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 701 to 800 (from 4,347) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 44  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 701  A*02:09-B*35:03:01-C*04:01:01-DRB1*09:01:02-DQB1*03:03:02  India Karnataka Kannada Speaking 0.2870174
 702  A*02:11:01-B*15:05:01-C*16:02:01-DRB1*13:01:01-DQB1*03:03:02  India Karnataka Kannada Speaking 0.2870174
 703  A*24:02:01-B*40:06:01-C*12:02:02-DRB1*07:01:01-DQB1*03:03:02  India Karnataka Kannada Speaking 0.2870174
 704  A*26:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  India Karnataka Kannada Speaking 0.2870174
 705  A*33:01:02-B*18:01:01-C*07:01:01-DRB1*03:01:01-DQB1*03:03:02  India Karnataka Kannada Speaking 0.2870174
 706  A*33:03:01-B*15:02:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  India Karnataka Kannada Speaking 0.2870174
 707  A*33:03:01-B*38:01:01-C*07:02:01-DRB1*04:03:01-DQB1*03:03:02  India Karnataka Kannada Speaking 0.2870174
 708  A*26-B*35-DRB1*07-DQB1*03:03  Mexico Michoacan Rural 0.2865348
 709  DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*02:07-DPB1*19:01  China Zhejiang Han pop 2 0.2850833
 710  A*02:11:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  India Kerala Malayalam speaking 0.2810356
 711  A*11:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*04:01  Sri Lanka Colombo 0.2801714
 712  A*24:02-B*27:07-C*15:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*26:01  Sri Lanka Colombo 0.2801714
 713  A*24:02-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*13:01  Sri Lanka Colombo 0.2801714
 714  A*02:01-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03-DPB1*04:02  Russia Karelia 0.27791,075
 715  A*03-B*57-DRB1*07-DQB1*03:03  Mexico Veracruz Rural 0.2773539
 716  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Germany DKMS - Turkey minority 0.27704,856
 717  A*02-B*18-DRB1*09-DQB1*03:03  Mexico Tlaxcala, Tlaxcala city 0.2762181
 718  A*25-B*51-DRB1*09-DQB1*03:03  Mexico Tlaxcala, Tlaxcala city 0.2762181
 719  A*33-B*14:02-DRB1*07-DQB1*03:03  Mexico Tlaxcala, Tlaxcala city 0.2762181
 720  A*66-B*41-DRB1*07-DQB1*03:03  Mexico Tlaxcala, Tlaxcala city 0.2762181
 721  A*24:02-B*51:01-C*14:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Chinese 0.275299,672
 722  A*24:02-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India East UCBB 0.27512,403
 723  A*02:01-B*44:02-C*05:01-DRB1*07:01-DQB1*03:03  USA NMDP Alaska Native or Aleut 0.27341,376
 724  A*01-B*57-DRB1*07-DQB1*03:03  Mexico Sinaloa Rural 0.2732183
 725  A*02-B*07-DRB1*09-DQB1*03:03  Mexico Sinaloa Rural 0.2732183
 726  A*02-B*15:02-DRB1*09-DQB1*03:03  Mexico Sinaloa Rural 0.2732183
 727  A*02-B*57-DRB1*07-DQB1*03:03  Mexico Sinaloa Rural 0.2732183
 728  A*11-B*15:01-DRB1*09-DQB1*03:03  Mexico Sinaloa Rural 0.2732183
 729  A*11-B*49-DRB1*07-DQB1*03:03  Mexico Sinaloa Rural 0.2732183
 730  A*66-B*41-DRB1*08-DQB1*03:03  Mexico Sinaloa Rural 0.2732183
 731  A*24:02-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.27101,772
 732  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
 733  A*02:01:01-B*15:11:01-C*03:03:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.27011,734
 734  A*03:01:01:01-B*07:02:01-C*07:02:01:03-DRB1*07:01:01-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.26911,510
