CVD GENETIC PREDISPOSITIONS AND GENOMIC SIGNATURES

CVD遗传易感性和基因组特征 主要研究者：Cam Patterson，MD，MBA 心血管疾病（CVD）是美国发病和死亡的最常见原因，占所有死亡和医疗保健支出的1/2。在少数民族和社会经济地位较低的阶层中，其发病率更高。我们提出的临床研究计划的目标是不成比例地折磨服务不足和处于不利地位的北卡罗来纳州人的心血管疾病，利用系统方法开发模型，将临床数据与来自临床基因组学和其他诱导的数据集相结合，非常详细的方法。为此，我们开发了一种称为萨马拉（支持动脉粥样硬化研究的多学科方法）项目的方法，该项目应用了生物医学和计算科学的最新重大进展，以更深入地了解人类CVD。 使用高通量数据收集、计算数据挖掘和模型构建的组合，我们在200名心血管疾病高风险受试者身上测试了这种系统方法。我们发现了大量的基因，这些基因在自认为是非洲或欧洲血统的美国受试者之间存在差异表达，并且还包含单核苷酸多态性，可以区分远亲。 祖先的人口 在建立和测试了这种综合方法之后，我们现在准备通过使用这种方法将我们的研究扩展到社区：1）。使用全基因组SNP分析确定高风险社区人群中基因组风险特征的患病率; 2）.开发新的基因组模型，将高风险特征纳入该人群; 3）。确定基因组特征是否可用于预测对CVD差异的干预措施的反应性。

CVD Genetic Predispositions and Genomic Signatures Principal Investigator: Cam Patterson, MD, MBA Cardiovascular diseases (CVD) are the most common cause of morbidity and mortality in the US, accounting for 1/2 of all deaths and health care expenditures. Their incidence is magnified in minority populations and in lower socioeconomic strata. Our proposed clinical research plan targets the cardiovascular diseases that disproportionately afflict underserved and disadvantaged North Carolinians, utilizing a systems approach to develop models that will integrate clinical data with datasets derived from clinical genomics and other inductive, highly detailed methods. To this end, we have developed an approach we refer to as the SAMARA (Supporting A Multidisciplinary Approach to Research in Atherosclerosis) Project that applies recent, major advances in biomedical and computational sciences at UNC to develop deeper understanding of human CVD. Using a combination of high-throughput data collection and computational data mining and model building, we have tested this systems approach on 200 subjects at high risk for CVD. We identified a large number of genes that were both differentially expressed between American subjects self-identified to be of either African or European ancestry and that also contained single nucleotide polymorphisms that distinguish distantly related ancestral populations. Having established and tested this integrative approach, we are now prepared to expand our studies into the community by using this methodology to: 1). Determine the prevalence of genomic risk signatures in high-risk community populations using genome-wide SNP analysis; 2). Develop novel genomic models incorporating high-risk features in this population; 3). Determine whether genomic signatures can be used to predict responsiveness to interventions that underiie CVD disparities.