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 1,100) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 11  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 301  A*02:01:01-B*50:02-C*06:02:01-DRB1*13:05:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 302  A*02:01:01-B*51:01:01-C*16:01:01-DRB1*08:01:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 303  A*02:05:01-B*50:01:01-C*02:02:02-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 304  A*03:01:01-B*44:03:01-C*04:01:01-DRB1*01:01:01-DQB1*05:01:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 305  A*03:01:01-B*45:01:01-C*04:01:01-DRB1*03:01:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 306  A*11:01:01-B*39:02:02-C*04:01:01-DRB1*01:01:01-DQB1*05:01:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 307  A*23:01:01-B*44:03:01-C*04:01:01-DRB1*13:01:01-DQB1*06:03:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 308  A*23:01:01-B*44:03:01-C*14:03:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 309  A*24:02:01-B*35:02:01-C*04:01:01-DRB1*11:01:01-DQB1*03:01:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 310  A*24:02:01-B*35:08:01-C*16:01:01-DRB1*13:01:01-DQB1*06:03:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 311  A*24:03:01-B*40:02:01-C*14:02:01-DRB1*04:05:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 312  A*26:01:01-B*58:01:01-C*12:03:01-DRB1*07:01:01-DQB1*03:01:01-DPA1*02:01:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 313  A*29:02:01-B*15:03:01-C*16:01:01-DRB1*04:03:01-DQB1*03:04:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 314  A*32:01:01-B*27:03-C*02:02:02-DRB1*10:01:01-DQB1*03:01:04-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 315  A*32:01:01-B*40:02:01-C*02:02:02-DRB1*13:01:01-DQB1*06:03:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 316  A*68:01:01-B*15:01:01-C*03:03:01-DRB1*04:04:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 317  DRB1*03:01-DQB1*02:01-DPB1*03:01  Gambia pop 3 0.1919939
 318  A*01:01-B*44:02-C*05:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Panama 0.1900462
 319  A*02:01-B*35:04-C*12:03-DRB1*14:02-DQB1*03:01-DPB1*03:01  Panama 0.1900462
 320  A*02:01-B*35:43-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*03:01  Panama 0.1900462
 321  A*02:01-B*39:06-C*07:02-DRB1*07:01-DQB1*03:01-DPB1*03:01  Panama 0.1900462
 322  A*02:01-B*45:01-C*16:01-DRB1*04:03-DQB1*03:02-DPB1*03:01  Panama 0.1900462
 323  A*02:02-B*13:02-C*08:04-DRB1*09:01-DQB1*02:02-DPB1*03:01  Panama 0.1900462
 324  A*23:01-B*35:14-C*04:01-DRB1*15:03-DQB1*06:02-DPB1*03:01  Panama 0.1900462
 325  A*24:02-B*07:02-C*04:01-DRB1*14:01-DQB1*05:03-DPB1*03:01  Panama 0.1900462
 326  A*24:02-B*15:01-C*01:02-DRB1*11:01-DQB1*03:01-DPB1*03:01  Panama 0.1900462
 327  A*24:02-B*35:12-C*04:01-DRB1*14:02-DQB1*03:01-DPB1*03:01  Panama 0.1900462
 328  A*24:02-B*40:02-C*03:04-DRB1*08:02-DQB1*04:02-DPB1*03:01  Panama 0.1900462
 329  A*24:02-B*40:02-C*03:04-DRB1*14:02-DQB1*03:01-DPB1*03:01  Panama 0.1900462
 330  A*24:02-B*40:02-C*04:01-DRB1*04:03-DQB1*03:02-DPB1*03:01  Panama 0.1900462
 331  A*24:02-B*40:05-C*03:05-DRB1*04:07-DQB1*03:02-DPB1*03:01  Panama 0.1900462
