Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

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Population:  Country:  Source of dataset : 
Region:  Ethnic Origin:     Type of study :  Sort by: 
Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 100 (from 4,813) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 49  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*33-B*58-C*03  Pakistan Baloch 11.100066
 2  A*33-B*58  Pakistan Baloch 10.800066
 3  A*33:03-B*44:03-C*14:03  Japan pop 5 10.7000117
 4  A*33-B*44-C*07-DRB1*07  Myanmar Rakhine 9.375048
 5  A*33:03-C*03:02  Taiwan Minnan pop 1 8.3000102
 6  A*33:03-B*58:01  Taiwan Hakka 8.200055
 7  A*33:03-C*03:02  Taiwan Hakka 8.200055
 8  A*33-B*58-DRB1*03  Pakistan Baloch 8.200066
 9  A*33:03-B*58:01  Singapore Chinese 8.0000149
 10  A*33:03-B*58:01  Taiwan Minnan pop 1 7.8000102
 11  A*33:03-B*58:01-C*03:02  China Southwest Dai 7.7000124
 12  A*33-B*44-DRB1*13  Brazil Parana Oriental 7.600033
 13  A*33:03-B*58:01-C*03:02  China Canton Han 7.4000264
 14  A*33-B*44-C*07-DRB1*07  Myanmar Mon 7.031064
 15  A*33-B*08-DRB1*03  Pakistan Burusho 6.700092
 16  A*33-B*14:02-DRB1*01-DQB1*05  Mexico San Luis Potosi, San Luis Potosi city 6.666730
 17  A*33:03-B*58:01-DRB1*03:01  Taiwan Tzu Chi Cord Blood Bank 6.6000710
 18  A*33-B*58-C*03  Malaysia Perak Rawa 6.500023
 19  A*33:03-B*58:01  Hong Kong Chinese 6.4000569
 20  A*33-B*44-DRB1*07  Bangladesh Dhaka Bangalee 6.3000141
 21  A*33:03-B*58:01  USA Asian 6.1000358
 22  A*33:03-B*58:01-DRB1*03:01  Malaysia Patani 6.000025
 23  A*33-B*14:02-DRB1*01  Pakistan Karachi Parsi 6.000091
 24  A*33:03-B*58:01-C*03:02  USA Asian 5.9000358
 25  A*33-B*08  Pakistan Burusho 5.900092
 26  A*33:03-B*58:01-C*03:02  South Korea pop 3 5.8000485
 27  A*33:01-B*14:02-DRB1*01:02  Israel Kavkazi Jews 5.74002,840
 28  A*33-B*44-C*14  South Korea pop 1 5.6000324
 29  A*33-B*44-DRB1*13:02  South Korea pop 1 5.5000324
 30  A*33-B*58-C*03:02  South Korea pop 1 5.4000324
 31  A*33:03-B*44:03-DRB1*07:01  Malaysia Kelantan 5.357128
 32  A*33-B*58-C*03-DRB1*03  Myanmar Bamar 5.319047
 33  A*33-B*58-C*03-DRB1*03-DQB1*02-DPB1*04  Myanmar Bamar 5.319047
 34  A*33-B*08-C*07:02  Pakistan Burusho 5.200092
 35  A*33:03-B*44:06-C*15:02:01  India Mumbai Maratha 5.100091
 36  A*33:03:01-B*44:03:02-C*07:06  South African Indian population 5.000050
 37  A*33:03:01-B*58:01:01-C*03:02:02  South African Indian population 5.000050
 38  A*33:03-B*58:01  Taiwan Thao 5.000030
 39  A*33:03-C*03:02  Taiwan Thao 5.000030
 40  A*33:03-B*53:01  Cuba Mulatto 4.800042
 41  A*33:03-B*44:03-C*14:03  South Korea pop 3 4.7000485
 42  A*33:03-B*58:01-C*03:02-DRB1*03:01  Hong Kong Chinese BMDR 4.64297,595
 43  A*33:03-B*58:01-C*03:02-DRB1*03:01-DRB3*02:02-DQB1*02:01  USA NMDP Chinese 4.60045599,672
 44  A*33:03-B*35:03-DRB1*13:01  China Yunnan Province Wa 4.6000119
 45  A*33:03-B*44:03-DRB1*13:02  South Korea pop 3 4.6000485
 46  A*33:03-B*44:03-DRB1*13:02  South Korea pop 10 4.60004,128
 47  A*33-B*58  Pakistan Burusho 4.600092
 48  A*33-B*58-C*03  Pakistan Burusho 4.600092
 49  A*33-B*58-DRB1*03  Thailand pop 4 4.540016,807
 50  A*33:03-B*58:01  USA Asian pop 2 4.53101,772
 51  A*33:03-B*44:03-C*14:03-DRB1*13:02  Japan pop 16 4.473018,604
 52  A*33:03-B*58:01-C*03:02-DRB1*03:01  China Southwest Dai 4.4000124
 53  A*33:03-B*58:01-DRB1*03:01  China Southwest Dai 4.4000124
