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 : 
Region:  Ethnic Origin:     Type of study :  Sort by: 
Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 100 (from 3,493) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 35  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  DRB1*13:01-DQA1*03-DQB1*03:03:02  Equatorial Guinea Bioko Island Bubi 27.5000100
 2  DRB1*09:01:02-DQA1*03-DQB1*03:03  Russia Siberia Khabarovsk Evenki 26.000025
 3  DRB1*09:01:02-DQB1*03:03  Samoa 25.900029
 4  DRB1*09:01:02-DQA1*03:01/03:02/03:03-DQB1*03:03:02  Russia Siberia Dudinka Nganasan 25.000024
 5  DQA1*03-DQB1*03:03:02  Cameroon Saa 24.1000172
 6  DRB1*09:01:02-DQA1*03-DQB1*03:03:02  Russia Siberia North East Kamchatka Koryak 22.300092
 7  DQA1*03-DQB1*03:03:02  Ecuador Cayapa 21.1000183
 8  DRB1*09:01:02-DQA1*03-DQB1*03:03  Russia Siberia Negidal 20.000035
 9  DRB1*09:01:02-DQA1*03-DQB1*03:03:02  Russia Siberia Gvaysugi Udege 19.000025
 10  DQA1*03-DQB1*03:03:02  Japan Fukuoka 17.400086
 11  DRB1*09:01-DQB1*03:03  Taiwan pop 2 16.3000364
 12  DRB1*09:01-DQB1*03:03  Sweden Northern Sami 16.0000154
 13  DRB1*09:01:02-DQA1*03-DQB1*03:03  Russia Siberia Ulchi 15.800073
 14  DRB1*09:01:02-DQA1*03-DQB1*03:03:02  Russia Siberia Chukotka Peninsula Chukchi 14.700059
 15  DRB1*09:01-DQB1*03:03  Vietnam Hanoi Kinh 14.1000103
 16  DRB1*09:01-DQB1*03:03  Japan Hokkaido Ainu 14.000050
 17  DRB1*09:01:02-DQB1*03:03  China Inner Mongolia Autonomous Region Northeast 12.3990496
 18  DRB1*09:01-DQB1*03:03  Japan Central 12.3000371
 19  DRB1*09:01:02-DQA1*03:01-DQB1*03:03  China Urumqi Han 11.900059
 20  DRB1*09-DQA1*03:01-DQB1*03:03  Russia Mari 11.4000202
 21  DQA1*03-DQB1*03:03  China, Xinjiang Uyghur Autonomous Region Hui 11.250040
 22  DRB1*07:01-DQB1*03:03  Germany pop 3 10.8000111
 23  DRB1*09:01:02-DQA1*03:01-DQB1*03:03  Japan pop 2 10.8000916
 24  DRB1*09:01-DQA1*03:01-DQB1*03:03:02  Canada British Columbia Athabaskan 10.500062
 25  DRB1*09:01:02-DQB1*03:03:02  China Shandong Province Han 9.700098
 26  DRB1*09:01-DQB1*03:03  USA Asian pop 2 9.66401,772
 27  DRB1*09:01:02-DQA1*03-DQB1*03:03  Russia Siberia Irkutsk Tofalar 9.300043
 28  DRB1*09:01-DQA1*03:02-DQB1*03:03:02  South Korea pop 5 9.1000467
 29  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Tunisia 9.0000100
 30  DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  China Zhejiang Han pop 2 8.5301833
 31  DRB1*09:01-DQA1*03:02-DQB1*03:03  South Korea pop 1 8.3000324
 32  DRB1*13:01-DQA1*03:02-DQB1*03:03:02  Cameroon Yaounde 7.900092
 33  DRB1*09:01-DQB1*03:03  USA Alaska Yupik 7.7000252
 34  DRB1*09:01-DQB1*03:03  Sweden Southern Sami 7.6000130
 35  DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Hong Kong Chinese HKBMDR. DQ and DP 7.58251,064
 36  DRB1*04:02-DQA1*03:01-DQB1*03:03  Iran Azeri 7.5000100
 37  DQA1*03-DQB1*03:03  China, Xinjiang Uyghur Autonomous Region Han 7.140070
 38  DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*05:01  China Canton Han 6.8000264
 39  DRB1*09:01:02-DQB1*03:03  Philippines 6.700034
 40  DRB1*09:01-DQB1*03:03  Vietnam HoaBinh Muong 6.700083
 41  DRB1*07:01-DQA1*02:01-DQB1*03:03  India Northeast Vaish 6.3000198
 42  DRB1*07-DQA1*02:01-DQB1*03:03  Italy pop 2 5.700053
 43  DQA1*02:01-DQB1*03:03:02  France Ceph 5.6000124
 44  DRB1*09:01:02-DQB1*03:03  Papua New Guinea Lowland 5.600047
 45  DRB1*04:03-DQA1*03:01-DQB1*03:03  Iran Kurd 5.5000100
 46  A*02-B*35-DRB1*09:01-DQB1*03:03  Bolivia La Paz Aymaras 5.491087
 47  DRB1*07-DQA1*02:01-DQB1*03:03  Russia Mari 5.4000202
 48  DQB1*03:03-DPB1*05:01:01  China Inner Mongolia Autonomous Region Northeast 5.3300496
 49  DRB1*07:01-DQA1*02:01-DQB1*03:03  India Uttar Pradesh 5.2000202
 50  DQA1*02:01-DQB1*03:03  Tunisia 5.1000100
 51  DRB1*07:01-DQA1*02:01-DQB1*03:03  India Northeast Mathur 5.1000155
 52  DRB1*09:01:02-DQA1*03-DQB1*03:03  Russia Siberia Khanty Mansi 5.100068
