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

Authors

Xiaoshuai Zhang, Shandong University
Xiaowei Yang, CUNY School of Public Health
Zhongshang Yuan, Shandong University
Yanxun Liu, Shandong University
Fangyu Li, Shandong University
Bin Peng, Chongqing Medical University
Dianwen Zhu, CUNY School of Public Health
Jinghua Zhao, MRC Epidemiology Unit and Institute of Metabolic Science
Fuzhong Xue, Shandong University

Document Type

Article

Publication Date

April 2013

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.