 735  A*01:01:01-B*35:03:01-C*04:01:01-DRB1*07:01:01-DQB1*03:03:02  India Andhra Pradesh Telugu Speaking 0.2688186
 736  A*01:01:01-B*40:06:04-C*15:02:01-DRB1*10:01:01-DQB1*03:03:02  India Andhra Pradesh Telugu Speaking 0.2688186
 737  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
 738  A*24:11N-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  India Andhra Pradesh Telugu Speaking 0.2688186
 739  A*33:03:01-B*40:06:01-C*07:06-DRB1*15:01:01-DQB1*03:03:02  India Andhra Pradesh Telugu Speaking 0.2688186
 740  A*69:01:01-B*37:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  India Andhra Pradesh Telugu Speaking 0.2688186
 741  A*02:06:01-B*51:01:01-C*14:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.26841,734
 742  A*01-B*35-DRB1*07-DQB1*03:03  Mexico Veracruz, Xalapa 0.2674187
 743  A*03-B*07-DRB1*07-DQB1*03:03  Mexico Veracruz, Xalapa 0.2674187
 744  A*24-B*40:02-DRB1*07-DQB1*03:03  Mexico Veracruz, Xalapa 0.2674187
 745  A*31-B*57-DRB1*09-DQB1*03:03  Mexico Veracruz, Xalapa 0.2674187
 746  A*24:02-B*38:02-C*07:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.26501,772
 747  A*01:01:01:01-B*48:01:01-C*08:03:01-DRB1*09:01:02-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 748  A*01:01:01-B*57:01:01-C*08:02:01-DRB1*07:01:01:01-DQB1*03:03:02:01  Russia Bashkortostan, Tatars 0.2604192
 749  A*02:01:01:01-B*35:03:01-C*03:03:01-DRB1*07:01:01:01-DQB1*03:03:02:01  Russia Bashkortostan, Tatars 0.2604192
 750  A*02:01:01:01-B*40:01:02-C*03:04:01:01-DRB1*07:01:01:01-DQB1*03:03:02:01  Russia Bashkortostan, Tatars 0.2604192
 751  A*02:01:01:01-B*40:06:01-C*08:01:01-DRB1*13:01:01-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 752  A*02:05:01-B*58:01:01-C*03:02:02-DRB1*09:01:02-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 753  A*02:06:01-B*40:02:01-C*03:04:01:01-DRB1*14:03:01-DQB1*03:03:02:01  Russia Bashkortostan, Tatars 0.2604192
 754  A*03:01:01:01-B*35:08:01-C*04-DRB1*01:01:01-DQB1*03:03:02:01  Russia Bashkortostan, Tatars 0.2604192
 755  A*03:01:01:01-B*58:01:01-C*03:02:02-DRB1*09:01:02-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 756  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
 757  A*11:01:01:01-B*35:03:01-C*04:01:01-DRB1*07:01:01:01-DQB1*03:03:02:01  Russia Bashkortostan, Tatars 0.2604192
 758  A*11:01:01:01-B*50:01:01-C*06:02:01:02-DRB1*07:01:01:01-DQB1*03:03:02:01  Russia Bashkortostan, Tatars 0.2604192
 759  A*11:01:01-B*15:01:01:01-C*03:03:01-DRB1*04:01:01-DQB1*03:03:02:01  Russia Bashkortostan, Tatars 0.2604192
 760  A*24:02:01:01-B*40:01:02-C*03:04:01:01-DRB1*09:01-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 761  A*25:01:01-B*44:03:02-C*07:06-DRB1*09:01:02-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 762  A*26:01:01-B*48:01:01-C*07:01:01-DRB1*09:01-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 763  A*30-B*07:02:01-C*06:02:01:01-DRB1*03:01:01-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 764  A*32:01:01-B*35:01:01-C*04:01:01-DRB1*09:01:02-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 765  A*34:02:01-B*14:02:01-C*06:02:01:01-DRB1*07:01:01:01-DQB1*03:03:02:01  Russia Bashkortostan, Tatars 0.2604192
 766  A*69:01-B*35:08:01-C*12:03:01:01-DRB1*09:01:02-DQB1*03:03:02  Russia Bashkortostan, Tatars 0.2604192
 767  A*01:01-B*08:01-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*04:01  USA San Diego 0.2600496
