Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

Please specify your search by selecting options from boxes. Then, click "Search" to find HLA Haplotype frequencies that match your criteria. Remember at least one option must be selected.
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 67 (from 67) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*04:01  South Korea pop 2 4.7000207
 2  DRB1*13:02-DQB1*06:04-DPB1*04:01  South Korea pop 1 4.2000324
 3  DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*04:01  South Korea pop 1 3.7000324
 4  A*33:03-B*44:03-C*14:03-DRB1*13:02-DQB1*06:04-DPB1*04:01  Japan Central 3.6000371
 5  A*33:03-B*44:03-C*14:03-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 3.13003,078
 6  DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*04:01  South Korea pop 11 2.6000149
 7  A*02:01:01-B*49:01:01-C*07:01:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*02:01:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Black 2.381073
 8  DRB1*13:02-DRB3*03:01-DQA1*01:02-DQB1*06:04-DPB1*04:01  USA San Francisco Caucasian 1.3000220
 9  DRB1*13:02-DRB3*03:01-DQA1*01:02-DQB1*06:04-DPB1*04:01  USA San Francisco Caucasian 1.3000220
 10  DRB1*13:02-DQB1*06:04-DPB1*04:01  Greece pop3 1.0000246
 11  A*29:02:01-B*35:01:01-C*16:01:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Parda 0.5882170
 12  A*26:01:01-B*07:02:01-C*07:02:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.5837521
 13  DRB1*13:02:01-DQB1*06:04-DPB1*04:01:01  China Inner Mongolia Autonomous Region Northeast 0.5730496
 14  A*03:01:01-B*07:02:01-C*07:02:01-DRB1*13:02:01-DQA1*01:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01  Russian Federation Vologda Region 0.4202119
 15  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.3891521
 16  A*68:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Russia Karelia 0.33871,075
 17  A*01:01-B*41:01-C*17:01-DRB1*13:02-DQB1*06:04-DPB1*04:01  Tanzania Maasai 0.3195336
 18  A*01:01:01-B*35:03:01-C*15:02:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 19  A*03:01:01-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 20  A*23:01:01-B*49:01:01-C*07:01:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 21  A*29:02:01-B*41:02:01-C*17:03:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 22  A*24:02:01-B*15:17:01-C*07:01:01-DRB1*13:02:01-DQB1*06:04:01-DPB1*04:01:01  Saudi Arabia pop 6 (G) 0.305028,927
 23  A*03:01-B*40:01-C*03:04-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*04:01  South Africa Worcester 0.3000159
 24  A*03:01-B*07:02-C*07:02-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*04:01  USA San Diego 0.2600496
 25  A*24:02-B*53:01-C*04:01-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*04:01  USA San Diego 0.2600496
 26  A*30:02-B*40:01-C*03:04-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*04:01  USA San Diego 0.2600496
 27  A*68:01-B*35:01-C*04:01-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPB1*04:01  USA San Diego 0.2600496
 28  DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  China Zhejiang Han pop 2 0.2348833
 29  A*02:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.23243,456,066
 30  A*02:01:01-B*40:01:02-C*03:04:01-DRB1*13:02:01-DQB1*06:04:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 31  A*01:01:01-B*15:17:01-C*07:01:01-DRB1*13:02:01-DQB1*06:04:01-DPB1*04:01:01  Saudi Arabia pop 6 (G) 0.177528,927
 32  A*02:01:01-B*49:01:01-C*07:01:01-DRB1*13:02:01-DQB1*06:04:01-DPB1*04:01:01  Saudi Arabia pop 6 (G) 0.168928,927
 33  A*03:01:01-B*51:01:01-C*15:02:01-DRB1*13:02:01-DQB1*06:04:01-DPB1*04:01:01  Saudi Arabia pop 6 (G) 0.168128,927
 34  A*30:01-B*15:10-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Tanzania Maasai 0.1597336
 35  A*31:04-B*45:01-C*15:05-DRB1*13:02-DQB1*06:04-DPB1*04:01  Tanzania Maasai 0.1597336
 36  A*68:02-B*18:01-C*07:01-DRB1*13:02-DQB1*06:04-DPB1*04:01  Tanzania Maasai 0.1597336
 37  A*24:02-B*44:03-C*14:03-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 0.13003,078
 38  A*32:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Russia Karelia 0.11921,075
 39  A*02:01:01-B*51:08-C*16:02:01-DRB1*13:02:01-DQB1*06:04:01-DPB1*04:01:01  Saudi Arabia pop 6 (G) 0.106628,927
 40  A*02:01-B*35:03-C*04:01-DRB1*13:02-DQB1*06:04-DPB1*04:01  Russia Karelia 0.10031,075
 41  A*31:01-B*44:03-C*14:03-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 0.10003,078
 42  A*02:01-B*44:03-C*14:03-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 0.07003,078
 43  DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*02:02-DPB1*04:01  China Zhejiang Han pop 2 0.0653833
 44  A*68:02-B*53:01-C*04:01-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.05953,456,066
 45  A*26:01-B*38:01-C*12:03-DRB1*13:02-DQB1*06:04-DPB1*04:01  Russia Karelia 0.05651,075
 46  A*24:02-B*35:03-C*04:01-DRB1*13:02-DQB1*06:04-DPB1*04:01  Russia Karelia 0.05651,075
 47  A*26:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Russia Karelia 0.05641,075
 48  A*68:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.03623,456,066
 49  A*03:01-B*07:02-C*07:02-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.03263,456,066
 50  A*02:01-B*40:06-C*03:04-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 0.03003,078
 51  A*02:06-B*44:03-C*14:03-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 0.03003,078
 52  A*02:06-B*46:01-C*01:02-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 0.03003,078
 53  A*02:07-B*44:03-C*14:03-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 0.03003,078
 54  A*02:07-B*46:01-C*01:02-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 0.03003,078
 55  A*11:01-B*15:07-C*01:02-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 0.03003,078
 56  A*11:01-B*54:01-C*01:02-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 0.03003,078
 57  A*26:03-B*15:01-C*03:03-DRB1*13:02-DQA1*01:02-DQB1*06:04-DPA1*01:03-DPB1*04:01  Japan pop 17 0.03003,078
 58  A*24:02-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.02993,456,066
 59  A*01:01-B*08:01-C*07:01-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.02773,456,066
 60  A*01:01-B*15:17-C*07:01-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.02433,456,066
 61  A*03:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.02263,456,066
 62  A*01:01-B*35:03-C*04:01-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.01963,456,066
 63  A*68:01-B*44:02-C*07:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.01923,456,066
 64  A*02:01-B*07:02-C*07:02-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.01893,456,066
 65  A*01:01-B*40:01-C*03:04-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.01703,456,066
 66  A*02:01-B*44:02-C*05:01-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.01413,456,066
 67  A*32:01-B*15:17-C*07:01-DRB1*13:02-DQB1*06:04-DPB1*04:01  Germany DKMS - German donors 0.01183,456,066

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.

support@allelefrequencies.net

Valid XHTML 1.0 Transitional