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Identifying functional modules for coronary artery disease by a prior knowledge-based approach.

Li H1, Zuo X2, Ouyang P1, Lin M1, Zhao Z3, Liang Y4, Zhong S4, Rao S5.

Gene. 2014 Mar 10;537(2):260-8.

 

1Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China.

2Institute 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.

3Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China; Department of Statistical Sciences, School of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou 510080, China.

4Department of Internal Cardiovascular Medicine, Maoming People’s Hospital, Maoming 525000, China.

5Institute 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; Department of Statistical Sciences, School of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: [email protected]

 

Abstract

Until recently, the underlying genetic mechanisms for coronary artery disease (CAD) have been largely unknown, with just a list of genes identified accounting for very little of the disease in the population. Hence, a systematic dissection of the sophisticated interplays between these individualdisease genes and their functional involvements becomes essential. Here, we presented a novel knowledge-based approach to identify the functionalmodules for CAD. First, we selected 266 disease genes in CAD gene database as the initial seed genes, and used PPI knowledge as a guide to expand these genes into a CAD-specific gene network. Then, we used Newman’s algorithm to decompose the primary network into 14 compactmodules with high modularity. By analysis of these modules, we further identified 114 hub genes, all either directly or indirectly associated with CAD. Finally, by functional analysis of these modules, we revealed several novel pathogenic mechanisms for CAD (for examples, some yet rarely concerned like peptide YY receptor activity, Fc gamma R-mediated phagocytosis and actin cytoskeleton regulation etc.).

Copyright © 2013 Elsevier B.V. All rights reserved.

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CAD genetics