | 3DL1*043 |  | Taiwan Han KIR | | | 0.000 | | 157 | See |  | |
2,513 | 3DL1*043 |  | Tanzania KIR | | | 0.000 | | 77 | See |  | |
2,514 | 3DL1*043 |  | Tanzania pop 2 KIR | | | 0.000 | | 67 | See |  | |
2,515 | 3DL1*043 |  | Thailand Bangkok KIR | | | 0.000 | | 119 | See |  | |
2,516 | 3DL1*043 |  | Trinidad Africans KIR | | | 0.000 | | 62 | See |  | |
2,517 | 3DL1*043 |  | Trinidad South Asians KIR | | | 0.005 |  | 108 | See |  | |
2,518 | 3DL1*043 |  | Turkey KIR | | | 0.000 | | 76 | See |  | |
2,519 | 3DL1*043 |  | USA Boston KIR | | | 0.000 | | 53 | See |  | |
2,520 | 3DL1*043 |  | USA California African American pop 2 KIR | | | 0.000 | | 92 | See |  | |
2,521 | 3DL1*043 |  | USA California Asian American pop 2 KIR | | | 0.000 | | 37 | See |  | |
2,522 | 3DL1*043 |  | USA California Caucasians pop 2 KIR | | | 0.000 | | 100 | See |  | |
2,523 | 3DL1*043 |  | USA California Hispanic pop 2 KIR | | | 0.000 | | 156 | See |  | |
2,524 | 3DL1*043 |  | Venezuela Bari KIR | | | 0.000 | | 80 | See |  | |
2,525 | 3DL1*043 |  | Venezuela Warao KIR | | | 0.000 | | 89 | See |  | |
2,526 | 3DL1*043 |  | Venezuela Yucpa KIR | | | 0.000 | | 61 | See |  | |
2,527 | 3DL1*043 |  | Zimbabwe KIR | | | 0.000 | | 93 | See |  | |
2,528 | 3DL1*044 |  | Ghana Prampram KIR | | | 0.000 | | 154 | See |  | |
2,529 | 3DL1*044 |  | India Madurai Piramalai Kallar KIR | | | 0.000 | | 107 | See |  | |
2,530 | 3DL1*044 |  | India Madurai Yadhav KIR | | | 0.000 | | 117 | See |  | |
2,531 | 3DL1*044 |  | India Sourashtran KIR | | | 0.000 | | 104 | See |  | |
2,532 | 3DL1*044 |  | Nigeria KIR | | | 0.000 | | 164 | See |  | |
2,533 | 3DL1*044 |  | Spain Majorca KIR | | | 0.008 |  | 63 | See |  | |
2,534 | 3DL1*044 |  | Taiwan Han KIR | | | 0.000 | | 157 | See |  | |
2,535 | 3DL1*044 |  | Tanzania KIR | | | 0.000 | | 77 | See |  | |
2,536 | 3DL1*044 |  | Tanzania pop 2 KIR | | | 0.000 | | 67 | See |  | |
2,537 | 3DL1*044 |  | Thailand Bangkok KIR | | | 0.000 | | 119 | See |  | |
2,538 | 3DL1*044 |  | Trinidad Africans KIR | | | 0.000 | | 62 | See |  | |
2,539 | 3DL1*044 |  | Trinidad South Asians KIR | | | 0.000 | | 108 | See |  | |
2,540 | 3DL1*044 |  | Turkey KIR | | | 0.000 | | 76 | See |  | |
2,541 | 3DL1*044 |  | USA Boston KIR | | | 0.000 | | 53 | See |  | |
2,542 | 3DL1*044 |  | USA California African American pop 2 KIR | | | 0.000 | | 92 | See |  | |
2,543 | 3DL1*044 |  | USA California Asian American pop 2 KIR | | | 0.000 | | 37 | See |  | |
2,544 | 3DL1*044 |  | USA California Caucasians pop 2 KIR | | | 0.000 | | 100 | See |  | |
2,545 | 3DL1*044 |  | USA California Hispanic pop 2 KIR | | | 0.000 | | 156 | See |  | |
2,546 | 3DL1*044 |  | Venezuela Bari KIR | | | 0.000 | | 80 | See |  | |
2,547 | 3DL1*044 |  | Venezuela Warao KIR | | | 0.000 | | 89 | See |  | |
2,548 | 3DL1*044 |  | Venezuela Yucpa KIR | | | 0.000 | | 61 | See |  | |
2,549 | 3DL1*044 |  | Zimbabwe KIR | | | 0.000 | | 93 | See |  | |
2,550 | 3DL1*059 |  | Ghana Ga-Adangbe KIR | | | 0.041 |  | 131 | See |  | |
2,551 | 3DL1*060 |  | Ghana Ga-Adangbe KIR | | | 0.016 |  | 131 | See |  | |
2,552 | 3DL1*070 |  | China Shenzhen Han KIR |  | 0.7 | | | 306 | See |  | |
2,553 | 3DL2 |  | Argentina Chaco Region Wichi KIR |  | 100.0 | | | 82 | See |  | |
2,554 | 3DL2 |  | Argentina Chiriguano KIR |  | 100.0 | | | 54 | See |  | |
2,555 | 3DL2 |  | Argentina Salta Wichi KIR |  | 100.0 | | | 19 | See |  | |
2,556 | 3DL2 |  | Australia South Aborigine KIR |  | 100.0 | | | 67 | See |  | |
2,557 | 3DL2 |  | Borneo Kalimantan KIR |  | 100.0 | | | 48 | See |  | |
2,558 | 3DL2 |  | Brazil Amazon KIR |  | 98.0 | | | 40 | See |  | |
2,559 | 3DL2 |  | Brazil Bahia Ilhéus KIR |  | 100.0 | 1.000 |  | 102 | See |  | |
