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 83 (from 83) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  DRB1*09:01:02-DQA1*03:01/03:02/03:03-DQB1*03:03:02  Russia Siberia Dudinka Nganasan 25.000024
 2  DRB1*09:01:02-DQA1*03:01-DQB1*03:03  China Urumqi Han 11.900059
 3  DRB1*09-DQA1*03:01-DQB1*03:03  Russia Mari 11.4000202
 4  DRB1*09:01:02-DQA1*03:01-DQB1*03:03  Japan pop 2 10.8000916
 5  DRB1*09:01-DQA1*03:01-DQB1*03:03:02  Canada British Columbia Athabaskan 10.500062
 6  DRB1*04:02-DQA1*03:01-DQB1*03:03  Iran Azeri 7.5000100
 7  DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*05:01  China Canton Han 6.8000264
 8  DRB1*04:03-DQA1*03:01-DQB1*03:03  Iran Kurd 5.5000100
 9  DRB1*04:02-DQA1*03:01-DQB1*03:03  Iran Kurd 5.0000100
 10  DRB1*09:01:02-DQA1*03:01-DQB1*03:03  China Urumqi Kazak 4.800042
 11  DRB1*04:03-DQA1*03:01-DQB1*03:03  Iran Yazd Zoroastrian 4.600065
 12  DRB1*04-DQA1*03:01-DQB1*03:03  Mexico Guadalajara Mestizo 3.700054
 13  DRB1*09-DQA1*03:01-DQB1*03:03  Russia Arkhangelsk 3.100081
 14  DRB1*04:03-DQA1*03:01-DQB1*03:03  Iran Azeri 3.0000100
 15  DRB1*09:01-DQA1*03:01-DQB1*03:03  Mongolia Tarialan Khoton 3.000085
 16  DRB1*09:01:02-DQA1*03:01/03:02/03:03-DQB1*03:03:02  Russia Siberia Lower Yenisey Ket 2.900017
 17  DRB1*13-DQA1*03:01-DQB1*03:03  Gabon Haut-Ogooue Dienga 2.7000167
 18  DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*02:01  China Canton Han 2.5000264
 19  DRB1*09-DQA1*03:01-DQB1*03:03  Russia Vologda 2.5000121
 20  DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*02:02  China Canton Han 2.1000264
 21  DRB1*09:01-DQA1*03:01-DQB1*03:03  Russia Northwest Slavic 2.0000200
 22  DRB1*09-DQA1*03:01-DQB1*03:03  Belarus Brest Region 1.9000105
 23  DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*13:01  China Canton Han 1.7000264
 24  DRB1*09:01-DQA1*03:01-DQB1*03:03  USA San Francisco Caucasian 1.6000220
 25  DRB1*09:01:02-DQA1*03:01/03:02-DQB1*03:03  England pop 6 1.4000177
 26  DRB1*09:01-DRB4*01:01-DQA1*03:01-DQB1*03:03-DPB1*04:01  USA San Francisco Caucasian 1.4000220
 27  DRB1*09:01-DRB4*01:01-DQA1*03:01-DQB1*03:03-DPB1*04:01  USA San Francisco Caucasian 1.4000220
 28  DRB1*09:01-DQA1*03:01-DQB1*03:03  Spain Las Alpujarras 1.180085
 29  DRB1*09:01:02-DQA1*03:01-DQB1*03:03  Australia New South Wales Aborigine 1.1000177
 30  A*68:01-B*15:01-C*01:02-DRB1*04:04-DQA1*03:01-DQB1*03:03  Mexico Chichen Itza Maya (prehispanic) 1.063847
 31  A*68:01-B*35:01-C*04:01-DRB1*04:07-DQA1*03:01-DQB1*03:03  Mexico Chichen Itza Maya (prehispanic) 1.063847
 32  A*24:02-B*54:01-C*03:03-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*02:01  USA San Diego 0.5210496
 33  A*02:01-B*40:02-C*03:04-DRB1*04:04-DQA1*03:01-DQB1*03:03  Brazil Puyanawa 0.3333150
 34  A*25:01:01-B*44:03:01-C*06:02:01-DRB1*07:01:01-DQA1*03:01:01-DQB1*03:03:02-DPA1*02:01:01-DPB1*10:01  Russia Belgorod region 0.3268153
 35  A*32:01-B*40:02-C*02:10-DRB1*04:01-DQA1*03:01-DQB1*03:03-DPB1*04:01  South Africa Worcester 0.3000159
 36  A*66:01-B*58:02-C*06:02-DRB1*13:01-DQA1*03:01-DQB1*03:03-DPB1*02:01  South Africa Worcester 0.3000159
 37  A*02:01-B*44:02-C*05:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*04:02  USA San Diego 0.2600496
 38  A*02:07-B*15:01-C*04:01-DRB1*04:06-DQA1*03:01-DQB1*03:03-DPB1*05:01  USA San Diego 0.2600496
 39  A*02:07-B*51:01-C*01:02-DRB1*04:05-DQA1*03:01-DQB1*03:03-DPB1*05:01  USA San Diego 0.2600496
 40  A*23:01-B*50:01-C*06:02-DRB1*13:01-DQA1*03:01-DQB1*03:03-DPB1*04:01  USA San Diego 0.2600496
 41  A*24:02-B*15:01-C*03:03-DRB1*15:02-DQA1*03:01-DQB1*03:03-DPB1*05:01  USA San Diego 0.2600496
 42  A*26:01-B*40:06-C*08:01-DRB1*14:02-DQA1*03:01-DQB1*03:03-DPB1*02:01  USA San Diego 0.2600496
 43  A*32:01-B*15:01-C*05:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*04:02  USA San Diego 0.2600496
 44  A*32:01-B*44:03-C*05:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*02:01  USA San Diego 0.2600496
 45  A*33:03-B*56:01-C*04:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*05:01  USA San Diego 0.2600496
