ReviewMethods in molecular biology (Clifton, N.J.)2021
Gene-Environment Interaction: A Variable Selection Perspective.
Review in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association between IL10 rs1800896 polymorphism and risk of pediatric asthma: A meta-analysis.The clinical respiratory journal · 2023Pooled it
- Analysis of Health-Related Quality of Life in Multiple Sclerosis: A Bayesian Quantile LASSO Approach.Healthcare (Basel, Switzerland) · 2026Article
- MixedBayes: An R Package for Longitudinal Gene-Environment Interaction Analysis Using Robust Sparse Bayesian Mixed Models.Entropy (Basel, Switzerland) · 2026Article
- A trans-omics gene-smoking interaction study of lung cancer based on consortium data.American journal of respiratory and critical care medicine · 2026Article
- Article
- The Multifactorial Causes of Down Syndrome During Pregnancy: A Narrative Review of Genetic, Environmental, and Maternal Influences.Health science reports · 2025Article
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- Article
- Using Quantitative Trait Locus Mapping and Genomic Resources to Improve Breeding Precision in Peaches: Current Insights and Future Prospects.Plants (Basel, Switzerland) · 2025Review
- Interactive effects between lead-cadmium co-exposure and VEGFA gene polymorphisms on renal dysfunction: a gene-environment interaction study.Frontiers in public health · 2025Article
- Mining for gene-environment and gene-gene interactions: parametric and non-parametric tests for detecting variance quantitative trait loci.Frontiers in genetics · 2025Article
- The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.Statistics in medicine · 2024Article
- DNA methylation at AHRR as a master predictor of smoke exposure and a biomarker for sleep and exercise.Clinical epigenetics · 2024Article
- Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.Entropy (Basel, Switzerland) · 2024Review
- Gene-environment interactions within a precision environmental health framework.Cell genomics · 2024Review
- Poor statistical power in population-based association study of gene interaction.BMC medical genomics · 2024Article
- The Bayesian Regularized Quantile Varying Coefficient Model.Computational statistics & data analysis · 2023Article
- Cauchy combination methods for the detection of gene-environment interactions for rare variants related to quantitative phenotypes.Heredity · 2023Article
- Which Factors Influence Healthy Aging? A Lesson from the Longevity Village of Bama in China.Aging and disease · 2023Review
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene-environment interactions have important implications for elucidating the genetic basis of complex diseases beyond the joint function of multiple genetic factors and their interactions (or epistasis). In the past, G × E interactions have been mainly conducted within the framework of genetic association studies. The high dimensionality of G × E interactions, due to the complicated form of environmental effects and the presence of a large number of genetic factors including gene expressions and SNPs, has motivated the recent development of penalized variable selection methods for dissecting G × E interactions, which has been ignored in the majority of published reviews on genetic interaction studies. In this article, we first survey existing studies on both gene-environment and gene-gene interactions. Then, after a brief introduction to the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms, respectively, under a variety of conceptual models. Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G × E studies, have also been provided.
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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.