Bayesian combinatorial partitioning for detecting interactions among genetic variants
Shyam Visweswaran1, An-Kwok Ian Wong
1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA.
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.
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