ReviewNature reviews. Genetics2014
Detecting epistasis in human complex traits.
Review in Nature reviews. Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 231 papers, 4 of them syntheses that pooled it.
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
231 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Meta-analysis of dispensable essential genes and their interactions with bypass suppressors.Life science alliance · 2024Pooled it
- Detecting fitness epistasis in recently admixed populations with genome-wide data.BMC genomics · 2020Pooled it
- Detecting epistasis within chromatin regulatory circuitry reveals CAND2 as a novel susceptibility gene for obesity.International journal of obesity (2005) · 2019Pooled it
- A meta-analytic framework for detection of genetic interactions.Genetic epidemiology · 2016Pooled it
- An epistasis between dopaminergic and oxytocinergic systems confers risk of post-traumatic stress disorder in a traumatized Chinese cohort.Scientific reports · 2019Trial
- Robust cis-by-trans epistasis in the human plasma proteome highlights an ABO-centered interaction network.American journal of human genetics · 2026Article
- Article
- EpiSNPdb: A Comprehensive Database of Genetic Epistasis Across Multiple Cancer Types.Current issues in molecular biology · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Polygenic backgrounds influence phenotypic consequences of variants in cells, individuals, and populations.Cell genomics · 2026Review
- Review
- Predicting epistasis across proteins by structural logic.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Phantom epistasis through the lens of genealogies.Genetics · 2026Article
- GWAIS-Web: a free and secure web service for ultra-fast and large-scale genome-wide association interaction studies.Bioinformatics advances · 2026Article
- Semiparametric efficient estimation of small genetic effects in large-scale population cohorts.Biostatistics (Oxford, England) · 2025Article
- Interactions with polygenic background impact quantitative traits in the UK Biobank.medRxiv : the preprint server for health sciences · 2025Article
- iRUNNER: A Baseline Mutation Burden Regression for Identifying Gene Interaction Between Rare Variants for Diseases.Genomics, proteomics & bioinformatics · 2025Article
- Improving accuracy in genome-wide association studies: a two-step approach for handling below limit of detection biomarker measurements.NAR genomics and bioinformatics · 2025Article
- Exploring genome, transcriptome, and microbiome interactions related to feed efficiency and methane emissions in Bos indicus through multi-omics network analysis.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Article
- A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic interaction effects.Nature genetics · 2025Article
171 more citing papers are in PubMed but not listed here.
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
Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.
Indexed as
Identifiers
25200660What Socratic 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 Socratic graph.