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 901 to 1,000 (from 15,210) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 153  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 901  A*24:02:01-B*44:02:01-C*05:01:01-DRB1*13:01:01-DQB1*06:03:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.7782521
 902  A*24:02:01-B*51:01:01-C*16:02:01-DRB1*01:01:01-DQB1*05:01:01  India Karnataka Kannada Speaking 0.7760174
 903  A*24:02-B*40:01-C*07:02-DRB1*08:03-DQB1*06:01  Malaysia Peninsular Chinese 0.7732194
 904  A*24:02-B*35:12-C*04:01-DRB1*04:07-DQB1*03:02  Colombia Bogotá Cord Blood 0.77091,463
 905  A*24:02-B*13:01  USA Asian pop 2 0.77001,772
 906  A*24:02-B*35:20-DRB1*04:02-DQB1*03:02  Chile Mapuche 0.770066
 907  A*24:02-B*48:01-DRB1*01:01-DQB1*05:01  Chile Mapuche 0.770066
 908  A*24:02:01-B*07:02:01-C*07:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.768423,595
 909  A*24:02:01-B*52:01:01-C*07:02:01-DRB1*14:04  China Jingpo Minority 0.7660105
 910  A*24:02-C*03:03  Italy pop 5 0.7600975
 911  A*24:02-C*15:02  Italy pop 5 0.7600975
 912  A*24:02-B*35:01-C*04:01-DRB1*14:02-DRB3*01:01-DQB1*03:01  USA NMDP North American Amerindian 0.758435,791
 913  A*24:02-B*59:01-C*01:02-DRB1*04:05-DRB4*01:01-DQB1*04:01  USA NMDP Korean 0.751177,584
 914  A*24:02-B*07:02-C*07:02-DRB1*03:01-DQA1*05:01-DQB1*02:01  Mexico Tixcacaltuyub Maya 0.746367
 915  A*24:02-B*14:01-C*08:02-DRB1*13:03-DQA1*02:01-DQB1*02:02  Mexico Tixcacaltuyub Maya 0.746367
 916  A*24:02-B*15:01-C*01:02-DRB1*04:03-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 917  A*24:02-B*35:01-C*03:04-DRB1*14:02-DQA1*05:03-DQB1*03:01  Mexico Tixcacaltuyub Maya 0.746367
 918  A*24:02-B*35:01-C*04:01-DRB1*04:07-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 919  A*24:02-B*35:01-C*04:01-DRB1*04:25-DQA1*03:03-DQB1*04:02  Mexico Tixcacaltuyub Maya 0.746367
 920  A*24:02-B*35:17-C*04:01-DRB1*14:02-DQA1*05:03-DQB1*03:01  Mexico Tixcacaltuyub Maya 0.746367
 921  A*24:02-B*35:20-C*03:04-DRB1*14:02-DQA1*05:03-DQB1*03:01  Mexico Tixcacaltuyub Maya 0.746367
 922  A*24:02-B*35:20-C*03:05-DRB1*04:03-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 923  A*24:02-B*35:20-C*04:01-DRB1*14:02-DQA1*05:03-DQB1*03:01  Mexico Tixcacaltuyub Maya 0.746367
 924  A*24:02-B*39:05-C*07:02-DRB1*04:07-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 925  A*24:02-B*39:06-C*07:02-DRB1*14:06-DQA1*05:03-DQB1*03:01  Mexico Tixcacaltuyub Maya 0.746367
 926  A*24:02-B*40:02-C*03:04-DRB1*14:02-DQA1*05:03-DQB1*03:01  Mexico Tixcacaltuyub Maya 0.746367
 927  A*24:02-B*40:02-C*03:05-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 928  A*24:02-B*40:02-C*04:01-DRB1*04:03-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 929  A*24:02-B*52:01-C*12:02-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.74112,492
 930  A*24:02-B*35:02-C*04:01-DRB1*11:04-DQB1*03:01  Italy pop 5 0.7400975
 931  A*24:02-B*07:02-C*07:02-DRB1*15:01-DRB5*01:01-DQB1*06:02  USA NMDP European Caucasian 0.73111,242,890
 932  A*24:02-B*52:01-DRB1*04:02  Israel Kavkazi Jews 0.73102,840
 933  A*24:02-B*52:01-C*12:02-DRB1*15:02-DQB1*06:01  India Central UCBB 0.72554,204
 934  A*24:02-B*51:01-C*14:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Korean 0.725077,584
 935  A*24:02-B*08:01-C*07:02-DRB1*03:01-DQB1*02:01  India North UCBB 0.72445,849
