Metabolomic Quantitative Trait Locus (mQTL) Genetic Mapping in Human CVD

冠状动脉疾病（CAD）是美国死亡的主要原因， 在许多发展中国家，肥胖和糖尿病正在成为死亡的主要原因。遗传 对CAD的偏爱是公认的。尽管如此，CAD的遗传学仍然在很大程度上未知。鉴于 CAD的复杂性，用更全面的分析工具评价疾病可能提供 需要深入了解这种异质性表型的生物学途径。许多 普遍接受的CAD风险因素是代谢性的。代谢组学，研究小分子 代谢物，是一个新兴的学科，可能是特别有用的了解代谢失衡 和疾病的诊断。利用这些反映生物反应的颗粒状代谢表型 外源性和内源性输入，在遗传筛选中可能提供更强大的方法， 揭示心血管疾病的分子机制。 我们先前已经发现了一个新的发现，即家族中代谢组学谱的高遗传率 患有早发性CAD，这表明这些代谢产物谱具有遗传基础。而且我们 已经表明这些代谢组学特征强烈且独立地区分CAD个体 并预测未来心血管事件的风险。因此，我们建议执行 在一个大型的、表型良好的心血管队列中进行代谢数量性状基因座（mQTL）定位 使用全基因组关联（GWAS）和靶向定量代谢分析，目标是 阐明了易患CAD的代谢特征的潜在遗传结构。我们 假设该队列中代谢组学分析将鉴定CAD潜在的新表型 mQTL作图将鉴定通过代谢途径介导的CAD风险的新基因， 途径。该提案的具体目标是：（1）在一个特定的环境中进行有针对性的定量代谢分析， 表型良好的心血管队列1000人;（2）进行全基因组关联（GWAS）， 1000人的同一心血管队列，并进行遗传作图以识别代谢 数量性状基因座（mQTL）;（3）在计算机模拟和独立的家族和非家族性重复GWAS结果 （4）对从目的2和3鉴定的候选基因座进行重测序，以鉴定 新的遗传变异。这项提案有可能对一个主要的公众产生重大影响。 健康问题有很强的遗传成分，

Coronary artery disease (CAD) is the leading cause of the death in the US and, in concert with the epidemic of obesity and diabetes, is becoming the leading cause of death in many developing countries. The genetic predilection of CAD is well-established. Despite this, the genetics of CAD remain largely unknown. Given the complex nature of CAD, evaluation of the disease with more comprehensive analytical tools may provide needed insights into biological pathways converging on this heterogeneous phenotype. Many of the commonly accepted risk factors for CAD are metabolic. Metabolomics, the study of small-molecule metabolites, is an emerging discipline that may be particularly useful for understanding metabolic imbalances and for diagnosis of disease. Using these granular metabolic phenotypes, which reflect biological responses to exogenous and endogenous inputs, in a genetic screen may provide more a more powerful method for uncovering molecular mechanisms of cardiovascular disease. We have previously shown a novel finding of high heritabilities of metabolomic profiles in families burdened with early-onset CAD, suggesting a genetic basis to these metabolite profiles. Furthermore, we have shown that these metabolomic profiles strongly, and independently, discriminate individuals with CAD from those without, and predict risk of future cardiovascular events. Therefore, we propose to perform metabolic quantitative trait loci (mQTL) mapping in a large, well-phenotyped cardiovascular cohort using genomewide association (GWAS) and targeted, quantitative metabolic profiling, with the goal of elucidating the underlying genetic architecture of metabolic traits predisposing to CAD. We hypothesize that metabolomic profiling in this cohort will identify novel phenotypes underlying CAD pathophysiology, and that mQTL mapping will identify novel genes for CAD risk mediated through metabolic pathways. The specific aims of this proposal are to: (1) perform targeted, quantitative metabolic profiling in a well-phenotyped cardiovascular cohort of 1000 individuals; (2) perform genomewide association (GWAS) in the same cardiovascular cohort of 1000 individuals and perform genetic mapping to identify metabolic quantitative trait loci (mQTLs); (3) replicate GWAS findings in silico and in independent familial and nonfamilial cardiovascular cohorts; and (4) resequence candidate loci identified from Aims 2 and 3 to identify novel genetic variants. This proposal has the potential for having a significant impact on a major public health problem that has a very strong heritable component that is poorly