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 1 to 100 (from 107) records   Pages: 1 2 of 2  

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*02:01-B*40:01-DRB1*04:04-DQB1*03:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 7  A*24:02-B*40:01-DRB1*04:04-DQB1*03:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 8  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
 9  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.37604,335
 10  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.34204,335
 11  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
 12  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
 13  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
 14  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.23401,999
 15  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
 16  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA NMDP Alaska Native or Aleut 0.20181,376
 17  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02:01  England North West 0.2000298
 18  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
 19  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*06:01  Russia Karelia 0.18331,075
 20  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
 21  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA African American pop 4 0.17402,411
 22  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.17091,463
 23  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*05:01  Russia Karelia 0.16801,075
 24  A*30:01-B*40:01-DRB1*04:04-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 25  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Italy pop 5 0.1500975
 26  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.13671,463
 27  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
 28  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
 29  A*11:01:01-B*40:01:02-C*07:02:01-DRB1*04:04:01-DQB1*03:02:01  China Zhejiang Han 0.12141,734
 30  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
 31  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
 32  A*03:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 33  A*30:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.10304,335
 34  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Germany DKMS - Italy minority 0.08601,159
 35  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.08122,492
 36  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
 37  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
 38  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 39  A*24:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 40  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
 41  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India South UCBB 0.057311,446
 42  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.05721,075
 43  A*68:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.05651,075
 44  A*24:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02-DPB1*04:01  Russia Karelia 0.05651,075
 45  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.05432,492
 46  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
 47  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
 48  A*68:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 49  A*02:11-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Asian pop 2 0.04401,772
 50  A*01:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.04372,492
 51  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.04104,856
 52  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
 53  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
 54  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India West UCBB 0.03425,829
 55  A*02:11-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 56  A*29:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 57  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
 58  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
 59  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
 60  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
 61  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
 62  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
 63  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
 64  A*26:01:01-B*40:01:02-C*08:01:01-DRB1*04:04:01-DQB1*03:02:01  China Zhejiang Han 0.02881,734
 65  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India South UCBB 0.028611,446
 66  A*24:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.02704,856
 67  A*02:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.02304,856
 68  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
 69  A*01:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India South UCBB 0.021911,446
 70  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
 71  A*02:11-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India South UCBB 0.021611,446
 72  A*11:01-B*40:01-C*06:02-DRB1*04:04-DQB1*03:02  India East UCBB 0.02082,403
 73  A*26:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.02012,492
 74  A*29:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Tamil Nadu 0.02012,492
 75  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
 76  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
 77  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
 78  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
 79  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India North UCBB 0.01715,849
 80  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
 81  A*02:11-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 82  A*29:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Hispanic pop 2 0.01201,999
 83  A*02:11-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Central UCBB 0.01194,204
 84  A*03:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Central UCBB 0.01194,204
 85  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India Central UCBB 0.01194,204
 86  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
 87  A*31:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Asian pop 2 0.01101,772
 88  A*33:03-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  USA Asian pop 2 0.01101,772
 89  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
 90  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
 91  A*24:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India West UCBB 0.01045,829
 92  A*29:02-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  Germany DKMS - Turkey minority 0.01004,856
 93  A*01:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India North UCBB 0.00995,849
 94  A*32:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India West UCBB 0.00865,829
 95  A*68:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India West UCBB 0.00865,829
 96  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India North UCBB 0.00725,849
 97  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
 98  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
 99  A*11:01-B*40:01-C*03:04-DRB1*04:04-DQB1*03:02  India South UCBB 0.004811,446
 100  A*32:01:01-B*40:01:02-C*05:01:01-DRB1*04:04:01-DQB1*03:02:01  Poland BMR 0.004223,595

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 1 to 100 (from 107) records   Pages: 1 2 of 2  


   

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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