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 90 (from 90) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02-B*48-DRB1*09:01-DQB1*03:03  Bolivia Quechua 2.100069
 2  A*02-B*48-DRB1*09:01-DQB1*03:03  Bolivia La Paz Aymaras 1.688087
 3  A*02-B*48-DRB1*09-DQB1*03:03  Ecuador Andes Mixed Ancestry 1.3956824
 4  A*02-B*48-DRB1*09-DQB1*03:03  Mexico Coahuila Rural 1.3761216
 5  A*32-B*48-DRB1*09-DQA1*03-DQB1*03:03  Russia, South Ural, Chelyabinsk region, Nagaybaks 1.3400112
 6  A*02-B*48-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 1.06561,173
 7  A*31:01-B*48:01-C*08:01-DRB1*09:01-DQA1*03:02-DQB1*03:03  Brazil Puyanawa 1.0000150
 8  A*02-B*48-DRB1*09-DQB1*03:03  Mexico Durango, Durango city 0.6452153
 9  A*02-B*48-DRB1*09-DQA1*03-DQB1*03:03  Russia, South Ural, Chelyabinsk region, Nagaybaks 0.4400112
 10  A*02-B*48-DRB1*09-DQB1*03:03  Ecuador Coast Mixed Ancestry 0.4202238
 11  A*02-B*48-DRB1*09-DQB1*03:03  Mexico Chihuahua Rural 0.4184236
 12  A*02:01:01-B*48:01:01-C*08:03:01-DRB1*09:01-DQB1*03:03:02  Russia Bashkortostan, Bashkirs 0.4167120
 13  A*02:01-B*48:01-DRB1*09:01-DQB1*03:03  Peru Titikaka Lake Uros 0.4000105
 14  A*31:01-B*48:01-C*08:03-DRB1*09:01-DQA1*03:02-DQB1*03:03  Brazil Puyanawa 0.3333150
 15  A*24-B*48-DRB1*09-DQB1*03:03  Ecuador Andes Mixed Ancestry 0.3034824
 16  A*01:01:01:01-B*48:01:01-C*08:03:01-DRB1*09:01:02-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 17  A*26:01:01-B*48:01:01-C*07:01:01-DRB1*09:01-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 18  A*24-B*48-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 0.21311,173
 19  A*24:02-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  USA NMDP Hawaiian or other Pacific Islander 0.209511,499
 20  A*68:01-B*48:01-C*08:03:01-DRB1*09:01:02-DQB1*03:03:02  England North West 0.2000298
 21  A*68-B*48-DRB1*09-DQB1*03:03  Mexico Zacatecas Rural 0.1859266
 22  A*02-B*48-DRB1*09-DQB1*03:03  Mexico Durango Rural 0.1529326
 23  A*11:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.13301,772
 24  A*24:02:01-B*48:01:01-C*08:01:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.11531,734
 25  A*02:06-B*48:01-C*08:03-DRB1*09:01-DQB1*03:03-DPB1*02:01  Russia Karelia 0.11291,075
 26  A*24:02-B*48:01-C*08:03-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.08901,772
 27  A*34-B*48-DRB1*09:01-DQA1*03:02-DQB1*03:03  Brazil Paraná Caucasian 0.0780641
 28  A*68-B*48-DRB1*09:01-DQA1*01:02-DQB1*03:03  Brazil Paraná Caucasian 0.0780641
 29  A*24:02-B*48:01-C*08:01-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.07003,078
 30  A*31:01-B*48:01-C*04:01-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.07003,078
 31  A*01:01:01:01-B*48:01:01-C*08:03:01-DRB1*09:01:02-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.06621,510
 32  A*02:01:01:01-B*48:01:01-C*08:01:01-DRB1*09:01:02-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.06621,510
 33  A*02:01:01-B*48:01:01-C*08:03:01-DRB1*09:01:02-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.06621,510
 34  A*02:06:01-B*48:01:01-C*08:22:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.05771,734
 35  A*24:02-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India North UCBB 0.04925,849
 36  A*24:02-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.04401,772
 37  A*31:01-B*48:01-C*04:01-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.04401,772
 38  A*24:02-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India East UCBB 0.04162,403
 39  A*24:02-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India Central UCBB 0.03574,204
 40  A*02:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 41  A*24:02:01-B*48:01:01-C*08:03:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.03351,734
 42  A*02:01:01:01-B*48:01:01-C*03:03:01-DRB1*09:01:02-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.03311,510
 43  A*03:01:01:01-B*48:01:01-C*08:03:01-DRB1*09:01:02-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.03311,510
 44  A*11:01-B*48:01-C*08:01-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 45  A*11:01-B*48:01-C*08:01-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:01-DPB1*05:01  Japan pop 17 0.03003,078
