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 201 to 300 (from 7,333) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 74  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 201  B*13:02-C*06:02  Uganda Kampala pop 2 1.1000175
 202  B*57:01-C*06:02  USA Hispanic 1.1000234
 203  A*30:01-B*13:02-C*06:02-DRB1*07:01  USA Italy Ancestry 1.0990273
 204  A*11:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  India Andhra Pradesh Telugu Speaking 1.0944186
 205  B*53:01-C*06:02  USA African American pop 4 1.08902,411
 206  A*30:01:01-B*13:02:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 1.0753186
 207  A*01:01-B*57:01-C*06:02-DRB1*07:01  Germany DKMS - France minority 1.07301,406
 208  A*02:01-B*13:02-C*06:02-DRB1*07:01  Germany DKMS - Croatia minority 1.07102,057
 209  B*13:02-C*06:02  Mexico Mexico City Mestizo pop 2 1.0600234
 210  A*30:01:01-B*13:02:01-C*06:02:01:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 1.05641,510
 211  A*29:11-B*13:02:01-C*06:02:01-DRB1*12:01:01-DQB1*05:01:01-DPB1*105:01:01  South African Black 1.0560142
 212  A*02:01-B*13:02-C*06:02-DRB1*07:01  Germany DKMS - Portugal minority 1.05301,176
 213  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  India North UCBB 1.04995,849
 214  A*01:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01  China Jingpo Minority 1.0420105
 215  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*04:01  USA San Diego 1.0420496
 216  A*30:01-B*13:02-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*17:01  USA San Diego 1.0420496
 217  B*37:01:01-C*06:02:01-DRB1*10:01:01  China Jingpo Minority 1.0420105
 218  A*26:08-B*57:01-C*06:02-DRB1*01:01  Brazil Vale do Ribeira Quilombos 1.0417144
 219  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP Southeast Asian 1.034527,978
 220  A*01:01:01-C*06:02:01-DRB1*10:01:01  China Jingpo Minority 1.0310105
 221  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India Northeast UCBB 1.0135296
 222  A*01:01:01-B*50:01:01-C*06:02:01  South African Indian population 1.000050
 223  A*01:01-B*37:01:01-C*06:02-DRB1*15:01-DQB1*06:02:01  England North West 1.0000298
 224  A*01:01-B*37:01-C*06:02  Ireland Northern 1.00001,000
 225  A*01:01-B*37:01-C*06:02  Italy pop 5 1.0000975
 226  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  South Korea pop 3 1.0000485
 227  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*02:01  Poland 1.0000200
 228  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Poland 1.0000200
 229  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*13:01  Ireland South 1.0000250
 230  A*01-B*37-C*06:02-DRB1*15:01-DQB1*06  Russia Transbaikal Territory Buryats 1.0000150
 231  A*02:01:01-B*15:16:01-C*06:02:01  South African Mixed ancestry 1.000050
 232  A*02:01-B*08:01-C*06:02  Kenya Nandi 1.0000240
 233  A*02:02-B*58:02-C*06:02-DRB1*11:01-DQA1*01:02-DQB1*05:01-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 1.0000100
 234  A*02:06:01-B*57:01:01-C*06:02:01  South African Indian population 1.000050
 235  A*02:11:01-B*57:01:01-C*06:02:01  South African Indian population 1.000050
 236  A*03:01:01-B*44:02:01-C*06:02:01  South African Indian population 1.000050
 237  A*03:01:01-B*49:01:01-C*06:02:01  South African Mixed ancestry 1.000050
 238  A*03:01-B*13:02-C*06:02-DRB1*07:01-DQB1*02:02  Poland 1.0000200
 239  A*03:01-B*58:02-C*06:02  Kenya Nandi 1.0000240
 240  A*23:01:01-B*45:01-C*06:02:01  South African Mixed ancestry 1.000050
 241  A*23:01:01-B*58:02-C*06:02:01  South African Indian population 1.000050
 242  A*23:01-B*53:01-C*06:02-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*13:01  Kenya, Nyanza Province, Luo tribe 1.0000100
 243  A*23:01-B*58:02-C*06:02  Kenya Luo 1.0000265
 244  A*24:02:01-B*58:02-C*06:02:01  South African Mixed ancestry 1.000050
 245  A*24:07-B*13:01:01-C*06:02:01  South African Indian population 1.000050
 246  A*26:01:01-B*35:03:01-C*06:02:01  South African Mixed ancestry 1.000050
 247  A*29:02:01-B*45:01-C*06:02:01  South African Mixed ancestry 1.000050
 248  A*29:02-B*45:01-C*06:02  Kenya Nandi 1.0000240
 249  A*30:01-B*58:02-C*06:02  Kenya Luo 1.0000265
 250  A*30:04:01-B*58:02-C*06:02:01  South African Mixed ancestry 1.000050
 251  A*31:01:02-B*40:06:01-C*06:02:01  South African Indian population 1.000050
 252  A*34:01:01-B*57:01:01-C*06:02:01  South African Mixed ancestry 1.000050
 253  A*66:01-B*45:01-C*06:02  Kenya Nandi 1.0000240