 332  A*24:02-B*51:01-C*03:05-DRB1*04:07-DQB1*03:02-DPB1*03:01  Panama 0.1900462
 333  A*24:02-B*51:01-C*04:01-DRB1*16:11-DQB1*03:02-DPB1*03:01  Panama 0.1900462
 334  A*24:02-B*57:02-C*14:02-DRB1*04:05-DQB1*03:02-DPB1*03:01  Panama 0.1900462
 335  A*26:01-B*35:04-C*04:01-DRB1*11:04-DQB1*03:01-DPB1*03:01  Panama 0.1900462
 336  A*29:02-B*50:01-C*06:02-DRB1*07:01-DQB1*02:02-DPB1*03:01  Panama 0.1900462
 337  A*30:01-B*15:31-C*04:07-DRB1*14:02-DQB1*02:02-DPB1*03:01  Panama 0.1900462
 338  A*30:02-B*27:03-C*02:02-DRB1*13:01-DQB1*06:03-DPB1*03:01  Panama 0.1900462
 339  A*31:01-B*35:43-C*07:02-DRB1*14:02-DQB1*03:01-DPB1*03:01  Panama 0.1900462
 340  A*34:02-B*15:10-C*03:04-DRB1*13:02-DQB1*05:01-DPB1*03:01  Panama 0.1900462
 341  A*66:03-B*53:01-C*03:03-DRB1*04:05-DQB1*02:02-DPB1*03:01  Panama 0.1900462
 342  A*68:01-B*40:02-C*03:05-DRB1*16:02-DQB1*03:01-DPB1*03:01  Panama 0.1900462
 343  A*69:01-B*49:01-C*07:01-DRB1*13:02-DQB1*06:04-DPB1*03:01  Panama 0.1900462
 344  DQB1*03:02-DPB1*03:01:01  China Inner Mongolia Autonomous Region Northeast 0.1840496
 345  DQA1*01:04-DQB1*05:03-DPA1*01:03-DPB1*03:01  Hong Kong Chinese HKBMDR. DQ and DP 0.18241,064
 346  DRB1*15:02-DQA1*01:01-DQB1*05:02-DPA1*02:02-DPB1*03:01  China Zhejiang Han pop 2 0.1801833
 347  DQA1*01:02-DQB1*06:01-DPA1*02:02-DPB1*03:01  Hong Kong Chinese HKBMDR. DQ and DP 0.17811,064
 348  DQB1*05:02-DPB1*03:01:01  China Inner Mongolia Autonomous Region Northeast 0.1780496
 349  DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*02:02-DPB1*03:01  China Zhejiang Han pop 2 0.1756833
 350  DRB1*11:04:01-DQB1*03:01-DPB1*03:01:01  China Inner Mongolia Autonomous Region Northeast 0.1720496
 351  DRB1*04:05-DQA1*03:03-DQB1*04:01-DPA1*01:03-DPB1*03:01  China Zhejiang Han pop 2 0.1714833
 352  A*03:01-B*40:02-C*02:02-DRB1*14:01-DQB1*05:03-DPB1*03:01  Russia Karelia 0.16981,075
 353  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*03:01  Russia Karelia 0.16931,075
 354  DRB1*11:04:01-DPB1*03:01:01  China Inner Mongolia Autonomous Region Northeast 0.1660496
 355  DRB1*03:01:01:01-DPB1*03:01:01  China Inner Mongolia Autonomous Region Northeast 0.1640496
 356  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*03:01  Russia Karelia 0.16391,075
 357  DRB1*15:01:01:01-DQB1*06:02-DPB1*03:01:01  China Inner Mongolia Autonomous Region Northeast 0.1630496
 358  A*03:01-B*07:02-C*07:02-DRB1*08:01-DQB1*04:02-DPB1*03:01  Russia Karelia 0.16231,075
 359  A*33:03-B*58:01-C*03:02-DRB1*13:02-DQA1*01:02-DQB1*06:09-DPA1*01:03-DPB1*03:01  Japan pop 17 0.16003,078
 360  A*01:01-B*08:01-C*07:328-DRB1*13:02-DQB1*06:04-DPB1*03:01  Tanzania Maasai 0.1597336
 361  A*01:01-B*15:03-C*07:01-DRB1*08:04-DQB1*04:02-DPB1*03:01  Tanzania Maasai 0.1597336
 362  A*01:01-B*39:10-C*12:03-DRB1*04:05-DQB1*02:02-DPB1*03:01  Tanzania Maasai 0.1597336
 363  A*01:01-B*40:12-C*07:01-DRB1*08:04-DQB1*03:01-DPB1*03:01  Tanzania Maasai 0.1597336
 364  A*01:01-B*45:01-C*06:02-DRB1*08:04-DQB1*03:01-DPB1*03:01  Tanzania Maasai 0.1597336
 365  A*01:01-B*45:01-C*07:328-DRB1*13:02-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 366  A*01:02-B*15:03-C*02:10-DRB1*11:01-DQB1*03:19-DPB1*03:01  Tanzania Maasai 0.1597336
