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: 10017121
• 负责人: Stephen J Glatt
• 金额: $ 76.05万
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10017121
• 关键词: 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 (Brain); Individual; Knowledge; Light; Magnetic Resonance Imaging; Maps; Methods; Mining; Molecular; NIH Program Announcements; Neurobiology; Phenotype; Prevention; Research; Residual state; Risk; Risk Factors; Sampling; Schizophrenia; Scientist; Single Nucleotide Polymorphism; Structure; Subgroup; Testing; Therapeutic; Thick; Tissues; Variant; Work; apolipoprotein E-4; base; 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; polygenic risk score; prevent; psychologic; resilience; secondary analysis; stem; structured data; transcriptome; 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.

项目概要 在过去的十年中，科学家们加快了了解阿尔茨海默病的努力 疾病（AD）。这带来了前所未有的遗传和生物学知识 AD 风险的基础，以及用于进一步挖掘的大量有价值的数据存储。了解 AD 的遗传和生物风险状态本身对于指导非常有价值 机制研究，开发更好的诊断方法，并制定治疗方法。但是一个 了解风险状态的另一个好处是可以进行复原力研究 广告。对复原力的遗传和生物学基础的研究必然滞后 风险因素的发现。现在，随着 AD 的风险架构逐渐显现， 研究认知能力正常的个体对 AD 的适应力是可行的 患该病的风险较高。我们设计的用于识别弹性的方法 据我们所知，这些因素是前所未有的简单明了。我们确定未受影响 多变量风险水平最高的个体，将他们与处于多变量风险水平的受影响个体进行匹配 同等的风险水平，并对比这两个亚组以找到剩余变异 与没有疾病有关。在这个项目中，我们将利用 现有的高通量AD风险因素结果和数据，以及我们参与的许多 世界上最大的 AD 联盟，在三个层面上有效地绘制 AD 的复原力 （遗传学、转录组学和神经影像学），并跨这些水平进行整合。在 目标 1，我们将识别与 AD 恢复力相关的遗传变异 APOE ε4 等位基因带来的遗传风险升高，AD 多基因风险升高 评分，或 AD 多基因风险评分升高。在目标 2 中，我们将对所有 来自死后海马组织研究的可用转录组数据 AD 中的外周血可识别转录组风险评分和机器学习 最大程度区分 AD 和认知正常对照受试者的算法，以及 然后识别抵消转录组变异的分数和算法 弹性控制中的转录组风险。在目标 3 中，我们将确定基于 MRI 的结构 在 AD 存在的情况下，与 AD 恢复力相关的大脑特征 相关的皮质风险特征。最后，在我们的探索性目标 4 中，我们将整合 遗传、转录组、脑结构和临床数据来识别生物 跨目标的关系，以及弹性的新表型。总的来说，这些目标 将识别复原力的多变量、遗传、转录组和大脑结构特征 AD 以及源自 AD 的分子、神经生物学和临床表型 复原力基因型。