title: RCM: a novel association approach to search for coronary artery disease genetic related metabolites based on SNPs and metabolic network.
abstract: Integration of genetic and metabolic network holds promise for providing insight into human disease. Coronary artery disease (CAD) is strongly heritable, but the heritability of metabolic compounds has not been evaluated in human metabolic context. Here we performed a genetic-based computational approach within eight sub-cellular networks from Edinburgh Human Metabolic Network to identify significant genetic risk compounds (SGRCs) of CAD. Our results provide the evidence that the high heritabilities of SGRCs played an important role in CAD pathogenesis. Besides, SGRCs were discovered to be strongly associated with lipid metabolism. We also established a possible disease-causing reference table to decipher genetic associations of SGRCs with CAD. Comparing with traditional method, RCM experienced better performance in CAD genetic risk compounds' identification. These findings provided novel insights into CAD pathogenesis from a genetic perspective.
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China.
authors: Xu
authors: X
authors: Li
authors: Lina
authors: L
authors: Chen
authors: Liangcai
authors: L
authors: Zhang
authors: Wan
authors: W
authors: Li
authors: Xu
authors: X
authors: Jia
authors: Weiguo
authors: W
authors: Li
authors: Xiaoli
authors: X
authors: Qu
authors: Jingxie
authors: J
authors: Tai
authors: Chenchen
authors: C
authors: Feng
authors: Fan
authors: F
authors: Zhang
authors: Weiming
authors: W
authors: He
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003324:Coronary Artery Disease / Q000235:genetics* / Q000378:metabolism; D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012307:Risk Factors