Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

Please specify your search by selecting options from boxes. Then, click "Search" to find HLA Haplotype frequencies that match your criteria. Remember at least one option must be selected.
A B C DRB1 DPA1 DPB1 DQA1 DQB1

Population:  Country:  Source of dataset : 
Region:  Ethnic Origin:     Type of study :  Sort by: 
Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 100 (from 100) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02-B*15:01-DRB1*07-DQB1*02  Mexico Tamaulipas, Ciudad Victoria 13.043523
 2  A*25-B*15:01-DRB1*07-DQB1*02  Mexico Mexico City West 1.470633
 3  A*25-B*15:01-DRB1*07-DQB1*02  Mexico Zacatecas, Fresnillo 1.4286103
 4  A*02-B*15:01-DRB1*07-DQB1*02  Mexico Chiapas, Tuxtla Gutierrez 0.943452
 5  A*24-B*15:01-DRB1*07-DQB1*02  Mexico Sonora, Ciudad Obregón 0.6993143
 6  A*32-B*15:01-DRB1*07-DQB1*02  Mexico Sonora, Ciudad Obregón 0.6993143
 7  A*31:01-B*15:01-DRB1*07:01-DQB1*02:01  Mexico Veracruz Xalapa 0.595284
 8  A*03:01-B*15:01-DRB1*07:01-DQB1*02:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 9  A*02-B*15:01-DRB1*07-DQB1*02  Mexico Sinaloa Rural 0.5464183
 10  A*68-B*15:01-DRB1*07-DQB1*02  Mexico Sinaloa, Culiacán 0.4854103
 11  A*24-B*15:01-DRB1*07-DQB1*02  Mexico Chihuahua, Ciudad Juarez 0.4630106
 12  A*26-B*15:01-DRB1*07-DQB1*02  Mexico Chihuahua Chihuahua City 0.4202119
 13  A*02-B*15:01-DRB1*07-DQB1*02  Mexico Chihuahua Rural 0.4184236
 14  A*02-B*15:01-DRB1*07-DQB1*02  Mexico Oaxaca, Oaxaca city 0.3311151
 15  A*25-B*15:01-DRB1*07-DQB1*02  Mexico Mexico City Center 0.3247152
 16  A*02:01-B*15:01-DRB1*07:01-DQB1*02:01  Mexico Mexico City Tlalpan 0.3030330
 17  A*25-B*15:01-DRB1*07-DQB1*02  Mexico Jalisco, Zapopan 0.2976168
 18  A*30-B*15:01-DRB1*07-DQB1*02  Mexico Sinaloa Rural 0.2732183
 19  A*02:01:01:01-B*15:01:01:01-C*03:04:01:01-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Tatars 0.2604192
 20  A*23:01:01-B*15:01:01:01-C*03:03:01-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Tatars 0.2604192
 21  A*24:02-B*15:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*05:01  USA San Diego 0.2600496
 22  A*29:02:01-B*15:01:01-C*03:03:01-DRB1*07:01:01-DQB1*02:02:01  Spain, Canary Islands, Gran canaria island 0.2300215
 23  A*02-B*15:01-DRB1*07-DQB1*02  Mexico Oaxaca Rural 0.2053485
 24  A*01:01-B*15:01-C*01:02-DRB1*07:01:01-DQB1*02:01  England North West 0.2000298
 25  A*24:02-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Indian 0.1845271
 26  A*02:01-B*15:01-C*03:04-DRB1*07:01-DQB1*02:02  India Northeast UCBB 0.1689296
 27  A*02:01-B*15:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.1515330
 28  A*24:02-B*15:01-C*07:04-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1400975
 29  A*11:01-B*15:01-C*12:03-DRB1*07:01-DQB1*02:02  India North UCBB 0.13685,849
 30  A*24-B*15:01-DRB1*07-DQB1*02  Ecuador Andes Mixed Ancestry 0.1214824
 31  A*02:01:01-B*15:01:01-C*18:01-DRB1*07:01-DQB1*02:01  Costa Rica Central Valley Mestizo (G) 0.0932221
 32  A*24-B*15:01-DRB1*07-DQB1*02  Ecuador Mixed Ancestry 0.08531,173
 33  A*02:01:01:01-B*15:01:01-C*01:02:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.06621,510
 34  A*32:01-B*15:01-C*04:01-DRB1*07:01-DQB1*02:02  India Central UCBB 0.06474,204
 35  A*02-B*15:01-DRB1*07-DQB1*02  Mexico Tlaxcala Rural 0.0602830
 36  A*02-B*15:01-DRB1*07-DQB1*02  Mexico Puebla Rural 0.0600833
 37  A*11:01-B*15:01-C*12:03-DRB1*07:01-DQB1*02:02  India Central UCBB 0.05954,204
 38  A*24:02:01-B*15:01:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01  China Zhejiang Han 0.05771,734
 39  A*24:02-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  India East UCBB 0.05472,403
 40  A*24-B*15:01-DRB1*07-DQB1*02  Mexico Puebla, Puebla city 0.05011,994
 41  A*11:01-B*15:01-C*12:03-DRB1*07:01-DQB1*02:01  USA Asian pop 2 0.04401,772
 42  A*02:01:01-B*15:01:01-C*01:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.042923,595
 43  A*31-B*15:01-DRB1*07-DQB1*02  Mexico Jalisco, Guadalajara city 0.04191,189
 44  A*02:01:01-B*15:01:01-C*03:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.037423,595
 45  A*03:01-B*15:01-C*03:04-DRB1*07:01-DQB1*02:01  Colombia Bogotá Cord Blood 0.03421,463
 46  A*11:01-B*15:01-C*04:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 47  A*24:02-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 48  A*25:01-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 49  A*32:01-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 50  A*24:02:01:01-B*15:01:01-C*12:03:01:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 51  A*29:01:01:01-B*15:01:01:01-C*03:03:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 52  A*02:01-B*15:01-C*12:03-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.03104,856
