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 1 to 100 (from 100) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Ireland South 1.0000250
 2  A*24:02:01-B*40:01:01-C*03:04:01-DRB1*04:04:01-DQB1*03:02  Costa Rica Central Valley Mestizo (G) 0.9050221
 3  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02:01  England North West 0.8000298
 4  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 0.746367
 5  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02:01  England North West 0.7000298
 6  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA NMDP American Indian South or Central America 0.40605,926
 7  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.37604,335
 8  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.34204,335
 9  A*02:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQA1*02:01:01-DQB1*03:02-DPA1*01:03:01-DPB1*16:01:01  Russia Belgorod region 0.3268153
 10  A*31:01:02:01-B*40:01:02-C*03:04:01:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.29541,510
 11  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA NMDP American Indian South or Central America 0.23805,926
 12  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.23401,999
 13  A*02:01:01-B*40:01:01-C*03:04:01-DRB1*04:04:01-DQB1*03:02  Costa Rica Central Valley Mestizo (G) 0.2262221
 14  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA NMDP Alaska Native or Aleut 0.20181,376
 15  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02:01  England North West 0.2000298
 16  A*03:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 17  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*06:01  Russia Karelia 0.18331,075
 18  A*02:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.175323,595
 19  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA African American pop 4 0.17402,411
 20  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.17091,463
 21  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*05:01  Russia Karelia 0.16801,075
 22  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Italy pop 5 0.1500975
 23  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.13671,463
 24  A*02:01:01:01-B*40:01:02-C*03:04:01:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.13511,510
 25  A*24:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA NMDP American Indian South or Central America 0.13015,926
 26  A*31:01:02-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.115323,595
 27  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA NMDP Black South or Central American 0.11234,889
 28  A*03:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 29  A*30:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 30  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Germany DKMS - Italy minority 0.08601,159
 31  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.08122,492
 32  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*06:01  Germany DKMS - German donors 0.07833,456,066
 33  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*06:01  Germany DKMS - German donors 0.07643,456,066
 34  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 35  A*24:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 36  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.05753,456,066
 37  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India South UCBB 0.057311,446
 38  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.05721,075
 39  A*68:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.05651,075
 40  A*24:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.05651,075
 41  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.05432,492
 42  A*11:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.053523,595
 43  A*03:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.051823,595
 44  A*68:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 45  A*02:11-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Asian pop 2 0.04401,772
 46  A*01:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.04372,492
 47  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.04104,856
 48  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*03:01  Germany DKMS - German donors 0.03943,456,066
 49  A*32:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.036723,595
 50  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India West UCBB 0.03425,829
 51  A*02:11-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 52  A*29:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 53  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.03353,456,066
 54  A*01:01:01:01-B*40:01:02-C*03:04:01:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.03311,510
 55  A*03:01:01:01-B*40:01:02-C*03:04:01:01-DRB1*04:04:01-DQB1*03:02  Russia Nizhny Novgorod, Russians 0.03311,510
 56  A*24:02:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.032523,595
 57  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*02:01  Germany DKMS - German donors 0.03093,456,066
 58  A*26:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.029223,595
 59  A*24:02:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  China Zhejiang Han 0.02881,734
 60  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India South UCBB 0.028611,446
 61  A*24:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.02704,856
 62  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.02304,856
 63  A*32:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*02:01  Germany DKMS - German donors 0.02213,456,066
 64  A*01:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India South UCBB 0.021911,446
 65  A*03:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*06:01  Germany DKMS - German donors 0.02173,456,066
 66  A*02:11-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India South UCBB 0.021611,446
 67  A*26:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.02012,492
 68  A*29:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.02012,492
 69  A*68:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.01883,456,066
 70  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*02:01  Germany DKMS - German donors 0.01863,456,066
 71  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*03:01  Germany DKMS - German donors 0.01843,456,066
 72  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:02  Germany DKMS - German donors 0.01723,456,066
 73  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India North UCBB 0.01715,849
 74  A*03:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:01  Germany DKMS - German donors 0.01423,456,066
 75  A*02:11-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 76  A*29:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 77  A*02:11-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Central UCBB 0.01194,204
 78  A*03:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Central UCBB 0.01194,204
 79  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Central UCBB 0.01194,204
 80  A*03:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*02:01  Germany DKMS - German donors 0.01153,456,066
 81  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Asian pop 2 0.01101,772
 82  A*33:03-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Asian pop 2 0.01101,772
 83  A*24:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:02  Germany DKMS - German donors 0.01053,456,066
 84  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:02  Germany DKMS - German donors 0.01053,456,066
 85  A*24:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India West UCBB 0.01045,829
 86  A*29:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.01004,856
 87  A*01:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India North UCBB 0.00995,849
 88  A*32:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India West UCBB 0.00865,829
 89  A*68:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India West UCBB 0.00865,829
 90  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India North UCBB 0.00725,849
 91  A*30:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.006623,595
 92  A*25:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.005823,595
 93  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India South UCBB 0.004811,446
 94  A*68:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.003923,595
 95  A*68:01:02-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.003223,595
 96  A*33:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.003123,595
 97  A*66:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002323,595
 98  A*01:01:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002223,595
 99  A*02:05:01-B*40:01:02-C*03:04:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.002123,595
 100  A*33:03-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India South UCBB 0.001511,446

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).




   

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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