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 267) records   Pages: 1 2 3 of 3  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*33-B*44-C*07-DRB1*07  Myanmar Rakhine 9.375048
 2  A*33-B*44-C*07-DRB1*07  Myanmar Mon 7.031064
 3  A*33-B*44-C*07-DRB1*07  Myanmar Shan 3.704054
 4  B*44:03-C*07:01/07:06-DRB1*07:01  South Korea pop 3 3.1000485
 5  A*33:03-B*44:03-C*07:01/07:06-DRB1*07:01-DQB1*02:01/02:02  South Korea pop 3 3.0000485
 6  A*33:03-B*44:03-C*07:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP South Asian Indian 2.7980185,391
 7  A*33-B*44-C*07-DRB1*07  Myanmar Kayah 2.727055
 8  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  Vietnam Hanoi Kinh pop 2 2.6000170
 9  A*33:03-B*44:03-C*07:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Korean 2.596977,584
 10  A*33:03-B*44:03-C*07:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Southeast Asian 2.512727,978
 11  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 2.30612,492
 12  A*33:03:01-B*44:03:02-C*07:01:01-DRB1*07:01:01  China Jingpo Minority 2.0830105
 13  B*44:03:02-C*07:01:01-DRB1*07:01:01  China Jingpo Minority 2.0830105
 14  A*33:03-B*44:03-C*07:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Vietnamese 1.880143,540
 15  A*33-B*44-C*07-DRB1*07  Myanmar Chin 1.818055
 16  A*33:03:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 1.7240174
 17  A*33:03:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 1.6150356
 18  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  USA Asian pop 2 1.50401,772
 19  A*23:01:01-B*44:03:01-C*07:01:01-DRB1*07:01:01-DQB1*02:01:01-DPA1*02:01:01-DPB1*17:01:01  Brazil Rio de Janeiro Black 1.470668
 20  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*02:01  Sri Lanka Colombo 1.4006714
 21  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  Sri Lanka Colombo 1.2605714
 22  A*24:02:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 1.1490174
 23  A*33:03:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 1.1208186
 24  A*33:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Indian 1.1070271
 25  A*33:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 1.1041951
 26  A*11-B*44-C*07-DRB1*07  Myanmar Shan 0.926054
 27  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.8403951
 28  A*01:01:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 0.8213186
 29  A*11-B*44-C*07-DRB1*07  Myanmar Mon 0.781064
 30  A*33:03-B*44:03-C*07:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Filipino 0.743850,614
 31  A*33:03:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 0.7020356
 32  A*30:04-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  USA NMDP Caribean Indian 0.676614,339
 33  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*13:01  Sri Lanka Colombo 0.6303714
 34  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  USA NMDP Hawaiian or other Pacific Islander 0.610011,499
 35  A*33:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  Malaysia Peninsular Malay 0.6072951
 36  A*01:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.60292,492
 37  A*01:01:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 0.5750174
 38  A*02:11:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 0.5750174
 39  A*11:01:01-B*44:03:01-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 0.5750174
 40  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:02  Sri Lanka Colombo 0.5602714
 41  A*01:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Indian 0.5535271
 42  A*11:01:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*03:03:02  India Andhra Pradesh Telugu Speaking 0.5376186
 43  A*33:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Chinese 0.5155194
 44  A*33:03-B*44:03-C*07:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Chinese 0.513999,672
 45  A*24:02:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 0.4910356
 46  A*33:03-B*44:03-C*07:01-DRB1*07:01  Hong Kong Chinese BMDR 0.47707,595
 47  A*24:02:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 0.4345186
 48  A*02:01:01-B*44:05:01-C*07:02:01-DRB1*07:01:01-DQA1*02:01-DQB1*03:03:02-DPA1*02:01:02-DPB1*01:01:01  Russian Federation Vologda Region 0.4202119
 49  A*23:01:01-B*44:03:01-C*07:02:01-DRB1*07:01:01-DQA1*01:02:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01  Russian Federation Vologda Region 0.4202119
 50  A*11:01-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  Sri Lanka Colombo 0.3501714
 51  A*02:01-B*44:02-C*07:04-DRB1*07:01  Brazil Vale do Ribeira Quilombos 0.3472144
 52  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  USA NMDP Caribean Indian 0.336014,339
 53  A*02:01:01-B*44:03:01-C*07:01:01-DRB1*07:01:01-DQA1*05:05:01-DQB1*03:01-DPA1*01:03:01-DPB1*13:01  Russia Belgorod region 0.3268153