 768  A*02:01-B*15:01-C*03:03-DRB1*11:01-DQA1*05:01-DQB1*03:03-DPB1*04:02  USA San Diego 0.2600496
 769  A*02:01-B*40:02-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*13:01  USA San Diego 0.2600496
 770  A*02:01-B*40:06-C*07:01-DRB1*03:01-DQA1*02:01-DQB1*03:03-DPB1*14:01  USA San Diego 0.2600496
 771  A*02:01-B*44:02-C*05:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*04:02  USA San Diego 0.2600496
 772  A*02:01-B*57:01-C*04:01-DRB1*13:02-DQA1*02:01-DQB1*03:03-DPB1*04:01  USA San Diego 0.2600496
 773  A*02:07-B*15:01-C*04:01-DRB1*04:06-DQA1*03:01-DQB1*03:03-DPB1*05:01  USA San Diego 0.2600496
 774  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
 775  A*03:01-B*57:01-C*07:01-DRB1*15:01-DQA1*02:01-DQB1*03:03-DPB1*15:01  USA San Diego 0.2600496
 776  A*23:01-B*50:01-C*06:02-DRB1*13:01-DQA1*03:01-DQB1*03:03-DPB1*04:01  USA San Diego 0.2600496
 777  A*24:02-B*15:01-C*03:03-DRB1*15:02-DQA1*03:01-DQB1*03:03-DPB1*05:01  USA San Diego 0.2600496
 778  A*24:02-B*15:01-C*07:01-DRB1*07:01-DQA1*04:01-DQB1*03:03-DPB1*02:01  USA San Diego 0.2600496
 779  A*24:02-B*40:01-C*04:01-DRB1*09:01-DQA1*05:01-DQB1*03:03-DPB1*05:01  USA San Diego 0.2600496
 780  A*26:01-B*40:06-C*08:01-DRB1*14:02-DQA1*03:01-DQB1*03:03-DPB1*02:01  USA San Diego 0.2600496
 781  A*32:01-B*15:01-C*05:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*04:02  USA San Diego 0.2600496
 782  A*32:01-B*44:03-C*05:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*02:01  USA San Diego 0.2600496
 783  A*33:03-B*56:01-C*04:01-DRB1*09:01-DQA1*03:01-DQB1*03:03-DPB1*05:01  USA San Diego 0.2600496
 784  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
 785  A*11:01-B*40:01-C*07:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.25901,772
 786  A*01:01-B*55:02-C*08:01-DRB1*14:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 787  A*02:01-B*27:06-C*03:04-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 788  A*02:01-B*40:06-C*01:02-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 789  A*02:01-B*46:01-C*03:04-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 790  A*02:01-B*52:01-C*03:03-DRB1*15:02-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 791  A*02:01-B*67:01-C*07:05-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 792  A*02:02-B*38:02-C*15:04-DRB1*10:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 793  A*02:03-B*58:01-C*03:15-DRB1*03:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 794  A*02:06-B*40:04-C*08:01-DRB1*12:02-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 795  A*02:06-B*40:06-C*08:01-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 796  A*02:07-B*39:01-C*01:02-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 797  A*02:07-B*46:01-C*01:16-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 798  A*11:01-B*15:01-C*16:02-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 799  A*11:01-B*15:11-C*03:02-DRB1*15:02-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 800  A*11:01-B*27:04-C*07:01-DRB1*09:01-DQB1*03:03  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 701 to 800 (from 4,347) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 44  


   

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.
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