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 48 (from 48) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*68:02-B*44:02-DRB1*07:01-DQB1*02:02  Tunisia Gabes 1.570095
 2  A*69:01-B*44:02-DRB1*07:01-DQB1*02:02  Tunisia Gabes 1.570095
 3  A*02:01-B*44:02-DRB1*07:01-DQB1*02:02  USA South Dakota Lakota Sioux 1.4000302
 4  A*29:01-B*44:02-DRB1*07:01-DQB1*02:02  Mexico Chihuahua Chihuahua City Pop 2 1.136488
 5  A*03:01-B*44:02-C*05:01-E*01:03:02-F*01:01:01-G*01:01-DRB1*07:01-DQA1*02:01-DQB1*02:02  Portugal Azores Terceira Island 0.8772130
 6  A*02:01:01-B*44:02:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 7  A*02:01-B*44:02-DRB1*07:01-DQB1*02:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 8  A*03:01:01-B*44:02:01-C*05:01:01-DRB1*07:01:01-DQA1*02:01:01-DQB1*02:02-DPA1*01:03:01-DPB1*04:02  Russian Federation Vologda Region 0.4202119
 9  A*02:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.41004,335
 10  A*26:01-B*44:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.3030330
 11  A*29:01-B*44:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.3030330
 12  A*24:02:01:01-B*44:02:01:01-C*16:04:01-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Tatars 0.2604192
 13  A*25:01-B*44:02-C*05:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  USA San Diego 0.2600496
 14  A*02:01:01-B*44:02:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:01:01-DPB1*17:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 15  A*25:01:01-B*44:02:01-C*12:03:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*01:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 16  A*68:01:02-B*44:02:01-C*07:04:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*11:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 17  A*03:01-B*44:02-C*12:03-DRB1*07:01-DQB1*02:02-DPB1*02:01  Panama 0.1900462
 18  A*01:01-B*44:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.1515330
 19  A*24:02-B*44:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.1515330
 20  A*02:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1400975
 21  A*03:01-B*44:02-C*04:01-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1400975
 22  A*02:01:01:01-B*44:02:01:01-C*05:01:01:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.10581,510
 23  A*02:01-B*44:02-C*07:04-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*17:01  Sri Lanka Colombo 0.0700714
 24  A*24:02-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 25  A*02:01:01-B*44:02:01-C*05:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.052623,595
 26  A*02:01-B*44:02-C*05:09-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 27  A*26:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 28  A*68:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 29  A*32:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  India North UCBB 0.02635,849
 30  A*01:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  India East UCBB 0.02082,403
 31  A*03:01-B*44:02-C*07:04-DRB1*07:01-DQB1*02:02  India East UCBB 0.02082,403
 32  A*11:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  India North UCBB 0.01405,849
 33  A*32:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  India West UCBB 0.01265,829
 34  A*02:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 35  A*11:01-B*44:02-C*07:04-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 36  A*32:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  India Central UCBB 0.01194,204
 37  A*26:01:01-B*44:02:01-C*05:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.011123,595
 38  A*68:01:02-B*44:02:01-C*07:04:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.010923,595
 39  A*02:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  India North UCBB 0.00925,849
 40  A*30:01:01-B*44:02:01-C*07:04:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.008523,595
 41  A*32:01:01-B*44:02:01-C*05:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.006323,595
 42  A*02:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  India South UCBB 0.006211,446
 43  A*24:02:01-B*44:02:01-C*05:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.004623,595
 44  A*31:01:02-B*44:02:01-C*07:04:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.004123,595
 45  A*02:01:01-B*44:02:01-C*07:04:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.003023,595
 46  A*25:01:01-B*44:02:01-C*07:04:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002323,595
 47  A*29:02:01-B*44:02:01-C*05:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002223,595
 48  A*01:01:01-B*44:02:01-C*01:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.001623,595

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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