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 210) records   Pages: 1 2 3 of 3  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02-B*51-DRB1*09-DQB1*03:03  Ecuador Amazonia Mixed Ancestry 2.564139
 2  A*11:01:01-B*51-C*14:02:01-DRB1*09:01:02-DQB1*03:03:02-DPB1*04:01:01  China Yunnan Province Lisu 1.7000111
 3  A*02:01-B*51:01-DRB1*09:01:02-DQB1*03:03  USA South Dakota Lakota Sioux 1.4000302
 4  A*24:02-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03-DPB1*02:01  Japan Central 1.1000371
 5  A*02-B*51-DRB1*09-DQB1*03:03  Mexico Yucatan Rural 0.7463132
 6  A*24:02-B*51:01-C*14:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Korean 0.725077,584
 7  A*02-B*51-DRB1*09:01-DQB1*03:03  Bolivia La Paz Aymaras 0.706087
 8  A*24:02-B*51:01-C*14:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Japanese 0.681124,582
 9  A*02-B*51-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 0.55411,173
 10  A*02:01-B*51:06-DRB1*09:01-DQB1*03:03  Iran Tabriz Azeris 0.515597
 11  A*02-B*51-DRB1*09-DQB1*03:03  Ecuador Andes Mixed Ancestry 0.4854824
 12  A*02:01-B*51:01-DRB1*09:01-DQB1*03:03  Peru Titikaka Lake Uros 0.4600105
 13  A*02-B*51-DRB1*09-DQA1*03-DQB1*03:03  Russia, South Ural, Chelyabinsk region, Nagaybaks 0.4400112
 14  A*02:01-B*51:01-C*14:02-E*01:03:02-F*01:01:02-G*01:01-DRB1*09:01:02-DQA1*03:02-DQB1*03:03  Portugal Azores Terceira Island 0.4386130
 15  A*02-B*51-DRB1*09-DQB1*03:03  Ecuador Coast Mixed Ancestry 0.4202238
 16  A*24-B*51-DRB1*09-DQB1*03:03  Ecuador Coast Mixed Ancestry 0.4202238
 17  A*31:01:02:01-B*51:01:01-C*15:02:01:01-DRB1*09:01-DQB1*03:03:02  Russia Bashkortostan, Bashkirs 0.4167120
 18  A*11:01:01-B*51:01:01-C*14:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.40741,734
 19  A*24:02-B*51:01-C*14:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.39003,078
 20  A*02:01-B*51:08-C*07:02-DRB1*09:01-DQB1*03:03  Mexico Mexico City Mestizo population 0.3497143
 21  B*51:08-C*07:02-DRB1*09:01-DQB1*03:03  Mexico Mexico City Mestizo population 0.3497143
 22  A*31-B*51-DRB1*09-DQB1*03:03  Mexico Mexico City Metropolitan Area Rural 0.3289150
 23  A*02:06-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.31101,772
 24  A*24-B*51-DRB1*09-DQB1*03:03  Ecuador Andes Mixed Ancestry 0.3034824
 25  A*24-B*51-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 0.29841,173
 26  A*02:01-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03-DPB1*04:02  Russia Karelia 0.27791,075
 27  A*25-B*51-DRB1*09-DQB1*03:03  Mexico Tlaxcala, Tlaxcala city 0.2762181
 28  A*24:02-B*51:01-C*14:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Chinese 0.275299,672
 29  A*24:02-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.27101,772
 30  A*02:06:01-B*51:01:01-C*14:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.26841,734
 31  A*24:02-B*51:01-C*03:03-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 32  A*24:02-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 33  A*68-B*51-DRB1*09-DQB1*03:03  Ecuador Andes Mixed Ancestry 0.2427824
 34  A*24:02-B*51:01-C*14:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.23003,078
 35  A*31-B*51-DRB1*09-DQB1*03:03  Mexico Coahuila Rural 0.2294216
 36  A*24:02:01-B*51:01:01-C*14:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.22731,734
 37  A*02:01:01-B*51:01:01-C*14:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.21231,734
 38  A*02-B*51-DRB1*09-DQB1*03:03  Mexico Chihuahua Rural 0.2092236
 39  A*03-B*51-DRB1*09-DQB1*03:03  Mexico Chihuahua Rural 0.2092236
 40  A*11:01-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.20801,772
 41  A*02:01-B*51:01-C*15:02-DRB1*09:01:02-DQB1*03:03:02  England North West 0.2000298
 42  A*11:01-B*51:01-C*05:01-DRB1*09:01:02-DQB1*03:03:02  England North West 0.2000298
 43  A*02-B*51-DRB1*09-DQB1*03:03  Mexico Zacatecas Rural 0.1859266
 44  A*25-B*51-DRB1*09-DQB1*03:03  Mexico Puebla Rural 0.1799833
 45  A*25:01-B*51:01-C*03:03-DRB1*09:01-DQB1*03:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.17104,335
 46  A*68-B*51-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 0.17051,173
 47  A*02:05-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  Italy pop 5 0.1400975
 48  A*24:02-B*51:01-C*14:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Vietnamese 0.137343,540
 49  A*31:01-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.13201,772
 50  A*31:01-B*51:01-C*14:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.13003,078
 51  A*25-B*51-DRB1*09-DQB1*03:03  Mexico Coahuila, Torreon 0.1250396
 52  A*11:01:01-B*51:02:01-C*15:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.11531,734
 53  A*02:01-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03-DPB1*02:01  Russia Karelia 0.11291,075