 54  A*33-B*14:01-C*08:02  Pakistan Karachi Parsi 4.400091
 55  A*33-B*44-C*07:01  Pakistan Karachi Parsi 4.400091
 56  A*33:01-B*14:02-DRB1*01:02:01  Portugal North 4.300046
 57  A*33-B*58  Pakistan Kalash 4.300069
 58  A*33-B*58-DRB1*03  Pakistan Kalash 4.300069
 59  A*33:03-B*58:01-DRB1*03:01  Hong Kong Chinese cord blood registry 4.200354293,892
 60  A*33:03-B*44:03-C*14:03-DRB1*13:02-DQB1*06:04  South Korea pop 3 4.2000485
 61  A*33-B*58-C*03-DRB1*03  Myanmar Rakhine 4.167048
 62  A*33-B*44-C*14  Brazil Parana Japanese 4.1600192
 63  A*33:01-B*58:01-C*03:02-DRB1*03:01-DQB1*02:01  Malaysia Peninsular Chinese 4.1240194
 64  A*33:01-B*14:02-C*08:02-DRB1*01:02-DQB1*05:01  Tunisia 4.0000100
 65  A*33:03-B*40:01-DRB1*15:02  Malaysia Patani 4.000025
 66  A*33:03-B*44:03-C*14:03-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP Japanese 3.92665524,582
 67  A*33:03-B*58:01-C*03:02-DRB1*03:01  Taiwan pop 2 3.9000364
 68  A*33-B*44-DRB1*07  Pakistan Karachi Parsi 3.900091
 69  A*33:01-B*14:02-DRB1*01:02  Azores Oriental Islands 3.800043
 70  A*33-B*44-DRB1*07  Malaysia pop 3 3.80001,445
 71  A*33-B*58-DRB1*03  United Arab Emirates 3.8000298
 72  A*33-B*44-C*07-DRB1*07  Myanmar Shan 3.704054
 73  A*33-B*58-C*03-DRB1*15  Myanmar Shan 3.704054
 74  A*33:03-B*58:01-DRB1*03:01  China Guangxi Region Maonan 3.7000108
 75  A*33:03-B*58:01-C*03:02-DRB1*03:01-DRB3*02:02-DQB1*02:01  USA NMDP Vietnamese 3.66814743,540
 76  A*33-B*14:02-DRB1*01-DQB1*05  Mexico Hidalgo, Pachuca 3.658541
 77  A*33:03-B*44:03  USA Asian 3.6000358
 78  A*33:03-B*44:03-C*14:03-DRB1*13:02-DQB1*06:04-DPB1*04:01  Japan Central 3.6000371
 79  A*33:03-B*58:01  Taiwan Pazeh 3.600055
 80  A*33:03-C*03:02  Taiwan Pazeh 3.600055
 81  A*33:03-B*18:01-DRB1*07:01  Malaysia Kelantan 3.571428
 82  A*33:03-B*58:01-C*03:02-DRB1*03:01-DQB1*02:01  Vietnam Hanoi Kinh pop 2 3.5000170
 83  A*33-B*35-DRB1*15  Bangladesh Dhaka Bangalee 3.5000141
 84  A*33-B*44-DRB1*15  Bangladesh Dhaka Bangalee 3.5000141
 85  A*33:03-B*44:03-C*14:03-DRB1*13:02-DRB3*03:01-DQB1*06:04  USA NMDP Korean 3.49153977,584
 86  A*33:03-B*44:03-DRB1*07:01  Malaysia Champa 3.448329
 87  A*33:03-B*58:01-DRB1*03:01  Malaysia Champa 3.448329
 88  A*33-B*58  Pakistan Mixed Pathan 3.4000100
 89  A*33-B*14:02-DRB1*01-DQB1*05  Mexico Jalisco, Tlajomulco 3.333330
 90  A*33:03-B*14:05-C*05:09  India West Coast Parsi 3.300050
 91  A*33-B*58-DRB1*03  Pakistan Burusho 3.200092
 92  A*33-B*44-C*07-DRB1*07  Myanmar Bamar 3.191047
 93  A*33-B*14:02-DRB1*01-DQB1*05  Mexico Sonora, Ciudad Obregón 3.1469143
 94  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
 95  A*33:03-B*18:01-DRB1*12:01  Sao Tome Island Angolar 3.100032
 96  A*33:03-B*44:03-C*07:01/07:06  South Korea pop 3 3.1000485
 97  A*33-B*15-DRB1*15  Bangladesh Dhaka Bangalee 3.1000141
 98  A*33-B*58-DRB1*13:02  South Korea pop 1 3.1000324
 99  A*33:01-B*14:01  Tunisia 3.0000100
 100  A*33:01-B*14:02-DRB1*01:02  Armenia combined Regions 3.0000100

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 4,813) records   Pages: 1 2 3 4 5 6 7 8 9 10 of 49  


   

Allele frequency net 2015 update: new features for HLA epitopes, KIR and disease and HLA adverse drug reaction associations.
Gonzalez-Galarza FF, Takeshita LY, Santos EJ, Kempson F, Maia MH, Silva AL, Silva AL, Ghattaoraya GS, Alfirevic A, Jones AR and Middleton D Nucleic Acid Research 2015, 39, 28, D784-8.
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