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 152) records   Pages: 1 2 of 2  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*01:01:01-B*50:01-C*06:02-DRB1*03:01-DQA1*05:01-DQB1*02:01  Morocco Atlantic Coast Chaouya 2.100098
 2  A*02-B*50-DRB1*03:01-DQB1*02  Mexico Chihuahua Chihuahua City 1.2605119
 3  A*02:01-B*50:01-DRB1*03:01-DQB1*02:01  Iran Saqqez-Baneh Kurds 0.833360
 4  A*01-B*50-DRB1*03:01-DQB1*02  Mexico Sonora, Ciudad Obregón 0.6993143
 5  A*24-B*50-DRB1*03:01-DQB1*02  Mexico Michoacan, Morelia 0.6623150
 6  A*02:01-B*50:01-DRB1*03:01-DQB1*02:01  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 7  A*02:05-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India North UCBB 0.51975,849
 8  A*03:02-B*50:01-DRB1*03:01-DQB1*02:01  Iran Tabriz Azeris 0.515597
 9  A*33-B*50-DRB1*03:01-DQB1*02  Mexico Chihuahua, Ciudad Juarez 0.4630106
 10  B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Mexico Mexico City Mestizo pop 2 0.4300234
 11  A*11:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Mexico Mexico City Mestizo population 0.3497143
 12  B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Mexico Mexico City Mestizo population 0.3497143
 13  A*26-B*50-DRB1*03:01-DQB1*02  Mexico Michoacan, Morelia 0.3311150
 14  A*32-B*50-DRB1*03:01-DQB1*02  Mexico Michoacan, Morelia 0.3311150
 15  A*02-B*50-DRB1*03:01-DQB1*02  Mexico Mexico City Metropolitan Area Rural 0.3289150
 16  A*02:05-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India Central UCBB 0.31794,204
 17  A*24:02:01-B*50:01:01-C*07:02:01-DRB1*03:01:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 18  A*33-B*50-DRB1*03:01-DQB1*02  Mexico Guanajuato Rural 0.3067162
 19  A*01-B*50-DRB1*03:01-DQB1*02  Mexico Sinaloa Rural 0.2732183
 20  A*02:05-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India West UCBB 0.26745,829
 21  A*32-B*50-DRB1*03:01-DQB1*02  Mexico Veracruz, Xalapa 0.2674187
 22  A*68:01:02:01-B*50:01:01-C*06:02:01:02-DRB1*03:01:01-DQB1*02:01:01  Russia Bashkortostan, Tatars 0.2604192
 23  A*03:02-B*50:01-C*15:02-DRB1*03:01-DQA1*05:01-DQB1*02:01-DPB1*04:01  USA San Diego 0.2600496
 24  A*01:01:01-B*50:01:01-C*06:02:01-DRB1*03:01:01-DQB1*02:01:01  Spain, Canary Islands, Gran canaria island 0.2300215
 25  A*02-B*50-DRB1*03:01-DQB1*02  Mexico Chihuahua Rural 0.2092236
 26  A*23-B*50-DRB1*03:01-DQB1*02  Mexico Chihuahua Rural 0.2092236
 27  A*80-B*50-DRB1*03:01-DQB1*02  Mexico Chihuahua Rural 0.2092236
 28  A*02:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Colombia Bogotá Cord Blood 0.20511,463
 29  A*02:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.20504,335
 30  A*02:01-B*50:01-C*06:02-DRB1*03:01:01-DQB1*02:01  England North West 0.2000298
 31  A*23:01-B*50:01-C*06:02-DRB1*03:01:01-DQB1*02:01  England North West 0.2000298
 32  A*33:03-B*50:01-C*06:02-DRB1*03:01:01-DQB1*02:01  England North West 0.2000298
 33  A*23:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Germany DKMS - Turkey minority 0.19904,856
 34  A*02-B*50-DRB1*03:01-DQB1*02  Mexico Jalisco Rural 0.1706585
 35  A*02-B*50-DRB1*03:01-DQA1*05:01-DQB1*02:01  Brazil Paraná Caucasian 0.1560641
 36  A*02-B*50-DRB1*03:01-DQB1*02  Mexico Durango Rural 0.1529326
 37  A*02-B*50-DRB1*03:01-DQB1*02  Mexico Michoacan Rural 0.1433348
 38  A*02:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Germany DKMS - Turkey minority 0.14104,856
 39  A*11:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Italy pop 5 0.1400975
 40  A*24:02-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Italy pop 5 0.1400975
 41  A*03:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India North UCBB 0.13365,849
 42  A*02:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Germany DKMS - Italy minority 0.12901,159
 43  A*02:05-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India East UCBB 0.12532,403
 44  A*02-B*50-DRB1*03:01-DQB1*02  Mexico Coahuila, Torreon 0.1250396
 45  A*68:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India North UCBB 0.12325,849
 46  A*03:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  USA Hispanic pop 2 0.12201,999
 47  A*01:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India Central UCBB 0.10234,204
 48  A*01:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India North UCBB 0.09515,849
 49  A*23:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India North UCBB 0.09435,849
 50  A*32-B*50-DRB1*03:01-DQB1*02  Mexico Veracruz Rural 0.0924539
 51  A*02:05-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  USA Asian pop 2 0.08901,772
