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

项目摘要 在过去的十年里，科学家们加快了了解阿尔茨海默氏症的努力 疾病(AD)。这导致了对基因和生物学的前所未有的了解 AD风险的基础，以及用于进一步挖掘的大量有价值的数据。了解 阿尔茨海默病的遗传和生物风险状态本身就具有非凡的指导价值 机制研究，开发更好的诊断学，并制定治疗学。但是一个 对风险状态的理解还有利于对弹性的研究 广告。恢复力的遗传和生物学基础研究必然滞后 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的复原力是可行的 有更高的患病风险。我们为识别韧性而设计的方法 据我们所知，因素是直截了当的，是前所未有的。我们确认了未受影响的 多变量风险水平最高的个体，将他们与受影响的个体进行匹配 相同的风险水平，并对比这两个子组以找出残差 与没有疾病相关的。在这个项目中，我们将利用 现有的高吞吐量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，在三个级别高效地映射AD的复原力 (遗传学、转录学和神经成像)，并在这些层面上整合。在……里面 目标1，我们将确定与AD恢复力相关的遗传变异 载脂蛋白Eε4等位基因导致的遗传风险增加，AD多基因风险增加 得分，或AD多基因危险得分升高。在目标2中，我们将对所有 可获得的转录数据来自死后海马组织和 AD患者外周血识别转录风险评分和机器学习 最大限度地区分AD和认知正常对照受试者的算法，以及 分数和算法，然后识别剩余的转录变异，以抵消 弹性对照中的转录切割风险。在目标3中，我们将确定一种基于MRI的结构 大脑信号与在AD存在的情况下对AD的弹性有关- 相关的皮质风险签名。最后，在我们的探索性目标4中，我们将整合 基因、转录本、脑结构和临床数据，以确定生物学 目标之间的关系，以及新的韧性表型。总而言之，这些目标 将确定复原力的多元、遗传、转录和脑结构特征 AD，以及AD的分子、神经生物学和临床表型- 韧性基因分型。

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.