Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

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Population:  Country:  Source of dataset : 
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Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 83 (from 83) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*24:02:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 2  A*02:13-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.67411,463
 3  A*02:06-B*51:01-C*15:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 4  A*11:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02:01  England North West 0.2000298
 5  A*02:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  USA NMDP American Indian South or Central America 0.18775,926
 6  A*11:01:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.167023,595
 7  A*24:02-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.13611,463
 8  A*11:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Italy minority 0.12901,159
 9  A*02:01:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.115823,595
 10  A*02:01:01:01-B*51:01:01-C*15:02:01:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.09931,510
 11  A*02:13-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.09401,999
 12  A*24:02-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.08801,999
 13  A*24:02-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.07615,849
 14  A*11:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 15  A*02:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.06651,463
 16  A*11:01:01:01-B*51:01:01-C*15:02:01:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.06621,510
 17  A*24:02:01:01-B*51:01:01-C*15:02:01:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.06621,510
 18  A*11:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.06304,856
 19  A*24:02-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.05651,075
 20  A*24:02-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India South UCBB 0.055211,446
 21  A*11:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India South UCBB 0.052111,446
 22  A*11:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02-DPB1*03:01  Germany DKMS - German donors 0.04973,456,066
 23  A*02:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.04653,456,066
 24  A*11:08-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Italy minority 0.04301,159
 25  A*31:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.04275,849
 26  A*32:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.04185,849
 27  A*68:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.04104,856
 28  A*11:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.03783,456,066
 29  A*03:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03671,463
 30  A*24:14-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 31  A*68:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 32  A*25:01:01-B*51:01:01-C*15:02:01:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.03311,510
 33  A*31:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.03104,856
 34  A*24:02-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.03004,856
 35  A*32:01:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.026523,595
 36  A*26:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.02645,849
 37  A*02:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.02562,492
 38  A*68:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.02493,456,066
 39  A*01:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.02384,204
 40  A*32:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.02384,204
 41  A*68:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India Central UCBB 0.02384,204
 42  A*11:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.02302,492
 43  A*02:11-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.02192,492
 44  A*32:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.02104,856
 45  A*24:02-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India East UCBB 0.02082,403
 46  A*30:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India East UCBB 0.02082,403
 47  A*03:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.02012,492
 48  A*01:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.01735,849
 49  A*11:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India West UCBB 0.01725,829
 50  A*24:02:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.015123,595
 51  A*02:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02-DPB1*03:01  Germany DKMS - German donors 0.01453,456,066
 52  A*68:01:02-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.012723,595
 53  A*01:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.01204,856
 54  A*01:01:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.011223,595
 55  A*32:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.01113,456,066
 56  A*02:13-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  USA African American pop 4 0.01102,411
 57  A*11:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  USA African American pop 4 0.01102,411
 58  A*26:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  USA African American pop 4 0.01102,411
 59  A*30:02-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  USA African American pop 4 0.01102,411
 60  A*26:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.01004,856
 61  A*02:06-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India West UCBB 0.00865,829
 62  A*24:02-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India West UCBB 0.00865,829
 63  A*26:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India West UCBB 0.00865,829
 64  A*68:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India West UCBB 0.00865,829
 65  A*03:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.00855,849
 66  A*33:03-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.00855,849
 67  A*68:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.00855,849
 68  A*01:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India West UCBB 0.00775,829
 69  A*03:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India South UCBB 0.007711,446
 70  A*30:01:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.007523,595
 71  A*25:01:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.005923,595
 72  A*26:01:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.005423,595
 73  A*03:01:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.005423,595
 74  A*03:02-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India North UCBB 0.00485,849
 75  A*02:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India South UCBB 0.004411,446
 76  A*03:02:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.004023,595
 77  A*31:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  India South UCBB 0.003311,446
 78  A*11:01:79-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002523,595
 79  A*23:01:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002323,595
 80  A*24:203-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002123,595
 81  A*31:01:02-B*51:01:02-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002123,595
 82  A*31:01:02-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.001323,595
 83  A*02:01-B*51:01-C*15:02-DRB1*04:04-DQB1*03:02  Italy pop 5 0.0000000975

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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