 54  A*33:03-B*44:03-C*07:01-DRB1*07:01  Germany DKMS - China minority 0.31501,282
 55  A*33:03:01-B*44:03:02-C*07:06:01-DRB1*07:01:01-DPB1*104:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.31485,266
 56  A*24:02:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*03:03:02  India Andhra Pradesh Telugu Speaking 0.3116186
 57  A*29:01-B*44:03-C*07:01-DRB1*07:01-DQA1*03:03-DQB1*02:02-DPB1*02:01  South Africa Worcester 0.3000159
 58  A*33:01-B*44:03-C*07:01-DRB1*07:01-DQA1*01:02-DQB1*02:02-DPB1*04:01  South Africa Worcester 0.3000159
 59  A*03:01:01-B*44:03:01-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 0.2870174
 60  A*24:02:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 0.2870174
 61  A*33:03:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*06:01:01  India Karnataka Kannada Speaking 0.2870174
 62  A*33:03:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 0.2870174
 63  A*68:01:02-B*44:03:02-C*07:06-DRB1*07:03-DQB1*02:02:01  India Karnataka Kannada Speaking 0.2870174
 64  A*03:01:01-B*44:03:02-C*07:01:18-DRB1*07:01:01-DQB1*06:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 65  A*23:01:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 66  A*24:02:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 67  A*68:01:02-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 68  A*11:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.2629951
 69  A*34:02-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*11:01  USA San Diego 0.2600496
 70  A*68:03-B*44:03-C*07:02-DRB1*07:01-DQA1*03:01-DQB1*02:02-DPB1*17:01  USA San Diego 0.2600496
 71  A*33:03:01-B*44:03:02-C*07:06:01-DRB1*07:01:01-DQB1*02:02:01  China Zhejiang Han 0.25951,734
 72  A*02:01-B*44:03-C*07:01-DRB1*07:01  Germany DKMS - Greece minority 0.24401,894
 73  A*32:01-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:02  Nicaragua Managua 0.2165339
 74  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*04:01  Sri Lanka Colombo 0.2101714
 75  A*68:01:02-B*44:02:01-C*07:04:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*11:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 76  A*02:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.18804,856
 77  A*30:04-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  USA Hispanic pop 2 0.18701,999
 78  A*01:01-B*44:03-C*07:01-DRB1*07:08-DQB1*05:01  Malaysia Peninsular Indian 0.1845271
 79  A*02:57-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Indian 0.1845271
 80  A*11:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  USA Asian pop 2 0.17801,772
 81  A*01:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.1577951
 82  A*33:03-B*44:03-C*07:01-DRB1*07:16-DQB1*02:02  Malaysia Peninsular Malay 0.1577951
 83  A*33:03-B*44:03-C*07:02-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.1577951
 84  A*33:03-B*44:03-C*07:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Caribean Black 0.152933,328
 85  A*01:01-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  Sri Lanka Colombo 0.1401714
 86  A*01:01-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:02  Sri Lanka Colombo 0.1401714
 87  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*03:01  Sri Lanka Colombo 0.1401714
 88  A*24:02:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*06:03:01  India Kerala Malayalam speaking 0.1400356
 89  A*33:03:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 0.1400356
 90  A*33:03:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*03:01:01  India Kerala Malayalam speaking 0.1400356
 91  A*33:03:01-B*44:03:02-C*07:01:01-DRB1*07:03-DQB1*02:02:01  India Kerala Malayalam speaking 0.1400356
 92  A*68:01:01-B*44:03:02-C*07:02:01-DRB1*07:01:01-DQB1*05:01:01  India Kerala Malayalam speaking 0.1400356
 93  A*68:01:02-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 0.1400356
 94  A*24:02-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.13642,492
 95  A*02:11-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  USA Asian pop 2 0.13301,772
 96  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  USA NMDP Black South or Central American 0.13274,889
 97  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQB1*03:03  India Tamil Nadu 0.13022,492
 98  A*11:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.12672,492
 99  A*24:02-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  Malaysia Peninsular Malay 0.1205951
 100  A*68:01-B*44:02-C*07:04-DRB1*07:01-DQB1*02:01  USA NMDP Black South or Central American 0.11774,889

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 267) records   Pages: 1 2 3 of 3  


   

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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