ArticleGenomics2013
Identification of gene-environment interactions in cancer studies using penalization.
Article in Genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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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.
The trial behind it
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
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An updated meta-analysis of 37 case-control studies on the association between NFKB1 -94ins/del ATTG promoter polymorphism and cancer susceptibility.Oncotarget · 2016Pooled it
- Ordinal Sparse Neural Networks for Modeling Gene- and Imaging-Environment Interactions.Statistics in medicine · 2025Article
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.Statistics in medicine · 2025Article
- Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues.Human genomics · 2025Review
- FunctanSNP: an R package for functional analysis of dense SNP data (with interactions).Bioinformatics (Oxford, England) · 2023Article
- Gene-environment interaction analysis via deep learning.Genetic epidemiology · 2023Article
- Gene-environment interaction identification via penalized robust divergence.Biometrical journal. Biometrische Zeitschrift · 2022Article
- A scalable hierarchical lasso for gene-environment interactions.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Gene-Environment Interaction: A Variable Selection Perspective.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes.Journal of clinical and translational science · 2020Article
- Structured gene-environment interaction analysis.Biometrics · 2020Article
- Identification of gene-environment interactions with marginal penalization.Genetic epidemiology · 2020Article
- Robust identification of gene-environment interactions for prognosis using a quantile partial correlation approach.Genomics · 2019Article
- A Novel Gene Underlies Bleomycin-Response Variation inGenetics · 2019Article
- Identifying gene-environment interactions incorporating prior information.Statistics in medicine · 2019Article
- Article
- Robust genetic interaction analysis.Briefings in bioinformatics · 2019Review
- Identifying gene-gene interactions using penalized tensor regression.Statistics in medicine · 2018Article
- Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures.Statistics in medicine · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
High-throughput cancer studies have been extensively conducted, searching for genetic markers associated with outcomes beyond clinical and environmental risk factors. Gene-environment interactions can have important implications beyond main effects. The commonly-adopted single-marker analysis cannot accommodate the joint effects of a large number of markers. The existing joint-effects methods also have limitations. Specifically, they may suffer from high computational cost, do not respect the "main effect, interaction" hierarchical structure, or use ineffective techniques. We develop a penalization method for the identification of important G × E interactions and main effects. It has an intuitive formulation, respects the hierarchical structure, accommodates the joint effects of multiple markers, and is computationally affordable. In numerical study, we analyze prognosis data under the AFT (accelerated failure time) model. Simulation shows satisfactory performance of the proposed method. Analysis of an NHL (non-Hodgkin lymphoma) study with SNP measurements shows that the proposed method identifies markers with important implications and satisfactory prediction performance.
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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.