ReviewFrontiers in genetics2014
Analysis pipeline for the epistasis search - statistical versus biological filtering.
Review in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 36 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- Phantom epistasis through the lens of genealogies.Genetics · 2026Article
- Interactions with polygenic background impact quantitative traits in the UK Biobank.medRxiv : the preprint server for health sciences · 2025Article
- Genome-wide epistasis analysis reveals significant epistatic signals associated with Parkinson's disease risk.Brain : a journal of neurology · 2025Article
- Genetic architecture of ideotype-related traits in middle American beans through single trait, multi-trait and epistatic genome-wide analyses.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS.Nature protocols · 2024Review
- Genetic interactions of schizophrenia using gene-based statistical epistasis exclusively identify nervous system-related pathways and key hub genes.Frontiers in genetics · 2023Article
- A pharmacogenetic interaction analysis of bevacizumab with paclitaxel in advanced breast cancer patients.NPJ breast cancer · 2022Article
- Cis-epistasis at the LPA locus and risk of cardiovascular diseases.Cardiovascular research · 2022Article
- RIL-StEp: epistasis analysis of rice recombinant inbred lines reveals candidate interacting genes that control seed hull color and leaf chlorophyll content.G3 (Bethesda, Md.) · 2021Article
- Using C-JAMP to Investigate Epistasis and Pleiotropy.Methods in molecular biology (Clifton, N.J.) · 2021Article
- A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models.Bioinformatics (Oxford, England) · 2020Article
- Genome-wide association and epistatic interactions of flowering time in soybean cultivar.PloS one · 2020Article
- Discovering genetic interactions bridging pathways in genome-wide association studies.Nature communications · 2019Article
- How to increase our belief in discovered statistical interactions via large-scale association studies?Human genetics · 2019Review
- The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation.Annals of translational medicine · 2018Article
- Review
- The Influence of Higher-Order Epistasis on Biological Fitness Landscape Topography.Journal of statistical physics · 2018Article
- Collective feature selection to identify crucial epistatic variants.BioData mining · 2018Article
- Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.American journal of epidemiology · 2017Article
- Functional regression method for whole genome eQTL epistasis analysis with sequencing data.BMC genomics · 2017Article
Corrections and comments
- Erratum issuedMukheerjee, Shubhabrata [corrected to Mukherjee, Shubhabrata]
Authors and funding
7 authors.
Funding
Abstract
Gene-gene interactions may contribute to the genetic variation underlying complex traits but have not always been taken fully into account. Statistical analyses that consider gene-gene interaction may increase the power of detecting associations, especially for low-marginal-effect markers, and may explain in part the "missing heritability." Detecting pair-wise and higher-order interactions genome-wide requires enormous computational power. Filtering pipelines increase the computational speed by limiting the number of tests performed. We summarize existing filtering approaches to detect epistasis, after distinguishing the purposes that lead us to search for epistasis. Statistical filtering includes quality control on the basis of single marker statistics to avoid the analysis of bad and least informative data, and limits the search space for finding interactions. Biological filtering includes targeting specific pathways, integrating various databases based on known biological and metabolic pathways, gene function ontology and protein-protein interactions. It is increasingly possible to target single-nucleotide polymorphisms that have defined functions on gene expression, though not belonging to protein-coding genes. Filtering can improve the power of an interaction association study, but also increases the chance of missing important findings.
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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.