title: SNP selection in genome-wide association studies via penalized support vector machine with MAX test.
abstract: One of main objectives of a genome-wide association study (GWAS) is to develop a prediction model for a binary clinical outcome using single-nucleotide polymorphisms (SNPs) which can be used for diagnostic and prognostic purposes and for better understanding of the relationship between the disease and SNPs. Penalized support vector machine (SVM) methods have been widely used toward this end. However, since investigators often ignore the genetic models of SNPs, a final model results in a loss of efficiency in prediction of the clinical outcome. In order to overcome this problem, we propose a two-stage method such that the the genetic models of each SNP are identified using the MAX test and then a prediction model is fitted using a penalized SVM method. We apply the proposed method to various penalized SVMs and compare the performance of SVMs using various penalty functions. The results from simulations and real GWAS data analysis show that the proposed method performs better than the prediction methods ignoring the genetic models in terms of prediction power and selectivity.
authors.affiliation: Department of Statistics and Information Science, Dongguk University, Gyeongju 780-714, Republic of Korea.
authors.forename: Jinseog
authors.initials: J
authors.lastname: Kim
authors.forename: Insuk
authors.initials: I
authors.lastname: Sohn
authors.forename: Dennis Dong Hwan
authors.initials: DD
authors.lastname: Kim
authors.forename: Sin-Ho
authors.initials: SH
authors.lastname: Jung
mesh_terms: D056808:Biostatistics; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011379:Prognosis; D060388:Support Vector Machine