Metabolomic Signatures of CAD Associated Genotypes

项目摘要 目前有153种共识与冠心病(CAD)相关的常见基因变异 通过全基因组关联研究的荟萃分析确定。1.对一些人来说，分子 解释它们与冠心病关联的机制是明确的(例如，PCSK9、LDL-R)。然而，对于 绝大多数情况下，分子机制还不太清楚或完全未知。A更多 对这些CAD GWAS型变种的机制进行全面表征可能会带来新的见解 关于冠状动脉疾病的发病机制，或提出新的治疗或预防策略。 高通量核磁共振谱和质谱学的新进展 (Ms)使对数以千计的 生物样本2-6份。与传统的靶向代谢组学不同，非靶向代谢组学使用 结合核磁共振和质谱学分析以获得更广泛的代谢物(包括已知和未知的) 可能来自任何单一的代谢物分析或靶点清单。 这项提议的总体目标是使用非靶向代谢组学来表征代谢组 与每一次共识CAD GWAS命中相关的签名。我们的目标是产生关于 冠心病发病机制及生物学途径的研究。我们建议使用以前的 从MESA(N=4,000)，RHS(N=2,000)，AirWave(N=4,000)， 1.对每个共识进行单变量和多变量代谢全组关联分析 CAD GWASS命中。代谢数据将包括两个核磁共振分析(NOESY和CPMG)和四个MS 代表&gt；100,000种不同代谢组学特征的分析(脂类/-，HILIC/-)。 2.利用统计、生物信息学和分析化学方法鉴定特定代谢物 以特定目标1中确定的核磁共振和MS特征为代表。 3.使用无监督和有监督的网络和系统生物学分析来表征 代谢物和与每一次GWA命中相关的途径。 4.创建所有代谢组数据以及所生成的关联和网络的数据储存库 为更广泛的科学界造福的分析。 该项目将由一个由具有以下专长的国际科学家组成的合作小组实施 心血管疾病，代谢组学，生物化学，统计遗传学，计算和系统生物学， 和项目管理。由此产生的数据可能会提供关于新陈代谢和 冠状动脉粥样硬化性心脏病影响心血管危险因素和危险的生理机制 临床心血管事件。

Project Summary 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.