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 70 (from 70) 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*29:01:01-B*44:08-C*16:01:01-DRB1*07:01:01-DQB1*02:02  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 20  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
 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 Barra Mansa Rio State Caucasian 0.6270405
 22  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
 23  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02-DPB1*01:01  Panama 0.5700462
 24  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
 25  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo pop 2 0.4274234
 26  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo pop 2 0.4274234
 27  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02-DPB1*11:01  Panama 0.3800462
 28  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 0.3497143
 29  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
 30  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
 31  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
 32  A*03:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Italy pop 5 0.2900975
 33  A*03:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.27404,335
 34  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
 35  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
 36  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.23904,335
 37  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
 38  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
 39  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
 40  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
 41  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
 42  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
 43  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
 44  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
 45  A*01:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02-DPB1*04:01  Panama 0.1900462
 46  A*01:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.17104,335
 47  A*24:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1400975
 48  A*11:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 49  A*30:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 50  A*24:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 51  A*31:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 52  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
 53  A*32:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 54  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
 55  A*23:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 56  A*26:08-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 57  A*33:03-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 58  A*68:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 59  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
 60  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
 61  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
 62  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 63  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
 64  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
 65  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  India South UCBB 0.004411,446
 66  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
 67  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
 68  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
 69  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
 70  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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