Towards Precision Medicine for Thoracic Aortic Disease: Defining the Clinical and Genomic Drivers of Bicuspid Aortopathy

项目摘要/摘要 这是为医学博士Jason P.Glotzbach颁发的K08临床科学家导师研究职业发展奖。Dr。 Glotzbach是一名很有前途的早期职业翻译研究临床医生兼科学家。他是一名心脏和主动脉外科医生 犹他大学终身教职的外科学助理教授。他在这方面的主要导师 建议是马丁·特里斯塔尼-菲鲁齐博士，医学博士，儿科心脏病专家和精确医学和基因组学专家 心血管疾病。这项计划为期五年，包括三个研究目标和四个职业生涯 发展目标。 摘要二尖瓣是最常见的先天性心血管畸形，与主动脉相关。 动脉瘤和主动脉夹层，一种被定义为BAV大动脉病变的情况。虽然BAV和BAV都是大动脉病变 被认为是高度可遗传的疾病，导致临床因素和基因组变异与 这种疾病的发展和进展仍然知之甚少。当前提案的目的是 通过三管齐下的方法填补这一知识空白：1)我们将使用创新的统计方法 称为泊松二项共病发现，用于定义相关的临床和人口学变量 与BAV大动脉病变；2)我们将开发BAV大动脉病变风险的预测模型，使用状态 ART人工智能方法称为概率图形模型；3)我们将使用详细的 多代高发家系的家系驱动全基因组测序分析 BAV大动脉病变和接受BAV大动脉病变手术以确定基因变异的患者 与BAV大动脉病变有关。通过将临床风险模型与对基因组的理解相结合 与BAV大动脉病变相关的变异，我们期望对其发病机制有新的理解。 非常有影响力的临床情况。这条调查路线产生的信息有很大的希望 通过建立一个允许发展的基础来帮助完善主动脉疾病的临床治疗范例 在个体患者水平上预测主动脉疾病风险的精确医学工具。这条调查路线，如果 成功，将导致改善这些复杂和异质患者的临床结果。 通过追求这项提案的研究目标，Glotzbach博士将在 统计学、预测建模、流行病学、生物信息学、基因组分析和 研究团队的领导力，使他能够成为一名独立的翻译调查员。 1

PROJECT SUMMARY/ABSTRACT This is a K08 Mentored Clinical Scientist Research Career Development Award for Jason P. Glotzbach, MD. Dr. Glotzbach is a promising early career translational research clinician-scientist. He is a cardiac and aortic surgeon and Assistant Professor of Surgery on the tenure track at the University of Utah. His primary mentor for this proposal is Dr. Martin Tristani-Firouzi, MD, a pediatric cardiologist and expert in precision medicine and genomics of cardiovascular disease. This proposal spans five years and includes three Research Aims and four Career Development Aims. Bicuspid aortic valve (BAV) is the most common congenital cardiovascular anomaly and is associated with aortic aneurysm and aortic dissection, a condition defined as BAV aortopathy. Although both BAV and BAV aortopathy are thought to be highly heritable conditions, the causative clinical factors and genomic variants associated with development and progression of this disease remain poorly understood. The aim of the current proposal is to fill this knowledge gap through a three-pronged approach: 1) we will use an innovative statistical method called Poisson binomial comorbidity discovery to define clinical and demographic variables associated with BAV aortopathy; 2) we will develop a predictive model for BAV aortopathy risk using a state-of-the- art artificial intelligence method called probabilistic graphical models; and 3) we will utilize detailed pedigree-driven whole genome sequencing analysis of multigenerational families with a high prevalence of BAV aortopathy and patients undergoing surgery for BAV aortopathy to define genetic variants associated with BAV aortopathy. By combining a clinical risk model with an understanding of the genomic variants associated with BAV aortopathy, we expect to gain novel understanding of the pathogenesis of this highly impactful clinical condition. The information produced by this line of investigation has significant promise to help refine the clinical paradigms for treatment of aortic disease by building a foundation to allow development of precision medicine tools to predict aortic disease risk at the individual patient level. This line of inquiry, if successful, will lead to improved clinical outcomes in these complex and heterogenous patients. Through pursuit of the Research Aims of this proposal, Dr. Glotzbach will develop his expertise with the fundamental skills of statistics, predictive modeling, epidemiology, bioinformatics, genomic analysis, and research team leadership that will enable him to build a career as an independent translational investigator. 1