 46  A*11:01-B*48:01-C*08:01-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 47  A*24:02-B*48:01-C*03:03-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 48  A*24:02-B*48:01-C*08:03-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*05:01  Japan pop 17 0.03003,078
 49  A*24:02-B*48:01-C*08:03-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 50  A*26:01-B*48:01-C*08:01-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 51  A*26:02-B*48:01-C*08:01-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 52  A*26:03-B*48:01-C*03:03-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 53  A*31:01-B*48:01-C*03:04-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 54  A*31:01-B*48:01-C*08:01-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 55  A*31:01-B*48:01-C*08:01-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 56  A*02:06:01-B*48:01:01-C*08:01:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.02881,734
 57  A*03:01:01-B*48:01:01-C*03:03:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.02881,734
 58  A*31:01:02-B*48:01:01-C*04:01:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.02881,734
 59  A*02:07:01-B*48:01:01-C*08:03:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.02411,734
 60  A*24:02-B*48:01-C*08:03-DRB1*09:01-DQB1*03:03  Germany DKMS - Turkey minority 0.02104,856
 61  A*01:01-B*48:01-C*15:04-DRB1*09:01-DQB1*03:03  India East UCBB 0.02082,403
 62  A*11:01-B*48:01-C*08:03-DRB1*09:01-DQB1*03:03  India Tamil Nadu 0.02012,492
 63  A*32:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India North UCBB 0.01785,849
 64  A*11:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India West UCBB 0.01725,829
 65  A*24:02-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India West UCBB 0.01725,829
 66  A*32:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India West UCBB 0.01725,829
 67  A*01:01-B*48:01-C*08:22-DRB1*09:01-DQB1*03:03  India Central UCBB 0.01194,204
 68  A*02:06-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India Central UCBB 0.01194,204
 69  A*02:11-B*48:01-C*08:03-DRB1*09:01-DQB1*03:03  India Central UCBB 0.01194,204
 70  A*32:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India Central UCBB 0.01194,204
 71  A*02:01-B*48:01-C*03:04-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.01101,772
 72  A*03:01-B*48:01-C*04:01-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.01101,772
 73  A*11:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India North UCBB 0.01105,849
 74  A*24:02-B*48:01-C*03:04-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.01101,772
 75  A*26:02-B*48:01-C*04:01-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.01101,772
 76  A*01:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India North UCBB 0.00855,849
 77  A*24:02-B*48:01-C*08:03-DRB1*09:01-DQB1*03:03  India North UCBB 0.00855,849
 78  A*02:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India North UCBB 0.00745,849
 79  A*31:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India South UCBB 0.005311,446
 80  A*02:01-B*48:01-C*07:02-DRB1*09:01-DQB1*03:03  India South UCBB 0.004411,446
 81  A*02:06-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India South UCBB 0.004411,446
 82  A*03:01-B*48:01-C*08:03-DRB1*09:01-DQB1*03:03  India South UCBB 0.004411,446
 83  A*24:02-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India South UCBB 0.004411,446
 84  A*30:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India South UCBB 0.004411,446
 85  A*69:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India South UCBB 0.004411,446
 86  A*02:01-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India South UCBB 0.004311,446
 87  A*02:11-B*48:01-C*08:01-DRB1*09:01-DQB1*03:03  India South UCBB 0.003511,446
 88  A*02:01:01-B*48:01:01-C*08:03:01-DRB1*09:01:02-DQB1*03:03:02  Poland BMR 0.002123,595
 89  A*02:06:01-B*48:01:01-C*08:03:01-DRB1*09:01:02-DQB1*03:03:02  Poland BMR 0.002123,595
 90  A*03:01:01-B*48:01:01-C*08:03:01-DRB1*09:01:02-DQB1*03:03:02  Poland BMR 0.002123,595

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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