Comput Math Methods Med - SNP selection in genome-wide association studies via penalized support vector machine with MAX test.

Tópicos

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Resumo

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.

Resumo Limpo

one main object genomewid associ studi gwas develop predict model binari clinic outcom use singlenucleotid polymorph snps can use diagnost prognost purpos better understand relationship diseas snps penal support vector machin svm method wide use toward end howev sinc investig often ignor genet model snps final model result loss effici predict clinic outcom order overcom problem propos twostag method genet model snp identifi use max test predict model fit use penal svm method appli propos method various penal svms compar perform svms use various penalti function result simul real gwas data analysi show propos method perform better predict method ignor genet model term predict power select

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