ArticleBioinformatics (Oxford, England)2018
MatrixEpistasis: ultrafast, exhaustive epistasis scan for quantitative traits with covariate adjustment.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Evaluation of epistasis detection methods for quantitative phenotypes.Bioinformatics (Oxford, England) · 2026Article
- A Forward Genetics Strategy for High-Throughput Gene Identification via Precise Image-Based Phenotyping of an Indexed EMS Mutant Library.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Epistasis regulates genetic control of cardiac hypertrophy.Nature cardiovascular research · 2025Article
- Deciphering epistatic genetic regulation of cardiac hypertrophy.medRxiv : the preprint server for health sciences · 2025Article
- Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer.medRxiv : the preprint server for health sciences · 2025Article
- An overview of recent technological developments in bovine genomics.Veterinary and animal science · 2024Review
- Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis.BioData mining · 2024Article
- Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies.G3 (Bethesda, Md.) · 2023Article
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes.Biology · 2021Article
- Genetic Interactions Affect Lung Function in Patients with Systemic Sclerosis.G3 (Bethesda, Md.) · 2020Article
- Quantitative Trait Module-Based Genetic Analysis of Alzheimer's Disease.International journal of molecular sciences · 2019Article
- A time-dependent genome-wide SNP-SNP interaction analysis of chicken body weight.BMC genomics · 2019Article
- Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect.DNA research : an international journal for rapid publication of reports on genes and genomes · 2019Article
- Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency.The Plant journal : for cell and molecular biology · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Motivation: For many traits, causal loci uncovered by genetic mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this 'missing heritability' have been proposed. Single nucleotide polymorphism (SNP)-SNP interaction (epistasis), as one of the compelling models, has been widely studied. However, the genome-wide scan of epistasis, especially for quantitative traits, poses huge computational challenges. Moreover, covariate adjustment is largely ignored in epistasis analysis due to the massive extra computational undertaking. Results: In the current study, we found striking differences among epistasis models using both simulation data and real biological data, suggesting that not only can covariate adjustment remove confounding bias, it can also improve power. Furthermore, we derived mathematical formulas, which enable the exhaustive epistasis scan together with full covariate adjustment to be expressed in terms of large matrix operation, therefore substantially improving the computational efficiency (∼104× faster than existing methods). We call the new method MatrixEpistasis. With MatrixEpistasis, we re-analyze a large real yeast dataset comprising 11 623 SNPs, 1008 segregants and 46 quantitative traits with covariates fully adjusted and detect thousands of novel putative epistasis with P-values < 1.48e-10. Availability and implementation: The method is implemented in R and available at https://github.com/fanglab/MatrixEpistasis. Supplementary information: Supplementary data are available at Bioinformatics online.
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.