ArticleBMC medical genomics2014
IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis.
Article in BMC medical genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transferring entropy to the realm of GxG interactions.Briefings in bioinformatics · 2018Pooled it
- Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies.BioData mining · 2024Article
- Biomarker interaction selection and disease detection based on multivariate gain ratio.BMC bioinformatics · 2022Article
- Detecting gene-gene interactions from GWAS using diffusion kernel principal components.BMC bioinformatics · 2022Article
- Optimized permutation testing for information theoretic measures of multi-gene interactions.BMC bioinformatics · 2021Article
- Modified entropy-based procedure detects gene-gene-interactions in unconventional genetic models.BMC medical genomics · 2020Article
- Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network.BMC bioinformatics · 2019Article
- GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data.BMC systems biology · 2018Article
- Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations.Scientific reports · 2017Article
- GRACOMICS: software for graphical comparison of multiple results with omics data.BMC genomics · 2015Article
- An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.BioMed research international · 2015Article
- New horizons in translational bioinformatics: TBC 2013.BMC medical genomics · 2014Article
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3 authors.
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Abstract
backgroundWith the development of high-throughput genotyping and sequencing technology, there are growing evidences of association with genetic variants and complex traits. In spite of thousands of genetic variants discovered, such genetic markers have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. Gene-gene interaction (GGI) analysis is expected to unveil a large portion of unexplained heritability of complex traits.
methodsIn this work, we propose IGENT, Information theory-based GEnome-wide gene-gene iNTeraction method. IGENT is an efficient algorithm for identifying genome-wide gene-gene interactions (GGI) and gene-environment interaction (GEI). For detecting significant GGIs in genome-wide scale, it is important to reduce computational burden significantly. Our method uses information gain (IG) and evaluates its significance without resampling.
resultsThrough our simulation studies, the power of the IGENT is shown to be better than or equivalent to that of that of BOOST. The proposed method successfully detected GGI for bipolar disorder in the Wellcome Trust Case Control Consortium (WTCCC) and age-related macular degeneration (AMD).
conclusionsThe proposed method is implemented by C++ and available on Windows, Linux and MacOSX.
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