Statistical Methods for Network-Based Integrative Analysis of CVD Epigenetic Data

基于网络的 CVD 表观遗传数据综合分析统计方法

• 批准号: 9032704
• 负责人: ALI SHOJAIE
• 金额: $ 14.28万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-15 至 2020-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9032704
• 关键词: Accounting; Address; Area; Atherosclerosis; Biological; Biological Markers; Biology; Biomedical Research; Cardiovascular Diseases; Cause of Death; Cell physiology; Cells; Communities; Complement; Complex; DNA Methylation; Data; Data Analyses; Data Set; Development; Diabetes Mellitus; Disease; Disease Outcome; Dyslipidemias; Environmental Risk Factor; Epigenetic Process; Exogenous Factors; Gene Expression; Gene Proteins; Genes; Genetic; Genetic Variation; Genomics; Histones; Hypertension; Individual; Information Networks; Joints; Knowledge; Lead; Machine Learning; Malignant Neoplasms; Maps; Measurement; Methodology; Methods; Methylation; Molecular; Myocardial Infarction; Nature; Network-based; Obesity; Outcome; Pathway Analysis; Pathway interactions; Patients; Phenotype; Physiology; Post-Transcriptional Regulation; Process; Prognostic Marker; Research; Research Personnel; Risk Factors; Software Tools; Source; Statistical Methods; Stroke; Structure; System; Testing; Training Activity; base; cardiovascular disorder risk; career; computerized tools; design; diagnostic biomarker; disease phenotype; epigenetic marker; genetic risk factor; improved; learning network; learning strategy; mRNA Expression; member; multiple omics; new technology; novel; novel diagnostics; public health relevance; response; targeted treatment; therapeutic target; training opportunity; user friendly software; whole genome

 DESCRIPTION (provided by applicant): 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 efficiently 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 refining 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 efficient 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.

 描述（由申请人提供）：该项目涉及开发新的统计方法和计算工具，用于心血管疾病（CVD）表观遗传风险因素的网络综合分析，而新技术组学数据的出现促进了心血管疾病（CVD）的研究。由于表观遗传因素，现有方法通常无法考虑生物数据的复杂性，例如由于作为生物途径一部分的基因/蛋白质相互作用而导致的相关性，并且无法有效整合不同的组学数据 该项目提出的方法包括遗传变异、DNA 甲基化和基因表达，以及将开发的软件工具来实现这些方法，解决这些缺点，并促进生物医学界的进一步研究，以更好地了解这些方法。 CVD 的基础生物学，并开发新的诊断生物标志物和潜在的治疗靶标。所提出的方法是受到动脉粥样硬化多种族研究 (MESA) 的表观遗传数据的启发，包括 (i) 基于网络的途径。富集分析方法，结合了基因和蛋白质之间相互作用的现有知识，同时补充和完善这些信息（目标 1A），及其对多种类型组学数据分析的扩展（目标 1B），以及（ii）综合分析框架确定基因表达水平和 DNA 甲基化之间的关联（目标 2A），并通过 DNA 甲基化和 mRNA 表达数据的综合分析来确定多种 CVD 表型的常见表观遗传因素（目标 2B）。将为所提出的方法（目标 3）开发高效且用户友好的软件工具，在使用模拟数据以及来自 MESA 的真实数据进行广泛测试后，将免费向公众提供。