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 100 (from 164) records   Pages: 1 2 of 2  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*68-B*15-DRB1*04:03-DQB1*03:02  Colombia Wayu from Guajira Peninsula 4.170048
 2  A*31:01:02-B*15:01-DRB1*04:03-DQB1*03:02  USA South Dakota Lakota Sioux 2.2000302
 3  A*02-B*15-DRB1*04:03-DQB1*03:02  Colombia Wayu from Guajira Peninsula 2.080048
 4  B*15:01-DRB1*04:03-DQB1*03:02  South Korea pop 3 1.6000485
 5  A*24-B*15-DRB1*04:03-DQB1*03:02  Colombia Wayu from Guajira Peninsula 1.200048
 6  A*02-B*15-DRB1*04:03-DQB1*03:02  Colombia San Basilio de Palenque 1.191042
 7  A*31:01-B*15:01-C*01:02-DRB1*04:03-DQA1*03:01-DQB1*03:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 8  A*31-B*15-DRB1*04:03-DQB1*03:02  Colombia Wayu from Guajira Peninsula 1.040048
 9  A*68:19-B*15:01-C*03:28-DRB1*04:03-DQB1*03:02  Colombia North Wiwa El Encanto 0.961552
 10  A*24-B*15-DRB1*04:03-DQB1*03:02  Mexico San Vicente Tancuayalab Teenek/Huastecos 0.940053
 11  A*32:01-B*15:01-DRB1*04:03-DQB1*03:02  Chile Mapuche 0.770066
 12  A*24:02-B*15:01-C*01:02-DRB1*04:03-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 13  A*69-B*15-DRB1*04:03-DQB1*03:02  Bolivia Quechua 0.720069
 14  A*26:01:01-B*15:18:01-C*07:04:01-DRB1*04:03:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.5620356
 15  A*02:01-B*15:01-DRB1*04:03-DQB1*03:02  Peru Titikaka Lake Uros 0.4800105
 16  A*68:01-B*15:18-C*07:04-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*02:01  Sri Lanka Colombo 0.4202714
 17  A*31:01-B*15:01-C*01:02-DRB1*04:03-DQB1*03:02  USA NMDP Alaska Native or Aleut 0.39651,376
 18  A*02:06-B*15:01-C*01:02-DRB1*04:03-DQB1*03:02  USA NMDP Alaska Native or Aleut 0.28991,376
 19  A*33:03:01-B*15:18:01-C*07:04:01-DRB1*04:03:01-DQB1*03:02:01  India Karnataka Kannada Speaking 0.2870174
 20  A*68:01:02-B*15:18:01-C*16:02:01-DRB1*04:03:01-DQB1*03:02:01  India Karnataka Kannada Speaking 0.2870174
 21  A*01:01:01-B*15:25:01-C*07:26:01-DRB1*04:03:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.2810356
 22  A*11:01:01-B*15:17:01-C*07:01:01-DRB1*04:03:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.2810356
 23  A*68:01:02-B*15:18:01-C*07:04:01-DRB1*04:03:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.2810356
 24  A*02:11:01-B*15:02:01-C*04:03:01-DRB1*04:03:01-DQB1*03:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 25  A*11:01:01-B*15:05:01-C*07:02:01-DRB1*04:03:01-DQB1*03:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 26  A*24:02:13-B*15:18:01-C*16:02:01-DRB1*04:03:01-DQB1*03:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 27  A*02:01-B*15:07-C*03:03-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Chinese 0.2577194
 28  A*24:02-B*15:01-C*03:03-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Chinese 0.2577194
 29  A*24:02-B*15:07-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.23003,078
 30  A*68:01-B*15:18-C*07:04-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.22952,492
 31  A*26:01-B*15:18-C*07:04-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*04:01  Sri Lanka Colombo 0.2101714
 32  A*31:01-B*15:01-C*01:02-DRB1*04:03-DQB1*03:02-DPB1*04:02  Panama 0.1900462
 33  A*02:11-B*15:05-C*03:03-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Indian 0.1845271
 34  A*03:01-B*15:18-C*07:04-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Indian 0.1845271
 35  A*24:02-B*15:01-C*04:01-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Indian 0.1845271
 36  A*68:01-B*15:18-C*07:04-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Indian 0.1845271
 37  A*24:02-B*15:01-DRB1*04:03-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 38  A*11:01-B*15:18-C*16:02-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*04:01  Sri Lanka Colombo 0.1401714
 39  A*24:02:01-B*15:18:01-C*16:02:01-DRB1*04:03:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.1400356
 40  A*26:01:01-B*15:01:01-C*03:03:01-DRB1*04:03:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.1400356
 41  A*02:01-B*15:01-C*04:01-DRB1*04:03-DQB1*03:02-DPB1*04:01  Russia Karelia 0.11211,075
 42  A*01:01-B*15:18-C*07:04-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.10172,492
 43  A*31:01-B*15:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.10003,078
 44  A*02:06-B*15:30-C*01:02-DRB1*04:03-DQB1*03:02  USA Hispanic pop 2 0.09401,999
 45  A*24:07-B*15:01-C*03:03-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.08901,772
 46  A*02-B*15-DRB1*04:03-DQA1*03:01-DQB1*03:02  Brazil Paraná Caucasian 0.0780641
 47  A*24:02-B*15:25-C*07:26-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.07302,492
 48  A*01:01-B*15:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*04:01  Sri Lanka Colombo 0.0700714
 49  A*01:01-B*15:17-C*07:02-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*04:01  Sri Lanka Colombo 0.0700714
