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 84 (from 84) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*23-B*56-DRB1*04-DQB1*03:02  Ecuador Amazonia Mixed Ancestry 1.282139
 2  A*24:02-B*56:04-C*01:02-DRB1*04:03-DQB1*03:02  USA NMDP Hawaiian or other Pacific Islander 0.641611,499
 3  A*02-B*56-DRB1*04-DQB1*03:02  Mexico Veracruz, Veracruz city 0.5814171
 4  A*02:01-B*56:01-C*07:01-E*01:01:01-F*01:01:01-G*01:01-DRB1*04:02-DQA1*03:01-DQB1*03:02  Portugal Azores Terceira Island 0.4386130
 5  A*02-B*56-DRB1*04-DQB1*03:02  Mexico Mexico City Center 0.3247152
 6  A*11:01:01-B*56:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.2810356
 7  A*24:02-B*56:01-C*12:02-DRB1*04:06-DQB1*03:02  Malaysia Peninsular Chinese 0.2577194
 8  A*24:02-B*56:01-C*07:02-DRB1*04:03-DQB1*03:02  USA NMDP Hawaiian or other Pacific Islander 0.243611,499
 9  A*02-B*56-DRB1*04-DQB1*03:02  Ecuador Coast Mixed Ancestry 0.2101238
 10  A*24:02-B*56:02-C*01:02-DRB1*04:03-DQB1*03:02  USA NMDP Hawaiian or other Pacific Islander 0.184911,499
 11  A*11:01-B*56:01-C*04:01-DRB1*04:01-DQB1*03:02  Malaysia Peninsular Indian 0.1845271
 12  A*24:02-B*56:01-C*01:02-DRB1*04:01-DQB1*03:02  Malaysia Peninsular Indian 0.1845271
 13  A*02:01:01-B*56:01:01-C*15:02:01-DRB1*04:01:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.1400356
 14  A*02-B*56-DRB1*04-DQB1*03:02  Ecuador Mixed Ancestry 0.12791,173
 15  A*02-B*56-DRB1*04-DQB1*03:02  Ecuador Andes Mixed Ancestry 0.1214824
 16  A*02:01-B*56:01-C*01:02-DRB1*04:01-DQB1*03:02-DPB1*02:01  Russia Karelia 0.11271,075
 17  A*03-B*56-DRB1*04:04-DQA1*03:01-DQB1*03:02  Brazil Paraná Caucasian 0.0780641
 18  A*24:02-B*56:01-C*04:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*13:01  Sri Lanka Colombo 0.0700714
 19  A*26:01-B*56:01-C*04:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*01:01  Sri Lanka Colombo 0.0700714
 20  A*26:01:01-B*56:01:01-C*01:02:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.06621,510
 21  A*26-B*56-DRB1*04-DQB1*03:02  Ecuador Andes Mixed Ancestry 0.0607824
 22  A*68-B*56-DRB1*04-DQB1*03:02  Ecuador Andes Mixed Ancestry 0.0607824
 23  A*68-B*56-DRB1*04-DQB1*03:02  Mexico Tlaxcala Rural 0.0602830
 24  A*24:02-B*56:01-C*01:02-DRB1*04:07-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 25  A*11:01-B*56:01-C*07:02-DRB1*04:06-DQB1*03:02  USA Asian pop 2 0.04401,772
 26  A*11:01-B*56:03-C*01:02-DRB1*04:06-DQB1*03:02  USA Asian pop 2 0.04401,772
 27  A*32:01-B*56:01-C*01:02-DRB1*04:01-DQB1*03:02  USA African American pop 4 0.04402,411
 28  A*02:01-B*56:01-C*01:02-DRB1*04:02-DQB1*03:02  Germany DKMS - Italy minority 0.04301,159
 29  A*30:01-B*56:01-C*01:02-DRB1*04:04-DQB1*03:02  Germany DKMS - Italy minority 0.04301,159
 30  A*23-B*56-DRB1*04-DQB1*03:02  Ecuador Mixed Ancestry 0.04261,173
 31  A*26-B*56-DRB1*04-DQB1*03:02  Ecuador Mixed Ancestry 0.04261,173
 32  A*68-B*56-DRB1*04-DQB1*03:02  Ecuador Mixed Ancestry 0.04261,173
 33  A*03:01:01-B*56:01:01-C*01:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.032823,595
 34  A*26:01-B*56:01-C*04:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 35  A*74-B*56-DRB1*04-DQB1*03:02  Mexico Puebla, Puebla city 0.02511,994
 36  A*01:01-B*56:01-C*04:10-DRB1*04:03-DQB1*03:02  India East UCBB 0.02082,403
 37  A*24:02-B*56:01-C*04:10-DRB1*04:03-DQB1*03:02  India East UCBB 0.02082,403
 38  A*24:07-B*56:01-C*01:02-DRB1*04:01-DQB1*03:02  India East UCBB 0.02082,403
 39  A*26:01-B*56:01-C*04:10-DRB1*04:03-DQB1*03:02  India East UCBB 0.02082,403
 40  A*11:01-B*56:01-C*01:02-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.02012,492
 41  A*11:01-B*56:01-C*12:98-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.02012,492
 42  A*24:07-B*56:01-C*03:03-DRB1*04:01-DQB1*03:02  India Tamil Nadu 0.02012,492
