Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

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A B C DRB1 DPA1 DPB1 DQA1 DQB1

Population:  Country:  Source of dataset : 
Region:  Ethnic Origin:     Type of study :  Sort by: 
Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 100 (from 265) records   Pages: 1 2 3 of 3  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Tamil Nadu 1.95162,492
 2  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India South UCBB 1.785911,446
 3  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India West UCBB 1.43205,829
 4  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP South Asian Indian 1.3953185,391
 5  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Northeast UCBB 1.3514296
 6  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India East UCBB 1.34252,403
 7  B*37:01-DRB1*10:01-DQB1*05:01  South Korea pop 3 1.3000485
 8  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  Malaysia Peninsular Indian 1.2915271
 9  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Central UCBB 1.27444,204
 10  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQA1*01:01-DQB1*05:01-DPB1*02:01  Sri Lanka Colombo 1.2605714
 11  A*01:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  India Karnataka Kannada Speaking 1.1490174
 12  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India North UCBB 1.04995,849
 13  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP Southeast Asian 1.034527,978
 14  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  South Korea pop 3 1.0000485
 15  A*11:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  India Kerala Malayalam speaking 0.9490356
 16  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP Korean 0.923177,584
 17  A*02:01-B*37:01-DRB1*10:01-DQB1*05:01  Iran Yazd 0.892956
 18  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  USA Asian pop 2 0.88901,772
 19  A*02:06:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  India Andhra Pradesh Telugu Speaking 0.8065186
 20  A*01:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  India Kerala Malayalam speaking 0.5960356
 21  A*02:11-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  Malaysia Peninsular Indian 0.5535271
 22  A*02:11:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  India Andhra Pradesh Telugu Speaking 0.5376186
 23  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQA1*03:01-DQB1*05:01-DPB1*02:01  USA San Diego 0.5210496
 24  A*01:01:01:01-B*37:01:01-C*06:02:01:01-DRB1*10:01:01-DQB1*05:01  Russia Bashkortostan, Tatars 0.5208192
 25  A*02:01-B*37:01-DRB1*10:01-DQB1*05:01  Iran Tabriz Azeris 0.515597
 26  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  Italy pop 5 0.4400975
 27  A*02:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  India Kerala Malayalam speaking 0.4210356
 28  A*02:06:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  India Kerala Malayalam speaking 0.4210356
 29  A*31:01:02:01-B*37:01:01-C*03:03:01-DRB1*10:01:01-DQB1*05:01  Russia Bashkortostan, Bashkirs 0.4167120
 30  A*11:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Tamil Nadu 0.38852,492
 31  A*02:06-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Tamil Nadu 0.37902,492
 32  A*03:01-B*37:01-C*01:02-DRB1*10:01-DQB1*05:01  Malaysia Peninsular Indian 0.3690271
 33  A*33:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  Malaysia Peninsular Indian 0.3690271
 34  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP Vietnamese 0.366843,540
 35  A*11:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 36  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP Chinese 0.310399,672
 37  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  Germany DKMS - Turkey minority 0.29404,856
 38  A*01:01:01-B*37:04:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  India Karnataka Kannada Speaking 0.2870174
 39  A*33:03:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  India Kerala Malayalam speaking 0.2810356
 40  A*26:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  USA NMDP Caribean Indian 0.273914,339
 41  A*11:01:01-B*37:01:01-C*03:04:01-DRB1*10:01:01-DQB1*05:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 42  A*24:02:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 43  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  Malaysia Peninsular Malay 0.2629951
 44  A*02:06-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India South UCBB 0.242611,446
 45  A*33:03-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India East UCBB 0.23802,403
 46  A*01:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01  Costa Rica Central Valley Mestizo (G) 0.2262221
 47  A*02:06-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India West UCBB 0.21505,829
 48  A*11:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India South UCBB 0.202811,446
 49  A*68:01-B*37:01:01-C*06:02-DRB1*10:01:01-DQB1*05:01:01  England North West 0.2000298
 50  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP Japanese 0.199324,582
