Genome-wide pathway association studies of multiple correlated quantitative phenotypes using principle component analyses

• Published in: PloS one 2012-12, Vol.7 (12), p.e53320-e53320
• Main Authors: Zhang, Feng, Guo, Xiong, Wu, Shixun, Han, Jing, Liu, Yongjun, Shen, Hui, Deng, Hong-Wen
• Format: Article
• Language: English
• Subjects: Analysis; Association analysis; Automatic Data Processing - methods; Biological effects; Biology; Bone density; Bone Density - genetics; Bone mineral density; Cardiovascular disease; Computer Simulation; Correlation analysis; Diseases; Education; Female; Genes; Genetic aspects; Genetic effects; Genetic Predisposition to Disease; Genome-wide association studies; Genome-Wide Association Study - methods; Genome-Wide Association Study - statistics & numerical data; Genomes; Genomics; Genotype & phenotype; Growth hormones; HapMap Project; Hip; Humans; Insulin-like growth factors; Laboratories; Male; Mathematics; Medical research; Medicine; Metabolic Networks and Pathways - genetics; Ontology; Osteoporosis; Phenotype; Phenotypes; Principal Component Analysis; Principal components analysis; Public health; Quantitative Trait Loci - genetics; Researchers; Simulation; Spine; Statistical analysis; Studies
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0053320

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.