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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Displaying 1 to 100 (from 498) records   Pages: 1 2 3 4 5 of 5  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 5.40472,403
 2  A*29-B*44-DRB1*07:01-DRB4*01:01-DQA1*02:01-DQB1*02:02  Spain Murcia 5.1000173
 3  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Tunisia 4.0000100
 4  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Colombia North Wiwa El Encanto 3.846252
 5  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Northeast UCBB 3.7162296
 6  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 3.42004,335
 7  B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 3.1469143
 8  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Tunisia 3.0000100
 9  B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Ireland South 3.0000250
 10  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 2.7972143
 11  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India West UCBB 2.79385,829
 12  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 2.58154,204
 13  A*23:01-B*44:03-C*04:01-DRB1*07:01-DQA1*02:01-DQB1*02:02  Kosovo 2.4190124
 14  A*02-B*44-DRB1*07:01-DQB1*02:02  Tunisia Ghannouch 2.400082
 15  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India South UCBB 2.381211,446
 16  A*02:01:01-B*44:03:01-C*15:05:02-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:01:01-DPB1*13:01:01  Brazil Barra Mansa Rio State Black 2.381073
 17  A*33:03-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 2.18365,849
 18  A*29-B*44-DRB1*07:01-DQA1*02:01-DQB1*02:02  Brazil Paraná Caucasian 2.1611641
 19  A*02:11-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 1.89865,849
 20  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Spain, Canary Islands, Gran canaria island 1.8600215
 21  A*02:11-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Northeast UCBB 1.8581296
 22  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*11:01  USA San Diego 1.8230496
 23  A*23:01:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*02:01:02  Brazil Rio de Janeiro Parda 1.7647170
 24  A*33:03:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 1.7240174
 25  A*33:03:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 1.6150356
 26  A*68:02-B*44:02-DRB1*07:01-DQB1*02:02  Tunisia Gabes 1.570095
 27  A*69:01-B*44:02-DRB1*07:01-DQB1*02:02  Tunisia Gabes 1.570095
 28  A*02-B*44-DRB1*07:01-DRB4*01:01-DQA1*02:01-DQB1*02:02  Spain Murcia 1.5000173
 29  A*02:01:01-B*44:03:01-C*16:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*02:01:02  Brazil Rio de Janeiro Black 1.470668
 30  A*30:02:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*03:01:01-DPB1*105:01:01  Brazil Rio de Janeiro Black 1.470668
 31  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*02:01  Sri Lanka Colombo 1.4006714
 32  A*02:01-B*44:02-DRB1*07:01-DQB1*02:02  USA South Dakota Lakota Sioux 1.4000302
 33  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Italy pop 5 1.3300975
 34  A*02:11-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 1.32552,403
 35  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Ireland South 1.3000250
 36  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  Nicaragua Managua 1.2987339
 37  B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo pop 2 1.2800234
 38  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  Sri Lanka Colombo 1.2605714
 39  A*30:01-B*44-DRB1*07:01-DQB1*02:02  Tunisia pop 3 1.2000104
 40  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:01:04-DPB1*13:01:01  Brazil Rio de Janeiro Parda 1.1765170
 41  A*02:11-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 1.15544,204
 42  A*24:02:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 1.1490174
 43  A*29:01-B*44:02-DRB1*07:01-DQB1*02:02  Mexico Chihuahua Chihuahua City Pop 2 1.136488
 44  A*33:03:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 1.1208186
 45  A*33:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Indian 1.1070271
 46  A*33:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 1.1041951
 47  B*44:03-C*04:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo population 1.0490143
 48  A*29:02:01:01-B*44:03:01-C*16:01:01-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Tatars 1.0417192
 49  A*23:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:02  Poland 1.0000200
 50  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Caucasian 0.9727521
 51  A*33:18-B*44:45-C*04:01-DRB1*07:01-DQB1*02:02  Colombia North Wiwa El Encanto 0.961552
 52  A*02:01-B*44:03-C*04:01-DRB1*07:01-DQA1*02:01-DQB1*02:02  United Arab Emirates Abu Dhabi 0.960052
 53  A*11:01-B*44:03-C*04:01-DRB1*07:01-DQA1*02:01-DQB1*02:02  United Arab Emirates Abu Dhabi 0.960052
