Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

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Population:  Country:  Source of dataset : 
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Displaying 201 to 300 (from 963) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 10  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 201  A*24:02-B*51:01-C*03:04-DRB1*13:02-DQB1*06:09  Malaysia Peninsular Indian 0.1845271
 202  A*24:02-B*58:01-C*03:02-DRB1*13:02-DQB1*06:09  Malaysia Peninsular Indian 0.1845271
 203  A*32:01-B*58:01-C*12:02-DRB1*13:02-DQB1*06:09  Malaysia Peninsular Indian 0.1845271
 204  A*33:01-B*58:01-C*03:02-DRB1*13:02-DQB1*06:09  Malaysia Peninsular Indian 0.1845271
 205  A*74:01-B*15:03-C*02:02-DRB1*13:02-DQB1*06:09  USA NMDP Black South or Central American 0.18384,889
 206  A*11:01-B*58:01-C*03:02-DRB1*13:02-DQB1*06:09  USA Asian pop 2 0.17801,772
 207  A*33:03-B*58:01-C*03:02-DRB1*13:02-DRB3*03:01-DQB1*06:09  USA NMDP Japanese 0.175624,582
 208  A*23:01-B*15:03-C*02:02-DRB1*13:02-DQB1*06:09  USA African American pop 4 0.17402,411
 209  A*11:01:01-B*58:01:01-C*03:02:02-DRB1*13:02:01-DQB1*06:09:01  China Zhejiang Han 0.17321,734
 210  A*01:01-B*58:01-C*03:02-DRB1*13:02-DQB1*06:09  Germany DKMS - Italy minority 0.17301,159
 211  A*24:02-B*58:01-C*03:02-DRB1*04:06-DQB1*06:09  India Northeast UCBB 0.1689296
 212  A*32:01-B*53:01-C*04:01-DRB1*13:02-DQB1*06:09  India Northeast UCBB 0.1689296
 213  A*33:03-B*52:01-C*07:06-DRB1*04:01-DQB1*06:09  India Northeast UCBB 0.1689296
 214  DRB1*13:02:01-DQB1*06:09-DPB1*09:01:01  China Inner Mongolia Autonomous Region Northeast 0.1650496
 215  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
 216  A*01:01-B*08:01-C*07:04-DRB1*13:02-DQB1*06:09-DPB1*04:01  Tanzania Maasai 0.1597336
 217  A*01:01-B*08:01-C*07:04-DRB1*13:02-DQB1*06:09-DPB1*17:01  Tanzania Maasai 0.1597336
 218  A*01:01-B*08:01-C*07:181-DRB1*13:02-DQB1*06:09-DPB1*04:01  Tanzania Maasai 0.1597336
 219  A*01:01-B*42:01-C*07:328-DRB1*13:02-DQB1*06:09-DPB1*17:01  Tanzania Maasai 0.1597336
 220  A*01:01-B*45:01-C*06:66-DRB1*13:02-DQB1*06:09-DPB1*17:01  Tanzania Maasai 0.1597336
 221  A*02:01-B*07:02-C*07:01-DRB1*11:189-DQB1*06:09-DPB1*04:01  Tanzania Maasai 0.1597336
 222  A*02:01-B*44:03-C*04:113-DRB1*13:02-DQB1*06:09-DPB1*03:01  Tanzania Maasai 0.1597336
 223  A*02:01-B*53:01-C*07:01-DRB1*03:01-DQB1*06:09-DPB1*04:02  Tanzania Maasai 0.1597336
 224  A*02:01-B*57:02-C*08:13-DRB1*13:02-DQB1*06:09-DPB1*105:01  Tanzania Maasai 0.1597336
 225  A*02:14-B*15:03-C*02:10-DRB1*13:02-DQB1*06:09-DPB1*04:01  Tanzania Maasai 0.1597336
 226  A*03:01-B*53:01-C*06:02-DRB1*04:05-DQB1*06:09-DPB1*03:01  Tanzania Maasai 0.1597336
 227  A*03:01-B*58:01-C*07:136-DRB1*13:02-DQB1*06:09-DPB1*02:01  Tanzania Maasai 0.1597336
 228  A*24:02-B*53:01-C*07:328-DRB1*13:02-DQB1*06:09-DPB1*02:01  Tanzania Maasai 0.1597336
 229  A*29:02-B*15:17-C*17:01-DRB1*10:01-DQB1*06:09-DPB1*105:01  Tanzania Maasai 0.1597336
 230  A*30:04-B*14:02-C*05:01-DRB1*03:02-DQB1*06:09-DPB1*01:01  Tanzania Maasai 0.1597336
 231  A*31:04-B*45:01-C*06:02-DRB1*13:02-DQB1*06:09-DPB1*34:01  Tanzania Maasai 0.1597336
 232  A*32:01-B*81:01-C*05:11-DRB1*12:01-DQB1*06:09-DPB1*04:02  Tanzania Maasai 0.1597336
 233  A*33:03-B*53:01-C*08:02-DRB1*13:02-DQB1*06:09-DPB1*55:01  Tanzania Maasai 0.1597336
