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 284) records   Pages: 1 2 3 of 3  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  DRB1*12:02-DQA1*06:01-DQB1*03:01  China Urumqi Han 5.900059
 2  DQA1*06:01-DQB1*03:01  China, Xinjiang Uyghur Autonomous Region Han 5.000070
 3  DQA1*06:01-DQB1*03:01-DPA1*02:02-DPB1*05:01  Hong Kong Chinese HKBMDR. DQ and DP 4.11181,064
 4  DQA1*06:01-DQB1*03:01  China, Xinjiang Uyghur Autonomous Region Hui 3.750040
 5  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*05:01  China Canton Han 3.5000264
 6  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*05:01  South Korea pop 11 3.5000149
 7  DRB1*12:01/12:06-DQA1*06:01-DQB1*03:01/03:09  Russia Tuva pop3 3.400044
 8  DQA1*06:01-DQB1*03:01-DPA1*01:03-DPB1*21:01  Hong Kong Chinese HKBMDR. DQ and DP 3.34571,064
 9  DRB1*12:02-DQA1*06:01:01-DQB1*03:01  South Korea pop 5 3.3000467
 10  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*02:02-DPB1*05:01  China Zhejiang Han pop 2 2.8945833
 11  DRB1*15:01-DQA1*06:01-DQB1*02:01  India Northeast Rajbanshi 2.500098
 12  DRB1*12:02-DQA1*06:01-DQB1*03:01  Japan pop 2 2.3000916
 13  DRB1*12:02-DQA1*06:01-DQB1*03:01  South Korea pop 1 2.3000324
 14  DRB1*12:02-DQA1*06:01-DQB1*03:01  India Uttar Pradesh 2.0000202
 15  DRB1*12:02-DQA1*06:01-DQB1*03:01/03:09  Russia Siberia Kushun Buryat 2.000025
 16  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*13:01  China Canton Han 1.8000264
 17  DQA1*06:01-DQB1*03:01  Japan Fukuoka 1.700086
 18  DQA1*06:01-DQB1*03:01  India Bombay 1.700059
 19  DRB1*12:02-DQA1*06:01:02-DQB1*03:01-DPB1*05:01  South Korea pop 2 1.7000207
 20  DRB1*12:02-DQA1*06:01-DQB1*03:01  India Northeast Rastogi 1.7000196
 21  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*03:01  China Canton Han 1.7000264
 22  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*21:01  China Canton Han 1.6000264
 23  DRB1*08-DQA1*06:01-DQB1*03:01  Belarus Vitebsk Region 1.500070
 24  DRB1*12:02-DQA1*06:01-DQB1*03:01  India Northeast Kayastha 1.5000190
 25  DRB1*12:02-DQA1*06:01-DQB1*03:01  India Northeast Shia 1.5000190
 26  DRB1*15:01-DQA1*06:01-DQB1*02:01  India Northeast Mech 1.500063
 27  DQA1*06:01-DQB1*03:03  China, Xinjiang Uyghur Autonomous Region Uyghur 1.410071
 28  DQA1*06:01-DQB1*06:03  China, Xinjiang Uyghur Autonomous Region Uyghur 1.410071
 29  DRB1*12:01/12:06-DQA1*06:01-DQB1*03:01/03:09  Russia Siberia Negidal 1.400035
 30  DQA1*06:01-DQB1*03:03  China, Xinjiang Uyghur Autonomous Region Hui 1.250040
 31  DRB1*12:02-DQA1*06:01:02-DQB1*03:01-DPB1*02:02  South Korea pop 2 1.2000207
 32  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*01:03-DPB1*21:01  China Zhejiang Han pop 2 1.1312833
 33  DQA1*06:01-DQB1*03:01-DPA1*01:03-DPB1*02:01  Hong Kong Chinese HKBMDR. DQ and DP 1.12731,064
 34  DRB1*08:03:02-DQA1*06:01:01-DQB1*03:01  South Korea pop 5 1.1000467
 35  DRB1*12:02-DQA1*06:01-DQB1*03:01/03:09  Russia Tuva pop3 1.100044
 36  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*02:01  South Korea pop 1 1.1000324
 37  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*04:01  China Canton Han 1.1000264
 38  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*14:01  China Canton Han 1.1000264
 39  DRB1*08:02-DQA1*06:01-DQB1*03:01  Mexico Guanajuato and Jalisco Mestizo 1.0000101
