Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

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A B C DRB1 DPA1 DPB1 DQA1 DQB1

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Displaying 1 to 61 (from 61) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*24:02-B*15:04-DRB1*14:02-DQB1*03:01  Peru Titikaka Lake Uro 3.2000105
 2  A*24:02-B*15:04-DRB1*14:02-DQB1*03:01  Peru Titikaka Lake Uros 3.2000105
 3  A*31:01:02-B*15:04:01-C*03:03:01-DRB1*14:02:01-DQB1*04:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Barra Mansa Rio State Black 2.381073
 4  A*02:01-B*15:04-C*03:03-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Puyanawa 1.3333150
 5  A*68:01:02-B*15:04-DRB1*08:02-DQB1*04:02  Peru Titikaka Lake Uros 0.9100105
 6  A*02:01-B*15:04-C*03:03-DRB1*14:06-DQA1*05:03-DQB1*03:01  Brazil Puyanawa 0.6667150
 7  A*02:01-B*15:04-DRB1*14:02-DQB1*03:01  Peru Titikaka Lake Uros 0.6600105
 8  A*02:01:01-B*15:04:01-C*02:10:01-DRB1*14:54:01-DQB1*06:04:01-DPA1*02:02:02-DPB1*01:01:01  Brazil Rio de Janeiro Parda 0.5882170
 9  A*31:15-B*15:04:01-C*03:03:01-DRB1*04:11:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*14:01:01  Brazil Rio de Janeiro Parda 0.5882170
 10  A*68:01:02-B*15:04-DRB1*09:01-DQB1*03:03  Peru Titikaka Lake Uros 0.4800105
 11  A*02:01-B*15:04-C*03:03-DRB1*16:02  Brazil Vale do Ribeira Quilombos 0.3472144
 12  A*02:02-B*15:04-C*16:01-DRB1*14:02  Brazil Vale do Ribeira Quilombos 0.3472144
 13  A*02:01-B*15:04-C*02:02-DRB1*11:01-DQA1*01:02-DQB1*06:02  Brazil Puyanawa 0.3333150
 14  A*02:01-B*15:04-C*03:03-DRB1*14:02-DQA1*05:03-DQB1*06:02  Brazil Puyanawa 0.3333150
 15  A*02:01:01-B*15:04:01-C*03:03:01-DRB1*03:01:01-DQB1*03:01:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 16  A*02:20:01-B*15:04:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01-DPA1*01:03:01-DPB1*126:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 17  A*68:01:02-B*15:04:01-C*05:01:01-DRB1*04:01:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*126:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 18  A*31:01-B*15:04-C*05:01-DRB1*10:01-DQA1*01:01-DQB1*05:01-DPB1*11:01  Nicaragua Managua 0.2165339
 19  A*02:04-B*15:04:01-C*03:03:01-DRB1*16:02:01-DQB1*03:01:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 20  A*31:01:02-B*15:04:01-C*03:03:01-DRB1*15:03:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*416:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 21  A*31:01-B*15:04-C*07:02-DRB1*08:03-DQB1*03:01  Malaysia Peninsular Indian 0.1845271
 22  B*15:04-C*01:02  USA Hispanic pop 2 0.10001,999
 23  A*68:01-B*15:04-C*03:03-DRB1*14:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.06841,463
 24  A*02:22-B*15:04  USA Hispanic pop 2 0.05001,999
 25  A*02:01-B*15:04-C*01:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:01-DPB1*14:01  United Arab Emirates Pop 1 0.0467570
 26  A*31:01-B*15:04-C*01:02-DRB1*08:02-DQB1*04:02  Colombia Bogotá Cord Blood 0.03421,463
