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 : 
Region:  Ethnic Origin:     Type of study :  Sort by: 
Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 71 (from 71) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*68-B*56-DRB1*11-DQB1*03:01  Mexico Tamaulipas, Ciudad Victoria 4.347823
 2  A*02:01:01-B*56:01:01-C*01:02:01-DRB1*11:03:01-DQB1*03:01:01-DPA1*02:01:01-DPB1*10:01:01  Brazil Barra Mansa Rio State Black 2.381073
 3  A*02-B*56-DRB1*11-DQB1*03:01  Ecuador Amazonia Mixed Ancestry 1.282139
 4  A*24:02-B*56:04-C*01:02-DRB1*11:01-DQB1*03:01  USA NMDP Hawaiian or other Pacific Islander 0.853611,499
 5  A*66:01:01-B*56:01:01-C*01:02:01-DRB1*11:04:01-DQA1*03:03:01-DQB1*03:01:01-DPA1*01:03:01-DPB1*02:01:02  Russian Federation Vologda Region 0.4202119
 6  A*11:01-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  USA NMDP Hawaiian or other Pacific Islander 0.252511,499
 7  A*24:02-B*56:02-C*01:02-DRB1*11:01-DQB1*03:01  USA NMDP Hawaiian or other Pacific Islander 0.221411,499
 8  A*03-B*56-DRB1*11:01-DQA1*05:05-DQB1*03:01  Brazil Paraná Caucasian 0.1560641
 9  A*02-B*56-DRB1*11-DQB1*03:01  Mexico Tlaxcala Rural 0.1205830
 10  A*11:01-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  India Tamil Nadu 0.08032,492
 11  A*29-B*56-DRB1*11:01-DQA1*05:05-DQB1*03:01  Brazil Paraná Caucasian 0.0780641
 12  A*68:01-B*56:01-C*01:02-DRB1*11:01-DQA1*05:01-DQB1*03:01-DPB1*09:01  Sri Lanka Colombo 0.0700714
 13  A*68:01-B*56:01-C*04:01-DRB1*11:01-DQA1*05:01-DQB1*03:01-DPB1*03:01  Sri Lanka Colombo 0.0700714
 14  A*26:01-B*56:01-C*07:02-DRB1*11:01-DQA1*05:05-DQB1*03:01-DPA1*01:03-DPB1*02:02  Japan pop 17 0.07003,078
 15  A*03-B*56-DRB1*11-DQB1*03:01  Ecuador Andes Mixed Ancestry 0.0607824
 16  A*03:01-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  USA Asian pop 2 0.04401,772
 17  A*31:01-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  Germany DKMS - Italy minority 0.04301,159
 18  A*02-B*56-DRB1*11-DQB1*03:01  Ecuador Mixed Ancestry 0.04261,173
 19  A*03-B*56-DRB1*11-DQB1*03:01  Ecuador Mixed Ancestry 0.04261,173
 20  A*11:01-B*56:01-C*04:10-DRB1*11:01-DQB1*03:01  India Tamil Nadu 0.04012,492
 21  A*11:01-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  India South UCBB 0.037611,446
 22  A*11:01-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 23  A*24:02-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 24  A*02:01:01:01-B*56:01:01-C*01:02:01-DRB1*11:04:01-DQB1*03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 25  A*32:01:01-B*56:01:01:02-C*01:02:01-DRB1*11:28:01-DQB1*03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 26  A*01:01-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  India South UCBB 0.031511,446
 27  A*24:02-B*56:01-C*12:02-DRB1*11:01-DQA1*05:05-DQB1*03:01-DPA1*01:03-DPB1*02:02  Japan pop 17 0.03003,078
 28  A*31:01-B*56:01-C*07:02-DRB1*11:01-DQA1*05:05-DQB1*03:01-DPA1*01:03-DPB1*04:01  Japan pop 17 0.03003,078
 29  A*02:01:01-B*56:03-C*01:02:01-DRB1*11:01:01-DQB1*03:01:01  China Zhejiang Han 0.02881,734
 30  A*01:01-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  India East UCBB 0.02082,403
 31  A*24:02-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  India East UCBB 0.02082,403
 32  A*74:01-B*56:01-C*04:10-DRB1*11:01-DQB1*03:01  India East UCBB 0.02082,403
 33  A*68:01-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  India Tamil Nadu 0.02012,492
 34  A*02:01:01-B*56:01:01-C*01:02:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.017223,595
 35  A*01:01-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  India North UCBB 0.01415,849
