Epigenetic fine-mapping of cardiometabolic disease loci in the human liver

人类肝脏中心脏代谢疾病基因座的表观遗传精细定位 总结 心血管疾病（CVD）是世界上死亡的主要原因：估计有17; 500; 000 2012年，全世界死于心血管病相关疾病的人。虽然疾病改变疗法，如他汀类药物， 对健康产生了巨大的影响，许多人对治疗反应迟钝或未被诊断出来。 直到致命事件发生。此外，虽然临床风险因素和家族史是显著的预测因素， 必须改善风险预测和早期临床干预，以减少心血管疾病的致命性。 这种疾病科学研究已经发现了超过182种不同的遗传变异， 导致CVD、冠状动脉疾病（CAD）、心肌梗死（MI）风险的变异性的基因座， 以及包括血脂在内的相关代谢物。然而，几个关键的限制限制了 这些研究结果对临床医学的转化影响。重要的是，虽然我们知道时间， 基因组和细胞背景在个体之间变化很大，目前的GWAS研究假设一个静态的 所有样本的上下文。事实上，正是这种动态的背景将揭示一个特定的 遗传变异影响分子特征，而分子特征又反过来调节疾病风险。此外，初级组织 涉及CVD的是人的肝脏，由于其难以表达，因此难以深入表型。 获取肝脏样本 在这项提议中，PI将通过创建一个深层分子表型来解决这些关键限制 200份人类肝脏活检样本的图谱，通过开发必要的统计工具来预测基因组 这些丰富的肝脏数据中的调节信号，并通过使用这些预测来驱动实验验证 通过报告基因分析和基因组编辑来研究调控信号的机制， CVD风险的遗传调节。在目标1中，与宾夕法尼亚大学的两名移植外科医生合作，PI支持， pose建立一个200个人类肝脏活检样本的遗传和表观遗传特征的综合图谱。 在目标2中，PI建议开发统计方法，使用配对的 样本设计在多种表观遗传性状中共享优势。虽然这种类型的研究数据是当前的- 虽然这类研究很少，但我们预计这类研究将大幅增长，我们的分析方法也将得到广泛应用。 在目标3中，PI建议开发实验方法来验证功能性 SNP影响CVD风险。特别是，我们将开发大规模平行的CRE报告基因测定和基因组测序。 在iPSC衍生的肝细胞中进行工程化，以表征多种CVD风险瓦里的精确机制， 蚂蚁在整个提案中，PI将开发、评估并公布新的分析工具， 众核计算环境的优势，并将公开提供所有的遗传和 从肝脏样本产生的表观遗传数据。

Epigenetic fine-mapping of cardiometabolic disease loci in the human liver Summary Cardiovascular disease (CVD) is the leading cause of mortality in the world: an estimated 17; 500; 000 people worldwide died from CVD-related illness in 2012. While disease altering therapies such as statins have had a tremendous health impact, many individuals are unresponsive to treatment or go undiagnosed until a fatal event occurs. Moreover, while clinical risk factors and family history are significantly predictive of CVD risk, risk prediction and early clinical intervention must be improved to diminish the lethality of the disease. Scientific studies have uncovered common genetic variation at more than 182 separate genetic loci that contribute to variability in CVD, coronary artery disease (CAD), myocardial infarction (MI) risk, and associated metabolites including blood lipids. However, several critical limitations have restricted the translational impact of these study findings on clinical medicine. Importantly, while we know that temporal, genomic, and cellular context varies dramatically across individuals, current GWAS studies assume a static context across all samples. Indeed, it is precisely this dynamic context that will shed light on how a specific genetic variant impacts molecular traits, which, in turn, modulate disease risk. Furthermore, a primary tissue involved in CVD is the human liver, which has been difficult to deeply phenotype because of the difficulty of acquiring liver samples. In this proposal, the PIs will address these critical limitations by creating a deep molecular phenotype map of 200 human liver biopsy samples, by developing essential statistical tools to predict the genomic regulatory signals in these rich liver data, and by using these predictions to drive experimental validation of regulatory signals through reporter assays and genome editing in order to study the mechanisms of the genetic regulation of CVD risk. In Aim 1, in collaboration with two transplant surgeons at Penn, the PIs pro- pose to build a comprehensive map of the genetic and epigenetic traits of 200 human liver biopsy samples. In Aim 2, the PIs propose to develop statistical methods to identify regulatory genetic variants using paired sample design to share strength across the multiple epigenetic traits. While study data of this type is cur- rently rare, we anticipate substantial growth in studies of this type and broad use of our analytic approaches. In Aim 3, the PIs propose to develop experimental methods to validate the mechanisms by which functional SNPs impact CVD risk. In particular, we will develop massively parallel CRE reporter assays and genome engineering in iPSC derived hepatocytes to characterize the precise mechanism of multiple CVD risk vari- ants. Throughout this proposal, the PIs will develop, evaluate, and make public new analytic tools that take advantage of many-core computing environments, and will make publicly available all of the genetic and epigenetic data generated from the liver samples.