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 901 to 1,000 (from 3,183) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 32  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 901  A*68:01-B*51:01-C*04:01-DRB1*16:01-DQB1*04:02-DPB1*04:02  Panama 0.1900462
 902  A*68:07-B*44:02-C*05:01-DRB1*08:02-DQB1*04:02-DPB1*02:01  Panama 0.1900462
 903  A*02:01-B*35:01-C*04:01-DRB1*08:02-DQB1*04:02  USA Hispanic pop 2 0.18701,999
 904  A*68:02-B*57:03-C*07:01-DRB1*03:02-DQB1*04:02  USA Hispanic pop 2 0.18701,999
 905  A*02:01-B*35:03-C*04:25-DRB1*08:02-DQB1*04:02  Malaysia Peninsular Indian 0.1845271
 906  A*02:11-B*40:06-C*15:02-DRB1*04:10-DQB1*04:02  Malaysia Peninsular Indian 0.1845271
 907  A*02:11-B*50:01-C*06:02-DRB1*04:06-DQB1*04:02  Malaysia Peninsular Indian 0.1845271
 908  A*03:34-B*51:01-C*16:02-DRB1*08:02-DQB1*04:02  Malaysia Peninsular Indian 0.1845271
 909  A*24:02-B*50:01-C*06:02-DRB1*04:06-DQB1*04:02  Malaysia Peninsular Indian 0.1845271
 910  A*24:02-B*51:04-C*01:02-DRB1*15:01-DQB1*04:02  Malaysia Peninsular Indian 0.1845271
 911  A*24:02-B*57:01-C*06:02-DRB1*04:10-DQB1*04:02  Malaysia Peninsular Indian 0.1845271
 912  A*26:01-B*55:01-C*14:02-DRB1*04:10-DQB1*04:02  Malaysia Peninsular Indian 0.1845271
 913  A*31:01-B*35:01-C*04:01-DRB1*04:10-DQB1*04:02  Malaysia Peninsular Indian 0.1845271
 914  A*33:01-B*35:01-C*04:01-DRB1*04:10-DQB1*04:02  Malaysia Peninsular Indian 0.1845271
 915  A*02:01:01-B*40:01:02-C*03:04:01-DRB1*08:01:01-DQB1*04:02:01  Poland BMR 0.183423,595
 916  A*02:17-B*15:40-C*03:03-DRB1*08:02-DQB1*04:02  USA NMDP Caribean Indian 0.176814,339
 917  A*02:01-B*42:01-C*17:01-DRB1*03:02-DQB1*04:02  USA African American pop 4 0.17602,411
 918  A*23:01-B*14:03-C*08:02-DRB1*03:02-DQB1*04:02  USA African American pop 4 0.17402,411
 919  A*02:06-B*35:01-C*04:01-DRB1*08:02-DQB1*04:02  USA NMDP Alaska Native or Aleut 0.17251,376
 920  A*02:01-B*27:05-C*01:02-DRB1*08:01-DQB1*04:02  USA NMDP Alaska Native or Aleut 0.16971,376
 921  A*03:01-B*35:03-C*07:02-DRB1*03:01-DQB1*04:02  India Northeast UCBB 0.1689296
 922  A*11:01-B*35:01-C*03:04-DRB1*08:02-DQB1*04:02  India Northeast UCBB 0.1689296
 923  A*11:01-B*35:03-C*04:01-DRB1*08:01-DQB1*04:02  India Northeast UCBB 0.1689296
 924  A*11:03-B*48:04-C*08:01-DRB1*08:02-DQB1*04:02  India Northeast UCBB 0.1689296
 925  A*24:02-B*56:01-C*04:01-DRB1*07:01-DQB1*04:02  India Northeast UCBB 0.1689296
 926  A*26:01-B*35:01-C*04:01-DRB1*04:05-DQB1*04:02  India Northeast UCBB 0.1689296
 927  A*03:01-B*35:01-C*03:03-DRB1*08:01-DQB1*04:02-DPB1*04:01  Russia Karelia 0.16621,075
 928  A*68:01-B*35:20-C*04:01-DRB1*08:02-DQB1*04:02  Colombia Bogotá Cord Blood 0.16601,463
 929  A*03:01-B*07:02-C*07:02-DRB1*08:01-DQB1*04:02-DPB1*03:01  Russia Karelia 0.16231,075
 930  A*03:01-B*35:01-C*04:01-DRB1*08:01-DQB1*04:02  Italy pop 5 0.1600975
 931  DQB1*04:02-DPB1*04:01:01  China Inner Mongolia Autonomous Region Northeast 0.1600496
 932  DRB1*08:02-DQB1*04:02  Italy pop 5 0.1600975
 933  DRB1*08:04-DQB1*04:02  Italy pop 5 0.1600975
