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Gene-environment interaction (GxE) models improve genetic predictions for complex traits by balancing bias and variance. Polygenic GxE analysis, considering multiple genetic variants, offers better estimation and prediction than single-variant approaches.

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Area of Science:

  • Genetics
  • Statistical Genomics
  • Complex Traits

Background:

  • Genetic effects on complex traits can be influenced by environmental context.
  • Gene-environment interaction (GxE) models are often complex and their utility in genome-wide association studies (GWAS) is debated compared to standard additive models.

Purpose of the Study:

  • To evaluate the trade-offs between bias and variance when using GxE models versus additive models in GWAS.
  • To develop a decision rule for selecting appropriate models based on estimation noise and bias.

Main Methods:

  • Derived a decision rule for model selection in GWAS, weighing estimation noise against potential bias.
  • Applied the rule to GxSex interactions in human physiology and GxDiet interactions in fruit fly longevity.
  • Investigated the utility of polygenic GxE models for complex traits.

Main Results:

  • Independently assessed GxE effects (e.g., GxSex) often show increased estimation noise that outweighs bias reduction, limiting their utility.
  • Polygenic GxE models, considering interactions across many variants, can mitigate both noise and bias.
  • Analysis of 'top hits' alone can be misleading; polygenic GxE patterns improve interpretation.

Conclusions:

  • The choice between additive and GxE models in GWAS depends on a bias-variance trade-off.
  • Polygenic GxE models offer improved estimation and prediction for complex traits.
  • Considering polygenic patterns of GxE is crucial for accurate interpretation of genetic effects.