title: A Bayesian method for identifying genetic interactions.
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.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Shyam
authors.initials: S
authors.lastname: Visweswaran
authors.forename: An-Kwok Ian
authors.initials: AK
authors.lastname: Wong
authors.forename: M Michael
authors.initials: MM
authors.lastname: Barmada
mesh_terms: D001499:Bayes Theorem; D004843:Epistasis, Genetic; D006801:Humans; D008957:Models, Genetic; D058011:Multifactor Dimensionality Reduction; D020641:Polymorphism, Single Nucleotide