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 305) records   Pages: 1 2 3 4 of 4  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*31-B*27-DRB1*04-DQB1*03:02  Mexico San Luis Potosi, San Luis Potosi city 3.333330
 2  A*11-B*27-DRB1*04-DQB1*03:02  Mexico Nayarit Rural 1.562564
 3  A*03:01-B*27:05-DRB1*04:03-DQB1*03:02  Chile Mapuche 1.540066
 4  A*02:01:01-B*27:05:02-C*01:02:01-DRB1*04:04:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Black 1.470668
 5  A*24-B*27-DRB1*04-DQB1*03:02  Mexico Guanajuato, Leon 1.282178
 6  A*24-B*27-DRB1*04-DQB1*03:02  Mexico Campeche Rural 1.063847
 7  A*01:01-B*27:01-DRB1*04:01-DQB1*03:02  Iran Tabriz Azeris 1.030997
 8  A*24-B*27-DRB1*04-DQB1*03:02  Mexico Nayarit, Tepic 1.030997
 9  A*02-B*27-DRB1*04-DQB1*03:02  Mexico Sinaloa, Culiacán 0.9709103
 10  A*02-B*27-DRB1*04:03-DQB1*03:02  Mexico Sinaloa Capomos Mayo Yoremes 0.833360
 11  A*02:34-B*27:01-C*02:02-DRB1*04:01-DQB1*03:02  Iran Gorgan 0.780064
 12  A*11-B*27-DRB1*04-DQB1*03:02  Mexico Durango, Durango city 0.6452153
 13  A*26:01:01-B*27:05:02-C*02:02:02-DRB1*04:04:01-DQB1*03:02:01-DPA1*01:03:05-DPB1*02:01:02  Brazil Rio de Janeiro Parda 0.5882170
 14  A*02-B*27-DRB1*04-DQB1*03:02  Mexico Veracruz, Xalapa 0.5348187
 15  A*26-B*27-DRB1*04-DQB1*03:02  Mexico Nayarit, Tepic 0.515597
 16  A*31-B*27-DRB1*04-DQB1*03:02  Mexico Nayarit, Tepic 0.515597
 17  A*31-B*27-DRB1*04-DQB1*03:02  Mexico Sonora, Hermosillo 0.505199
 18  A*31:01-B*27:05-C*02:02-E*01:03:01-F*01:01:01-G*01:01-DRB1*04:04-DQA1*03:01-DQB1*03:02  Portugal Azores Terceira Island 0.4386130
 19  A*02:01:01-B*27:05:03-C*02:02:02-DRB1*04:04:01-DQA1*03:01:01-DQB1*03:02-DPA1*01:03:01-DPB1*04:01:01  Russian Federation Vologda Region 0.4202119
 20  A*32-B*27-DRB1*04-DQB1*03:02  Mexico Jalisco, Guadalajara city 0.37691,189
 21  A*02-B*27-DRB1*04-DQB1*03:02  Mexico Sonora, Ciudad Obregón 0.3497143
 22  A*11-B*27-DRB1*04-DQB1*03:02  Mexico Sonora, Ciudad Obregón 0.3497143
 23  A*02-B*27-DRB1*04-DQB1*03:02  Mexico Oaxaca, Oaxaca city 0.3311151
 24  A*11-B*27-DRB1*04-DQB1*03:02  Mexico Mexico City Metropolitan Area Rural 0.3289150
 25  A*11:01:01-B*27:02:01-C*03:04:01-DRB1*04:11:01-DQB1*03:02:01-DPA1*02:01:01-DPB1*14:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 26  A*24-B*27-DRB1*04-DQB1*03:02  Mexico Guanajuato Rural 0.3067162
 27  A*31-B*27-DRB1*04-DQB1*03:02  Mexico Guanajuato Rural 0.3067162
 28  A*31:01-B*27:05-C*02:02:02-DRB1*04:01:01-DQB1*03:02:01  England North West 0.3000298
 29  A*43:01-B*27:05-C*02:02-DRB1*04:01-DQA1*03:03-DQB1*03:02-DPB1*01:01  South Africa Worcester 0.3000159
 30  A*11:01:01-B*27:02:01-C*02:02:02-DRB1*04:04:01-DQB1*03:02:01  India Karnataka Kannada Speaking 0.2870174
 31  A*31-B*27-DRB1*04-DQB1*03:02  Mexico Sinaloa Rural 0.2732183
 32  A*02:131-B*27:05:02-C*02:02:02-DRB1*04:04:01-DQB1*03:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 33  A*33:01-B*27:05-C*02:02-DRB1*04:04-DQA1*01:01-DQB1*03:02-DPB1*04:01  USA San Diego 0.2600496
 34  A*11-B*27-DRB1*04-DQB1*03:02  Mexico Coahuila, Torreon 0.2500396
 35  A*11-B*27-DRB1*04-DQB1*03:02  Mexico Coahuila Rural 0.2294216
 36  A*11-B*27-DRB1*04-DQB1*03:02  Mexico Nuevo Leon, Monterrey city 0.2212226
 37  A*24-B*27-DRB1*04-DQB1*03:02  Ecuador Coast Mixed Ancestry 0.2101238
 38  A*01:06-B*27:05-C*02:02:02-DRB1*04:01:01-DQB1*03:02:01  England North West 0.2000298
 39  A*02:01-B*27:05-C*01:02-DRB1*04:04-DQB1*03:02:01  England North West 0.2000298
 40  A*02:01-B*27:05-C*02:02:02-DRB1*04:01:01-DQB1*03:02:01  England North West 0.2000298
 41  A*11:01-B*27:05-C*02:02:02-DRB1*04:04-DQB1*03:02:01  England North West 0.2000298
 42  A*02:01:01-B*27:05:02-C*06:02:01-DRB1*04:01:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*02:01:02  Brazil Rio de Janeiro Caucasian 0.1946521
 43  A*11-B*27-DRB1*04-DQB1*03:02  Mexico Zacatecas Rural 0.1859266
 44  A*24-B*27-DRB1*04-DQB1*03:02  Mexico Jalisco Rural 0.1706585
 45  A*02:01:01-B*27:05:03-C*02:02:02-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.158823,595
 46  A*68-B*27-DRB1*04:03-DQA1*03:01-DQB1*03:02  Brazil Paraná Caucasian 0.1560641
 47  A*01:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  India West UCBB 0.15375,829
 48  A*24:02-B*27-DRB1*04:07-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 49  A*01:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  India East UCBB 0.14922,403
 50  A*11-B*27-DRB1*04-DQB1*03:02  Mexico Michoacan Rural 0.1433348
