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 84 (from 84) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*26-B*38-DRB1*07-DQB1*02  Mexico Coahuila, Saltillo 1.369972
 2  A*26:01-B*38:01-DRB1*07:01-DQB1*02:02  Mexico Veracruz Xalapa 0.595284
 3  A*23:01-B*38:01-DRB1*07:01-DQB1*02:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 4  A*33-B*38-DRB1*07-DQA1*02-DQB1*02:02  Russia, South Ural, Chelyabinsk region, Nagaybaks 0.4400112
 5  B*38:01-C*12:03-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo pop 2 0.4300234
 6  A*03:01:01-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQA1*02:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Russian Federation Vologda Region 0.4202119
 7  A*02-B*38-DRB1*07-DQB1*02  Mexico Chihuahua Rural 0.4184236
 8  A*24:02:01-B*38:01:01-C*12:03:01:01-DRB1*07:01-DQB1*02  Russia Bashkortostan, Bashkirs 0.4167120
 9  A*26:01-B*38:01-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02  Kosovo 0.4030124
 10  A*24-B*38-DRB1*07-DQB1*02  Mexico Sonora, Ciudad Obregón 0.3497143
 11  A*02-B*38-C*12:03-DRB1*07:01-DQB1*02  Russia Transbaikal Territory Buryats 0.3340150
 12  A*24-B*38-C*12:03-DRB1*07:01-DQB1*02  Russia Transbaikal Territory Buryats 0.3340150
 13  A*30-B*38-C*12:03-DRB1*07:01-DQB1*02  Russia Transbaikal Territory Buryats 0.3340150
 14  A*01:01:01-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQA1*01:03:01-DQB1*02:02-DPA1*02:02:02-DPB1*104:01:01  Russia Belgorod region 0.3268153
 15  A*26-B*38-DRB1*07-DQB1*02  Mexico Mexico City Center 0.3247152
 16  A*01:01:01-B*38:01:01-C*12:03:06-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 17  A*01-B*38-DRB1*07-DQB1*02  Mexico Jalisco, Zapopan 0.2976168
 18  A*03:01-B*38:01-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Chinese 0.2577194
 19  A*24:02-B*38:01-C*12:03-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Chinese 0.2577194
 20  A*26-B*38-DRB1*07-DQB1*02  Mexico Chihuahua Rural 0.2092236
 21  A*02:01-B*38:01-C*04:01-DRB1*07:08-DQB1*02:02-DPB1*14:01  Panama 0.1900462
 22  A*03:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:02-DPB1*09:01  Panama 0.1900462
 23  A*01-B*38-DRB1*07-DQB1*02  Mexico Jalisco Rural 0.1706585
 24  A*02-B*38-DRB1*07-DQB1*02  Mexico Tlaxcala Rural 0.1205830
 25  A*02:03-B*38:02-C*07:02-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.11942,492
 26  A*01:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:01-DPB1*04:01  Russia Karelia 0.11291,075
 27  A*68-B*38-DRB1*07-DQB1*02  Mexico Oaxaca Rural 0.1027485
 28  A*03:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:01  USA Asian pop 2 0.08901,772
 29  A*11:01-B*38:02-C*07:02-DRB1*07:01-DQB1*02:01  USA Asian pop 2 0.08901,772
 30  A*24:02-B*38:01-C*12:03-DRB1*07:01-DQB1*02:01  Germany DKMS - Italy minority 0.08601,159
 31  A*03-B*38-DRB1*07-DQB1*02  Mexico Jalisco Rural 0.0853585
 32  A*24-B*38-DRB1*07-DQB1*02  Mexico Jalisco Rural 0.0853585
 33  A*29-B*38-DRB1*07-DQB1*02  Mexico Jalisco Rural 0.0853585
 34  A*11:01-B*38:02-C*07:02-DRB1*07:01-DQB1*02:02  India East UCBB 0.08322,403
 35  A*66:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.08104,856
 36  A*26-B*38-C*12-DRB1*07-DQA1*02-DQB1*02  Spain, Castilla y Leon, Northwest, 0.07971,743
 37  A*01-B*38-DRB1*07:01-DQA1*02:01-DQB1*02:02  Brazil Paraná Caucasian 0.0780641
 38  A*26:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:01  Colombia Bogotá Cord Blood 0.06931,463
 39  A*26-B*38-DRB1*07-DQB1*02  Mexico Mexico City North 0.0664751
 40  A*26-B*38-DRB1*07-DQB1*02  Ecuador Andes Mixed Ancestry 0.0607824
