Significant variation between SNP-based HLA imputations in diverse populations: the last mile is the hardest.
Identifieur interne : 000363 ( PubMed/Checkpoint ); précédent : 000362; suivant : 000364Significant variation between SNP-based HLA imputations in diverse populations: the last mile is the hardest.
Auteurs : D J Pappas [États-Unis] ; A. Lizee [États-Unis] ; V. Paunic [États-Unis] ; K R Beutner [États-Unis] ; A. Motyer [Australie] ; D. Vukcevic [Australie] ; S. Leslie [Australie] ; J. Biesiada [États-Unis] ; J. Meller [États-Unis] ; K D Taylor [États-Unis] ; X. Zheng [États-Unis] ; L P Zhao [États-Unis] ; P-A Gourraud [États-Unis] ; J A Hollenbach [États-Unis] ; S J Mack [États-Unis] ; M. Maiers [États-Unis]Source :
- The pharmacogenomics journal [ 1473-1150 ] ; 2017.
Abstract
Four single nucleotide polymorphism (SNP)-based human leukocyte antigen (HLA) imputation methods (e-HLA, HIBAG, HLA*IMP:02 and MAGPrediction) were trained using 1000 Genomes SNP and HLA genotypes and assessed for their ability to accurately impute molecular HLA-A, -B, -C and -DRB1 genotypes in the Human Genome Diversity Project cell panel. Imputation concordance was high (>89%) across all methods for both HLA-A and HLA-C, but HLA-B and HLA-DRB1 proved generally difficult to impute. Overall, <27.8% of subjects were correctly imputed for all HLA loci by any method. Concordance across all loci was not enhanced via the application of confidence thresholds; reliance on confidence scores across methods only led to noticeable improvement (+3.2%) for HLA-DRB1. As the HLA complex is highly relevant to the study of human health and disease, a standardized assessment of SNP-based HLA imputation methods is crucial for advancing genomic research. Considerable room remains for the improvement of HLA-B and especially HLA-DRB1 imputation methods, and no imputation method is as accurate as molecular genotyping. The application of large, ancestrally diverse HLA and SNP reference data sets and multiple imputation methods has the potential to make SNP-based HLA imputation methods a tractable option for determining HLA genotypes.The Pharmacogenomics Journal advance online publication, 25 April 2017; doi:10.1038/tpj.2017.7.
DOI: 10.1038/tpj.2017.7
PubMed: 28440342
Affiliations:
- Australie, États-Unis
- Californie, Minnesota, Ohio, Victoria (État), Washington (État)
- Melbourne, Seattle
- Université de Melbourne, Université de Washington
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<front><div type="abstract" xml:lang="en">Four single nucleotide polymorphism (SNP)-based human leukocyte antigen (HLA) imputation methods (e-HLA, HIBAG, HLA*IMP:02 and MAGPrediction) were trained using 1000 Genomes SNP and HLA genotypes and assessed for their ability to accurately impute molecular HLA-A, -B, -C and -DRB1 genotypes in the Human Genome Diversity Project cell panel. Imputation concordance was high (>89%) across all methods for both HLA-A and HLA-C, but HLA-B and HLA-DRB1 proved generally difficult to impute. Overall, <27.8% of subjects were correctly imputed for all HLA loci by any method. Concordance across all loci was not enhanced via the application of confidence thresholds; reliance on confidence scores across methods only led to noticeable improvement (+3.2%) for HLA-DRB1. As the HLA complex is highly relevant to the study of human health and disease, a standardized assessment of SNP-based HLA imputation methods is crucial for advancing genomic research. Considerable room remains for the improvement of HLA-B and especially HLA-DRB1 imputation methods, and no imputation method is as accurate as molecular genotyping. The application of large, ancestrally diverse HLA and SNP reference data sets and multiple imputation methods has the potential to make SNP-based HLA imputation methods a tractable option for determining HLA genotypes.The Pharmacogenomics Journal advance online publication, 25 April 2017; doi:10.1038/tpj.2017.7.</div>
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<ArticleTitle>Significant variation between SNP-based HLA imputations in diverse populations: the last mile is the hardest.</ArticleTitle>
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<Abstract><AbstractText>Four single nucleotide polymorphism (SNP)-based human leukocyte antigen (HLA) imputation methods (e-HLA, HIBAG, HLA*IMP:02 and MAGPrediction) were trained using 1000 Genomes SNP and HLA genotypes and assessed for their ability to accurately impute molecular HLA-A, -B, -C and -DRB1 genotypes in the Human Genome Diversity Project cell panel. Imputation concordance was high (>89%) across all methods for both HLA-A and HLA-C, but HLA-B and HLA-DRB1 proved generally difficult to impute. Overall, <27.8% of subjects were correctly imputed for all HLA loci by any method. Concordance across all loci was not enhanced via the application of confidence thresholds; reliance on confidence scores across methods only led to noticeable improvement (+3.2%) for HLA-DRB1. As the HLA complex is highly relevant to the study of human health and disease, a standardized assessment of SNP-based HLA imputation methods is crucial for advancing genomic research. Considerable room remains for the improvement of HLA-B and especially HLA-DRB1 imputation methods, and no imputation method is as accurate as molecular genotyping. The application of large, ancestrally diverse HLA and SNP reference data sets and multiple imputation methods has the potential to make SNP-based HLA imputation methods a tractable option for determining HLA genotypes.The Pharmacogenomics Journal advance online publication, 25 April 2017; doi:10.1038/tpj.2017.7.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Pappas</LastName>
