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 : 
Region:  Ethnic Origin:     Type of study :  Sort by: 
Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 69 (from 69) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02-B*48-DRB1*04:03-DQB1*03:02  Peru Lamas City Lama 7.800083
 2  A*02-B*48-DRB1*04-DQB1*03:02  Mexico San Luis Potosi, San Luis Potosi city 5.000030
 3  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Coahuila, Saltillo 4.109672
 4  A*02-B*48-DRB1*04:04-DQB1*03:02  Mexico Sinaloa Capomos Mayo Yoremes 3.333360
 5  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Chiapas Rural 2.4793121
 6  A*02-B*48-DRB1*04:07-DQB1*03:02  Peru Lamas City Lama 2.400083
 7  A*02-B*48-DRB1*04:11-DQB1*03:02  Peru Lamas City Lama 2.400083
 8  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Queretaro, Queretaro city 2.222245
 9  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Baja California, La Paz 2.000075
 10  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Zacatecas, Zacatecas city 1.785784
 11  A*02-B*48-DRB1*04:07-DQB1*03:02  Mexico Sinaloa Capomos Mayo Yoremes 1.666760
 12  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Jalisco, Tlajomulco 1.666730
 13  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Durango, Durango city 1.6129153
 14  A*02:01-B*48:01-DRB1*04:03-DQB1*03:02  Peru Titikaka Lake Uros 1.5400105
 15  A*02-B*48-DRB1*04:07-DQB1*03:02  Bolivia Quechua 1.450069
 16  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Veracruz, Xalapa 1.3369187
 17  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Tlaxcala Rural 1.3253830
 18  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Guanajuato, Leon 1.282178
 19  A*02-B*48-DRB1*04-DQB1*03:02  Ecuador Amazonia Mixed Ancestry 1.282139
 20  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Mexico City East 1.250079
 21  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Queretaro Rural 1.162843
 22  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Sinaloa Rural 1.0929183
 23  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Baja Californa, Mexicali 1.0000100
 24  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Zacatecas Rural 0.9294266
 25  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Coahuila Rural 0.9174216
 26  A*02-B*48-DRB1*04:07-DQB1*03:02  Bolivia La Paz Aymaras 0.856087
 27  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Tlaxcala, Tlaxcala city 0.8287181
 28  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Colima, Colima city 0.819761
 29  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Tamaulipas Rural 0.7937125
 30  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Nayarit Rural 0.781264
 31  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Sonora Rural 0.7614197
 32  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Jalisco, Guadalajara city 0.67001,189
 33  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Michoacan, Morelia 0.6623150
 34  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Veracruz Rural 0.6470539
 35  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Guanajuato Rural 0.6135162
 36  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Puebla, Puebla city 0.60121,994
 37  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Jalisco Rural 0.5973585
 38  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Michoacan Rural 0.5731348
 39  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Puebla Rural 0.5396833
 40  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Mexico City North 0.5312751
 41  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Aguascalientes state 0.526395
 42  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Nayarit, Tepic 0.515597
 43  A*02-B*48-DRB1*04-DQB1*03:02  Ecuador Andes Mixed Ancestry 0.4854824
 44  A*02-B*48-DRB1*04-DQB1*03:02  Ecuador Mixed Ancestry 0.46891,173
 45  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Nuevo Leon Rural 0.4545439
 46  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Nuevo Leon, Monterrey city 0.4425226
 47  A*02-B*48-DRB1*04-DQB1*03:02  Ecuador Coast Mixed Ancestry 0.4202238
 48  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Chihuahua Chihuahua City 0.4202119
 49  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Coahuila, Torreon 0.3750396
 50  A*02:01-B*48:01-C*08:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 51  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Mexico City Center 0.3247152
 52  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Veracruz, Veracruz city 0.2907171
 53  A*02:06-B*48:01-C*08:01-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPB1*04:02  USA San Diego 0.2600496
 54  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
 55  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Oaxaca Rural 0.2053485
 56  A*02:06-B*48:01-C*08:01-DRB1*04:07-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.20003,078
 57  A*02:06-B*48:01-C*08:01-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.18701,999
 58  A*02:06-B*48:01-C*08:22-DRB1*04:03-DQB1*03:02  India Northeast UCBB 0.1689296
 59  A*02:01-B*48:01-DRB1*04:04-DQB1*03:02  Peru Titikaka Lake Uros 0.1600105
 60  A*02-B*48-DRB1*04-DQB1*03:02  Mexico Durango Rural 0.1529326
 61  A*02:01-B*48:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 62  A*02:01-B*48:01-DRB1*04:07-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 63  A*02:02-B*48:01-DRB1*04:03-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 64  A*02:11:01-B*48:02:01-C*04:01:01-DRB1*04:11:01-DQB1*03:02  Costa Rica Central Valley Mestizo (G) 0.1105221
 65  A*02:01-B*48:01-C*08:01-DRB1*04:07-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.07003,078
 66  A*02:01-B*48:01-C*08:01-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 67  A*02:06-B*48:01-C*12:03-DRB1*04:03-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 68  A*02:06-B*48:01-C*08:01-DRB1*04:03-DQB1*03:02  India North UCBB 0.01715,849
 69  A*02:06:01-B*48:01:01-C*08:03:01-DRB1*04:03:01-DQB1*03:02:01  Poland BMR 0.006423,595

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.

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