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 : 
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Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 100 (from 100) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*66-B*41-DRB1*11-DQB1*03:01  Mexico San Luis Potosi Rural 2.298987
 2  A*11-B*41-DRB1*11-DQB1*03:01  Mexico Jalisco, Tlajomulco 1.666730
 3  A*02-B*41-DRB1*11-DQB1*03:01  Mexico Queretaro, Queretaro city 1.111145
 4  A*02-B*41-DRB1*11-DQB1*03:01  Mexico Mexico City East 0.625079
 5  A*02-B*41-DRB1*11-DQB1*03:01  Mexico Veracruz, Veracruz city 0.5814171
 6  A*11-B*41-DRB1*11-DQB1*03:01  Guatemala, Guatemala City Mixed Ancestry 0.3900127
 7  A*23:01:01-B*41:01:01-C*17:01:01-DRB1*11:01:02-DQB1*03:01:01-DPA1*02:01:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.3891521
 8  A*24:02:01-B*41:02:01-C*17:03:01-DRB1*11:01:01-DQA1*05:05:01-DQB1*03:01-DPA1*01:03:01-DPB1*04:01:01  Russia Belgorod region 0.3268153
 9  A*66-B*41-DRB1*11-DQB1*03:01  Mexico Mexico City Center 0.3247152
 10  A*24-B*41-DRB1*11-DQB1*03:01  Mexico Guanajuato Rural 0.3067162
 11  A*02:05-B*41:01-C*17:01-DRB1*11:01-DQA1*03:01-DQB1*03:01-DPB1*01:01  South Africa Worcester 0.3000159
 12  A*24-B*41-DRB1*11-DQB1*03:01  Mexico Veracruz, Xalapa 0.2674187
 13  A*23-B*41-DRB1*11-DQB1*03:01  Mexico Sonora Rural 0.2538197
 14  A*02-B*41-DRB1*11-DQB1*03:01  Mexico Chihuahua Rural 0.2092236
 15  A*03:02-B*41:02:01-C*17:01-DRB1*11:04:01-DQB1*03:01  England North West 0.2000298
 16  A*23-B*41-DRB1*11-DQB1*03:01  Mexico Zacatecas Rural 0.1859266
 17  A*02:01-B*41:01-C*07:328-DRB1*11:01-DQB1*03:01-DPB1*233:01  Tanzania Maasai 0.1597336
 18  A*23:01-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01-DPB1*04:01  Tanzania Maasai 0.1597336
 19  A*26-B*41-DRB1*11-DQB1*03:01  Mexico Michoacan Rural 0.1433348
 20  A*02:02-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01  Italy pop 5 0.1400975
 21  A*02:05-B*41:01-C*07:01-DRB1*11:01-DQB1*03:01  Italy pop 5 0.1400975
 22  A*30-B*41-DRB1*11-DQB1*03:01  Mexico Coahuila, Torreon 0.1250396
 23  A*02:01-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01  Germany DKMS - Italy minority 0.08601,159
 24  A*11-B*41-DRB1*11-DQB1*03:01  Mexico Jalisco Rural 0.0853585
 25  A*24-B*41-DRB1*11:01-DQA1*05:05-DQB1*03:01  Brazil Paraná Caucasian 0.0780641
 26  A*32-B*41-DRB1*11:04-DQA1*05:01-DQB1*03:01  Brazil Paraná Caucasian 0.0780641
 27  A*02:01:01:01-B*41:01:01-C*17:01:01-DRB1*11:04:01-DQB1*03:01  Russia Nizhny Novgorod, Russians 0.06621,510
 28  A*11-B*41-DRB1*11-DQB1*03:01  Ecuador Andes Mixed Ancestry 0.0607824
 29  A*23-B*41-DRB1*11-DQB1*03:01  Ecuador Andes Mixed Ancestry 0.0607824
 30  A*02-B*41-DRB1*11-DQB1*03:01  Mexico Puebla Rural 0.0600833
 31  A*03:01-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01-DPB1*13:01  Russia Karelia 0.05651,075
 32  A*03:01-B*41:01-C*07:01-DRB1*11:01-DQB1*03:01-DPB1*02:01  Russia Karelia 0.05581,075
 33  A*23:01-B*41:02-C*17:01-DRB1*11:04-DQB1*03:01  USA Hispanic pop 2 0.04701,999
 34  A*01:01-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01  Germany DKMS - Italy minority 0.04301,159
 35  A*11-B*41-DRB1*11-DQB1*03:01  Ecuador Mixed Ancestry 0.04261,173
 36  A*23-B*41-DRB1*11-DQB1*03:01  Ecuador Mixed Ancestry 0.04261,173
 37  A*66:01-B*41:02-C*17:01-DRB1*11:01-DQB1*03:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 38  A*68:01-B*41:02-C*17:01-DRB1*11:01-DQB1*03:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 39  A*01:01:01:01-B*41:01:01-C*17:01:01-DRB1*11:01:01-DQB1*03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 40  A*02:01:01:01-B*41:02:01-C*17:03-DRB1*11:03-DQB1*03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 41  A*23:01:01-B*41:02:01-C*17:03-DRB1*11:04-DQB1*03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 42  A*02:03:01-B*41:01:01-C*17:01:01-DRB1*11:04:01-DQB1*03:01:01  China Zhejiang Han 0.02881,734
 43  A*33:01-B*41:02-C*17:01-DRB1*11:02-DQB1*03:01  USA African American pop 4 0.02202,411
 44  A*66:01-B*41:02-C*17:01-DRB1*11:04-DQB1*03:01  Germany DKMS - Turkey minority 0.01504,856
 45  A*66:01:01-B*41:02:01-C*17:03-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.014223,595
 46  A*01:01-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  Germany DKMS - Turkey minority 0.01404,856
 47  A*01:01-B*41:01-C*02:08-DRB1*11:01-DQB1*03:01  India UCBB_Central Indian HLA 0.01194,204
 48  A*31:06-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  India UCBB_Central Indian HLA 0.01194,204
 49  A*02:02-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  Germany DKMS - Turkey minority 0.01004,856
 50  A*23:01-B*41:02-C*17:01-DRB1*11:01-DQB1*03:01  Germany DKMS - Turkey minority 0.01004,856
 51  A*24:02-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  Germany DKMS - Turkey minority 0.01004,856
 52  A*30:02-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  Germany DKMS - Turkey minority 0.01004,856
