ArticleBiometrics2023
Robust Bayesian variable selection for gene-environment interactions.
Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Robust prioritization of genomic features with stability selection.Bioinformatics (Oxford, England) · 2026Article
- Mapping the Analytical Landscape of Gene-Diet Interactions in Epidemiology: From Classical Models to Causal and Multi-Omics Frameworks.Nutrients · 2026Review
- Benchmarking Sparse Variable Selection Methods for Genomic Data Analyses.Statistics in medicine · 2026Article
- Robust Bayesian model averaging for linear regression models with heavy-tailed errors.Journal of applied statistics · 2026Article
- Robust sparse Bayesian regression for longitudinal gene-environment interactions.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- BHCox: Bayesian heredity-constrained Cox proportional hazards models for detecting gene-environment interactions.BMC bioinformatics · 2025Article
- The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.Statistics in medicine · 2024Article
- Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.Entropy (Basel, Switzerland) · 2024Review
- Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions.Computational statistics & data analysis · 2024Article
- Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.Cancer genomics & proteomics · 2023Review
- The Bayesian Regularized Quantile Varying Coefficient Model.Computational statistics & data analysis · 2023Article
- Article
- Springer: An R package for bi-level variable selection of high-dimensional longitudinal data.Frontiers in genetics · 2023Article
- Sparse group variable selection for gene-environment interactions in the longitudinal study.Genetic epidemiology · 2022Article
- Overlapping group screening for detection of gene-environment interactions with application to TCGA high-dimensional survival genomic data.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Gene-environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.
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