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（由适用提供）：该项目涉及开发新的统计方法和计算工具，用于基于网络的心血管疾病（CVD）的表观遗传风险因素的集成分析。虽然新技术的OMIC数据的冒险已经准备好了表观遗传因素的研究，但现有方法通常不会说明生物学数据的复杂性，例如由于基因/蛋白质的相互作用而导致的相关性，这是生物学途径的一部分，并且无法有效地整合多样化的OMICS数据 例如遗传变异，DNA甲基化和基因表达。该项目中提出的方法以及将开发出来的软件工具来实施它们，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CVD的基本生物学，并开发新的诊断生物标志物和潜在的疗法。提出的方法是通过研究动脉粥样硬化研究（MESA）的表观遗传数据的动机，包括（i）一种基于网络的途径富集分析方法，该方法结合了基因和蛋白质之间可用的相互作用知识，同时完成和重新填充了此类信息（AIM 1A），以及对多种类型的分析（II）（目标1B）（目标）（目标1B）（目标）（目标1B）（目标）（目标）（目标1B），并且通过对DNA甲基化和mRNA表达数据的综合分析（AIM 2B），基因表达水平和DNA甲基化（AIM 2A）之间的关联（AIM 2A）并鉴定了多种CVD表型的常见表观遗传因子。我们将为所提出的方法开发有效和用户友好的软件工具（AIM 3），使用模拟数据以及MESA的真实数据，将在广泛的测试之后免费向公众提供。