Efficient inference for genetic association studies with multiple outcomes.
Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes.
Nestlé Institute of Health Sciences SA, EPFL Innovation Park, 1015 Lausanne, Switzerland Ecole Polytechnique Fédérale de Lausanne, EPFL SB MATH STAT, Station 8, 1015 Lausanne, Switzerland.
Helene
H
Ruffieux
Ecole Polytechnique Fédérale de Lausanne, EPFL SB MATH STAT, Station 8, 1015 Lausanne, Switzerland.
Anthony C
AC
Davison
Nestlé Institute of Health Sciences SA, EPFL Innovation Park, 1015 Lausanne, Switzerland.
Jorg
J
Hager
Nestlé Institute of Health Sciences SA, EPFL Innovation Park, 1015 Lausanne, Switzerland.
Irina
I
Irincheeva
D056726:Genetic Association Studies / Q000379:methods*; D014644:Genetic Variation; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method
High-dimensional data; Molecular quantitative trait locus analysis; Sparse multivariate regression; Statistical genetics; Variable selection; Variational inference