<ForeName>D J</ForeName>
<Initials>DJ</Initials>
<AffiliationInfo><Affiliation>Center for Genetics, Children's Hospital Research Institute, Oakland, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Lizee</LastName>
<ForeName>A</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Department of Neurology, University of California, San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Paunic</LastName>
<ForeName>V</ForeName>
<Initials>V</Initials>
<AffiliationInfo><Affiliation>Bioinformatics Research Department, National Marrow Donor Program, Minneapolis, MN, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Beutner</LastName>
<ForeName>K R</ForeName>
<Initials>KR</Initials>
<AffiliationInfo><Affiliation>Department of Neurology, University of California, San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Motyer</LastName>
<ForeName>A</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Centre for Systems Genomics, Schools of Mathematics and Statistics, and BioSciences, The University of Melbourne, Melbourne, VIC, Australia.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Data Science, Murdoch Childrens Research Institute, Melbourne, VIC, Australia.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Vukcevic</LastName>
<ForeName>D</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Centre for Systems Genomics, Schools of Mathematics and Statistics, and BioSciences, The University of Melbourne, Melbourne, VIC, Australia.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Data Science, Murdoch Childrens Research Institute, Melbourne, VIC, Australia.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Leslie</LastName>
<ForeName>S</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Centre for Systems Genomics, Schools of Mathematics and Statistics, and BioSciences, The University of Melbourne, Melbourne, VIC, Australia.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Data Science, Murdoch Childrens Research Institute, Melbourne, VIC, Australia.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Biesiada</LastName>
<ForeName>J</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center & Department of Environmental Health, University of Cincinnati, Cincinnati, OH, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Meller</LastName>
<ForeName>J</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center & Department of Environmental Health, University of Cincinnati, Cincinnati, OH, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Taylor</LastName>
<ForeName>K D</ForeName>
<Initials>KD</Initials>
<AffiliationInfo><Affiliation>Los Angeles Biomedical Research Institute, Torrance, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Zheng</LastName>
<ForeName>X</ForeName>
<Initials>X</Initials>
<AffiliationInfo><Affiliation>Department of Biostatistics, University of Washington, Seattle, WA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Zhao</LastName>
<ForeName>L P</ForeName>
<Initials>LP</Initials>
<AffiliationInfo><Affiliation>Fred Hutchinson Cancer Research Center, Seattle, WA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Gourraud</LastName>
<ForeName>P-A</ForeName>
<Initials>PA</Initials>
<AffiliationInfo><Affiliation>Department of Neurology, University of California, San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>ATIP-Avenir Team UMR Inserm, 1064, Nantes, France.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Nantes University Hospital Department of Public Health, Nantes, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Hollenbach</LastName>
<ForeName>J A</ForeName>
<Initials>JA</Initials>
<AffiliationInfo><Affiliation>Department of Neurology, University of California, San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Mack</LastName>
<ForeName>S J</ForeName>
<Initials>SJ</Initials>
<AffiliationInfo><Affiliation>Center for Genetics, Children's Hospital Research Institute, Oakland, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Maiers</LastName>
<ForeName>M</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>Bioinformatics Research Department, National Marrow Donor Program, Minneapolis, MN, USA.</Affiliation>
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<Language>eng</Language>
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<Country>United States</Country>
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