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 301 to 400 (from 3,776) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 38  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 301  A*24:02-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.27101,772
 302  A*01:01:01-B*07:06:01-C*14:02:01-DRB1*13:02:01-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 303  A*01:01:01-B*51:01:01-C*14:02:01-DRB1*10:01:01-DQB1*05:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 304  A*02:01:01-B*51:06:01-C*14:02:01-DRB1*15:02:01-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 305  A*02:03:01-B*18:01:01-C*14:02:01-DRB1*15:02:01-DQB1*05:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 306  A*03:01:01-B*51:01:01-C*14:02:01-DRB1*08:03:02-DQB1*03:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 307  A*11:01:01-B*51:01:01-C*14:02:01-DRB1*13:01:01-DQB1*03:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 308  A*11:01:01-B*51:01:05-C*14:02:01-DRB1*11:08:01-DQB1*03:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 309  A*24:02:01-B*51:01:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 310  A*24:02:01-B*51:01:05-C*14:02:01-DRB1*14:10-DQB1*05:10  India Andhra Pradesh Telugu Speaking 0.2688186
 311  A*24:02:01-B*51:06:01-C*14:02:01-DRB1*14:04:01-DQB1*05:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 312  A*26:01:01-B*40:06:04-C*14:02:01-DRB1*14:04:01-DQB1*05:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 313  A*31:01:02-B*40:02:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 314  A*33:03:01-B*51:01:01-C*14:02:01-DRB1*15:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 315  A*33:03:01-B*58:01:01-C*14:02:01-DRB1*15:01:01-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 316  A*68:01:01-B*35:03:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  India Andhra Pradesh Telugu Speaking 0.2688186
 317  A*68:01:01-B*51:01:01-C*14:02:01-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 0.2688186
 318  A*68:01:01-B*51:01:01-C*14:02:01-DRB1*15:01:01-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 319  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
 320  A*24:02-B*51:01-C*14:02-DRB1*09:01  Hong Kong Chinese BMDR 0.26767,595
 321  A*11:01-B*51:01-C*14:02-DRB1*14:04-DQB1*05:03  Malaysia Peninsular Malay 0.2629951
 322  A*03:01-B*51:01-C*14:02-DRB1*04:01-DQB1*03:02  USA NMDP Caribean Indian 0.260914,339
 323  A*24:02:01:01-B*51:01:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  Russia Bashkortostan, Tatars 0.2604192
 324  A*01:01-B*51:01-C*14:02-DRB1*03:01-DQA1*03:01-DQB1*03:02-DPB1*04:01  USA San Diego 0.2600496
 325  A*01:01-B*51:01-C*14:02-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*04:01  USA San Diego 0.2600496
 326  A*02:01-B*51:01-C*14:02-DRB1*11:01-DQA1*05:01-DQB1*03:01-DPB1*04:01  USA San Diego 0.2600496
 327  A*03:01-B*51:01-C*14:02-DRB1*01:01-DQA1*01:01-DQB1*05:01-DPB1*02:01  USA San Diego 0.2600496
 328  A*11:01-B*51:01-C*14:02-DRB1*16:02-DQA1*01:02-DQB1*05:02-DPB1*05:01  USA San Diego 0.2600496
 329  A*11-B*51-C*14  Brazil Parana Japanese 0.2600192
 330  A*23:01-B*44:03-C*14:02-DRB1*07:01-DQA1*05:01-DQB1*03:01-DPB1*10:01  USA San Diego 0.2600496
 331  A*24:02-B*44:03-C*14:02-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*05:01  USA San Diego 0.2600496
