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 419) records   Pages: 1 2 3 4 5 of 5  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02-B*51-DRB1*13:01-DQB1*06:03  Mexico Sinaloa Capomos Mayo Yoremes 1.666760
 2  A*24:02:01-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 1.0753186
 3  A*02:01:01-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01  Spain, Canary Islands, Gran canaria island 0.9300215
 4  A*02:01-B*51:01-C*06:02-E*01:01:01-F*01:01:01-G*01:01-DRB1*13:01-DQA1*02:01-DQB1*06:03  Portugal Azores Terceira Island 0.8772130
 5  A*24:02:01-B*51:01:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  India Karnataka Kannada Speaking 0.8620174
 6  A*31:01:02-B*51:01:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.8065186
 7  A*31:01:02-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01-DPB1*13:01:01  Saudi Arabia pop 6 (G) 0.629428,927
 8  A*74:01-B*51:01-DRB1*13:01-DQB1*06:03  Mexico Veracruz Xalapa 0.595284
 9  A*24:02-B*51:01-DRB1*13:01-DQB1*06:03  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 10  A*11:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  Malaysia Peninsular Indian 0.5535271
 11  A*68:01-B*51:01-C*15:02-E*01:01:01-F*01:01:02-G*01:01-DRB1*13:01-DQA1*01:03-DQB1*06:03  Portugal Azores Terceira Island 0.4386130
 12  A*02:01:01-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01  India Kerala Malayalam speaking 0.4210356
 13  A*02:01:01:01-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01  Russia Bashkortostan, Bashkirs 0.4167120
 14  A*02:06:01-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03  Russia Bashkortostan, Bashkirs 0.4167120
 15  A*02:01-B*51:01-C*01:02-DRB1*13:01-DQA1*01:03-DQB1*06:03  Kosovo 0.4030124
 16  A*25:01-B*51:01-C*02:02-DRB1*13:01-DQA1*01:03-DQB1*06:03  Kosovo 0.4030124
 17  A*68:01-B*51:01-C*05:01-DRB1*13:01-DQA1*01:03-DQB1*06:03  Kosovo 0.4030124
 18  A*02:01:01-B*51:01:01-C*07:01:01-DRB1*13:01:01-DQB1*06:03:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.3891521
 19  A*31:01:02-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01-DPB1*04:01:01  Saudi Arabia pop 6 (G) 0.377128,927
 20  A*02:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  Malaysia Peninsular Indian 0.3690271
 21  A*02:01:01-B*51:01:01-C*05:01:01-DRB1*13:01:01-DQB1*06:03:01-DPB1*04:01:01  South African Black 0.3520142
 22  A*11:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  Mexico Mexico City Mestizo population 0.3497143
 23  B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  Mexico Mexico City Mestizo population 0.3497143
 24  A*31:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India South UCBB 0.312511,446
 25  A*02-B*51-DRB1*13:01-DQA1*01:03-DQB1*06:03  Brazil Paraná Caucasian 0.2998641
 26  A*02:11:01-B*51:01:01-C*07:02:01-DRB1*13:01:01-DQB1*06:03:01  India Kerala Malayalam speaking 0.2810356
 27  A*24:02:01-B*51:01:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 28  A*68:01:02-B*51:01:01-C*16:02:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 29  A*02:01:01:01-B*51:01:01-C*06:02:01:01-DRB1*13:01:01-DQB1*06:03:01  Russia Bashkortostan, Tatars 0.2604192
 30  A*02:06:01-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01  Russia Bashkortostan, Tatars 0.2604192
 31  A*24:02:01:01-B*51:01:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  Russia Bashkortostan, Tatars 0.2604192