2,560 | 3DL2 |  | Brazil Curitiba Japanese KIR |  | 100.0 | | | 51 | See |  | |
2,561 | 3DL2 |  | Brazil Curitiba KIR |  | 100.0 | 1.000 |  | 164 | See |  | |
2,562 | 3DL2 |  | Brazil Guarani Kaiowá KIR |  | 100.0 | | | 96 | See |  | |
2,563 | 3DL2 |  | Brazil Guarani MByá KIR |  | 100.0 | | | 81 | See |  | |
2,564 | 3DL2 |  | Brazil Guarani Ñandeva KIR |  | 100.0 | | | 50 | See |  | |
2,565 | 3DL2 |  | Brazil Kaingang KIR |  | 100.0 | | | 100 | See |  | |
2,566 | 3DL2 |  | Brazil Rondonia Porto Velho KIR |  | 100.0 | | | 377 | See |  | |
2,567 | 3DL2 |  | China Eastern Mainland Han KIR |  | 100.0 | | | 106 | See |  | |
2,568 | 3DL2 |  | China Hubei Province Tujia KIR |  | 100.0 | | | 124 | See |  | |
2,569 | 3DL2 |  | China Mongolia Province KIR |  | 100.0 | | | 90 | See |  | |
2,570 | 3DL2 |  | China Shaanxi Province Han KIR |  | 100.0 | | | 104 | See |  | |
2,571 | 3DL2 |  | China Tibet Lhasa KIR |  | 100.0 | | | 102 | See |  | |
2,572 | 3DL2 |  | China Xinjiang Region Urumqi Uygur KIR |  | 100.0 | | | 120 | See |  | |
2,573 | 3DL2 |  | China Yunnan Han KIR |  | 100.0 | | | 404 | See |  | |
2,574 | 3DL2 |  | China Yunnan Province Dali Bai KIR |  | 100.0 | | | 100 | See |  | |
2,575 | 3DL2 |  | China Yunnan Province Drung KIR |  | 100.0 | | | 152 | See |  | |
2,576 | 3DL2 |  | China Yunnan Province Fengyandong Han KIR |  | 100.0 | | | 93 | See |  | |
2,577 | 3DL2 |  | China Yunnan Province Fugong Nu KIR |  | 100.0 | | | 112 | See |  | |
2,578 | 3DL2 |  | China Yunnan Province Menghai Bulang KIR |  | 100.0 | | | 109 | See |  | |
2,579 | 3DL2 |  | China Yunnan Province Sunan Yugu KIR |  | 100.0 | | | 104 | See |  | |
2,580 | 3DL2 |  | China Yunnan Province Tiandeng Zhuang KIR |  | 100.0 | | | 98 | See |  | |
2,581 | 3DL2 |  | China Zhejiang Province Han KIR |  | 100.0 | | | 104 | See |  | |
2,582 | 3DL2 |  | Comoros Mixed KIR |  | 100.0 | | | 54 | See |  | |
2,583 | 3DL2 |  | Cook Islands KIR |  | 100.0 | | | 48 | See |  | |
2,584 | 3DL2 |  | Croatia KIR pop 3 |  | 100.0 | | | 121 | See |  | |
2,585 | 3DL2 |  | Czech Republic KIR |  | 100.0 | | | 125 | See |  | |
2,586 | 3DL2 |  | East Timor KIR |  | 100.0 | | | 50 | See |  | |
2,587 | 3DL2 |  | England KIR pop 5 |  | 100.0 | | | 584 | See |  | |
2,588 | 3DL2 |  | Equatorial Guinea Bioko Island Bubi KIR |  | 100.0 | | | 95 | See |  | |
2,589 | 3DL2 |  | France Southeast KIR |  | 100.0 | | | 130 | See |  | |
2,590 | 3DL2 |  | Guadeloupe Mixed KIR |  | 100.0 | | | 90 | See |  | |
2,591 | 3DL2 |  | India Madurai Piramalai Kallar Dravidian KIR |  | 97.0 | | | 100 | See |  | |
2,592 | 3DL2 |  | India Mumbai Maharashtrian KIR |  | 100.0 | | | 139 | See |  | |
2,593 | 3DL2 |  | India Mumbai Parsi KIR |  | 100.0 | | | 145 | See |  | |
2,594 | 3DL2 |  | India Tamil Nadu Kanikar KIR |  | 100.0 | | | 35 | See |  | |
2,595 | 3DL2 |  | India Tamil Nadu Mollukurumba KIR |  | 100.0 | | | 41 | See |  | |
2,596 | 3DL2 |  | India Tamil Nadu Paravar KIR |  | 100.0 | | | 77 | See |  | |
2,597 | 3DL2 |  | India West Bengal Rabha KIR |  | 100.0 | | | 50 | See |  | |
2,598 | 3DL2 |  | India West Bengal Rajbanshi KIR |  | 100.0 | | | 75 | See |  | |
2,599 | 3DL2 |  | Indonesia Java KIR |  | 100.0 | | | 45 | See |  | |
2,600 | 3DL2 |  | Iran East Azerbaijan Azerbaijani KIR |  | 100.0 | | | 100 | See |  | |
* Allele Frequency: Total number of copies of the allele in the population sample (Alleles / 2n) in three decimal format.
* % of individuals that have the allele: Percentage of individuals who have the allele or gene in the population (Individuals / n).
¹ IPD-KIR Database - For more details of the allele.
² Distribution - Graphical display of the distribution of the allele.
³ Notes - See notes for ambiguous combinations of alleles.