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 69 (from 69) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Tunisia 4.0000100
 2  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Colombia North Wiwa El Encanto 3.846252
 3  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 3.42004,335
 4  B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 3.1469143
 5  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Tunisia 3.0000100
 6  B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Ireland South 3.0000250
 7  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 2.7972143
 8  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Spain, Canary Islands, Gran canaria island 1.8600215
 9  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*11:01  USA San Diego 1.8230496
 10  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Italy pop 5 1.3300975
 11  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Ireland South 1.3000250
 12  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  Nicaragua Managua 1.2987339
 13  B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo pop 2 1.2800234
 14  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:01:04-DPB1*13:01:01  Brazil Rio de Janeiro Parda 1.1765170
 15  A*29:02:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Tatars 1.0417192
 16  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.9727521
 17  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02-DPB1*02:01  Panama 0.9500462
 18  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:01:01-DPB1*11:01:01  Brazil Rio de Janeiro Caucasian 0.7782521
 19  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.667623,595
 20  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.6270405
 21  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Parda 0.5882170
 22  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02-DPB1*01:01  Panama 0.5700462
 23  A*68:02-B*44:03-C*16:01-E*01:03:02-F*01:01:01-G*01:01-DRB1*07:01-DQA1*01:03-DQB1*02:02  Portugal Azores Terceira Island 0.4386130
 24  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo pop 2 0.4274234
 25  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo pop 2 0.4274234
 26  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02-DPB1*11:01  Panama 0.3800462
 27  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 0.3497143
 28  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*02:01:02  Brazil Rio de Janeiro Caucasian 0.3423521
 29  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:02:02-DPB1*01:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 30  A*29:02:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.29801,510
 31  A*03:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Italy pop 5 0.2900975
 32  A*03:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.27404,335
 33  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  USA San Diego 0.2600496
 34  A*02:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.2414521
 35  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.23904,335
 36  A*03:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Spain, Canary Islands, Gran canaria island 0.2300215
 37  A*23:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Spain, Canary Islands, Gran canaria island 0.2300215
 38  A*31:01:02-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Spain, Canary Islands, Gran canaria island 0.2300215
 39  A*03:01-B*44:03-C*16:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:02  Nicaragua Managua 0.2165339
 40  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*11:01  Nicaragua Managua 0.2165339
 41  A*03:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:01:04-DPB1*13:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 42  A*23:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:01:02-DPB1*01:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 43  A*26:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*02:01:02  Brazil Rio de Janeiro Caucasian 0.1946521
 44  A*01:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02-DPB1*04:01  Panama 0.1900462
 45  A*01:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.17104,335
 46  A*24:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1400975
 47  A*11:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 48  A*30:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 49  A*24:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 50  A*31:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 51  A*03:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.082523,595
 52  A*32:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 53  A*02:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.056223,595
 54  A*23:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 55  A*26:08-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 56  A*33:03-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 57  A*68:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 58  A*01:01:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.03311,510
 59  A*01:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.028023,595
 60  A*24:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.023423,595
 61  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 62  A*11:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.007223,595
 63  A*31:01:02-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.006623,595
 64  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  India South UCBB 0.004411,446
 65  A*32:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.004223,595
 66  A*68:01:02-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.003623,595
 67  A*25:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.003323,595
 68  A*03:01:01-B*44:03:01-C*16:01:15-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002123,595
 69  A*26:08-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  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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