 332  A*26-B*44-C*14  Brazil Parana Japanese 0.2600192
 333  A*31:01-B*46:01-C*14:02-DRB1*12:01-DQA1*03:01-DQB1*04:01-DPB1*05:01  USA San Diego 0.2600496
 334  A*31:01-B*51:01-C*14:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*02:02  USA San Diego 0.2600496
 335  A*31:01-B*54:01-C*14:02-DRB1*04:05-DQA1*03:01-DQB1*04:01-DPB1*02:01  USA San Diego 0.2600496
 336  A*31-B*40-C*14  Brazil Parana Japanese 0.2600192
 337  A*33:03-B*44:03-C*14:03-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*01:01  USA San Diego 0.2600496
 338  A*33:03-B*51:02-C*14:02-DRB1*11:05-DQA1*03:01-DQB1*06:02-DPB1*05:01  USA San Diego 0.2600496
 339  A*33-B*58-C*14  Brazil Parana Japanese 0.2600192
 340  A*02:01-B*40:88-C*14:02-DRB1*16:02-DQB1*04:02  Malaysia Peninsular Chinese 0.2577194
 341  A*02:03-B*38:02-C*14:02-DRB1*16:02-DQB1*03:02  Malaysia Peninsular Chinese 0.2577194
 342  A*02:03-B*51:02-C*14:02-DRB1*14:04-DQB1*03:01  Malaysia Peninsular Chinese 0.2577194
 343  A*11:01-B*13:01-C*14:02-DRB1*09:01-DQB1*05:01  Malaysia Peninsular Chinese 0.2577194
 344  A*11:01-B*51:01-C*14:02-DRB1*16:02-DQB1*05:02  Malaysia Peninsular Chinese 0.2577194
 345  A*24:02-B*15:02-C*14:02-DRB1*16:02-DQB1*03:01  Malaysia Peninsular Chinese 0.2577194
 346  A*24:02-B*46:01-C*14:02-DRB1*14:04-DQB1*04:01  Malaysia Peninsular Chinese 0.2577194
 347  A*24:02-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  Malaysia Peninsular Chinese 0.2577194
 348  A*24:02-B*54:01-C*14:02-DRB1*04:05-DQB1*05:02  Malaysia Peninsular Chinese 0.2577194
 349  A*32:03-B*51:01-C*14:02-DRB1*01:01-DQB1*05:01  Malaysia Peninsular Chinese 0.2577194
 350  A*33:03-B*44:03-C*14:03-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP Chinese 0.254799,672
 351  A*11:01-B*51:06-C*14:02-DRB1*15:01-DQB1*06:01  India Tamil Nadu 0.24922,492
 352  B*44:03-C*14:03  USA African American pop 4 0.24902,411
 353  A*02:01-B*51:01-C*14:02-DRB1*01:01  Germany DKMS - France minority 0.24801,406
 354  A*11:01:01-B*51:01:01-C*14:02:01-DRB1*11:01:01-DQB1*03:01:01  China Zhejiang Han 0.24521,734
 355  A*02:01-B*51:01-C*14:02-DRB1*11:01  Germany DKMS - Austria minority 0.24301,698
 356  A*24:02-B*51:01-C*14:02-DRB1*08:03-DQB1*06:01  USA Asian pop 2 0.24101,772
 357  A*03:01-C*14:02  Italy pop 5 0.2400975
 358  A*24:02-C*14:02  Italy pop 5 0.2400975
 359  A*31:01-B*51:01-C*14:02-DRB1*04:05  Japan pop 16 0.240018,604
 360  A*24:02-B*44:03-C*14:03-DRB1*13:02  Japan pop 16 0.235018,604
 361  A*01:01:01-B*39:01:01-C*14:02:01-DRB1*13:01:01-DQB1*06:03:01  Spain, Canary Islands, Gran canaria island 0.2300215
 362  A*24:02-B*51:01-C*14:02-DRB1*09:01-DQA1*03:02-DQB1*03:03-DPA1*01:03-DPB1*02:01  Japan pop 17 0.23003,078
 363  A*31:01-B*51:01-C*14:02-DRB1*08:02-DQA1*04:01-DQB1*04:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.23003,078
 364  A*31:01-B*51:01-C*14:02-DRB1*14:03-DQA1*05:03-DQB1*03:01-DPA1*02:02-DPB1*05:01  Japan pop 17 0.23003,078
 365  A*32:01:01-B*51:01:01-C*14:02:01-DRB1*11:04:01-DQB1*03:01:01  Spain, Canary Islands, Gran canaria island 0.2300215
 366  A*32:01:01-B*51:01:01-C*14:02:01-DRB1*11:04:01-DQB1*03:02:01  Spain, Canary Islands, Gran canaria island 0.2300215
 367  A*03:01-B*51:01-C*14:02-DRB1*13:01-DQB1*06:03  India Tamil Nadu 0.22972,492
 368  A*31:01-B*51:01-C*14:02-DRB1*09:01  Japan pop 16 0.229018,604
