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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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High-Dimensional Gene-Environment Interaction Analysis.

Mengyun Wu1, Yingmeng Li1, Shuangge Ma2

  • 1School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.

Annual Review of Statistics and Its Application
|August 29, 2025
PubMed
Summary
This summary is machine-generated.

Gene-environment interactions are crucial for complex diseases. This review covers statistical methods for analyzing these gene-environment interactions, aiding research into disease development.

Keywords:
dimension reductiongene–environment interactionhypothesis testingmarginal and joint analysisvariable selection

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Area of Science:

  • Genetics
  • Environmental Health
  • Biostatistics

Background:

  • Complex diseases arise from genetic and environmental factors, with gene-environment (G-E) interactions playing a significant role.
  • Current G-E interaction analyses often employ supervised frameworks for genetic and environmental factors in relation to disease.
  • A statistical perspective is needed to review methodological advancements in G-E interaction analysis.

Purpose of the Study:

  • To provide a selective review of statistical methodologies for gene-environment interaction analysis.
  • To categorize and discuss the main frameworks and techniques used in G-E interaction studies.
  • To highlight considerations for applying these methods in diverse research scenarios.

Main Methods:

  • Review of hypothesis testing, variable selection, and dimension reduction techniques.
  • Discussion of testing-based, estimation-based, and prediction-based analytical frameworks.
  • Exploration of linear/nonlinear, fixed/random effects, marginal/joint, and Bayesian/frequentist analyses.

Main Results:

  • Identified three primary statistical frameworks: testing-based, estimation-based, and prediction-based.
  • Detailed various analytical approaches including linear/nonlinear and Bayesian/frequentist methods.
  • Highlighted statistical properties, computational aspects, and practical applications of G-E interaction methods.

Conclusions:

  • Methodological diversity exists for analyzing gene-environment interactions, catering to different research goals.
  • The review facilitates the appropriate application of statistical techniques for G-E interaction analysis.
  • Future research directions in statistical G-E interaction analysis are outlined.