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 185) records   Pages: 1 2 of 2  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  DRB1*04:03-DQA1*03:01-DQB1*03:04  Italy Sardinia Sorgono 3.800093
 2  DRB1*04:03-DQA1*03:01-DQB1*03:04  Italy Sardinia Oristano 3.300091
 3  A*32:01:01-B*15:01:01-C*03:03:01-DRB1*10:01:01-DQB1*03:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Black 2.381073
 4  DRB1*04:03-DQA1*03:01-DQB1*03:04  Italy Sardinia Sassari 2.200091
 5  DRB1*04:03-DQA1*03:01-DQB1*03:04  Italy Sardinia Cagliari 1.700087
 6  DRB1*04:03-DQA1*03:01-DQB1*03:04  Italy Sardinia Lanusei 1.700087
 7  DRB1*04:03-DQA1*03:01-DQB1*03:04  Italy Sardinia Carbonia 1.600091
 8  DRB1*04:03-DQA1*03:01-DQB1*03:04  Italy Sardinia Tempio 1.600091
 9  DRB1*04-DQA1*03:01-DQB1*03:04  Russia Smolensk 1.6000156
 10  A*24-B*40-DRB1*16:02-DQB1*03:04  Bolivia La Paz Aymaras 1.149087
 11  A*68:01-B*51:01-C*08:01-DRB1*14:02-DQA1*05:03-DQB1*03:04  Mexico Chichen Itza Maya (prehispanic) 1.063847
 12  A*68:03-B*35:01-C*07:02-DRB1*04:07-DQA1*05:03-DQB1*03:04  Mexico Chichen Itza Maya (prehispanic) 1.063847
 13  A*68:03-B*40:02-C*07:02-DRB1*14:02-DQA1*05:03-DQB1*03:04  Mexico Chichen Itza Maya (prehispanic) 1.063847
 14  A*02:01-B*51:24-C*01:02-DRB1*04:07-DQB1*03:04  Colombia North Chimila Amerindians 1.063847
 15  A*02-B*35-DRB1*14:46-DQB1*03:04  Mexico San Vicente Tancuayalab Teenek/Huastecos 0.940053
 16  A*02-B*52-DRB1*14:06-DQB1*03:04  Mexico San Vicente Tancuayalab Teenek/Huastecos 0.940053
 17  A*24-B*51-DRB1*14:06-DQB1*03:04  Mexico San Vicente Tancuayalab Teenek/Huastecos 0.940053
 18  A*68-B*39-DRB1*04:04-DQB1*03:04  Mexico San Vicente Tancuayalab Teenek/Huastecos 0.940053
 19  A*11:01-B*44:02-C*05:01-E*01:03:02-F*01:01:01-G*01:01-DRB1*04:03-DQA1*03:01-DQB1*03:04  Portugal Azores Terceira Island 0.8772130
 20  A*01:01-B*07:02-C*07:08-DRB1*14:01-DQB1*03:04  Iran Gorgan 0.780064
 21  A*02:01-B*38:01-C*07:03-DRB1*08:01-DQB1*03:04  Iran Gorgan 0.780064
 22  A*02:01-B*35:01-C*07:02-DRB1*14:02-DQA1*05:03-DQB1*03:04  Mexico Tixcacaltuyub Maya 0.746367
 23  A*68:01-B*35:17-C*04:01-DRB1*14:02-DQA1*05:03-DQB1*03:04  Mexico Tixcacaltuyub Maya 0.746367
 24  A*31-B*40-DRB1*07:01-DQB1*03:04  Bolivia Quechua 0.720069
 25  A*31:01-B*40:02-DRB1*14:02-DQB1*03:04  Mexico Veracruz Xalapa 0.595284
 26  A*31:01-B*44:02-DRB1*14:02-DQB1*03:04  Mexico Veracruz Xalapa 0.595284
 27  A*24-B*15-DRB1*14:02-DQB1*03:04  Bolivia La Paz Aymaras 0.575087
 28  A*68-B*15-DRB1*14:02-DQB1*03:04  Bolivia La Paz Aymaras 0.575087
 29  A*02:01-B*07:02-C*04:01-E*01:03:01-F*01:01:01-G*01:01-DRB1*04:03-DQA1*03:01-DQB1*03:04  Portugal Azores Terceira Island 0.4386130
 30  A*01:01:01-B*27:14-C*12:03:01-DRB1*15:02:01-DQA1*01:03:01-DQB1*03:04:01-DPA1*01:03:01-DPB1*03:01:01  Russian Federation Vologda Region 0.4202119
 31  A*02:01:01-B*44:27:01-C*06:02:01:01-DRB1*13:02:01-DQB1*03:04:01  Russia Bashkortostan, Bashkirs 0.4167120
 32  A*24:02:01-B*51:01:01-C*14:02:01-DRB1*04:01:01-DQB1*03:04:01  Russia Bashkortostan, Bashkirs 0.4167120
 33  A*68-B*39-DRB1*14-DQB1*03:04  Mexico Tabasco Rural 0.3521142
 34  A*24:02-B*40:02-C*03:04-DRB1*14:02-DQB1*03:04  Mexico Mexico City Mestizo population 0.3497143
 35  A*31:01-B*18:01-C*07:01-DRB1*11:04-DQB1*03:04  Mexico Mexico City Mestizo population 0.3497143
