Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

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A B C DRB1 DPA1 DPB1 DQA1 DQB1

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Displaying 1 to 100 (from 389) records   Pages: 1 2 3 4 of 4  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02:11:01-B*35:03:01-C*04:01:01-DRB1*15:02:02-DQB1*06:01:01  India Karnataka Kannada Speaking 1.7240174
 2  A*11:01-B*35:03-C*04:01-DRB1*15:01-DQA1*01:02-DQB1*06:02  Mexico Tixcacaltuyub Maya 1.492567
 3  A*03-B*35-C*04:01-DRB1*15-DQB1*06  Russia North Ossetian 1.1800127
 4  A*74:01-B*35:01-C*04:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 1.0000100
 5  A*32:01-B*35:08-C*04:01-DRB1*15:02-DQA1*01:03-DQB1*06:02  United Arab Emirates Abu Dhabi 0.960052
 6  A*02:11-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India South UCBB 0.881911,446
 7  A*32-B*35-C*04:01-DRB1*15-DQB1*06  Russia North Ossetian 0.7800127
 8  A*74:01:01-B*35:01:01-C*04:01:01-DRB1*15:03:01-DQB1*06:02:01-DPB1*02:01:02  South African Black 0.7040142
 9  A*24:02-B*35:17-C*04:01-DRB1*15:03-DQB1*06:02  Mexico Mexico City Mestizo population 0.6993143
 10  B*35:17-C*04:01-DRB1*15:03-DQB1*06:02  Mexico Mexico City Mestizo population 0.6993143
 11  A*03:01-B*35:01-C*04:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*02:01  South Africa Worcester 0.6000159
 12  A*11:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India East UCBB 0.59882,403
 13  A*11:01-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India Northeast UCBB 0.5068296
 14  A*29:02-B*35:01-C*04:01-DRB1*15:03-DQA1*05:01-DQB1*06:02-DPB1*01:01  Kenya, Nyanza Province, Luo tribe 0.5000100
 15  A*02:11-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India Tamil Nadu 0.49112,492
 16  A*68:01-B*35:33-C*04:01-E*01:03:02-F*01:01:01-G*01:01-DRB1*15:01-DQA1*04:01-DQB1*06:02  Portugal Azores Terceira Island 0.4386130
 17  A*02:01:01-B*35:02:01-C*04:01:01-DRB1*15:01:01-DQA1*01:02:01-DQB1*06:02-DPA1*01:03:01-DPB1*03:01:01  Russian Federation Vologda Region 0.4202119
 18  A*32:01:01-B*35:01:01-C*04:01:01-DRB1*15:02:01-DQA1*05:05:01-DQB1*06:01:01-DPA1*01:03:01-DPB1*02:01:02  Russian Federation Vologda Region 0.4202119
 19  A*11:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India North UCBB 0.40565,849
 20  A*24:02-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  Malaysia Peninsular Indian 0.3690271
 21  A*03:01:01-B*35:01:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01-DPB1*03:01:01  South African Black 0.3520142
 22  A*24:02-B*35:01-C*04:01-DRB1*15:03-DQB1*06:02  Mexico Mexico City Mestizo population 0.3497143
 23  A*31:01-B*35:12-C*04:01-DRB1*15:01-DQB1*06:02  Mexico Mexico City Mestizo population 0.3497143
 24  B*35:01-C*04:01-DRB1*15:03-DQB1*06:02  Mexico Mexico City Mestizo population 0.3497143
 25  B*35:12-C*04:01-DRB1*15:01-DQB1*06:02  Mexico Mexico City Mestizo population 0.3497143
 26  A*03-B*35-C*04:01-DRB1*15:01-DQB1*06  Russia Transbaikal Territory Buryats 0.3340150
 27  A*36:01-B*35:01-C*04:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  South Africa Worcester 0.3000159
 28  A*11:01:01-B*35:03:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01  India Karnataka Kannada Speaking 0.2870174
 29  A*11:01:01-B*35:03:01-C*04:01:01-DRB1*15:01:01-DQB1*06:01:01  India Kerala Malayalam speaking 0.2810356
 30  A*02:11-B*35:03-C*04:01-DRB1*15:02-DQA1*01:03-DQB1*06:01-DPB1*04:01  Sri Lanka Colombo 0.2801714