 254  A*66:01-B*58:02-C*06:02:01  South African Mixed ancestry 1.000050
 255  A*66:01-B*58:02-C*06:02-DRB1*13:01-DQA1*01:03-DQB1*06:02-DPB1*19:01  South Africa Worcester 1.0000159
 256  A*68:02-B*13:02-C*06:02  Kenya Nandi 1.0000240
 257  A*74:01-B*58:02-C*06:02  Kenya Luo 1.0000265
 258  B*08:01-C*06:02  Kenya Nandi 1.0000240
 259  A*01:01:01-B*37:01:01-C*06:02:01  China Jingpo Minority 0.9900105
 260  A*30:01-B*13:02-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Southeast Asian 0.989427,978
 261  A*30:01-B*13:02-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP South Asian Indian 0.9874185,391
 262  B*45:01-C*06:02  USA African American pop 4 0.98502,411
 263  A*30:01-B*13:02-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP Middle Eastern or North Coast of Africa 0.983870,890
 264  A*30:01-B*13:02-C*06:02-DRB1*07:01-DQB1*02:01  Germany DKMS - Turkey minority 0.98204,856
 265  A*03:01:01-B*58:02-C*06:02:01-DRB1*03:01  Costa Rica African -Caribbean (G) 0.9804102
 266  A*03:01:01-B*58:02-C*06:02:01-DRB1*07:01  Costa Rica African -Caribbean (G) 0.9804102
 267  A*74:01-B*58:02-C*06:02:01-DRB1*12:01  Costa Rica African -Caribbean (G) 0.9804102
 268  B*37:01:01-C*06:02:01  China Jingpo Minority 0.9800105
 269  A*30:01-B*13:02-C*06:02-DRB1*07:01-DQB1*02:01  USA Asian pop 2 0.97801,772
 270  A*01:01:01-C*06:02:01  China Jingpo Minority 0.9710105
 271  A*30:01:01-B*13:02:01-C*06:02:01  England Blood Donors of Mixed Ethnicity 0.9634519
 272  A*03:01-B*47:01-C*06:02-DRB1*10:01  Russia Bering Island Aleuts 0.9615104
 273  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Italy pop 5 0.9600975
 274  A*02:05-B*50:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02  United Arab Emirates Abu Dhabi 0.960052
 275  A*02:06-B*50:01-C*06:02-DRB1*04:05-DQA1*03:03-DQB1*03:02  United Arab Emirates Abu Dhabi 0.960052
 276  A*03:01-B*49:01-C*06:02-DRB1*11:04-DQA1*03:01-DQB1*03:02  United Arab Emirates Abu Dhabi 0.960052
 277  A*03:01-B*50:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02  United Arab Emirates Abu Dhabi 0.960052
 278  A*24:02-B*50:01-C*06:02-DRB1*11:01-DQA1*05:05-DQB1*03:01  United Arab Emirates Abu Dhabi 0.960052
 279  A*24:02-C*06:02  Italy pop 5 0.9600975
 280  A*29:01-B*50:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02  United Arab Emirates Abu Dhabi 0.960052
 281  A*30:01-B*51:01-C*06:02-DRB1*04:02-DQA1*03:01-DQB1*02:02  United Arab Emirates Abu Dhabi 0.960052
 282  A*30:02-B*57:01-C*06:02-DRB1*03:01-DQA1*05:01-DQB1*03:03  United Arab Emirates Abu Dhabi 0.960052
 283  A*30:04-B*50:01-C*06:02-DRB1*04:05-DQA1*03:03-DQB1*03:02  United Arab Emirates Abu Dhabi 0.960052
 284  A*33:03-B*35:02-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*02:02  United Arab Emirates Abu Dhabi 0.960052
 285  A*68:01-B*58:02-C*06:02-DRB1*12:01-DQA1*01:05-DQB1*05:01  United Arab Emirates Abu Dhabi 0.960052
 286  A*74:01-B*38:01-C*06:02-DRB1*08:04-DQA1*05:05-DQB1*03:01  United Arab Emirates Abu Dhabi 0.960052
 287  A*11:01:01-B*37:01:01-C*06:02:01-DRB1*10:01:01-DQB1*05:01:01  India Kerala Malayalam speaking 0.9490356
 288  A*30:01:01-B*13:02:01-C*06:02:01-DRB1*07:01:01-DQB1*02:02:01  Spain, Canary Islands, Gran canaria island 0.9300215
 289  A*01:01-B*37:01-C*06:02-DRB1*10:01-DRBX*NNNN-DQB1*05:01  USA NMDP Korean 0.923177,584
 290  A*01:01-B*57:01-C*06:02-DRB1*14:04-DQB1*05:03  Malaysia Peninsular Indian 0.9225271
 291  A*30:01-C*06:02  Italy pop 5 0.9200975
 292  A*02:05:01-B*50:01:01-C*06:02:01-DRB1*07:01  Costa Rica Guanacaste Mestizo (G) 0.9091110
 293  A*01:01-B*57:01-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*02:01  USA NMDP South Asian Indian 0.9040185,391
 294  A*30:01-B*13:02-C*06:02-DRB1*07:01  Poland DKMS 0.898720,653
 295  A*24:02-B*13:02-C*06:02-DRB1*07:01  Poland DKMS 0.891820,653
 296  A*01:01-B*37:01-C*06:02-DRB1*10:01-DQB1*05:01  USA Asian pop 2 0.88901,772
 297  A*30:01-B*13:02-C*06:02-DRB1*07:01  Germany DKMS - Romania minority 0.88901,234
 298  A*30:01-B*13:02-C*06:02-DRB1*07:01  Germany DKMS - France minority 0.88701,406
 299  A*02:01-B*13:02-C*06:02-DRB1*07:01  Germany DKMS - Romania minority 0.88201,234
 300  A*01:01-B*57:01-C*06:02-DRB1*01:01-DQB1*05:01  Italy pop 5 0.8800975

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 201 to 300 (from 7,333) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 74  


   

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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