Investigating a molecular basis for Alzheimer's disease subtypes using multiomic data integration and machine-learning

项目摘要 利用多组学数据整合研究AD亚型的分子基础 和机器学习 尽管对临床前阿尔茨海默病（AD）疾病模型进行了深入研究，但所有潜在的疾病- 修饰药物在临床试验中失败了。许多遗传学研究已经提出了一些生物学上的 机制，但一直没有达成共识的遗传病因学的AD。这可能是因为 AD作为单一疾病的流行观点过于简单化，没有考虑异质性致病性 AD遗传结构的变异。高通量研究表明，AD是复杂的、非线性的 基因组、转录组、表观基因组和蛋白质组内部和之间的相互作用。虽然全基因组 关联研究已经成功地揭示了与AD相关的基因，这些基因以一种 这是一个小比例的患者群体，“缺失遗传性”的问题仍然存在。因此，在目标1中， 建议在机器学习的集成多组学框架中使用线性和非线性方法， 识别AD中重要的通路。虽然几乎所有的AD患者都表现出标志性的b-淀粉样蛋白， 神经系统缠结病理，他们也提出了显着的变化，认知症状，行为， 神经生理学鉴于此，我假设AD相关和免疫途径的个体间差异 在患者群体中驱动不同的疾病病因，最终导致共同的病理生理学。一 异质性的来源可能是在免疫途径中差异调节神经炎症反应， AD.在目标2中，我建议使用无监督分类方法来确定AD的亚型， 患者在组学水平、成像数据和表型数据上的途径变异的相似性。我特别 假设先天免疫途径中的致病性变异在驱动不同的免疫途径中起着关键作用， 患者之间的疾病病因。在目标3中，我建议通过生成蛋白质来表征每个组学亚型， 药物靶向优先化的相互作用网络。从这些目标中获得的知识将为当前AD的转变提供信息 药物开发范例，通过告知精确医学方法来靶向AD的特定组学亚型 而不是迄今为止失败的“一刀切”的方法。研究AD的基因组异质性， 这些目标有可能影响症状前AD个体的检测， 深入了解AD的复杂遗传结构。

PROJECT ABSTRACT Investigating a molecular basis for AD subtypes using multiomic data integration and machine-learning Despite intense investigation into preclinical Alzheimer’s Disease (AD) disease models, all potential disease- modifying drugs have failed in clinical trials. Numerous genetic studies have proposed a number of biological mechanisms, however there has been no consensus on the genetic etiology of AD. This is likely because the prevailing view of AD as a singular disease is oversimplified and does not consider heterogeneous pathogenic variation in AD genetic architecture. High-throughput studies indicate that AD is a result of complex, nonlinear interactions within and between the genome, transcriptome, epigenome, and proteome. While genome-wide association studies have successfully revealed genes associated with AD, these genes explain disease in a small proportion of the patient population, and the question of “missing heritability” remains. Thus, in Aim 1, I propose using linear and nonlinear methods in an integrated multiomics framework with machine learning to identify pathways significant in AD. While almost all AD patients present the hallmark b-amyloid and neurofibrillary tangle pathology, they also present significant variability in cognitive symptoms, behaviors, and neurophysiology. Given this, I hypothesize that inter-individual variation in AD-associated and immune pathways drives different disease etiologies across the patient population culminating in a common pathophysiology. One source of heterogeneity may be in immune pathways differentially regulating neuroinflammatory response during AD. In Aim 2, I propose using an unsupervised classification approach to determine subtypes of AD based on patient similarity in pathway variation across omic levels, imaging data, and phenotypic data. Specifically, I hypothesize that pathogenic variation within innate immunity pathways plays a critical role in driving different disease etiologies between patients. In aim 3, I propose characterizing each omic subtype by generating protein interaction networks for drug target prioritization. Knowledge from these aims will inform a shift in the current AD drug development paradigm by informing a precision medicine approach to target specific omic subtypes of AD instead of a “one size fits all” approach that has failed to date. Investigating genomic heterogeneity in AD through these aims has the potential to impact detection of pre-symptomatic AD individuals as well as reveal more insights into the complex genetic architecture of AD.