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 129) records   Pages: 1 2 of 2  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*24-B*35:04-DRB1*04:04-DQA1*03-DQB1*03:02  Colombia Jaidukama 9.600039
 2  A*02-B*35-DRB1*04:04-DQB1*03:02  Bolivia Quechua 6.000069
 3  A*24:02-B*35:01-DRB1*04:04-DQB1*03:02  USA South Dakota Lakota Sioux 4.3000302
 4  A*68-B*35-DRB1*04:04-DQB1*03:02  Bolivia La Paz Aymaras 3.930087
 5  A*24-B*35-DRB1*04:04-DQA1*03-DQB1*03:02  Mexico Mazatecan 2.500089
 6  A*02-B*35-DRB1*04:04-DQA1*03-DQB1*03:02  Colombia Jaidukama 1.900039
 7  A*02-B*35-DRB1*04:04-DQB1*03:02  Mexico San Vicente Tancuayalab Teenek/Huastecos 1.890053
 8  A*24:14-B*35:01-C*04:01-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Mexico Chiapas Lacandon Mayans 1.6055218
 9  A*02-B*35-DRB1*04:04-DQB1*03:02  Bolivia La Paz Aymaras 1.540087
 10  A*31-B*35-DRB1*04:04-DQB1*03:02  Bolivia Quechua 1.450069
 11  A*68:01-B*35:01-DRB1*04:04-DQB1*03:02  Mexico Veracruz Xalapa 1.190584
 12  A*31:01-B*35:01-C*04:01-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 13  A*68:01:02-B*35:05-DRB1*04:04-DQB1*03:02  Peru Titikaka Lake Uros 1.0500105
 14  A*31-B*35-DRB1*04:04-DQB1*03:02  Colombia Wayu from Guajira Peninsula 1.040048
 15  A*24-B*35-DRB1*04:04-DQB1*03:02  Bolivia La Paz Aymaras 0.984087
 16  A*24:02-B*35:05-DRB1*04:04-DQB1*03:02  Peru Titikaka Lake Uros 0.9700105
 17  A*02:01-B*35:05-DRB1*04:04-DQB1*03:02  Peru Titikaka Lake Uros 0.9500105
 18  A*68-B*35-DRB1*04:04-DQB1*03:02  Bolivia Quechua 0.720069
 19  A*68:01-B*35:01-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.6993143
 20  B*35:01-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.6993143
 21  B*35:12-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.6993143
 22  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*04:04:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 23  A*24:14-B*35:01-C*04:01-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.6881218
 24  A*24:02-B*35:01-C*04:01-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPB1*04:02  Nicaragua Managua 0.6494339
 25  B*35:01-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo pop 2 0.6400234
 26  A*02:01-B*35:01-DRB1*04:04-DQB1*03:02  Mexico Veracruz Xalapa 0.595284
 27  A*23:01-B*35:01-DRB1*04:04-DQB1*03:02  Mexico Veracruz Xalapa 0.595284
 28  A*68:01-B*35:02-DRB1*04:04-DQB1*03:02  Mexico Veracruz Xalapa 0.595284
 29  A*25:01-B*35:01-DRB1*04:04-DQB1*03:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 30  A*02:01-B*35:01-DRB1*04:04-DQB1*03:02  Peru Titikaka Lake Uros 0.4800105
 31  A*31:09-B*35:05-DRB1*04:04-DQB1*03:02  Peru Titikaka Lake Uros 0.4800105
 32  B*35:12-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo pop 2 0.4300234
 33  B*35:43-C*01:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo pop 2 0.4300234
 34  A*24:02-B*35:12-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo pop 2 0.4274234
 35  A*24:02-B*35:43-C*01:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo pop 2 0.4274234
 36  A*68:01:02-B*35:01-DRB1*04:04-DQB1*03:02  Peru Titikaka Lake Uros 0.3800105
 37  A*02:06-B*35:17-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 38  A*24:02-B*35:14-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 39  A*31:01-B*35:12-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 40  A*68:03-B*35:12-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 41  B*35:14-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 42  B*35:17-C*04:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 43  A*02:01-B*35:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Tlalpan 0.3030330
 44  A*24:02-B*35:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Tlalpan 0.3030330
 45  A*68:01:02-B*35:03:01-C*12:03:01-DRB1*04:04:01-DQB1*03:02:01  India Karnataka Kannada Speaking 0.2870174
 46  A*02:01:01:01-B*35:03:01-C*04:01:01-DRB1*04:04:01-DQB1*03:02  Russia Bashkortostan, Tatars 0.2604192
 47  A*03:01-B*35:03-C*12:03-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPB1*05:01  USA San Diego 0.2600496
 48  A*11:01-B*35:01-C*04:01-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPB1*03:01  USA San Diego 0.2600496
 49  A*68:01-B*35:01-C*03:04-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPB1*04:01  USA San Diego 0.2600496
 50  A*02:01-B*35:17-C*04:01-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 51  A*02:06-B*35:01-C*04:01-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 52  A*02:06-B*35:01-C*04:01-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Mexico Chiapas Lacandon Mayans 0.2294218
