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

TY - JOUR

T1 - Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank

AU - Cortes, Adrian

AU - Dendrou, Calliope A.

AU - Motyer, Allan

AU - Jostins, Luke

AU - Vukcevic, Damjan

AU - Dilthey, Alexander

AU - Donnelly, Peter

AU - Leslie, Stephen

AU - Fugger, Lars

AU - McVean, Gil

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

U2 - 10.1038/ng.3926

DO - 10.1038/ng.3926

M3 - Journal article

C2 - 28759005

AN - SCOPUS:85028691452

SN - 1061-4036

VL - 49

SP - 1311

EP - 1318

JO - Nature Genetics

JF - Nature Genetics

IS - 9

ER -