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 85 (from 85) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02:01-B*39:01-DRB1*04:04-DQB1*03:02  USA South Dakota Lakota Sioux 5.1000302
 2  A*02-B*39-DRB1*04:04-DQB1*03:02  Mexico San Vicente Tancuayalab Teenek/Huastecos 1.890053
 3  A*02:01:01-B*39:02:01-C*03:02:01-DRB1*04:04:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 1.388972
 4  A*24:02-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*02:01  Russia Karelia 1.27031,075
 5  A*68:01-B*39:05-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 6  A*31:01-B*39:05-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 7  A*68:01-B*39:05-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 8  A*68:01-B*39:06-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 9  A*68:03-B*39:05-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 10  A*31-B*39-DRB1*04:04-DQB1*03:02  Bolivia Quechua 0.720069
 11  A*24:02:01-B*39:01:01-C*04:01:01-DRB1*04:04:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 12  A*24:02-B*39:06-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.6881218
 13  A*31:01-B*39:01-DRB1*04:04-DQB1*03:02  Mexico Veracruz Xalapa 0.595284
 14  A*68:01-B*39:01-DRB1*04:04-DQB1*03:02  Mexico Veracruz Xalapa 0.595284
 15  A*02:01-B*39:01-DRB1*04:04-DQB1*03:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 16  A*24:02:01:01-B*39:01:01:03-C*07:02:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.52751,510
 17  B*39:02-C*03:04-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo pop 2 0.4300234
 18  B*39:06-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo pop 2 0.4300234
 19  A*02:01-B*39:02-C*03:04-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo pop 2 0.4274234
 20  A*24:02:01-B*39:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02  Russia Bashkortostan, Bashkirs 0.4167120
 21  A*24:02-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.36131,075
 22  A*02:01-B*39:02-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 23  A*24:02-B*39:05-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 24  B*39:02-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 25  B*39:05-C*07:02-DRB1*04:04-DQB1*03:02  Mexico Mexico City Mestizo population 0.3497143
 26  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*04:04:01-DQA1*03:01:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*04:01:01  Russia Belgorod region 0.3268153
 27  A*24:02-B*39:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Tlalpan 0.3030330
 28  A*68:01-B*39:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Tlalpan 0.3030330
 29  A*03:01:01-B*39:01:01-C*12:03:01-DRB1*04:04:01-DQB1*03:02:01  India Karnataka Kannada Speaking 0.2870174
 30  A*30:01:01-B*39:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02  Russia Bashkortostan, Tatars 0.2604192
 31  A*02:01-B*39:05-C*07:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.23801,463
 32  A*24:02-B*39:06-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.23401,999
 33  A*02:01-B*39:39-C*01:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Mexico Chiapas Lacandon Mayans 0.2294218
 34  A*24:02-B*39:05-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 35  A*02:01-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*02:01  Russia Karelia 0.16191,075
 36  A*36-B*39:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 37  A*11:01:01-B*39:01:01-C*03:03:01-DRB1*04:04:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.1400356
 38  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*04:04:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.1400356
 39  A*02:01-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.13411,075
 40  A*24:02-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:02  Russia Karelia 0.12171,075
 41  A*02:01:01-B*39:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  China Zhejiang Han 0.11531,734
 42  A*24:02-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*03:01  Russia Karelia 0.10431,075
 43  A*68:01-B*39:05-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.09401,999
 44  A*29-B*39-DRB1*04:04-DQA1*03:01-DQB1*03:02  Brazil Paraná Caucasian 0.0780641
 45  A*31-B*39-DRB1*04:04-DQA1*03:01-DQB1*03:02  Brazil Paraná Caucasian 0.0780641
 46  A*68:01-B*39:01-C*12:03-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPB1*04:01  Sri Lanka Colombo 0.0700714
 47  A*03:01:01:01-B*39:01:01:03-C*07:02:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.06851,510
 48  A*31:01-B*39:01-C*12:03-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.06022,492
 49  A*31:01-B*39:01-C*12:03-DRB1*04:04-DQB1*03:02  India South UCBB 0.059411,446
 50  A*68:01-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02-DPB1*04:02  Russia Karelia 0.05651,075
 51  A*02:01-B*39:08-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 52  A*24:02-B*39:14-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 53  A*31:01-B*39:05-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 54  A*31:01-B*39:06-C*01:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 55  A*68:01-B*39:02-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 56  A*68:01-B*39:06-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 57  A*24:02-B*39:05-C*08:01-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 58  A*24:02-B*39:11-C*07:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 59  A*01:01-B*39:01-C*12:03-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 60  A*02:22-B*39:09-C*07:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 61  A*24:02-B*39:06-C*07:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 62  A*68:01-B*39:14-C*07:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 63  A*24:02:01:01-B*39:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.03311,510
 64  A*11:01-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02  USA Asian pop 2 0.02201,772
 65  A*02:01-B*39:01-C*12:03-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.02104,856
 66  A*11:01-B*39:01-C*12:03-DRB1*04:04-DQB1*03:02  India East UCBB 0.02082,403
 67  A*31:01-B*39:01-C*12:03-DRB1*04:04-DQB1*03:02  India East UCBB 0.02082,403
 68  A*33:03-B*39:01-C*12:03-DRB1*04:04-DQB1*03:02  India East UCBB 0.02082,403
 69  A*11:01-B*39:01-C*12:03-DRB1*04:04-DQB1*03:02  India South UCBB 0.019311,446
 70  A*24:02-B*39:01-C*12:03-DRB1*04:04-DQB1*03:02  India South UCBB 0.017911,446
 71  A*02:01-B*39:09-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 72  A*02:01-B*39:11-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 73  A*23:01-B*39:09-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 74  A*31:01-B*39:11-C*07:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 75  A*02:01:01-B*39:06:02-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.008523,595
 76  A*24:02:01-B*39:06:02-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.007923,595
 77  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.007223,595
 78  A*03:01-B*39:01-C*12:03-DRB1*04:04-DQB1*03:02  India South UCBB 0.004411,446
 79  A*32:01-B*39:01-C*12:03-DRB1*04:04-DQB1*03:02  India South UCBB 0.003511,446
 80  A*01:01:01-B*39:01:01-C*12:03:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002323,595
 81  A*24:02:01-B*39:01:01-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002123,595
 82  A*02:01:01-B*39:01:01-C*12:03:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002023,595
 83  A*31:01:02-B*39:06:02-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.001923,595
 84  A*02:06-B*39:01-C*07:02-DRB1*04:04-DQB1*03:02  India South UCBB 0.001111,446
 85  A*01:01-B*39:01-C*12:04-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.00075302,492

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).




   

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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