Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

Please specify your search by selecting options from boxes. Then, click "Search" to find HLA Haplotype frequencies that match your criteria. Remember at least one option must be selected.
A B C DRB1 DPA1 DPB1 DQA1 DQB1

Population:  Country:  Source of dataset : 
Region:  Ethnic Origin:     Type of study :  Sort by: 
Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 100 (from 702) records   Pages: 1 2 3 4 5 6 7 8 of 8  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*11:01:01-B*44:03:02-C*07:06  South African Indian population 6.000050
 2  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 5.40472,403
 3  A*33:03:01-B*44:03:02-C*07:06  South African Indian population 5.000050
 4  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Northeast UCBB 3.7162296
 5  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India West UCBB 2.79385,829
 6  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 2.58154,204
 7  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India South UCBB 2.381211,446
 8  A*03:01:01-B*08:01:01-C*07:06:01-DRB1*03:01:01-DQB1*02:01:01-DPA1*02:01:08-DPB1*01:01:01  Brazil Barra Mansa Rio State Black 2.381073
 9  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 2.18365,849
 10  A*02:11-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 1.89865,849
 11  A*02:11-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Northeast UCBB 1.8581296
 12  A*29:02:01-B*44:03:02-C*07:06:01-DRB1*11:01:02-DQB1*06:02:01-DPB1*04:01:01  South African Black 1.4080142
 13  A*02:11-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 1.32552,403
 14  A*02:11-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 1.15544,204
 15  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
 16  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
 17  A*11:01:01-B*18:01:01-C*07:06  South African Mixed ancestry 1.000050
 18  A*26:01:01-B*27:07:01-C*07:06  South African Indian population 1.000050
 19  A*24:02-B*44:03-C*07:06-DRB1*13:01-DQA1*01:03-DQB1*06:03  United Arab Emirates Abu Dhabi 0.960052
 20  A*11:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 0.82892,403
 21  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
 22  A*24:02-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 0.71115,849
 23  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
 24  A*11:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Northeast UCBB 0.6757296
 25  A*11:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 0.60944,204
 26  A*26:01:01-B*51:01:01-C*07:06:01-DRB1*14:54:01-DQB1*03:02:01-DPA1*02:02:02-DPB1*04:02:01  Brazil Rio de Janeiro Parda 0.5882170
 27  A*11:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India West UCBB 0.54275,829
 28  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
 29  A*33:03-B*44:03-C*07:06-DRB1*10:01-DQB1*05:01  India Northeast UCBB 0.5068296
 30  A*24:03:01-B*15:12-C*07:06-DRB1*07:01:01-DQB1*05:03:01  Vietnam Kinh 0.4950101
 31  A*11:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 0.49105,849
 32  A*24:02-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 0.48744,204
 33  A*02:11-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India West UCBB 0.48375,829
 34  A*01:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India West UCBB 0.46755,829
 35  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
 36  A*01:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 0.42042,403
 37  A*24:02-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India West UCBB 0.40315,829
 38  A*24:02-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 0.38652,403
 39  A*01:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India South UCBB 0.361911,446
 40  A*02:01:01-B*44:03:02-C*07:06:01-DRB1*04:01:01-DQB1*06:09:01-DPB1*01:01:01  South African Black 0.3520142
 41  A*03:01:01-B*44:03:02-C*07:06:01-DRB1*11:01:02-DQB1*02:02:01-DPB1*01:01:01  South African Black 0.3520142
 42  A*03:01:01-B*58:02:01-C*07:06:01-DRB1*11:02:01-DQB1*02:02:01-DPB1*04:01:01  South African Black 0.3520142
 43  A*36:01-B*44:03:02-C*07:06:01-DRB1*13:02:01-DQB1*06:09:01-DPB1*01:01:01  South African Black 0.3520142
 44  A*24:02-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India South UCBB 0.341211,446
 45  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
 46  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
 47  A*01:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 0.29525,849
 48  A*03:01:01-B*44:03:02-C*07:06-DRB1*04:03:01-DQB1*03:02:01  India Karnataka Kannada Speaking 0.2870174
 49  A*33:03:01-B*07:06-C*07:06-DRB1*04:08:01-DQB1*03:02:01  India Karnataka Kannada Speaking 0.2870174
