Non-subjective power analysis to detect G*E interactions in Genome-Wide Association Studies in presence of confounding factor

TL;DR

This paper evaluates the effectiveness of existing methods for detecting gene-environment interactions in GWAS, highlighting the challenge posed by confounding factors and introducing a simulated dataset for future method development.

Contribution

It provides a simulated dataset using real genotypes to assess the impact of confounding factors on G*E interaction detection methods in GWAS.

Findings

Existing methods have low power in presence of confounding factors.

Confounding factors significantly hinder detection of G*E interactions.

The simulated dataset can facilitate development of more robust detection methods.

Abstract

It is generally acknowledged that most complex diseases are affected in part by interactions between genes and genes and/or between genes and environmental factors. Taking into account environmental exposures and their interactions with genetic factors in genome-wide association studies (GWAS) can help to identify high-risk subgroups in the population and provide a better understanding of the disease. For this reason, many methods have been developed to detect gene-environment (G*E) interactions. Despite this, few loci that interact with environmental exposures have been identified so far. Indeed, the modest effect of G*E interactions as well as confounding factors entail low statistical power to detect such interactions. In this work, we provide a simulated dataset in order to study methods for detecting G*E interactions in GWAS in presence of confounding factor and population structure. Our work applies a recently introduced non-subjective method for H1 simulations called waffect and exploits the publicly available HapMap project to build a datasets with real genotypes and population structures. We use this dataset to study the impact of confounding factors and compare the relative performance of popular methods such as PLINK, random forests and linear mixed models to detect G*E interactions. Presence of confounding factor is an obstacle to detect G*E interactions in GWAS and the approaches considered in our power study all have insufficient power to detect the strong simulated interaction. Our simulated dataset could help to develop new methods which account for confounding factors through latent exposures in order to improve power.