title: Genome-wide pathway association studies of multiple correlated quantitative phenotypes using principle component analyses.
abstract: Genome-wide pathway association studies provide novel insight into the biological mechanism underlying complex diseases. Current pathway association studies primarily focus on single important disease phenotype, which is sometimes insufficient to characterize the clinical manifestations of complex diseases. We present a multi-phenotypes pathway association study(MPPAS) approach using principle component analysis(PCA). In our approach, PCA is first applied to multiple correlated quantitative phenotypes for extracting a set of orthogonal phenotypic components. The extracted phenotypic components are then used for pathway association analysis instead of original quantitative phenotypes. Four statistics were proposed for PCA-based MPPAS in this study. Simulations using the real data from the HapMap project were conducted to evaluate the power and type I error rates of PCA-based MPPAS under various scenarios considering sample sizes, additive and interactive genetic effects. A real genome-wide association study data set of bone mineral density (BMD) at hip and spine were also analyzed by PCA-based MPPAS. Simulation studies illustrated the performance of PCA-based MPPAS for identifying the causal pathways underlying complex diseases. Genome-wide MPPAS of BMD detected associations between BMD and KENNY_CTNNB1_TARGETS_UP as well as LONGEVITYPATHWAY pathways in this study. We aim to provide a applicable MPPAS approach, which may help to gain deep understanding the potential biological mechanism of association results for complex diseases.
authors.affiliation: Key Laboratory of Environment and Gene Related Diseases of Ministry Education, Faculty of Public Health, College of Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Zhang
authors.forename: Xiong
authors.initials: X
authors.lastname: Guo
authors.forename: Shixun
authors.initials: S
authors.lastname: Wu
authors.forename: Jing
authors.initials: J
authors.lastname: Han
authors.forename: Yongjun
authors.initials: Y
authors.lastname: Liu
authors.forename: Hui
authors.initials: H
authors.lastname: Shen
authors.forename: Hong-Wen
authors.initials: HW
authors.lastname: Deng
mesh_terms: D015519:Bone Density / Q000235:genetics; D003198:Computer Simulation; D001330:Electronic Data Processing / Q000379:methods; D005260:Female; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data*; D060148:HapMap Project; D006615:Hip; D006801:Humans; D008297:Male; D053858:Metabolic Networks and Pathways / Q000235:genetics; D010641:Phenotype; D025341:Principal Component Analysis; D040641:Quantitative Trait Loci / Q000235:genetics; D013131:Spine