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 120) records   Pages: 1 2 of 2  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*31:01-B*39:02-DRB1*16:02  Mexico Oaxaca Mixe 10.000055
 2  A*02:01-B*39:02-DRB1*16:02  Mexico Oaxaca Mixtec 6.0000103
 3  A*02:01-B*39:02-DRB1*16:02  Mexico Oaxaca Mixe 6.000055
 4  A*31:01-B*39:01-C*08:03-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Puyanawa 5.9180150
 5  A*02-B*39-DRB1*16:02-DQA1*05-DQB1*03:01  Mexico Mazatecan 3.300089
 6  A*24-B*39-DRB1*16:02-DQB1*03:02  Colombia Wayu from Guajira Peninsula 3.130048
 7  A*02:06-B*39:01-DRB1*16:02  Mexico Oaxaca Mixtec 3.0000103
 8  A*02:06-B*39:05-DRB1*16:02  Mexico Oaxaca Mixtec 3.0000103
 9  A*31:01-B*39:02-DRB1*16:02  Mexico Oaxaca Zapotec 3.000090
 10  A*24-B*39-DRB1*16:02  Malaysia Sarawak Iban 2.900051
 11  A*24:02-B*39:06-DRB1*16:02  Mexico Mixtec 2.600097
 12  A*24:02-B*39:06-DRB1*16:02  Mexico Oaxaca Jamiltepec Mixtec 2.600096
 13  A*01:01:01-B*39:09:01-C*01:02:01-DRB1*16:02:01-DQB1*03:01:01-DPA1*01:03:01-DPB1*11:01:01  Brazil Barra Mansa Rio State Black 2.381073
 14  A*11:02-B*39:01-DRB1*16:02  China Guangxi Region Maonan 2.3000108
 15  A*24:02-B*39:02-C*07:02-DRB1*16:02-DQA1*05:05-DQB1*03:01  Mexico Chichen Itza Maya (prehispanic) 2.127747
 16  A*31:01-B*39:01-C*08:01-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Puyanawa 1.7486150
 17  A*31:01-B*39:05-C*08:03-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Puyanawa 1.7486150
 18  A*31:01-B*39:05-C*08:01-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Puyanawa 1.5847150
 19  A*02:01-B*39:09-DRB1*16:02-DQB1*03:01  Chile Mapuche 1.540066
 20  A*24:02-B*39:01-DRB1*16:02-DQB1*03:01  Chile Mapuche 1.540066
 21  A*68:01-B*39:09-DRB1*16:02-DQB1*03:01  Chile Mapuche 1.540066
 22  A*02:01-B*39:01-DRB1*16:02:01-DQB1*03:01  USA South Dakota Lakota Sioux 1.4000302
 23  A*68-B*39-DRB1*16:02-DQB1*03:01  Mexico San Vicente Tancuayalab Teenek/Huastecos 1.240053
 24  A*02:04-B*39:05-C*07:02-DRB1*16:02-DQA1*05:01-DQB1*03:01-DPA1*01-DPB1*04:02  Venezuela Sierra de Perija Yucpa 1.200073
 25  A*68-B*39-DRB1*16:02-DQB1*03:01  Colombia San Basilio de Palenque 1.191042
 26  A*68-B*39-DRB1*16:02  Chile Santiago 1.1229920
 27  A*02:06-B*39:02-DRB1*16:02  Mexico Oaxaca Jamiltepec Mixtec 1.040096
 28  A*02:06-B*39:05-DRB1*16:02  Mexico Oaxaca Jamiltepec Mixtec 1.040096
 29  A*02-B*39-DRB1*16:02-DQB1*03:01  Colombia Wayu from Guajira Peninsula 1.040048
 30  A*68-B*39-DRB1*16:02-DQB1*03:01  Colombia Wayu from Guajira Peninsula 1.040048
 31  A*02:01-B*39:06-DRB1*16:02-DQB1*03:01  Chile Mapuche 0.770066
 32  A*68:16-B*39:09-DRB1*16:02-DQB1*03:01  Chile Mapuche 0.770066
 33  A*68:03-B*39:05-C*07:02-DRB1*16:02-DQA1*05:05-DQB1*03:01  Mexico Tixcacaltuyub Maya 0.746367
 34  A*02:01:01-B*39:01:01-C*07:01:01-DRB1*16:02:01-DQB1*03:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 35  A*68:01:01-B*39:01:01-C*07:01:01-DRB1*16:02:01-DQB1*03:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 36  A*68:01-B*39:05-C*07:02-DRB1*16:02-DQA1*05:05-DQB1*03:01-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.6881218
