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 228) records   Pages: 1 2 3 of 3  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02-B*40:01-DRB1*13-DQB1*06  Mexico Chihuahua Chihuahua City 0.8403119
 2  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*02:01  USA San Diego 0.7810496
 3  A*02:01-B*40:01-C*03:04-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP European Caucasian 0.76601,242,890
 4  A*24:02:01-B*40:01:02-C*03:04:01-DRB1*13:01:01-DQB1*06:03:01  Spain, Canary Islands, Gran canaria island 0.7000215
 5  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04  USA NMDP American Indian South or Central America 0.69025,926
 6  A*02:01:01-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQA1*01:03:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*02:01:02  Russia Belgorod region 0.6536153
 7  A*02:01-B*40:01-C*03:04-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP North American Amerindian 0.621035,791
 8  A*11:01:01-B*40:01:02-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.5376186
 9  A*68:01:02-B*40:01:02-C*03:04:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.5376186
 10  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*03:01  Germany DKMS - German donors 0.51493,456,066
 11  A*02:01-B*40:01-C*03:04-DRB1*13:02:01-DQB1*06:04  England North West 0.5000298
 12  A*32:01:01-B*40:01:02-C*03:04:01-DRB1*13:01:01-DQB1*06:03:01  Spain, Canary Islands, Gran canaria island 0.4700215
 13  A*02:01:01-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQB1*06:04:01  Poland BMR 0.449623,595
 14  A*24-B*40:01-DRB1*13-DQB1*06  Mexico Nuevo Leon, Monterrey city 0.4425226
 15  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*03:01  Nicaragua Managua 0.4329339
 16  A*02:01:01:01-B*40:01:02-C*03:04:01:01-DRB1*13:02:01-DQB1*06:04:01  Russia Nizhny Novgorod, Russians 0.42721,510
 17  A*02:01:01-B*40:01:02-C*05:01:01-DRB1*13:01:01-DQA1*01:03:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:01  Russian Federation Vologda Region 0.4202119
 18  A*68:01:01-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQA1*01:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*02:01  Russian Federation Vologda Region 0.4202119
 19  A*23:01:01-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.3891521
 20  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04  Germany DKMS - Italy minority 0.38801,159
 21  A*31:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  Mexico Mexico City Mestizo population 0.3497143
 22  B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  Mexico Mexico City Mestizo population 0.3497143
 23  B*40:01-C*16:02-DRB1*13:01-DQB1*06:03  Mexico Mexico City Mestizo population 0.3497143
 24  A*68:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Russia Karelia 0.33871,075
 25  A*02:01:01-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*02:01:02  Brazil Barra Mansa Rio State Caucasian 0.3125405
 26  A*03:01:01-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 27  A*03:01-B*40:01-C*03:04-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*03:01  South Africa Worcester 0.3000159
 28  A*03:01-B*40:01-C*03:04-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*04:01  South Africa Worcester 0.3000159
 29  A*26:01-B*40:01-C*03:04-DRB1*13:01:01-DQB1*06:03:01  England North West 0.3000298
 30  A*03:01:01-B*40:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  India Karnataka Kannada Speaking 0.2870174
 31  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*03:01  Russia Karelia 0.27151,075
 32  A*24:02:01-B*40:01:02-C*06:02:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 33  A*30:02-B*40:01-C*03:04-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*04:01  USA San Diego 0.2600496
 34  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.23243,456,066
 35  A*02:01-B*40:01-C*03:04-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP Middle Eastern or North Coast of Africa 0.228370,890
 36  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04  USA African American pop 4 0.21802,411
 37  A*01:01-B*40:01-C*03:04-DRB1*13:02:01-DQB1*06:09  England North West 0.2000298
 38  A*03:01-B*40:01-C*03:04-DRB1*13:01:01-DQB1*06:03:01  England North West 0.2000298
 39  A*24:02-B*40:01-C*03:04-DRB1*13:02:01-DQB1*06:04  England North West 0.2000298
 40  A*02:01:01-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 41  A*01:01-B*40:01-C*03:02-DRB1*13:01-DQB1*06:04-DPB1*13:01  Panama 0.1900462
 42  A*24-B*40:01-DRB1*13-DQB1*06  Mexico Zacatecas Rural 0.1859266
 43  A*02-B*40:01-DRB1*13-DQB1*06  Mexico Veracruz Rural 0.1848539
 44  A*03-B*40:01-DRB1*13-DQB1*06  Mexico Jalisco Rural 0.1706585
 45  A*11:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  India Northeast UCBB 0.1689296
 46  A*02:01-B*40:01-C*03:04-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP Mexican or Chicano 0.1530261,235
 47  A*02-B*40:01-DRB1*13-DQB1*06  Mexico Durango Rural 0.1529326
 48  A*11:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  India South UCBB 0.134311,446
 49  A*02:01-B*40:01-C*03:04-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP Hispanic South or Central American 0.1286146,714
