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 123) records   Pages: 1 2 of 2  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Ireland South 2.3000250
 2  A*24:02-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*02:01  Russia Karelia 1.27031,075
 3  A*68:01-B*39:05-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 4  A*02:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Ireland South 1.0000250
 5  A*31:01-B*39:05-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 6  A*68:01-B*39:05-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 7  A*68:01-B*39:06-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 8  A*68:03-B*39:05-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 9  A*24:02-B*39:06-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.6881218
 10  A*24:02:01:01-B*39:01:01:03-C*07:02:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.52751,510
 11  B*39:06-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo pop 2 0.4300234
 12  A*03:01:01-B*07:02:01-C*07:02:01-DRB1*04:04:01-DQA1*03:01:01-DQB1*03:02:01-DPA1*01:04:01-DPB1*04:01:01  Russian Federation Vologda Region 0.4202119
 13  A*24:02:01-B*39:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02  Russia Bashkortostan, Bashkirs 0.4167120
 14  A*24:02-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.36131,075
 15  A*02:01-B*39:02-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 16  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 17  A*24:02-B*39:05-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 18  B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 19  B*39:02-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 20  B*39:05-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 21  A*02:01:01-B*18:01:01-C*07:02:01-DRB1*04:04:01-DQA1*04:02-DQB1*03:02-DPA1*01:03:01-DPB1*06:01  Russia Belgorod region 0.3268153
 22  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02:01  England North West 0.3000298
 23  A*03:01:01-B*07:02:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 24  A*30:01:01-B*39:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02  Russia Bashkortostan, Tatars 0.2604192
 25  A*02:01-B*39:05-C*07:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.23801,463
 26  A*24:02-B*39:06-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.23401,999
 27  A*03:01:01:01-B*07:02:01-C*07:02:01:03-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.23181,510
 28  A*02:06-B*35:01-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 29  A*02:06-B*35:01-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Mexico Chiapas Lacandon Mayans 0.2294218
 30  A*24:02-B*39:05-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 31  A*02:06-B*48:01-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPB1*14:01  Nicaragua Managua 0.2165339
 32  A*11:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02:01  England North West 0.2000298
 33  A*31:01:02-B*35:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 34  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.17104,335
 35  A*02:01-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*02:01  Russia Karelia 0.16191,075
 36  A*02:01-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.13411,075
 37  A*02:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  USA African American pop 4 0.13102,411
 38  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Italy minority 0.12901,159
 39  A*24:02-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:02  Russia Karelia 0.12171,075
 40  A*11:01:01-B*40:01:02-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  China Zhejiang Han 0.12141,734
 41  A*02:01:01-B*39:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  China Zhejiang Han 0.11531,734
 42  A*24:02-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*03:01  Russia Karelia 0.10431,075
 43  A*68:01-B*39:05-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.09401,999
 44  A*01:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.08401,463
 45  A*03:01:01:01-B*39:01:01:03-C*07:02:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.06851,510
 46  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.06841,463
 47  A*68:01-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:02  Russia Karelia 0.05651,075
 48  A*02:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:02  Russia Karelia 0.05641,075
 49  A*03:01:01-B*07:02:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.052023,595
 50  A*02:01-B*39:08-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 51  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 52  A*24:02-B*39:14-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 53  A*31:01-B*39:05-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 54  A*68:01-B*39:02-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 55  A*68:01-B*39:06-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 56  A*24:02-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.04605,849
 57  A*11:02-B*55:02-C*07:02-DRB1*04:04-DQB1*03:02  USA Asian pop 2 0.04401,772
 58  A*24:02-B*51:01-C*07:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Italy minority 0.04301,159
 59  A*24:02:01-B*07:02:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.036523,595
 60  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.03604,204
 61  A*26:01-B*08:01-C*07:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.03584,204
 62  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.03504,856
 63  A*02:01:01-B*07:02:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.034223,595
 64  A*24:02-B*39:11-C*07:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 65  A*02:22-B*39:09-C*07:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 66  A*03:01-B*51:01-C*07:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 67  A*24:02-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 68  A*24:02-B*39:06-C*07:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 69  A*32:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 70  A*68:01-B*39:14-C*07:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 71  A*24:02:01:01-B*39:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.03311,510
 72  A*02:01:01-B*15:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  China Zhejiang Han 0.02881,734
 73  A*11:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.02402,492
 74  A*11:01-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02  USA Asian pop 2 0.02201,772
 75  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*02:01  Germany DKMS - German donors 0.02143,456,066
 76  A*02:03-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  India East UCBB 0.02082,403
 77  A*24:02-B*40:06-C*07:02-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.02012,492
 78  A*01:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.02002,492
 79  A*02:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*06:01  Germany DKMS - German donors 0.01863,456,066
 80  A*02:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.01793,456,066
 81  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.01723,456,066
 82  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  India West UCBB 0.01495,829
 83  A*02:01-B*51:01-C*07:02-DRB1*04:04-DQB1*03:02  India South UCBB 0.013111,446
 84  A*03:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*06:01  Germany DKMS - German donors 0.01253,456,066
 85  A*33:03-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.01234,204
 86  A*02:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.01204,856
 87  A*02:01-B*39:09-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 88  A*02:01-B*39:11-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 89  A*23:01-B*39:09-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 90  A*31:01-B*39:11-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 91  A*02:01-B*51:01-C*07:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.01194,204
 92  A*03:01-B*51:01-C*07:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.01194,204
 93  A*31:12-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.01194,204
 94  A*68:01-B*15:18-C*07:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.01194,204
 95  A*24:02-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.01083,456,066
 96  A*24:02-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*02:01  Germany DKMS - German donors 0.01073,456,066
 97  A*32:01-B*51:01-C*07:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.01004,856
 98  A*68:02-B*51:01-C*07:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.01004,856
 99  A*01:01-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  India West UCBB 0.00955,829
 100  A*24:02-B*07:02-C*07:02-DRB1*04:04-DQB1*03:02  India South UCBB 0.008911,446

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 123) 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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