ReviewBriefings in bioinformatics2019
Robust genetic interaction analysis.
Review in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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.
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
20 citing papers in PubMed.
- 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
- Article
- Role of gene interactions in the pathophysiology of skeletal dysplasias: A case report in Colombia.Journal, genetic engineering & biotechnology · 2024Article
- The significance of CDT1 expression in non-cancerous and cancerous liver in cases with hepatocellular carcinoma.Journal of clinical biochemistry and nutrition · 2023Article
- Gene-environment interaction analysis via deep learning.Genetic epidemiology · 2023Article
- A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification.Bioinformatics advances · 2023Article
- A tree-based gene-environment interaction analysis with rare features.Statistical analysis and data mining · 2022Article
- Gene-environment interaction identification via penalized robust divergence.Biometrical journal. Biometrische Zeitschrift · 2022Article
- Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Gene-gene interaction analysis incorporating network information via a structured Bayesian approach.Statistics in medicine · 2021Article
- Vertical integration methods for gene expression data analysis.Briefings in bioinformatics · 2021Review
- Structured gene-environment interaction analysis.Biometrics · 2020Article
- Effects ofNeuropsychiatric disease and treatment · 2020Article
- Penalized Variable Selection for Lipid-Environment Interactions in a Longitudinal Lipidomics Study.Genes · 2019Article
- Robust semiparametric gene-environment interaction analysis using sparse boosting.Statistics in medicine · 2019Article
- Article
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.High-throughput · 2019Review
- Robust gene-environment interaction analysis using penalized trimmed regression.Journal of statistical computation and simulation · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that genetic interactions (including gene-gene and gene-environment interactions) play important roles beyond the main genetic and environmental effects. In practical genetic interaction analyses, model mis-specification and outliers/contaminations in response variables and covariates are not uncommon, and demand robust analysis methods. Compared with their nonrobust counterparts, robust genetic interaction analysis methods are significantly less popular but are gaining attention fast. In this article, we provide a comprehensive review of robust genetic interaction analysis methods, on their methodologies and applications, for both marginal and joint analysis, and for addressing model mis-specification as well as outliers/contaminations in response variables and covariates.
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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.