 53  DRB1*09:01:02-DQB1*03:03-DPB1*05:01:01  China Inner Mongolia Autonomous Region Northeast 5.0170496
 54  B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Ireland South 5.0000250
 55  DQA1*03-DQB1*03:03:02  Russia Tuva pop 2 5.0000169
 56  DRB1*04:02-DQA1*03:01-DQB1*03:03  Iran Kurd 5.0000100
 57  DRB1*07:01-DQA1*02:01-DQB1*03:03  Slovenia pop 2 5.0000140
 58  DRB1*07-DQA1*02:01-DQB1*03:03  Belarus Gomel Region 5.0000100
 59  DRB1*07-DQA1*02:01-DQB1*03:03  Russia Arkhangelsk 4.900081
 60  DQA1*03-DQB1*03:03  China, Xinjiang Uyghur Autonomous Region Kazakh 4.810052
 61  DRB1*09:01:02-DQA1*03:01-DQB1*03:03  China Urumqi Kazak 4.800042
 62  DRB1*04:03-DQA1*03:01-DQB1*03:03  Iran Yazd Zoroastrian 4.600065
 63  DRB1*09:01:02-DQB1*03:03:02-DPB1*04:02  Mexico Chihuahua Tarahumara 4.600044
 64  DRB1*09:01:02-DQA1*03-DQB1*03:03  Russia Tuva Todja 4.500022
 65  DRB1*09:01:02-DQA1*03-DQB1*03:03:02  Russia Siberia Chukotka Peninsula Eskimo 4.400080
 66  DRB1*09:01-DQB1*03:03-DPB1*05:01  South Korea pop 1 4.4000324
 67  A*23-B*50-DRB1*07:01-DQB1*03:03  Tunisia Ghannouch 4.300082
 68  A*24:02-B*40:02-DRB1*09:01-DQB1*03:03  USA Alaska Yupik 4.3000252
 69  DRB1*09:01:02-DQA1*03-DQB1*03:03:02  Russia Siberia Polygus Evenk 4.300035
 70  DRB1*07:01-DQA1*02:01-DQB1*03:03  India Northeast Kayastha 4.2000190
 71  DRB1*09:01-DQA1*03:02-DQB1*03:03-DPB1*05:01  South Korea pop 1 4.2000324
 72  DQA1*05:05-DQB1*03:03  Tunisia 4.1000100
 73  DRB1*07-DQA1*02:01-DQB1*03:03  Russia Vologda 4.1000121
 74  DRB1*07:01-DQA1*02:01-DQB1*03:03  India Northeast Rastogi 4.0000196
 75  DRB1*07:01-DQA1*02:01-DQB1*03:03  Russia Siberia Kushun Buryat 4.000025
 76  A*02:07-B*46:01-C*01:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Chinese 3.974199,672
 77  DRB1*09:01-DQA1*03:02-DQB1*03:03-DPB1*02:01  South Korea pop 11 3.9000149
 78  DRB1*07-DQA1*02:01-DQB1*03:03  Russia Smolensk 3.8000156
 79  DQB1*03:03-DPB1*02:01:02  China Inner Mongolia Autonomous Region Northeast 3.7390496
 80  DRB1*04-DQA1*03:01-DQB1*03:03  Mexico Guadalajara Mestizo 3.700054
 81  DRB1*09:01:02-DQA1*03:02-DQB1*03:03:02-DPB1*05:01  South Korea pop 2 3.7000207
 82  DRB1*07:01-DQA1*02:01-DQB1*03:03  USA European American 3.66001,899
 83  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India Tamil Nadu 3.63482,492
 84  DRB1*07-DQA1*02:01-DQB1*03:03  Czech Republic pop 3 3.6000180
 85  DRB1*09:01:02-DQB1*03:03:02  China Yunnan Province Lahu 3.600070
 86  DRB1*12:01-DQA1*05:01-DQB1*03:03  Slovenia pop 2 3.6000140
 87  A*02:01-B*40:01-C*03:04-DRB1*09:01-DQB1*03:03  USA NMDP Hawaiian or other Pacific Islander 3.507211,499
 88  A*02-B*39-DRB1*09:01-DQB1*03:03  Bolivia La Paz Aymaras 3.448087
 89  DRB1*07:01-DQA1*02:01-DQB1*03:03  Iran Fars Parsi 3.400073
 90  DRB1*07:01-DQB1*03:03-DPB1*04:01  Ireland South 3.4000250
 91  DRB1*07-DQA1*02:01-DQB1*03:03  Ukraine Lvov 3.4000102
 92  DRB1*09:01:02-DQA1*03-DQB1*03:03  China Urumqi Uyghur 3.400057
 93  A*02:07:01-B*46:01:01-C*01:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 3.37431,734
 94  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03  Brazil Puyanawa 3.3333150
 95  A*34-B*40:01-DRB1*09-DQB1*03:03  Mexico Jalisco, Tlajomulco 3.333330
 96  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Malaysia Peninsular Indian 3.3210271
 97  DRB1*09:01-DQA1*03:02-DQB1*03:03-DPB1*05:01  South Korea pop 11 3.3000149
 98  DRB1*09:01:02-DQB1*03:03-DPB1*02:01:02  China Inner Mongolia Autonomous Region Northeast 3.1810496
 99  A*02:07-B*46:01-C*01:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Vietnamese 3.132643,540
 100  A*01:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  India Andhra Pradesh Telugu Speaking 3.1035186

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 3,493) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 35  


   

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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