 367  A*01:03-B*18:06-C*07:181-DRB1*03:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 368  A*01:03-B*47:01-C*06:02-DRB1*01:01-DQB1*05:01-DPB1*03:01  Tanzania Maasai 0.1597336
 369  A*02:01-B*08:01-C*07:328-DRB1*03:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 370  A*02:01-B*15:03-C*16:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 371  A*02:01-B*44:03-C*04:113-DRB1*13:02-DQB1*06:09-DPB1*03:01  Tanzania Maasai 0.1597336
 372  A*02:01-B*48:15-C*06:02-DRB1*13:02-DQB1*06:58-DPB1*03:01  Tanzania Maasai 0.1597336
 373  A*02:01-B*48:15-C*17:02-DRB1*13:03-DQB1*03:01-DPB1*03:01  Tanzania Maasai 0.1597336
 374  A*02:01-B*51:01-C*16:08-DRB1*13:03-DQB1*03:01-DPB1*03:01  Tanzania Maasai 0.1597336
 375  A*02:01-B*53:01-C*06:02-DRB1*04:05-DQB1*02:02-DPB1*03:01  Tanzania Maasai 0.1597336
 376  A*02:01-B*57:03-C*07:01-DRB1*14:54-DQB1*05:01-DPB1*03:01  Tanzania Maasai 0.1597336
 377  A*02:05-B*42:01-C*07:01-DRB1*03:02-DQB1*03:02-DPB1*03:01  Tanzania Maasai 0.1597336
 378  A*03:01-B*41:01-C*15:05-DRB1*03:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 379  A*03:01-B*47:01-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 380  A*03:01-B*53:01-C*06:02-DRB1*04:05-DQB1*06:09-DPB1*03:01  Tanzania Maasai 0.1597336
 381  A*03:01-B*53:01-C*07:65-DRB1*11:01-DQB1*03:02-DPB1*03:01  Tanzania Maasai 0.1597336
 382  A*26:30-B*41:01-C*06:02-DRB1*07:01-DQB1*05:01-DPB1*03:01  Tanzania Maasai 0.1597336
 383  A*29:01-B*14:02-C*04:01-DRB1*08:04-DQB1*03:01-DPB1*03:01  Tanzania Maasai 0.1597336
 384  A*29:02-B*45:01-C*16:01-DRB1*07:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 385  A*30:01-B*35:01-C*04:01-DRB1*01:02-DQB1*04:02-DPB1*03:01  Tanzania Maasai 0.1597336
 386  A*30:01-B*35:01-C*07:05-DRB1*01:02-DQB1*06:04-DPB1*03:01  Tanzania Maasai 0.1597336
 387  A*30:01-B*35:01-C*07:05-DRB1*13:01-DQB1*05:01-DPB1*03:01  Tanzania Maasai 0.1597336
 388  A*30:02-B*45:01-C*16:04-DRB1*03:01-DQB1*05:01-DPB1*03:01  Tanzania Maasai 0.1597336
 389  A*30:04-B*44:03-C*04:143-DRB1*07:01-DQB1*03:01-DPB1*03:01  Tanzania Maasai 0.1597336
 390  A*30:04-B*45:01-C*08:04-DRB1*03:02-DQB1*04:02-DPB1*03:01  Tanzania Maasai 0.1597336
 391  A*30:04-B*57:03-C*08:13-DRB1*03:01-DQB1*06:02-DPB1*03:01  Tanzania Maasai 0.1597336
 392  A*33:03-B*81:01-C*04:01-DRB1*13:02-DQB1*06:04-DPB1*03:01  Tanzania Maasai 0.1597336
 393  A*34:02-B*47:01-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 394  A*68:01-B*07:02-C*07:02-DRB1*01:02-DQB1*05:01-DPB1*03:01  Tanzania Maasai 0.1597336
 395  A*68:01-B*40:12-C*06:02-DRB1*01:02-DQB1*05:01-DPB1*03:01  Tanzania Maasai 0.1597336
 396  A*68:02-B*13:02-C*06:02-DRB1*01:02-DQB1*05:01-DPB1*03:01  Tanzania Maasai 0.1597336
 397  A*68:02-B*13:02-C*06:02-DRB1*10:01-DQB1*05:01-DPB1*03:01  Tanzania Maasai 0.1597336
 398  A*68:02-B*18:01-C*07:04-DRB1*13:02-DQB1*06:09-DPB1*03:01  Tanzania Maasai 0.1597336
 399  A*68:02-B*27:03-C*06:02-DRB1*13:02-DQB1*06:09-DPB1*03:01  Tanzania Maasai 0.1597336
 400  A*68:02-B*35:01-C*04:01-DRB1*15:03-DQB1*06:02-DPB1*03:01  Tanzania Maasai 0.1597336

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 1,100) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 11  


   

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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