 234  A*34:02-B*40:12-C*04:04-DRB1*13:02-DQB1*06:09-DPB1*02:01  Tanzania Maasai 0.1597336
 235  A*34:02-B*40:12-C*04:04-DRB1*13:02-DQB1*06:09-DPB1*105:01  Tanzania Maasai 0.1597336
 236  A*68:02-B*14:02-C*08:02-DRB1*13:02-DQB1*06:09-DPB1*01:01  Tanzania Maasai 0.1597336
 237  A*68:02-B*18:01-C*07:04-DRB1*13:02-DQB1*06:09-DPB1*03:01  Tanzania Maasai 0.1597336
 238  A*68:02-B*27:03-C*06:02-DRB1*13:02-DQB1*06:09-DPB1*03:01  Tanzania Maasai 0.1597336
 239  A*68:02-B*53:01-C*04:01-DRB1*13:02-DQB1*06:09-DPB1*03:01  Tanzania Maasai 0.1597336
 240  A*74:01-B*15:03-C*02:10-DRB1*13:02-DQB1*06:09-DPB1*02:01  Tanzania Maasai 0.1597336
 241  A*74:01-B*44:03-C*04:01-DRB1*15:03-DQB1*06:09-DPB1*02:01  Tanzania Maasai 0.1597336
 242  A*01-B*58-DRB1*13:02-DQA1*01:02-DQB1*06:09  Brazil Paraná Caucasian 0.1560641
 243  A*24-B*58-DRB1*13:02-DQA1*01:02-DQB1*06:09  Brazil Paraná Caucasian 0.1560641
 244  A*11:01-B*35:01-DRB1*13:02-DQB1*06:09  Mexico Mexico City Tlalpan 0.1515330
 245  A*23:01-B*41:01-DRB1*13:01-DQB1*06:09  Mexico Mexico City Tlalpan 0.1515330
 246  A*02:01-B*35:01-C*16:01-DRB1*13:02-DQB1*06:09  USA NMDP Black South or Central American 0.14744,889
 247  DRB1*13:02-DQB1*06:09-DPB1*39:01  Gambia pop 3 0.1451939
 248  A*24:02-B*58:01-C*03:02-DRB1*13:02-DQA1*01:02-DQB1*06:09-DPB1*09:01  Sri Lanka Colombo 0.1401714
 249  A*33:03-B*58:01-C*03:02-DRB1*13:02-DQA1*01:02-DQB1*06:09-DPB1*09:01  Sri Lanka Colombo 0.1401714
 250  A*02:01:01-B*40:06:01-C*03:02:01-DRB1*15:01:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 251  A*02:01:01-B*56:01:01-C*04:03:01-DRB1*14:04:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 252  A*03:01:01-B*38:02:01-C*14:02:01-DRB1*13:02:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 253  A*24:02:01-B*13:01:01-C*04:03:01-DRB1*13:02:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 254  A*24:02-B*15:17-C*07:01-DRB1*13:02-DQB1*06:09  Italy pop 5 0.1400975
 255  A*25:01-B*56:01-C*01:02-DRB1*08:01-DQB1*06:09  Italy pop 5 0.1400975
 256  A*26:01:01-B*15:18:01-C*16:02:01-DRB1*10:01:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 257  A*26:01:01-B*58:01:01-C*03:02:02-DRB1*13:02:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 258  A*30:02:01-B*58:01:01-C*03:146-DRB1*13:02:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 259  A*31:01:02-B*51:01:01-C*07:04:01-DRB1*14:04:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 260  A*31:01:02-B*52:01:01-C*03:02:01-DRB1*13:01:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 261  A*31:01:02-B*58:34-C*03:02:01-DRB1*15:01:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 262  A*32:01:01-B*40:06:01-C*03:02:02-DRB1*04:10:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 263  A*33:03:01-B*07:02:01-C*07:02:01-DRB1*13:02:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 264  A*68:01:02-B*07:06:01-C*03:02:02-DRB1*13:02:01-DQB1*06:09:01  India Kerala Malayalam speaking 0.1400356
 265  A*80:01-B*44:03-C*04:01-DRB1*13:02-DQB1*06:09  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.13704,335
 266  A*03:01:01:01-B*14:02:01-C*08:02:01-DRB1*13:02:01-DQB1*06:09:01  Russia Nizhny Novgorod, Russians 0.13251,510
 267  A*33:03-B*58:01-C*03:02-DRB1*13:02-DQA1*01:02-DQB1*06:09-DPA1*01:03-DPB1*02:01  Japan pop 17 0.13003,078
