Currently there are 153 consensus coronary artery disease (CAD) associated common genetic variants identified through meta-analysis of genome-wide association studies (GWAS).1 For some, the molecular mechanisms explaining their association with CAD are well-defined (e.g. PCSK9, LDL-R). However, for the vast majority, the molecular mechanisms are less well understood or completely unknown. A more comprehensive characterization of the mechanisms for these CAD GWAS variants could lead to new insights concerning the pathogenesis of coronary disease or suggest novel therapeutic or preventive strategies. Recent advances in high-throughput nuclear magnetic resonance (NMR) spectroscopy and mass-spectrometry (MS) make it possible to perform highly accurate, precise, and sensitive metabolomic profiling on thousands of biologic samples 2-6. Unlike conventional targeted metabolomics, un-targeted metabolomics uses a combination of NMR and MS assays to access a broader range of metabolites (both known and unknown) than possible from any single metabolomic assay or target list. The overall goal of this proposal is to use un-targeted metabolomics to characterize the metabolomic signatures associated with each consensus CAD GWAS hit.
We aim to generate new knowledge about the mechanisms and biological pathways involved in the pathogenesis of CAD. We propose to use previously obtained GWAS and metabolomic data from subsets of MESA (N=4,000), RHS (N=2,000), Airwave (N=4,000), 1. To perform univariate and multivariate metabolome-wide association analyses with each of the consensus CAD GWAS hits. The metabolomic data will include two NMR assays (NOESY and CPMG) and four MS assays (lipid+/-, HILIC+/-) representing >100,000 distinct metabolomic features. 2. To use statistical, bioinformatic and analytic chemistry methods to identify the specific metabolites represented by the NMR and MS features identified in Specific Aim 1. 3. To use unsupervised and supervised network and systems biology analyses to characterize the groups of metabolites and pathways associated with each GWAS hit. 4. To create a data repository of all the metabolomic data and the generated association and network analyses for the benefit of the wider scientific community. This project will be carried out by a collaborating group of international scientists with expertise in cardiovascular disease, metabolomics, biochemistry, statistical genetics, computational and systems biology, and project management. The resulting data may provide novel insights concerning the metabolic and physiologic mechanisms through which CAD GWAS hits influence cardiovascular risk factors and risk for clinical cardiovascular events.
The overall goal of this proposal is to use un-targeted metabolomics to characterize the metabolomic signatures associated with genomic variants associated with CAD. In this way we aim to generate new knowledge about the mechanisms and biological pathways involved in the pathogenesis of CAD. We expect this knowledge to lead to the development of better biomarkers for risk prediction and more mechanistically targeted and individually tailored interventions for treatment and prevention of coronary disease that can be used to reduce the human and financial costs of cardiovascular disease worldwide.
