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 127) records   Pages: 1 2 of 2  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*24-B*07-DRB1*07:01-DQB1*02:02  Tunisia Ghannouch 3.700082
 2  A*03:01:01-B*07:06:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:01:04-DPB1*11:01:01  Brazil Barra Mansa Rio State Black 2.381073
 3  A*24:02:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQA1*02:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Russian Federation Vologda Region 0.8403119
 4  A*03:01:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Parda 0.5882170
 5  A*24:02:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 0.5750174
 6  A*32:01-B*07:02-DRB1*07:01-DQB1*02:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 7  A*24:02-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.47982,403
 8  A*02:01:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQA1*01:02:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:02  Russian Federation Vologda Region 0.4202119
 9  A*24:02:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQA1*02:01:01-DQB1*02:02-DPA1*02:01:02-DPB1*17:01:01  Russian Federation Vologda Region 0.4202119
 10  A*02:01:01-B*07:02:01-C*07:02:01:03-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Bashkirs 0.4167120
 11  A*32:01-B*07:02-C*07:02-DRB1*07:01-DQA1*02:01-DQB1*02:02  Kosovo 0.4030124
 12  A*23:17-B*07:06:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01-DPB1*02:01:02  South African Black 0.3520142
 13  A*03:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India Northeast UCBB 0.3378296
 14  A*02:01-B*07:02-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02  Brazil Puyanawa 0.3333150
 15  A*01:01:01-B*07:02:01-C*07:01:01-DRB1*07:01:01-DQA1*05:05:01-DQB1*02:02-DPA1*01:03:01-DPB1*04:01  Russia Belgorod region 0.3268153
 16  A*11:01:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQA1*02:01:01-DQB1*02:02-DPA1*01:03:01-DPB1*02:01  Russia Belgorod region 0.3268153
 17  A*02:01:01:01-B*07:02:01-C*03:03:01-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Tatars 0.2604192
 18  A*02:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.23904,335
 19  A*24:02-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.231111,446
 20  A*68:01-B*07:05-C*07:02-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*13:01  Sri Lanka Colombo 0.2101714
 21  A*30:04:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:01:01-DPB1*104:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 22  A*80:01-B*07:05-C*02:02-DRB1*07:01-DQB1*02:02-DPB1*02:01  Panama 0.1900462
 23  A*24:02-B*07:02-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Indian 0.1845271
 24  A*02:01:01:01-B*07:02:01-C*07:02:01:03-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.16631,510
 25  A*11:01:01:01-B*07:02:01-C*07:02:01:03-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.16151,510
 26  A*11-B*07-DRB1*07:01-DQA1*02:01-DQB1*02:02  Brazil Paraná Caucasian 0.1560641
 27  A*01:01-B*07:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.1515330
 28  A*25:01-B*07:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.1515330
 29  A*24:02-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.14085,829
 30  A*03:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 31  A*03:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.12442,403
 32  A*33:03-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.12182,403
 33  A*01:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.11512,403
 34  A*03:01:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.101523,595
 35  A*24:02-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.08674,204
 36  A*01:01-B*07:02-C*07:02-DRB1*07:01-DQA1*05:01-DQB1*02:02-DPB1*26:01  Sri Lanka Colombo 0.0700714
 37  A*03:01-B*07:02-C*07:02-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:02  Sri Lanka Colombo 0.0700714
 38  A*11:01-B*07:02-C*04:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*26:01  Sri Lanka Colombo 0.0700714
 39  A*11:01-B*07:05-C*07:02-DRB1*07:01-DQA1*06:01-DQB1*02:02-DPB1*26:01  Sri Lanka Colombo 0.0700714
 40  A*24:07-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.06865,829
 41  A*24:02-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 42  A*29:01-B*07:05-C*15:05-DRB1*07:01-DQB1*02:02  India West UCBB 0.06795,829
 43  A*03:01:01:01-B*07:02:01-C*07:02:01:03-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.06731,510
 44  A*02:01:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.060323,595
 45  A*03:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.05864,204
 46  A*01:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.058111,446
 47  A*32:01:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01  China Zhejiang Han 0.05771,734
 48  A*01:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.05565,849
 49  A*11:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.054611,446
 50  A*03:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.0526951
 51  A*11:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.05205,829
 52  A*24:07-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.04764,204
 53  A*11:01:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.044323,595
 54  A*03:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.04295,849
 55  A*31:12-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.04162,403
 56  A*24:02-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.04025,849
 57  A*01:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.03764,204
 58  A*02:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.03674,204
 59  A*31:12-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.03534,204
 60  A*01:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 61  A*02:01-B*07:05-C*15:05-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 62  A*03:01-B*07:05-C*15:05-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 63  A*29:01-B*07:05-C*15:05-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 64  A*31:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 65  A*32:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 66  A*23:01:01-B*07:02:01-C*07:02:01:03-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 67  A*01:01:01-B*07:05:01-C*15:05:02-DRB1*07:01:01-DQB1*02:02:01  China Zhejiang Han 0.02881,734
 68  A*02:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.02775,849
 69  A*33:03-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.02605,829
 70  A*02:11-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.024211,446
 71  A*02:11-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.02102,403
 72  A*02:09-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.02082,403
 73  A*11:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.02084,204
 74  A*29:01-B*07:05-C*15:05-DRB1*07:01-DQB1*02:02  India North UCBB 0.01945,849
 75  A*02:11-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.01835,849
 76  A*02:05-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.01775,849
 77  A*03:01-B*07:05-C*15:05-DRB1*07:01-DQB1*02:02  India West UCBB 0.01745,829
 78  A*24:02-B*07:06-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.01725,829
 79  A*32:01:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.013123,595
 80  A*32:01-B*07:06-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.01315,829
 81  A*33:03-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.012911,446
 82  A*68:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01284,204
 83  A*02:11-B*07:06-C*07:01-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 84  A*02:11-B*07:06-C*14:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 85  A*29:01-B*07:05-C*15:05-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 86  A*11:01-B*07:06-C*07:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.011711,446
 87  A*26:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.011211,446
 88  A*02:09-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.009711,446
 89  A*01:01:01-B*07:02:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.009223,595
 90  A*01:01-B*07:06-C*07:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.008811,446
 91  A*24:02-B*07:05-C*15:05-DRB1*07:01-DQB1*02:02  India North UCBB 0.00875,849
 92  A*26:01-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.00875,829
 93  A*01:01-B*07:02-C*12:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 94  A*02:05-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 95  A*02:11-B*07:06-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 96  A*24:17-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 97  A*29:02-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 98  A*31:12-B*07:02-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 99  A*03:01-B*07:05-C*15:05-DRB1*07:01-DQB1*02:02  India North UCBB 0.00855,849
 100  A*24:02-B*07:02-C*06:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.00855,849

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 127) 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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