Support Vector Machines with L₁ penalty for detecting gene-gene interactions
by Yuanyuan Shen; Zhe Liu; Jurg Ott
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 6, No. 5, 2012

Abstract: Interactions among genetic variants are likely to affect risk for human complex diseases, and their identification should increase the power to detect disease-associated variants and elucidate biological pathways underlying diseases. We propose a two-stage approach: 1) model selection with Support Vector Machines identifies the most promising Single Nucleotide Polymorphisms and interactions; 2) logistic regression ensures a valid type I error by excluding non-significant candidates after Bonferroni correction. Simulation studies for case-control data suggest that our method powerfully detects gene-gene interactions. We analyze a published genome-wide case-control dataset, where our method successfully identifies an interaction term, which was missed in previous studies.

Online publication date: Wed, 17-Dec-2014

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