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 76 (from 76) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02-B*39-DRB1*15:01-DQB1*06:02  Bolivia Quechua 1.450069
 2  A*01-B*39-DRB1*15:01-DQB1*06:02  Colombia San Basilio de Palenque 1.191042
 3  A*68:01-B*39:01-DRB1*15:01-DQB1*06:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 4  A*11:02:01-B*39:01:01-C*04:03:01-DRB1*15:01:01-DQB1*06:02:01  Vietnam Kinh 0.4950101
 5  A*24:02-B*39:01-C*07:02-DRB1*15:01-DQB1*06:02  Mexico Mexico City Mestizo population 0.3497143
 6  B*39:01-C*07:02-DRB1*15:01-DQB1*06:02  Mexico Mexico City Mestizo population 0.3497143
 7  A*25:01:01-B*39:06:02-C*07:02:01-DRB1*15:01:01-DQA1*01:02:01-DQB1*06:02-DPA1*01:03:01-DPB1*03:01  Russia Belgorod region 0.3268153
 8  A*02:01:01-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 9  A*26:01-B*39:10-C*17:01-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*11:01  South Africa Worcester 0.3000159
 10  A*24:02-B*39:06-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*04:01  USA San Diego 0.2600496
 11  A*02:06-B*39:01-C*07:02-DRB1*15:01-DQB1*06:02  USA NMDP Hawaiian or other Pacific Islander 0.255311,499
 12  A*02:06-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.20003,078
 13  A*24:02-B*39:01-C*12:03-DRB1*15:01-DQB1*06:02:01  England North West 0.2000298
 14  A*31:01-B*39:02-C*05:01-DRB1*15:01-DQB1*06:02-DPB1*04:01  Panama 0.1900462
 15  A*24:02-B*39:01-DRB1*15:01-DQB1*06:02  Mexico Mexico City Tlalpan 0.1515330
 16  A*24:02-B*39:06-C*07:02-DRB1*15:01-DQB1*06:02  Colombia Bogotá Cord Blood 0.10801,463
 17  A*02:01-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.07003,078
 18  A*02:06-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*02:01-DPB1*13:01  Japan pop 17 0.07003,078
 19  A*02:06-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.07003,078
 20  A*24:02-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.07003,078
 21  A*26:01-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.07003,078
 22  A*02:06:01-B*39:08-C*06:02:01-DRB1*15:01-DQB1*06:02  Costa Rica Central Valley Mestizo (G) 0.0628221
 23  A*30:02-B*39:06-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*02:01  Russia Karelia 0.05651,075
 24  A*02:01-B*39:06-C*12:03-DRB1*15:01-DQB1*06:02-DPB1*04:01  Russia Karelia 0.05651,075
 25  A*31:01-B*39:01-C*12:03-DRB1*15:01-DQB1*06:02  Germany DKMS - Italy minority 0.05201,159
 26  A*02:01-B*39:06-C*03:03-DRB1*15:01-DQB1*06:02  USA Hispanic pop 2 0.04701,999
 27  A*11:01-B*39:01-C*07:02-DRB1*15:01-DQB1*06:02  USA Hispanic pop 2 0.04701,999
 28  A*01:01-B*39:01-C*12:03-DRB1*15:01-DQB1*06:02  USA African American pop 4 0.04402,411
 29  A*24:02:01-B*39:01:01-C*12:03:01:01-DRB1*15:01:01-DQB1*06:02:01  Russia Nizhny Novgorod, Russians 0.03911,510
 30  A*68:24-B*39:01:01-C*12:03:01:01-DRB1*15:01:01-DQB1*06:02:01  Russia Nizhny Novgorod, Russians 0.03311,510
 31  A*02:01-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*02:02  Japan pop 17 0.03003,078
 32  A*02:01-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*02:01-DPB1*13:01  Japan pop 17 0.03003,078
 33  A*02:06-B*39:01-C*03:04-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*02:02-DPB1*02:01  Japan pop 17 0.03003,078
 34  A*11:01-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*03:01  Japan pop 17 0.03003,078
 35  A*24:02-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*02:01-DPB1*13:01  Japan pop 17 0.03003,078
 36  A*24:02-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 37  A*24:20-B*39:01-C*12:03-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 38  A*26:01-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*03:01  Japan pop 17 0.03003,078
