ArticleWiley interdisciplinary reviews. Computational statistics
Statistical methods for gene-environment interaction analysis.
Article in Wiley interdisciplinary reviews. Computational statistics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Gene-Environment Interplay Beyond Interactions: Psychosocial Environments Mediate Genetic Effects Underlying Externalizing-Behavior Trajectories.Clinical psychological science : a journal of the Association for Psychological Science · 2026Article
- Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.Journal of the American Statistical Association · 2026Article
- A Narrative Review of Gene-Environment Interactions in Pediatric Pulmonology.Pediatric pulmonology · 2026Review
- Leveraging polygenic risk scores to infer causal directions in genotype-by-environment interactions between complex traits.Human genetics · 2026Article
- Causal effect heterogeneity estimation using summary statistics.Research square · 2026Article
- Impact of prenatal environmental exposure on offspring neurodevelopment and susceptibility to neurodegenerative diseases: mechanisms and perspectives.Frontiers in public health · 2026Review
- Methods for modeling gene-environment interplay using polygenic risk scores.Statistical applications in genetics and molecular biology · 2026Review
- Modulation of Gut Microbes and Hepatic Metabolites by PCP Ameliorates NASH and Fatigue-like Performance in Mice.Nutrients · 2025Article
- PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits.Nature human behaviour · 2025Article
- Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks.Nature communications · 2025Article
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning.BMC public health · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Most human complex phenotypes result from multiple genetic and environmental factors and their interactions. Understanding the mechanisms by which genetic and environmental factors interact offers valuable insights into the genetic architecture of complex traits and holds great potential for advancing precision medicine. The emergence of large population biobanks has led to the development of numerous statistical methods aiming at identifying gene-environment interactions (G × E). In this review, we present state-of-the-art statistical methodologies for G × E analysis. We will survey a spectrum of approaches for single-variant G × E mapping, followed by various techniques for polygenic G × E analysis. We conclude this review with a discussion on the future directions and challenges in G × E research.
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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.