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 50 (from 50) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*25:01-B*44:02-DRB1*14:54  New Zealand Maori with Admixed History 0.4762105
 2  A*25:01:01-B*44:27:01-C*07:04:01-DRB1*14:54:01-DQA1*01:02:02-DQB1*05:02-DPA1*01:03:01-DPB1*04:02  Russian Federation Vologda Region 0.4202119
 3  A*01:01-B*44:02-C*05:01-DRB1*14:54  Italy pop 5 0.1000975
 4  A*68:01-B*44:02-C*07:01-DRB1*14:54  Italy pop 5 0.1000975
 5  B*44:02-DRB1*14:54  Italy pop 5 0.1000975
 6  A*02:01-B*44:03-C*14:03-DRB1*14:54-DQA1*01:04-DQB1*05:03-DPA1*02:02-DPB1*04:01  Japan pop 17 0.07003,078
 7  A*33:03-B*44:03-C*14:03-DRB1*14:54  Japan pop 16 0.068018,604
 8  A*29-B*44-DRB1*14:54  Chile Santiago 0.0577920
 9  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.037423,595
 10  A*02:01-B*44:05-C*02:02-DRB1*14:54-DQB1*05:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 11  A*24:02-B*44:03-C*04:01-DRB1*14:54-DQB1*05:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 12  A*29:02-B*44:03-C*16:01-DRB1*14:54-DQB1*05:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 13  A*29:02-B*44:04-C*16:01-DRB1*14:54-DQB1*05:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 14  A*33:01-B*44:02-C*06:02-DRB1*14:54-DQB1*05:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 15  A*33:03-B*44:03-C*07:01-DRB1*14:54-DQB1*05:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 16  A*29:02:01:01-B*44:03:01-C*16:01:01-DRB1*14:54-DQB1*05:03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 17  A*02:01:01:01-B*44:02:01:01-C*05:01:01-DRB1*14:54-DQB1*05:03:01  Russia Nizhny Novgorod, Russians 0.03211,510
 18  A*33:03-B*44:03-C*14:03-DRB1*14:54-DQA1*01:04-DQB1*05:03-DPA1*02:02-DPB1*03:01  Japan pop 17 0.03003,078
 19  A*11:01-B*44:03-C*07:06-DRB1*14:54-DQB1*05:03  India North UCBB 0.02985,849
 20  A*02:01:01-B*44:27:01-C*07:04:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.028523,595
 21  A*26-B*44-DRB1*14:54  Chile Santiago 0.0272920
 22  A*02-B*44-DRB1*14:54  Chile Santiago 0.0243920
 23  A*24:02-B*44:03-C*07:06-DRB1*14:54-DQB1*05:03  India North UCBB 0.02145,849
 24  A*02:06-B*44:03-C*07:06-DRB1*14:54-DQB1*05:03  India East UCBB 0.02082,403
 25  A*11:01-B*44:02-C*05:01-DRB1*14:54-DQB1*05:03  India North UCBB 0.01715,849
 26  A*01:01-B*44:03-C*07:06-DRB1*14:54-DQB1*05:03  India Central UCBB 0.01194,204
 27  A*24:02-B*44:03-C*07:06-DRB1*14:54-DQB1*05:03  India Central UCBB 0.01194,204
 28  A*02:01:01-B*44:02:01-C*05:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.011823,595
 29  A*33:03-B*44:03-C*07:06-DRB1*14:54-DQB1*05:03  India West UCBB 0.01135,829
 30  A*33:03:01-B*44:03:02-C*07:06:01-DRB1*14:54:01-DPB1*05:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00955,266
 31  A*01:01-B*44:06-C*05:01-DRB1*14:54-DQB1*05:03  India North UCBB 0.00855,849
 32  A*23:01:01-B*44:03:01-C*04:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.005023,595
 33  A*02:07-B*44:03-C*14:03-DRB1*14:54  Japan pop 16 0.005018,604
 34  A*33:03-B*44:03-C*07:06-DRB1*14:54-DQB1*05:03  India South UCBB 0.004711,446
 35  A*02:11-B*44:03-C*07:06-DRB1*14:54-DQB1*05:02  India South UCBB 0.004411,446
 36  A*02:131-B*44:03-C*07:06-DRB1*14:54-DQB1*05:03  India South UCBB 0.004411,446
 37  A*33:03-B*44:03-C*07:06-DRB1*14:54-DQB1*05:02  India South UCBB 0.004411,446
 38  A*23:01:01-B*44:03:01-C*16:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.004223,595
 39  A*24:02:01-B*44:05:01-C*02:02:02-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.004223,595
 40  A*32:01:01-B*44:03:01-C*04:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.004223,595
 41  A*02:01:01-B*44:05:01-C*02:02:02-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.002823,595
 42  A*01:01:01-B*44:03:01-C*16:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.002123,595
 43  A*01:01:01-B*44:02:01-C*04:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.002123,595
 44  A*02:05:01-B*44:05:01-C*02:02:02-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.002123,595
 45  A*31:01:02-B*44:02:01-C*05:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.002123,595
 46  A*68:01:02-B*44:02:01-C*05:01:01-DRB1*14:54:01-DQB1*03:01:01  Poland BMR 0.002123,595
 47  A*24:02:01-B*44:03:01-C*04:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.002023,595
 48  A*68:01:02-B*44:02:01-C*05:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.001823,595
 49  A*02:01:01-B*44:03:01-C*16:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.001823,595
 50  A*03:01:01-B*44:05:01-C*02:02:02-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.001423,595

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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