 31  A*02:11:01-B*35:03:01-C*04:01:01-DRB1*15:02:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 32  A*03:01:01-B*35:03:01-C*04:01:01-DRB1*15:02:01-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 33  A*11:01:01-B*35:03:01-C*04:01:01-DRB1*15:02:01-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 34  A*11:01:01-B*35:03:01-C*04:01:01-DRB1*15:02:02-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 35  A*11:01:01-B*35:03:01-C*04:01:01-DRB1*15:38-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 36  A*24:02:01-B*35:03:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 37  A*01:01:01:01-B*35:03:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01  Russia Bashkortostan, Tatars 0.2604192
 38  A*11:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India West UCBB 0.24085,829
 39  A*33:03-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India East UCBB 0.23602,403
 40  A*11:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India Central UCBB 0.23464,204
 41  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01  Spain, Canary Islands, Gran canaria island 0.2300215
 42  A*33:01:01-B*35:01:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01  Spain, Canary Islands, Gran canaria island 0.2300215
 43  A*68:01:01-B*35:03:01-C*04:01:01-DRB1*15:01-DQB1*06:02  Costa Rica Central Valley Mestizo (G) 0.2262221
 44  A*02:01-B*35:03-C*04:01-DRB1*15:01-DQB1*06:02-DPB1*04:01  Russia Karelia 0.22381,075
 45  A*03:01-B*35:01-C*04:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.21802,411
 46  A*02:06-B*35:123-C*04:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Nicaragua Managua 0.2165339
 47  A*01:01-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India East UCBB 0.20172,403
 48  A*68:01:02-B*35:01:01-C*04:01:01-DRB1*15:01:01-DQB1*06:01:01  India Kerala Malayalam speaking 0.1970356
 49  A*03:01:01-B*35:03:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 50  A*32:01:01-B*35:01:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 51  A*24:02:01-B*35:03:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.1945521
 52  A*03:01-B*35:01-C*04:01-DRB1*15:01-DQB1*06:02-DPB1*04:01  Panama 0.1900462
 53  A*23:01-B*35:14-C*04:01-DRB1*15:03-DQB1*06:02-DPB1*03:01  Panama 0.1900462
 54  A*11:01-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India East UCBB 0.18602,403
 55  A*03:01-B*35:03-C*04:01-DRB1*15:01-DQB1*06:01  Malaysia Peninsular Indian 0.1845271
 56  A*31:18-B*35:03-C*04:01-DRB1*15:01-DQB1*06:03  Malaysia Peninsular Indian 0.1845271
 57  A*31:01:02-B*35:08-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01-DPB1*02:01:02  Saudi Arabia pop 6 (G) 0.180228,927
 58  A*02:01-B*35:01-C*04:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.17402,411
 59  A*33:03-B*35:01-C*04:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.17402,411
 60  A*11:01-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India Central UCBB 0.17174,204
 61  A*11:01:01-B*35:01:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01  India Kerala Malayalam speaking 0.1690356
 62  A*11:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India Northeast UCBB 0.1689296
 63  A*11:01-B*35:03-C*04:01-DRB1*15:01-DQB1*06:01  India Northeast UCBB 0.1689296
 64  A*24:07-B*35:05-C*04:01-DRB1*15:01-DQB1*06:01  India Northeast UCBB 0.1689296
 65  A*31:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India Northeast UCBB 0.1689296
 66  A*02:11-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India West UCBB 0.16615,829
 67  A*03:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India East UCBB 0.16522,403
 68  A*68:02-B*35:01-C*04:01-DRB1*15:03-DQB1*06:02-DPB1*03:01  Tanzania Maasai 0.1597336
 69  A*24:02-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India Tamil Nadu 0.15502,492
 70  A*68:01-B*35:03-C*04:01-DRB1*15:01-DQA1*01:03-DQB1*06:01-DPB1*01:01  Sri Lanka Colombo 0.1401714
