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.

阿尔茨海默氏病（AD）是最常见的神经退行性疾病，后期发作的广告影响约1 在美国，有9人超过65岁。在美国，越来越多的人口使广告成为主要的公众 健康关注和财务上最昂贵的疾病之一。目前，一个主要的挑战是缺乏可靠， 微创，廉价的生物标志物，以帮助诊断，预后和最终发展新的 广告处理策略。 AD的生物标志物的一种潜在来源是DNA甲基化（DNAM）。变更 Dnam在衰老和AD中都隐含。而且，dnam相对稳定，很容易 检测到。 DNAM是基因组和环境界面上的表观遗传机制，它受到影响 通过衰老和许多生活方式因素，例如吸烟，饮食和运动，这反过来可能会改变 广告。在过去的几年中，我们为DNA和其他其他创新的开源软件开发了几种创新的开源软件 基因组学数据分析。在此提案中，基于我们以前在开发和应用工具方面的经验 对于大规模异质数据集的集成基因组学分析，我们建议协调一个大型 DNAM数据集的数量来阐明DNAM在衰老和AD中的作用，以开发一个网络界面 传播分析结果，并开发用于预测AD表型的表观遗传钟。我们 假设许多基于DNAM的监管变化与衰老和AD有关，以及某些年龄 相关的DNAM变化也有助于AD发作和进展。在AIM 1中，我们将汇总，协调， 并分析了在大脑和血液样本中测量的大量DNAM衰老数据集，以识别 DNAM变化与衰老和AD相关，并确定与年龄相关的DNAM差异 为广告做出贡献。我们将开发两个工具：（1）可搜索的Web界面，阐明了DNA的作用 衰老和AD中的甲基化以及（2）用于执行DNAM的荟萃分析的开源R包装 甲基化区域。在AIM 2中，我们将开发一个针对预测AD表型的新表观遗传时钟。这 将使用可用的CSF生物标志物评估新表观遗传时钟的诊断和预后值 和临床认知结果，并将其与已知的临床和遗传因素以及当前 可用的血浆生物标志物。可搜索的Web界面将显着增强我们的理解和 对AD中与年龄相关的表观遗传变化的作用启用新的生物学见解。新表观遗传钟 针对预测AD表型的量身定制将有助于替代生物标志物的发展 在临床试验中监测疾病进展的客观性以及评估个性化的风险概况 用于广告诊断和预后。该项目的成功完成还将为我们提供计算 可以轻松调整和应用以分析针对其他类型的数据集的管道和工具 痴呆症。