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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Sample Size:      Sample Year:     Loci Tested: 
Displaying 101 to 200 (from 299) records   Pages: 1 2 3 of 3  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 101  A*24:02-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Germany DKMS - Italy minority 0.19301,159
 102  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*04:01  Panama 0.1900462
 103  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*26:01  Panama 0.1900462
 104  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*04:01  Panama 0.1900462
 105  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  USA Hispanic pop 2 0.18701,999
 106  A*02:05-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Malaysia Peninsular Indian 0.1845271
 107  A*11:19-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Malaysia Peninsular Indian 0.1845271
 108  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*13:01  Germany DKMS - German donors 0.18213,456,066
 109  A*01:01-B*57:01-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*03:03  USA NMDP Filipino 0.181550,614
 110  A*24:02-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India Central UCBB 0.18114,204
 111  A*01:01-B*57:01-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*03:03  USA NMDP Chinese 0.177199,672
 112  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  USA African American pop 4 0.17402,411
 113  A*25:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  Poland BMR 0.171623,595
 114  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*03:01  Russia Karelia 0.16931,075
 115  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India Northeast UCBB 0.1689296
 116  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  USA NMDP Caribean Indian 0.168714,339
 117  A*24:02-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India North UCBB 0.16465,849
 118  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*03:01  Russia Karelia 0.16391,075
 119  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Germany DKMS - Turkey minority 0.15304,856
 120  A*11:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India North UCBB 0.15105,849
 121  A*24:02-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India West UCBB 0.14655,829
 122  A*32:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  India Kerala Malayalam speaking 0.1450356
 123  A*01:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  China Zhejiang Han 0.14421,734
 124  A*24:02:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  China Zhejiang Han 0.14421,734
 125  A*01:01-B*57:01-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*03:03  USA NMDP Mexican or Chicano 0.1418261,235
 126  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*03:01  Sri Lanka Colombo 0.1401714
 127  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*16:01  Sri Lanka Colombo 0.1401714
 128  A*02:11-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*02:01  Sri Lanka Colombo 0.1401714
 129  A*26:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*26:01  Sri Lanka Colombo 0.1401714
 130  A*33:03-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*03:01  Sri Lanka Colombo 0.1401714
 131  A*02:05:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  India Kerala Malayalam speaking 0.1400356
 132  A*24:02:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  India Kerala Malayalam speaking 0.1400356
 133  A*31:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Italy pop 5 0.1400975
 134  A*33:03-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  USA Asian pop 2 0.13801,772
 135  A*33:03-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India Central UCBB 0.13034,204
 136  A*31:01:02:01-B*57:01:01-C*06:02:01:01-DRB1*07:01:01-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.12921,510
 137  A*68:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India South UCBB 0.127111,446
 138  A*24:02:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  Poland BMR 0.116623,595
 139  A*03:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India Tamil Nadu 0.11622,492
 140  A*02:11-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India South UCBB 0.112711,446
 141  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*13:01  Russia Karelia 0.11261,075
 142  A*31:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India Tamil Nadu 0.11012,492
 143  A*01:01-B*57:01-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*03:03  USA NMDP Hispanic South or Central American 0.1063146,714
 144  A*03:01:01:01-B*57:01:01-C*06:02:01:01-DRB1*07:01:01-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.10581,510
 145  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Colombia Bogotá Cord Blood 0.10251,463
 146  A*24:02:01:01-B*57:01:01-C*06:02:01:01-DRB1*07:01:01-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.09931,510
 147  A*11:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  Poland BMR 0.098923,595
 148  A*33:03-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India West UCBB 0.09295,829
 149  A*24:02-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*04:01  Germany DKMS - German donors 0.09193,456,066
 150  A*01:01-B*57:01-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*03:03  USA NMDP Korean 0.087777,584
 151  A*33:03:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  China Zhejiang Han 0.08651,734
 152  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India South UCBB 0.083811,446
 153  A*26:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India East UCBB 0.08342,403
 154  A*03:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India South UCBB 0.082711,446
 155  A*11:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  China Zhejiang Han 0.07851,734
 156  A*01:01-B*57:01-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*03:03  USA NMDP Caribean Black 0.078233,328
 157  A*01:01-B*57:01-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*03:03  USA NMDP African American pop 2 0.0772416,581
 158  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India East UCBB 0.07712,403
 159  A*32:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  Poland BMR 0.073723,595
 160  A*26:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  Poland BMR 0.072323,595
 161  A*01:01-B*57:01-C*06:02-DRB1*07:01-DRB4*01:01-DQB1*03:03  USA NMDP Caribean Hispanic 0.0705115,374
 162  A*03:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*14:01  Sri Lanka Colombo 0.0700714
 163  A*03:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*26:01  Sri Lanka Colombo 0.0700714
 164  A*11:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*15:01  Sri Lanka Colombo 0.0700714
 165  A*24:02-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*04:02  Sri Lanka Colombo 0.0700714
 166  A*26:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*02:01  Sri Lanka Colombo 0.0700714
 167  A*33:03-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*15:01  Sri Lanka Colombo 0.0700714
 168  A*68:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*02:01  Sri Lanka Colombo 0.0700714
 169  A*68:01-B*57:01-C*06:02-DRB1*07:01-DQA1*02:01-DQB1*03:03-DPB1*26:01  Sri Lanka Colombo 0.0700714
 170  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Italy pop 5 0.0700975
 171  A*31:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 172  A*32:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 173  A*68:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 174  A*02:01:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.06621,510
 175  A*26:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  Russia Nizhny Novgorod, Russians 0.06621,510
 176  A*26:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*04:01  Russia Karelia 0.06281,075
 177  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*02:01  Germany DKMS - German donors 0.06253,456,066
 178  A*26:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India South UCBB 0.062011,446
 179  A*68:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India Tamil Nadu 0.06192,492
 180  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India West UCBB 0.06185,829
 181  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*02:01  Germany DKMS - German donors 0.06103,456,066
 182  A*03:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*04:01  Germany DKMS - German donors 0.06013,456,066
 183  A*11:02:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  China Zhejiang Han 0.05771,734
 184  A*03:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DQB1*03:03:02  Poland BMR 0.057023,595
 185  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*04:02  Russia Karelia 0.05681,075
 186  A*03:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India West UCBB 0.05665,829
 187  A*23:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*13:01  Russia Karelia 0.05651,075
 188  A*29:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*04:01  Russia Karelia 0.05641,075
 189  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*03:01  Germany DKMS - German donors 0.05613,456,066
 190  A*24:02-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  Malaysia Peninsular Malay 0.0531951
 191  A*31:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India South UCBB 0.052711,446
 192  A*24:11N-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India South UCBB 0.051511,446
 193  A*24:07-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India East UCBB 0.05032,403
 194  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India Central UCBB 0.05004,204
 195  A*01:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*04:02  Germany DKMS - German donors 0.05003,456,066
 196  A*33:03-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  India North UCBB 0.04745,849
 197  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03-DPB1*04:02  Germany DKMS - German donors 0.04733,456,066
 198  A*02:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  USA African American pop 4 0.04402,411
 199  A*11:01-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  USA Asian pop 2 0.04401,772
 200  A*24:02-B*57:01-C*06:02-DRB1*07:01-DQB1*03:03  USA Asian pop 2 0.04401,772

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 101 to 200 (from 299) records   Pages: 1 2 3 of 3  


   

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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