Allele Frequencies in World Populations

HLA > Allele Frequency Search > Classical

Please specify your search by selecting options from boxes. Then, click "Search" to find HLA allele frequencies that match your criteria. Remember at least one option must be selected.
Locus:  Starting Allele:  Ending Allele:  �� (Type your allele e.g. A*01:01, etc. or leave both empty to include all alleles)
Select specific alleles (If you want to pick specific alleles, make sure your alleles are within the Start-End range above)
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Select specific populations
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Population:      Country:      Source of dataset: 
Region:  Ethnic Origin:     Type of Study:  Sort by: 
Sample Size:      Sample Year:      Level of resolution :   (Click here for further details)
Population standard: Gold only Gold and Silver All Show frequencies: All Only positives Only negatives
   

Note: The aim of this search option is to display frequencies organised by population. If your selected criteria retrieves alleles that have been found in more than one hundred populations the number of columns will be limited. For a best view please reduce the number of populations to compare.


Line Allele
Argentina Rosario Toba (n=86)
Australia Cape York Peninsula Aborigine (n=103)
Australia New South Wales Caucasian (n=134)
Australia Yuendumu Aborigine (n=191)
Austria (n=200)
Azores Central Islands (n=59)
Azores Oriental Islands (n=43)
Azores Terceira Island (n=130)
Brazil Belo Horizonte Caucasian (n=95)
Brazil Mixed (n=108)
Brazil Vale do Ribeira Quilombos (n=144)
Bulgaria (n=55)
Burkina Faso Fulani (n=49)
Burkina Faso Mossi (n=53)
Burkina Faso Rimaibe (n=47)
Cameroon Baka Pygmy (n=10)
Cameroon Bakola Pygmy (n=50)
Cameroon Bamileke (n=77)
Cameroon Beti (n=174)
Cameroon Sawa (n=13)
Cameroon Yaounde (n=92)
Cape Verde Northwestern Islands (n=62)
Cape Verde Southeastern Islands (n=62)
Central African Republic Mbenzele Pygmy (n=36)
Chile Mapuche (n=66)
Chile Santiago Mixed (n=70)
China Beijing (n=67)
China Beijing Shijiazhuang Tianjian Han (n=618)
China Canton Han (n=264)
China Guangdong Province Meizhou Han (n=100)
China Guangxi Region Maonan (n=108)
China Guangzhou (n=102)
China Guangzhou Han (n=106)
China Guizhou Province Bouyei (n=109)
China Guizhou Province Miao pop 2 (n=85)
China Guizhou Province Shui (n=153)
China Han HIV negative (n=72)
China Henan HIV negative (n=16)
China Hubei Han (n=3732)
China Inner Mongolia Region (n=102)
China Jiangsu Han (n=3238)
China Jiangsu Province Han (n=334)
China North Han (n=105)
China Qinghai Province Hui (n=110)
China Shanxi HIV negative (n=22)
China Sichuan HIV negative (n=34)
China South Han (n=284)
China Southwest Dai (n=124)
China Tibet Region Tibetan (n=158)
China Yunnan Bulang (n=116)
China Yunnan Hani (n=150)
China Yunnan Province Han (n=101)
China Yunnan Province Jinuo (n=109)
China Yunnan Province Lisu (n=111)
China Yunnan Province Nu (n=107)
China Yunnan Province Wa (n=119)
Colombia Bogotá Cord Blood (n=1463)
Croatia (n=150)
Croatia pop 4 (n=4000)
Cuba Caucasian (n=70)
Cuba Mixed Race (n=42)
Czech Republic (n=106)
Czech Republic NMDR (n=5099)
England North West (n=298)
France Corsica Island (n=100)
France French Bone Marrow Donor Registry (n=42623)
France Southeast (n=130)
Gaza (n=42)
Georgia Tibilisi (n=109)
Germany DKMS - Austria minority (n=1698)
Germany DKMS - Bosnia and Herzegovina minority (n=1028)
Germany DKMS - China minority (n=1282)
Germany DKMS - Croatia minority (n=2057)
Germany DKMS - France minority (n=1406)
Germany DKMS - German donors (n=3456066)
Germany DKMS - Greece minority (n=1894)
Germany DKMS - Italy minority (n=1159)
Germany DKMS - Netherlands minority (n=1374)
Germany DKMS - Portugal minority (n=1176)
Germany DKMS - Romania minority (n=1234)
Germany DKMS - Spain minority (n=1107)
Germany DKMS - Turkey minority (n=4856)
Germany DKMS - United Kingdom minority (n=1043)
Germany pop 6 (n=8862)
Germany pop 8 (n=39689)
Ghana Ga-Adangbe (n=131)
Greece pop 8 (n=83)
Guatemala Mayan (n=132)
Guinea Bissau (n=65)
Hong Kong Chinese (n=569)
Hong Kong Chinese BMDR (n=7595)
Hong Kong Chinese cord blood registry (n=3892)
India Andhra Pradesh Golla (n=111)
India Delhi pop 2 (n=90)
India Khandesh Region Pawra (n=50)
India Mumbai Maratha (n=91)
India New Delhi (n=71)
India North pop 2 (n=72)
India Tamil Nadu (n=2492)
India UCBB_Central Indian HLA (n=4204)
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1B*58:010.0060.0100.0490.0000.0080.0090.0260.0120.0210.0220.0000.0180.0610.0570.0430.1500.0520.0370.1150.0540.0320.0400.0150.0230.0600.0890.1700.0420.0850.0470.0830.0480.0150.0630.0630.0620.0880.0710.0710.0290.0230.0230.0880.0890.0770.0160.0040.0270.0740.0050.0070.0190.0170.0170.0130.0110.0290.0480.0140.006860.0050.0450.0100.0160.0480.0140.0120.009730.0590.0120.0120.007890.0150.0200.007280.0160.009720.0130.0180.004790.008090.009040.0420.0240.0070.0780.0730.0890.0840.0720.0880.1500.0740.0680.0580.0370.039
Totals0.0060.0100.0490.0000.0080.0090.0260.0120.0210.0220.0000.0180.0610.0570.0430.1500.0000.0520.0370.1150.0540.0320.0400.0000.0150.0000.0230.0600.0890.1700.0420.0850.0470.0830.0480.0150.0630.0630.0620.0880.0710.0710.0290.0230.0230.0880.0890.0770.0160.0040.0270.0740.0050.0070.0190.0170.0170.0130.0110.0290.0480.0140.0070.0050.0450.0100.0160.0480.0140.0120.0100.0590.0120.0120.0080.0150.0200.0070.0160.0100.0130.0180.0050.0080.0090.0420.0240.0070.0780.0730.0890.0840.0720.0880.1500.0740.0680.0580.0370.039
Number of Alleles (k)1111111111111111011111101011111111111111111111111111111111111111111111111111111111111111111111111111
Heterozygosity (h)1.00581.00481.00131.00261.00241.00851.01111.00371.00481.00421.00351.00881.00651.00621.00891.02891.01011.00381.00151.02621.00251.00711.00651.01411.00741.00721.00700.99720.99400.97601.00290.99771.00250.99771.00361.00311.00301.02820.99630.99710.99510.99651.00391.00401.02271.00710.99380.99811.00291.00431.00260.99951.00461.00451.00431.00391.00011.00321.00001.00631.00971.00451.00011.00171.00300.99991.00361.00971.00441.00021.00040.99691.00011.00020.99991.00001.00001.00031.00021.00031.00030.99981.00051.00000.99991.00211.00551.00381.00160.99550.99210.99310.99930.99780.98741.00001.00241.00360.99880.9986
   

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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