 936  A*24:02-B*40:01-C*03:04-DRB1*14:02-DQB1*03:01  USA NMDP Alaska Native or Aleut 0.72111,376
 937  A*24:02:01:01-B*07:02:01-C*07:02:01:03-DRB1*15:01:01-DQB1*06:02:01  Russia Nizhny Novgorod, Russians 0.71531,510
 938  A*24:02-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 0.71115,849
 939  A*24:02-B*15:17-C*07:01  Italy pop 5 0.7100975
 940  A*24:02-B*40:06-C*08:01-DRB1*09:01  Japan pop 16 0.709018,604
 941  A*24:02-B*15:01-C*03:03-DRB1*14:02-DQB1*03:01  USA NMDP Alaska Native or Aleut 0.70351,376
 942  A*24:02:01-B*07:06:01-C*07:02:01-DRB1*15:01:01-DQB1*06:01:01  India Kerala Malayalam speaking 0.7020356
 943  A*24:02:01-B*35:01:01-C*04:01:01-DRB1*11:01:01-DQB1*03:01:01  India Kerala Malayalam speaking 0.7020356
 944  A*24:02:01-B*07:02:01-C*07:02:01-DRB1*15:01:01-DQB1*06:02:01  Spain, Canary Islands, Gran canaria island 0.7000215
 945  A*24:02:01-B*40:01:02-C*03:04:01-DRB1*13:01:01-DQB1*06:03:01  Spain, Canary Islands, Gran canaria island 0.7000215
 946  A*24:02:01-B*40:02:01-C*02:02:02-DRB1*11:01:01-DQB1*03:01:01  Spain, Canary Islands, Gran canaria island 0.7000215
 947  A*24:02-B*08:01-C*07:01-DRB1*01:01:01-DQB1*05:01:01  England North West 0.7000298
 948  A*24:02-B*15:17-C*07:01-DRB1*13:02  Italy pop 5 0.7000975
 949  A*24:02-B*35:01-C*04:01-DRB1*08:02-DQB1*04:02  Mexico Mexico City Mestizo population 0.6993143
 950  A*24:02-B*35:02-C*04:01-DRB1*11:04-DQB1*03:01  Mexico Mexico City Mestizo population 0.6993143
 951  A*24:02-B*35:12-C*04:01-DRB1*08:02-DQB1*04:02  Mexico Mexico City Mestizo population 0.6993143
 952  A*24:02-B*35:17-C*04:01-DRB1*08:02-DQB1*04:02  Mexico Mexico City Mestizo population 0.6993143
 953  A*24:02-B*35:17-C*04:01-DRB1*15:03-DQB1*06:02  Mexico Mexico City Mestizo population 0.6993143
 954  A*24:02-B*39:06-C*07:02-DRB1*14:06-DQB1*03:01  Mexico Mexico City Mestizo population 0.6993143
 955  A*24:02:01-B*40:06:01-C*08:01:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.69911,734
 956  A*24:02-B*07:02-C*07:02-DRB1*15:01  Germany DKMS - France minority 0.69801,406
 957  A*24:02:01-C*12:03:01  China Jingpo Minority 0.6950105
 958  A*24:02:01-B*07:02:01-C*07:01:01-DRB1*08:02:01-DQB1*04:02  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 959  A*24:02:01-B*08:12:01-C*07:01:01-DRB1*15:02:01-DQB1*06:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 960  A*24:02:01-B*15:01:01-C*01:02:01-DRB1*04:07:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 961  A*24:02:01-B*15:08:01-C*01:02:01-DRB1*04:04:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 962  A*24:02:01-B*15:08:01-C*01:02:01-DRB1*16:02:01-DQB1*03:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 963  A*24:02:01-B*35:01:01-C*04:01:01-DRB1*08:02:01-DQB1*04:02  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 964  A*24:02:01-B*39:01:01-C*04:01:01-DRB1*04:04:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 965  A*24:02:01-B*39:01:01-C*07:01:01-DRB1*08:02:01-DQB1*04:02  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 966  A*24:02:01-B*39:01:01-C*07:01:01-DRB1*14:06:01-DQB1*03:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 967  A*24:02:01-B*39:01:01-C*08:01:01-DRB1*08:02:01-DQB1*04:02  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 968  A*24:02:01-B*39:06:01-C*07:01:01-DRB1*14:06:01-DQB1*03:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 969  