 36  A*32:01-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  India Central UCBB 0.01194,204
 37  A*33:03-B*56:01-C*03:02-DRB1*11:01-DQB1*03:01  India Central UCBB 0.01194,204
 38  A*02:01-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01-DPB1*04:01  Germany DKMS - German donors 0.01163,456,066
 39  A*03:01-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  India West UCBB 0.01015,829
 40  A*01:01-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  Germany DKMS - Turkey minority 0.01004,856
 41  A*03:01-B*56:01-C*04:10-DRB1*11:01-DQB1*03:01  India South UCBB 0.010011,446
 42  A*03:02-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  Germany DKMS - Turkey minority 0.01004,856
 43  A*29:02-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  Germany DKMS - Turkey minority 0.01004,856
 44  A*02:01:01-B*56:01:01-C*01:02:01-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.009223,595
 45  A*11:01-B*56:01-C*04:10-DRB1*11:01-DQB1*03:01  India South UCBB 0.008711,446
 46  A*11:01-B*56:22-C*04:10-DRB1*11:01-DQB1*03:01  India South UCBB 0.008711,446
 47  A*24:02:01-B*56:01:01-C*01:02:01-DRB1*11:03:01-DQB1*03:01:01  Poland BMR 0.008723,595
 48  A*32:01-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  India West UCBB 0.00865,829
 49  A*68:01-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  India West UCBB 0.00865,829
 50  A*11:01-B*56:01-C*01:02-DRB1*11:04-DQB1*03:01  India North UCBB 0.00855,849
 51  A*24:02-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  India North UCBB 0.00855,849
 52  A*24:02-B*56:01-C*12:09-DRB1*11:01-DQB1*03:01  India North UCBB 0.00855,849
 53  A*02:01-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  India South UCBB 0.008011,446
 54  A*26:01:01-B*56:01:01-C*01:02:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.006623,595
 55  A*26:01:01-B*56:01:01-C*01:02:01-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.006423,595
 56  A*02:01-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  India West UCBB 0.00515,829
 57  A*31:01-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  India South UCBB 0.004911,446
 58  A*11:01:01-B*56:01:01-C*01:02:01-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.004823,595
 59  A*02:03-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  India South UCBB 0.004411,446
 60  A*02:11-B*56:01-C*14:02-DRB1*11:01-DQB1*03:01  India South UCBB 0.004411,446
 61  A*03:01-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  India South UCBB 0.004411,446
 62  A*03:01-B*56:01-C*15:02-DRB1*11:11-DQB1*03:01  India South UCBB 0.004411,446
 63  A*24:02-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  India South UCBB 0.004411,446
 64  A*24:07-B*56:01-C*01:02-DRB1*11:01-DQB1*03:01  India South UCBB 0.004411,446
 65  A*33:03-B*56:01-C*04:01-DRB1*11:04-DQB1*03:01  India South UCBB 0.004411,446
 66  A*02:06-B*56:01-C*04:01-DRB1*11:01-DQB1*03:01  India South UCBB 0.003111,446
 67  A*01:01:01-B*56:01:01-C*01:02:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.002223,595
 68  A*11:01:01-B*56:01:01-C*01:02:01-DRB1*11:69-DQB1*03:01:01  Poland BMR 0.002123,595
 69  A*23:01:01-B*56:01:01-C*01:02:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.002123,595
 70  A*24:02:01-B*56:01:01-C*01:02:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.000516223,595
 71  A*30:01:01-B*56:01:01-C*01:02:01-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.000239523,595

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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