 27  A*31:01-B*15:04-C*03:03-DRB1*16:02-DQB1*03:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 28  A*02:01-B*15:04  USA Hispanic pop 2 0.02501,999
 29  A*74:01-B*15:04  USA Hispanic pop 2 0.02501,999
 30  B*15:04-C*03:03  USA Hispanic pop 2 0.02501,999
 31  B*15:04-C*04:01  USA Hispanic pop 2 0.02501,999
 32  A*24:07-B*15:04-C*04:01-DRB1*14:04-DQB1*05:03  India East UCBB 0.02082,403
 33  A*01:01-B*15:04-C*04:01-DRB1*14:04-DQB1*05:03  India Tamil Nadu 0.02012,492
 34  A*02:01-B*15:04-C*04:01-DRB1*08:02-DQB1*04:02  USA Hispanic pop 2 0.01201,999
 35  A*02:01-B*15:04-C*04:01-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.01201,999
 36  A*02:01-B*15:04-C*07:02-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.01201,999
 37  A*02:01-B*15:04-C*07:02-DRB1*14:02-DQB1*03:01  USA Hispanic pop 2 0.01201,999
 38  A*02:11-B*15:04-C*04:01-DRB1*08:02-DQB1*04:02  USA Hispanic pop 2 0.01201,999
 39  A*02:11-B*15:04-C*04:01-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.01201,999
 40  A*02:22-B*15:04-C*07:02-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.01201,999
 41  A*02:22-B*15:04-C*07:02-DRB1*14:02-DQB1*03:01  USA Hispanic pop 2 0.01201,999
 42  A*31:01-B*15:04-C*03:03-DRB1*14:02-DQB1*03:01  USA Hispanic pop 2 0.01201,999
 43  A*31:01-B*15:04-C*03:03-DRB1*16:02-DQB1*05:02  USA Hispanic pop 2 0.01201,999
 44  A*74:01-B*15:04-C*03:03-DRB1*14:02-DQB1*03:01  USA Hispanic pop 2 0.01201,999
 45  A*74:01-B*15:04-C*03:03-DRB1*16:02-DQB1*05:02  USA Hispanic pop 2 0.01201,999
 46  A*11:01-B*15:04-C*04:01-DRB1*10:01-DQB1*05:01  India Central UCBB 0.01194,204
 47  A*24:04-B*15:04-DRB1*08:02  Israel Argentina Jews 0.01164,307
 48  A*29:02-B*15:04-DRB1*04:05  Israel Argentina Jews 0.01164,307
 49  A*68:02-B*15:04-DRB1*14:02  Israel Argentina Jews 0.01164,307
 50  A*02:11-B*15:04-C*04:01-DRB1*08:01-DQB1*04:02  India Tamil Nadu 0.01002,492
 51  A*02:11-B*15:04-C*04:01-DRB1*08:04-DQB1*03:01  India Tamil Nadu 0.01002,492
 52  A*02:01:01-B*15:04:01-C*01:02:01-DRB1*03:01:01-DPB1*04:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00955,266
 53  A*02:11-B*15:04-C*07:02-DRB1*10:01-DQB1*03:01  India West UCBB 0.00865,829
 54  A*24:02-B*15:04-C*04:01-DRB1*07:01-DQB1*02:02  India West UCBB 0.00865,829
 55  A*31:01-B*15:04-C*04:01-DRB1*11:01-DQB1*03:01  India South UCBB 0.004411,446
 56  A*31:01-B*15:04-C*04:01-DRB1*14:04-DQB1*05:03  India South UCBB 0.004411,446
 57  A*31:01-B*15:04-C*04:01-DRB1*15:02-DQB1*06:01  India South UCBB 0.004411,446
 58  A*33:03-B*15:04-C*04:01-DRB1*07:01-DQB1*03:03  India South UCBB 0.004411,446
 59  A*74:03-B*15:04-C*04:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.00402,492
 60  A*02:01-B*15:04-C*01:02-DRB1*14:02-DQB1*03:01  Belgium 0.002031,412
 61  A*02:11-B*15:04-C*04:01  Pakistan Mixed Punjabi 0.0013389

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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