RT Journal Article
SR Electronic
T1 Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 105122
DO 10.1101/105122
A1 Adrian Cortes
A1 Calliope A. Dendrou
A1 Allan Motyer
A1 Luke Jostins
A1 Damjan Vukcevic
A1 Alexander Dilthey
A1 Peter Donnelly
A1 Stephen Leslie
A1 Lars Fugger
A1 Gil McVean
YR 2017
UL http://biorxiv.org/content/early/2017/02/01/105122.abstract
AB Genetic discovery from the multitude of phenotypes extractable from routine healthcare data has the ability to radically transform our understanding of the human phenome, thereby accelerating progress towards precision medicine. However, a critical question when analysing high-dimensional and heterogeneous data is how to interrogate increasingly specific subphenotypes whilst retaining statistical power to detect genetic associations. Here we develop and employ a novel Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to jointly analyse genetic variants against UK Biobank healthcare phenotypes. Our method displays a more than 20% increase in power to detect genetic effects over other approaches, such that we uncover the broader burden of genetic variation: we identify associations with over 2,000 diagnostic terms. We find novel associations with common immune-mediated diseases (IMD), we reveal the extent of genetic sharing between specific IMDs, and we expose differences in disease perception or diagnosis with potential clinical implications.