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 801 to 900 (from 7,664) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 77  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 801  A*24:02-B*55:01-C*01:02-DRB1*13:01-DQA1*01:03-DQB1*06:03-DPB1*04:01  Sri Lanka Colombo 0.2801714
 802  A*02:01-B*56:01-C*01:02-DRB1*01:01-DQB1*05:01  USA Hispanic pop 2 0.28001,999
 803  A*02:01-B*15:01-C*01:02-DRB1*08:02-DQB1*04:02  USA NMDP Alaska Native or Aleut 0.27781,376
 804  A*11:01-B*27:05-C*01:02-DRB1*01:01-DQB1*05:01  USA NMDP Caribean Indian 0.277614,339
 805  A*02:01-B*46:01-C*01:03-DRB1*09:01  Hong Kong Chinese BMDR 0.27657,595
 806  B*55:01-C*01:02  USA Hispanic pop 2 0.27601,999
 807  A*11:01-B*27:05-C*01:02-DRB1*01:01  Germany DKMS - Spain minority 0.27101,107
 808  A*11:01-B*46:01-C*01:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Southeast Asian 0.270927,978
 809  A*02:01-B*27:05-C*01:02-DRB1*01:01  Poland DKMS 0.270320,653
 810  A*02:01-B*15:01-C*01:02  Italy pop 5 0.2700975
 811  A*02:01-B*27:05-C*01:02  Italy pop 5 0.2700975
 812  A*02:01-B*56:01-C*01:02  Italy pop 5 0.2700975
 813  A*24:02-B*15:01-C*01:02-DRB1*16:02-DQB1*03:01-DPB1*04:02  Panama 0.2700462
 814  B*27:05-C*01:02  USA African American pop 4 0.27002,411
 815  A*02:07-B*46:01-C*01:02-DRB1*15:01  Germany DKMS - China minority 0.26901,282
 816  A*01:01:01-B*55:01:01-C*01:02:01-DRB1*15:01:01-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 817  A*02:01:01-B*55:01:01-C*01:02:01-DRB1*07:01:01-DQB1*03:03:02  India Andhra Pradesh Telugu Speaking 0.2688186
 818  A*02:11:01-B*15:01:01-C*01:02:01-DRB1*04:01:01-DQB1*03:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 819  A*02:11:01-B*37:01:01-C*01:44-DRB1*13:01:01-DQB1*05:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 820  A*03:01:01-B*40:06:04-C*01:02:01-DRB1*14:04:01-DQB1*05:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 821  A*03:01:01-B*56:01:01-C*01:02:01-DRB1*10:01:01-DQB1*05:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 822  A*11:01:01-B*35:01:01-C*01:02:01-DRB1*13:01:01-DQB1*04:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 823  A*11:01:01-B*50:01:01-C*01:02:01-DRB1*13:02:01-DQB1*03:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 824  A*11:01:01-B*55:01:01-C*01:02:01-DRB1*15:01:01-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 825  A*24:02:01-B*15:01:01-C*01:02:01-DRB1*15:02:02-DQB1*03:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 826  A*24:02:01-B*15:08:01-C*01:02:01-DRB1*04:01:01-DQB1*03:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 827  A*24:02:01-B*15:08:01-C*01:02:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 828  A*24:02:01-B*40:06:01-C*01:02:01-DRB1*08:03:02-DQB1*03:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 829  A*26:01:01-B*40:06:01-C*01:17-DRB1*15:02:01-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 830  A*26:01:01-B*55:01:01-C*01:02:01-DRB1*04:04:01-DQB1*03:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 831  A*30:02:01-B*55:01:01-C*01:02:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 832  A*31:01:02-B*55:01:01-C*01:02:01-DRB1*10:01:01-DQB1*05:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 833  A*68:01:01-B*55:01:04-C*01:02:01-DRB1*04:03:01-DQB1*03:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 834  A*68:01:02-B*40:06:01-C*01:02:01-DRB1*15:02:01-DQB1*05:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 835  A*02:03-B*46:01-C*01:02-DRB1*09:01  Hong Kong Chinese BMDR 0.26867,595
 836  A*02:01-B*51:01-C*01:02-DRB1*14:01  Germany DKMS - Croatia minority 0.26802,057
 837  A*68:01-B*35:43-C*01:02-DRB1*04:07-DQB1*03:02  Colombia Bogotá Cord Blood 0.26711,463
 838  A*02:01-B*27:05-C*01:02-DRB1*01:01-DRBX*NNNN-DQB1*05:01  USA NMDP European Caucasian 0.26651,242,890
 839  A*11:01:01-B*27:05:02-C*01:02:01  England Blood Donors of Mixed Ethnicity 0.2658519
 840  A*26:01:01-B*27:14-C*01:02:01-DRB1*01:01:01-DQB1*05:01  Russia Nizhny Novgorod, Russians 0.26491,510
 841  A*24:02-B*56:04-C*01:02-DRB1*04:05-DQB1*04:02  USA NMDP Hawaiian or other Pacific Islander 0.263611,499
 842  A*02:01-B*46:01-C*01:02-DRB1*04:04  Hong Kong Chinese BMDR 0.26137,595
 843  A*02:01:01:01-B*27:05:02-C*01:02:01-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Tatars 0.2604192
 844  A*02:01:01:01-B*53:01:01-C*01:02:01-DRB1*01:02:01-DQB1*05:01  Russia Bashkortostan, Tatars 0.2604192
 845  A*02:01:01:01-B*55:01:01-C*01:02:01-DRB1*11:04:01-DQB1*03:01  Russia Bashkortostan, Tatars 0.2604192
 846  A*02:05:01-B*27:05:02-C*01:02:01-DRB1*01:03-DQB1*03:01  Russia Bashkortostan, Tatars 0.2604192
 847  A*03:01:01:01-B*55:01:01-C*01:02:01-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Tatars 0.2604192
 848  A*11:01:01:01-B*08:01:01-C*01:02:01-DRB1*01:01:01-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 849  A*11:01:01:01-B*27:05:02-C*01:02:01-DRB1*08:01:01-DQB1*04:02:01  Russia Bashkortostan, Tatars 0.2604192
 850  A*24:02:01:01-B*46:01:01-C*01:02:01-DRB1*13:01:01-DQB1*06:03:01  Russia Bashkortostan, Tatars 0.2604192
 851  A*25:01:01-B*27:02:01-C*01:02:01-DRB1*11:01:01-DQB1*03:01  Russia Bashkortostan, Tatars 0.2604192
 852  A*26:01:01-B*27:05:02-C*01:02:01-DRB1*01:01:01-DQB1*05:01  Russia Bashkortostan, Tatars 0.2604192
 853  A*68:01:02:01-B*27:05:02-C*01:02:01-DRB1*01:01:01-DQB1*05:01  Russia Bashkortostan, Tatars 0.2604192
 854  A*01-B*35-C*01  Brazil Parana Japanese 0.2600192
 855  A*02:01-B*08:01-C*01:02-DRB1*01:01-DQA1*01:01-DQB1*05:01-DPB1*01:01  USA San Diego 0.2600496
 856  A*02:01-B*15:01-C*01:02-DRB1*04:02-DQA1*04:01-DQB1*04:02-DPB1*05:01  USA San Diego 0.2600496
 857  A*02:01-B*27:05-C*01:02-DRB1*01:01-DQA1*01:01-DQB1*05:01-DPB1*02:01  USA San Diego 0.2600496
 858  A*02:01-B*27:05-C*01:02-DRB1*01:01-DQA1*01:01-DQB1*05:01-DPB1*03:01  USA San Diego 0.2600496
 859  A*02:01-B*27:05-C*01:02-DRB1*01:03-DQA1*01:01-DQB1*05:01-DPB1*02:01  USA San Diego 0.2600496
 860  A*02:01-B*27:05-C*01:02-DRB1*11:04-DQA1*05:01-DQB1*03:01-DPB1*04:01  USA San Diego 0.2600496
 861  A*02:01-B*40:01-C*01:02-DRB1*04:05-DQA1*03:01-DQB1*04:01-DPB1*05:01  USA San Diego 0.2600496
 862  A*02:03-B*40:01-C*01:02-DRB1*11:01-DQA1*05:01-DQB1*04:01-DPB1*14:01  USA San Diego 0.2600496
 863  A*02:05-B*51:01-C*01:02-DRB1*13:01-DQA1*03:01-DQB1*03:01-DPB1*04:01  USA San Diego 0.2600496
 864  A*02:06-B*54:01-C*01:02-DRB1*04:05-DQA1*03:01-DQB1*04:01-DPB1*05:01  USA San Diego 0.2600496
 865  A*02:07-B*46:01-C*01:02-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*05:01  USA San Diego 0.2600496
 866  A*02:07-B*51:01-C*01:02-DRB1*04:05-DQA1*03:01-DQB1*03:03-DPB1*05:01  USA San Diego 0.2600496
 867  A*02-B*44-C*01  Brazil Parana Japanese 0.2600192
 868  A*03:01-B*13:02-C*01:02-DRB1*01:01-DQA1*01:01-DQB1*05:01-DPB1*04:01  USA San Diego 0.2600496
 869  A*03:01-B*27:05-C*01:02-DRB1*01:01-DQA1*01:01-DQB1*05:01-DPB1*03:01  USA San Diego 0.2600496
 870  A*11:01-B*15:01-C*01:02-DRB1*01:01-DQA1*01:01-DQB1*05:01-DPB1*39:01  USA San Diego 0.2600496
 871  A*11:01-B*27:05-C*01:02-DRB1*04:01-DQA1*01:01-DQB1*05:01-DPB1*51:01  USA San Diego 0.2600496
 872  A*11:01-B*40:02-C*01:02-DRB1*12:02-DQA1*06:01-DQB1*05:03-DPB1*05:01  USA San Diego 0.2600496
 873  A*11:01-B*46:01-C*01:02-DRB1*09:01-DQA1*03:01-DQB1*03:02-DPB1*05:01  USA San Diego 0.2600496
 874  A*11-B*35-C*01  Brazil Parana Japanese 0.2600192
 875  A*24:02-B*40:02-C*01:02-DRB1*13:01-DQA1*01:01-DQB1*05:03-DPB1*04:01  USA San Diego 0.2600496
 876  A*24:02-B*46:01-C*01:02-DRB1*12:02-DQA1*06:01-DQB1*03:01-DPB1*13:01  USA San Diego 0.2600496
 877  A*24:02-B*51:01-C*01:02-DRB1*01:01-DQA1*01:01-DQB1*05:01-DPB1*03:01  USA San Diego 0.2600496
 878  A*24:02-B*54:01-C*01:02-DRB1*04:05-DQA1*01:03-DQB1*04:01-DPB1*05:01  USA San Diego 0.2600496
 879  A*24:02-B*59:01-C*01:02-DRB1*04:05-DQA1*03:03-DQB1*04:01-DPA1*02:02-DPB1*05:01  Japan pop 17 0.26003,078
 880  A*24-B*27-C*01  Brazil Parana Japanese 0.2600192
 881  A*25:01-B*56:01-C*01:02-DRB1*08:01-DQA1*04:01-DQB1*04:02-DPB1*04:01  USA San Diego 0.2600496
 882  A*26:01-B*48:01-C*01:02-DRB1*14:07-DQA1*01:03-DQB1*06:01-DPB1*14:01  USA San Diego 0.2600496
 883  A*26-B*55-C*01  Brazil Parana Japanese 0.2600192
 884  A*31:01-B*15:01-C*01:02-DRB1*08:02-DQA1*04:01-DQB1*04:02-DPB1*04:01  USA San Diego 0.2600496
 885  A*31:01-B*35:01-C*01:02-DRB1*01:01-DQA1*01:01-DQB1*05:01-DPB1*05:01  USA San Diego 0.2600496
 886  A*31:01-B*49:01-C*01:02-DRB1*08:02-DQA1*02:01-DQB1*04:02-DPB1*17:01  USA San Diego 0.2600496
 887  A*31-B*46-C*01  Brazil Parana Japanese 0.2600192
 888  A*33:03-B*58:01-C*01:02-DRB1*13:02-DQA1*01:02-DQB1*03:01-DPB1*02:02  USA San Diego 0.2600496
 889  A*33-B*58-C*01  Brazil Parana Japanese 0.2600192
 890  A*11:01:01-B*15:01:01-C*01:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.25951,734
 891  A*02:01-B*15:01-C*01:02-DRB1*14:01-DQB1*05:02  Malaysia Peninsular Chinese 0.2577194
 892  A*02:01-B*15:02-C*01:02-DRB1*12:01-DQB1*03:01  Malaysia Peninsular Chinese 0.2577194
 893  A*02:01-B*40:01-C*01:03-DRB1*08:03-DQB1*06:01  Malaysia Peninsular Chinese 0.2577194
 894  A*02:01-B*40:04-C*01:02-DRB1*08:03-DQB1*04:01  Malaysia Peninsular Chinese 0.2577194
 895  A*02:01-B*40:06-C*01:02-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 896  A*02:01-B*46:01-C*01:02-DRB1*04:05-DQB1*04:01  Malaysia Peninsular Chinese 0.2577194
 897  A*02:01-B*46:01-C*01:02-DRB1*04:06-DQB1*03:02  Malaysia Peninsular Chinese 0.2577194
 898  A*02:01-B*46:01-C*01:02-DRB1*14:01-DQB1*05:01  Malaysia Peninsular Chinese 0.2577194
 899  A*02:01-B*46:01-C*01:02-DRB1*14:01-DQB1*05:02  Malaysia Peninsular Chinese 0.2577194
 900  A*02:01-B*46:01-C*01:02-DRB1*15:01-DQB1*06:01  Malaysia Peninsular Chinese 0.2577194

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 801 to 900 (from 7,664) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 77  


   

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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