 32  A*24-B*51-DRB1*13:01-DQA1*01:03-DQB1*06:03  Brazil Paraná Caucasian 0.2542641
 33  A*29:02-B*51:01-C*15:02-DRB1*13:01-DQB1*06:03  USA NMDP Caribean Indian 0.233714,339
 34  A*33:01:01-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01  Spain, Canary Islands, Gran canaria island 0.2300215
 35  A*03:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India Tamil Nadu 0.22972,492
 36  A*02:01-B*51:01-C*15:09-DRB1*13:01-DQA1*01:03-DQB1*06:03-DPB1*04:01  Nicaragua Managua 0.2165339
 37  A*11:01-B*51:01-C*05:01-DRB1*13:01:01-DQB1*06:03:01  England North West 0.2000298
 38  A*68:01:01-B*51:75-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01-DPA1*02:02:02-DPB1*02:01:02  Brazil Rio de Janeiro Caucasian 0.1946521
 39  A*31:01:02-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01-DPA1*02:01:01-DPB1*11:01:01  Brazil Rio de Janeiro Caucasian 0.1945521
 40  A*24:07-B*51:01-C*07:04-DRB1*13:01-DQB1*06:03  Malaysia Peninsular Indian 0.1845271
 41  A*31:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  Malaysia Peninsular Indian 0.1845271
 42  A*32:01-B*51:01-C*04:01-DRB1*13:01-DQB1*06:03  Malaysia Peninsular Indian 0.1845271
 43  A*31:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India West UCBB 0.18315,829
 44  A*02:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  Colombia Bogotá Cord Blood 0.17061,463
 45  A*03:02-B*51:01-C*15:02-DRB1*13:01-DQB1*06:03  India Northeast UCBB 0.1689296
 46  A*24:02-B*51:01-C*16:02-DRB1*13:01-DQB1*06:03  India Northeast UCBB 0.1689296
 47  A*26:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India Northeast UCBB 0.1689296
 48  A*31:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India North UCBB 0.16755,849
 49  A*68:01-B*51:01-C*16:02-DRB1*13:01-DQB1*06:03  India North UCBB 0.14215,849
 50  A*02:01-B*51:01-C*01:02-DRB1*13:01-DQB1*06:03  Italy pop 5 0.1400975
 51  A*02:01-B*51:01-C*15:02-DRB1*13:01-DQB1*06:03  USA Hispanic pop 2 0.14001,999
 52  A*03:01-B*51:01-C*04:01-DRB1*13:01-DQB1*06:03  Italy pop 5 0.1400975
 53  A*03:01-B*51:01-C*05:01-DRB1*13:01-DQB1*06:03  Italy pop 5 0.1400975
 54  A*24:02-B*51:01-C*01:02-DRB1*13:01-DQB1*06:03  Italy pop 5 0.1400975
 55  A*31:01:02-B*51:01:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  India Kerala Malayalam speaking 0.1400356
 56  A*31:01:02-B*51:01:05-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  India Kerala Malayalam speaking 0.1400356
 57  A*31:01-B*51:01-C*07:18-DRB1*13:01-DQB1*06:03  Italy pop 5 0.1400975
 58  A*02:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  USA NMDP Black South or Central American 0.13924,889
 59  A*11:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India Tamil Nadu 0.13782,492
 60  A*02:01-B*51:01-C*15:02-DRB1*13:01-DQB1*06:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 61  A*02:01-B*51:01-C*02:02-DRB1*13:01-DQB1*06:03  Colombia Bogotá Cord Blood 0.13671,463
 62  A*02:01-B*51:01-C*07:01-DRB1*13:01-DQB1*06:03  Colombia Bogotá Cord Blood 0.13671,463
 63  A*31:01:02-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.125028,927
 64  A*02:01-B*51:01-C*15:02-DRB1*13:01-DQB1*06:03  India Tamil Nadu 0.12362,492
 65  A*11:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India West UCBB 0.12285,829
 66  A*01:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India Tamil Nadu 0.11922,492
