A simulation study of gene-by-environment interactions in GWAS implies ample hidden effects.

Area of Science:

• Genetics
• Environmental Health
• Complex Trait Analysis

Background:

• Modern lifestyles correlate with increased disease prevalence, potentially due to gene-by-environment (GxE) interactions.
• Detecting GxE interactions typically requires large sample sizes and has faced replication challenges.
• Existing methods often rely on aggregated genetic risk scores (GRS) to infer environmental effects.

Purpose of the Study:

• To investigate the feasibility of detecting GxE interactions within Genome-Wide Association Studies (GWAS).
• To simulate a quantitative trait influenced by numerous causal variants under varying environmental conditions.
• To assess the impact of environmental changes on the detectability of genetic susceptibility variants.

Main Methods:

• Extensive simulations of a quantitative trait controlled by 2500 causal variants.
• Simulated individuals were assigned to ancestral or modern environments, modifying phenotype effect sizes.
• Varied the fraction of single nucleotide polymorphisms (SNPs) affected by environmental perturbation (1-20%).

Main Results:

• Highly significant GxE risk scores (GRSxE) were detected even without individual genotype GxE evidence.
• Increased phenotypic variance due to environmental changes reduced GWAS power in mixed cohorts.
• Pervasive GxE effects contributing to complex traits can remain undetected.

Conclusions:

• Gene-by-environment interactions are crucial in the genetic architecture of complex traits.
• GRSxE can reveal GxE effects missed by traditional GWAS approaches.
• Environmental perturbations can obscure genetic susceptibility signals in mixed populations.