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 73 (from 73) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02-B*39-DRB1*09:01-DQB1*03:03  Bolivia La Paz Aymaras 3.448087
 2  A*02-B*39-DRB1*09:01:02-DQB1*03:03  Peru Lamas City Lama 3.000083
 3  A*02-B*39-DRB1*09-DQB1*03:03  Ecuador Amazonia Mixed Ancestry 1.282139
 4  A*68-B*39-DRB1*09:01-DQB1*03:03  Bolivia La Paz Aymaras 0.802087
 5  A*02-B*39-DRB1*09-DQB1*03:03  Ecuador Andes Mixed Ancestry 0.4854824
 6  A*24:02-B*39:03-DRB1*09:01-DQB1*03:03  Peru Titikaka Lake Uros 0.4800105
 7  A*24:02-B*39:04-DRB1*09:01-DQB1*03:03  Peru Titikaka Lake Uros 0.4800105
 8  A*24:02-B*39:14-DRB1*09:01-DQB1*03:03  Peru Titikaka Lake Uros 0.4800105
 9  A*02-B*39-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 0.42631,173
 10  A*03:01:01-B*39:01:01-C*07:02:01-DRB1*09:01:02-DQA1*03:02:01-DQB1*03:03:02-DPA1*02:02:02-DPB1*05:01:01  Russian Federation Vologda Region 0.4202119
 11  A*24-B*39-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 0.34101,173
 12  A*02:07-B*39:01-C*01:02-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 13  A*11:01-B*39:05-C*07:02-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 14  A*24-B*39-DRB1*09-DQB1*03:03  Ecuador Andes Mixed Ancestry 0.2427824
 15  A*24-B*39-DRB1*09-DQB1*03:03  Ecuador Coast Mixed Ancestry 0.2101238
 16  A*30-B*39-DRB1*09-DQB1*03:03  Ecuador Coast Mixed Ancestry 0.2101238
 17  A*31-B*39-DRB1*09-DQB1*03:03  Ecuador Coast Mixed Ancestry 0.2101238
 18  A*68-B*39-DRB1*09-DQB1*03:03  Ecuador Coast Mixed Ancestry 0.2101238
 19  A*31-B*39-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 0.17051,173
 20  A*11:01:01-B*39:01:01-C*07:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.13421,734
 21  A*31-B*39-DRB1*09-DQB1*03:03  Ecuador Andes Mixed Ancestry 0.1214824
 22  A*31:01-B*39:02-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.10003,078
 23  A*11:01-B*39:01-C*07:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.08901,772
 24  A*68-B*39-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 0.08531,173
 25  A*24-B*39-DRB1*09-DQB1*03:03  Mexico Jalisco, Guadalajara city 0.08381,189
 26  A*24-B*39-DRB1*09-DQB1*03:03  Mexico Puebla, Puebla city 0.07521,994
 27  A*02:03-B*39:01-C*12:04-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*04:02  Sri Lanka Colombo 0.0700714
 28  A*02:06-B*39:02-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.07003,078
 29  A*03-B*39-DRB1*09-DQB1*03:03  Ecuador Andes Mixed Ancestry 0.0607824
 30  A*68-B*39-DRB1*09-DQB1*03:03  Ecuador Andes Mixed Ancestry 0.0607824
 31  A*02:01-B*39:05-C*07:02-DRB1*09:01-DQB1*03:03  Colombia Bogotá Cord Blood 0.04831,463
 32  A*02:03-B*39:01-C*07:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.04401,772
 33  A*03-B*39-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 0.04261,173
 34  A*30-B*39-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 0.04261,173
 35  A*29:02-B*39:01-C*12:03-DRB1*09:01-DQB1*03:03  Colombia Bogotá Cord Blood 0.03421,463
 36  A*31:01-B*39:01-C*12:03-DRB1*09:01-DQB1*03:03  Colombia Bogotá Cord Blood 0.03421,463
 37  A*02:11-B*39:06-C*07:02-DRB1*09:01-DQB1*03:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 38  A*25:01:01-B*39:01:01-C*12:03:01:01-DRB1*09:01:02-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.03311,510
 39  A*02:01-B*39:01-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 40  A*02:01-B*39:01-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 41  A*02:01-B*39:02-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 42  A*02:01-B*39:04-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:01-DPB1*05:01  Japan pop 17 0.03003,078
 43  A*02:01-B*39:04-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 44  A*02:06-B*39:01-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 45  A*11:01-B*39:01-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*02:01  Japan pop 17 0.03003,078
 46  A*11:01-B*39:01-C*07:02-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*39:01-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 48  A*24:02-B*39:01-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:01-DPB1*09:01  Japan pop 17 0.03003,078
 49  A*26:01-B*39:01-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 50  A*26:02-B*39:01-C*07:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 51  A*33:03-B*39:01-C*07:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 52  A*31:01:02-B*39:01:01-C*03:04:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.02881,734
 53  A*24:02:01-B*39:01:01-C*07:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.02321,734
 54  A*11:02:01-B*39:05:01-C*07:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.02151,734
 55  A*01:01-B*39:01-C*12:04-DRB1*09:01-DQB1*03:03  India Tamil Nadu 0.02012,492
 56  A*24:02-B*39:01-C*12:04-DRB1*09:01-DQB1*03:03  India West UCBB 0.01725,829
 57  A*02:01-B*39:12-C*01:02-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.01201,999
 58  A*02:01-B*39:13-C*07:02-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.01201,999
 59  A*02:11-B*39:13-C*07:02-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.01201,999
 60  A*02:22-B*39:12-C*01:02-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.01201,999
 61  A*24:02-B*39:05-C*07:02-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.01201,999
 62  A*31:01-B*39:05-C*07:02-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.01201,999
 63  A*03:01-B*39:01-C*12:03-DRB1*09:01-DQB1*03:03  Germany DKMS - Turkey minority 0.01004,856
 64  A*24:02-B*39:01-C*12:03-DRB1*09:01-DQB1*03:03  Germany DKMS - Turkey minority 0.01004,856
 65  A*02:01-B*39:06-C*12:03-DRB1*09:01-DQB1*03:03  India West UCBB 0.00865,829
 66  A*11:03-B*39:01-C*12:04-DRB1*09:01-DQB1*03:03  India West UCBB 0.00865,829
 67  A*02:09-B*39:01-C*12:04-DRB1*09:01-DQB1*03:03  India South UCBB 0.004411,446
 68  A*24:02-B*39:01-C*12:04-DRB1*09:01-DQB1*03:03  India South UCBB 0.004411,446
 69  A*11:01:01-B*39:06:02-C*12:03:01-DRB1*09:01:02-DQB1*03:03:02  Poland BMR 0.002123,595
 70  A*24:02:01-B*39:06:02-C*12:03:01-DRB1*09:01:02-DQB1*03:03:02  Poland BMR 0.002123,595
 71  A*26:01:01-B*39:01:01-C*07:02:01-DRB1*09:01:02-DQB1*03:03:02  Poland BMR 0.002123,595
 72  A*68:01:01-B*39:06:02-C*07:02:01-DRB1*09:01:02-DQB1*03:03:02  Poland BMR 0.002123,595
 73  A*68:01:02-B*39:01:01-C*07:02:01-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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