 54  A*02:01-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03  Germany DKMS - Turkey minority 0.11204,856
 55  A*68:01-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03  India West UCBB 0.11145,829
 56  A*11:01-B*51:02-C*04:01-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Malay 0.1052951
 57  A*24:02-B*51:01-C*07:04-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Malay 0.1052951
 58  A*24:02-B*51:02-C*14:02-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Malay 0.1052951
 59  A*02:07-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.08901,772
 60  A*01:01:01-B*51:01:01-C*14:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.08651,734
 61  A*31-B*51-DRB1*09-DQB1*03:03  Ecuador Mixed Ancestry 0.08531,173
 62  A*25-B*51-DRB1*09-DQB1*03:03  Mexico Jalisco, Guadalajara city 0.08381,189
 63  A*24-B*51-DRB1*09:01-DQA1*03:01-DQB1*03:03  Brazil Paraná Caucasian 0.0780641
 64  A*11:01-B*51:01-C*03:03-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*02:01  Sri Lanka Colombo 0.0700714
 65  A*02:01-B*51:01-C*14:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.07003,078
 66  A*02:01-B*51:01-C*14:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:02-DPB1*05:01  Japan pop 17 0.07003,078
 67  A*01:01:01:01-B*51:01:01-C*15:02:01:01-DRB1*09:01:02-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.06621,510
 68  A*02:01-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.06401,772
 69  A*31-B*51-DRB1*09-DQB1*03:03  Ecuador Andes Mixed Ancestry 0.0607824
 70  A*03:01-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  India West UCBB 0.05915,829
 71  A*03:01-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03-DPB1*04:02  Russia Karelia 0.05651,075
 72  A*24:02-B*51:01-C*01:02-DRB1*09:01-DQB1*03:03-DPB1*16:01  Russia Karelia 0.05651,075
 73  A*24:02-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03-DPB1*15:01  Russia Karelia 0.05651,075
 74  A*02:02-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Malay 0.0526951
 75  A*11:01-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Malay 0.0526951
 76  A*24:07-B*51:02-C*17:01-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Malay 0.0526951
 77  A*24:10-B*51:01-C*04:01-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Malay 0.0526951
 78  A*11:01-B*51:01-C*03:03-DRB1*09:01-DQB1*03:03  Colombia Bogotá Cord Blood 0.05131,463
 79  A*02:01-B*51:01-C*01:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.05101,772
 80  A*24:02-B*51:01-C*14:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Southeast Asian 0.050827,978
 81  A*02:01-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.04701,999
 82  A*11:01-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03  USA Hispanic pop 2 0.04701,999
 83  A*11:02:01-B*51:01:01-C*14:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.04671,734
 84  A*02:01-B*51:01-C*03:04-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.04401,772
 85  A*24:02-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03  USA African American pop 4 0.04402,411
 86  A*68:01-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.04401,772
 87  A*02:01-B*51:01-C*07:01-DRB1*09:01-DQB1*03:03  Germany DKMS - Italy minority 0.04301,159
 88  A*23:01-B*51:01-C*03:03-DRB1*09:01-DQB1*03:03  Germany DKMS - Italy minority 0.04301,159
 89  A*24:02-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03  Germany DKMS - Italy minority 0.04301,159
 90  A*02-B*51-DRB1*09-DQB1*03:03  Mexico Jalisco, Guadalajara city 0.04191,189
 91  A*02:01-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  India East UCBB 0.04162,403
 92  A*26:01-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  India West UCBB 0.03695,829
 93  A*24:02-B*51:01-C*14:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Filipino 0.035750,614
 94  A*02:01-B*51:01-C*03:04-DRB1*09:01-DQB1*03:03  Colombia Bogotá Cord Blood 0.03421,463
 95  A*24:02-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03  Colombia Bogotá Cord Blood 0.03421,463
 96  A*29:02-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03  Colombia Bogotá Cord Blood 0.03421,463
 97  A*02:01-B*51:01-C*03:03-DRB1*09:01-DQB1*03:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 98  A*03:01-B*51:01-C*16:02-DRB1*09:01-DQB1*03:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 99  A*02:05:01-B*51:01:01-C*16:02:01-DRB1*09:01:02-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.03311,510
 100  A*03:01:01:01-B*51:01:01-C*15:02:01:01-DRB1*09:01:02-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.03311,510

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 210) 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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