 37  A*31:01-B*39:05-C*08:03-DRB1*16:02-DQA1*01:02-DQB1*03:02  Brazil Puyanawa 0.6667150
 38  A*02-B*39-DRB1*16:02  Chile Santiago 0.6505920
 39  B*39:05-C*07:02-DRB1*16:02-DQB1*03:01  Mexico Mexico City Mestizo pop 2 0.6400234
 40  A*24:02-B*39:01-DRB1*16:02-DQB1*03:01  Mexico Veracruz Xalapa 0.595284
 41  A*24:02-B*39:02-DRB1*16:02-DQB1*03:01  Mexico Veracruz Xalapa 0.595284
 42  A*36-B*39:01-DRB1*16:02-DQB1*03:01  Mexico Veracruz Xalapa 0.595284
 43  A*68:01-B*39:01-DRB1*16:02-DQB1*03:02  Mexico Veracruz Xalapa 0.595284
 44  A*02:01-B*39:02-DRB1*16:02  Mexico Oaxaca Jamiltepec Mixtec 0.520096
 45  A*68:03-B*39:05-C*07:02-DRB1*16:02-DQA1*05:05-DQB1*03:01-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.4587218
 46  B*39:02-C*07:02-DRB1*16:02-DQB1*03:01  Mexico Mexico City Mestizo pop 2 0.4300234
 47  A*02:01-B*39:05-C*07:02-DRB1*16:02-DQB1*03:01  Mexico Mexico City Mestizo pop 2 0.4274234
 48  A*68:01-B*39:02-C*07:02-DRB1*16:02-DQB1*03:01  Mexico Mexico City Mestizo pop 2 0.4274234
 49  A*02-B*39-DRB1*16:02-DQB1*03:01  Guatemala, Guatemala City Mixed Ancestry 0.3900127
 50  A*24:02-B*39:05-C*07:01-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Puyanawa 0.3333150
 51  A*31:01-B*39:01-C*08:03-DRB1*16:02-DQA1*05:05-DQB1*03:02  Brazil Puyanawa 0.3333150
 52  A*68:01-B*39:01-C*07:02-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Puyanawa 0.3333150
 53  A*68:01-B*39:01-C*08:03-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Puyanawa 0.3333150
 54  A*26:01-B*39:01-C*03:04-DRB1*16:02-DQB1*05:02  Malaysia Peninsular Chinese 0.2577194
 55  A*24:02-B*39:05-C*07:02-DRB1*16:02-DQA1*05:05-DQB1*03:01-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 56  A*31:01-B*39:05-C*07:02-DRB1*16:02-DQA1*05:05-DQB1*03:01-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 57  A*24:02-B*39:11-C*08:03-DRB1*16:02-DQA1*03:01-DQB1*04:02-DPB1*04:01  Nicaragua Managua 0.2165339
 58  A*02:11-B*39:05-C*07:02-DRB1*16:02-DQB1*03:01-DPB1*14:01  Panama 0.1900462
 59  A*02:01-B*39:02-DRB1*16:02-DQB1*03:01  Mexico Mexico City Tlalpan 0.1515330
 60  A*02:01-B*39:06-DRB1*16:02-DQB1*03:01  Mexico Mexico City Tlalpan 0.1515330
 61  A*02:01-B*39:11-C*07:02-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.13671,463
 62  A*02:01-B*39:05-C*07:02-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.11501,463
 63  A*31:01-B*39:05-C*07:02-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.10251,463
 64  A*68:01-B*39:11-C*07:02-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.10251,463
 65  A*68:01-B*39:19-C*07:02-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.10251,463
 66  A*02-B*39-DRB1*16:02  USA NMDP Hispanic 0.1000449,844
 67  A*24-B*39-DRB1*16:02  USA NMDP Hispanic 0.1000449,844
 68  A*26:01-B*39:01-C*12:03-DRB1*16:02  Italy pop 5 0.1000975
 69  A*24:02-B*39:05-C*07:02-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.07831,463