 40  A*11:01-B*15:02-C*08:01-DRB1*07:01-DQA1*06:01-DQB1*03:01  United Arab Emirates Abu Dhabi 0.960052
 41  A*11:01-B*15:13-C*05:01-DRB1*11:04-DQA1*06:01-DQB1*03:01  United Arab Emirates Abu Dhabi 0.960052
 42  DQA1*06:01-DQB1*03:01  China, Xinjiang Uyghur Autonomous Region Kazakh 0.960052
 43  DQA1*06:01-DQB1*04:02  China, Xinjiang Uyghur Autonomous Region Kazakh 0.960052
 44  DQA1*06:01-DQB1*06:01  China, Xinjiang Uyghur Autonomous Region Kazakh 0.960052
 45  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*01:03-DPB1*02:01  China Zhejiang Han pop 2 0.7495833
 46  DQA1*06:01-DQB1*02:01  China, Xinjiang Uyghur Autonomous Region Han 0.710070
 47  DQA1*06:01-DQB1*04:02  China, Xinjiang Uyghur Autonomous Region Han 0.710070
 48  DQA1*06:01-DQB1*02:01  China, Xinjiang Uyghur Autonomous Region Uyghur 0.700071
 49  A*02-B*15-C*12-DRB1*12-DQA1*06-DQB1*03  Mexico Tapachula, Chiapas Mestizo Population 0.694472
 50  B*15-C*12-DRB1*12-DQA1*06-DQB1*03  Mexico Tapachula, Chiapas Mestizo Population 0.694472
 51  DRB1*12-DQA1*06-DQB1*03  Mexico Tapachula, Chiapas Mestizo Population 0.694472
 52  DQA1*06:01-DQB1*03:01-DPA1*01:03-DPB1*03:01  Hong Kong Chinese HKBMDR. DQ and DP 0.65021,064
 53  DQA1*06:01-DQB1*03:01-DPA1*02:02-DPB1*02:02  Hong Kong Chinese HKBMDR. DQ and DP 0.61411,064
 54  A*24:17-B*15:02-C*08:01-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*02:01  Sri Lanka Colombo 0.5602714
 55  DQA1*06:01-DQB1*03:01-DPA1*02:02-DPB1*03:01  Hong Kong Chinese HKBMDR. DQ and DP 0.55191,064
 56  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*02:02-DPB1*02:02  China Zhejiang Han pop 2 0.5157833
 57  DQA1*06:01-DQB1*03:01-DPA1*02:01-DPB1*14:01  Hong Kong Chinese HKBMDR. DQ and DP 0.47771,064
 58  A*11:01-B*44:02-C*07:04-E*01:01:01-F*01:01:02-G*01:06-DRB1*11:01-DQA1*06:01-DQB1*03:01  Portugal Azores Terceira Island 0.4386130
 59  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*02:02-DPB1*02:01  China Zhejiang Han pop 2 0.3544833
 60  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*01:03-DPB1*03:01  China Zhejiang Han pop 2 0.3294833
 61  A*24:07-B*35:05-C*04:01-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*02:02-DPB1*05:01  United Arab Emirates Pop 1 0.3271570
 62  A*03:01:01-B*07:02:01-C*07:02:01-DRB1*08:03:02-DQA1*06:01:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*02:01:02  Russia Belgorod region 0.3268153
 63  A*11:01:01-B*51:01:01-C*03:03:01-DRB1*15:01:01-DQA1*06:01:01-DQB1*03:01-DPA1*01:03:01-DPB1*04:01  Russia Belgorod region 0.3268153
 64  DQA1*06:01-DQB1*03:01-DPA1*02:01-DPB1*13:01  Hong Kong Chinese HKBMDR. DQ and DP 0.31711,064
 65  A*02:01-B*51:01-C*14:02-DRB1*08:03-DQA1*06:01-DQB1*03:01-DPB1*03:01  South Africa Worcester 0.3000159
 66  A*11:01-B*35:05-C*04:01-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*04:01  South Africa Worcester 0.3000159
 67  A*24:02-B*08:01-C*04:01-DRB1*15:03-DQA1*06:01-DQB1*03:01-DPB1*49:01  South Africa Worcester 0.3000159
 68  A*24:02-B*15:02-C*08:01-DRB1*03:02-DQA1*06:01-DQB1*03:02-DPB1*04:02  South Africa Worcester 0.3000159
 69  A*24:02-B*18:01-C*07:04-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*04:01  South Africa Worcester 0.3000159
 70  A*24:02-B*45:01-C*07:02-DRB1*13:01-DQA1*06:01-DQB1*06:03-DPB1*18:01  South Africa Worcester 0.3000159
 71  A*30:01-B*58:02-C*06:02-DRB1*12:02-DQA1*06:01-DQB1*06:03-DPB1*01:01  South Africa Worcester 0.3000159