 50  A*02-B*40:01-DRB1*13-DQB1*06  Mexico Coahuila, Torreon 0.1250396
 51  A*32:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Russia Karelia 0.11921,075
 52  A*11:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*03:01  Russia Karelia 0.10991,075
 53  A*02:01-B*40:01-C*03:04-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP African American pop 2 0.1094416,581
 54  A*02:01-B*40:01-C*03:04-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP Caribean Hispanic 0.1082115,374
 55  A*26:01:01-B*40:01:02-C*03:04:01:01-DRB1*13:02:01-DQB1*06:04:01  Russia Nizhny Novgorod, Russians 0.10261,510
 56  A*02:01:01-B*40:01:02-C*03:04:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.098123,595
 57  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*02:01  Germany DKMS - German donors 0.09423,456,066
 58  A*01:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04  USA Hispanic pop 2 0.09401,999
 59  A*68-B*40:01-DRB1*13-DQB1*06  Mexico Veracruz Rural 0.0924539
 60  A*02:01-B*40:01-C*03:04-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP Caribean Black 0.086233,328
 61  A*02:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03-DPB1*02:01  Russia Karelia 0.08321,075
 62  A*02:01-B*40:01-C*03:04-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP African 0.070528,557
 63  A*01:01-B*40:01-C*03:04-DRB1*13:01-DQA1*01:03-DQB1*06:03-DPB1*13:01  Sri Lanka Colombo 0.0700714
 64  A*01:01-B*40:01-C*12:03-DRB1*13:01-DQA1*01:03-DQB1*06:03-DPB1*04:01  Sri Lanka Colombo 0.0700714
 65  A*33:03-B*40:01-C*03:04-DRB1*13:02-DQA1*01:02-DQB1*06:09-DPB1*02:01  Sri Lanka Colombo 0.0700714
 66  A*02:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03-DPB1*04:01  Russia Karelia 0.06971,075
 67  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04  Colombia Bogotá Cord Blood 0.06841,463
 68  A*24:02:01-B*40:01:02-C*03:04:01-DRB1*13:01:01-DQB1*06:03:01  Russia Nizhny Novgorod, Russians 0.06621,510
 69  A*24:02:01-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQB1*06:04:01  Poland BMR 0.063323,595
 70  A*11-B*40:01-DRB1*13-DQB1*06  Ecuador Andes Mixed Ancestry 0.0607824
 71  A*03:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  Colombia Bogotá Cord Blood 0.05931,463
 72  A*02:01-B*40:01-C*07:02-DRB1*13:02-DQB1*06:04-DPB1*03:01  Russia Karelia 0.05661,075
 73  A*03:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:09-DPB1*03:01  Russia Karelia 0.05651,075
 74  A*32:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03-DPB1*04:01  Russia Karelia 0.05651,075
 75  A*68:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03-DPB1*02:01  Russia Karelia 0.05651,075
 76  A*31:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03-DPB1*02:01  Russia Karelia 0.05651,075
 77  A*02:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03-DPB1*19:01  Russia Karelia 0.05651,075
 78  A*26:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Russia Karelia 0.05641,075
 79  A*02:11-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  India South UCBB 0.054111,446
 80  A*24:02-B*40:01-C*03:04-DRB1*13:02-DQB1*06:09  Malaysia Peninsular Malay 0.0526951
 81  A*24:02-B*40:01-C*08:01-DRB1*13:01-DQB1*06:09  Malaysia Peninsular Malay 0.0526951
 82  A*68:01:01-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQB1*06:04:01  Poland BMR 0.051223,595
 83  A*02:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  Germany DKMS - Turkey minority 0.05104,856
 84  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:02  Germany DKMS - German donors 0.04833,456,066
 85  A*24:02-B*40:01-C*12:03-DRB1*13:01-DQB1*06:03  India West UCBB 0.04745,829
 86  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04  USA Hispanic pop 2 0.04701,999
 87  A*31:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  USA Hispanic pop 2 0.04701,999
 88  A*02:01-B*40:01-C*03:04-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP Japanese 0.045924,582
 89  A*01:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04  USA African American pop 4 0.04402,411
 90  A*02:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  USA Asian pop 2 0.04401,772
 91  A*11:01-B*40:01-C*07:02-DRB1*13:02-DQB1*06:09  USA Asian pop 2 0.04401,772
 92  A*03:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*03:01  Germany DKMS - German donors 0.04373,456,066
 93  A*03:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  Germany DKMS - Italy minority 0.04301,159
 94  A*11-B*40:01-DRB1*13-DQB1*06  Ecuador Mixed Ancestry 0.04261,173
 95  A*02-B*40:01-DRB1*13-DQB1*06  Mexico Jalisco, Guadalajara city 0.04191,189
 96  A*11:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  India East UCBB 0.04162,403
 97  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*05:01  Germany DKMS - German donors 0.04133,456,066
 98  A*31:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  India West UCBB 0.04045,829
 99  A*01:01-B*40:01-C*03:04-DRB1*13:01-DQB1*06:03  India Tamil Nadu 0.04012,492
 100  A*02:01-B*40:01-C*12:03-DRB1*13:01-DQB1*06:03  India Tamil Nadu 0.04012,492

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 228) 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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