Lars Henrik Fugger

Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

DOI

  • Adrian Cortes, Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.
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  • Calliope A Dendrou, MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.
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  • Allan Motyer, Centre for Systems Genomics, Schools of Mathematics and Statistics and of BioSciences, University of Melbourne, Parkville, Victoria, Australia.
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  • Luke Jostins, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
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  • Damjan Vukcevic, Murdoch Children's Research Institute, Parkville, Victoria, Australia.
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  • Alexander Dilthey, Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, US National Institutes of Health, Bethesda, Maryland, USA.
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  • Peter Donnelly, University of Oxford
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  • Stephen Leslie, Murdoch Children's Research Institute, Parkville, Victoria, Australia.
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  • Lars Fugger
  • Gil McVean, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK.

Genetic discovery from the multitude of phenotypes extractable from routine healthcare data can transform understanding of the human phenome and accelerate progress toward precision medicine. However, a critical question when analyzing high-dimensional and heterogeneous data is how best to interrogate increasingly specific subphenotypes while retaining statistical power to detect genetic associations. Here we develop and employ a new Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to analyze genetic variants against UK Biobank disease phenotypes derived from self-reporting and hospital episode statistics. Our method displays a more than 20% increase in power to detect genetic effects over other approaches and identifies new associations between classical human leukocyte antigen (HLA) alleles and common immune-mediated diseases (IMDs). By applying the approach to genetic risk scores (GRSs), we show the extent of genetic sharing among IMDs and expose differences in disease perception or diagnosis with potential clinical implications.

Original languageEnglish
JournalNature Genetics
Volume49
Issue9
Pages (from-to)1311-1318
Number of pages8
ISSN1061-4036
DOIs
Publication statusPublished - Sep 2017

    Research areas

  • Adult, Aged, Alleles, Bayes Theorem, Cluster Analysis, Delivery of Health Care, Female, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, HLA Antigens, Health Information Systems, Humans, International Classification of Diseases, Logistic Models, Male, Middle Aged, Polymorphism, Single Nucleotide, United Kingdom, Journal Article

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