A PLSPM-Based Test Statistic for Detecting Gene-Gene Co-Association in Genome-Wide Association Study with Case-Control Design

Xiaoshuai Zhang, Xiaowei Yang, Zhongshang Yuan, Yanxun Liu, Fangyu Li, Bin Peng, Dianwen Zhu, Jinghua Zhao, Fuzhong Xue

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods.

Original languageEnglish (US)
Article numbere62129
JournalPLoS One
Volume8
Issue number4
DOIs
StatePublished - Apr 19 2013
Externally publishedYes

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

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