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 501 to 600 (from 7,533) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 76  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 501  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*07:01:01-DQB1*03:01:01  Poland BMR 0.004223,595
 502  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*07:01:01-DQB1*03:03:02  India Kerala Malayalam speaking 0.1400356
 503  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*07:01:01-DQB1*03:03:02  Poland BMR 0.009223,595
 504  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*08:01:01-DQB1*03:02:01  Poland BMR 0.002023,595
 505  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*08:04:01-DQB1*03:01:01  Poland BMR 0.002123,595
 506  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.036023,595
 507  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*11:03:01-DQB1*03:01:01  Poland BMR 0.002123,595
 508  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.002123,595
 509  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*12:01:01-DQB1*03:01:01  Poland BMR 0.114623,595
 510  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*03:01:01  Poland BMR 0.002123,595
 511  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  India Kerala Malayalam speaking 0.1400356
 512  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.020023,595
 513  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*13:02:01-DQB1*06:09:01-DPA1*01:03:01-DPB1*02:01:02  Brazil Rio de Janeiro Caucasian 0.1946521
 514  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*13:03:01-DQB1*03:01:01  Poland BMR 0.005723,595
 515  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*13:03:01-DQB1*06:02:01  Poland BMR 0.002123,595
 516  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*14:54:01-DQB1*05:03:01  Poland BMR 0.016023,595
 517  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*03:01:01  Poland BMR 0.006323,595
 518  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*05:02:01  Poland BMR 0.001523,595
 519  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.017023,595
 520  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*15:01:01-DQB1*06:03:01  Poland BMR 0.006423,595
 521  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*16:01:01-DQB1*03:01:01  Poland BMR 0.002223,595
 522  A*31:01:02-B*39:01:01-C*12:03:01-DRB1*16:01:01-DQB1*05:02:01  Poland BMR 0.006423,595
 523  A*31:01:02-B*39:01:01-DRB1*15:01:01  Portugal North 1.100046
 524  A*31:01:02-B*39:01-DRB1*04:07-DQB1*03:02  USA South Dakota Lakota Sioux 1.6000302
 525  A*31:01:02-B*39:03:01-C*18:02:01-DRB1*13:01:01-DQB1*05:01:01-DPA1*01:03:01-DPB1*14:01:01  Brazil Rio de Janeiro Black 1.470668
 526  A*31:01:02-B*39:05:01-C*07:02:01-DRB1*04:07  Nicaragua Mestizo (G) 0.3226155
 527  A*31:01:02-B*39:05:01-C*07:02:01-DRB1*04:07-DQB1*05:01  Costa Rica Central Valley Mestizo (G) 0.2262221
 528  A*31:01:02-B*39:05:01-C*12:03:01-DRB1*01:01:01-DQB1*04:02:01  Poland BMR 0.002123,595
 529  A*31:01:02-B*39:05:01-C*12:03:01-DRB1*16:01:01-DQB1*05:02:01  Poland BMR 0.001923,595
 530  A*31:01:02-B*39:06:01-C*07:01:01-DRB1*08:02:01-DQB1*04:02  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 531  A*31:01:02-B*39:06:01-C*07:02:01-DRB1*08:04:01  Costa Rica Amerindians (G) 0.4032125
 532  A*31:01:02-B*39:06:02-C*07:02:01-DRB1*01:01:01-DQB1*04:02:01  Poland BMR 0.002123,595
 533  A*31:01:02-B*39:06:02-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.001923,595
 534  A*31:01:02-B*39:06:02-C*12:03:01-DRB1*16:02:01-DQB1*05:02:01  Poland BMR 0.002123,595
 535  A*31:01:02-B*39:08-C*07:02:01-DRB1*04:07  Nicaragua Mestizo (G) 0.3226155
 536  A*31:01:02-B*39:10:01-C*03:04:01  South African Mixed ancestry 1.000050
 537  A*31:01:02-B*39:11-C*12:02:02-DRB1*14:02  Nicaragua Mestizo (G) 0.3226155
 538  A*31:01:02-B*40:01:01-C*03:04:01-DRB1*04:03:01  Nicaragua Mestizo (G) 0.3226155
 539  A*31:01:02-B*40:01:01-C*03:04:01-DRB1*14:02  Nicaragua Mestizo (G) 0.3226155
 540  A*31:01:02-B*40:01:01-C*07:02:01-DRB1*08:03:02-DQB1*03:01:01  India Kerala Malayalam speaking 0.1400356
 541  A*31:01:02-B*40:01:01-C*12:03:01-DRB1*14:04:01-DQB1*05:03:01  India Karnataka Kannada Speaking 0.2870174
 542  A*31:01:02-B*40:01:01-DRB1*03:01:01  Portugal Center 1.000050
 543  A*31:01:02-B*40:01:02-C*03:04:01  England Blood Donors of Mixed Ethnicity 1.2127519
 544  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*03:01:01-DQB1*02:01:01  Poland BMR 0.015923,595
 545  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*04:01:01-DQB1*03:02:01  Poland BMR 0.004323,595
 546  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*04:03:01-DQB1*03:02:01  Poland BMR 0.002623,595
 547  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*02:01:01  Poland BMR 0.002123,595
 548  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.115323,595
 549  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*06:03:01  Poland BMR 0.002123,595
 550  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*04:05:01-DPB1*04:02:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00945,266
 551  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*04:05:01-DPB1*05:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00645,266
 552  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*04:07:01-DQB1*03:01:01  Poland BMR 0.004123,595