 67  A*02:01-B*51:01-C*16:02-DRB1*13:01-DQB1*06:03  Germany DKMS - Turkey minority 0.11904,856
 68  A*31:01:02-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01-DPB1*02:01:02  Saudi Arabia pop 6 (G) 0.118428,927
 69  A*11:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India Central UCBB 0.11764,204
 70  A*68:01-B*51:01-C*15:02-DRB1*13:01-DQB1*06:03  India North UCBB 0.11365,849
 71  A*31:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India Central UCBB 0.11344,204
 72  A*02:01-B*51:01-C*07:01-DRB1*13:01-DQB1*06:03-DPB1*03:01  Russia Karelia 0.11291,075
 73  A*02:06-B*51:01-C*15:02-DRB1*13:01-DQB1*06:03-DPB1*05:01  Russia Karelia 0.10751,075
 74  A*01:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India South UCBB 0.107211,446
 75  A*68:01-B*51:01-C*16:02-DRB1*13:01-DQB1*06:03  India East UCBB 0.10402,403
 76  A*02:01-B*51:01-C*02:02-DRB1*13:01-DQB1*06:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 77  A*11:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India South UCBB 0.098111,446
 78  A*02:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  Germany DKMS - Turkey minority 0.09604,856
 79  A*02:01-B*51:01-C*07:01-DRB1*13:01-DQB1*06:03  USA Hispanic pop 2 0.09401,999
 80  A*68:01-B*51:01-C*16:02-DRB1*13:01-DQB1*06:03  India West UCBB 0.09385,829
 81  A*02:01-B*51:05-C*04:01-DRB1*13:01-DQB1*06:03  Germany DKMS - Turkey minority 0.09304,856
 82  A*02:01:01-B*51:01:01-C*16:04-DRB1*13:01:01-DQB1*06:03:01-DPB1*04:01:01  Saudi Arabia pop 6 (G) 0.091928,927
 83  A*24:02-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India North UCBB 0.09165,849
 84  A*01:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  USA Asian pop 2 0.08901,772
 85  A*11:01-B*51:01-C*12:09-DRB1*13:01-DQB1*06:03  India North UCBB 0.08705,849
 86  A*24:02-B*51:01-C*15:02-DRB1*13:01-DQB1*06:03  Germany DKMS - Italy minority 0.08601,159
 87  A*24:03-B*51:01-C*07:01-DRB1*13:01-DQB1*06:03  Germany DKMS - Italy minority 0.08601,159
 88  A*68:01-B*51:01-C*15:02-DRB1*13:01-DQB1*06:03  Germany DKMS - Italy minority 0.08601,159
 89  A*11:01-B*51:01-C*16:02-DRB1*13:01-DQB1*06:03  India North UCBB 0.08245,849
 90  A*68:01:02-B*51:01:01-C*15:02:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.082023,595
 91  A*68:01-B*51:01-C*16:02-DRB1*13:01-DQB1*06:03  India Central UCBB 0.08164,204
 92  A*29-B*51-DRB1*13:01-DQA1*01:03-DQB1*06:03  Brazil Paraná Caucasian 0.0813641
 93  A*31:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India Tamil Nadu 0.08062,492
 94  A*02:01:01:01-B*51:01:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  Russia Nizhny Novgorod, Russians 0.07951,510
 95  A*11-B*51-DRB1*13:01-DQA1*01:03-DQB1*06:03  Brazil Paraná Caucasian 0.0780641
 96  A*03:01-B*51:01-C*16:02-DRB1*13:01-DQB1*06:03  India Tamil Nadu 0.07642,492
 97  A*03:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India South UCBB 0.074711,446
 98  A*31:01-B*51:01-C*16:02-DRB1*13:01-DQB1*06:03  India Central UCBB 0.07414,204
 99  A*68:01-B*51:01-C*16:02-DRB1*13:01-DQB1*06:03  India South UCBB 0.073611,446
 100  A*68:01-B*51:01-C*15:02-DRB1*13:01-DQB1*06:03  India Tamil Nadu 0.07312,492

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).


Displaying 1 to 100 (from 419) records   Pages: 1 2 3 4 5 of 5  


   

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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