 36  B*18:01-C*07:01-DRB1*11:04-DQB1*03:04  Mexico Mexico City Mestizo population 0.3497143
 37  B*40:02-C*03:04-DRB1*14:02-DQB1*03:04  Mexico Mexico City Mestizo population 0.3497143
 38  DRB1*11:04-DQB1*03:04  Mexico Mexico City Mestizo population 0.3497143
 39  A*11:01:01-B*35:03:01-C*12:03:01-DRB1*07:01:01-DQB1*03:04:01-DPA1*02:02:02-DPB1*158:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 40  A*02:05-B*18:01-C*07:01-DRB1*04:03-DQB1*03:04  Italy pop 5 0.2900975
 41  A*24:02:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQB1*03:04:01  India Karnataka Kannada Speaking 0.2870174
 42  A*11:01:01-B*35:03:01-C*12:03:01-DRB1*04:08-DQB1*03:04:01  Poland BMR 0.274223,595
 43  A*25:01:01-B*51:01:01-C*07:02:01:01-DRB1*04:03:01-DQB1*03:04:01  Russia Bashkortostan, Tatars 0.2604192
 44  A*32:01:01-B*18:01:01-C*07:02:01:01-DRB1*04:03:01-DQB1*03:04:01  Russia Bashkortostan, Tatars 0.2604192
 45  A*02:01-B*40:02-C*03:05-DRB1*14:02-DQA1*05:03-DQB1*03:04-DPA1*01:03-DPB1*03:01  Mexico Chiapas Lacandon Mayans 0.2294218
 46  A*24:02-B*35:01-C*07:02-DRB1*04:07-DQA1*03:01-DQB1*03:04-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 47  A*24:14-B*40:02-C*03:05-DRB1*14:06-DQA1*05:05-DQB1*03:04-DPA1*01:03-DPB1*03:01  Mexico Chiapas Lacandon Mayans 0.2294218
 48  A*11:01-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04-DPB1*15:01  Russia Karelia 0.22581,075
 49  A*24:02-B*53:01-C*03:06-DRB1*13:02-DQA1*05:01-DQB1*03:04-DPB1*04:02  Nicaragua Managua 0.2165339
 50  A*68:01-B*35:01-C*04:01-DRB1*14:02-DQA1*05:01-DQB1*03:04-DPB1*04:02  Nicaragua Managua 0.2165339
 51  A*68:03-B*35:43-C*01:02-DRB1*14:02-DQA1*05:01-DQB1*03:04-DPB1*01:01  Nicaragua Managua 0.2165339
 52  DQB1*03:04-DPB1*04:02:01  China Inner Mongolia Autonomous Region Northeast 0.2020496
 53  A*24:02-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04  England North West 0.2000298
 54  A*26:01-B*45:01-C*06:02-DRB1*04:03:01-DQB1*03:04  England North West 0.2000298
 55  A*02:01:01-B*38:01:01-C*12:03:01-DRB1*11:04:01-DQB1*03:04:01-DPA1*01:03:01-DPB1*13:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 56  A*24:02:01-B*39:01:01-C*05:01:01-DRB1*12:10-DQB1*03:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 57  A*29:02:01-B*15:03:01-C*16:01:01-DRB1*04:03:01-DQB1*03:04:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 58  DRB1*04:03-DQB1*03:04  Italy pop 5 0.1900975
 59  A*11:01:01:01-B*35:03:01-C*12:03:01:01-DRB1*04:08:01-DQB1*03:04:01  Russia Nizhny Novgorod, Russians 0.16561,510
 60  A*01:01-B*40:02-DRB1*11:04-DQB1*03:04  Mexico Mexico City Tlalpan 0.1515330
 61  A*02:01-B*08:01-DRB1*14:06-DQB1*03:04  Mexico Mexico City Tlalpan 0.1515330
 62  A*02:01-B*39:01-DRB1*04:03-DQB1*03:04  Mexico Mexico City Tlalpan 0.1515330
 63  A*68:01-B*35:01-DRB1*14:02-DQB1*03:04  Mexico Mexico City Tlalpan 0.1515330
 64  A*11:01-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04  USA Hispanic pop 2 0.14001,999
 65  A*24:02-B*14:01-C*12:03-DRB1*07:01-DQB1*03:04  Italy pop 5 0.1400975
 66  DRB1*04:08-DQB1*03:04  USA Hispanic pop 2 0.13901,999
 67  A*02:01-B*51:01-C*02:02-DRB1*04:03-DQB1*03:04  Germany DKMS - Italy minority 0.12901,159
 68  A*11:01-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04-DPB1*02:01  Russia Karelia 0.11231,075
