title: Support vector machines with L1 penalty for detecting gene-gene interactions.
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: model selection with support vector machines identifies the most promising single nucleotide polymorphisms and interactions; 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.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. yushen@hsph.harvard.edu
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Shen
authors.forename: Zhe
authors.initials: Z
authors.lastname: Liu
authors.forename: Jurg
authors.initials: J
authors.lastname: Ott
mesh_terms: D000465:Algorithms; D015870:Gene Expression; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D060388:Support Vector Machine