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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Kim, J; Sohn, I; Kim, DDH; Jung, S-H

Published Date

  • 2013

Published In

Volume / Issue

  • 2013 /

Start / End Page

  • 340678 -

PubMed ID

  • 24174989

Pubmed Central ID

  • 24174989

Electronic International Standard Serial Number (EISSN)

  • 1748-6718

Digital Object Identifier (DOI)

  • 10.1155/2013/340678

Language

  • eng

Conference Location

  • United States