New computational tools for understanding and predicting AD via age-associated DNA methylation changes

通过与年龄相关的 DNA 甲基化变化来理解和预测 AD 的新计算工具

• 批准号: 10509428
• 负责人: Lily Wang
• 金额: $ 202.07万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10509428
• 关键词: Acceleration; Affect; Age; Aging; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Attenuated; Bioconductor; Bioinformatics; Biological; Biological Aging; Biological Markers; Blood; Blood specimen; Brain; Chronology; Clinical; Clinical Trials; Cognitive; Communities; Computer software; Cost of Illness; DNA Methylation; Data; Data Analyses; Data Set; Databases; Dementia; Development; Diagnosis; Diet; Disease; Disease Progression; Elderly; Environment; Epigenetic Process; Financial cost; Genome; Genomic approach; Genomics; Knowledge; Late Onset Alzheimer Disease; Measures; Medical Genetics; Meta-Analysis; Methylation; Modeling; Monitor; Neurodegenerative Disorders; Onset of illness; Outcome; Persons; Plasma; Population; Prognosis; Public Health; Research; Research Personnel; Risk; Role; Sampling; Smoking; Source; Surrogate Markers; Tissues; Training; base; computational pipelines; computerized tools; diagnostic value; diet and exercise; disease phenotype; disease prognosis; experience; functional decline; genomic data; heterogenous data; human old age (65+); innovation; insight; lifestyle factors; minimally invasive; open source; predictive marker; predictive modeling; prognosis biomarker; prognostic value; progression marker; tool; treatment strategy; web interface

Alzheimer’s disease (AD) is the most common neurodegenerative disorder, with late-onset AD affecting about 1 in 9 people over 65 years old in the US. The increasing elderly population in the US makes AD a major public health concern and one of the most financially costly diseases. Currently, a major challenge is the lack of reliable, minimally invasive, inexpensive biomarkers to aid the diagnosis, prognosis, and ultimately development of new AD treatment strategies. One potential source of biomarkers for AD is DNA methylation (DNAm). Changes in DNAm have been implicated in both aging and AD. Moreover, DNAm is relatively stable and can be easily detected. DNAm is an epigenetic mechanism at the interface of the genome and environment, and it is influenced by aging and many lifestyle factors such as smoking, diet, and exercise, which in turn might modify the risk of AD. In the past few years, we have developed several innovative open-source software for DNAm and other genomics data analyses. In this proposal, building on our previous experiences in developing and applying tools for integrative genomics analyses of large-scale heterogeneous datasets, we propose to harmonize a large number of DNAm datasets to clarify the role of DNAm in aging and AD, to develop a web interface that disseminates the analyses results, and to develop epigenetic clocks tailored for predicting AD phenotypes. We hypothesize a number of DNAm-based regulatory changes are relevant to both aging and AD, and some age- associated DNAm changes also contribute to AD onset and progression. In Aim 1, we will aggregate, harmonize, and meta-analyze a large number of DNAm aging datasets measured in brain and blood samples to identify DNAm changes associated with aging and AD, and determine age-associated DNAm differences that also contribute to AD. We will develop two tools: (1) a searchable web interface that clarifies the role of DNA methylation in aging and AD and (2) an open-source R package for performing meta-analyses of DNAm methylation regions. In Aim 2, we will develop a new epigenetic clock tailored for predicting AD phenotypes. The diagnostic and prognostic values of the new epigenetic clock will be evaluated using available CSF biomarkers and clinical cognitive outcomes and compared with known clinical and genetic factors, as well as currently available plasma biomarkers. The searchable web interface will significantly enhance our understanding and enable new biological insights on the role of age-associated epigenetic changes in AD. The new epigenetic clock tailored to predicting AD phenotypes will facilitate the development of surrogate biomarkers that provide a degree of objectivity for monitoring disease progression in clinical trials, as well as assessing individualized risk profiles for AD diagnosis and prognosis. The successful completion of the project will also provide us with computational pipelines and tools that can be easily adapted and applied to analyze datasets generated for other types of dementias.

阿尔茨海默病（AD）是最常见的神经退行性疾病，其中晚发性AD影响约1 在美国，65岁以上的人中有9人。美国老年人口的增加使AD成为一个主要的公共 健康问题和最昂贵的疾病之一。目前，一个主要的挑战是缺乏可靠的， 微创，廉价的生物标志物，以帮助诊断，预后，并最终开发新的 AD治疗策略。AD生物标志物的一个潜在来源是DNA甲基化（DNAm）。变化 DNA m与衰老和AD都有关系。此外，DNAm相对稳定，并且可以容易地被 检测到DNAm是基因组和环境界面上的一种表观遗传机制， 年龄和许多生活方式因素，如吸烟，饮食和锻炼，这反过来可能会改变风险， AD.在过去的几年里，我们已经开发了几个创新的开源软件的DNAm和其他 基因组学数据分析。在本提案中，基于我们以前在开发和应用工具方面的经验， 对于大规模异构数据集的综合基因组学分析，我们建议协调一个大的 一些DNAm数据集，以阐明DNAm在衰老和AD中的作用，开发一个网络界面， 传播分析结果，并开发用于预测AD表型的表观遗传时钟。我们 假设一些基于DNAm的调节变化与衰老和AD相关，并且一些年龄- 相关的DNAm变化也有助于AD的发作和进展。在目标1中，我们将聚集、协调、 并荟萃分析大量的DNA老化数据集测量大脑和血液样本，以确定 与衰老和AD相关的DNA m变化，并确定年龄相关的DNA m差异， 为AD做贡献我们将开发两个工具：（1）一个可搜索的网络界面，阐明DNA的作用 甲基化在衰老和AD和（2）一个开源的R包进行荟萃分析的DNAm 甲基化区域。在目标2中，我们将开发一种新的表观遗传时钟，用于预测AD表型。的 新的表观遗传时钟的诊断和预后价值将使用可用的CSF生物标志物进行评估 和临床认知结果，并与已知的临床和遗传因素进行比较，以及目前 可用的血浆生物标志物。可搜索的网络界面将大大提高我们的理解， 使新的生物学见解的作用，年龄相关的表观遗传变化在AD。新的表观遗传时钟 专为预测AD表型而设计的生物标志物将促进替代生物标志物的开发，从而提供学位 在临床试验中监测疾病进展以及评估个体化风险特征的客观性 AD的诊断和预后。该项目的成功完成还将为我们提供计算 管道和工具，可以很容易地适应和应用于分析为其他类型的 痴呆症