Structures and Assumptions: Strategies to Harness Gene $\times$ Gene and Gene $\times$ Environment Interactions in GWAS

TL;DR

This paper reviews strategies for modeling gene-gene and gene-environment interactions in GWAS, emphasizing data structure and assumptions, and discusses the importance of SNP selection for identifying meaningful interactions.

Contribution

It provides a comprehensive overview of methods tailored to genetic data structure and assumptions, highlighting the role of SNP selection in interaction detection.

Findings

Interaction modeling often requires SNP selection based on prior knowledge or significance.

Considering larger numbers of interactions can help identify marginal SNP associations.

Appropriate modeling assumptions are crucial for effective interaction analysis.

Abstract

Genome-wide association studies, in which as many as a million single nucleotide polymorphisms (SNP) are measured on several thousand samples, are quickly becoming a common type of study for identifying genetic factors associated with many phenotypes. There is a strong assumption that interactions between SNPs or genes and interactions between genes and environmental factors substantially contribute to the genetic risk of a disease. Identification of such interactions could potentially lead to increased understanding about disease mechanisms; drug $\times$ gene interactions could have profound applications for personalized medicine; strong interaction effects could be beneficial for risk prediction models. In this paper we provide an overview of different approaches to model interactions, emphasizing approaches that make specific use of the structure of genetic data, and those that make specific modeling assumptions that may (or may not) be reasonable to make. We conclude that to identify interactions it is often necessary to do some selection of SNPs, for example, based on prior hypothesis or marginal significance, but that to identify SNPs that are marginally associated with a disease it may also be useful to consider larger numbers of interactions.