Mapping Molecular and Phenotypic Interactions in Alzheimers Disease

摘要 阿尔茨海默病(AD)是老年人认知功能减退和死亡的主要原因。治疗 治疗AD的选择是有限的，而且对更好的医疗选择有巨大的需求。广告与以下内容关联 随着时间的推移，大脑中的β淀粉样斑块逐渐扩大，导致脑组织和 认知功能。然而，目前对AD的根本驱动因素的理解还不完全。在这 项目中，我们将使用遗传和表型数据的大型数据集的组合，以及功能性 基因组学，以进一步阐明导致AD的生物学过程、途径和细胞类型。具体来说， 我们将使用现有的功能基因组学数据，以及新生成的大规模平行报道 以及先进的共局定位和基于异常值的统计方法，以识别功能调控变体 在疾病相关的基因座上；这些将被用来增加检测AD相关变异的能力，特别是 对于低频率站点。此外，我们将专注于将代谢物等中间生物标记物联系起来 和脑成像数据，以及糖尿病、心血管疾病和睡眠模式等特征对AD风险的影响 孟德尔随机化和聚类技术。我们将进一步的目标是将GWAS信号划分为 离散的生物因素，既有途径/过程，也有组织。总之，这项工作将导致更深层次的 了解阿尔茨海默病的致病基因驱动因素。

Abstract Alzheimer’s Disease (AD) is a leading cause of cognitive decline and mortality in the elderly. Treatment options for AD are limited, and there is a huge need for better medical options. AD is associated with progressive expansion of beta amyloid plaques in the brain, leading over time to loss of brain tissue and cognitive function. However, at present, understanding of the underlying drivers of AD is incomplete. In this project, we will use a combination of large data sets of genetic and phenotypic data, as well as functional genomics, to further elucidate the biological processes, pathways, and cell types leading to AD. Specifically, we will use existing functional genomics data, as well as newly generated Massively Parallel Reporter Assays and advanced colocalization and outlier-based statistical approaches to identify functional regulatory variants at disease-relevant loci; these will be used to increase the power to detect AD-associated variants, particularly for low-frequency sites. Furthermore, we will focus on linking intermediate biomarkers such as metabolites and brain imaging data, and traits such as diabetes, cardiovascular disease and sleep patterns to AD risk using Mendelian Randomization and clustering techniques. We will further aim to partition the GWAS signal into discrete biological factors, both by pathways/processes and by tissue. In sum, this work will lead to deeper understanding of the causal genetic drivers of Alzheimer’s Disease.