 52  A*32-B*50-DRB1*03:01-DQB1*02  Mexico Jalisco Rural 0.0853585
 53  A*33-B*50-DRB1*03:01-DQB1*02  Mexico Jalisco Rural 0.0853585
 54  A*02-B*50-DRB1*03:01-DQB1*02  Mexico Jalisco, Guadalajara city 0.08381,189
 55  A*01:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India West UCBB 0.07895,829
 56  A*02:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01-DPB1*04:01  Germany DKMS - German donors 0.07373,456,066
 57  A*03:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India Central UCBB 0.07364,204
 58  A*03:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Germany DKMS - Turkey minority 0.07104,856
 59  A*01:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 60  A*23:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 61  A*03:01:01:01-B*50:01:01-C*06:02:01:02-DRB1*03:01:01-DQB1*02:01  Russia Nizhny Novgorod, Russians 0.06621,510
 62  A*01:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Germany DKMS - Turkey minority 0.06604,856
 63  A*02:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  USA Hispanic pop 2 0.06501,999
 64  A*02:05-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India Tamil Nadu 0.06172,492
 65  A*02-B*50-DRB1*03:01-DQB1*02  Ecuador Andes Mixed Ancestry 0.0607824
 66  A*11-B*50-DRB1*03:01-DQB1*02  Mexico Puebla Rural 0.0600833
 67  A*23:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India West UCBB 0.06005,829
 68  A*02:06:01-B*50:01:01-C*06:02:01-DRB1*03:01:01-DQB1*02:01:01  China Zhejiang Han 0.05771,734
 69  A*31:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01-DPB1*02:01  Russia Karelia 0.05651,075
 70  A*11:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India Tamil Nadu 0.05122,492
 71  A*23:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India Central UCBB 0.04834,204
 72  A*31:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  USA Hispanic pop 2 0.04701,999
 73  A*24:02-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India North UCBB 0.04505,849
 74  A*23:01:01-B*50:01:01-C*06:02:01-DRB1*03:01:01-DQB1*02:01:01  Poland BMR 0.044423,595
 75  A*02:01:01-B*50:01:01-C*06:02:01-DRB1*03:01:01-DQB1*02:01:01  Poland BMR 0.044323,595
 76  A*02:01-B*50:01-C*15:02-DRB1*03:01-DQB1*02:01  USA Asian pop 2 0.04401,772
 77  A*24:02-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  USA Asian pop 2 0.04401,772
 78  A*02:05-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Germany DKMS - Italy minority 0.04301,159
 79  A*02-B*50-DRB1*03:01-DQB1*02  Ecuador Mixed Ancestry 0.04261,173
 80  A*33:03-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India Central UCBB 0.04234,204
 81  A*01-B*50-DRB1*03:01-DQB1*02  Mexico Jalisco, Guadalajara city 0.04191,189
 82  A*23-B*50-DRB1*03:01-DQB1*02  Mexico Jalisco, Guadalajara city 0.04191,189
 83  A*32-B*50-DRB1*03:01-DQB1*02  Mexico Jalisco, Guadalajara city 0.04191,189
 84  A*03:02-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Germany DKMS - Turkey minority 0.03804,856
 85  A*11:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India North UCBB 0.03705,849
 86  A*32:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Colombia Bogotá Cord Blood 0.03421,463
 87  A*02:05-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 88  A*03:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 89  A*30:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 90  A*30:02-B*50:01-C*04:01-DRB1*03:01-DQB1*02:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 91  A*30:01:01-B*50:01:01-C*06:02:01:02-DRB1*03:01:01-DQB1*02:01  Russia Nizhny Novgorod, Russians 0.03311,510
 92  A*68:01:02:02-B*50:01:01-C*06:02:01:02-DRB1*03:01:01-DQB1*02:01  Russia Nizhny Novgorod, Russians 0.03311,510
 93  A*03:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India South UCBB 0.031011,446
 94  A*24:02-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India West UCBB 0.02805,829
 95  A*68:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India West UCBB 0.02625,829
 96  A*02:05-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  Germany DKMS - Turkey minority 0.02604,856
 97  A*02:08-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India North UCBB 0.02595,849
 98  A*02:06-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India North UCBB 0.02585,849
 99  A*32-B*50-DRB1*03:01-DQB1*02  Mexico Puebla, Puebla city 0.02511,994
 100  A*68:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01  India Central UCBB 0.02364,204

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 152) 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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