 51  A*31-B*27-DRB1*04-DQB1*03:02  Mexico Michoacan Rural 0.1433348
 52  A*24:02:01-B*27:07:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.1400356
 53  A*02-B*27-DRB1*04-DQB1*03:02  Mexico Mexico City North 0.1328751
 54  A*01:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.12745,849
 55  A*26-B*27-DRB1*04-DQB1*03:02  Mexico Jalisco, Guadalajara city 0.12561,189
 56  A*24-B*27-DRB1*04-DQB1*03:02  Mexico Coahuila, Torreon 0.1250396
 57  A*02:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.12291,075
 58  A*32-B*27-DRB1*04-DQB1*03:02  Mexico Puebla Rural 0.1199833
 59  A*03:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.10124,204
 60  A*02:01:01-B*27:05:02-C*02:02:02-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.09931,510
 61  A*11:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.09401,999
 62  A*31-B*27-DRB1*04-DQB1*03:02  Mexico Jalisco Rural 0.0853585
 63  A*32-B*27-DRB1*04-DQB1*03:02  Mexico Jalisco Rural 0.0853585
 64  A*26:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.07535,849
 65  A*26:01-B*27:05-C*07:02-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*02:01  Sri Lanka Colombo 0.0700714
 66  A*11:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 67  A*29:02-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 68  A*11-B*27-DRB1*04-DQB1*03:02  Mexico Mexico City North 0.0664751
 69  A*02:01:01:01-B*27:05:02-C*01:02:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.06621,510
 70  A*02:01:01:01-B*27:05:03-C*02:02:02-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.06621,510
 71  A*02:01-B*27:05-C*02:02-DRB1*04:01-DQB1*03:02-DPB1*04:01  Russia Karelia 0.06191,075
 72  A*03:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.06155,849
 73  A*68:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  India East UCBB 0.06142,403
 74  A*26:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.06134,204
 75  A*24:02-B*27:07-C*15:02-DRB1*04:04-DQB1*03:02  India South UCBB 0.061111,446
 76  A*02-B*27-DRB1*04-DQB1*03:02  Ecuador Andes Mixed Ancestry 0.0607824
 77  A*24:02-B*27:07-C*15:02-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.06022,492
 78  A*11:01:01-B*27:05:02-C*02:02:02-DRB1*04:04:01-DQB1*03:02:01  China Zhejiang Han 0.05771,734
 79  A*32:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02-DPB1*02:01  Russia Karelia 0.05651,075
 80  A*26:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02-DPB1*05:01  Russia Karelia 0.05651,075
 81  A*31:01-B*27:05-C*02:02-DRB1*04:01-DQB1*03:02-DPB1*19:01  Russia Karelia 0.05651,075
 82  A*01:01-B*27:02-C*02:02-DRB1*04:01-DQB1*03:02-DPB1*03:01  Russia Karelia 0.05651,075
 83  A*02:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.05604,856
 84  A*01:01-B*27:06-C*12:02-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Malay 0.0526951
 85  A*24:02-B*27:03-C*08:01-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Malay 0.0526951
 86  A*33:01-B*27:06-C*04:01-DRB1*04:04-DQB1*03:02  Malaysia Peninsular Malay 0.0526951
 87  A*26:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  India West UCBB 0.05185,829
 88  A*01:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.05134,204
 89  A*68:01-B*27:05-C*02:02-DRB1*04:03-DQB1*03:02  India North UCBB 0.05075,849
 90  A*24:02-B*27:02-C*02:02-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.05032,492
 91  A*24-B*27-DRB1*04-DQB1*03:02  Mexico Puebla, Puebla city 0.05011,994
 92  A*24:02-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.04904,856
 93  A*68:01-B*27:05-C*02:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.04875,849
 94  A*02:06-B*27:05-C*02:02-DRB1*04:03-DQB1*03:02  India Central UCBB 0.04764,204
 95  A*01:01-B*27:05-C*02:02-DRB1*04:01-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 96  A*02:01-B*27:05-C*01:02-DRB1*04:01-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 97  A*31:01-B*27:05-C*01:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 98  A*68:01-B*27:05-C*02:02-DRB1*04:03-DQB1*03:02  India Central UCBB 0.04524,204
 99  A*31:01-B*27:05-C*02:02-DRB1*04:01-DQB1*03:02-DPB1*04:02  Germany DKMS - German donors 0.04463,456,066
 100  A*02:01-B*27:05-C*02:02-DRB1*04:01-DQB1*03:02  USA Asian pop 2 0.04401,772

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 305) records   Pages: 1 2 3 4 of 4  


   

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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