Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

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Displaying 1 to 100 (from 166) records   Pages: 1 2 of 2  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*24:02:01-B*51:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Black 1.470668
 2  A*01:01-B*51:01-C*14:02-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
 3  A*02:01:01-B*51:01:01-C*01:02:01-DRB1*07:01-DQB1*02:02  Russia Bashkortostan, Bashkirs 0.8333120
 4  A*26:01:01-B*51:01:01-C*15:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.6250405
 5  A*32:01:01-B*51:01:01-C*03:03:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.6250405
 6  A*02:02-B*51:01-DRB1*07:01-DQB1*02:02  Mexico Veracruz Xalapa 0.595284
 7  A*23:01-B*51:01-DRB1*07:01-DQB1*02:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 8  A*01:01-B*51:01-C*16:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*01:01  Kenya, Nyanza Province, Luo tribe 0.5000100
 9  A*03:01-B*51:01-C*15:02-E*01:03:02-F*01:01:02-G*01:01-DRB1*07:01-DQA1*05:01-DQB1*02:02  Portugal Azores Terceira Island 0.4386130
 10  A*25:01:01-B*51:01:01-C*12:03:01:01-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Bashkirs 0.4167120
 11  A*03:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 0.3497143
 12  B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 0.3497143
 13  B*51:01-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 0.3497143
 14  A*31:01-B*51:01-C*15:02-DRB1*07:01-DQA1*02:01-DQB1*02:02  Brazil Puyanawa 0.3333150
 15  A*02:05:01-B*51:01:01-C*05:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 16  A*02:01-B*51:01-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*01:01  South Africa Worcester 0.3000159
 17  A*02:01-B*51:01-C*02:02-DRB1*07:01-DQB1*02:02  Italy pop 5 0.2900975
 18  A*02:01-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  Italy pop 5 0.2900975
 19  A*03:02-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  Italy pop 5 0.2900975
 20  A*32:01-B*51:01-C*01:02-DRB1*07:01-DQB1*02:02  Italy pop 5 0.2900975
 21  A*11:01:01-B*51:01:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 0.2810356
 22  A*68:01:01-B*51:01:01-C*14:02:01-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 23  A*24:02:01-B*51:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Spain, Canary Islands, Gran canaria island 0.2300215
 24  A*66:01:01-B*51:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Spain, Canary Islands, Gran canaria island 0.2300215
 25  A*02:01-B*51:01-C*01:02-DRB1*07:01-DQA1*01:01-DQB1*02:02-DPB1*04:02  Nicaragua Managua 0.2165339
 26  A*24:02-B*51:01-C*04:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:02  Nicaragua Managua 0.2165339
 27  A*25:01:01-B*51:01:01-C*02:02:02-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:01:01-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 28  A*30:01:01-B*51:01:01-C*14:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 29  A*23:01-B*51:01-C*16:46-DRB1*07:01-DQB1*02:02-DPB1*131:01  Tanzania Maasai 0.1597336
 30  A*68:01:02-B*51:01:01-C*03:03:01-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 0.1400356
 31  A*02:01-B*51:01-C*02:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 32  A*24:02-B*51:01-C*02:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 33  A*30:02-B*51:01-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 34  A*68:01-B*51:01-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  Sri Lanka Colombo 0.0700714
 35  A*33:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.0526951
 36  A*33:01-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.0526951
 37  A*26:01-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.04395,849
 38  A*24:02-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.04162,403
 39  A*32:01-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.04162,403
 40  A*02:06-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.03925,829
 41  A*01:01-B*51:01-C*02:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 42  A*02:01-B*51:01-C*05:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 43  A*02:01-B*51:01-C*08:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 44  A*02:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 45  A*02:01-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 46  A*03:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 47  A*03:01-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 48  A*11:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 49  A*11:01-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 50  A*24:02-B*51:01-C*03:03-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 51  A*24:02-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 52  A*26:01-B*51:01-C*02:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 53  A*68:01-B*51:01-C*01:02-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 54  A*68:02-B*51:01-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 55  A*02:01:01:01-B*51:01:01-C*07:01:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 56  A*02:01:01-B*51:01:01-C*14:02:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 57  A*24:02:01-B*51:01:01-C*15:02:01:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 58  A*30:04:01-B*51:01:01:01-C*14:02:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 59  A*33:03-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.02585,829
 60  A*02:11-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.02575,829
 61  A*02:01:01-B*51:01:01-C*14:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.025123,595
 62  A*24:02-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.02454,204
 63  A*11:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.02415,849
 64  A*01:01-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.02384,204
 65  A*24:02-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.02384,204
 66  A*26:01:01-B*51:01:01-C*01:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.023323,595
 67  A*24:02-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.02322,403
 68  A*68:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.022811,446
 69  A*03:01-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.021911,446
 70  A*24:02-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.02175,829
 71  A*01:01-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.02082,403
 72  A*01:01-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.02082,403
 73  A*02:03-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.02082,403
 74  A*11:01-B*51:01-C*07:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.02082,403
 75  A*01:01-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.01765,849
 76  A*33:03-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.01745,849
 77  A*02:01-B*51:01-C*15:04-DRB1*07:01-DQB1*02:02  India West UCBB 0.01725,829
 78  A*02:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.01715,849
 79  A*03:02-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.01515,849
 80  A*26:01-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.01485,829
 81  A*25:01:01-B*51:01:01-C*15:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.014423,595
 82  A*02:01:01-B*51:01:01-C*15:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.014423,595
 83  A*03:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.013511,446
 84  A*68:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01254,204
 85  A*24:02:01-B*51:01:01-C*14:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.012523,595
 86  A*11:01-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.012111,446
 87  A*31:01-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01214,204
 88  A*01:01-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 89  A*02:06-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 90  A*03:02-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 91  A*11:01-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 92  A*24:02-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 93  A*24:07-B*51:01-C*15:04-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 94  A*11:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.011311,446
 95  A*02:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.01115,829
 96  A*02:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01004,204
 97  A*33:03-B*51:01-C*15:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.009711,446
 98  A*11:01-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.00885,849
 99  A*11:01-B*51:01-C*16:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.00875,829
 100  A*26:01-B*51:01-C*14:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.00875,829

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 166) 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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