 46  A*24:02-B*39:05-C*07:02-DRB1*04:10-DQA1*03:01-DQB1*03:03-DPA1*02:01-DPB1*14:01  Mexico Chiapas Lacandon Mayans 0.2294218
 47  A*02:01-B*15:01-C*01:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*14:01  Nicaragua Managua 0.2165339
 48  A*02:01-B*44:02-C*05:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*04:02  Nicaragua Managua 0.2165339
 49  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQA1*03:01-DQB1*03:03-DPB1*05:01  Nicaragua Managua 0.2165339
 50  A*68:01-B*27:05-C*02:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*26:01  Sri Lanka Colombo 0.1401714
 51  A*03-B*07-DRB1*04:03-DQA1*03:01-DQB1*03:03  Brazil Paraná Caucasian 0.0780641
 52  A*23-B*07-DRB1*09:01-DQA1*03:01-DQB1*03:03  Brazil Paraná Caucasian 0.0780641
 53  A*24-B*51-DRB1*09:01-DQA1*03:01-DQB1*03:03  Brazil Paraná Caucasian 0.0780641
 54  A*01:01-B*55:01-C*01:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*02:01  Sri Lanka Colombo 0.0700714
 55  A*02:03-B*39:01-C*12:04-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*04:02  Sri Lanka Colombo 0.0700714
 56  A*03:01-B*27:07-C*15:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*04:01  Sri Lanka Colombo 0.0700714
 57  A*11:01-B*15:02-C*08:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*02:01  Sri Lanka Colombo 0.0700714
 58  A*11:01-B*40:06-C*15:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*04:01  Sri Lanka Colombo 0.0700714
 59  A*11:01-B*51:01-C*03:03-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*02:01  Sri Lanka Colombo 0.0700714
 60  A*11:01-B*52:01-C*12:02-DRB1*04:03-DQA1*03:01-DQB1*03:03-DPB1*04:01  Sri Lanka Colombo 0.0700714
 61  A*24:02-B*27:07-C*15:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*02:01  Sri Lanka Colombo 0.0700714
 62  A*24:02-B*35:01-C*04:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*04:01  Sri Lanka Colombo 0.0700714
 63  A*26:01-B*13:01-C*04:03-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*26:01  Sri Lanka Colombo 0.0700714
 64  A*68:01-B*07:05-C*14:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*05:01  Sri Lanka Colombo 0.0700714
 65  A*68:01-B*27:07-C*04:03-DRB1*12:02-DQA1*03:01-DQB1*03:03-DPB1*10:01  Sri Lanka Colombo 0.0700714
 66  A*02:06-B*40:06-C*08:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.07003,078
 67  A*24:02-B*40:02-C*03:03-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.07003,078
 68  DRB1*04:03-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*04:02  China Zhejiang Han pop 2 0.0600833
 69  A*02:01-B*15:11-C*03:03-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*14:01  Japan pop 17 0.03003,078
 70  A*02:01-B*51:01-C*14:02-DRB1*12:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 71  A*02:01-B*55:02-C*01:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 72  A*11:01-B*15:01-C*04:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 73  A*11:01-B*51:01-C*14:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 74  A*24:02-B*07:02-C*07:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 75  A*24:02-B*40:06-C*01:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*03:01  Japan pop 17 0.03003,078
 76  A*24:02-B*40:06-C*08:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 77  A*24:02-B*51:01-C*14:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*01:03-DPB1*03:01  Japan pop 17 0.03003,078
 78  A*24:02-B*51:01-C*14:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:01-DPB1*05:01  Japan pop 17 0.03003,078
 79  A*24:20-B*51:01-C*14:02-DRB1*12:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 80  A*26:01-B*40:02-C*03:03-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 81  A*26:02-B*40:06-C*08:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 82  A*33:03-B*39:01-C*07:02-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 83  A*33:03-B*44:03-C*14:03-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078

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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