Bayesian combinatorial partitioning for detecting interactions among genetic variants.

Shyam Visweswaran, An-Kwok Ian Wong
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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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遗传变异间相互作用检测的贝叶斯组合划分。
检测多位点单核苷酸多态性(SNPs)之间的上位性(非线性)相互作用在关联研究的基因组数据分析中是重要的。我们开发了一种贝叶斯组合划分(BCP)来检测预测疾病的snp之间的这种相互作用。与多因素降维法(MDR)相比,BCP具有更强的能力和更高的计算效率。
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