 69  DRB1*04:04:04-DQB1*03:04-DPB1*04:02:01  China Inner Mongolia Autonomous Region Northeast 0.1010496
 70  DRB1*04:08:01-DQB1*03:04-DPB1*04:02:01  China Inner Mongolia Autonomous Region Northeast 0.1010496
 71  DRB1*11:01-DQB1*03:04  Italy pop 5 0.0900975
 72  A*11:01-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04  Germany DKMS - Italy minority 0.08601,159
 73  A*24:02-B*51:01-C*12:03-DRB1*04:03-DQB1*03:04  Germany DKMS - Italy minority 0.08601,159
 74  A*24-B*44-DRB1*04:03-DQA1*03:01-DQB1*03:04  Brazil Paraná Caucasian 0.0780641
 75  A*11:01-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04  Colombia Bogotá Cord Blood 0.06841,463
 76  A*11:01-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04-DPB1*15:01  Germany DKMS - German donors 0.05993,456,066
 77  A*24:02-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04  Germany DKMS - Turkey minority 0.05804,856
 78  A*11:01-B*18:03-C*07:01-DRB1*04:08-DQB1*03:04-DPB1*02:01  Russia Karelia 0.05651,075
 79  A*02:01-B*15:01-C*03:04-DRB1*04:08-DQB1*03:04-DPB1*15:01  Russia Karelia 0.05651,075
 80  A*11:01-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04-DPB1*03:01  Russia Karelia 0.05651,075
 81  A*11:01-B*07:05-C*07:02-DRB1*12:01-DQB1*03:04  Malaysia Peninsular Malay 0.0526951
 82  A*02:01-B*51:01-C*15:09-DRB1*14:02-DQB1*03:04  USA Hispanic pop 2 0.04701,999
 83  DRB1*14:02-DQB1*03:04  USA Hispanic pop 2 0.04601,999
 84  A*01:01-B*40:02-C*02:02-DRB1*04:03-DQB1*03:04  Germany DKMS - Italy minority 0.04301,159
 85  A*31:01-B*51:01-C*02:02-DRB1*04:03-DQB1*03:04  Germany DKMS - Italy minority 0.04301,159
 86  A*69:01-B*51:01-C*07:02-DRB1*04:03-DQB1*03:04  Germany DKMS - Turkey minority 0.04104,856
 87  A*11:01-B*51:01-C*15:02-DRB1*04:01-DQB1*03:04  Colombia Bogotá Cord Blood 0.03421,463
 88  A*24:14-B*40:02-C*03:04-DRB1*14:02-DQB1*03:04  Colombia Bogotá Cord Blood 0.03421,463
 89  A*32:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:04  Colombia Bogotá Cord Blood 0.03421,463
 90  A*02:01-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 91  A*02:01-B*44:02-C*05:01-DRB1*04:03-DQB1*03:04  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 92  A*02:01-B*51:01-C*02:02-DRB1*04:03-DQB1*03:04  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 93  A*03:01-B*35:01-C*04:01-DRB1*04:08-DQB1*03:04  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 94  A*24:02-B*51:01-C*02:02-DRB1*04:03-DQB1*03:04  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 95  A*32:01-B*51:01-C*02:02-DRB1*04:03-DQB1*03:04  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 96  A*02:01:01-B*35:03:01-C*12:03:01-DRB1*04:08-DQB1*03:04:01  Poland BMR 0.034023,595
 97  A*02:01:01:01-B*13:02:01-C*07:01:01-DRB1*04:08:01-DQB1*03:04:01  Russia Nizhny Novgorod, Russians 0.03311,510
 98  A*02:01:01:01-B*27:05:02-C*04:01:01-DRB1*04:08:01-DQB1*03:04:01  Russia Nizhny Novgorod, Russians 0.03311,510
 99  A*02:01:01:01-B*39:05:01-C*12:03:01:01-DRB1*04:03:01-DQB1*03:04:01  Russia Nizhny Novgorod, Russians 0.03311,510
 100  A*24:02:01:01-B*35:03:01-C*12:03:01:01-DRB1*04:08:01-DQB1*03:04:01  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 185) 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.
Liverpool, U.K.

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