A*24:02:01-B*40:01:05-C*05:01:01-DRB1*03:01:01-DQB1*02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 970  A*24:02:01-B*40:02:01-C*04:01:01-DRB1*04:07:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 971  A*24:02:01-B*40:02:01-C*08:01:01-DRB1*08:02:01-DQB1*04:02  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 972  A*24:02:01-B*44:02:01-C*04:01:01-DRB1*01:03:01-DQB1*05:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 973  A*24:02:01-B*44:08-C*05:01:01-DRB1*04:04:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 974  A*24:02:01-B*48:01:01-C*08:01:01-DRB1*01:02:01-DQB1*05:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 975  A*24:02:01-B*48:01:01-C*08:01:01-DRB1*04:04:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 976  A*24:02:01-B*51:01:01-C*08:01:01-DRB1*04:07:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 977  A*24:02:01-B*51:01:01-C*15:02:01-DRB1*04:04:01-DQB1*03:02:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 978  A*24:02:01-B*52:01:02-C*04:01:01-DRB1*14:06:01-DQB1*03:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 979  A*24:02:01-B*53:03-C*12:03:01-DRB1*14:02:01-DQB1*03:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 980  A*24:02-B*44:03-C*02:02-DRB1*01:02  Brazil Vale do Ribeira Quilombos 0.6944144
 981  A*24:02:01-B*13:02:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.691023,595
 982  A*24:02-B*39:06-C*07:02-DRB1*04:04-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.6881218
 983  A*24:02-B*51:01-C*14:02-DRB1*09:01-DRB4*01:01-DQB1*03:03  USA NMDP Japanese 0.681124,582
 984  A*24:02-B*40:06-C*15:02-DRB1*14:04-DQB1*05:03  India North UCBB 0.68095,849
 985  A*24:02-B*14:02-DRB1*01:02  Israel Poland Jews 0.680013,871
 986  A*24:02-B*27:05-C*02:02-DRB1*08:02-DQB1*04:02  USA NMDP Alaska Native or Aleut 0.67931,376
 987  A*24:02:01-B*35:12:01-C*04:01:01-DRB1*04:07-DQB1*03:02  Costa Rica Central Valley Mestizo (G) 0.6787221
 988  A*24:02:01-B*53:01:01-C*04:01:01-DRB1*03:01-DQB1*02:01  Costa Rica Central Valley Mestizo (G) 0.6787221
 989  A*24:02-B*35:03-C*12:03-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Caribean Hispanic 0.6778115,374
 990  A*24:02-B*18:01-C*07:01-DRB1*11:04-DQB1*03:01  Germany DKMS - Italy minority 0.67701,159
 991  A*24:02-B*35:03-C*12:03-DRB1*13:01-DQB1*06:03  India Northeast UCBB 0.6757296
 992  A*24:02-B*15:01  USA Asian pop 2 0.67501,772
 993  A*24:02-B*07:02-C*07:02-DRB1*15:01  Germany DKMS - Croatia minority 0.67102,057
 994  A*24:02-B*07:02-C*07:02-DRB1*15:01  Poland DKMS 0.669920,653
 995  A*24:02-B*15:35-C*07:02-DRB1*15:02-DRB5*01:01-DQB1*05:02  USA NMDP Filipino 0.669850,614
 996  A*24:02:01-B*08:01:01-C*07:02:01-DRB1*03:01:01-DQB1*02:01:01-DPB1*04:01:01  Saudi Arabia pop 6 (G) 0.667228,927
 997  A*24:02-B*45:01-C*06:02-DRB1*04:02-DQA1*03:01-DQB1*03:02  Brazil Puyanawa 0.6667150
 998  A*24:02-B*52:01-C*03:03-DRB1*04:04-DQA1*03:01-DQB1*03:02  Brazil Puyanawa 0.6667150
 999  A*24:02-B*35:01-C*04:01-DRB1*14:02-DQB1*03:01  USA NMDP Alaska Native or Aleut 0.66491,376
 1,000  A*24:02-B*07:02-C*07:02-DRB1*15:01  Germany DKMS - Netherlands minority 0.65901,374

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 15,210) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 153  


   

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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