 43  A*11:01-B*56:01-C*04:01-DRB1*04:03-DQB1*03:02  India West UCBB 0.01725,829
 44  A*26:01:01-B*56:01:01-C*01:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.014723,595
 45  A*31:01-B*56:01-C*01:02-DRB1*04:01-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.01323,456,066
 46  A*11:01-B*56:01-C*04:01-DRB1*04:03-DQB1*03:02  India South UCBB 0.013111,446
 47  A*03:01-B*56:01-C*04:01-DRB1*04:01-DQB1*03:02  India Central UCBB 0.01194,204
 48  A*11:01-B*56:01-C*07:04-DRB1*04:03-DQB1*03:02  India Central UCBB 0.01194,204
 49  A*24:02-B*56:01-C*04:10-DRB1*04:03-DQB1*03:02  India Central UCBB 0.01194,204
 50  A*29:01-B*56:01-C*04:01-DRB1*04:03-DQB1*03:02  India Central UCBB 0.01194,204
 51  A*32:01-B*56:01-C*01:02-DRB1*04:03-DQB1*03:02  India Central UCBB 0.01194,204
 52  A*02:01-B*56:01-C*01:02-DRB1*04:05-DQB1*03:02  USA African American pop 4 0.01102,411
 53  A*02:60-B*56:01-C*01:02-DRB1*04:05-DQB1*03:02  USA African American pop 4 0.01102,411
 54  A*11:01:01-B*56:01:01-C*01:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.010523,595
 55  A*02:01-B*56:01-C*01:02-DRB1*04:03-DQB1*03:02  India West UCBB 0.01025,829
 56  A*32:01-B*56:01-C*01:02-DRB1*04:03-DQB1*03:02  Germany DKMS - Turkey minority 0.01004,856
 57  A*24:02:01-B*56:01:01-C*01:02:01-DRB1*04:01:01-DQB1*03:02:01  Poland BMR 0.009523,595
 58  A*02:11-B*56:01-C*04:01-DRB1*04:03-DQB1*03:02  India South UCBB 0.008711,446
 59  A*24:02-B*56:01-C*04:10-DRB1*04:01-DQB1*03:02  India South UCBB 0.008711,446
 60  A*26:01-B*56:01-C*01:02-DRB1*04:03-DQB1*03:02  India South UCBB 0.008711,446
 61  A*02:06-B*56:01-C*01:02-DRB1*04:03-DQB1*03:02  India West UCBB 0.00865,829
 62  A*33:03-B*56:01-C*04:01-DRB1*04:03-DQB1*03:02  India West UCBB 0.00865,829
 63  A*11:01-B*56:01-C*04:10-DRB1*04:06-DQB1*03:02  India North UCBB 0.00855,849
 64  A*24:02-B*56:01-C*04:10-DRB1*04:03-DQB1*03:02  India North UCBB 0.00855,849
 65  A*68:01-B*56:01-C*04:10-DRB1*04:03-DQB1*03:02  India West UCBB 0.00845,829
 66  A*02:01-B*56:01-C*04:10-DRB1*04:03-DQB1*03:02  India South UCBB 0.008311,446
 67  A*02:01:01-B*56:01:01-C*01:02:01-DRB1*04:01:01-DQB1*03:02:01  Poland BMR 0.007923,595
 68  A*31:01:02-B*56:01:01-C*01:02:01-DRB1*04:01:01-DQB1*03:02:01  Poland BMR 0.006423,595
 69  A*02:11-B*56:01-C*04:10-DRB1*04:01-DQB1*03:02  India South UCBB 0.004411,446
 70  A*11:58-B*56:01-C*01:02-DRB1*04:02-DQB1*03:02  India South UCBB 0.004411,446
 71  A*33:03-B*56:01-C*04:10-DRB1*04:03-DQB1*03:02  India South UCBB 0.004411,446
 72  A*31:01:02-B*56:01:01-C*12:03:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.004223,595
 73  A*03:01:01-B*56:01:01-C*01:02:01-DRB1*04:03:01-DQB1*03:02:01  Poland BMR 0.004123,595
 74  A*24:02:01-B*56:01:01-C*01:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.003623,595
 75  A*02:11-B*56:01-C*04:10-DRB1*04:03-DQB1*03:02  India South UCBB 0.003511,446
 76  A*25:01:01-B*56:01:01-C*01:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002623,595
 77  A*03:01:01-B*56:01:01-C*01:02:01-DRB1*04:01:01-DQB1*03:02:01  Poland BMR 0.002523,595
 78  A*02:01:01-B*56:01:01-C*01:02:01-DRB1*04:03:01-DQB1*03:02:01  Poland BMR 0.002423,595
 79  A*01:01:01-B*56:01:01-C*01:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002223,595
 80  A*26:01:01-B*56:01:01-C*01:02:01-DRB1*04:01:01-DQB1*03:02:01  Poland BMR 0.002123,595
 81  A*23:01:01-B*56:01:01-C*01:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002123,595
 82  A*25:01:01-B*56:01:01-C*02:02:02-DRB1*04:01:01-DQB1*03:02:01  Poland BMR 0.002123,595
 83  A*68:01:02-B*56:01:01-C*01:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002123,595
 84  A*25:01:01-B*56:01:01-C*01:02:01-DRB1*04:03:01-DQB1*03:02:01  Poland BMR 0.002123,595

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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