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 75 (from 75) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  DPA1*01:04-DPB1*15:01  India Bombay 1.700059
 2  A*03:01-B*47:01-C*06:02-DRB1*03:01-DQA1*05:01-DQB1*02:01-DPB1*15:01  South Africa Worcester 1.6000159
 3  DPA1*01:04-DPB1*15:01  Russia Tuva pop 2 1.2000169
 4  DRB1*07:01-DQB1*02:02-DPB1*15:01  Gambia pop 3 1.1611939
 5  A*01:01-B*37:01-C*06:02-DRB1*03:01-DQB1*02:01-DPB1*15:01  Tanzania Maasai 0.6390336
 6  A*29:15-B*58:01-C*04:01-DRB1*08:04-DQA1*05:01-DQB1*03:01-DPB1*15:01  Kenya, Nyanza Province, Luo tribe 0.5000100
 7  A*11:01:01-B*35:03:01-C*01:02:01-DRB1*04:08:01-DQA1*03:03:01-DQB1*06:01:01-DPA1*01:04:01-DPB1*15:01:01  Russian Federation Vologda Region 0.4202119
 8  A*24:02:01-B*39:01:01-C*07:02:01-DRB1*11:01:01-DQA1*05:05:01-DQB1*03:01-DPA1*01:03:01-DPB1*15:01:01  Russian Federation Vologda Region 0.4202119
 9  A*25:01:01-B*18:01-C*07:01:01-DRB1*15:01:01-DQA1*01:02:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*15:01:01  Russian Federation Vologda Region 0.4202119
 10  A*03:01:01-B*58:02:01-C*06:02:01-DRB1*04:01:01-DQB1*03:02:01-DPB1*15:01:01  South African Black 0.3520142
 11  A*68:02:01-B*15:16:01-C*03:04:02-DRB1*03:02:01-DQB1*03:19:01-DPB1*15:01:01  South African Black 0.3520142
 12  A*25:03-B*18:01:01-C*04:01:01-DRB1*07:01:01-DQA1*01:02:01-DQB1*06:02-DPA1*01:03:01-DPB1*15:01:01  Russia Belgorod region 0.3268153
 13  A*68:02-B*41:01-C*17:01-DRB1*03:01-DQB1*02:01-DPB1*15:01  Tanzania Maasai 0.3195336
 14  A*02:01:01-B*45:01:01-C*16:01:01-DRB1*13:02:01-DQB1*05:01:01-DPA1*02:01:08-DPB1*15:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 15  A*02:05:01-B*45:01:01-C*05:01:01-DRB1*13:03:01-DQB1*03:01:01-DPA1*01:04-DPB1*15:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 16  A*03:01:01-B*57:01:01-C*07:02:01-DRB1*03:01:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*15:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 17  A*24:02:01-B*07:02:01-C*07:02:01-DRB1*16:01:01-DQB1*03:03:02-DPA1*01:03:01-DPB1*15:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 18  A*11:01-B*07:02-C*07:02-DRB1*14:04-DQA1*01:02-DQB1*05:03-DPB1*15:01  South Africa Worcester 0.3000159
 19  A*01:01-B*15:01-C*03:03-DRB1*04:01-DQA1*02:01-DQB1*02:02-DPB1*15:01  USA San Diego 0.2600496
 20  A*03:01-B*35:01-C*04:01-DRB1*04:04-DQA1*01:01-DQB1*05:01-DPB1*15:01  USA San Diego 0.2600496
 21  A*03:01-B*57:01-C*07:01-DRB1*15:01-DQA1*02:01-DQB1*03:03-DPB1*15:01  USA San Diego 0.2600496
 22  A*11:01-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04-DPB1*15:01  Russia Karelia 0.22581,075
