ArticleStatistics in medicine2018
Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 38 citations in OpenAlex.
- MixedBayes: An R Package for Longitudinal Gene-Environment Interaction Analysis Using Robust Sparse Bayesian Mixed Models.Entropy (Basel, Switzerland) · 2026Article
- Ethnic genomic differences in esophageal squamous cell carcinoma: Whole-exome sequencing of Han and Kazakh populations in China.World journal of gastroenterology · 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
- Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions.Computational statistics & data analysis · 2024Article
- Gene-environment interaction analysis under the Cox model.Annals of the Institute of Statistical Mathematics · 2023Article
- Article
- Gene-environment interaction analysis via deep learning.Genetic epidemiology · 2023Article
- Springer: An R package for bi-level variable selection of high-dimensional longitudinal data.Frontiers in genetics · 2023Article
- A tree-based gene-environment interaction analysis with rare features.Statistical analysis and data mining · 2022Article
- Sparse group variable selection for gene-environment interactions in the longitudinal study.Genetic epidemiology · 2022Article
- Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data.Genes · 2022Article
- 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
- Review
- Integrating Multi-Omics Data for Gene-Environment Interactions.Biotech (Basel (Switzerland)) · 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
- Impact of Gene-Environment Interactions on Cancer Development.International journal of environmental research and public health · 2020Review
- Identification of gene-environment interactions with marginal penalization.Genetic epidemiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
Identification of gene-environment (G × E) interactions associated with disease phenotypes has posed a great challenge in high-throughput cancer studies. The existing marginal identification methods have suffered from not being able to accommodate the joint effects of a large number of genetic variants, while some of the joint-effect methods have been limited by failing to respect the "main effects, interactions" hierarchy, by ignoring data contamination, and by using inefficient selection techniques under complex structural sparsity. In this article, we develop an effective penalization approach to identify important G × E interactions and main effects, which can account for the hierarchical structures of the 2 types of effects. Possible data contamination is accommodated by adopting the least absolute deviation loss function. The advantage of the proposed approach over the alternatives is convincingly demonstrated in both simulation and a case study on lung cancer prognosis with gene expression measurements and clinical covariates under the accelerated failure time model.
Indexed as
Identifiers
What OpenQuestion holds
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.