INTEGRATION OF METABOLOMICS AND GENOMICS DATA TO INVESTIGATE CARDIOVASCULAR RISK

项目摘要 常见和复杂疾病的诊断、治疗选择和预后，如 心血管疾病仍然主要基于少数生物标志物。虽然这些生物标志物改善了 人群水平的临床实践，其在特定亚群中的敏感性和特异性 可能会更有限。随着经济实惠和高度可重复的组学测量的出现， 是一个很好的机会，通过综合分析更大的 几组标记。一个特别有前途的途径是代谢组学，因为它允许识别和 通过核技术等高性能技术， 磁共振（NMR）光谱。 在这里，我们建议测试的假设，即聚合的NMR功能，与 基因型数据，并可以匹配到一个或几个代谢物可以作为一个新的类型 定量生物标志物。这样的标记物可能比单个光谱特征更强大， 不能与已知代谢物相关联的特征组合。该提案的主要目标是 1)为了测试这些伪化合物是否比它们的任何一种都具有更强的遗传关联性， 个体特征和2）调查这些伪化合物是否与已建立的 心血管危险因素。 为此，我们将利用现有的基因型、代谢组学和其他表型数据， 已经测量了来自CoLaus（Cohorte Lausannoise）研究的983个个体的子集。然后， 我们将使用JUPITER的一个子集（n=500）验证我们的研究结果（JUPITER使用他汀类药物的理由）。 预防：一项评价瑞舒伐他汀的干预试验）研究。回归分析将用于 对基因型和代谢组学数据之间的关系建模，以及对基因型和代谢组学数据之间的关系建模。 假化合物和已知风险因素之间的关系 我们的建议是创新的，因为它不限于一组预定义的代谢物，如在靶向 代谢组学同时，我们提出的伪化合物概念的一个很大的优点是， 通常可以将它们与具有已知谱的一种或几种代谢物相匹配， 提供了一个有形的实体，可以涉及这些化学和生物途径， 代谢物。我们将把我们的方法作为用户友好的软件公开给非专家使用， 允许对疾病或相关性状具有特定兴趣的临床研究人员直接提取和 测试伪化合物作为潜在的生物标志物。这将有助于翻译非目标 代谢组学数据转化为潜在的临床相关生物标志物。

Project summary Diagnosis, choice of treatment, and prognosis of common and complex diseases, such as cardiovascular disease are still mostly based on a few biomarkers. While these biomarkers improve clinical practice at the population level, their sensitivity and specificity in particular subpopulation groups may be more limited. With the advent of affordable and highly reproducible omics measurements, there is a great opportunity to advance personalized medicine through the integrative analysis of much larger sets of markers. A particularly promising avenue is metabolomics, as it allows for the identification and quantification of hundreds of metabolites enabled by high performance technologies such as nuclear magnetic resonance (NMR) spectroscopy. Here, we propose to test the hypothesis that the aggregation of the NMR features that correlate with genotypic data and which can be matched to one or several metabolites could function as a novel type of quantitative biomarker. Such markers could be more powerful than individual spectral features or feature combinations that cannot be linked to known metabolites. The primary goals of the proposal are 1) to test whether these pseudo-compounds achieve stronger genetic association than any of their individual features and 2) to investigate whether such pseudo-compounds associate with established cardiovascular risk factors. For this purpose, we will leverage existing genotype, metabolomics, and other phenotype data, which has been measured for a subset of 983 individuals from the CoLaus (Cohorte Lausannoise) study. Then, we will validate our findings using a subset (n=500) of the JUPITER (Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin) study. Regression analysis will be used to model the relationship between genotype and metabolomics data, as well as to model the relationship between pseudo-compounds and established risk factors. Our proposal is innovative because it is not limited to a predefined set of metabolites as in targeted metabolomics. At the same time, a great advantage of our proposed concept of pseudo-compounds is that often it may be feasible to match them to one or several metabolites with a known spectrum, thus providing a tangible entity that can be related to the chemical and biological pathways of these metabolites. We will make our method publicly available for non-experts as user-friendly software, allowing clinical researchers with a specific interest in a disease or a related trait to directly extract and test pseudo-compounds as potential biomarkers. This will facilitate the translation of untargeted metabolomic data into potentially clinically relevant biomarkers.