ArticleBioinformatics (Oxford, England)2014
EPIQ-efficient detection of SNP-SNP epistatic interactions for quantitative traits.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.BMC genomics · 2025Article
- Association between polymorphisms of glucagon-like peptide-1 receptor gene and susceptibility to osteoporosis in Chinese postmenopausal women.Journal of orthopaedic surgery and research · 2024Article
- Identification of disease-associated loci using machine learning for genotype and network data integration.Bioinformatics (Oxford, England) · 2019Article
- Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network.BMC bioinformatics · 2019Article
- Article
- Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data.Briefings in bioinformatics · 2018Review
- MatrixEpistasis: ultrafast, exhaustive epistasis scan for quantitative traits with covariate adjustment.Bioinformatics (Oxford, England) · 2018Article
- Local Joint Testing Improves Power and Identifies Hidden Heritability in Association Studies.Genetics · 2016Article
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6 authors.
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Abstract
motivationGene-gene interactions are of potential biological and medical interest, as they can shed light on both the inheritance mechanism of a trait and on the underlying biological mechanisms. Evidence of epistatic interactions has been reported in both humans and other organisms. Unlike single-locus genome-wide association studies (GWAS), which proved efficient in detecting numerous genetic loci related with various traits, interaction-based GWAS have so far produced very few reproducible discoveries. Such studies introduce a great computational and statistical burden by necessitating a large number of hypotheses to be tested including all pairs of single nucleotide polymorphisms (SNPs). Thus, many software tools have been developed for interaction-based case-control studies, some leading to reliable discoveries. For quantitative data, on the other hand, only a handful of tools exist, and the computational burden is still substantial.
resultsWe present an efficient algorithm for detecting epistasis in quantitative GWAS, achieving a substantial runtime speedup by avoiding the need to exhaustively test all SNP pairs using metric embedding and random projections. Unlike previous metric embedding methods for case-control studies, we introduce a new embedding, where each SNP is mapped to two Euclidean spaces. We implemented our method in a tool named EPIQ (EPIstasis detection for Quantitative GWAS), and we show by simulations that EPIQ requires hours of processing time where other methods require days and sometimes weeks. Applying our method to a dataset from the Ludwigshafen risk and cardiovascular health study, we discovered a pair of SNPs with a near-significant interaction (P = 2.2 × 10(-13)), in only 1.5 h on 10 processors. AVAILABILITY: https://github.com/yaarasegre/EPIQ
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