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 580) records   Pages: 1 2 3 4 5 6 of 6  

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-DQB1*03:01  Mexico Oaxaca, Oaxaca city 3.9735151
 6  A*02-B*39-DRB1*16:02-DQA1*05-DQB1*03:01  Mexico Mazatecan 3.300089
 7  A*24-B*39-DRB1*16:02-DQB1*03:02  Colombia Wayu from Guajira Peninsula 3.130048
 8  A*02:06-B*39:01-DRB1*16:02  Mexico Oaxaca Mixtec 3.0000103
 9  A*02:06-B*39:05-DRB1*16:02  Mexico Oaxaca Mixtec 3.0000103
 10  A*31:01-B*39:02-DRB1*16:02  Mexico Oaxaca Zapotec 3.000090
 11  A*24-B*39-DRB1*16:02  Malaysia Sarawak Iban 2.900051
 12  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Veracruz, Coatzacoalcos 2.678655
 13  A*24:02-B*39:06-DRB1*16:02  Mexico Mixtec 2.600097
 14  A*24:02-B*39:06-DRB1*16:02  Mexico Oaxaca Jamiltepec Mixtec 2.600096
 15  A*68-B*39-DRB1*16-DQB1*03:01  Mexico Jalisco, Tlaquepaque 2.564139
 16  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Puebla Mestizo 2.500099
 17  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Oaxaca Rural 2.4641485
 18  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
 19  A*11:02-B*39:01-DRB1*16:02  China Guangxi Region Maonan 2.3000108
 20  A*68-B*39-DRB1*16-DQB1*03:01  Mexico Guanajuato, Guanajuato city 2.272722
 21  B*39-DRB1*16  Russia South Ural Tatar 2.2000135
 22  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
 23  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Veracruz, Cordoba 1.785756
 24  A*31:01-B*39:01-C*08:01-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Puyanawa 1.7486150
 25  A*31:01-B*39:05-C*08:03-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Puyanawa 1.7486150
 26  A*24-B*39-DRB1*16-DQB1*03:01  Mexico San Luis Potosi Rural 1.724187
 27  A*26-B*39-DRB1*16-DQB1*05  Mexico Veracruz, Orizaba 1.666760
 28  A*68-B*39-DRB1*16-DQB1*03:01  Mexico Veracruz, Orizaba 1.666760
 29  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Tlaxcala, Tlaxcala city 1.6575181
 30  A*31:01-B*39:05-C*08:01-DRB1*16:02-DQA1*05:05-DQB1*03:01  Brazil Puyanawa 1.5847150
 31  A*02:01-B*39:09-DRB1*16:02-DQB1*03:01  Chile Mapuche 1.540066
 32  A*24:02-B*39:01-DRB1*16:02-DQB1*03:01  Chile Mapuche 1.540066
 33  A*68:01-B*39:09-DRB1*16:02-DQB1*03:01  Chile Mapuche 1.540066
 34  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Sonora, Hermosillo 1.515299
 35  A*26-B*39-DRB1*16  Russia South Ural Tatar 1.5000135
 36  A*68-B*39-DRB1*16  Brazil Parana Oriental 1.500033
 37  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Mexico City West 1.470633
 38  A*02:01-B*39:01-DRB1*16:02:01-DQB1*03:01  USA South Dakota Lakota Sioux 1.4000302
 39  B*39-DRB1*16  Macedonia 1.3986286
 40  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Guerrero state 1.3889144
 41  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Coahuila, Saltillo 1.369972
 42  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Nuevo Leon Rural 1.3636439
 43  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Veracruz, Xalapa 1.3369187
 44  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Puebla, Puebla city 1.32771,994
 45  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Nuevo Leon, Monterrey city 1.3274226
 46  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Mexico City Metropolitan Area Rural 1.3158150
 47  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Mexico City Center 1.2987152
 48  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Jalisco, Tlaquepaque 1.282139
 49  A*24-B*39-DRB1*16-DQB1*03:01  Ecuador Amazonia Mixed Ancestry 1.282139
 50  A*68-B*39-DRB1*16:02-DQB1*03:01  Mexico San Vicente Tancuayalab Teenek/Huastecos 1.240053
 51  A*31-B*39-DRB1*16-DQB1*03:01  Mexico Hidalgo, Pachuca 1.219541
 52  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