 23  DRB1*07:01-DQB1*02:01-DPB1*15:01  Gambia pop 3 0.2177939
 24  A*03:01-B*07:02-C*01:02-DRB1*04:07-DQA1*03:01-DQB1*03:01-DPB1*15:01  Nicaragua Managua 0.2165339
 25  A*11:01:01-B*44:02:01-C*04:01:01-DRB1*04:04:01-DQB1*03:02:01-DPA1*01:04-DPB1*15:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 26  A*02:17-B*51:01-C*15:02-DRB1*09:01-DQB1*03:01-DPB1*15:01  Panama 0.1900462
 27  A*01:02-B*41:01-C*17:01-DRB1*13:02-DQB1*06:85-DPB1*15:01  Tanzania Maasai 0.1597336
 28  A*02:02-B*57:03-C*07:371-DRB1*15:03-DQB1*06:02-DPB1*15:01  Tanzania Maasai 0.1597336
 29  A*03:01-B*45:01-C*18:01-DRB1*15:03-DQB1*06:02-DPB1*15:01  Tanzania Maasai 0.1597336
 30  A*29:01-B*44:15-C*07:76-DRB1*15:03-DQB1*06:02-DPB1*15:01  Tanzania Maasai 0.1597336
 31  A*29:02-B*13:02-C*06:27-DRB1*01:01-DQB1*03:01-DPB1*15:01  Tanzania Maasai 0.1597336
 32  A*30:01-B*13:03-C*01:11-DRB1*15:03-DQB1*06:02-DPB1*15:01  Tanzania Maasai 0.1597336
 33  A*30:01-B*57:03-C*01:35-DRB1*15:03-DQB1*06:02-DPB1*15:01  Tanzania Maasai 0.1597336
 34  A*30:02-B*15:03-C*06:04-DRB1*15:03-DQB1*05:01-DPB1*15:01  Tanzania Maasai 0.1597336
 35  A*34:02-B*35:02-C*04:01-DRB1*11:04-DQB1*05:01-DPB1*15:01  Tanzania Maasai 0.1597336
 36  A*11:01-B*15:01-C*04:01-DRB1*15:02-DQA1*01:03-DQB1*06:01-DPB1*15:01  Sri Lanka Colombo 0.1401714
 37  DRB1*03:01-DQB1*02:01-DPB1*15:01  Gambia pop 3 0.0726939
 38  DRB1*09:01-DQB1*02:01-DPB1*15:01  Gambia pop 3 0.0726939
 39  DRB1*10:01-DQB1*02:02-DPB1*15:01  Gambia pop 3 0.0726939
 40  A*02:01-B*35:01-C*04:01-DRB1*15:01-DQA1*01:02-DQB1*05:02-DPB1*15:01  Sri Lanka Colombo 0.0700714
 41  A*02:11-B*35:03-C*04:01-DRB1*04:03-DQA1*05:01-DQB1*03:01-DPB1*15:01  Sri Lanka Colombo 0.0700714
 42  A*02:16-B*35:03-C*12:03-DRB1*14:01-DQA1*01:01-DQB1*05:03-DPB1*15:01  Sri Lanka Colombo 0.0700714
 43  A*03:01-B*07:02-C*07:02-DRB1*16:01-DQA1*01:02-DQB1*06:02-DPB1*15:01  Sri Lanka Colombo 0.0700714
 44  A*03:01-B*35:03-C*04:01-DRB1*14:33-DQA1*01:03-DQB1*06:03-DPB1*15:01  Sri Lanka Colombo 0.0700714
 45  A*03:01-B*40:06-C*15:02-DRB1*14:06-DQA1*03:01-DQB1*03:02-DPB1*15:01  Sri Lanka Colombo 0.0700714
 46  A*11:01-B*51:06-C*14:02-DRB1*14:04-DQA1*03:01-DQB1*03:01-DPB1*15:01  Sri Lanka Colombo 0.0700714
 47  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
 48  A*11:01-B*58:01-C*03:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*15:01  Sri Lanka Colombo 0.0700714
 49  A*24:02-B*51:01-C*07:02-DRB1*15:02-DQA1*05:01-DQB1*05:02-DPB1*15:01  Sri Lanka Colombo 0.0700714
 50  A*24:02-B*51:01-C*15:02-DRB1*15:06-DQA1*01:01-DQB1*05:01-DPB1*15:01  Sri Lanka Colombo 0.0700714
