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

用于估计高血压个性化基因组变异以实现精准医疗的新框架

基本信息

批准号：
10685475
负责人：
Jinling Liu
金额：
$ 14.42万
依托单位：
MISSOURI UNIVERSITY OF SCIENCE & TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-17 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10685475
关键词：
Address African American population American Appointment Automobile Driving Biological Sciences Blood Pressure Cardiovascular Diseases Cessation of life Clinical Data Complex Data Data Set Development Development Plans Disease Ecosystem Engineering Environmental Risk Factor Ethnic Origin European Extramural Activities Fellowship Frequencies Genome Genome Stability Genomics Genotype Goals Hypertension Individual Institution Intervention Joints Learning Longevity Malignant Neoplasms Mentored Research Scientist Development Award Mentors Mentorship Methods Missouri Modeling Multiomic Data National Heart, Lung, and Blood Institute Parents Patients Performance Phenotype Play Population Population Sizes Postdoctoral Fellow Prevalence Prevention Research Research Personnel Research Support Risk Risk Factors Role Science Signal Pathway Single Nucleotide Polymorphism System Technology Time Training Trans-Omics for Precision Medicine United States National Library of Medicine Universities Variant biomedical informatics blood pressure regulation cardiovascular risk factor career development catalyst causal model causal variant cloud based cohort data integration deep learning feature selection genetic architecture genetic variant genome sequencing genome wide association study genomic data high dimensionality machine learning model machine learning prediction member multiple omics novel personalized genomics population based precision medicine predictive modeling prevent professor programs racial difference racial disparity skills social determinants success theories tumor variant detection whole genome

项目摘要

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.

项目概要/摘要该候选人目前担任工程管理学助理教授和系统工程（EMSE）与密苏里州生物科学联合任命科学技术大学（Missouri S&T），密苏里大学成员机构加入密苏里科技大学之前，候选人获得了密苏里州 (UM) 系统的硕士学位。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 研究员（皮特）候选人的长期研究目标是成为一名独立研究员。外部支持的研究计划，专注于推断激活状态多组学数据的信号通路及其在心血管精准医学中的应用在这个 K01 申请中，候选人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。该 K01 奖项将显着增强她在多组学分析、因果分析等方面的专业知识推理、深度学习，更重要的是，将有助于培养她在复杂领域的专业知识 K01 奖项对于心血管疾病及其危险因素的转变至关重要。精准医学多组学分析独立研究员候选人在此提案中，候选人建议追求以下目标：开发和评估特定于实例的因果推理 (ICI) 框架来识别因果关系血压调节的基因组变异（目标 1）；协调大型混合种族群体来自 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来预测高血压的模型（目标 3）。成功后，该项目将开发和评估一种新颖的、针对特定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种族差异的基因组基础，以便更准确和及时预测高血压的发展以进行干预和预防。开发的方法也适用于其他心血管疾病和危险因素。