Genomic Data Analysis using a Two Stage Expectation Propagation Algorithm for Analysis of Sparse Bayesian High-Dimensional Instrumental Variables Regression

TL;DR

This paper introduces a novel two-stage expectation propagation algorithm for Bayesian high-dimensional sparse instrumental variables regression, enabling effective analysis of gene expression and genetic variant data where traditional methods struggle.

Contribution

It develops a two-stage EP algorithm tailored for sparse Bayesian IV models with spike-and-slab priors, improving inference in high-dimensional genetic data analysis.

Findings

The method performs well in simulations.

Applied successfully to mouse obesity data.

Enhances Bayesian sparse IV modeling.

Abstract

Simultaneous analysis of gene expression data and genetic variants is highly of interest, especially when the number of gene expressions and genetic variants are both greater than the sample size. Association of both causal genes and effective SNPs makes the use of sparse modeling of such genetic data sets, highly important. The high-dimensional sparse instrumental variables models are one of such useful association models, which models the simultaneous relation of the gene expressions and genetic variants with complex traits. From a Bayesian viewpoint, the sparsity can be favored using sparsity-enforcing priors such as spike-and-slab priors. A two-stage modification of the expectation propagation (EP) algorithm is proposed and examined for approximate inference in high-dimensional sparse instrumental variables models with spike-and-slab priors. This method is an adoption of the classical two-stage least squares method, to be used with the Bayes context. A simulation study is performed to examine the performance of the methods. The proposed method is applied to analysis of the mouse obesity data.