 50  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
 51  A*68:01:02-B*18:01:01-C*07:06-DRB1*13:02:01-DQB1*06:09:01  India Karnataka Kannada Speaking 0.2870174
 52  A*68:01:02-B*44:03:02-C*07:06-DRB1*07:03-DQB1*02:02:01  India Karnataka Kannada Speaking 0.2870174
 53  A*03:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 0.28494,204
 54  A*33:03-B*44:03-C*07:06-DRB1*14:04-DQB1*05:03  India East UCBB 0.26972,403
 55  A*02:11:01-B*44:03:02-C*07:06-DRB1*04:03:01-DQB1*03:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 56  A*11:01:01-B*44:03:02-C*07:06-DRB1*10:01:01-DQB1*05:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 57  A*11:01:01-B*44:03:02-C*07:06-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 58  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
 59  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
 60  A*33:03:01-B*40:06:01-C*07:06-DRB1*15:01:01-DQB1*03:03:02  India Andhra Pradesh Telugu Speaking 0.2688186
 61  A*33:03:01-B*51:01:01-C*07:06-DRB1*15:01:01-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 62  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
 63  A*03:01:01:01-B*44:03:02-C*07:06-DRB1*15:02-DQB1*06:01  Russia Bashkortostan, Tatars 0.2604192
 64  A*25:01:01-B*44:03:02-C*07:06-DRB1*09:01:02-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 65  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
 66  A*11:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India South UCBB 0.258011,446
 67  A*03:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 0.24175,849
 68  A*68:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 0.24004,204
 69  A*33:03-B*44:03-C*07:06-DRB1*04:03-DQB1*03:02  India North UCBB 0.23155,849
 70  A*01:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 0.23004,204
 71  A*03:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 0.22932,403
 72  A*02:11-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India South UCBB 0.221311,446
 73  A*33:03-B*44:03-C*07:06-DRB1*11:01-DQB1*03:01  India East UCBB 0.20812,403
 74  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*03:03  India East UCBB 0.20802,403
 75  A*01:01:01-B*15:03:01-C*07:06:01-DRB1*16:02:01-DQB1*03:01:01-DPA1*01:03:01-DPB1*105:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 76  A*68:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 0.19402,403
 77  A*02:11:01-B*44:03:02-C*07:06  England Blood Donors of Mixed Ethnicity 0.1927519
 78  A*33:03-B*44:03-C*07:06-DRB1*15:02-DQB1*06:01  India Central UCBB 0.19104,204
 79  A*31:12-B*39:11-C*07:06-DRB1*01:02-DQB1*05:01-DPB1*04:02  Panama 0.1900462
 80  A*33:03-B*44:03-C*07:06-DRB1*10:01-DQB1*05:01  India Central UCBB 0.17884,204
 81  A*68:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 0.17535,849
 82  A*02:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 0.17334,204
 83  A*01:01-B*18:01-C*07:06-DRB1*15:02-DQB1*02:02  India Northeast UCBB 0.1689296
 84  A*03:01-B*44:03-C*07:06-DRB1*01:01-DQB1*05:01  India Northeast UCBB 0.1689296
 85  A*03:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Northeast UCBB 0.1689296
 86  A*03:01-B*57:01-C*07:06-DRB1*07:01-DQB1*03:03  India Northeast UCBB 0.1689296
 87  A*11:01-B*44:03-C*07:06-DRB1*01:01-DQB1*05:01  India Northeast UCBB 0.1689296
 88  A*24:02-B*44:03-C*07:06-DRB1*15:02-DQB1*06:01  India Northeast UCBB 0.1689296
 89  A*31:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Northeast UCBB 0.1689296
 90  A*32:01-B*27:05-C*07:06-DRB1*01:01-DQB1*05:01  India Northeast UCBB 0.1689296
 91  A*33:03-B*44:03-C*07:06-DRB1*01:01-DQB1*05:01  India Northeast UCBB 0.1689296
 92  A*33:03-B*44:03-C*07:06-DRB1*04:01-DQB1*03:02  India Northeast UCBB 0.1689296
 93  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*03:03  India Northeast UCBB 0.1689296
 94  A*33:03-B*44:03-C*07:06-DRB1*11:01-DQB1*03:01  India Northeast UCBB 0.1689296
 95  A*33:03-B*44:03-C*07:06-DRB1*13:01-DQB1*06:03  India Northeast UCBB 0.1689296
 96  A*33:03-B*44:03-C*07:06-DRB1*14:04-DQB1*05:03  India Northeast UCBB 0.1689296
 97  A*33:03-B*44:03-C*07:06-DRB1*15:04-DQB1*05:02  India Northeast UCBB 0.1689296
 98  A*33:03-B*52:01-C*07:06-DRB1*04:01-DQB1*06:09  India Northeast UCBB 0.1689296
 99  A*02:01-B*47:03-C*07:06-DRB1*11:01-DQB1*02:01-DPB1*55:01  Tanzania Maasai 0.1597336
 100  A*29:02-B*47:03-C*07:06-DRB1*13:01-DQB1*04:02-DPB1*14:01  Tanzania Maasai 0.1597336

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 702) records   Pages: 1 2 3 4 5 6 7 8 of 8  


   

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.

support@allelefrequencies.net


Valid XHTML 1.0 Transitional