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Related Experiment Video

Updated: Jun 7, 2025

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Marginal interaction test for detecting interactions between genetic marker sets and environment in genome-wide

Linchuan Shen1, Amei Amei1,2, Bowen Liu1,3

  • 1Department of Mathematical Sciences, University of Nevada, Las Vegas, Las Vegas, NV 89154, USA.

G3 (Bethesda, Md.)
|November 14, 2024
PubMed
Summary

New methods, MAGEIT_RAN and MAGEIT_FIX, identify gene-environment interactions (G×E) using both rare and common variants. MAGEIT_RAN proved most powerful in detecting G×E related to hypertension and blood pressure influenced by alcohol.

Keywords:
gene–environment interactiongenome-wide studymethod of momentsmixed effects model

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Area of Science:

  • Genetics
  • Environmental Health
  • Biostatistics

Background:

  • Complex human diseases arise from gene-environment interactions (G×E), necessitating advanced analytical tools for risk prediction and mechanistic understanding.
  • Current G×E analysis methods often focus on single genetic variants (common or rare) and a single environmental factor, limiting comprehensive investigation.
  • Developing robust statistical methods is crucial for dissecting the complex interplay between genetic and environmental factors in disease etiology.

Purpose of the Study:

  • To develop and validate novel statistical methods, MAGEIT_RAN and MAGEIT_FIX, for identifying gene-environment interactions (G×E).
  • To enable the analysis of G×E involving both rare and common genetic variants simultaneously with an environmental factor.
  • To apply these methods to identify novel G×E associated with hypertension and blood pressure in a multiethnic population.

Main Methods:

  • Development of MAGEIT_RAN and MAGEIT_FIX, which utilize the MinQue statistic to detect G×E.
  • MAGEIT_RAN models genetic main effects as random effects, while MAGEIT_FIX models them as fixed effects.
  • Simulation studies were conducted to assess type I error control and statistical power, followed by genome-wide application to gene-alcohol interactions in the Multiethnic Study of Atherosclerosis.

Main Results:

  • Both MAGEIT_RAN and MAGEIT_FIX demonstrated controlled type I error rates in simulations.
  • MAGEIT_RAN exhibited superior statistical power compared to MAGEIT_FIX.
  • Genome-wide analysis identified significant gene-alcohol interactions for hypertension and seated systolic blood pressure, highlighting genes such as EIF2AK2, CCNDBP1, and EPB42.

Conclusions:

  • MAGEIT_RAN and MAGEIT_FIX are effective tools for identifying G×E, accommodating both rare and common variants.
  • The application of MAGEIT_RAN revealed specific genes and pathways (apoptosis, signal transduction) involved in alcohol-related hypertension.
  • These findings underscore the utility of MAGEIT_RAN in uncovering biologically relevant G×E for complex diseases.