ArticleStatistics in medicine2020
Semiparametric Bayesian variable selection for gene-environment interactions.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- 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
- Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.Entropy (Basel, Switzerland) · 2024Review
- The Bayesian Regularized Quantile Varying Coefficient Model.Computational statistics & data analysis · 2023Article
- Detection of Interaction Effects in a Nonparametric Concurrent Regression Model.Entropy (Basel, Switzerland) · 2023Article
- Article
- Springer: An R package for bi-level variable selection of high-dimensional longitudinal data.Frontiers in genetics · 2023Article
- Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data.Genes · 2022Article
- Gene-gene interaction analysis incorporating network information via a structured Bayesian approach.Statistics in medicine · 2021Article
- Bayesian variable selection for understanding mixtures in environmental exposures.Statistics in medicine · 2021Article
- Integrating Multi-Omics Data for Gene-Environment Interactions.Biotech (Basel (Switzerland)) · 2021Article
- Identifying Gene-Environment Interactions With Robust Marginal Bayesian Variable Selection.Frontiers in genetics · 2021Article
- EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models.Frontiers in genetics · 2021Article
- Gene-Environment Interaction: A Variable Selection Perspective.Methods in molecular biology (Clifton, N.J.) · 2021Review
- A general index for linear and nonlinear correlations for high dimensional genomic data.BMC genomics · 2020Article
- Penalized Variable Selection for Lipid-Environment Interactions in a Longitudinal Lipidomics Study.Genes · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Study of gene-environment (G×E) interactions is important for elucidating the disease etiology. Existing Bayesian methods for G×E interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. Many studies have shown the advantages of penalization methods in detecting G×E interactions in "large p, small n" settings. However, Bayesian variable selection, which can provide fresh insight into G×E study, has not been widely examined. We propose a novel and powerful semiparametric Bayesian variable selection model that can investigate linear and nonlinear G×E interactions simultaneously. Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main-effects-only case within the Bayesian framework. Spike-and-slab priors are incorporated on both individual and group levels to identify the sparse main and interaction effects. The proposed method conducts Bayesian variable selection more efficiently than existing methods. Simulation shows that the proposed model outperforms competing alternatives in terms of both identification and prediction. The proposed Bayesian method leads to the identification of main and interaction effects with important implications in a high-throughput profiling study with high-dimensional SNP data.
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Registered trials
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