title: Bayesian combinatorial partitioning for detecting interactions among genetic variants.
abstract: Detecting epistatic (nolinear) interactions among single nucleotide polymorphisms (SNPs) at multiple loci is important in the analysis of genomic data in association studies. We developed a Bayesian combinatorial partitioning (BCP) for detecting such interactions among SNPs that are predictive of disease. When compared with multifactor dimensionality reduction (MDR), a widely used combinatorial partitioning method for detecting interactions, BCP has significantly greater power and is computationally more efficient.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA.
authors.forename: Shyam
authors.initials: S
authors.lastname: Visweswaran
authors.forename: An-Kwok Ian
authors.initials: AK
authors.lastname: Wong