 934  A*01:01-B*15:03-C*07:01-DRB1*08:04-DQB1*04:02-DPB1*03:01  Tanzania Maasai 0.1597336
 935  A*01:01-B*41:01-C*07:95-DRB1*03:02-DQB1*04:02-DPB1*02:01  Tanzania Maasai 0.1597336
 936  A*01:01-B*42:01-C*17:01-DRB1*03:02-DQB1*04:02-DPB1*107:01  Tanzania Maasai 0.1597336
 937  A*02:01-B*15:03-C*17:01-DRB1*11:01-DQB1*04:02-DPB1*01:01  Tanzania Maasai 0.1597336
 938  A*02:01-B*45:01-C*02:09-DRB1*08:04-DQB1*04:02-DPB1*02:01  Tanzania Maasai 0.1597336
 939  A*02:01-B*47:03-C*07:01-DRB1*15:03-DQB1*04:02-DPB1*01:01  Tanzania Maasai 0.1597336
 940  A*24:02-B*35:02-C*17:17-DRB1*03:02-DQB1*04:02-DPB1*55:01  Tanzania Maasai 0.1597336
 941  A*29:01-B*47:02-C*07:04-DRB1*07:01-DQB1*04:02-DPB1*01:01  Tanzania Maasai 0.1597336
 942  A*29:02-B*18:01-C*17:01-DRB1*04:10-DQB1*04:02-DPB1*01:01  Tanzania Maasai 0.1597336
 943  A*29:02-B*47:03-C*07:06-DRB1*13:01-DQB1*04:02-DPB1*14:01  Tanzania Maasai 0.1597336
 944  A*29:02-B*81:01-C*17:01-DRB1*03:02-DQB1*04:02-DPB1*04:02  Tanzania Maasai 0.1597336
 945  A*30:01-B*08:01-C*04:01-DRB1*11:01-DQB1*04:02-DPB1*18:01  Tanzania Maasai 0.1597336
 946  A*30:01-B*35:01-C*04:01-DRB1*01:02-DQB1*04:02-DPB1*03:01  Tanzania Maasai 0.1597336
 947  A*30:02-B*45:01-C*16:24-DRB1*01:02-DQB1*04:02-DPB1*01:01  Tanzania Maasai 0.1597336
 948  A*30:04-B*45:01-C*08:04-DRB1*03:02-DQB1*04:02-DPB1*03:01  Tanzania Maasai 0.1597336
 949  A*30:09-B*58:02-C*06:02-DRB1*03:02-DQB1*04:02-DPB1*01:01  Tanzania Maasai 0.1597336
 950  A*31:06-B*42:01-C*17:01-DRB1*03:02-DQB1*04:02-DPB1*01:01  Tanzania Maasai 0.1597336
 951  A*36:01-B*53:01-C*04:04-DRB1*03:02-DQB1*04:02-DPB1*01:01  Tanzania Maasai 0.1597336
 952  A*36:01-B*53:01-C*08:04-DRB1*03:02-DQB1*04:02-DPB1*01:01  Tanzania Maasai 0.1597336
 953  A*36:01-B*81:01-C*03:04-DRB1*08:04-DQB1*04:02-DPB1*105:01  Tanzania Maasai 0.1597336
 954  A*68:02-B*07:02-C*17:01-DRB1*11:01-DQB1*04:02-DPB1*13:01  Tanzania Maasai 0.1597336
 955  A*68:02-B*45:01-C*08:04-DRB1*13:02-DQB1*04:02-DPB1*04:01  Tanzania Maasai 0.1597336
 956  A*74:01-B*07:04-C*07:14-DRB1*15:03-DQB1*04:02-DPB1*01:01  Tanzania Maasai 0.1597336
 957  A*74:01-B*18:01-C*07:04-DRB1*03:02-DQB1*04:02-DPB1*39:01  Tanzania Maasai 0.1597336
 958  A*74:01-B*49:01-C*07:01-DRB1*11:04-DQB1*04:02-DPB1*463:01  Tanzania Maasai 0.1597336
 959  A*02:01-B*07:02-C*07:02-DRB1*08:01-DQB1*04:02-DPB1*04:01  Russia Karelia 0.15911,075
 960  DRB1*08:02-DQA1*04:01-DQB1*04:02-DPA1*01:03-DPB1*02:01  China Zhejiang Han pop 2 0.1584833
 961  A*33:03-B*42:01-C*17:01-DRB1*03:02-DQB1*04:02  USA NMDP Black South or Central American 0.15814,889
 962  A*11:01-B*18:01-C*07:04-DRB1*04:05-DQB1*04:02  Malaysia Peninsular Malay 0.1577951
 963  A*68:01-B*39:05-C*07:02-DRB1*08:02-DQB1*04:02  Colombia Bogotá Cord Blood 0.15691,463
 964  A*02-B*39-DRB1*08:02-DQA1*04:01-DQB1*04:02  Brazil Paraná Caucasian 0.1560641
 965  A*02-B*44-DRB1*08:01-DQA1*04:01-DQB1*04:02  Brazil Paraná Caucasian 0.1560641
 966  A*03-B*44-DRB1*08:01-DQA1*04:01-DQB1*04:02  Brazil Paraná Caucasian 0.1560641
