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

• We use PPI as a guide to expand CAD initial genes into a specific gene network.
• We use Newman's algorithm to decompose the network into compact modules.
• We identify their hub genes, all either direct or indirectly associated with CAD.
• We reveal several novel pathogenic mechanisms for CAD.
• The proposed approach is an efficient way to identify functional modules.

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 individual disease genes and their functional involvements becomes essential. Here, we presented a novel knowledge-based approach to identify the functional modules for CAD. First, we selected 266 disease genes in CADgene 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 compact modules 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.).