ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2009
A Bayesian method for identifying genetic interactions.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Article
- Article
- epiACO - a method for identifying epistasis based on ant Colony optimization algorithm.BioData mining · 2017Article
- FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm.PloS one · 2016Article
- Cuckoo search epistasis: a new method for exploring significant genetic interactions.Heredity · 2014Article
- Identifying genetic interactions associated with late-onset Alzheimer's disease.BioData mining · 2014Article
- Learning genetic epistasis using Bayesian network scoring criteria.BMC bioinformatics · 2011Article
- Article
- A fast algorithm for learning epistatic genomic relationships.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2010Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An important challenge in the analysis of single nucleotide polymorphism (SNP) data is the identification of SNPs that interact in a nonlinear fashion in their association with disease. Such epistatic interactions among genetic variants at multiple loci likely underlie the inheritance of common diseases. We have developed a novel method called the Bayesian combinatorial method (BCM) for detecting combination of genetic variants that are predictive of disease. When compared with the multifactor dimensionality reduction (MDR), a widely used combinatorial method, BCM has significantly greater power to detect interactions and is computationally more efficient.
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