TY - JOUR
T1 - A PLSPM-Based Test Statistic for Detecting Gene-Gene Co-Association in Genome-Wide Association Study with Case-Control Design
AU - Zhang, Xiaoshuai
AU - Yang, Xiaowei
AU - Yuan, Zhongshang
AU - Liu, Yanxun
AU - Li, Fangyu
AU - Peng, Bin
AU - Zhu, Dianwen
AU - Zhao, Jinghua
AU - Xue, Fuzhong
PY - 2013/4/19
Y1 - 2013/4/19
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0062129
DO - 10.1371/journal.pone.0062129
M3 - Article
C2 - 23620809
AN - SCOPUS:84876439990
VL - 8
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 4
M1 - e62129
ER -