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

项目总结/摘要 候选人目前担任工程管理助理教授， 系统工程（EMSE）与密苏里州生物科学联合任命 科技大学（密苏里州S&T），是密苏里大学的成员机构。 密苏里州（UM）系统。在加入密苏里州科技之前，候选人获得了 生物医学信息学（BMI），并完成了国家医学图书馆（NLM）博士后 匹兹堡大学生物医学信息学系（DBMI）BMI奖学金 （皮特）。候选人的长期研究目标是成为一名独立的研究人员， 一个由校外资助的研究项目，专注于推断 多组学数据的信号通路，并将其用于心血管精准医学 疾病在K 01申请中，候选人组建了一个强大的指导委员会 皮特和UM系统的提供的培训，指导和研究机会， 这个K 01奖将大大加强她在多组学分析，因果关系， 推理，深度学习，更重要的是将有助于建立她在复杂领域的专业知识 心血管疾病及其危险因素。这个K 01奖项对于过渡到 候选人成为一个独立的研究员在多组学分析的精准医学， 心血管疾病在这一提议中，候选人提议追求以下目标： 开发和评估特定实例因果推理（ICI）框架，以确定因果关系 血压调节的基因组变异（目标1）;协调大型混合种族队列 将ICI和GWAS应用于更好地 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）; 应用和评估基于人群和特定实例的预测机器学习 通过整合基因组学和其他组学数据建立高血压预测模型（目标3）。如果 成功后，该项目将开发和评估一种新颖的，具体实例的方法， 发现高血压的个体化基因组变异，以更好地了解高血压的发病机制。 高血压种族差异的基因组基础，更准确和及时地 预测高血压的发展，以进行干预和预防。而且 所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.