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 100 (from 102) records   Pages: 1 2 of 2  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*68:01-B*35:01-DRB1*04:03-DQB1*03:02  Chile Mapuche 2.310066
 2  A*31:01-B*35:01-C*07:02-DRB1*04:03-DQA1*03:01-DQB1*03:02  Mexico Tixcacaltuyub Maya 1.492567
 3  A*24:02-B*35:01-DRB1*04:03-DQB1*03:02  Peru Titikaka Lake Uros 1.3700105
 4  A*02:06-B*35:01-C*04:01-DRB1*04:03-DQA1*03:01-DQB1*03:02  Mexico Chichen Itza Maya (prehispanic) 1.063847
 5  A*02:01-B*35:01-DRB1*04:03-DQB1*03:02  Peru Titikaka Lake Uros 1.0100105
 6  A*24:11N-B*35:01-C*12:02-DRB1*04:03-DQA1*01:02-DQB1*03:02  United Arab Emirates Abu Dhabi 0.960052
 7  A*68:01-B*35:01-DRB1*04:03-DQB1*03:02  Mexico Veracruz Xalapa 0.595284
 8  A*03:01:01-B*35:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01  India Karnataka Kannada Speaking 0.5750174
 9  A*26:01:01-B*35:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01  India Karnataka Kannada Speaking 0.5750174
 10  A*68:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Indian 0.5535271
 11  A*24:02:01-B*35:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01  India Andhra Pradesh Telugu Speaking 0.5376186
 12  A*24:02:01-B*35:01:01-C*03:03:01-DRB1*04:03:01-DQB1*03:02:01  Vietnam Kinh 0.4950101
 13  A*02:06-B*35:01-C*07:02-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.4587218
 14  A*02:01-B*35:01-DRB1*04:03-DQB1*03:02  Mexico Mexico City Tlalpan 0.4545330
 15  A*11:01-B*35:01-C*04:01-DRB1*04:03-DQA1*03:01-DQB1*03:02  Kosovo 0.4030124
 16  A*32:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Indian 0.3690271
 17  A*11:01:01-B*35:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.3110356
 18  A*29:01:01-B*35:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01  India Karnataka Kannada Speaking 0.2870174
 19  A*33:03:01-B*35:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.2510356
 20  A*68:03-B*35:01-C*07:02-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Mexico Chiapas Lacandon Mayans 0.2294218
 21  A*03:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.22402,492
 22  A*02:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  USA NMDP Alaska Native or Aleut 0.21801,376
 23  A*32:01-B*35:01-C*04:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPB1*02:01  Nicaragua Managua 0.2165339
 24  A*02:06-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  USA NMDP Alaska Native or Aleut 0.21001,376
 25  A*11:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India UCBB_Central Indian HLA 0.20734,204
 26  A*32:01:01-B*35:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.1946521
 27  A*02:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02-DPB1*04:01  Panama 0.1900462
 28  A*24:02-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02-DPB1*04:02  Panama 0.1900462
 29  A*02:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Indian 0.1845271
 30  A*26:01-B*35:01-DRB1*04:03-DQB1*03:02  Mexico Mexico City Tlalpan 0.1515330
 31  A*24:07:01-B*35:01:01-C*12:02:01-DRB1*04:03:01-DQB1*03:02:01  India Kerala Malayalam speaking 0.1400356
 32  A*26:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.10012,492
 33  A*26:03-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.10003,078
 34  A*26:03-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.10003,078
 35  A*03:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India UCBB_Central Indian HLA 0.08144,204
 36  A*24:02-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.07962,492
 37  A*33:03-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.07472,492
 38  A*11:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.07312,492
 39  A*03:01-B*35:01-C*04:01-DRB1*04:03-DQA1*01:01-DQB1*03:02-DPB1*04:02  Sri Lanka Colombo 0.0700714
 40  A*02:01-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.07003,078
 41  A*02:06-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.07003,078
 42  A*26:01-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.07003,078
 43  A*26:01-B*35:01-C*08:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*05:01  Japan pop 17 0.07003,078
 44  A*33:03-B*35:01-C*08:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*05:01  Japan pop 17 0.07003,078
 45  A*24:02-B*35:01-C*03:03-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.05401,772
 46  A*11:01-B*35:01-C*08:01-DRB1*04:03-DQB1*03:02  Malaysia Peninsular Malay 0.0526951
 47  A*03:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  USA Hispanic pop 2 0.04701,999
 48  A*24:02-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.04401,772
 49  A*24:02-B*35:01-C*08:01-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.04401,772
 50  A*26:02-B*35:01-C*08:01-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.04401,772
 51  A*31:01-B*35:01-C*03:03-DRB1*04:03-DQB1*03:02  USA Asian pop 2 0.04401,772