 53  A*68-B*39-DRB1*16:02-DQB1*03:01  Colombia San Basilio de Palenque 1.191042
 54  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Veracruz, Veracruz city 1.1628171
 55  A*26-B*39-DRB1*16-DQB1*05  Mexico Colima Rural 1.136443
 56  A*68-B*39-DRB1*16:02  Chile Santiago 1.1229920
 57  A*68-B*39-DRB1*16-DQB1*03:01  Mexico Queretaro, Queretaro city 1.111145
 58  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Campeche Rural 1.063847
 59  A*02-B*39-DRB1*16-DQB1*05  Mexico Aguascalientes state 1.052695
 60  A*68-B*39-DRB1*16-DQB1*03:01  Mexico Aguascalientes state 1.052695
 61  A*02:06-B*39:02-DRB1*16:02  Mexico Oaxaca Jamiltepec Mixtec 1.040096
 62  A*02:06-B*39:05-DRB1*16:02  Mexico Oaxaca Jamiltepec Mixtec 1.040096
 63  A*02-B*39-DRB1*16:02-DQB1*03:01  Colombia Wayu from Guajira Peninsula 1.040048
 64  A*68-B*39-DRB1*16:02-DQB1*03:01  Colombia Wayu from Guajira Peninsula 1.040048
 65  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Quintana Roo Rural 1.000050
 66  A*11:01:01-B*39:06:02-DRB1*16:01:01  Portugal Center 1.000050
 67  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Baja California Rural 1.000050
 68  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Oaxaca, Oaxaca city 0.9934151
 69  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Mexico City Metropolitan Area Rural 0.9868150
 70  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Sinaloa, Culiacán 0.9709103
 71  A*03:01-B*39:01-C*12:03-DRB1*16:01-DQB1*05:02  Colombia North Wiwa El Encanto 0.961552
 72  A*31-B*39-DRB1*16-DQB1*03:01  Mexico Mexico City South 0.961552
 73  A*26:01-B*39:01-C*12:03-DRB1*16:01-DQA1*01:02-DQB1*05:02  United Arab Emirates Abu Dhabi 0.960052
 74  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Puebla Rural 0.9592833
 75  A*02-B*39-DRB1*16-DQB1*03:03  Mexico Veracruz, Cordoba 0.892956
 76  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Veracruz, Coatzacoalcos 0.892955
 77  A*29-B*39-DRB1*16-DQB1*03:01  Mexico Veracruz, Coatzacoalcos 0.892955
 78  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Tlaxcala Rural 0.8434830
 79  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Veracruz Rural 0.8318539
 80  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Veracruz Rural 0.8318539
 81  A*31-B*39-DRB1*16-DQB1*03:01  Mexico Chiapas Rural 0.8264121
 82  A*02-B*39-DRB1*16  Brazil Para Cord Blood Unit 0.8060841
 83  A*02-B*39-DRB1*16  Iraq Erbil 0.8000372
 84  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Nuevo Leon Rural 0.7955439
 85  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Tamaulipas Rural 0.7937125
 86  A*02:01-B*39:06-DRB1*16:02-DQB1*03:01  Chile Mapuche 0.770066
 87  A*68:16-B*39:09-DRB1*16:02-DQB1*03:01  Chile Mapuche 0.770066
 88  A*68-B*39-DRB1*16-DQB1*03:01  Mexico Yucatan Rural 0.7463132
 89  A*68:03-B*39:05-C*07:02-DRB1*16:02-DQA1*05:05-DQB1*03:01  Mexico Tixcacaltuyub Maya 0.746367
 90  A*24-B*39-DRB1*16-DQB1*03:01  Mexico Puebla Rural 0.7194833
 91  A*68-B*39-DRB1*16-DQB1*03:01  Mexico Tabasco Rural 0.7042142
 92  A*02-B*39-C*12-DRB1*16  Macedonia 0.6993286
 93  A*02-B*39-DRB1*16  Macedonia 0.6993286
 94  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
 95  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
 96  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Coahuila Rural 0.6881216
 97  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
 98  A*31:01-B*39:05-C*08:03-DRB1*16:02-DQA1*01:02-DQB1*03:02  Brazil Puyanawa 0.6667150
 99  A*02-B*39-DRB1*16-DQB1*03:01  Mexico Mexico City North 0.6640751
 100  A*24-B*39-DRB1*16-DQB1*05  Mexico Oaxaca, Oaxaca city 0.6623151

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 580) records   Pages: 1 2 3 4 5 6 of 6  


   

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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