Genetic effects on complex traits may depend on context, such as age, sex, environmental exposures, or social settings. However, it remains often unclear if the extent of context dependency, or gene-by-environment interaction (GxE), merits more involved models than the additive model typically used to analyze data from genome-wide association studies (GWAS). Here, we suggest considering the utility of GxE models in GWAS as a trade-off between bias and variance parameters. In particular, we derive a decision rule for choosing between competing models for the estimation of allelic effects. The rule weighs the increased estimation noise when context is considered against the potential bias when context dependency is ignored. In the empirical example of GxSex in human physiology, the increased noise of context-specific estimation often outweighs the bias reduction, rendering GxE models less useful when variants are considered independently. However, for complex traits, we argue that the joint consideration of context dependency across many variants mitigates both noise and bias. As a result, polygenic GxE models can improve both estimation and trait prediction. Finally, we exemplify (using GxDiet effects on longevity in fruit flies) how analyses based on independently ascertained 'top hits' alone can be misleading, and that considering polygenic patterns of GxE can improve interpretation.
Weine et al. (Thu,) studied this question.