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

项目摘要 在过去的十年里，科学家们加快了对阿尔茨海默氏症的研究。 疾病（AD）。这导致了前所未有的遗传和生物学知识， 广告风险的基础，以及大量有价值的数据存储，以供进一步挖掘。了解 AD的遗传和生物学风险状态本身对于指导 机制研究，开发更好的诊断方法，制定治疗方案。而是一个 对风险状态的理解也有好处，可以让研究恢复力， AD.对恢复力的遗传和生物基础的研究必然滞后 风险因素的发现。现在，随着AD的风险架构进入人们的视野， 研究认知正常的个体对AD的恢复力是可行的，尽管他们 患病风险增加我们设计的识别弹性的方法 这些因素是直截了当的，但据我们所知，是前所未有的。我们识别未受影响的 多变量风险水平最高的个体，将其与受影响的个体进行匹配， 同等风险水平，并对比这两个亚组以寻找剩余变异 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映射AD的弹性 （遗传学，转录组学和神经影像学），并整合这些水平。在 目的1，我们将确定与AD恢复力相关的遗传变异， 由APOE ε4等位基因引起的遗传风险升高，AD多基因风险升高 评分或AD多基因危险评分升高。在目标2中，我们将分析所有 现有的转录组学数据来自死后海马组织的研究， AD患者的外周血，以确定转录组风险评分和机器学习 最大程度区分AD与认知正常对照受试者的算法，以及 分数和算法，然后确定残留的转录组变异， 转录组学风险在弹性控制。在目标3中，我们将确定一个基于MRI的结构 在AD存在的情况下与对AD的恢复力相关的大脑签名， 相关的皮质风险信号。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，以确定生物学特征。 目标之间的关系，以及新的弹性表型。总体而言，这些目标 将确定多变量，遗传，转录组学，和大脑结构的韧性概况 以及源自AD的分子、神经生物学和临床表型， 弹性基因型

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.