 53  A*30:04-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  Germany DKMS - Turkey minority 0.01004,856
 54  A*02:01:01-B*41:02:01-C*17:03-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.006923,595
 55  A*03:01:01-B*41:02:01-C*04:01:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.005623,595
 56  A*02:01:01-B*41:01:01-C*17:01:01-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.004723,595
 57  A*01:01:01-B*41:02:01-C*17:03-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.003723,595
 58  A*02:01:01-B*41:02:01-C*17:03-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.003223,595
 59  A*26:01:01-B*41:02:01-C*17:03-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.003123,595
 60  A*02:05:01-B*41:01:01-C*07:01:01-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.002123,595
 61  A*23:01:01-B*41:01:01-C*17:01:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.002123,595
 62  A*23:01:01-B*41:02:01-C*17:03-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.002123,595
 63  A*31:01:02-B*41:02:01-C*17:03-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.002123,595
 64  A*24:02-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01  India Tamil Nadu 0.00172,492
 65  A*24:03-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01  India Tamil Nadu 0.00172,492
 66  A*24:07-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01  India Tamil Nadu 0.00172,492
 67  A*24:10-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01  India Tamil Nadu 0.00172,492
 68  A*24:17-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01  India Tamil Nadu 0.00172,492
 69  A*24:32-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01  India Tamil Nadu 0.00172,492
 70  A*24:55-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01  India Tamil Nadu 0.00172,492
 71  A*23:01:01-B*41:01:01-C*07:01:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.000999323,595
 72  A*24:02-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  India Tamil Nadu 0.00030002,492
 73  A*24:02-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01  India Tamil Nadu 0.00030002,492
 74  A*24:02-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01  India Tamil Nadu 0.00030002,492
 75  A*24:02-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01  India Tamil Nadu 0.00030002,492
 76  A*24:03-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  India Tamil Nadu 0.00030002,492
 77  A*24:03-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01  India Tamil Nadu 0.00030002,492
 78  A*24:03-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01  India Tamil Nadu 0.00030002,492
 79  A*24:03-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01  India Tamil Nadu 0.00030002,492
 80  A*24:07-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  India Tamil Nadu 0.00030002,492
 81  A*24:07-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01  India Tamil Nadu 0.00030002,492
 82  A*24:07-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01  India Tamil Nadu 0.00030002,492
 83  A*24:07-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01  India Tamil Nadu 0.00030002,492
 84  A*24:10-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  India Tamil Nadu 0.00030002,492
 85  A*24:10-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01  India Tamil Nadu 0.00030002,492
 86  A*24:10-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01  India Tamil Nadu 0.00030002,492
 87  A*24:10-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01  India Tamil Nadu 0.00030002,492
 88  A*24:17-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  India Tamil Nadu 0.00030002,492
 89  A*24:17-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01  India Tamil Nadu 0.00030002,492
 90  A*24:17-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01  India Tamil Nadu 0.00030002,492
 91  A*24:17-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01  India Tamil Nadu 0.00030002,492
 92  A*24:32-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  India Tamil Nadu 0.00030002,492
 93  A*24:32-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01  India Tamil Nadu 0.00030002,492
 94  A*24:32-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01  India Tamil Nadu 0.00030002,492
 95  A*24:32-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01  India Tamil Nadu 0.00030002,492
 96  A*24:55-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01  India Tamil Nadu 0.00030002,492
 97  A*24:55-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01  India Tamil Nadu 0.00030002,492
 98  A*24:55-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01  India Tamil Nadu 0.00030002,492
 99  A*24:55-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01  India Tamil Nadu 0.00030002,492
 100  A*01:01:01-B*41:01:01-C*17:01:01-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.000264823,595

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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