Gene-Environment Interaction: A Variable Selection Perspective

TL;DR

This paper reviews recent advances in penalized variable selection methods for gene-environment interactions, emphasizing their importance in understanding complex diseases and addressing high-dimensional data challenges.

Contribution

It provides a comprehensive survey of variable selection techniques tailored for G×E studies, highlighting their strengths, limitations, and computational aspects.

Findings

Summarizes existing variable selection methods for G×E interactions.

Discusses strengths and limitations of penalization approaches.

Highlights computational challenges and future directions.

Abstract

Gene-environment interactions have important implications to elucidate the genetic basis of complex diseases beyond the joint function of multiple genetic factors and their interactions (or epistasis). In the past, G$\times$E interactions have been mainly conducted within the framework of genetic association studies. The high dimensionality of G$\times$E interactions, due to the complicated form of environmental effects and presence of a large number of genetic factors including gene expressions and SNPs, has motivated the recent development of penalized variable selection methods for dissecting G$\times$E interactions, which has been ignored in majority of published reviews on genetic interaction studies. In this article, we first survey existing overviews on both gene-environment and gene-gene interactions. Then, after a brief introduction on the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms respectively under a variety of conceptual models. Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G$\times$E studies have also been provided.