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

冠状动脉疾病(CAD)是美国人死亡的主要原因，与这种流行病相一致 在许多发展中国家，肥胖症和糖尿病正成为主要的死亡原因。基因 对CAD的偏好是根深蒂固的。尽管如此，CAD的遗传学在很大程度上仍然是未知的。给定 冠心病的复杂性质，用更全面的分析工具评估疾病可能会提供 需要对汇聚在这种异质表型上的生物途径有深入的了解。许多人 人们普遍接受的冠心病危险因素是代谢。代谢组学，对小分子物质的研究 代谢物，是一门新兴的学科，可能对理解代谢失衡特别有用 以及疾病的诊断。使用这些反映生物反应的颗粒状代谢表型 对于外源和内源的输入，在基因筛查中可能提供更多更有力的方法 揭示心血管疾病的分子机制。 我们之前已经发现了一个新的发现，在家族中代谢组谱的高遗传力。 患有早发性冠心病，提示这些代谢物的遗传基础。此外，我们 已经表明，这些代谢组特征强烈且独立地区分患有冠心病的个体 来自那些没有发生心血管事件的人，并预测未来心血管事件的风险。因此，我们建议执行 一个表型良好的大型心血管队列中的代谢数量性状基因座(MQTL)定位 使用全基因组关联(GWAS)和有针对性的定量代谢图谱，目标是 阐明易患冠心病的代谢性状的潜在遗传结构。我们 假设该队列中的代谢组谱将识别冠心病潜在的新表型 病理生理学，mQTL定位将确定通过代谢介导的冠心病风险的新基因 小路。这项建议的具体目标是：(1)在 1,000名表型良好的心血管队列；(2)在 相同的心血管队列中的1000人，并执行基因图谱以确定代谢 数量性状基因座(MQTL)；(3)在矽肺和独立的家族性和非家族性中重复GWAS的研究结果 心血管队列；以及(4)从AIMS 2和3识别的重新排序候选基因座以识别 新的基因变异。这项提议有可能对主要公众产生重大影响 具有非常强的可遗传成分的健康问题

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