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.

项目摘要/摘要该候选人目前是工程管理助理教授，系统工程（EMSE）在密苏里州的生物科学联合任命科学技术大学（密苏里S＆T），大学成员机构密苏里州（UM）系统。在加入密苏里S＆T之前，候选人获得了MS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（DBMI）的BMI奖学金（皮特）。候选人的长期研究目标是成为一名独立研究人员外部支持的研究计划集中于推断来自多媒体数据的信号通路，并在精确医学中使用它进行心血管疾病。在此K01申请中，候选人组成了一个强大的心理委员会来自皮特和UM系统。提供的培训，精神训练和研究机会该K01奖将大大增强她在多摩变分析，因果关系方面的专业知识推论，深度学习以及更重要的是，将有助于建立她在复杂方面的专业知识心血管疾病及其危险因素。该K01奖对于过渡至关重要候选人进入多摩变学分析的独立研究者的精确医学心血管疾病。在此提案中，候选人提出了以下目标：开发和评估特定实例的因果推理（ICI）框架以识别因果关系血压调节的基因组变体（AIM 1）；协调一个大型混合族裔队列从精密医学计划的跨词，并应用ICI和GWAS 了解基因组变异在种族差异中的作用在高血压患病率中（AIM 2）；应用和评估基于人群和实例的特定预测机器学习通过整合基因组学和其他OMIC数据来进行高血压预测的模型（AIM 3）。如果成功，该项目将开发和评估一种新颖的实例特定方法发现高血压的个性化基因组变异，以更好地理解高血压种族差异的基因组基础，更准确，及时预测高血压的发展以进行干预和预防。而且，开发的方法也将适用于其他心血管疾病和危险因素。