 51  A*24:07-B*35:05-C*04:01-DRB1*15:01-DQA1*01:02-DQB1*06:01-DPB1*15:01  Sri Lanka Colombo 0.0700714
 52  A*26:01-B*51:06-C*14:02-DRB1*07:01-DQA1*01:01-DQB1*05:03-DPB1*15:01  Sri Lanka Colombo 0.0700714
 53  A*31:01-B*18:01-C*04:01-DRB1*13:02-DQA1*01:02-DQB1*06:09-DPB1*15:01  Sri Lanka Colombo 0.0700714
 54  A*33:03-B*35:01-C*04:01-DRB1*13:01-DQA1*01:01-DQB1*06:03-DPB1*15:01  Sri Lanka Colombo 0.0700714
 55  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
 56  A*68:01-B*51:01-C*14:02-DRB1*15:02-DQA1*01:01-DQB1*05:03-DPB1*15:01  Sri Lanka Colombo 0.0700714
 57  DRB1*13:01-DQA1*01:03-DQB1*06:03-DPA1*01:04-DPB1*15:01  China Zhejiang Han pop 2 0.0600833
 58  A*11:01-B*35:03-C*12:03-DRB1*04:08-DQB1*03:04-DPB1*15:01  Germany DKMS - German donors 0.05993,456,066
 59  A*02:01-B*27:05-C*15:11-DRB1*11:01-DQB1*03:01-DPB1*15:01  Russia Karelia 0.05651,075
 60  A*24:02-B*49:01-C*07:01-DRB1*13:02-DQB1*06:04-DPB1*15:01  Russia Karelia 0.05651,075
 61  A*26:01-B*38:01-C*12:03-DRB1*15:01-DQB1*06:02-DPB1*15:01  Russia Karelia 0.05651,075
 62  A*26:01-B*40:02-C*03:04-DRB1*08:03-DQB1*03:01-DPB1*15:01  Russia Karelia 0.05651,075
 63  A*24:02-B*51:01-C*15:02-DRB1*09:01-DQB1*03:03-DPB1*15:01  Russia Karelia 0.05651,075
 64  A*02:01-B*40:01-C*03:04-DRB1*07:01-DQB1*02:01-DPB1*15:01  Russia Karelia 0.05651,075
 65  A*02:01-B*15:01-C*03:04-DRB1*04:08-DQB1*03:04-DPB1*15:01  Russia Karelia 0.05651,075
 66  A*03:01-B*15:01-C*03:04-DRB1*13:03-DQB1*03:01-DPB1*15:01  Russia Karelia 0.05641,075
 67  A*32:01-B*52:01-C*12:02-DRB1*13:01-DQB1*06:03-DPB1*15:01  Russia Karelia 0.05641,075
 68  A*02:01-B*27:05-C*01:02-DRB1*11:04-DQB1*03:01-DPB1*15:01  Russia Karelia 0.05581,075
 69  A*03:01-B*47:01-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*15:01  Germany DKMS - German donors 0.04773,456,066
 70  A*02:05-B*49:01-C*07:01-DRB1*11:02-DQB1*03:01-DPB1*15:01  Germany DKMS - German donors 0.01813,456,066
 71  A*01:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01-DPB1*15:01  Germany DKMS - German donors 0.01713,456,066
 72  A*29:02-B*44:03-C*16:01-DRB1*04:04-DQB1*03:02-DPB1*15:01  Germany DKMS - German donors 0.01213,456,066
 73  A*01:01-B*08:01-C*07:01-DRB1*07:01-DQB1*02:01-DPB1*15:01  Germany DKMS - German donors 0.01053,456,066
 74  A*11:01:01-B*57:01:01-C*06:02:01-DRB1*07:01:01-DPB1*15:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00945,266
 75  A*32:01:01-B*41:02:01-C*03:04:01-DRB1*04:04:01-DPB1*15:01:01  Hong Kong Chinese HKBMDR HLA 11 loci 0.00515,266

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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