ReviewFrontiers in genetics2015
A survey about methods dedicated to epistasis detection.
Review in Frontiers in genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detecting fitness epistasis in recently admixed populations with genome-wide data.BMC genomics · 2020Pooled it
- Evaluation of epistasis detection methods for quantitative phenotypes.Bioinformatics (Oxford, England) · 2026Article
- How population subsampling to concentrate selection effects can help to find epistatic interactions of transmission ratio distortion.Heredity · 2026Article
- A novel explainable deep-learning approach for network analysis of epistatic interactions.NAR genomics and bioinformatics · 2026Article
- Turning heterogeneity of statistical epistasis networks to an advantage.Briefings in bioinformatics · 2026Article
- iRUNNER: A Baseline Mutation Burden Regression for Identifying Gene Interaction Between Rare Variants for Diseases.Genomics, proteomics & bioinformatics · 2025Article
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Co-evolution of alpha-helical transmembrane protein residues: large-scale variant profiling and complete mutational landscape of 2277 known PDB entries representing 504 unique human protein sequences.Journal of molecular evolution · 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
- Metabolic reaction fluxes as amplifiers and buffers of risk alleles for coronary artery disease.Molecular systems biology · 2025Article
- Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer.medRxiv : the preprint server for health sciences · 2025Article
- Distributed transformer for high order epistasis detection in large-scale datasets.Scientific reports · 2024Article
- Next-Gen GWAS: full 2D epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction.Genome biology · 2024Article
- Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis.BioData mining · 2024Article
- Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding.Frontiers in plant science · 2024Review
- Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic-assisted selection.The plant genome · 2023Article
- Exhaustive Variant Interaction Analysis using Multifactor Dimensionality Reduction.Research square · 2023Article
- Faster and more accurate pathogenic combination predictions with VarCoPP2.0.BMC bioinformatics · 2023Article
- Genetic interactions of schizophrenia using gene-based statistical epistasis exclusively identify nervous system-related pathways and key hub genes.Frontiers in genetics · 2023Article
- Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests.Nucleic acids research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the past decade, findings of genome-wide association studies (GWAS) improved our knowledge and understanding of disease genetics. To date, thousands of SNPs have been associated with diseases and other complex traits. Statistical analysis typically looks for association between a phenotype and a SNP taken individually via single-locus tests. However, geneticists admit this is an oversimplified approach to tackle the complexity of underlying biological mechanisms. Interaction between SNPs, namely epistasis, must be considered. Unfortunately, epistasis detection gives rise to analytic challenges since analyzing every SNP combination is at present impractical at a genome-wide scale. In this review, we will present the main strategies recently proposed to detect epistatic interactions, along with their operating principle. Some of these methods are exhaustive, such as multifactor dimensionality reduction, likelihood ratio-based tests or receiver operating characteristic curve analysis; some are non-exhaustive, such as machine learning techniques (random forests, Bayesian networks) or combinatorial optimization approaches (ant colony optimization, computational evolution system).
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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.