Genetic Predictors, Transcriptomic Biomarkers, & Neurobiological Signatures of Resilience to Alzheimer's Disease

遗传预测因子、转录组生物标志物、

• 批准号: 10655365
• 负责人: CHRISTINE FENNEMA-NOTESTINE
• 金额: $ 75.87万
• 依托单位: UPSTATE MEDICAL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10655365
• 关键词: Acceleration; Address; Affect; Aging; Algorithms; Alleles; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease risk; Architecture; Autopsy; Biological; Biological Markers; Blood; Brain; Brain Diseases; Brain region; Clinical; Clinical Data; Cognitive; Complex; Data; Data Set; Diagnosis; Diagnostic; Disease; Early Intervention; Genes; Genetic; Genetic Risk; Genetic Variation; Genetic study; Genotype; Hippocampus; Individual; Knowledge; Magnetic Resonance Imaging; Maps; Methods; Mining; Molecular; NIH Program Announcements; Neurobiology; Persons; Phenotype; Prevention; Research; Residual state; Risk; Risk Factors; Sampling; Schizophrenia; Scientist; Single Nucleotide Polymorphism; Structure; Subgroup; Therapeutic; Thick; Tissues; Variant; Work; base; biomarker signature; brain tissue; clinical phenotype; cohort; disorder risk; evidence base; genetic predictors; genetic risk factor; genome wide association study; hazard; high dimensionality; high risk; innovation; interest; machine learning algorithm; neuroimaging; novel; peripheral blood; phenome; pilot test; polygenic risk score; prevent; psychologic; resilience; resilience factor; resilience research; secondary analysis; stem; transcriptomic profiling; transcriptomics

Project Summary Over the last decade, scientists have accelerated their efforts to understand Alzheimer’s disease (AD). This has led to unprecedented knowledge of the genetic and biological bases of AD risk, and vast stores of valuable data for further mining. Understanding the genetic and biological risk states for AD is, in itself, extraordinarily valuable for guiding mechanistic studies, developing better diagnostics, and formulating therapeutics. But an understanding of risk states also has the benefit of allowing research on resilience to AD. Research on the genetic and biological bases of resilience necessarily lags behind the discovery of risk factors. Now, as the risk architecture of AD is coming into view, it is feasible to study resilience to AD in individuals who are cognitively normal despite being at elevated risk for the disease. The approach we have devised for identifying resilience factors is straightforward yet, to our knowledge, unprecedented. We identify unaffected individuals at the highest levels of multivariate risk, match them to affected individuals at equivalent levels of risk, and contrast these two subgroups to find residual variation associated with the absence of disease. In this project, we will capitalize on the wealth of existing high-throughput AD risk-factor results and data, and our involvement in many of the world’s largest AD consortia, to efficiently map resilience to AD at three levels (genetics, transcriptomics, and neuroimaging), and to integrate across these levels. In Aim 1, we will identify genetic variation associated with resilience to AD in the presence of elevated genetic risk conferred by APOE ε4 alleles, an elevated AD polygenic risk score, or an elevated AD polygenic hazard score. In Aim 2, we will mega-analyze all available transcriptomic data from studies of postmortem hippocampal tissue and of peripheral blood in AD to identify transcriptomic risk scores and machine-learning algorithms that maximally distinguish AD from cognitively normal control subjects, and scores and algorithms that then identify residual transcriptomic variation that offsets the transcriptomic risk in resilient controls. In Aim 3, we will identify an MRI-based structural brain signature that is associated with resilience to AD in the presence of an AD- associated cortical risk signature. Lastly, in our exploratory Aim 4, we will integrate genetic, transcriptomic, brain structural, and clinical data to identify biological relationships across Aims, and novel phenotypes of resilience. Collectively, these Aims will identify multivariate, genetic, transcriptomic, and brain-structural profiles of resilience to AD, as well as molecular, neurobiological, and clinical phenotypes stemming from AD- resilience genotypes.

项目摘要 在过去的十年中，科学家们加快了了解阿尔茨海默氏症的努力 疾病（AD）。这导致了对遗传和生物学的前所未有的知识 广告风险基础和大量有价值的数据存储，以进一步采矿。了解 AD的遗传和生物风险状态本身对于指导非常有价值 机械研究，开发更好的诊断和制定治疗。但是 对风险状态的理解也可以使有关弹性的研究 广告。关于弹性必要滞后的遗传和生物学基础的研究 发现风险因素。现在，随着广告的风险架构即将到来，它是 可以在认知正常目的地的个人中研究对AD的弹性 疾病风险升高。我们为识别弹性而设计的方法 据我们所知，因素很简单。我们确定不受影响 具有最高水平的多元风险的个体，将他们与受影响的个体相匹配 等效的风险水平，并对比这两个亚组，以找到残差变化 与缺乏疾病有关。在这个项目中，我们将利用 现有的高通量广告风险因素结果和数据，以及我们参与许多 世界上最大的广告联盟，有效地将弹性绘制到三个级别的广告 （遗传学，转录组学和神经影像学），并在这些层面上整合。在 AIM 1，我们将确定与AD相关的遗传变异 APOEε4等位基因赋予的遗传风险升高，AD多基因风险升高 得分或升高的AD多基因危害得分。在AIM 2中，我们将大量分析 可用的转录组数据来自验尸后海马组织和 广告中的外围血液以识别转录组风险评分和机器学习 最大程度地区分AD和认知上正常对照对象的算法，以及 然后确定剩余转录组的分数和算法，以抵消 弹性控制中的转录组风险。在AIM 3中，我们将确定基于MRI的结构 在存在AD的情况下与AD相关的大脑签名 相关的皮质风险签名。最后，在我们的探索目标4中，我们将整合 遗传，转录组，大脑结构和临床数据以识别生物学 目的之间的关系和新颖的弹性表型。总的来说，这些目标 将识别弹性的多元，遗传，转录组和脑结构概况 以及AD，分子，神经生物学和临床表型 弹性基因型。