 53  A*01:01:01-B*15:01:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01  China Zhejiang Han 0.02881,734
 54  A*29-B*15:01-DRB1*07-DQB1*02  Mexico Puebla, Puebla city 0.02511,994
 55  A*68-B*15:01-DRB1*07-DQB1*02  Mexico Puebla, Puebla city 0.02511,994
 56  A*01:01-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  India Central UCBB 0.02224,204
 57  A*68:01-B*15:01-C*01:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.02082,403
 58  A*03:01-B*15:01-C*12:02-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.02012,492
 59  A*02:01-B*15:01-C*12:03-DRB1*07:01-DQB1*02:02  India West UCBB 0.01725,829
 60  A*33:03-B*15:01-C*12:03-DRB1*07:01-DQB1*02:02  India West UCBB 0.01725,829
 61  A*02:01-B*15:01-C*12:03-DRB1*07:01-DQB1*02:02  India North UCBB 0.01715,849
 62  A*24:02:01-B*15:01:01-C*03:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.015523,595
 63  A*33:03-B*15:01-C*12:03-DRB1*07:01-DQB1*02:02  India North UCBB 0.01495,849
 64  A*02:01-B*15:01-C*03:04-DRB1*07:01-DQB1*02:01-DPB1*04:01  Germany DKMS - German donors 0.01463,456,066
 65  A*02:06-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  India South UCBB 0.013111,446
 66  A*02:06-B*15:01-C*01:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 67  A*01:01:01-B*15:01:01-C*03:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.010823,595
 68  A*32:01-B*15:01-C*12:03-DRB1*07:01-DQB1*02:02  India North UCBB 0.01075,849
 69  A*02:01-B*15:01-C*04:01-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.01004,856
 70  A*32:01-B*15:01-C*03:03-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.01004,856
 71  A*68:01-B*15:01-C*03:03-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.01004,856
 72  A*69:01-B*15:01-C*12:03-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.01004,856
 73  A*02:01:01-B*15:01:01-C*03:04:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.009223,595
 74  A*01:01-B*15:01-C*06:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 75  A*02:06-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 76  A*03:01-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 77  A*11:01-B*15:01-C*12:03-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 78  A*32:01-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 79  A*02:06-B*15:01-C*12:03-DRB1*07:01-DQB1*02:02  India North UCBB 0.00855,849
 80  A*01:01-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  India East UCBB 0.00772,403
 81  A*31:01:02-B*15:01:01-C*03:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.006823,595
 82  A*32:01:01-B*15:01:01-C*03:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.005523,595
 83  A*02:05-B*15:01-C*04:01-DRB1*07:01-DQB1*02:02  India Central UCBB 0.00524,204
 84  A*03:01:01-B*15:01:01-C*03:04:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.004623,595
 85  A*01:01-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  India South UCBB 0.004411,446
 86  A*02:03-B*15:01-C*06:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.004411,446
 87  A*02:11-B*15:01-C*03:03-DRB1*07:01-DQB1*02:02  India South UCBB 0.004411,446
 88  A*03:01:01-B*15:01:01-C*07:04:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.004423,595
 89  A*74:03-B*15:01-C*04:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.00402,492
 90  A*11:01:01-B*15:01:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.003123,595
 91  A*68:01:02-B*15:01:01-C*02:02:02-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002523,595
 92  A*11:01:01-B*15:01:01-C*03:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002323,595
 93  A*31:01:02-B*15:01:01-C*03:04:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002123,595
 94  A*01:01:01-B*15:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002123,595
 95  A*02:05:01-B*15:01:01-C*01:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002123,595
 96  A*29:02:01-B*15:01:01-C*05:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002123,595
 97  A*02:01:01-B*15:01:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002123,595
 98  A*03:01:01-B*15:01:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002123,595
 99  A*25:01:01-B*15:01:01-C*03:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.001923,595
 100  A*68:01:02-B*15:01:01-C*03:04:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.001223,595

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).




   

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.

support@allelefrequencies.net

Valid XHTML 1.0 Transitional