Support Vector Machines with L1 penalty for detecting gene-gene interactions

Authors:
Yuanyuan Shen;Zhe Liu;Jurg Ott
Affiliations:
Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA;Department of Statistics, University of Chicago, 5734 S. University Avenue, Chicago, IL 60637, USA;Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 4A Datun Road, Beijing 100101, China
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2012

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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.