 39  A*31:01-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 40  A*31:01-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*02:01-DPB1*09:01  Japan pop 17 0.03003,078
 41  A*31:01-B*39:01-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 42  A*02:03:01-B*39:01:01-C*07:02:01-DRB1*15:01:01-DQB1*06:02:01  China Zhejiang Han 0.02881,734
 43  A*31:01:02-B*39:01:01-C*03:04:01-DRB1*15:01:01-DQB1*06:02:01  China Zhejiang Han 0.02881,734
 44  A*01:01-B*39:01-C*12:04-DRB1*15:01-DQB1*06:02  India East UCBB 0.02082,403
 45  A*11:01-B*39:01-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*04:01  Germany DKMS - German donors 0.01943,456,066
 46  A*03:01:01-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.019223,595
 47  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.017023,595
 48  A*24:02:01-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.013823,595
 49  A*02:03-B*39:01-C*12:04-DRB1*15:01-DQB1*06:02  India South UCBB 0.013111,446
 50  A*02:06-B*39:01-C*07:02-DRB1*15:01-DQB1*06:02  USA Hispanic pop 2 0.01201,999
 51  A*25:01-B*39:01-C*07:02-DRB1*15:01-DQB1*06:02  USA Hispanic pop 2 0.01201,999
 52  A*11:01-B*39:01-C*15:02-DRB1*15:01-DQB1*06:02  India Central UCBB 0.01194,204
 53  A*02:01:01-B*39:01:01-C*07:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.011323,595
 54  A*02:03-B*39:01-C*03:03-DRB1*15:01-DQB1*06:02  USA Asian pop 2 0.01101,772
 55  A*24:02-B*39:06-C*07:02-DRB1*15:01-DQB1*06:02  USA African American pop 4 0.01102,411
 56  A*26:01-B*39:01-C*03:03-DRB1*15:01-DQB1*06:02  USA Asian pop 2 0.01101,772
 57  A*68:02-B*39:06-C*07:02-DRB1*15:01-DQB1*06:02  USA African American pop 4 0.01102,411
 58  A*02:01:01-B*39:06:02-C*07:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.010623,595
 59  A*02:01:01-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.010523,595
 60  A*02:01-B*39:01-C*07:02-DRB1*15:01-DQB1*06:02  Germany DKMS - Turkey minority 0.01004,856
 61  A*03:01-B*39:01-C*12:03-DRB1*15:01-DQB1*06:02  Germany DKMS - Turkey minority 0.01004,856
 62  A*31:01-B*39:01-C*12:03-DRB1*15:01-DQB1*06:02  Germany DKMS - Turkey minority 0.01004,856
 63  A*32:01-B*39:01-C*12:03-DRB1*15:01-DQB1*06:02  Germany DKMS - Turkey minority 0.01004,856
 64  A*03:01-B*39:01-C*15:02-DRB1*15:01-DQB1*06:02  India West UCBB 0.00865,829
 65  A*26:01:01-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.007423,595
 66  A*31:01-B*39:01-C*12:03-DRB1*15:01-DQB1*06:02  India Tamil Nadu 0.00672,492
 67  A*24:02:01-B*39:06:02-C*07:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.005223,595
 68  A*32:01:01-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.005123,595
 69  A*25:01:01-B*39:01:01-C*01:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.004223,595
 70  A*26:01:01-B*39:01:01-C*07:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.003823,595
 71  A*02:01:01-B*39:24:01-C*05:01:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.002123,595
 72  A*32:01:01-B*39:01:01-C*01:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.002123,595
 73  A*32:01:01-B*39:01:01-C*07:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.002123,595
 74  A*01:01:01-B*39:01:01-C*07:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.002123,595
 75  A*25:01:01-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.002023,595
 76  A*24:02:01-B*39:01:01-C*07:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.000123023,595

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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