A novel framework for estimating personalized genomic variants of hypertension for precision medicine

项目摘要/摘要 该候选人目前担任工程管理学助理教授，并 系统工程(EMSE)与密苏里州生物科学联合任命 密苏里科技大学(密苏里州S&T)，密苏里州大学成员机构 密苏里州(UM)系统。在加入密苏里州S技术公司之前，这位候选人在 生物医学信息学(BMI)，并完成国家医学图书馆(NLM)博士后学位 匹兹堡大学生物医学信息学系BMI奖学金 (皮特)候选人的长期研究目标是成为一名拥有 外部支持的研究计划，专注于推断 多组学数据中的信号通路及其在心血管精确医学中的应用 疾病。在这份K01申请中，应聘者组建了一个强大的指导委员会 来自皮特和密歇根大学系统。提供的培训、指导和研究机会 这一K01奖项将显著加强她在多组学分析、因果分析方面的专业知识 推理，深度学习，更重要的是，将帮助她建立复杂的专业知识 心血管疾病及其危险因素。这一K01奖项在过渡到 成为精准医学多组学分析领域的独立研究员 心血管疾病。在这项提案中，候选人提议追求以下目标： 开发和评估特定于实例的因果推理(ICI)框架，以确定因果关系 调节血压的基因组变异(目标1)；协调一个大型混血人种队列 来自Trans-Omics for Precision Medicine计划，并应用ICI和GWAS来更好地 了解基因组变异在高血压患病率种族差异中的作用(目标2)； 应用和评估基于种群和特定实例的预测性机器学习 通过整合基因组学和其他组学数据预测高血压的模型(目标3)。如果 成功后，该项目将开发和评估一种新的、针对具体实例的方法 发现高血压的个体化基因组变异，以便更好地了解 高血压的种族差异的基因组基础，并更准确和及时 预测高血压的发展趋势，为干预和预防提供依据。此外， 开发的方法也将适用于其他心血管疾病和危险因素。

Project Summary/Abstract The candidate currently serves as an Assistant Professor of Engineering Management and Systems Engineering (EMSE) with a joint appointment in Biological Sciences at Missouri University of Science and Technology (Missouri S&T), a member institution of the University of Missouri (UM) System. Before joining Missouri S&T, the candidate obtained an MS degree in Biomedical Informatics (BMI) and completed a National Library of Medicine (NLM) Postdoctoral Fellowship in BMI at Department of Biomedical Informatics (DBMI) at University of Pittsburgh (Pitt). The candidate’s long-time research goal is to become an independent researcher with an extramurally supported research program concentrating on inferring the activation states of signaling pathways from multi-omics data and utilizing it in precision medicine for cardiovascular diseases. In this K01 application, the candidate has assembled a strong mentoring committee from both Pitt and UM System. The training, mentorship, and research opportunities provided by this K01 award will significantly strengthen her expertise in multi-omics analytics, causal inference, deep learning, and more importantly will help build her expertise in complex cardiovascular diseases and their risk factors. This K01 award is critical in transitioning the candidate into an independent investigator in multi-omics analytics for precision medicine in cardiovascular disease. In this proposal, the candidate proposes to pursue the following aims: develop and evaluate an instance-specific causal inference (ICI) framework to identify causative genomic variants for blood pressure regulation (Aim 1); harmonize a large mixed-ethnic cohort from The Trans-Omics for Precision Medicine program and apply ICI and GWAS to better understand the role of genomic variants in racial disparity in hypertension prevalence(Aim 2); apply and evaluate both population-based and instance-specific predictive machine learning models for hypertension prediction by integrating genomics and other omics data (Aim 3). If successful, this project will develop and evaluate a novel, instance-specific method for discovering individualized genomic variants of hypertension, for better understanding the genomic basis of racial differences in hypertension, and for more accurately and timely predicting the development of hypertension for intervention and prevention. Moreover, the developed methods will be applicable to other cardiovascular diseases and risk factor as well.