ArticleEuropean journal of human genetics : EJHG2011
EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units.
Article in European journal of human genetics : EJHG, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ultrafast genome-wide scan for SNP-SNP interactions in common complex disease.Genome research · 2012Pooled it
- Testing for Genetic Interactions in Complex Disease With Distance Correlation.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Turning heterogeneity of statistical epistasis networks to an advantage.Briefings in bioinformatics · 2026Article
- Detecting genetic interactions with visible neural networks.Communications biology · 2025Article
- Review on GPU accelerated methods for genome-wide SNP-SNP interactions.Molecular genetics and genomics : MGG · 2024Review
- Epistasis regulates genetic control of cardiac hypertrophy.Research square · 2023Article
- Missing Causality and Heritability of Autoimmune Hepatitis.Digestive diseases and sciences · 2023Article
- Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.Plants (Basel, Switzerland) · 2022Review
- Genetic control of non-genetic inheritance in mammals: state-of-the-art and perspectives.Mammalian genome : official journal of the International Mammalian Genome Society · 2020Review
- Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies.BMC bioinformatics · 2019Article
- How to increase our belief in discovered statistical interactions via large-scale association studies?Human genetics · 2019Review
- Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies.BioData mining · 2019Article
- Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data.Briefings in bioinformatics · 2018Review
- WISH-R- a fast and efficient tool for construction of epistatic networks for complex traits and diseases.BMC bioinformatics · 2018Article
- MatrixEpistasis: ultrafast, exhaustive epistasis scan for quantitative traits with covariate adjustment.Bioinformatics (Oxford, England) · 2018Article
- Performance of epistasis detection methods in semi-simulated GWAS.BMC bioinformatics · 2018Article
- Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis.GigaScience · 2017Article
- SHEsisPlus, a toolset for genetic studies on polyploid species.Scientific reports · 2016Article
- Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.PLoS genetics · 2016Article
- FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm.PloS one · 2016Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detection of epistatic interaction between loci has been postulated to provide a more in-depth understanding of the complex biological and biochemical pathways underlying human diseases. Studying the interaction between two loci is the natural progression following traditional and well-established single locus analysis. However, the added costs and time duration required for the computation involved have thus far deterred researchers from pursuing a genome-wide analysis of epistasis. In this paper, we propose a method allowing such analysis to be conducted very rapidly. The method, dubbed EPIBLASTER, is applicable to case-control studies and consists of a two-step process in which the difference in Pearson's correlation coefficients is computed between controls and cases across all possible SNP pairs as an indication of significant interaction warranting further analysis. For the subset of interactions deemed potentially significant, a second-stage analysis is performed using the likelihood ratio test from the logistic regression to obtain the P-value for the estimated coefficients of the individual effects and the interaction term. The algorithm is implemented using the parallel computational capability of commercially available graphical processing units to greatly reduce the computation time involved. In the current setup and example data sets (211 cases, 222 controls, 299468 SNPs; and 601 cases, 825 controls, 291095 SNPs), this coefficient evaluation stage can be completed in roughly 1 day. Our method allows for exhaustive and rapid detection of significant SNP pair interactions without imposing significant marginal effects of the single loci involved in the pair.
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