000232274 001__ 232274
000232274 005__ 20181203024847.0
000232274 0247_ $$2doi$$a10.1093/biostatistics/kxx007
000232274 022__ $$a1465-4644
000232274 02470 $$2ISI$$a000413247300003
000232274 037__ $$aARTICLE
000232274 245__ $$aEfficient inference for genetic association studies with multiple outcomes
000232274 260__ $$bOxford University Press$$c2017$$aOxford
000232274 269__ $$a2017
000232274 300__ $$a19
000232274 336__ $$aJournal Articles
000232274 520__ $$aCombined 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.
000232274 6531_ $$aHigh-dimensional data
000232274 6531_ $$aMolecular quantitative trait locus analysis
000232274 6531_ $$aSparse multivariate regression
000232274 6531_ $$aStatistical genetics
000232274 6531_ $$aVariable selection
000232274 6531_ $$aVariational inference
000232274 700__ $$aRuffieux, Hélène
000232274 700__ $$0240476$$g111184$$aDavison, Anthony C.
000232274 700__ $$uEcole Polytech Fed Lausanne, Nestle Inst Hlth Sci SA, Innovat Pk, CH-1015 Lausanne, Switzerland$$aHager, Jorg
000232274 700__ $$aIrincheeva, Irina$$uEcole Polytech Fed Lausanne, Nestle Inst Hlth Sci SA, Innovat Pk, CH-1015 Lausanne, Switzerland
000232274 773__ $$j18$$tBiostatistics$$k4$$q618-636
000232274 909C0 $$xU10124$$0252136$$pSTAT
000232274 909CO $$pSB$$particle$$ooai:infoscience.tind.io:232274
000232274 917Z8 $$x186552
000232274 937__ $$aEPFL-ARTICLE-232274
000232274 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000232274 980__ $$aARTICLE