Hypothesis Testing in High-Dimensional Instrumental Variables Regression with an Application to Genomics Data

TL;DR

This paper develops hypothesis testing methods for high-dimensional instrumental variables regression, addressing challenges in genomics data where both genetic variants and gene expressions are high-dimensional, and applies these methods to identify gene-phenotype associations.

Contribution

It introduces novel hypothesis testing procedures for sparse high-dimensional IV models, including single coefficient tests and FDR-controlled multiple testing, with theoretical and empirical validation.

Findings

Methods effectively control false discovery rate in simulations

Proposed tests accurately identify true gene-phenotype associations

Application reveals genes linked to yeast growth under oxidative stress

Abstract

Gene expression and phenotype association can be affected by potential unmeasured confounders from multiple sources, leading to biased estimates of the associations. Since genetic variants largely explain gene expression variations, they can be used as instruments in studying the association between gene expressions and phenotype in the framework of high dimensional instrumental variable (IV) regression. However, because the dimensions of both genetic variants and gene expressions are often larger than the sample size, statistical inferences such as hypothesis testing for such high dimensional IV models are not trivial and have not been investigated in literature. The problem is more challenging since the instrumental variables (e.g., genetic variants) have to be selected among a large set of genetic variants. This paper considers the problem of hypothesis testing for sparse IV regression models and presents methods for testing single regression coefficient and multiple testing of multiple coefficients, where the test statistic for each single coefficient is constructed based on an inverse regression. A multiple testing procedure is developed for selecting variables and is shown to control the false discovery rate. Simulations are conducted to evaluate the performance of our proposed methods. These methods are illustrated by an analysis of a yeast dataset in order to identify genes that are associated with growth in the presence of hydrogen peroxide.