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Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank by Adrian Cortes; Calliope A Dendrou; Allan Motyer; Luke Jostins; Damjan Vukcevic; Alexander Dilthey; Peter Donnelly; Stephen Leslie; Lars Fugger; Gil McVean is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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

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.

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

Author

Adrian Cortes; Calliope A Dendrou; Allan Motyer; Luke Jostins; Damjan Vukcevic; Alexander Dilthey; Peter Donnelly; Stephen Leslie; Lars Fugger; Gil McVean

Publisher

Springer Science and Business Media LLC

Published

2017

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)