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 73 (from 73) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  DRB1*03:01-DQB1*02:01-DPB1*03:01  Ireland South 1.7000250
 2  A*31:01:02-B*27:05:02-C*01:02:01-DRB1*03:01:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Black 1.470668
 3  DRB1*07:01-DQA1*02:01-DQB1*02:01-DPB1*03:01  China Canton Han 1.1000264
 4  DQB1*02:01-DPB1*03:01:01  China Inner Mongolia Autonomous Region Northeast 1.0210496
 5  A*03:01-B*08:01-C*07:01-DRB1*03:01-DQA1*05:01-DQB1*02:01-DPB1*03:01  USA San Diego 0.7810496
 6  A*23:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:01:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.667628,927
 7  A*01:01:01-B*40:01:02-C*03:04:01-DRB1*03:01:01-DQA1*05:01:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*03:01:01  Russia Belgorod region 0.6536153
 8  A*68:02-B*07:02-C*15:05-DRB1*03:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.6390336
 9  A*02:05:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:01:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.599428,927
 10  A*23:01:01-B*38:01:01-C*12:03:01-DRB1*03:01:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Parda 0.5882170
 11  A*74:03-B*18:01-C*05:01-DRB1*03:01-DQA1*01:01-DQB1*02:01-DPB1*03:01  Kenya, Nyanza Province, Luo tribe 0.5000100
 12  A*01:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.45243,456,066
 13  A*26:01:01-B*40:01:02-C*05:01:01-DRB1*03:01-DQA1*01:03:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*03:01  Russian Federation Vologda Region 0.4202119
 14  DRB1*07:01:01:01-DQB1*02:01-DPB1*03:01:01  China Inner Mongolia Autonomous Region Northeast 0.4110496
 15  A*02:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:01:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.346428,927
 16  A*24:02-B*41:01-C*17:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.3195336
 17  A*11:01-B*41:01-C*07:01-DRB1*03:01-DQA1*05:01-DQB1*02:01-DPB1*03:01  South Africa Worcester 0.3000159
 18  A*31:01:02-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:01:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.296728,927
 19  DQA1*05:01-DQB1*02:01-DPA1*01:03-DPB1*03:01  Hong Kong Chinese HKBMDR. DQ and DP 0.28051,064
 20  A*30:02-B*08:01-C*07:01-DRB1*03:01-DQA1*03:01-DQB1*02:01-DPB1*03:01  USA San Diego 0.2600496
 21  A*33:03:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:01:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.250828,927
 22  A*26:01:01-B*08:01:01-C*07:02:01-DRB1*03:01:01-DQB1*02:01:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.233228,927
 23  A*02:01-B*18:05-C*16:01-DRB1*03:01-DQA1*05:01-DQB1*02:01-DPB1*03:01  Nicaragua Managua 0.2165339
 24  A*03:01-B*51:01-C*15:02-DRB1*11:01-DQA1*05:01-DQB1*02:01-DPB1*03:01  Nicaragua Managua 0.2165339
 25  A*02:01:01-B*50:02-C*06:02:01-DRB1*13:05:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 26  A*03:01:01-B*45:01:01-C*04:01:01-DRB1*03:01:01-DQB1*02:01:01-DPA1*01:03:01-DPB1*03:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 27  DRB1*03:01-DQB1*02:01-DPB1*03:01  Gambia pop 3 0.1919939
 28  A*01:01-B*44:02-C*05:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Panama 0.1900462
 29  A*01:01-B*45:01-C*07:328-DRB1*13:02-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 30  A*01:03-B*18:06-C*07:181-DRB1*03:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 31  A*02:01-B*08:01-C*07:328-DRB1*03:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 32  A*02:01-B*15:03-C*16:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 33  A*03:01-B*41:01-C*15:05-DRB1*03:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 34  A*03:01-B*47:01-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 35  A*29:02-B*45:01-C*16:01-DRB1*07:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 36  A*34:02-B*47:01-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Tanzania Maasai 0.1597336
 37  DRB1*03:01:01:01-DQB1*02:01-DPB1*03:01:01  China Inner Mongolia Autonomous Region Northeast 0.1500496
 38  A*01:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Russia Karelia 0.13151,075
 39  A*24:02:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:01:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.128028,927
 40  A*02:01-B*73:01-C*15:05-DRB1*04:05-DQB1*02:01-DPB1*03:01  Russia Karelia 0.11291,075
 41  A*02:05-B*50:01-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Russia Karelia 0.11141,075
 42  A*26:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:01:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.111328,927
 43  A*68:01:01-B*08:01:01-C*07:02:01-DRB1*07:01:01-DQB1*02:01:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.106928,927
 44  A*03:01:01-B*50:01:01-C*06:02:01-DRB1*07:01:01-DQB1*02:01:01-DPB1*03:01:01  Saudi Arabia pop 6 (G) 0.106628,927
 45  DRB1*04:05:01-DQB1*02:01-DPB1*03:01:01  China Inner Mongolia Autonomous Region Northeast 0.1010496
 46  DRB1*03:01-DQA1*05:01-DQB1*02:01-DPA1*01:03-DPB1*03:01  China Zhejiang Han pop 2 0.0953833
 47  A*02:01-B*27:02-C*02:02-DRB1*03:01-DQB1*02:01-DPB1*03:01  Russia Karelia 0.08771,075
 48  A*02:05-B*50:01-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.07313,456,066
 49  DRB1*04:05-DQB1*02:01-DPB1*03:01  Gambia pop 3 0.0726939
 50  A*26:01-B*15:01-C*07:02-DRB1*15:06-DQA1*01:02-DQB1*02:01-DPB1*03:01  Sri Lanka Colombo 0.0700714
 51  A*02:01-B*13:02-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Russia Karelia 0.06381,075
 52  DRB1*03:01-DQA1*05:01-DQB1*02:01-DPA1*04:01-DPB1*03:01  China Zhejiang Han pop 2 0.0600833
 53  A*01:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Russia Karelia 0.05651,075
 54  A*24:02-B*38:01-C*12:03-DRB1*03:01-DQB1*02:01-DPB1*03:01  Russia Karelia 0.05651,075
 55  A*11:01-B*35:02-C*04:01-DRB1*07:01-DQB1*02:01-DPB1*03:01  Russia Karelia 0.05651,075
 56  A*01:01-B*57:01-C*07:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Russia Karelia 0.05581,075
 57  A*02:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.04883,456,066
 58  A*30:01-B*13:02-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.04613,456,066
 59  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.04393,456,066
 60  A*02:01-B*13:02-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.04173,456,066
 61  A*23:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.02803,456,066
 62  A*02:01-B*18:01-C*05:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.02573,456,066
 63  A*03:01-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.02473,456,066
 64  A*03:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.02423,456,066
 65  A*30:02-B*18:01-C*05:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.02383,456,066
 66  A*74:03-B*44:03-C*04:01-DRB1*07:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.01573,456,066
 67  A*02:01-B*50:01-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.01543,456,066
 68  A*02:01-B*73:01-C*15:05-DRB1*04:05-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.01523,456,066
 69  A*03:01-B*18:01-C*05:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.01383,456,066
 70  A*24:02-B*08:01-C*07:01-DRB1*03:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.01293,456,066
 71  A*33:01-B*14:02-C*08:02-DRB1*03:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.01143,456,066
 72  A*02:01-B*50:01-C*06:02-DRB1*03:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.01113,456,066
 73  A*24:02-B*13:02-C*06:02-DRB1*07:01-DQB1*02:01-DPB1*03:01  Germany DKMS - German donors 0.01053,456,066

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.

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