PMID- 23285279
OWN - NLM
STAT- MEDLINE
DCOM- 20130723
LR  - 20211021
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 7
IP  - 12
DP  - 2012
TI  - Genome-wide pathway association studies of multiple correlated quantitative 
      phenotypes using principle component analyses.
PG  - e53320
LID - 10.1371/journal.pone.0053320 [doi]
LID - e53320
AB  - 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.
FAU - Zhang, Feng
AU  - Zhang F
AD  - 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.
FAU - Guo, Xiong
AU  - Guo X
FAU - Wu, Shixun
AU  - Wu S
FAU - Han, Jing
AU  - Han J
FAU - Liu, Yongjun
AU  - Liu Y
FAU - Shen, Hui
AU  - Shen H
FAU - Deng, Hong-Wen
AU  - Deng HW
LA  - eng
GR  - R01 AR050496/AR/NIAMS NIH HHS/United States
GR  - R01 AR057049/AR/NIAMS NIH HHS/United States
PT  - Evaluation Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20121228
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Bone Density/genetics
MH  - Computer Simulation
MH  - Electronic Data Processing/methods
MH  - Female
MH  - Genetic Predisposition to Disease
MH  - Genome-Wide Association Study/*methods/*statistics & numerical data
MH  - HapMap Project
MH  - Hip
MH  - Humans
MH  - Male
MH  - Metabolic Networks and Pathways/genetics
MH  - Phenotype
MH  - *Principal Component Analysis
MH  - Quantitative Trait Loci/genetics
MH  - Spine
PMC - PMC3532454
COIS- Competing Interests: The authors have declared that no competing interests exist.
EDAT- 2013/01/04 06:00
MHDA- 2013/07/24 06:00
PMCR- 2012/12/28
CRDT- 2013/01/04 06:00
PHST- 2012/07/31 00:00 [received]
PHST- 2012/11/27 00:00 [accepted]
PHST- 2013/01/04 06:00 [entrez]
PHST- 2013/01/04 06:00 [pubmed]
PHST- 2013/07/24 06:00 [medline]
PHST- 2012/12/28 00:00 [pmc-release]
AID - PONE-D-12-22928 [pii]
AID - 10.1371/journal.pone.0053320 [doi]
PST - ppublish
SO  - PLoS One. 2012;7(12):e53320. doi: 10.1371/journal.pone.0053320. Epub 2012 Dec 28.