This project involves the development of new statistical methodologies and computational tools for network-based integrative analysis of epigenetic risk factors of cardiovascular diseases (CVD). While the advent of omics data from new technologies has facilitated the study of epigenetic factors, existing methodologies often do not account for complexities of biological data such as correlations due to interactions of genes/proteins as part of biological pathways and fail to e?ciently integrate diverse omics data sets for instance genetic variation, DNA methylation and gene expression. The methodologies proposed in this project, and the software tools that will be developed to implement them, address these shortcomings, and facilitate further research by the biomedical community to gain a better understanding of the underlying biology of CVD, and to develop new diagnostic biomarkers and potential targets for therapies. The proposed methodologies are motivated by the study of epigenetic data from the Multi-Ethnic Study of Atherosclerosis (MESA), and include (i) a network-based pathway enrichment analysis method that incorporates available knowledge of interactions among genes and proteins while complementing and re?ning such information (Aim 1A), as well as its extension for analysis of multiple types of omics data (Aim 1B), and (ii) an integrative analysis framework to identify associations among gene expression levels and DNA methylation (Aim 2A) and identify common epigenetic factors of multiple CVD phenotypes through integrated analysis of DNA methylation and mRNA expression data (Aim 2B). We will develop e?cient and user-friendly software tools for the proposed methods (Aim 3), which will be made freely available to the public after extensive tests using both simulated data, as well as real data from MESA.

Public Health Relevance

The proposed research addresses the need for development of new statistical machine learning methods for analysis of diverse epigenetic data from multiple cardiovascular disease outcomes. The proposed training activities are designed to enhance the applicant's knowledge of epigenetics and physiology of cardiovascular diseases (CVD), and to further his career as an independent investigator in the area of computational epigenetics for CVD.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Scientist Development Award - Research & Training (K01)
Project #
5K01HL124050-05
Application #
9838771
Study Section
NHLBI Mentored Clinical and Basic Science Review Committee (MCBS)
Program Officer
Wolz, Michael
Project Start
2015-12-15
Project End
2020-11-30
Budget Start
2019-12-01
Budget End
2020-11-30
Support Year
5
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Washington
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Mathur, Ravi; Rotroff, Daniel; Ma, Jun et al. (2018) Gene set analysis methods: a systematic comparison. BioData Min 11:8
Randolph, Timothy W; Zhao, Sen; Copeland, Wade et al. (2018) KERNEL-PENALIZED REGRESSION FOR ANALYSIS OF MICROBIOME DATA. Ann Appl Stat 12:540-566
Wang, Xiaoliang; Shojaie, Ali; Zhang, Yuzheng et al. (2017) Exploratory plasma proteomic analysis in a randomized crossover trial of aspirin among healthy men and women. PLoS One 12:e0178444
Chen, Shizhe; Witten, Daniela; Shojaie, Ali (2017) Nearly assumptionless screening for the mutually-exciting multivariate Hawkes process. Electron J Stat 11:1207-1234
Miles, Fayth L; Navarro, Sandi L; Schwarz, Yvonne et al. (2017) Plasma metabolite abundances are associated with urinary enterolactone excretion in healthy participants on controlled diets. Food Funct 8:3209-3218
Chen, Shizhe; Shojaie, Ali; Witten, Daniela M (2017) Network Reconstruction From High-Dimensional Ordinary Differential Equations. J Am Stat Assoc 112:1697-1707
Seshadri, Chetan; Sedaghat, Nafiseh; Campo, Monica et al. (2017) Transcriptional networks are associated with resistance to Mycobacterium tuberculosis infection. PLoS One 12:e0175844
Sas, Kelli M; Kayampilly, Pradeep; Byun, Jaeman et al. (2016) Tissue-specific metabolic reprogramming drives nutrient flux in diabetic complications. JCI Insight 1:e86976
Kaushik, Akash K; Shojaie, Ali; Panzitt, Katrin et al. (2016) Inhibition of the hexosamine biosynthetic pathway promotes castration-resistant prostate cancer. Nat Commun 7:11612
Zhao, Sen; Shojaie, Ali (2016) A significance test for graph-constrained estimation. Biometrics 72:484-93

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