 50  A*01:01-B*15:18-C*16:02-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*21:01  Sri Lanka Colombo 0.0700714
 51  A*02:06-B*15:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*02:01  Sri Lanka Colombo 0.0700714
 52  A*11:01-B*15:13-C*12:02-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*04:02  Sri Lanka Colombo 0.0700714
 53  A*30:01-B*15:18-C*06:02-DRB1*04:03-DQA1*02:01-DQB1*03:02-DPB1*26:01  Sri Lanka Colombo 0.0700714
 54  A*68:01-B*15:18-C*07:04-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*04:01  Sri Lanka Colombo 0.0700714
 55  A*31:01-B*15:07-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.07003,078
 56  A*03:01-B*15:09-C*07:04-DRB1*04:03-DQB1*03:02  Colombia Bogotá Cord Blood 0.06841,463
 57  A*11:01-B*15:01-C*03:03-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.06352,492
 58  A*02:01-B*15:18-C*07:04-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.06022,492
 59  A*26:01-B*15:18-C*16:02-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.06002,492
 60  A*11:01-B*15:18-C*07:04-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.05872,492
 61  A*02:02-B*15:44-C*04:03-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Malay 0.0526951
 62  A*11:01-B*15:07-C*12:02-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Malay 0.0526951
 63  A*11:01-B*15:25-C*12:02-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Malay 0.0526951
 64  A*26:01-B*15:25-C*04:03-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Malay 0.0526951
 65  A*68:01-B*15:18-C*07:04-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Malay 0.0526951
 66  A*68:01-B*15:18-C*07:12-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Malay 0.0526951
 67  A*01:01-B*15:25-C*07:26-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.04942,492
 68  A*02:01-B*15:01-C*04:01-DRB1*04:03-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 69  A*11:01-B*15:01-C*04:01-DRB1*04:03-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 70  A*11:01-B*15:25-C*07:26-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.04542,492
 71  A*02:01-B*15:02-C*08:01-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.04401,772
 72  A*02:01-B*15:18-C*08:01-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.04401,772
 73  A*02:07-B*15:02-C*08:01-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.04401,772
 74  A*24:02-B*15:18-C*07:04-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.04401,772
 75  A*24:02-B*15:18-C*16:02-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.04401,772
 76  A*26:01-B*15:21-C*04:03-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.04401,772
 77  A*33:03-B*15:16-C*14:02-DRB1*04:03-DQB1*03:02  USA African American pop 4 0.04402,411
 78  A*11:01-B*15:01-C*04:01-DRB1*04:03-DQB1*03:02  Germany DKMS - Italy minority 0.04301,159
 79  A*24:03-B*15:17-C*07:01-DRB1*04:03-DQB1*03:02  Germany DKMS - Italy minority 0.04301,159
 80  A*32:01-B*15:03-C*12:03-DRB1*04:03-DQB1*03:02  Germany DKMS - Italy minority 0.04301,159
 81  A*69:01-B*15:01-C*04:01-DRB1*04:03-DQB1*03:02  Germany DKMS - Italy minority 0.04301,159
 82  A*02:06-B*15:01-C*03:03-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.04222,492
 83  A*68:01-B*15:25-C*07:26-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.04012,492
 84  A*02:06-B*15:18-C*08:01-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.03501,772
 85  A*01:01-B*15:01-C*12:03-DRB1*04:03-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 86  A*02:01-B*15:01-C*03:03-DRB1*04:03-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 87  A*24:02-B*15:01-C*03:03-DRB1*04:03-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 88  A*24:02-B*15:18-C*07:04-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.03232,492
 89  A*11:01:01-B*15:02:01-C*08:01:01-DRB1*04:03:01-DQB1*03:02:01  China Zhejiang Han 0.03061,734
 90  A*02:01-B*15:01-C*03:04-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 91  A*02:01-B*15:18-C*07:04-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.03003,078
 92  A*02:06-B*15:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*13:01  Japan pop 17 0.03003,078
 93  A*02:06-B*15:07-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 94  A*11:01-B*15:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.03003,078
 95  A*11:01-B*15:07-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 96  A*11:01-B*15:18-C*08:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*02:01  Japan pop 17 0.03003,078
 97  A*24:02-B*15:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 98  A*24:02-B*15:07-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.03003,078
 99  A*24:02-B*15:07-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 100  A*24:02-B*15:07-C*07:02-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*09:01  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 164) 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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