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 : 
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Displaying 301 to 400 (from 828) records   Pages: 1 2 3 4 5 6 7 8 9 of 9  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 301  A*01-B*44-DRB1*07:01-DQA1*02:01-DQB1*02:02  Brazil Paraná Caucasian 0.1623641
 302  A*23:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.16062,492
 303  A*26:30-B*44:03-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*02:01  Tanzania Maasai 0.1597336
 304  A*01:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.1577951
 305  A*33:03-B*44:03-C*07:02-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.1577951
 306  A*02:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  USA NMDP Black South or Central American 0.15604,889
 307  A*02:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 0.15485,849
 308  A*33:03-B*44:03-C*07:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Caribean Black 0.152933,328
 309  A*01:01-B*44:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.1515330
 310  A*03:01-B*44:02-DRB1*07:01-DQB1*02:01  Mexico Mexico City Tlalpan 0.1515330
 311  A*24:02-B*44:02-DRB1*07:01-DQB1*02:02  Mexico Mexico City Tlalpan 0.1515330
 312  A*02:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  USA NMDP Caribean Indian 0.150914,339
 313  A*02:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  Germany DKMS - Italy minority 0.14201,159
 314  A*32:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:02  India Central UCBB 0.14194,204
 315  A*02:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  Colombia Bogotá Cord Blood 0.14161,463
 316  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:01  USA NMDP Black South or Central American 0.14104,889
 317  A*01:01-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  Sri Lanka Colombo 0.1401714
 318  A*01:01-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:02  Sri Lanka Colombo 0.1401714
 319  A*33:03-B*44:03-C*04:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*02:01  Sri Lanka Colombo 0.1401714
 320  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*03:01  Sri Lanka Colombo 0.1401714
 321  A*02:01-B*44:02-C*05:01-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1400975
 322  A*02:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1400975
 323  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:01  USA Hispanic pop 2 0.14001,999
 324  A*03:01-B*44:02-C*04:01-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1400975
 325  A*11:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:01  USA Hispanic pop 2 0.14001,999
 326  A*24:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Italy pop 5 0.1400975
 327  A*33:03:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 0.1400356
 328  A*68:01:02-B*44:03:02-C*02:75-DRB1*07:01:01-DQB1*02:01:10  India Kerala Malayalam speaking 0.1400356
 329  A*68:01:02-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 0.1400356
 330  A*02:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 331  A*11:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 332  A*30:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 333  A*24:02-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.13642,492
 334  A*29:02-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  USA NMDP Black South or Central American 0.13594,889
 335  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:01-DPB1*01:01  Germany DKMS - German donors 0.13423,456,066
 336  A*02:11-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  USA Asian pop 2 0.13301,772
 337  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  USA NMDP Black South or Central American 0.13274,889
 338  A*24:02-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  USA African American pop 4 0.13102,411
 339  A*29:02-B*44:03-C*16:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP African 0.130728,557
 340  A*24:02-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  Germany DKMS - Italy minority 0.12901,159
 341  A*24:07-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 0.12682,403
 342  A*11:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.12672,492
 343  A*02:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 0.12602,403
 344  A*24:02-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  Malaysia Peninsular Malay 0.1205951
 345  A*29:02-B*44:03-C*16:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Filipino 0.120150,614
 346  A*02:01:01:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.11951,510
 347  A*30-B*44-DRB1*07:01-DQA1*02:01-DQB1*02:02  Brazil Paraná Caucasian 0.1182641
 348  A*68:01-B*44:02-C*07:04-DRB1*07:01-DQB1*02:01  USA NMDP Black South or Central American 0.11774,889
 349  A*02:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01-DPB1*04:01  Russia Karelia 0.11671,075
 350  A*33:03:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:01:01-DPB1*04:01:01  Saudi Arabia pop 6 (G) 0.114228,927
 351  A*68:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India West UCBB 0.10855,829
 352  A*24:07-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.1061951
 353  A*03:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India South UCBB 0.105911,446
 354  A*02:01:01:01-B*44:02:01:01-C*05:01:01:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.10581,510
 355  A*02:03-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.1052951
 356  A*02:06-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.1052951
 357  A*02:11-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.10492,492
 358  A*01:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 359  A*02:01-B*44:03-C*05:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 360  A*24:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 361  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.10304,856
 362  A*31:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 363  A*33:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  Colombia Bogotá Cord Blood 0.10251,463
 364  A*30:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  USA African American pop 4 0.10202,411
 365  A*32:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:01  Colombia Bogotá Cord Blood 0.10141,463
 366  A*23:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:02  India South UCBB 0.100211,446
 367  A*24:02:01:01-B*44:05:01-C*02:02:02-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.09931,510
 368  A*03:01:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.097523,595
 369  A*24:02:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.096123,595
 370  A*03:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India West UCBB 0.09565,829
 371  A*29:02-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  USA Hispanic pop 2 0.09401,999
 372  A*68:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:01  USA Hispanic pop 2 0.09401,999
 373  A*01:01:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.089823,595
 374  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:01  USA Asian pop 2 0.08901,772
 375  A*24:02-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  USA Asian pop 2 0.08901,772
 376  A*02:02-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  USA African American pop 4 0.08702,411
 377  A*11:01:01-B*44:03:02-C*07:06:01-DRB1*07:01:01-DQB1*02:02:01  China Zhejiang Han 0.08651,734
 378  A*32:01:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01  China Zhejiang Han 0.08651,734
 379  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:01  Germany DKMS - Italy minority 0.08601,159
 380  A*29:02-B*44:03-C*16:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Japanese 0.085324,582
 381  A*23:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01-DPB1*04:02  Germany DKMS - German donors 0.08383,456,066
 382  A*23:01-B*44:03-C*04:01-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP South Asian Indian 0.0836185,391
 383  A*11:01-B*44:03-C*05:01-DRB1*07:01-DQB1*02:02  India East UCBB 0.08322,403
 384  A*03:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.082523,595
 385  A*11:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:02  India Central UCBB 0.08104,204
 386  A*03:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01  USA African American pop 4 0.08002,411
 387  A*02-B*44-DRB1*07:01-DQA1*01:01-DQB1*02:02  Brazil Paraná Caucasian 0.0780641
 388  A*30-B*44-DRB1*07:01-DQA1*02:01-DQB1*02:03  Brazil Paraná Caucasian 0.0780641
 389  A*02:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.07692,492
 390  A*32:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India South UCBB 0.073211,446
 391  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:01-DPB1*02:01  Germany DKMS - German donors 0.07273,456,066
 392  A*02:01:01-B*44:03:01-C*16:02:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.071323,595
 393  A*11:01-B*44:03-C*05:01-DRB1*07:01-DQB1*02:01  India Tamil Nadu 0.07042,492
 394  A*02:01-B*44:02-C*07:04-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*17:01  Sri Lanka Colombo 0.0700714
 395  A*02:01-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:02  Sri Lanka Colombo 0.0700714
 396  A*02:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:02  Sri Lanka Colombo 0.0700714
 397  A*02:11-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*02:01  Sri Lanka Colombo 0.0700714
 398  A*11:01-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*02:01  Sri Lanka Colombo 0.0700714
 399  A*11:01-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*13:01  Sri Lanka Colombo 0.0700714
 400  A*24:02-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:02  Sri Lanka Colombo 0.0700714

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 828) records   Pages: 1 2 3 4 5 6 7 8 9 of 9  


   

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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