Tradeoffs in Modeling Context Dependency in Complex Trait Genetics.

Genetic effects on complex traits may depend on context, such as age, sex, environmental exposures or social settings. However, it is 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 tradeoff 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, we argue that for complex traits, considering patterns of context dependency across many variants jointly can improve both estimation and trait prediction. Finally, we exemplify (using GxDiet effects on longevity in fruit flies) how considering polygenic trends of GxE may also be important for interpretation, as analyses based on independently ascertained "top hits" alone can be misleading.