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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures.

Yebin Tao1, Brisa N Sánchez, Bhramar Mukherjee

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, 48109, U.S.A.

Statistics in Medicine
|December 30, 2014
PubMed
Summary

This study introduces a novel latent variable (LV) modeling approach to analyze gene-environment interactions (G × E) in longitudinal cohort studies. The method enhances understanding of how genetic factors modify environmental exposure effects on health outcomes.

Keywords:
gene-environment dependencegene-environment interactiongrowth curveslatent variable modelshrinkage estimation

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Area of Science:

  • Environmental Epidemiology
  • Biostatistics
  • Genetic Epidemiology

Background:

  • Cohort studies often collect environmental exposure and genetic marker data.
  • Longitudinal data allows for novel investigations into gene-environment interaction (G × E).
  • Latent variable (LV) models can address challenges like multiple testing and multicollinearity with correlated exposures/outcomes.

Purpose of the Study:

  • Propose a modeling strategy using LV models for repeated outcome measures and correlated exposure biomarkers.
  • Develop novel tests for G × E effects within the LV framework.
  • Examine effect modification of outcome-exposure associations by genetic factors.

Main Methods:

  • Utilized latent variable (LV) models to analyze associations between repeated outcomes and correlated exposure biomarkers.
  • Developed new tests for gene-environment interaction (G × E) within the LV framework.
  • Employed shrinkage estimation to combine results from models with and without gene dependence, optimizing bias-efficiency trade-offs.

Main Results:

  • Evaluated the properties of shrinkage estimates through simulations.
  • Demonstrated the necessity of data-adaptive shrinkage for repeated measures, time-varying exposures, and G × E.

Conclusions:

  • The proposed LV modeling strategy effectively analyzes complex G × E in longitudinal studies.
  • Shrinkage estimation provides a data-adaptive approach to balance bias and efficiency in G × E analyses.
  • This methodology is crucial for understanding environmental health effects influenced by genetic variations.