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

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% increase in power to detect genetic effects over other approaches and identifies new associations bet

SUBMITTER: Cortes A 

PROVIDER: S-EPMC5580804 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature