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Related Concept Videos

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Gene-Environment Interactions

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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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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MixedBayes: An R Package for Longitudinal Gene-Environment Interaction Analysis Using Robust Sparse Bayesian Mixed

Kun Fan1, Xiaoxi Li2, Shejuty Devnath2

  • 1Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX 77030, USA.

Entropy (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces the R package mixedBayes for robustly analyzing gene-environment interactions in longitudinal data. It provides Bayesian methods to quantify uncertainty in these complex genetic and environmental interactions.

Keywords:
high-dimensional statistical inferencequantile mixed-effects modelrepeated measuresrobust Bayesian variable selectionuncertainty quantification

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • High-dimensional gene-environment interactions in longitudinal studies are complex.
  • Existing variable selection methods lack robust inferential tools for quantifying interaction uncertainty.
  • Longitudinal data analysis requires methods that handle intra-cluster correlations and repeated measures.

Purpose of the Study:

  • Introduce the R package mixedBayes (version 0.2.5) for robust high-dimensional gene-environment interaction analysis in longitudinal studies.
  • Implement fully Bayesian robust mixed-effects models to address limitations in current inferential tools.
  • Provide a framework for analyzing interactions between omics features, treatment effects, genetic main effects, and environmental factors.

Main Methods:

  • Developed the mixedBayes R package implementing two classes of Bayesian robust mixed-effects models.
  • Utilized Markov chain Monte Carlo (MCMC) for posterior Bayesian inference.
  • Applied the models to analyze longitudinal asthma data with high-dimensional SNP measurements.

Main Results:

  • The mixedBayes package enables robust analysis of high-dimensional gene-environment interactions in longitudinal data.
  • The implemented models successfully accommodate complex correlation structures and heavy-tailed repeated measures.
  • Demonstrated the utility of the package through numerical examples and a real-world asthma case study.

Conclusions:

  • The mixedBayes R package offers a valuable tool for robust and statistically sound analysis of gene-environment interactions in longitudinal omics data.
  • The Bayesian approach provides a robust framework for quantifying uncertainty in complex interaction models.
  • Facilitates advanced genetic association studies and personalized medicine research.