Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

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A B C DRB1 DPA1 DPB1 DQA1 DQB1

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Displaying 1 to 92 (from 92) records   Pages: 1 of 1  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  DRB1*15:03-DQB1*06:02-DPB1*04:02  Central African Republic Aka Pygmy 7.200093
 2  DRB1*15-DQB1*06:02-DPB1*04:02  Congo Kinshasa Bantu 5.600090
 3  DRB1*15:01-DRB5*01:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  USA San Francisco Caucasian 1.8000220
 4  DRB1*15:01-DRB5*01:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  USA San Francisco Caucasian 1.8000220
 5  A*02:01-B*07:02-C*04:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 1.0000100
 6  A*74:01-B*35:01-C*04:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 1.0000100
 7  DRB1*15:01:01:01-DQB1*06:02-DPB1*04:02:01  China Inner Mongolia Autonomous Region Northeast 0.7820496
 8  A*02:01-B*07:02-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  USA San Diego 0.7810496
 9  DQB1*06:02-DPB1*04:02:01  China Inner Mongolia Autonomous Region Northeast 0.6480496
 10  A*02:01:01-B*07:02:01-C*07:02:01-DRB1*15:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Barra Mansa Rio State Caucasian 0.6250405
 11  A*29:01-B*08:01-C*02:02-DRB1*11:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  South Africa Worcester 0.6000159
 12  A*43:01-B*18:01-C*07:04-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  South Africa Worcester 0.6000159
 13  A*02:01:01-B*18:04:01-C*12:12-DRB1*11:04:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Parda 0.5882170
 14  A*31:01:02-B*13:02:01-C*06:02:01-DRB1*15:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Parda 0.5882170
 15  A*33:03:01-B*58:01:01-C*04:01:01-DRB1*11:02:01-DQB1*06:02:01-DPA1*03:01:01-DPB1*04:02:01  Brazil Rio de Janeiro Parda 0.5882170
 16  A*01:03-B*58:01-C*07:01-DRB1*11:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 0.5000100
 17  A*02:01-B*51:01-C*06:02-DRB1*07:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 0.5000100
 18  A*02:01-B*51:01-C*16:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 0.5000100
 19  A*23:01-B*40:12-C*16:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 0.5000100
 20  A*23:01-B*45:01-C*06:02-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 0.5000100
 21  A*30:01-B*45:01-C*06:02-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 0.5000100
 22  A*30:02-B*13:02-C*06:02-DRB1*03:01-DQA1*05:01-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 0.5000100
 23  A*36:01-B*51:01-C*16:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 0.5000100
 24  A*68:02-B*53:01-C*07:02-DRB1*11:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  Kenya, Nyanza Province, Luo tribe 0.5000100
 25  A*03:01-B*07:02-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  Russia Karelia 0.43261,075
 26  DRB1*11:01-DQB1*06:02-DPB1*04:02  Gambia pop 3 0.4211939
 27  A*02:01:01-B*18:01:01-C*02:02:02-DRB1*08:03:02-DQA1*01:02:01-DQB1*06:02-DPA1*01:03:01-DPB1*04:02  Russia Belgorod region 0.3268153
 28  A*24:02:01-B*07:02:01-C*07:02:01-DRB1*15:01:01-DQA1*05:05:01-DQB1*06:02-DPA1*01:03:01-DPB1*04:02  Russia Belgorod region 0.3268153
 29  A*01:01:01-B*08:01:01-C*07:01:01-DRB1*15:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 30  A*03:01:01-B*07:02:01-C*07:01:01-DRB1*15:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 31  A*23:01:01-B*50:01:01-C*07:01:01-DRB1*01:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 32  A*68:01:01-B*15:17:01-C*07:01:02-DRB1*15:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Barra Mansa Rio State Caucasian 0.3125405
 33  A*23:01-B*08:01-C*12:03-DRB1*01:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  South Africa Worcester 0.3000159
 34  A*29:01-B*53:01-C*17:01-DRB1*13:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  South Africa Worcester 0.3000159
 35  A*29:02-B*07:02-C*07:02-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  South Africa Worcester 0.3000159
 36  A*30:04-B*58:02-C*04:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  South Africa Worcester 0.3000159
 37  A*36:01-B*35:01-C*04:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  South Africa Worcester 0.3000159
 38  A*43:01-B*18:01-C*07:04-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  South Africa Worcester 0.3000159
 39  A*68:01-B*58:02-C*06:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  South Africa Worcester 0.3000159
 40  A*68:02-B*14:02-C*08:02-DRB1*15:03-DQA1*02:01-DQB1*06:02-DPB1*04:02  South Africa Worcester 0.3000159
 41  A*02:01-B*40:01-C*02:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  USA San Diego 0.2600496
 42  A*03:01-B*53:01-C*04:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  USA San Diego 0.2600496
 43  A*23:01-B*44:03-C*04:01-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  USA San Diego 0.2600496
