ArticleHuman genetics2015
Bayesian variable selection for hierarchical gene-environment and gene-gene interactions.
Article in Human genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- The impact, mechanisms and prevention strategies of environmental endocrine disruptors on male reproductive health.Frontiers in endocrinology · 2025Review
- Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.Cancer genomics & proteomics · 2023Review
- Two-level Bayesian interaction analysis for survival data incorporating pathway information.Biometrics · 2023Article
- Bayesian group sequential enrichment designs based on adaptive regression of response and survival time on baseline biomarkers.Biometrics · 2022Article
- Gene-gene interaction analysis incorporating network information via a structured Bayesian approach.Statistics in medicine · 2021Article
- Identifying Gene-Environment Interactions With Robust Marginal Bayesian Variable Selection.Frontiers in genetics · 2021Article
- Gene-Environment Interaction: A Variable Selection Perspective.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Semiparametric Bayesian variable selection for gene-environment interactions.Statistics in medicine · 2020Article
- Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates.Journal of statistical computation and simulation · 2018Article
- Structured detection of interactions with the directed lasso.Statistics in biosciences · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions and gene by environment interactions in the same model. Our approach incorporates the natural hierarchical structure between the main effects and interaction effects into a mixture model, such that our methods tend to remove the irrelevant interaction effects more effectively, resulting in more robust and parsimonious models. We consider both strong and weak hierarchical models. For a strong hierarchical model, both the main effects between interacting factors must be present for the interactions to be considered in the model development, while for a weak hierarchical model, only one of the two main effects is required to be present for the interaction to be evaluated. Our simulation results show that the proposed strong and weak hierarchical mixture models work well in controlling false-positive rates and provide a powerful approach for identifying the predisposing effects and interactions in gene-environment interaction studies, in comparison with the naive model that does not impose this hierarchical constraint in most of the scenarios simulated. We illustrate our approach using data for lung cancer and cutaneous melanoma.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.