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Gene-Environment Interactions01:20

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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 Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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A linear mixed model framework for gene-based gene-environment interaction tests in twin studies.

Brandon J Coombes1, Saonli Basu1, Matt McGue2

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota.

Genetic Epidemiology
|September 12, 2018
PubMed
Summary

This study introduces new methods to analyze gene-environment interactions (G×E) in families, identifying a significant gene linked to alcohol consumption influenced by psychosocial factors.

Keywords:
candidate genesfamily studiesgene-environment interactionlinear mixed modelsridge penaltyscore tests

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Area of Science:

  • Genetics
  • Environmental Health
  • Biostatistics

Background:

  • Gene-environment interactions (G×E) are crucial for understanding complex traits.
  • Existing G×E tests are often limited to unrelated individuals, not family studies.
  • Analyzing G×E in families requires specialized statistical approaches.

Purpose of the Study:

  • To extend G×E interaction tests for family studies using a linear mixed model framework.
  • To investigate interactions between candidate genes and multiple correlated environmental factors.
  • To develop computationally efficient and powerful methods for detecting G×E in families.

Main Methods:

  • Extended existing G×E tests to a linear mixed model framework suitable for family data.
  • Modeled correlated environments either separately or jointly within the statistical model.
  • Proposed treating genetic main effects as random effects to enhance interaction detection power.
  • Introduced a generalized sequential algorithm for summing powered score (Seq-SPU) tests, including an adaptive version (Seq-aSPU).

Main Results:

  • Demonstrated theoretical validity and computational efficiency of separate modeling for correlated environments.
  • Showed that the Seq-aSPU test can outperform other methods when interaction effects have opposite directions.
  • Applied the novel methods to the Minnesota Center for Twin and Family Research data.
  • Identified a significant gene interacting with four psychosocial environmental factors influencing alcohol consumption.

Conclusions:

  • The developed linear mixed model framework effectively analyzes G×E interactions in family studies.
  • The Seq-SPU family of tests, particularly Seq-aSPU, offers a powerful approach for G×E detection.
  • These findings highlight the complex interplay of genetic and environmental factors in alcohol consumption.