 72  DQA1*06:01-DQB1*03:01-DPA1*02:02-DPB1*135:01  Hong Kong Chinese HKBMDR. DQ and DP 0.28121,064
 73  A*24:07-B*35:05-C*04:01-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*13:01  Sri Lanka Colombo 0.2801714
 74  A*24:17-B*15:02-C*08:01-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*04:01  Sri Lanka Colombo 0.2801714
 75  DQA1*06:01-DQB1*03:01-DPA1*01:03-DPB1*04:02  Hong Kong Chinese HKBMDR. DQ and DP 0.27151,064
 76  DQA1*06:01-DQB1*03:01-DPA1*02:07-DPB1*19:01  Hong Kong Chinese HKBMDR. DQ and DP 0.26571,064
 77  A*02:07-B*46:01-C*01:02-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*05:01  USA San Diego 0.2600496
 78  A*11:01-B*15:02-C*08:01-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*21:01  USA San Diego 0.2600496
 79  A*11:01-B*40:02-C*01:02-DRB1*12:02-DQA1*06:01-DQB1*05:03-DPB1*05:01  USA San Diego 0.2600496
 80  A*24:02-B*46:01-C*01:02-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*13:01  USA San Diego 0.2600496
 81  A*24:07-B*35:05-C*04:01-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*04:01  USA San Diego 0.2600496
 82  A*26:01-B*13:01-C*03:04-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*02:01  USA San Diego 0.2600496
 83  A*32:01-B*44:02-C*05:01-DRB1*08:03-DQA1*06:01-DQB1*03:01-DPB1*06:01  USA San Diego 0.2600496
 84  A*33:03-B*38:02-C*03:02-DRB1*12:02-DQA1*06:01-DQB1*02:01-DPB1*05:01  USA San Diego 0.2600496
 85  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*04:01-DPB1*13:01  China Zhejiang Han pop 2 0.2042833
 86  A*02:01-B*13:01-C*03:04-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*02:02-DPB1*05:01  Japan pop 17 0.20003,078
 87  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*01:03-DPB1*05:01  China Zhejiang Han pop 2 0.1998833
 88  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*04:01-DPB1*296:01  China Zhejiang Han pop 2 0.1801833
 89  DQA1*06:01-DQB1*03:01-DPA1*01:03-DPB1*04:01  Hong Kong Chinese HKBMDR. DQ and DP 0.17141,064
 90  DQA1*06:01-DQB1*03:01-DPA1*02:01-DPB1*01:01  Hong Kong Chinese HKBMDR. DQ and DP 0.16681,064
 91  DQA1*06:01-DQB1*03:01-DPA1*02:01-DPB1*05:01  Hong Kong Chinese HKBMDR. DQ and DP 0.15281,064
 92  A*32:01-B*51:01-C*14:02-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*02:01-DPB1*45:01  United Arab Emirates Pop 1 0.1402570
 93  A*02:06-B*15:02-C*08:01-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*02:01  Sri Lanka Colombo 0.1401714
 94  A*11:01-B*38:02-C*07:02-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*04:01  Sri Lanka Colombo 0.1401714
 95  A*24:02-B*40:06-C*14:02-DRB1*08:03-DQA1*06:01-DQB1*03:01-DPB1*02:01  Sri Lanka Colombo 0.1401714
 96  A*33:03-B*15:18-C*07:04-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*04:01  Sri Lanka Colombo 0.1401714
 97  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*02:01-DPB1*05:01  China Zhejiang Han pop 2 0.1383833
 98  A*02-B*51-C*14-DRB1*08-DQA1*06-DQB1*03  Spain, Castilla y Leon, Northwest, 0.13131,743
 99  DRB1*08:03-DQA1*06:01-DQB1*03:01-DPA1*02:02-DPB1*05:01  China Zhejiang Han pop 2 0.1200833
 100  DRB1*12:02-DQA1*06:01-DQB1*03:01-DPA1*01:03-DPB1*41:01  China Zhejiang Han pop 2 0.1200833

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 284) 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.

support@allelefrequencies.net


Valid XHTML 1.0 Transitional