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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Displaying 1 to 100 (from 135) records   Pages: 1 2 of 2  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  DRB1*04:10-DQA1*03:01-DQB1*04:02  Canada British Columbia Athabaskan 4.000062
 2  DQA1*03-DQB1*04:02  Papua New Guinea Highland pop2 3.800028
 3  DRB1*04:10-DQA1*03:01-DQB1*04:02  Australia New South Wales Aborigine 2.4000177
 4  DRB1*14:01-DQA1*03-DQB1*04:02  Russia Siberia Gvaysugi Udege 2.400025
 5  DRB1*14:02-DQA1*03-DQB1*04:02  Russia Siberia Gvaysugi Udege 2.400025
 6  DRB1*14:03-DQA1*03-DQB1*04:02  Russia Siberia Sulamai Ket 2.300022
 7  A*31:01-B*40:02-C*03:04-DRB1*04:11-DQA1*03:03-DQB1*04:02  Mexico Chichen Itza Maya (prehispanic) 2.127747
 8  DQA1*03-DQB1*04:02  China, Xinjiang Uyghur Autonomous Region Kazakh 1.920052
 9  DRB1*04:06-DQA1*03-DQB1*04:02  Algeria pop 2 1.9000106
 10  DRB1*04:04-DQA1*03:01/03:02-DQB1*04:02  Ethiopia Oromo 1.800083
 11  DQA1*03-DQB1*04:02  Japan Fukuoka 1.700086
 12  DRB1*04:04-DQA1*03:03-DQB1*04:02  South Korea pop 1 1.7000324
 13  DRB1*04:10-DQA1*03:01-DQB1*04:02  Japan pop 2 1.7000916
 14  DRB1*14:02-DQA1*03-DQB1*04:02  Russia Siberia North East Kamchatka Koryak 1.600092
 15  DRB1*04:06-DQA1*03:01/03:02-DQB1*04:02  Ethiopia Amhara 1.500098
 16  DRB1*04:03-DQA1*03-DQB1*04:02  Russia Siberia Negidal 1.400035
 17  DRB1*04:04-DQA1*03:03-DQB1*04:02  South Korea pop 5 1.4000467
 18  DRB1*04:04-DQA1*03:03-DQB1*04:02-DPB1*13:01  South Korea pop 1 1.4000324
 19  DRB1*14:03-DQA1*03-DQB1*04:02  Russia Siberia Polygus Evenk 1.400035
 20  DRB1*04:04-DQA1*03:03-DQB1*04:02-DPB1*13:01  South Korea pop 11 1.3000149
 21  DQA1*03-DQB1*04:02  China, Xinjiang Uyghur Autonomous Region Hui 1.250040
 22  DRB1*04:01-DQA1*03-DQB1*04:02  Russia Siberia Irkutsk Tofalar 1.200043
 23  DRB1*04:04-DQA1*03-DQB1*04:02  Russia Siberia Irkutsk Tofalar 1.200043
 24  DRB1*04:06-DQA1*03:01/03:02-DQB1*04:02  Ethiopia Oromo 1.200083
 25  DRB1*04:10-DQA1*03:03-DQB1*04:02  South Korea pop 5 1.2000467
 26  DRB1*04:10-DQA1*03-DQB1*04:02  Russia Siberia Irkutsk Tofalar 1.200043
 27  DRB1*04:01-DQA1*03-DQB1*04:02  Russia Siberia North East Kamchatka Koryak 1.100092
 28  DRB1*04:04-DQA1*03:01-DQB1*04:02  Australia New South Wales Aborigine 1.1000177
 29  DRB1*04:05-DQA1*03-DQB1*04:02  Russia Tuva pop3 1.100044
 30  DRB1*04:08-DQA1*03:01-DQB1*04:02  Australia New South Wales Aborigine 1.1000177
 31  DRB1*04:10-DQA1*03-DQB1*04:02  Russia Tuva pop3 1.100044
 32  DRB1*09:01:02-DQA1*03-DQB1*04:02  Russia Siberia North East Kamchatka Koryak 1.100092
 33  A*02:01-B*35:17-C*04:01-DRB1*04:11-DQA1*03:03-DQB1*04:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 34  A*02:06-B*39:02-C*03:04-DRB1*04:11-DQA1*03:03-DQB1*04:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 35  A*24:02-B*35:01-C*04:01-DRB1*04:10-DQA1*03:03-DQB1*04:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 36  A*24:02-B*40:02-C*03:04-DRB1*04:11-DQA1*03:03-DQB1*04:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 37  A*24:02-B*40:08-C*03:04-DRB1*04:11-DQA1*03:03-DQB1*04:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 38  A*31:01-B*35:01-C*04:01-DRB1*04:10-DQA1*03:03-DQB1*04:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 