Efficient inference for genetic association studies with multiple outcomes

TL;DR

This paper introduces a scalable variational inference method for joint analysis of high-dimensional genetic and molecular data, enabling efficient and accurate identification of associations in large-scale studies.

Contribution

It develops a variational inference approach for sparse multivariate regression, significantly reducing computation time while maintaining accuracy compared to MCMC methods.

Findings

Outperforms existing variable selection methods.

Handles hundreds of thousands of genetic variants efficiently.

Provides near-MCMC accuracy at a fraction of the computational cost.

Abstract

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 modelling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson et al. (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.