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

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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.