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Robust sparse Bayesian regression for longitudinal gene-environment interactions.

Kun Fan1, Yu Jiang2, Shuangge Ma3

  • 1Department of Health Data Science and Biostatistics, Peter O'Donnell Jr School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.

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|November 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a robust Bayesian model for analyzing complex genetic and environmental interactions in longitudinal data. The new method improves variable selection and prediction accuracy, especially with high-dimensional genetic factors.

Keywords:
MCMC (Markov Chain Monte Carlo)longitudinal gene–environment interactionquantile mixed-effects modelrobust Bayesian variable selectionstructured spike-and-slab priors

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Area of Science:

  • Biostatistics
  • Genomics
  • Statistical Genetics

Background:

  • Longitudinal studies require accurate estimation of main and interaction effects.
  • High-dimensional genetic data presents challenges for traditional analysis of variance (ANOVA).
  • Sparse longitudinal gene-environment (G×E) interactions are understudied, especially with skewed data and correlated observations.

Purpose of the Study:

  • To develop a novel robust sparse Bayesian mixed model for longitudinal gene-environment interaction analysis.
  • To address challenges including skewed phenotypic measurements, intra-cluster correlations, and structured sparsity.
  • To enable robust Bayesian variable selection for main and interaction effects.

Main Methods:

  • Developed a robust sparse Bayesian mixed model incorporating structured spike-and-slab priors.
  • Implemented Gibbs samplers and Markov Chain Monte Carlo (MCMC) algorithms for efficient computation and posterior inference.
  • Accommodated outliers and inter-relatedness among repeated measurements.

Main Results:

  • The proposed model demonstrated superior performance in variable selection and estimation compared to benchmark methods in extensive simulations.
  • Successfully analyzed longitudinal lipidomics data from a cancer prevention study in CD-1 mice.
  • Identified significant main and interaction effects with important biological implications.

Conclusions:

  • The novel robust sparse Bayesian mixed model effectively handles challenges in high-dimensional longitudinal gene-environment interaction analysis.
  • The method offers improved prediction performance over existing alternatives.
  • Provides a powerful tool for uncovering complex genetic and environmental influences in biological studies.