 70  A*02-B*39-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Paraná Caucasian 0.0780641
 71  A*24-B*39-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Paraná Caucasian 0.0780641
 72  A*24:02-B*39:06-C*07:02-DRB1*16:02-DQB1*03:01  USA Hispanic pop 2 0.06701,999
 73  A*01:01-B*39:01-C*07:02-DRB1*16:02-DQB1*03:01  USA Hispanic pop 2 0.04701,999
 74  A*02:01-B*39:01-C*07:02-DRB1*16:02-DQB1*03:01  USA Hispanic pop 2 0.04701,999
 75  A*24:02-B*39:13-C*07:02-DRB1*16:02-DQB1*03:01  USA Hispanic pop 2 0.04701,999
 76  A*31:01-B*39:11-C*07:02-DRB1*16:02-DQB1*03:01  USA Hispanic pop 2 0.04701,999
 77  A*24:02-B*39:05-C*03:05-DRB1*16:02  Germany DKMS - Spain minority 0.04501,107
 78  A*24:02-B*39:06-C*07:02-DRB1*16:02  Germany DKMS - Spain minority 0.04501,107
 79  A*24:02-B*39:01-C*07:02-DRB1*16:02-DQB1*05:02  USA Asian pop 2 0.04401,772
 80  A*32:01-B*39:06-C*12:03-DRB1*16:02  Germany DKMS - Romania minority 0.04101,234
 81  A*11:01-B*39:01-DRB1*16:02  Hong Kong Chinese cord blood registry 0.04073,892
 82  A*02:03-B*39:09-C*07:02-DRB1*16:02  Germany DKMS - China minority 0.03901,282
 83  A*11:01-B*39:01-C*07:02-DRB1*16:02  Germany DKMS - China minority 0.03901,282
 84  A*11:02-B*39:01-C*07:02-DRB1*16:02  Germany DKMS - China minority 0.03901,282
 85  A*02:03-B*39:01-DRB1*16:02  Hong Kong Chinese cord blood registry 0.03803,892
 86  A*03:01-B*39:05-C*07:02-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.03561,463
 87  A*01:01-B*39:05-C*03:04-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.03421,463
 88  A*02:01-B*39:06-C*04:01-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.03421,463
 89  A*02:01-B*39:08-C*07:02-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.03421,463
 90  A*02:04-B*39:02-C*07:02-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.03421,463
 91  A*24:02-B*39:05-C*03:05-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.03421,463
 92  A*24:02-B*39:06-C*07:02-DRB1*16:02-DQB1*03:01  Colombia Bogotá Cord Blood 0.03421,463
 93  A*24:02-B*39:01-C*07:02-DRB1*16:02-DQB1*05:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 94  A*68:01-B*39:09-C*07:02-DRB1*16:02-DQB1*03:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 95  A*30:01:01-B*39:01:01-C*07:02:01-DRB1*16:02:01-DQB1*05:02:01  China Zhejiang Han 0.02881,734
 96  A*11:01:01-B*39:01:01-C*07:02:01-DRB1*16:02:01-DPB1*05:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.02825,266
 97  A*26:01-B*39:01-DRB1*16:02  Hong Kong Chinese cord blood registry 0.02353,892
 98  A*02:01-B*39:01-C*07:02-DRB1*16:02  Hong Kong Chinese BMDR 0.02227,595
 99  A*33:03-B*39:01-C*12:04-DRB1*16:02-DQB1*03:01  India East UCBB 0.02082,403
 100  A*02:01-B*39:01-DRB1*16:02  Hong Kong Chinese cord blood registry 0.01543,892

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 120) records   Pages: 1 2 of 2  


   

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.
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