 369  A*02:01-B*51:01-C*14:02-DRB1*08:01  Germany DKMS - Croatia minority 0.22802,057
 370  A*24:02:01-B*51:01:01-C*14:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.22731,734
 371  A*68:01-B*51:01-C*14:02-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.22702,492
 372  A*03:01:01-B*51:01:01-C*14:02:01-DRB1*04:07-DQB1*03:02  Costa Rica Central Valley Mestizo (G) 0.2262221
 373  A*11:01:01-B*51:01:01-C*14:02:01-DRB1*07:01-DQB1*03:01  Costa Rica Central Valley Mestizo (G) 0.2262221
 374  A*30:02:01-B*15:16:01-C*14:02:01-DRB1*03:01-DQB1*03:02  Costa Rica Central Valley Mestizo (G) 0.2262221
 375  A*02:01-B*51:01-C*14:02-DRB1*11:04-DQB1*03:01  Germany DKMS - Turkey minority 0.22604,856
 376  A*02:01-B*51:01-C*14:02-DRB1*08:01-DQB1*04:02-DPB1*03:01  Russia Karelia 0.22531,075
 377  A*23:01-B*15:16-C*14:02-DRB1*14:06-DQA1*03:01-DQB1*03:02-DPB1*03:01  Nicaragua Managua 0.2165339
 378  A*74:01-B*35:01-C*14:02-DRB1*15:03-DQA1*01:02-DQB1*05:03-DPB1*01:01  Nicaragua Managua 0.2165339
 379  A*02:01:01-B*51:01:01-C*14:02:01-DRB1*04:03:01-DQB1*03:02:01-DPB1*04:01:01  Saudi Arabia pop 6 (G) 0.215328,927
 380  A*02:01:01-B*51:01:01-C*14:02:01-DRB1*09:01:02-DQB1*03:03:02  China Zhejiang Han 0.21231,734
 381  A*02:01-B*51:01-C*14:02-DRB1*11:01  Germany DKMS - Netherlands minority 0.21201,374
 382  A*02:01-B*51:01-C*14:02-DRB1*16:01  Germany DKMS - Romania minority 0.21201,234
 383  A*33:03-B*15:16-C*14:02-DRB1*01:02-DQB1*05:01  USA NMDP Black South or Central American 0.21004,889
 384  B*15:16-C*14:02  Mexico Mexico City Mestizo pop 2 0.2100234
 385  A*11:01-B*51:01-C*14:02-DRB1*09:01-DQB1*03:03  USA Asian pop 2 0.20801,772
 386  A*02:01-B*51:01-C*14:02-DRB1*11:01  Germany DKMS - Spain minority 0.20701,107
 387  A*01-B*51-C*14-DRB1*11  Macedonia MBMDR - Macedonian 0.20231,283
 388  A*01:01-B*37:01:01-C*14:02:01-DRB1*07:01:01-DQB1*03:03:02  England North West 0.2000298
 389  A*02:01-B*51:01-C*14:02:01-DRB1*04:01:01-DQB1*03:01  England North West 0.2000298
 390  A*02:01-B*51:01-C*14:02:01-DRB1*08:01-DQB1*04:02  England North West 0.2000298
 391  A*02:01-B*51:01-C*14:02-DRB1*03:01  Italy pop 5 0.2000975
 392  A*03:01-B*51:01-C*14:02:01-DRB1*11:03-DQB1*03:02:01  England North West 0.2000298
 393  A*25:01-B*51:01-C*14:02:01-DRB1*14:01-DQB1*05:03:01  England North West 0.2000298
 394  A*29:02:01-B*15:16-C*14:02:01-DRB1*04:01:01-DQB1*03:01  England North West 0.2000298
 395  A*68:01-B*51:01-C*14:02-DRB1*07:01  Italy pop 5 0.2000975
 396  A*66:01-B*51:01-C*14:02-DRB1*08:01  Germany DKMS - Bosnia and Herzegovina minority 0.19501,028
 397  A*01:01:01-B*49:01:01-C*14:02:01-DRB1*03:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 398  A*01:01:01-B*51:01:01-C*14:02:01-DRB1*11:01:01-DQB1*03:03:02-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 399  A*01:01:01-B*58:02:01-C*14:02:01-DRB1*12:02:02-DQB1*06:02:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 400  A*03:01:01-B*07:02:01-C*14:03:01-DRB1*15:03:01-DQB1*06:02:01-DPA1*02:06-DPB1*01:01:01  Brazil Rio de Janeiro Caucasian 0.1946521

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 301 to 400 (from 3,776) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 38  


   

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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