ArticleFrontiers in genetics2021
Identifying Gene-Environment Interactions With Robust Marginal Bayesian Variable Selection.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Analysis of Health-Related Quality of Life in Multiple Sclerosis: A Bayesian Quantile LASSO Approach.Healthcare (Basel, Switzerland) · 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
- 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
- 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
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Authors and funding
4 authors.
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Abstract
In high-throughput genetics studies, an important aim is to identify gene-environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using data from the Nurse Health Study (NHS). Some of the identified main and interaction effects from the real data analysis have important biological implications.
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