Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

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Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 74 (from 74) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*02:01:01/02:01:09-B*35:01/35:02/35:03-C*04:01:01  Mexico Chihuahua Tarahumara 3.400044
 2  A*02:01:01-B*35:01:01-C*04:01-DRB1*01:01  South Africa Caucasians 1.6700102
 3  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*08:02:01-DQB1*04:02  Mexico Hidalgo Mezquital Valley/ Otomi 1.388972
 4  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*14:06:01-DQB1*03:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 1.388972
 5  A*02:01:01-B*35:01:01-C*04:01:01  England Blood Donors of Mixed Ethnicity 1.3511519
 6  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*09:01:02  Costa Rica African -Caribbean (G) 0.9804102
 7  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQB1*05:01:01  Spain, Canary Islands, Gran canaria island 0.7000215
 8  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:02:01-DQB1*05:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 9  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*04:04:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 10  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*04:07:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 11  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01-DQB1*05:01:01  Russia Bashkortostan, Bashkirs 0.5387120
 12  A*02:01:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQB1*05:01  Russia Bashkortostan, Tatars 0.5208192
 13  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*11:02:01  Costa Rica African -Caribbean (G) 0.4902102
 14  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01  Costa Rica Guanacaste Mestizo (G) 0.4545110
 15  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01-DQA1*01:01:01-DQB1*03:01:01-DPA1*01:03:01-DPB1*02:01  Russian Federation Vologda Region 0.4202119
 16  A*02:01:01:01-B*35:01:01-C*04:01:01-DRB1*04:01:01-DQB1*03:02  Russia Bashkortostan, Bashkirs 0.4167120
 17  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQB1*05:01:01-DPA1*01:03:01-DPB1*02:01:02  Brazil Rio de Janeiro Caucasian 0.3891521
 18  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.3891521
 19  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQA1*01:01:01-DQB1*05:01-DPA1*01:03:01-DPB1*04:01  Russia Belgorod region 0.3268153
 20  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*03:01:01-DQA1*05:01:01-DQB1*03:01-DPA1*01:03:01-DPB1*02:01:02  Russia Belgorod region 0.3268153
 21  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*08:01-DQA1*02:01:01-DQB1*02:02-DPA1*02:01:01-DPB1*03:01:01  Russia Belgorod region 0.3268153
 22  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*04:03:01  Nicaragua Mestizo (G) 0.3226155
 23  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*04:07  Nicaragua Mestizo (G) 0.3226155
 24  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*13:01  Nicaragua Mestizo (G) 0.3226155
 25  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*08:03:02-DQB1*03:01:01  India Kerala Malayalam speaking 0.2810356
 26  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 27  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*13:01:01-DQB1*06:03:01  Spain, Canary Islands, Gran canaria island 0.2300215
 28  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01  Spain, Canary Islands, Gran canaria island 0.2300215
 29  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*16:02:01-DQB1*05:03  Costa Rica Central Valley Mestizo (G) 0.2262221
 30  A*02:01:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQB1*05:01  Russia Nizhny Novgorod, Russians 0.21911,510
 31  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQB1*05:01:01  Poland BMR 0.179723,595
 32  A*02:01:01:01-B*35:01:01-C*04:01:01-DRB1*08:03:02-DQB1*03:01  Russia Nizhny Novgorod, Russians 0.08771,510
 33  A*02:01:01:01-B*35:01:01-C*04:01:01-DRB1*16:01:01-DQB1*05:02  Russia Nizhny Novgorod, Russians 0.07151,510
 34  A*02:01:01:01-B*35:01:01-C*04:01:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.06891,510
 35  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*14:03:01-DQB1*03:01:01  China Zhejiang Han 0.05771,734
 36  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.048823,595
 37  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*14:01:01-DQB1*05:03:01  Poland BMR 0.042123,595
 38  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.040423,595
 39  A*02:01:01:01-B*35:01:01-C*04:01:01-DRB1*11:04:01-DQB1*03:01  Russia Nizhny Novgorod, Russians 0.03341,510
 40  A*02:01:01:01-B*35:01:01-C*04:01:01:05-DRB1*13:01:01-DQB1*06:03:01  Russia Nizhny Novgorod, Russians 0.03311,510
 41  A*02:01:01:01-B*35:01:01-C*04:01:01-DRB1*04:05:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.03311,510
 42  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.029623,595
 43  A*02:01:01-B*35:01:23-C*04:01:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.02881,734
 44  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.026223,595
 45  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQB1*03:01:01  Poland BMR 0.015523,595
 46  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQB1*02:01:01  Poland BMR 0.011623,595
 47  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.011523,595
 48  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*13:05:01-DQB1*03:01:01  Poland BMR 0.010123,595
 49  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*15:01:01-DQB1*06:03:01  Poland BMR 0.009523,595
 50  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*16:02:01-DPB1*02:02:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00925,266
 51  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQB1*02:02:01  Poland BMR 0.008723,595
 52  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQB1*06:03:01  Poland BMR 0.007923,595
 53  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*08:01:01-DQB1*04:02:01  Poland BMR 0.007823,595
 54  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*13:03:01-DQB1*03:01:01  Poland BMR 0.007523,595
 55  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*09:01:02-DPB1*02:01:02  Hong Kong Chinese HKBMDR HLA 11 loci 0.00635,266
 56  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*04:02:01-DQB1*03:02:01  Poland BMR 0.005823,595
 57  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*04:01:01-DQB1*03:02:01  Poland BMR 0.005623,595
 58  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.004123,595
 59  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQB1*03:02:01  Poland BMR 0.004023,595
 60  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*11:03:01-DQB1*03:01:01  Poland BMR 0.003723,595
 61  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*08:04:01-DQB1*04:02:01  Poland BMR 0.003123,595
 62  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*03:01:01-DQB1*02:01:01  Poland BMR 0.002823,595
 63  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*11:01:01-DQB1*05:02:01  Poland BMR 0.002723,595
 64  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*12:01:01-DQB1*03:01:01  Poland BMR 0.002523,595
 65  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:01:01-DQB1*03:03:02  Poland BMR 0.002523,595
 66  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*11:01:01-DQB1*02:02:01  Poland BMR 0.002423,595
 67  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*11:01:01-DQB1*05:01:01  Poland BMR 0.002323,595
 68  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*01:03-DQB1*03:01:01  Poland BMR 0.002323,595
 69  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*09:01:02-DQB1*03:03:02  Poland BMR 0.002223,595
 70  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01  Poland BMR 0.002223,595
 71  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*04:02:01-DQB1*02:02:01  Poland BMR 0.002123,595
 72  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*11:03:01-DQB1*06:04:01  Poland BMR 0.002123,595
 73  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*14:01:01-DQB1*03:01:01  Poland BMR 0.002123,595
 74  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*10:01:01-DQB1*06:02:01  Poland BMR 0.001323,595

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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