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

A Cortes; CA Dendrou; A Motyer; L Jostins; D Vukcevic; A Dilthey; P Donnelly; S Leslie; L Fugger; G McVean

Journal article

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

A Cortes, CA Dendrou, A Motyer, L Jostins, D Vukcevic, A Dilthey, P Donnelly, S Leslie, L Fugger, G McVean

Nature Genetics | NATURE PUBLISHING GROUP | Published : 2017

DOI: 10.1038/ng.3926

Abstract

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% i..

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University of Melbourne Researchers

Allan Motyer Author

Stephen Leslie Author

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Grants

Awarded by Medical Research Council

Funding Acknowledgements

This research has been conducted using the UK Biobank Resource (application number 10625). The research has been supported by the Wellcome Trust (095552/Z/11/Z to P.D., 100308/Z/12/Z to L.F., 100956/Z/13/Z to G.M., and 090532/Z/09/Z and 203141/Z/16/Z to the Wellcome Trust Centre for Human Genetics), the Danish National Research Foundation (grant number 126 to L.F.), the Wellcome Trust/Royal Society (204290/Z/16/Z to C.A.D.), Takeda, Ltd. (L.F. and C.A.D.), the Medical Research Council (grant number MC_UU_12010/3 to L.F.), and the Oak Foundation (OCAY-15-520 to L.F.). This work was supported by the Australian National Health and Medical Research Council (NHMRC), Career Development Fellowship 1053756 (S.L.), and by Victorian Life Sciences Computation Initiative (VLSCI) grant VR0240 on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian government in Australia (S.L.). Research at the Murdoch Children's Research Institute was supported by the Victorian government's Operational Infrastructure Support Program.