39  A*31:01-B*35:01-C*04:01-DRB1*04:11-DQA1*03:03-DQB1*04:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 40  A*31:01-B*35:20-C*04:01-DRB1*04:10-DQA1*03:03-DQB1*04:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 41  A*31:09-B*35:01-C*04:01-DRB1*04:11-DQA1*03:03-DQB1*04:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 42  A*68:01-B*39:02-C*07:02-DRB1*04:11-DQA1*03:03-DQB1*04:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 43  A*02-B*45-DRB1*04:04-DQA1*03-DQB1*04:02  Morocco 1.000096
 44  A*02-B*49-DRB1*04:06-DQA1*03-DQB1*04:02  Morocco 1.000096
 45  DRB1*04:06-DQA1*03:01-DQB1*04:02  Morocco Souss Region 1.000098
 46  A*02:01-B*35:01-C*16:02-DRB1*04:04-DQA1*03:03-DQB1*04:02  United Arab Emirates Abu Dhabi 0.960052
 47  A*68:03-B*35:43-C*01:02-DRB1*04:10-DQA1*03:01-DQB1*04:02-DPB1*03:01  Nicaragua Managua 0.8658339
 48  A*24:02-B*35:01-C*04:01-DRB1*04:25-DQA1*03:03-DQB1*04:02  Mexico Tixcacaltuyub Maya 0.746367
 49  A*29:02-B*58:01-C*06:02-DRB1*04:04-DQA1*03:01-DQB1*04:02-DPB1*40:01  South Africa Worcester 0.6000159
 50  A*26:01-B*35:01-C*03:03-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.33003,078
 51  A*68:01:01-B*07:02:01-C*07:01:01-DRB1*08:01:01-DQA1*03:01:01-DQB1*04:02:01-DPA1*02:01:01-DPB1*10:01  Russia Belgorod region 0.3268153
 52  A*01:01-B*37:01-C*06:02-DRB1*04:04-DQA1*03:01-DQB1*04:02-DPB1*04:01  South Africa Worcester 0.3000159
 53  A*02:06-B*15:02-C*08:01-DRB1*04:05-DQA1*03:01-DQB1*04:02-DPB1*05:01  South Africa Worcester 0.3000159
 54  A*23:01-B*58:02-C*07:01-DRB1*07:01-DQA1*03:01-DQB1*04:02-DPB1*04:01  South Africa Worcester 0.3000159
 55  A*43:01-B*58:02-C*04:01-DRB1*04:01-DQA1*03:01-DQB1*04:02-DPB1*01:01  South Africa Worcester 0.3000159
 56  A*26:01-B*15:01-C*03:03-DRB1*09:01-DQA1*03:01-DQB1*04:02-DPB1*05:01  USA San Diego 0.2600496
 57  A*33:01-B*15:03-C*02:10-DRB1*04:04-DQA1*03:01-DQB1*04:02-DPB1*04:01  USA San Diego 0.2600496
 58  A*34:01-B*15:35-C*07:02-DRB1*15:02-DQA1*03:01-DQB1*04:02-DPB1*01:01  USA San Diego 0.2600496
 59  A*24:02-B*35:12-C*04:01-DRB1*04:11-DQA1*03:01-DQB1*04:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 60  A*02:01-B*51:01-C*15:09-DRB1*04:11-DQA1*03:01-DQB1*04:02-DPB1*04:02  Nicaragua Managua 0.2165339
 61  A*02:05-B*41:01-C*07:01-DRB1*04:10-DQA1*03:01-DQB1*04:02-DPB1*01:01  Nicaragua Managua 0.2165339
 62  A*03:01-B*39:08-C*07:02-DRB1*04:05-DQA1*03:01-DQB1*04:02-DPB1*04:02  Nicaragua Managua 0.2165339
 63  A*23:01-B*44:03-C*04:01-DRB1*04:05-DQA1*03:01-DQB1*04:02-DPB1*04:01  Nicaragua Managua 0.2165339
 64  A*24:02-B*39:11-C*08:03-DRB1*16:02-DQA1*03:01-DQB1*04:02-DPB1*04:01  Nicaragua Managua 0.2165339
 65  A*30:02-B*07:02-C*07:02-DRB1*04:11-DQA1*03:01-DQB1*04:02-DPB1*18:01  Nicaragua Managua 0.2165339
 66  A*31:01-B*40:02-C*03:05-DRB1*14:02-DQA1*03:01-DQB1*04:02-DPB1*04:02  Nicaragua Managua 0.2165339
 67  A*68:02-B*40:01-C*03:04-DRB1*03:01-DQA1*03:01-DQB1*04:02-DPB1*02:02  Nicaragua Managua 0.2165339
 68  A*68:05-B*52:01-C*03:03-DRB1*04:11-DQA1*03:01-DQB1*04:02-DPB1*03:01  Nicaragua Managua 0.2165339
 69  DQA1*03:03-DQB1*04:02-DPA1*02:02-DPB1*05:01  Hong Kong Chinese HKBMDR. DQ and DP 0.12921,064
 70  DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*02:01-DPB1*14:01  China Zhejiang Han pop 2 0.1200833