 967  A*30-B*42-DRB1*03:02-DQA1*04:01-DQB1*04:02  Brazil Paraná Caucasian 0.1560641
 968  A*31-B*35-DRB1*08:07-DQA1*04:01-DQB1*04:02  Brazil Paraná Caucasian 0.1560641
 969  A*31-B*39-DRB1*08:02-DQA1*04:01-DQB1*04:02  Brazil Paraná Caucasian 0.1560641
 970  A*03:01:01-B*50:01:01-C*06:02:01-DRB1*04:06:01-DQB1*04:02:01-DPB1*04:01:01  Saudi Arabia pop 6 (G) 0.155728,927
 971  A*02:01-B*39:01-C*07:02-DRB1*08:11-DQB1*04:02  USA NMDP American Indian South or Central America 0.15495,926
 972  A*01:01-B*14:02-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 973  A*01:01-B*40:01-DRB1*08:01-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 974  A*02:01-B*14:01-DRB1*13:03-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 975  A*02:01-B*15:03-DRB1*03:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 976  A*02:01-B*44:02-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 977  A*02:01-B*48:01-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 978  A*02:01-B*51:01-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 979  A*02:01-B*52:01-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 980  A*02:02-B*15:03-DRB1*04:07-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 981  A*02:02-B*35:01-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 982  A*03:01-B*07:02-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 983  A*03:01-B*14:02-DRB1*08:01-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 984  A*23:01-B*35:01-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 985  A*24:02-B*35:01-DRB1*04:04-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 986  A*24:02-B*35:02-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 987  A*24:02-B*40:01-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 988  A*24:02-B*40:02-DRB1*08:01-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 989  A*24:02-B*40:02-DRB1*16:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 990  A*24:02-B*48:01-DRB1*08:01-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 991  A*24:02-B*48:01-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 992  A*24:02-B*52:02-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 993  A*26:01-B*40:02-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 994  A*29:01-B*40:02-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 995  A*31:01-B*15:02-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 996  A*31:01-B*35:01-DRB1*04:10-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 997  A*31:01-B*35:01-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 998  A*31:01-B*40:01-DRB1*04:07-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 999  A*31:01-B*40:02-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330
 1,000  A*68:01-B*15:05-DRB1*08:02-DQB1*04:02  Mexico Mexico City Tlalpan 0.1515330

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 901 to 1,000 (from 3,183) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 32  


   

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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