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Deriving stratified effects from joint models investigating gene-environment interactions.

Vincent Laville1, Timothy Majarian2, Paul S de Vries3

  • 1Department of Computational Biology, USR 3756 CNRS, Institut Pasteur, Paris, France. vincent.laville@pasteur.fr.

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Summary

This study introduces a method to derive gene-environment interaction summary statistics from joint models. The approach accurately estimates stratified and marginal effects, aiding genetic analyses.

Keywords:
Binary exposureGene-environment interactionStratified analysisSummary statistics

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Gene-environment interactions (GxE) are often identified using models with interaction terms and joint tests.
  • Stratified analyses, estimating genetic effects separately in exposed and unexposed groups, are valuable for binary environmental exposures.
  • Obtaining stratified and marginal model summary statistics can be difficult in large consortia that only performed joint tests for GxE interactions.

Purpose of the Study:

  • To develop a framework for estimating stratified and marginal summary statistics from joint GxE interaction models.
  • To assess the accuracy and potential biases of the proposed estimation method.
  • To provide a practical tool for applying the method to real-world genetic data.

Main Methods:

  • Developed a novel framework to estimate summary statistics for stratified and marginal models using data from a joint GxE interaction model.
  • Conducted simulation studies to evaluate estimator accuracy and investigate sources of bias, including genotype-exposure correlation and differing variances.
  • Applied the developed methods to real genetic datasets, assessing accuracy after SNP filtering based on sample size.

Main Results:

  • Simulation results demonstrated high theoretical accuracy of the proposed estimators.
  • Identified key sources of bias and their impact on estimation accuracy.
  • Showcased the retained accuracy of the estimators on real data after sample size-based SNP filtering.

Conclusions:

  • The developed method accurately estimates stratified and marginal summary statistics from joint GxE interaction models.
  • Facilitates the interpretation of GxE screening results and guides further functional genetic analyses.
  • A user-friendly Python script is provided for practical application on real datasets.