 54  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02-DPB1*02:01  Panama 0.9500462
 55  A*23:01:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.937323,595
 56  A*03:01-B*44:02-C*05:01-E*01:03:02-F*01:01:01-G*01:01-DRB1*07:01-DQA1*02:01-DQB1*02:02  Portugal Azores Terceira Island 0.8772130
 57  A*23:01-B*44:03-C*04:01-DRB1*07:01-DQB1*02:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.85504,335
 58  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Malay 0.8403951
 59  A*11:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 0.82892,403
 60  A*23:01:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02  Russia Nizhny Novgorod, Russians 0.82201,510
 61  A*01:01:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 0.8213186
 62  A*02:01-B*44:03-C*14:03-DRB1*07:01-DQB1*02:02-DPB1*14:01  Tanzania Maasai 0.7987336
 63  A*02:01:01:01-B*44:03:01-C*04:01:01-DRB1*07:01:01:01-DQB1*02:02  Russia Bashkortostan, Tatars 0.7812192
 64  A*01:01-B*44:03-C*04:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  USA San Diego 0.7810496
 65  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*02:01:01-DPB1*11:01:01  Brazil Rio de Janeiro Caucasian 0.7782521
 66  A*24:02-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 0.71115,849
 67  A*33:03:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 0.7020356
 68  A*02:01:01-B*44:02:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 69  A*29:01:01-B*44:08-C*16:01:01-DRB1*07:01:01-DQB1*02:02  Mexico Hidalgo Mezquital Valley/ Otomi 0.694472
 70  A*11:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Northeast UCBB 0.6757296
 71  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01  Poland BMR 0.667623,595
 72  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*13:01  Sri Lanka Colombo 0.6303714
 73  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.6270405
 74  A*23-B*44-DRB1*07:01-DQA1*02:01-DQB1*02:02  Brazil Paraná Caucasian 0.6240641
 75  A*23:01:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Barra Mansa Rio State Caucasian 0.6230405
 76  A*11:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 0.60944,204
 77  A*29:02:01-B*44:03:01-C*16:01:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Parda 0.5882170
 78  A*33:01:01-B*44:03:01-C*08:02:01-DRB1*07:01:01-DQB1*02:02:01-DPA1*01:03:01-DPB1*04:01:01  Brazil Rio de Janeiro Parda 0.5882170
 79  A*01:01:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 0.5750174
 80  A*02:11:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 0.5750174
 81  A*11:01:01-B*44:03:01-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Karnataka Kannada Speaking 0.5750174
 82  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02-DPB1*01:01  Panama 0.5700462
 83  A*02:01-B*44:02-DRB1*07:01-DQB1*02:02  Mexico Chihuahua Chihuahua City Pop 2 0.568288
 84  A*33:03-B*44:03-C*07:01-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:02  Sri Lanka Colombo 0.5602714
 85  A*01:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Indian 0.5535271
 86  A*11:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India West UCBB 0.54275,829
 87  A*33:01-B*44:03-C*07:01-DRB1*07:01-DQB1*02:02  Malaysia Peninsular Chinese 0.5155194
 88  A*11:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India North UCBB 0.49105,849
 89  A*24:02:01-B*44:03:02-C*07:01:01-DRB1*07:01:01-DQB1*02:02:01  India Kerala Malayalam speaking 0.4910356
 90  A*24:02-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India Central UCBB 0.48744,204
 91  A*02:11-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India West UCBB 0.48375,829
 92  A*01:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India West UCBB 0.46755,829
 93  A*68:02-B*44:03-C*16:01-E*01:03:02-F*01:01:01-G*01:01-DRB1*07:01-DQA1*01:03-DQB1*02:02  Portugal Azores Terceira Island 0.4386130
 94  A*24:02:01-B*44:03:02-C*07:06-DRB1*07:01:01-DQB1*02:02:01  India Andhra Pradesh Telugu Speaking 0.4345186
 95  A*03:01-B*44:03-C*12:03-DRB1*07:01-DQA1*02:01-DQB1*02:02-DPB1*04:01  Nicaragua Managua 0.4329339
 96  A*02:01-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo pop 2 0.4274234
 97  A*29:02-B*44:03-C*16:01-DRB1*07:01-DQB1*02:02  Mexico Mexico City Mestizo pop 2 0.4274234
 98  A*01:01-B*44:03-C*07:06-DRB1*07:01-DQB1*02:02  India East UCBB 0.42042,403
 99  A*03:01:01-B*44:02:01-C*05:01:01-DRB1*07:01:01-DQA1*02:01:01-DQB1*02:02-DPA1*01:03:01-DPB1*04:02  Russian Federation Vologda Region 0.4202119
 100  A*23:01:01-B*44:03:01-C*04:01:01-DRB1*07:01:01-DQA1*02:01:01-DQB1*02:02-DPA1*01:03:01-DPB1*13:01:01  Russian Federation Vologda Region 0.4202119

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 498) records   Pages: 1 2 3 4 5 of 5  


   

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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