 44  A*23:01-B*44:03-C*07:01-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  USA San Diego 0.2600496
 45  A*29:02-B*07:02-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  USA San Diego 0.2600496
 46  A*02:05-B*49:01-C*07:01-DRB1*01:02-DQA1*01:02-DQB1*06:02-DPB1*04:02  Nicaragua Managua 0.2165339
 47  A*02:06-B*35:123-C*04:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Nicaragua Managua 0.2165339
 48  A*03:01-B*07:02-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*04:02  Nicaragua Managua 0.2165339
 49  A*03:01-B*07:02-C*07:02-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Nicaragua Managua 0.2165339
 50  A*68:02-B*15:10-C*03:04-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*04:02  Nicaragua Managua 0.2165339
 51  A*01:01:01-B*49:01:01-C*14:02:01-DRB1*03:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 52  A*03:01:01-B*35:03:01-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 53  A*24:03:01-B*40:02:01-C*03:04:01-DRB1*15:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 54  A*68:01:01-B*51:01:01-C*15:02:01-DRB1*16:01:01-DQB1*06:02:01-DPA1*01:03:01-DPB1*04:02:01  Brazil Rio de Janeiro Caucasian 0.1946521
 55  A*02:01-B*44:03-C*04:01-DRB1*03:01-DQB1*06:02-DPB1*04:02  Panama 0.1900462
 56  A*02:06-B*35:32-C*01:02-DRB1*15:03-DQB1*06:02-DPB1*04:02  Panama 0.1900462
 57  A*29:02-B*49:01-C*07:01-DRB1*15:03-DQB1*06:02-DPB1*04:02  Panama 0.1900462
 58  A*33:01-B*15:01-C*15:05-DRB1*16:02-DQB1*06:02-DPB1*04:02  Panama 0.1900462
 59  A*66:02-B*14:02-C*08:02-DRB1*01:02-DQB1*06:02-DPB1*04:02  Panama 0.1900462
 60  A*03:01-B*07:02-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  Germany DKMS - German donors 0.17053,456,066
 61  A*01:01-B*58:01-C*03:02-DRB1*15:03-DQB1*06:02-DPB1*04:02  Tanzania Maasai 0.1597336
 62  A*24:02-B*13:03-C*06:27-DRB1*03:02-DQB1*06:02-DPB1*04:02  Tanzania Maasai 0.1597336
 63  A*33:03-B*58:02-C*06:02-DRB1*13:01-DQB1*06:02-DPB1*04:02  Tanzania Maasai 0.1597336
 64  DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*04:02  China Zhejiang Han pop 2 0.1580833
 65  A*02:01-B*07:02-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  Germany DKMS - German donors 0.09793,456,066
 66  A*25:01-B*18:01-C*12:03-DRB1*15:01-DQB1*06:02-DPB1*04:02  Russia Karelia 0.09121,075
 67  A*02:01-B*07:02-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  Russia Karelia 0.08401,075
 68  DRB1*15:03-DQB1*06:02-DPB1*04:02  Gambia pop 3 0.0726939
 69  DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*04:02  Hong Kong Chinese HKBMDR. DQ and DP 0.06061,064
 70  DRB1*15:02-DQA1*01:02-DQB1*06:02-DPA1*02:02-DPB1*04:02  China Zhejiang Han pop 2 0.0600833
 71  A*02:01-B*07:04-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  Russia Karelia 0.05651,075
 72  A*02:01-B*07:02-C*03:04-DRB1*15:01-DQB1*06:02-DPB1*04:02  Russia Karelia 0.05651,075
 73  A*30:01-B*41:01-C*17:01-DRB1*15:03-DQB1*06:02-DPB1*04:02  Russia Karelia 0.05641,075
 74  A*11:01-B*55:01-C*03:03-DRB1*15:01-DQB1*06:02-DPB1*04:02  Russia Karelia 0.05641,075
 75  A*01:01-B*37:01-C*06:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  Russia Karelia 0.05641,075
 76  A*68:01-B*07:02-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  Russia Karelia 0.05591,075
 77  A*02:01-B*44:02-C*05:01-DRB1*15:01-DQB1*06:02-DPB1*04:02  Russia Karelia 0.05561,075
 78  A*01:01-B*08:01-C*07:01-DRB1*15:01-DQB1*06:02-DPB1*04:02  Germany DKMS - German donors 0.04363,456,066
 79  A*24:02-B*07:02-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  Germany DKMS - German donors 0.03513,456,066
 80  A*02:01-B*15:01-C*03:03-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 81  A*02:01-B*35:01-C*03:03-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 82  A*02:01-B*51:01-C*15:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 83  A*02:06-B*35:01-C*03:04-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 84  A*24:02-B*07:02-C*07:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 85  A*24:02-B*15:01-C*15:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 86  A*24:02-B*35:01-C*03:03-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*02:02-DPB1*04:02  Japan pop 17 0.03003,078
 87  A*24:02-B*40:01-C*12:02-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 88  A*25:01-B*18:01-C*12:03-DRB1*15:01-DQB1*06:02-DPB1*04:02  Germany DKMS - German donors 0.02683,456,066
 89  A*01:01-B*07:02-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  Germany DKMS - German donors 0.02613,456,066
 90  A*01:01-B*37:01-C*06:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  Germany DKMS - German donors 0.02073,456,066
 91  A*32:01-B*07:02-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  Germany DKMS - German donors 0.01293,456,066
 92  A*11:01-B*07:02-C*07:02-DRB1*15:01-DQB1*06:02-DPB1*04:02  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.

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