 51  A*01:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01-DPA1*01:03:01-DPB1*02:01:02  Brazil Rio de Janeiro Caucasian 0.1946521
 52  A*02:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01-DPA1*01:03:01-DPB1*02:01:02  Brazil Rio de Janeiro Caucasian 0.1946521
 53  A*02:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01-DPA1*02:01:02-DPB1*01:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 54  A*11:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  Malaysia Peninsular Indian 0.1845271
 55  A*24:02-B*37:14-C*06:02-DRB1*10:01-DQB1*05:01  Malaysia Peninsular Indian 0.1845271
 56  A*24:07-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  Malaysia Peninsular Indian 0.1845271
 57  A*24:02-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Tamil Nadu 0.18122,492
 58  A*02:06-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Central UCBB 0.18074,204
 59  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  USA NMDP American Indian South or Central America 0.17915,926
 60  A*01:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  China Zhejiang Han 0.17301,734
 61  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  Germany DKMS - Italy minority 0.17301,159
 62  A*02:06-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Northeast UCBB 0.1689296
 63  A*03:02-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Northeast UCBB 0.1689296
 64  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP European Caucasian 0.15901,242,890
 65  A*03:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Tamil Nadu 0.15752,492
 66  A*02-B*37-DRB1*10:01-DQA1*01:01-DQB1*05:01  Brazil Paraná Caucasian 0.1560641
 67  A*32:01-B*37:01-DRB1*10:01-DQB1*05:01  Mexico Mexico City Tlalpan 0.1515330
 68  A*11:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Central UCBB 0.15134,204
 69  A*68:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Tamil Nadu 0.14502,492
 70  A*01:01-B*37:01-C*07:04-DRB1*10:01-DQA1*01:01-DQB1*05:01-DPB1*04:01  Sri Lanka Colombo 0.1401714
 71  A*11:01-B*37:01-C*07:02-DRB1*10:01-DQA1*01:01-DQB1*05:01-DPB1*04:01  Sri Lanka Colombo 0.1401714
 72  A*02:03:01-B*37:01:01-C*15:02:01-DRB1*10:01:01-DQB1*05:01:01  India Kerala Malayalam speaking 0.1400356
 73  A*32:01:01-B*37:01:01-C*05:01:01-DRB1*10:01:01-DQB1*05:01:01  India Kerala Malayalam speaking 0.1400356
 74  A*24:02-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India South UCBB 0.139711,446
 75  A*68:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India South UCBB 0.137211,446
 76  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQA1*01:05-DQB1*05:01-DPA1*01:03-DPB1*02:01  Japan pop 17 0.13003,078
 77  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP Middle Eastern or North Coast of Africa 0.125470,890
 78  A*02:06-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India North UCBB 0.12185,849
 79  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP Hispanic South or Central American 0.1163146,714
 80  A*24:02-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Central UCBB 0.11174,204
 81  A*24:02:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  China Zhejiang Han 0.10951,734
 82  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP Filipino 0.106750,614
 83  A*24:02-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  Malaysia Peninsular Malay 0.1052951
 84  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 85  A*03:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India North UCBB 0.10305,849
 86  A*01:01:01:01-B*37:01:01-C*06:02:01:01-DRB1*10:01:01-DQB1*05:01  Russia Nizhny Novgorod, Russians 0.09931,510
 87  A*01:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.096028,927
 88  A*03:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India South UCBB 0.095011,446
 89  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP North American Amerindian 0.094735,791
 90  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  USA Hispanic pop 2 0.09401,999
 91  A*24:02-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  USA Hispanic pop 2 0.09401,999
 92  A*02:11-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Central UCBB 0.09034,204
 93  A*01:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  Poland BMR 0.089723,595
 94  A*02:06-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  USA Asian pop 2 0.08901,772
 95  A*02:11-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India Tamil Nadu 0.08692,492
 96  A*02:11-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India South UCBB 0.083411,446
 97  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP Mexican or Chicano 0.0831261,235
 98  A*11:02:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  China Zhejiang Han 0.08091,734
 99  A*33:03-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India West UCBB 0.08095,829
 100  A*11-B*37-DRB1*10:01-DQA1*01:01-DQB1*05:01  Brazil Paraná Caucasian 0.0780641

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 265) records   Pages: 1 2 3 of 3  


   

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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