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 84 (from 84) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02:01-B*40:02-C*02:02-DRB1*13:01-DQA1*01:03-DQB1*06:03  Brazil Puyanawa 2.0000150
 2  A*11-B*40:02-DRB1*13-DQB1*06  Mexico Tamaulipas Rural 1.1905125
 3  A*24-B*40:02-DRB1*13-DQB1*06  Mexico Tamaulipas Rural 1.1905125
 4  A*31:01-B*40:02-C*03:04-DRB1*13:02-DQA1*01:02-DQB1*06:04  Mexico Tixcacaltuyub Maya 0.746367
 5  A*31:01-B*40:02-C*04:01-DRB1*13:02-DQA1*01:02-DQB1*06:04  Mexico Tixcacaltuyub Maya 0.746367
 6  A*02-B*40:02-DRB1*13-DQB1*06  Mexico San Luis Potosi Rural 0.574787
 7  A*02-B*40:02-DRB1*13-DQB1*06  Mexico Zacatecas, Fresnillo 0.4762103
 8  A*32-B*40:02-DRB1*13-DQB1*06  Mexico Chihuahua Chihuahua City 0.4202119
 9  A*02:01-B*40:02-C*02:02-DRB1*13:02-DQA1*01:02-DQB1*06:04  Kosovo 0.4030124
 10  A*68-B*40:02-DRB1*13-DQB1*06  Mexico Sonora, Ciudad Obregón 0.3497143
 11  A*02-B*40:02-DRB1*13-DQB1*06  Mexico Michoacan, Morelia 0.3311150
 12  A*31-B*40:02-DRB1*13-DQB1*06  Mexico Mexico City Center 0.3247152
 13  A*24-B*40:02-DRB1*13-DQB1*06  Mexico Guanajuato Rural 0.3067162
 14  A*31:01:02-B*40:02:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 15  A*26-B*40:02-DRB1*13-DQB1*06  Mexico Veracruz, Xalapa 0.2674187
 16  A*11:01-B*40:02-C*02:02-DRB1*13:01-DQA1*01:03-DQB1*06:03-DPB1*13:01  USA San Diego 0.2600496
 17  A*02-B*40:02-DRB1*13-DQB1*06  Mexico Puebla Rural 0.2398833
 18  A*32:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  Colombia Bogotá Cord Blood 0.23921,463
 19  A*03-B*40:02-DRB1*13-DQB1*06  Mexico Nuevo Leon Rural 0.2273439
 20  A*32:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  USA NMDP Caribean Indian 0.222214,339
 21  A*03-B*40:02-DRB1*13-DQB1*06  Mexico Nuevo Leon, Monterrey city 0.2212226
 22  A*32-B*40:02-DRB1*13-DQB1*06  Mexico Chihuahua Rural 0.2092236
 23  A*11:01-B*40:02-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01  England North West 0.2000298
 24  A*32:01:01-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 25  A*68-B*40:02-DRB1*13-DQB1*06  Mexico Zacatecas Rural 0.1859266
 26  A*03:01-B*40:02-DRB1*13:01-DQB1*06:01  Mexico Mexico City Tlalpan 0.1515330
 27  A*24:02-B*40:02-DRB1*13:01-DQB1*06:03  Mexico Mexico City Tlalpan 0.1515330
 28  A*68-B*40:02-DRB1*13-DQB1*06  Mexico Michoacan Rural 0.1433348
 29  A*02:06-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  Italy pop 5 0.1400975
 30  A*32:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 31  A*32:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  Germany DKMS - Italy minority 0.08601,159
 32  A*01-B*40:02-DRB1*13-DQB1*06  Mexico Jalisco Rural 0.0853585
 33  A*68-B*40:02-DRB1*13-DQB1*06  Mexico Jalisco, Guadalajara city 0.08381,189
 34  A*11-B*40:02-DRB1*13-DQB1*06  Ecuador Andes Mixed Ancestry 0.0607824
 35  A*32:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03-DPB1*04:02  Russia Karelia 0.05651,075
 36  A*24:02-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03-DPB1*03:01  Russia Karelia 0.05561,075
 37  A*24:02-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03-DPB1*04:02  Russia Karelia 0.05561,075
 38  A*32-B*40:02-DRB1*13-DQB1*06  Mexico Puebla, Puebla city 0.05011,994
 39  A*01:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  Germany DKMS - Italy minority 0.04301,159
 40  A*26:01-B*40:02-C*02:02-DRB1*13:02-DQB1*06:04  Germany DKMS - Italy minority 0.04301,159
 41  A*33:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  Germany DKMS - Italy minority 0.04301,159
 42  A*11-B*40:02-DRB1*13-DQB1*06  Ecuador Mixed Ancestry 0.04261,173
 43  A*02:01:01-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.040723,595
 44  A*32:01:01-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.035223,595
 45  A*02:22-B*40:02-C*03:05-DRB1*13:01-DQB1*06:02  Colombia Bogotá Cord Blood 0.03421,463
