High Dimensional Mediation Analysis for DNA Methylation Markers Mediating Cardiovascular Health Metrics and Cardiovascular Diseases

DNA 甲基化标记介导心血管健康指标和心血管疾病的高维中介分析

基本信息

批准号：
9918838
负责人：
Lifang Hou
金额：
$ 23.62万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-05-01 至 2023-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9918838
关键词：
Achievement Address Adult Advanced Development Air Pollutants Alcohol consumption American American Heart Association Area Atherosclerosis Biological Biological Markers Candidate Disease Gene Cardiovascular Diseases Cardiovascular system Cause of Death Clinical Clinical Research Computer software Coronary Artery Risk Development in Young Adults Study CpG dinucleotide Cytosine DNA DNA Methylation DNA analysis Data Data Analyses Development Dietary intake Disease Environmental Exposure Environmental Risk Factor Epigenetic Process Genes Genetic Grouping Health Human Individual Intervention Lead Life Link Longitudinal Studies Longitudinal cohort Malignant Neoplasms Measurement Mediating Mediation Mediator of activation protein Medical Care Costs Metabolic Diseases Methodology Methods Methylation Modeling Modification Morbidity - disease rate Nature Outcome Physical Exercise Play Population Positioning Attribute Prevention Research Research Personnel Respiratory physiology Risk Assessment Risk Factors Risk Marker Role Smoking Source Statistical Methods Techniques Technology Testing Work analytical method base bead chip cardiovascular health design disorder risk epidemiology study epigenome high dimensionality human disease improved insight lifestyle factors methyl group methylation biomarker middle age mortality multidimensional data population based response screening user friendly software user-friendly

项目摘要

Recent advances in the study of epigenetics and human diseases have prompted a substantial increase in research on the extent to which DNA methylation mediates the effects of environmental exposures on health outcomes. With new and advancing technologies, epigenome-wide DNA methylation data (EWAS) have become the standard for large, population-based epigenetic studies while simultaneously introducing new methodological challenges unique to the use of high-dimensional data in mediation analyses. Due to these challenges, previous studies on epigenetic-mediated effects were generally forced to focus on candidate (gene-specific) methylation markers. More systematic research into this area will remain stalled until the urgent need for analytical methods appropriate and specific to EWAS mediation analysis is met. We propose to develop and apply new analytical approaches to high-dimensional mediation analysis methods for epigenome-wide DNA methylation studies. We will integrate high-dimensional screening and model selection methods into the traditional mediation analysis framework. We will also address sophisticated practical issues in conducting this type of data analysis, specifically: grouping effects of DNA methylation markers on health outcomes, and the generally high correlation among methylation markers. Finally, we will compile and disseminate a user-friendly software package to facilitate implementation of the proposed methods. We will test our proposed methods using data from the Coronary Artery Risk Development in Young Adults Study (CARDIA), a large longitudinal study with available EWAS data that examines the risk factors, development, and determinants of clinical and subclinical cardiovascular disease. Our discoveries may lead to new insights into the mechanisms by which DNA methylation mediates the effects of environmental exposures on health outcomes. It may also inform the development of new interventions targeting these methylation markers and improving the prevention and treatment of disease.

表观遗传学和人类疾病研究的最新进展促使 DNA甲基化在多大程度上介导环境暴露对健康的影响的研究结果。借助先进的新技术，全表观基因组 DNA 甲基化数据 (EWAS) 已成为大规模、基于人群的表观遗传学研究的标准，同时引入新的在中介分析中使用高维数据所特有的方法论挑战。由于这些挑战，之前关于表观遗传介导效应的研究通常被迫集中于候选（基因特异性）甲基化标记。对该领域更系统的研究将继续停滞，直到出现紧急情况为止。满足了对适合 EWAS 中介分析的特定分析方法的需求。我们建议开发和应用新的分析方法来进行高维中介分析方法用于全表观基因组 DNA 甲基化研究。我们将高维筛选和模型结合起来选择方法纳入传统的中介分析框架。我们还将解决复杂的进行此类数据分析的实际问题，特别是：DNA 甲基化的分组效应健康结果的标记，以及甲基化标记之间普遍存在的高度相关性。最后，我们将编制并传播一个用户友好的软件包，以促进拟议的实施方法。我们将使用来自年轻人冠状动脉风险发展的数据来测试我们提出的方法研究 (CARDIA) 是一项大型纵向研究，利用可用的 EWAS 数据检查风险因素，临床和亚临床心血管疾病的发展和决定因素。我们的发现可能会导致对 DNA 甲基化介导环境暴露影响的机制的新见解关于健康结果。它还可能为针对这些甲基化的新干预措施的开发提供信息标记并改善疾病的预防和治疗。