Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0053320
Title: Genome-Wide Pathway Association Studies of Multiple Correlated Quantitative Phenotypes Using Principle Component Analyses
Authors: Zhang F.
Guo X.
Wu S.
Han J.
Liu Y.
Shen H. 
Deng H.-W.
Keywords: article
bone density
error
genetic association
heredity
hip
human
multiphenotypes pathway association study
phenotype
power analysis
principal component analysis
quantitative analysis
sample size
simulation
single nucleotide polymorphism
spine
trend study
Automatic Data Processing
Bone Density
Computer Simulation
Female
Genetic Predisposition to Disease
Genome-Wide Association Study
HapMap Project
Hip
Humans
Male
Metabolic Networks and Pathways
Phenotype
Principal Component Analysis
Quantitative Trait Loci
Spine
Issue Date: 2012
Citation: Zhang F., Guo X., Wu S., Han J., Liu Y., Shen H., Deng H.-W. (2012). Genome-Wide Pathway Association Studies of Multiple Correlated Quantitative Phenotypes Using Principle Component Analyses. PLoS ONE 7 (12) : e53320. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0053320
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. © 2012 Zhang et al.
Source Title: PLoS ONE
URI: https://scholarbank.nus.edu.sg/handle/10635/161356
ISSN: 19326203
DOI: 10.1371/journal.pone.0053320
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