 53  A*02:06-B*35:01-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 54  A*02:06-B*35:01-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Mexico Chiapas Lacandon Mayans 0.2294218
 55  A*24:02-B*35:01-C*04:01-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 56  A*24:02-B*35:01-C*04:01-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Mexico Chiapas Lacandon Mayans 0.2294218
 57  A*26:01-B*35:01-C*03:04-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPB1*04:02  Nicaragua Managua 0.2165339
 58  A*31:01:02-B*35:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 59  A*24:25-B*35:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*14:01  Panama 0.1900462
 60  A*03:01:01-B*35:01:01-C*06:02:01-DRB1*04:04:01-DQB1*03:02  Costa Rica Central Valley Mestizo (G) 0.1810221
 61  A*24:02-B*35:43-C*01:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.18091,463
 62  A*24-B*35-DRB1*04:04-DQA1*03:01-DQB1*03:02  Brazil Paraná Caucasian 0.1560641
 63  A*02:01-B*35:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 64  A*24:02-B*35:05-DRB1*04:04-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 65  A*31:01-B*35:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 66  A*68:02-B*35:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 67  A*26:01-B*35:03-C*04:01-DRB1*04:04-DQB1*03:02  Italy pop 5 0.1400975
 68  A*31:01-B*35:43-C*01:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.10631,463
 69  A*02:01-B*35:05-C*04:01-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.10251,463
 70  A*03-B*35-DRB1*04:04-DQA1*03:01-DQB1*03:02  Brazil Paraná Caucasian 0.0780641
 71  A*02:01:01:01-B*35:01:01-C*04:01:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.06891,510
 72  A*32:04-B*35:01-C*04:01-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.05641,075
 73  A*11:01-B*35:01-C*04:01-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.05504,856
 74  A*02:02-B*35:01-C*01:06-DRB1*04:04-DQB1*03:02  Malaysia Peninsular Malay 0.0526951
 75  A*02:01-B*35:03-C*04:01-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 76  A*02:11-B*35:10-C*03:04-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 77  A*23:01-B*35:01-C*04:01-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 78  A*24:02-B*35:17-C*04:01-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 79  A*31:01-B*35:08-C*15:27-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 80  A*68:01-B*35:03-C*12:03-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 81  A*02:01-B*35:01-C*04:01-DRB1*04:04-DQB1*03:02  Germany DKMS - Italy minority 0.04301,159
 82  A*66:01-B*35:02-C*17:01-DRB1*04:04-DQB1*03:02  Germany DKMS - Italy minority 0.04301,159
 83  A*01:01-B*35:01-C*03:04-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 84  A*02:01-B*35:01-C*03:04-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 85  A*02:01-B*35:11-C*04:01-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 86  A*11:01-B*35:30-C*03:04-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 87  A*24:02-B*35:30-C*03:04-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 88  A*24:03-B*35:12-C*04:01-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 89  A*24:03-B*35:21-C*04:01-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 90  A*24:03-B*35:43-C*01:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 91  A*24:14-B*35:30-C*01:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 92  A*24:15-B*35:05-C*04:01-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 93  A*68:01-B*35:12-C*04:01-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 94  A*02:01-B*35:01-C*04:01-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 95  A*11:01-B*35:01-C*04:01-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 96  A*29:02-B*35:01-C*04:01-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 97  A*32:01-B*35:01-C*04:01-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 98  A*68:17-B*35:21-C*04:01-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 99  A*03:01:01-B*35:03:01-C*04:01:01-DRB1*04:04:01-DQB1*03:02:01  China Zhejiang Han 0.02881,734
 100  A*33:03:01-B*35:01:01-C*03:03:01-DRB1*04:04:01-DQB1*03:02:01  China Zhejiang Han 0.02881,734

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 129) 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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