title: Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data.
abstract: Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium (WTCCC) SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene-gene interactions involved in these susceptible pathways with their protein-protein interaction (PPI) knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer's disease, non-alcoholic fatty liver disease, and Huntington's disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer's disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer's disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.
authors.affiliation: Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhao
authors.affiliation: School of Life Sciences, Sun Yat-sen University, Guangzhou 510080, China.
authors.forename: Yi-Zhao
authors.initials: YZ
authors.lastname: Luan
authors.affiliation: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Zuo
authors.affiliation: Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China.
authors.forename: Ye-Da
authors.initials: YD
authors.lastname: Chen
authors.affiliation: Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China.
authors.forename: Jiheng
authors.initials: J
authors.lastname: Qin
authors.affiliation: Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China.
authors.forename: Lv
authors.initials: L
authors.lastname: Jin
authors.affiliation: Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China.
authors.forename: Yiqing
authors.initials: Y
authors.lastname: Tan
authors.affiliation: Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China.
authors.forename: Meihua
authors.initials: M
authors.lastname: Lin
authors.affiliation: Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
authors.forename: Naizun
authors.initials: N
authors.lastname: Zhang
authors.affiliation: Maoming People's Hospital, Maoming 525000, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Liang
authors.affiliation: Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China; Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: raoshaoq@gdmc.edu.cn.
authors.forename: Shao-Qi
authors.initials: SQ
authors.lastname: Rao
mesh_terms: D016564:Amyloid beta-Protein Precursor / Q000235:genetics / Q000378:metabolism; D058543:Class Ia Phosphatidylinositol 3-Kinase; D003324:Coronary Artery Disease / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D030541:Databases, Genetic; D053263:Gene Regulatory Networks / Q000235:genetics*; D055106:Genome-Wide Association Study; D006801:Humans; D015810:Linkage Disequilibrium; D016015:Logistic Models; D019869:Phosphatidylinositol 3-Kinases / Q000235:genetics / Q000378:metabolism; D020641:Polymorphism, Single Nucleotide; D012306:Risk
chemical_list: D016564:Amyloid beta-Protein Precursor; D058543:Class Ia Phosphatidylinositol 3-Kinase; D019869:Phosphatidylinositol 3-Kinases; C588001:PIK3R1 protein, human
keywords: Coronary artery disease; Functional module; Genetic network; Genome-wide SNP profiling; Risk pathway