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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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Gene-Environment Interaction: A Variable Selection Perspective.

Fei Zhou1, Jie Ren2, Xi Lu1

  • 1Department of Statistics, Kansas State University, Manhattan, KS, USA.

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Summary
This summary is machine-generated.

Gene-environment interactions are key to understanding complex diseases. This review covers penalized variable selection methods for dissecting gene-environment (G × E) interactions, offering insights into their strengths and limitations.

Keywords:
Bayesian variable selectionGene–environment interactionLinear and nonlinear interactionMarginal and joint analysisPenalization

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Gene-environment (G × E) interactions are crucial for complex disease etiology, extending beyond simple genetic factors.
  • Traditional genetic association studies have limitations in analyzing high-dimensional G × E interactions.
  • Existing reviews often overlook the specific challenges and methods for G × E interaction analysis.

Purpose of the Study:

  • To survey existing studies on gene-environment and gene-gene interactions.
  • To review penalized variable selection methods for dissecting G × E interactions.
  • To discuss the strengths, limitations, and computational aspects of these methods.

Main Methods:

  • Review of existing literature on genetic and gene-environment interaction studies.
  • Introduction to variable selection methods, focusing on penalization techniques.
  • Analysis of marginal and joint paradigms for G × E interaction detection.

Main Results:

  • Identified a gap in reviews concerning penalized variable selection for G × E interactions.
  • Provided a comprehensive overview of methods applicable to high-dimensional G × E data.
  • Discussed the utility of penalized methods in addressing the complexity of G × E interactions.

Conclusions:

  • Penalized variable selection methods are essential for dissecting complex G × E interactions.
  • These methods offer advantages in handling high dimensionality and complex environmental effects.
  • Further research and discussion on computational aspects are needed for effective G × E studies.