 46  A*11:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  Colombia Bogotá Cord Blood 0.03421,463
 47  A*24:02-B*40:02-C*04:01-DRB1*13:02-DQB1*06:04  Colombia Bogotá Cord Blood 0.03421,463
 48  A*29:01-B*40:02-C*02:02-DRB1*13:02-DQB1*06:04  Colombia Bogotá Cord Blood 0.03421,463
 49  A*02:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 50  A*03:01-B*40:02-C*02:02-DRB1*13:02-DQB1*06:04  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 51  A*30:01:01-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 52  A*32:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03-DPB1*04:01  Germany DKMS - German donors 0.03153,456,066
 53  A*02-B*40:02-DRB1*13-DQB1*06  Mexico Puebla, Puebla city 0.02511,994
 54  A*68-B*40:02-DRB1*13-DQB1*06  Mexico Puebla, Puebla city 0.02511,994
 55  A*03:01:01-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.022123,595
 56  A*32:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03-DPB1*02:01  Germany DKMS - German donors 0.02083,456,066
 57  A*68:01-B*40:02-C*15:02-DRB1*13:02-DQB1*06:04  India Tamil Nadu 0.02012,492
 58  A*11:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03-DPB1*04:02  Germany DKMS - German donors 0.01763,456,066
 59  A*32:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  India North UCBB 0.01715,849
 60  A*02:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03-DPB1*02:01  Germany DKMS - German donors 0.01673,456,066
 61  A*32:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03-DPB1*04:02  Germany DKMS - German donors 0.01573,456,066
 62  A*02:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03-DPB1*04:01  Germany DKMS - German donors 0.01373,456,066
 63  A*24:02-B*40:02-C*14:02-DRB1*13:02-DQB1*06:09  India Central UCBB 0.01194,204
 64  A*24:02:01-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.011323,595
 65  A*02:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  Germany DKMS - Turkey minority 0.01004,856
 66  A*24:02-B*40:02-C*03:04-DRB1*13:01-DQB1*06:03  Germany DKMS - Turkey minority 0.01004,856
 67  A*32:01-B*40:02-C*02:02-DRB1*13:01-DQB1*06:03  Germany DKMS - Turkey minority 0.01004,856
 68  A*26:01-B*40:02-C*15:02-DRB1*13:01-DQB1*06:03  India North UCBB 0.00895,849
 69  A*02:11-B*40:02-C*03:03-DRB1*13:01-DQB1*06:03  India West UCBB 0.00865,829
 70  A*02:20-B*40:02-C*15:02-DRB1*13:01-DQB1*06:03  India North UCBB 0.00855,849
 71  A*01:01:01-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.006623,595
 72  A*11:01:01-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.003323,595
 73  A*02:01:01-B*40:02:01-C*02:02:02-DRB1*13:02:01-DQB1*06:04:01  Poland BMR 0.002223,595
 74  A*02:01:01-B*40:02:01-C*01:02:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.002223,595
 75  A*68:01:02-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.002223,595
 76  A*03:01:01-B*40:02:01-C*01:02:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.002223,595
 77  A*03:01:01-B*40:02:01-C*02:02:02-DRB1*13:02:01-DQB1*06:09:01  Poland BMR 0.002123,595
 78  A*11:01:01-B*40:02:01-C*01:02:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.002123,595
 79  A*11:01:01-B*40:02:01-C*07:04:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.002123,595
 80  A*32:01:01-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:02  Poland BMR 0.002123,595
 81  A*32:01:01-B*40:02:01-C*03:04:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.002123,595
 82  A*68:01:01-B*40:02:01-C*02:02:02-DRB1*13:02:01-DQB1*06:04:01  Poland BMR 0.002123,595
 83  A*32:01:01-B*40:02:01-C*01:02:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.001423,595
 84  A*11:01:79-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.001023,595

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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