 71  A*02:09-B*35:03:01-C*04:01:01-DRB1*15:02:01-DQB1*06:01:01  India Kerala Malayalam speaking 0.1400356
 72  A*11:01:01-B*35:01:01-C*04:01:01-DRB1*15:02:01-DQB1*06:01:01  India Kerala Malayalam speaking 0.1400356
 73  A*24:02:01-B*35:03:01-C*04:01:01-DRB1*15:02:02-DQB1*06:01:01  India Kerala Malayalam speaking 0.1400356
 74  A*24:02-B*35:03-C*04:01-DRB1*15:01-DQB1*06:03  Italy pop 5 0.1400975
 75  A*26:01:01-B*35:01:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01  India Kerala Malayalam speaking 0.1400356
 76  A*32:01:01-B*35:01:01-C*04:01:01-DRB1*15:02:01-DQB1*06:01:01  India Kerala Malayalam speaking 0.1400356
 77  A*24:02-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India Central UCBB 0.13744,204
 78  A*24:02-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India South UCBB 0.136211,446
 79  A*11:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  USA Asian pop 2 0.13301,772
 80  A*74:01-B*35:01-C*04:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.12602,411
 81  A*11:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India South UCBB 0.123911,446
 82  A*24:02-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India Central UCBB 0.12344,204
 83  A*24:02-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India South UCBB 0.116211,446
 84  A*03:01-B*35:01-C*04:01-DRB1*15:01-DQB1*06:02-DPB1*04:01  Russia Karelia 0.11391,075
 85  A*03:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India West UCBB 0.11225,829
 86  A*11:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India Tamil Nadu 0.11192,492
 87  A*31:01-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India Tamil Nadu 0.11082,492
 88  A*32:01-B*35:01-C*04:01-DRB1*15:01-DQB1*06:02  Germany DKMS - Italy minority 0.11001,159
 89  A*23:01-B*35:01-C*04:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.10602,411
 90  A*01:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India West UCBB 0.10465,829
 91  A*11:01-B*35:03-C*04:01-DRB1*15:01-DQB1*06:01  India East UCBB 0.10432,403
 92  A*02:11-B*35:03-C*04:01-DRB1*15:01-DQB1*06:01  India West UCBB 0.10055,829
 93  A*68:01-B*35:03-C*04:01-DRB1*15:01-DQB1*06:01  India East UCBB 0.09502,403
 94  A*02:01-B*35:01-C*04:01-DRB1*15:01-DQB1*06:02  USA Hispanic pop 2 0.09401,999
 95  A*03:01-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India South UCBB 0.092111,446
 96  A*03:01:01-B*35:03:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.090223,595
 97  A*11:01-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India North UCBB 0.08885,849
 98  A*33:03-B*35:01-C*04:01-DRB1*15:02-DQB1*06:01  India West UCBB 0.08835,829
 99  A*11:01-B*35:03-C*04:01-DRB1*15:01-DQB1*06:01  India South UCBB 0.087411,446
 100  A*03:01-B*35:03-C*04:01-DRB1*15:02-DQB1*06:01  India West UCBB 0.08455,829

Notes:

* Haplotype Frequencies: Total number of copies of the haplotype in the population sample (Haplotypes / 2n) shown in percentages (%).
   Important: This field has been expanded to two decimals to better represent frequencies of large datasets (e.g. where sample size > 1000 individuals)
¹ Distribution - Shows the geographic distribution in overlaid maps of the complete haplotype (left icon) or the input alleles if low level resolution was entered (right icon).


Displaying 1 to 100 (from 389) records   Pages: 1 2 3 4 of 4  


   

Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools
Gonzalez-Galarza FF, McCabe A, Santos EJ, Jones J, Takeshita LY, Ortega-Rivera ND, Del Cid-Pavon GM, Ramsbottom K, Ghattaoraya GS, Alfirevic A, Middleton D and Jones AR Nucleic Acid Research 2020, 48:D783-8.
Liverpool, U.K.

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