 52  A*24:02-B*35:01-C*03:03-DRB1*04:03-DQB1*03:02  Germany DKMS - Italy minority 0.04301,159
 53  A*26:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  Germany DKMS - Turkey minority 0.04304,856
 54  A*32:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.04242,492
 55  A*02:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  Germany DKMS - Turkey minority 0.04104,856
 56  A*32:02-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.03942,492
 57  A*34:02-B*35:01-C*07:01-DRB1*04:03-DQB1*03:02  Colombia Bogotá Cord Blood 0.03421,463
 58  A*11:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.03404,335
 59  A*02:01-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*05:01  Japan pop 17 0.03003,078
 60  A*02:01-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*09:01  Japan pop 17 0.03003,078
 61  A*02:01-B*35:01-C*08:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*05:01  Japan pop 17 0.03003,078
 62  A*02:06-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 63  A*02:06-B*35:01-C*03:04-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 64  A*02:06-B*35:01-C*08:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*05:01  Japan pop 17 0.03003,078
 65  A*02:06-B*35:01-C*08:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 66  A*02:07-B*35:01-C*08:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*05:01  Japan pop 17 0.03003,078
 67  A*11:01-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 68  A*24:02-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*02:01  Japan pop 17 0.03003,078
 69  A*24:02-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*05:01  Japan pop 17 0.03003,078
 70  A*24:02-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 71  A*24:02-B*35:01-C*08:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*05:01  Japan pop 17 0.03003,078
 72  A*24:02-B*35:01-C*08:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.03003,078
 73  A*24:02-B*35:01-C*08:01-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 74  A*26:01-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*03:01  Japan pop 17 0.03003,078
 75  A*26:01-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*05:01  Japan pop 17 0.03003,078
 76  A*26:01-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:01-DPB1*13:01  Japan pop 17 0.03003,078
 77  A*26:01-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*03:01  Japan pop 17 0.03003,078
 78  A*26:03-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 79  A*31:01-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*01:03-DPB1*04:02  Japan pop 17 0.03003,078
 80  A*31:01-B*35:01-C*03:03-DRB1*04:03-DQA1*03:01-DQB1*03:02-DPA1*02:02-DPB1*05:01  Japan pop 17 0.03003,078
 81  A*24:02:01-B*35:01:01-C*03:03:01-DRB1*04:03:01-DQB1*03:02:01  China Zhejiang Han 0.02881,734
 82  A*11:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  Germany DKMS - Turkey minority 0.02804,856
 83  A*24:02-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  Germany DKMS - Turkey minority 0.02804,856
 84  A*24:07-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India UCBB_Central Indian HLA 0.02584,204
 85  A*03:08-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.02502,492
 86  A*01:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.02452,492
 87  A*68:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India UCBB_Central Indian HLA 0.02444,204
 88  A*24:02-B*35:01-C*03:03-DRB1*04:03-DQB1*03:02  Germany DKMS - Turkey minority 0.02104,856
 89  A*01:01-B*35:01-C*12:03-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.02012,492
 90  A*68:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India Tamil Nadu 0.01972,492
 91  A*32:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  Germany DKMS - Turkey minority 0.01604,856
 92  A*02:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India UCBB_Central Indian HLA 0.01504,204
 93  A*24:02-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India UCBB_Central Indian HLA 0.01334,204
 94  A*02:11-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  India UCBB_Central Indian HLA 0.01234,204
 95  A*03:01-B*35:01-C*04:01-DRB1*04:03-DQB1*03:02  Germany DKMS - Turkey minority 0.01204,856
 96  A*02:01:01-B*35:01:01-C*03:03:01-DRB1*04:03:01-DQB1*03:02:01  Poland BMR 0.010723,595
 97  A*03:01-B*35:01-C*03:03-DRB1*04:03-DQB1*03:02  Germany DKMS - Turkey minority 0.01004,856
 98  A*01:01:01-B*35:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01  Poland BMR 0.006323,595
 99  A*24:02:01-B*35:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01  Poland BMR 0.004623,595
 100  A*02:01:01-B*35:01:01-C*04:01:01-DRB1*04:03:01-DQB1*03:02:01  Poland BMR 0.002223,595

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 102) records   Pages: 1 2 of 2  


   

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.
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