Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

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Population:  Country:  Source of dataset : 
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Displaying 1 to 59 (from 59) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*24:02-B*39:01-C*12:03-E*01:01:01-F*01:01:01-G*01:01-DRB1*13:01-DQA1*01:03-DQB1*06:03  Portugal Azores Terceira Island 0.8772130
 2  A*11:01:01-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  India Kerala Malayalam speaking 0.4210356
 3  A*32:01:01-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQA1*01:03:01-DQB1*06:03:01-DPA1*01:03:01-DPB1*04:01  Russian Federation Vologda Region 0.4202119
 4  A*32:01:01-B*39:01:01-C*12:03:01:01-DRB1*13:01:01-DQB1*06:03  Russia Bashkortostan, Bashkirs 0.4167120
 5  A*02:01-B*39:01-C*12:03-DRB1*13:01-DQA1*01:03-DQB1*06:03-DPB1*02:01  USA San Diego 0.2600496
 6  A*11:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India Tamil Nadu 0.22082,492
 7  A*68:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  USA NMDP Caribean Indian 0.181414,339
 8  A*02:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  Germany DKMS - Italy minority 0.16101,159
 9  A*02:01:01:01-B*39:01:01-C*12:03:01:01-DRB1*13:01:01-DQB1*06:03:01  Russia Nizhny Novgorod, Russians 0.15251,510
 10  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  India Kerala Malayalam speaking 0.1400356
 11  A*31:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03-DPB1*13:01  Russia Karelia 0.11291,075
 12  A*24:02-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03-DPB1*02:01  Russia Karelia 0.10801,075
 13  A*11:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India Central UCBB 0.10704,204
 14  A*11:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India North UCBB 0.09405,849
 15  A*03:01:01-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.070223,595
 16  A*24:02-B*39:01-C*12:03-DRB1*13:01-DQA1*01:03-DQB1*06:03-DPB1*10:01  Sri Lanka Colombo 0.0700714
 17  A*33:03-B*39:01-C*12:03-DRB1*13:01-DQA1*01:03-DQB1*06:03-DPB1*04:01  Sri Lanka Colombo 0.0700714
 18  A*24:02-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 19  A*03:01:01:01-B*39:01:01-C*12:03:01:01-DRB1*13:01:01-DQB1*06:03:01  Russia Nizhny Novgorod, Russians 0.06741,510
 20  A*02:01:01-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.065923,595
 21  A*32:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03-DPB1*04:01  Russia Karelia 0.05621,075
 22  A*11:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India West UCBB 0.05155,829
 23  A*11:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India South UCBB 0.051111,446
 24  A*03:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  USA Asian pop 2 0.04401,772
 25  A*68:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  USA African American pop 4 0.04402,411
 26  A*01:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India West UCBB 0.03435,829
 27  A*02:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 28  A*33:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 29  A*01:01:01:01-B*39:01:01-C*12:03:01:01-DRB1*13:01:01-DQB1*06:03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 30  A*11:01:01:01-B*39:01:01-C*12:03:01:01-DRB1*13:01:01-DQB1*06:03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 31  A*32:01:01-B*39:01:01-C*12:03:01:01-DRB1*13:01:01-DQB1*06:03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 32  A*32:01:01-B*39:01:01-C*12:03:01:01-DRB1*13:01-DQB1*06:03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 33  A*26:01:01-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.026923,595
 34  A*01:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India Central UCBB 0.02384,204
 35  A*01:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India South UCBB 0.023311,446
 36  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.020023,595
 37  A*02:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03-DPB1*02:01  Germany DKMS - German donors 0.01783,456,066
 38  A*24:02:01-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.015923,595
 39  A*31:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India West UCBB 0.01515,829
 40  A*03:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03-DPB1*02:01  Germany DKMS - German donors 0.01273,456,066
 41  A*32:01:01-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.012123,595
 42  A*68:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India Central UCBB 0.01194,204
 43  A*01:01:01-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.011123,595
 44  A*24:02-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India West UCBB 0.01065,829
 45  A*02:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03-DPB1*04:01  Germany DKMS - German donors 0.01043,456,066
 46  A*03:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India South UCBB 0.010111,446
 47  A*29:02-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  Germany DKMS - Turkey minority 0.01004,856
 48  A*32:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  Germany DKMS - Turkey minority 0.01004,856
 49  A*30:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India West UCBB 0.00865,829
 50  A*26:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India North UCBB 0.00855,849
 51  A*33:03-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India North UCBB 0.00855,849
 52  A*24:02-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India South UCBB 0.007911,446
 53  A*68:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India South UCBB 0.005311,446
 54  A*26:01-B*39:01-C*12:03-DRB1*13:01-DQB1*06:03  India South UCBB 0.004411,446
 55  A*68:01:02-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.003723,595
 56  A*11:01:01-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.002223,595
 57  A*29:02:01-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.002223,595
 58  A*02:01:01-B*39:01:24-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.002123,595
 59  A*26:08-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.000706423,595

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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