 41  A*11:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:01  USA Hispanic pop 2 0.04701,999
 42  A*26:01:01-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.044223,595
 43  A*24:02-B*38:01-C*12:03-DRB1*07:01-DQB1*02:01  USA Asian pop 2 0.04401,772
 44  A*26-B*38-DRB1*07-DQB1*02  Ecuador Mixed Ancestry 0.04261,173
 45  A*26-B*38-DRB1*07-DQB1*02  Mexico Jalisco, Guadalajara city 0.04191,189
 46  A*29-B*38-DRB1*07-DQB1*02  Mexico Jalisco, Guadalajara city 0.04191,189
 47  A*31-B*38-DRB1*07-DQB1*02  Mexico Jalisco, Guadalajara city 0.04191,189
 48  A*68-B*38-DRB1*07-DQB1*02  Mexico Jalisco, Guadalajara city 0.04191,189
 49  A*02:01:01-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.041723,595
 50  A*02:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 51  A*03:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 52  A*29:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 53  A*32:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 54  A*02-B*38-C*12-DRB1*07-DQA1*02-DQB1*02  Spain, Castilla y Leon, Northwest, 0.03351,743
 55  A*26:01:01-B*38:01:01-C*12:03:01:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 56  A*34:01:01-B*38:02:01-C*07:02:01-DRB1*07:01:01-DQB1*02:02:01  China Zhejiang Han 0.02881,734
 57  A*01-B*38-DRB1*07-DQB1*02  Mexico Puebla, Puebla city 0.02511,994
 58  A*03-B*38-DRB1*07-DQB1*02  Mexico Puebla, Puebla city 0.02511,994
 59  A*26:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.02404,856
 60  A*26:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:02  India Central UCBB 0.02384,204
 61  A*68:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.02104,856
 62  A*02:06-B*38:02-C*07:02-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.02012,492
 63  A*24:02-B*38:02-C*07:02-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.02012,492
 64  A*03:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.02004,856
 65  A*26:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:02  India West UCBB 0.01725,829
 66  A*02:03-B*38:02-C*07:02-DRB1*07:01-DQB1*02:02  India West UCBB 0.01675,829
 67  A*02:05:01-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.014823,595
 68  A*01:01:01-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.012523,595
 69  A*02:03-B*38:02-C*07:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01204,204
 70  A*74:02-B*38:02-C*07:02-DRB1*07:03-DQB1*02:02  India Central UCBB 0.01194,204
 71  A*24:02-B*38:02-C*07:02-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01024,204
 72  A*68:01:01-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.010123,595
 73  A*24-B*38-C*12-DRB1*07-DQB1*02-DPB1*04  Norway ethnic Norwegians 0.01004,510
 74  A*11:01-B*38:02-C*07:02-DRB1*07:03-DQB1*02:02  India West UCBB 0.00865,829
 75  A*01:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:02  India North UCBB 0.00855,849
 76  A*02:01-B*38:02-C*07:02-DRB1*07:01-DQB1*02:02  India North UCBB 0.00855,849
 77  A*11:01-B*38:02-C*07:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.004411,446
 78  A*26:01-B*38:01-C*12:03-DRB1*07:01-DQB1*02:02  India South UCBB 0.004411,446
 79  A*31:12-B*38:02-C*07:02-DRB1*07:01-DQB1*02:02  India South UCBB 0.004411,446
 80  A*24:02:01-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.004323,595
 81  A*68:01:02-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002223,595
 82  A*25:01:01-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002223,595
 83  A*29:01:01-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002123,595
 84  A*32:01:01-B*38:01:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.000419423,595

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