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 : 
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Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 100 (from 154) records   Pages: 1 2 of 2  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Tunisia 3.0000100
 2  A*02:01:01-B*50:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02  Morocco Atlantic Coast Chaouya 2.900098
 3  A*02:01-B*50:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02  United Arab Emirates Abu Dhabi 2.880052
 4  A*02:01-B*50:01-DRB1*07:01-DQB1*02:02  Tunisia Gabes 2.630095
 5  A*02-B*50-DRB1*07:01-DQB1*02:02  Tunisia Ghannouch 1.800082
 6  A*01:01-B*50:01-DRB1*07:01-DQB1*02:02  Tunisia Gabes 1.570095
 7  A*03:01-B*50:01-DRB1*07:01-DQB1*02:02  Tunisia Gabes 1.570095
 8  A*23:01-B*50:01-DRB1*07:01-DQB1*02:02  Tunisia Gabes 1.570095
 9  A*02:05-B*50:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02  United Arab Emirates Abu Dhabi 0.960052
 10  A*03:01-B*50:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02  United Arab Emirates Abu Dhabi 0.960052
 11  A*29:01-B*50:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02  United Arab Emirates Abu Dhabi 0.960052
 12  A*24:02-B*50:01-C*06:02-E*01:01:01-F*01:01:01-G*01:01-DRB1*07:01-DQA1*02:01-DQB1*02:02  Portugal Azores Terceira Island 0.8772130
 13  A*02:01:01:01-B*50:01:01-C*06:02:01:02-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Bashkirs 0.8333120
 14  A*32:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  Spain, Canary Islands, Gran canaria island 0.7000215
 15  A*02:01-B*50:01-DRB1*07:01-DQB1*02:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 16  A*02:05-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.55835,849
 17  A*03:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Indian 0.5535271
 18  A*02-B*50-DRB1*07:01-DQA1*02:01-DQB1*02:02  Brazil Paraná Caucasian 0.5359641
 19  A*01:01:01:01-B*50:01:01-C*06:02:01:02-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Tatars 0.5208192
 20  A*33:03:01-B*50:01:01-C*06:02:01:02-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Tatars 0.5208192
 21  A*02:05-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.46104,204
 22  A*01:01-B*50:01-C*04:01-E*01:01:01-F*01:01:01-G*01:06-DRB1*07:01-DQA1*02:01-DQB1*02:02  Portugal Azores Terceira Island 0.4386130
 23  A*02:01-B*50:01-C*07:01-E*01:01:01-F*01:01:01-G*01:01-DRB1*07:01-DQA1*01:03-DQB1*02:02  Portugal Azores Terceira Island 0.4386130
 24  A*11:01:01-B*50:01:01-C*06:02:01:01-DRB1*07:01-DQB1*02:02  Russia Bashkortostan, Bashkirs 0.4167120
 25  A*30:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Bashkirs 0.4167120
 26  A*02:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.3891521
 27  A*02:05-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.35685,829
 28  A*03:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.354111,446
 29  A*01:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 0.3497143
 30  B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 0.3497143
 31  A*02:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.34204,335
 32  A*33:03:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 33  A*74:01-B*50:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  South Africa Worcester 0.3000159
 34  A*02:05:01-B*50:01:01-C*06:02:01:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.29801,510
 35  A*24:02-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Italy pop 5 0.2700975
 36  A*03:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 37  A*02:05:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.268623,595
 38  A*03:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.20425,829
 39  A*24-B*50-DRB1*07:01-DQA1*02:01-DQB1*02:02  Brazil Paraná Caucasian 0.2039641
 40  A*02:05:01-B*50:01:01-C*02:02:02-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 41  A*24:02:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 42  A*33:03:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:02:02-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 43  A*29:02-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02-DPB1*03:01  Panama 0.1900462
 44  A*02:05-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.17104,335
 45  A*02:05-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.17072,403
 46  A*30:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1700975
 47  A*24:07-B*50:01-C*08:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.1577951
 48  A*24:02:01:01-B*50:01:01-C*06:02:01:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.15261,510
 49  A*01:01-B*50:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.1515330
 50  A*02:01-B*50:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.1515330
 51  A*23:01-B*50:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.1515330
 52  A*24:02-B*50:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.1515330
 53  A*02:05-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.146711,446
 54  A*02:08-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.14515,849
 55  A*01:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1400975
 56  A*24:02:01-B*50:01:01-C*15:02:01-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 0.1400356
 57  A*24:03-B*50:01-C*07:01-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1400975
 58  A*03-B*50-DRB1*07:01-DQA1*02:01-DQB1*02:02  Brazil Paraná Caucasian 0.1182641
 59  A*01:01:01:01-B*50:01:01-C*06:02:01:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.11231,510
 60  A*24:02-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.11084,204
 61  A*02:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.090023,595
 62  A*01:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  China Zhejiang Han 0.08651,734
 63  A*23:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.08345,849
 64  A*02:08-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.08334,204
 65  A*02:01-B*50:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  Sri Lanka Colombo 0.0700714
 66  A*01:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 67  A*03:02-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 68  A*32:01:01-B*50:01:01-C*06:02:01:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.06621,510
 69  A*68:01:01:02-B*50:01:01-C*06:02:01:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.06621,510
 70  A*03:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.05945,849
 71  A*11:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.05895,829
 72  A*02:05:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  China Zhejiang Han 0.05771,734
 73  A*01:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.05444,204
 74  A*11:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.04764,204
 75  A*03:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.04684,204
 76  A*01:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.045711,446
 77  A*24:02-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.04182,403
 78  A*11:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.041711,446
 79  A*68:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.03825,849
 80  A*01:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.03475,849
 81  A*01:03-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 82  A*03:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 83  A*23:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 84  A*23:01-B*50:02-C*04:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 85  A*24:02-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 86  A*29:02-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 87  A*31:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 88  A*33:03-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 89  A*23:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.03384,204
 90  A*01:01:01:01-B*50:01:01-C*02:02:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 91  A*02:01:01:01-B*50:01:01-C*06:02:01:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 92  A*02:01:04-B*50:01:01-C*06:02:01:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 93  A*68:01:02:02-B*50:01:01-C*06:02:01:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 94  A*68:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.031711,446
 95  A*11:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.03135,849
 96  A*03:01-B*50:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 97  A*68:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.02975,829
 98  A*32:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.02515,849
 99  A*03:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.022323,595
 100  A*32:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.021123,595

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 154) records   Pages: 1 2 of 2  


   

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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