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

人类肝脏心脏代谢疾病位点的表观遗传精细定位

基本信息

批准号：
9309707
负责人：
Christopher David Brown
金额：
$ 80.39万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2021-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9309707
关键词：
Address Affect Alleles Biological Biological Assay Biopsy Specimen Cardiovascular Diseases Cholesterol Homeostasis Chromatin Clinical Clinical Medicine Code Collaborations Complex Coronary Arteriosclerosis Data Data Set Diffuse Disease Elements Environment Epigenetic Process Etiology Event Family Follow-Up Studies Future Gene Expression Gene Targeting Genes Genetic Genetic Variation Genome Genome engineering Genomics Genotype Goals Growth Health Hepatocyte Heritability Histones Human Individual Institutes Intervention Joints Light Linear Regressions Link Linkage Disequilibrium Liver Maps Meta-Analysis Methods Mind Modeling Modernization Molecular Molecular Conformation Molecular Mechanisms of Action Molecular Profiling Myocardial Infarction Outcome Pathway interactions Phenotype Play Population Proteins Recording of previous events Regulation Regulator Genes Regulatory Element Reporter Reproducibility Research Research Design Resolution Risk Risk Factors Role Sample Size Sampling Signal Transduction Single Nucleotide Polymorphism Statistical Methods Statistical Models Tissue Sample Tissues Transplant Surgeon Transplantation Untranslated RNA Validation Variant Work analytical tool base blood lipid cardiovascular disorder risk clinical practice clinical risk design disorder risk experimental study genetic variant genome editing genome wide association study genomic predictors high dimensionality histone modification improved induced pluripotent stem cell insight lipid metabolism liver biopsy model development molecular phenotype mortality novel pleiotropism risk variant therapeutic development tool trait transcriptomics translational impact

项目摘要

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.

人类肝脏中心脏代谢疾病位点的表观遗传学精细定位