 553  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.008323,595
 554  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*07:01:01-DQB1*03:03:02  Poland BMR 0.002423,595
 555  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*08:01:01-DQB1*04:02:01  Poland BMR 0.016223,595
 556  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*08:03:02-DPB1*04:02:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00675,266
 557  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*08:03:02-DQB1*06:01:01  China Zhejiang Han 0.02881,734
 558  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*09:01:02-DQB1*03:03:02  Poland BMR 0.002123,595
 559  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*10:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 560  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*11:01:01-DPB1*05:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00955,266
 561  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*11:01:01-DQB1*03:01:01  China Zhejiang Han 0.05771,734
 562  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.004923,595
 563  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.001723,595
 564  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*13:01:01-DQB1*06:03:01  Poland BMR 0.003223,595
 565  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQB1*06:04:01  Poland BMR 0.009623,595
 566  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*15:01:01-DPB1*02:01:02  Hong Kong Chinese HKBMDR HLA 11 loci 0.00945,266
 567  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*15:01:01-DPB1*05:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00845,266
 568  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.016223,595
 569  A*31:01:02-B*40:01:02-C*03:54-DRB1*08:01:01-DQB1*04:02:01  Poland BMR 0.002123,595
 570  A*31:01:02-B*40:01:02-C*04:82-DRB1*04:03:01-DPB1*05:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.03545,266
 571  A*31:01:02-B*40:01:02-C*07:02:01-DRB1*04:03:01-DPB1*05:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.01905,266
 572  A*31:01:02-B*40:01:02-C*07:02:01-DRB1*04:05:01-DPB1*04:02:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00895,266
 573  A*31:01:02-B*40:01:02-C*07:02:01-DRB1*04:05:01-DQB1*04:01:01  China Zhejiang Han 0.08081,734
 574  A*31:01:02-B*40:01:02-C*07:02:01-DRB1*08:03:02-DQB1*06:01:01  China Zhejiang Han 0.02851,734
 575  A*31:01:02-B*40:01:02-C*07:02:01-DRB1*09:01:02-DPB1*05:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.01185,266
 576  A*31:01:02-B*40:01:02-C*07:02:01-DRB1*12:02:01-DPB1*05:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.01495,266
 577  A*31:01:02-B*40:01:02-C*07:02:01-DRB1*13:12:01-DPB1*05:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00935,266
 578  A*31:01:02-B*40:01:02-C*14:02:01-DRB1*03:01:01-DQB1*02:01:01  China Zhejiang Han 0.02881,734
 579  A*31:01:02-B*40:01:02-C*15:02:01-DRB1*04:05:01-DPB1*135:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00855,266
 580  A*31:01:02-B*40:01:02-C*15:02:01-DRB1*14:05:01-DQB1*03:03:02  China Zhejiang Han 0.02881,734
 581  A*31:01:02-B*40:01:02-C*16:01:01-DRB1*10:01:01-DQB1*02:02:01-DPA1*02:01:01-DPB1*17:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 582  A*31:01:02-B*40:01-C*03:04  Ireland South 1.0000250
 583  A*31:01:02-B*40:02  Mexico Guadalajara Mestizo pop 2 2.9000103
 584  A*31:01:02-B*40:02:01-C*01:02:01-DRB1*01:01:01-DQA1*01:01:01-DQB1*03:01:01-DPA1*01:03:01-DPB1*04:01  Russia Belgorod region 0.3268153
 585  A*31:01:02-B*40:02:01-C*01:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.002123,595
 586  A*31:01:02-B*40:02:01-C*01:02:01-DRB1*07:01:01-DQB1*03:03:02  Poland BMR 0.001923,595
 587  A*31:01:02-B*40:02:01-C*01:02:01-DRB1*11:01:01-DQB1*02:02:01  Poland BMR 0.002123,595
 588  A*31:01:02-B*40:02:01-C*01:02:01-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.022023,595
 589  A*31:01:02-B*40:02:01-C*01:02:01-DRB1*11:04:01-DQB1*03:01:01  Poland BMR 0.002123,595
 590  A*31:01:02-B*40:02:01-C*01:02:01-DRB1*12:01:01-DQB1*03:01:01  Poland BMR 0.007123,595
 591  A*31:01:02-B*40:02:01-C*01:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.002123,595
 592  A*31:01:02-B*40:02:01-C*02:02:02-DRB1*01:01:01-DQB1*03:03:02  Poland BMR 0.002123,595
 593  A*31:01:02-B*40:02:01-C*02:02:02-DRB1*01:02:01-DQB1*05:01:01  Poland BMR 0.002123,595
 594  A*31:01:02-B*40:02:01-C*02:02:02-DRB1*07:01:01-DQB1*06:03:01  Poland BMR 0.002123,595
 595  A*31:01:02-B*40:02:01-C*02:02:02-DRB1*11:01:01-DQB1*03:01:01  Poland BMR 0.025023,595
 596  A*31:01:02-B*40:02:01-C*02:02:02-DRB1*11:01:01-DQB1*06:03:01  Poland BMR 0.002123,595
 597  A*31:01:02-B*40:02:01-C*03:02:01-DRB1*08:02:01-DQB1*04:02  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 598  A*31:01:02-B*40:02:01-C*03:02:01-DRB1*14:02:01-DQB1*03:01:01  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 599  A*31:01:02-B*40:02:01-C*03:03:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.02881,734
 600  A*31:01:02-B*40:02:01-C*03:04:01-DRB1*08:07-DQB1*03:01:01-DPA1*03:01:01-DPB1*105:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405

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 501 to 600 (from 7,533) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 76  


   

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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