Understanding the protective and neuroinflammatory role of human brain immune cells in Alzheimer Disease

项目总结 精准医疗是指对危险因素的识别和医疗的定制化 每个病人的个性特征。推进精准医学，我们需要更好地 了解共同和复杂性状背后的分子机制。正在加速的 药物伙伴关系(AMP)一直在研究包括阿尔茨海默病(AD)在内的特征， 类风湿性关节炎(RA)、系统性红斑狼疮(SLE)、帕金森病(PD)、 精神分裂症(SCZ)和2型糖尿病(T2D)具有复杂和部分共有的多基因 结构，其病理生理涉及炎症机制，由先天和 在大脑和外周发现的适应性免疫。我们将利用EpiXcan，一种新的机器学习 方法，它利用表达参考小组(eQTL与表达和基因的队列 数据)以了解共享和不同免疫机制的遗传驱动的扰动 所有与AMP相关的性状。作为本次分析的输入数据，我们将整合多个GWAS摘要 用高维单细胞数据对人纯化小胶质细胞和小胶质细胞AMP相关性状的统计 来自匹配捐赠者的外周血单核细胞(PBMC)和扩大的内部人口- 人纯化小胶质细胞中RNA-seq和ATAC-seq水平的数据结合外部组学数据 脑免疫细胞、PBMC和滑膜。我们的分析将确定特质-特质的相关性，基因-特质 与AMP相关的共享基因相关的关联和分子通路 特征，作为未来新的治疗方法的潜在靶点。

PROJECT SUMMARY Precision Medicine refers to the identification of risk factors and customization of medical treatment to the individual characteristics of each patient. To advance Precision Medicine, we need to better understand the molecular mechanisms underlying common and complex traits. The Accelerating Medicines Partnership (AMP) has been investigating traits, including Alzheimer's disease (AD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), Parkinson's disease (PD), Schizophrenia (SCZ) and Type 2 diabetes (T2D), which have complex and partially shared polygenic architecture, and their pathophysiology involves inflammatory mechanisms regulated by the innate and adaptive immunity found in the brain and periphery. We will utilize EpiXcan, a novel machine learning approach, which leverages expression reference panels (eQTLs cohorts with expression and genotype data) to understand the genetically driven perturbations of shared and distinct immune mechanisms across AMP-related traits. As input data to this analysis, we will integrate multiple GWAS summary statistics for AMP-related traits with high dimensional single-cell data in human purified microglia and peripheral blood mononuclear cells (PBMCs) from matched donors and expanded in-house population- level RNA-seq and ATAC-seq data in human purified microglia combined with external omics data of brain immune cells, PBMC and synovium. Our analysis will identify trait-trait correlations, gene-trait associations and molecular pathways associated with shared genes implicated across AMP-related traits, serving as potential targets for future, novel therapeutic treatments.