 71  A*02-B*07-DRB1*04:04-DQA1*03:02-DQB1*04:02  Brazil Paraná Caucasian 0.0780641
 72  A*02-B*15-DRB1*04:11-DQA1*03:02-DQB1*04:02  Brazil Paraná Caucasian 0.0780641
 73  A*24-B*18-DRB1*08:01-DQA1*03:01-DQB1*04:02  Brazil Paraná Caucasian 0.0780641
 74  A*26-B*49-DRB1*04:06-DQA1*03:01-DQB1*04:02  Brazil Paraná Caucasian 0.0780641
 75  A*29-B*44-DRB1*11:01-DQA1*03:01-DQB1*04:02  Brazil Paraná Caucasian 0.0780641
 76  A*68-B*15-DRB1*04:11-DQA1*03:01-DQB1*04:02  Brazil Paraná Caucasian 0.0780641
 77  A*11:01-B*40:06-C*07:02-DRB1*04:05-DQA1*03:01-DQB1*04:02-DPB1*02:01  Sri Lanka Colombo 0.0700714
 78  A*11:01-B*40:06-C*15:02-DRB1*04:05-DQA1*03:01-DQB1*04:02-DPB1*01:01  Sri Lanka Colombo 0.0700714
 79  A*11:01-B*51:01-C*15:02-DRB1*04:10-DQA1*03:01-DQB1*04:02-DPB1*02:01  Sri Lanka Colombo 0.0700714
 80  A*24:02-B*40:06-C*15:02-DRB1*04:10-DQA1*03:01-DQB1*04:02-DPB1*02:01  Sri Lanka Colombo 0.0700714
 81  A*24:02-B*52:01-C*16:02-DRB1*04:06-DQA1*03:01-DQB1*04:02-DPB1*19:01  Sri Lanka Colombo 0.0700714
 82  A*26:01-B*51:01-C*14:02-DRB1*04:05-DQA1*03:01-DQB1*04:02-DPB1*16:01  Sri Lanka Colombo 0.0700714
 83  A*02:01-B*35:01-C*03:03-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*01:03-DPB1*03:01  Japan pop 17 0.07003,078
 84  A*02:01-B*39:01-C*07:02-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*01:03-DPB1*03:01  Japan pop 17 0.07003,078
 85  A*02:01-B*40:02-C*03:04-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.07003,078
 86  A*02:06-B*40:02-C*03:04-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.07003,078
 87  A*11:01-B*35:01-C*03:03-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.07003,078
 88  A*24:02-B*07:02-C*07:02-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.07003,078
 89  A*26:01-B*35:01-C*03:03-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.07003,078
 90  DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*01:03-DPB1*02:01  China Zhejiang Han pop 2 0.0600833
 91  DQA1*03:03-DQB1*04:02-DPA1*01:03-DPB1*03:01  Hong Kong Chinese HKBMDR. DQ and DP 0.03851,064
 92  A*02:01-B*15:01-C*03:03-DRB1*09:01-DQA1*03:03-DQB1*04:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 93  A*02:01-B*35:01-C*03:03-DRB1*04:07-DQA1*03:03-DQB1*04:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 94  A*02:01-B*35:01-C*03:03-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 95  A*02:01-B*35:01-C*03:03-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.03003,078
 96  A*02:01-B*35:01-C*03:04-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.03003,078
 97  A*02:01-B*38:02-C*07:02-DRB1*04:10-DQA1*03:01-DQB1*04:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 98  A*02:01-B*40:02-C*03:04-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*01:03-DPB1*05:01  Japan pop 17 0.03003,078
 99  A*02:01-B*48:01-C*08:01-DRB1*04:10-DQA1*03:03-DQB1*04:02-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 100  A*02:01-B*51:01-C*14:02-DRB1*04:05-DQA1*03:01-DQB1*04:02-DPA1*02:02-DPB1*05:01  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).


Displaying 1 to 100 (from 135) records   Pages: 1 2 of 2  


   

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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