 268  A*30:01-B*42:01-C*17:01-DRB1*13:02-DQB1*06:09  USA African American pop 4 0.12902,411
 269  A*01:01-B*58:01-C*03:02-DRB1*13:02-DQB1*06:09  India East UCBB 0.12482,403
 270  A*74:01-B*53:01-C*04:01-DRB1*13:02-DQB1*06:09  USA NMDP Black South or Central American 0.12384,889
 271  DRB1*13:02-DQB1*06:09-DPB1*85:01  Gambia pop 3 0.1235939
 272  A*01:01-B*58:01-C*03:02-DRB1*13:02-DQB1*06:09  India Central UCBB 0.12064,204
 273  A*33:03:01-B*58:01:01-C*03:02:02-DRB1*13:02:01-DQB1*06:09:01  Poland BMR 0.120623,595
 274  A*30:01:01-B*53:01:01-C*04:01:01-DRB1*13:02:01-DQB1*06:09:01  Costa Rica Central Valley Mestizo (G) 0.1175221
 275  DRB1*13:02:01-DQB1*06:09-DPB1*04:01:01  China Inner Mongolia Autonomous Region Northeast 0.1110496
 276  A*02:02-B*53:01-C*04:01-DRB1*13:02-DQB1*06:09  USA NMDP Black South or Central American 0.10914,889
 277  A*33:01-B*58:01-C*03:02-DRB1*13:01-DQB1*06:09  Malaysia Peninsular Malay 0.1052951
 278  A*33:03-B*58:01-C*03:02-DRB1*13:02-DQB1*06:09  Malaysia Peninsular Malay 0.1052951
 279  A*33:03-B*58:01-C*03:02-DRB1*13:02-DRB3*03:01-DQB1*06:09  USA NMDP Caribean Black 0.104833,328
 280  A*30:02-B*35:01-C*04:01-DRB1*13:02-DQB1*06:09  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 281  DRB1*03:01:12-DQB1*06:09-DPB1*04:01:01  China Inner Mongolia Autonomous Region Northeast 0.1010496
 282  A*32:01-B*51:01-C*15:02-DRB1*13:02-DQB1*06:09  India Tamil Nadu 0.10042,492
 283  A*33:03-B*58:01-C*03:02-DRB1*13:02-DQA1*01:02-DQB1*06:09-DPA1*02:02-DPB1*05:01  Japan pop 17 0.10003,078
 284  A*24:02:01-B*35:02:01-C*04:01:01-DRB1*13:02:01-DQB1*06:09:01  Costa Rica Central Valley Mestizo (G) 0.0994221
 285  A*32:01:01-B*39:24-C*07:01:01-DRB1*13:02:01-DQB1*06:09:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.099428,927
 286  A*03:01:01:01-B*58:01:01-C*03:02:02-DRB1*13:02:01-DQB1*06:09:01  Russia Nizhny Novgorod, Russians 0.09931,510
 287  DQA1*01:02-DQB1*06:09-DPA1*02:02-DPB1*05:01  Hong Kong Chinese HKBMDR. DQ and DP 0.09841,064
 288  A*02:05-B*58:01-C*07:01-DRB1*13:02-DQB1*06:09  USA Hispanic pop 2 0.09401,999
 289  A*01:01-B*58:01-C*03:02-DRB1*13:02-DQB1*06:09  India North UCBB 0.09235,849
 290  DRB1*01:02-DQB1*06:09-DPB1*01:01  Gambia pop 3 0.0921939
 291  DRB1*13:02-DQA1*01:02-DQB1*06:09-DPA1*02:02-DPB1*05:01  China Zhejiang Han pop 2 0.0909833
 292  DRB1*11:04-DQB1*06:09  Italy pop 5 0.0900975
 293  A*02:06-B*58:01-C*03:02-DRB1*13:02-DQB1*06:09  USA Asian pop 2 0.08901,772
 294  A*02:02-B*53:01-C*04:01-DRB1*13:02-DQB1*06:09  USA African American pop 4 0.08702,411
 295  A*26:01-B*50:01-C*04:01-DRB1*13:02-DQB1*06:09  USA African American pop 4 0.08702,411
 296  A*30:01-B*35:01-C*04:01-DRB1*13:02-DQB1*06:09  USA African American pop 4 0.08702,411
 297  A*34:02-B*42:01-C*17:01-DRB1*13:02-DQB1*06:09  USA African American pop 4 0.08702,411
 298  A*68:02-B*53:01-C*04:01-DRB1*13:02-DQB1*06:09  USA African American pop 4 0.08702,411
 299  A*01:01-B*52:01-C*12:02-DRB1*13:02-DQB1*06:09  Germany DKMS - Italy minority 0.08601,159
 300  A*02:05-B*58:01-C*07:01-DRB1*10:01-DQB1*06:09  Germany DKMS - Italy minority 0.08601,159

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 963) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 10  


   

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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