Statistical methods for population-level cell-type-specific analyses of tissue omics data for Alzheimer's disease

项目总结/摘要 阿尔茨海默病（AD）占痴呆病例的60 - 80%，并导致进行性神经变性 最终导致死亡虽然美国晚发性AD患者人数预计将达到1380万， 到2050年，预防和治疗艾滋病的效果仍然有限。人们对AD的研究做了很多努力 病理生理学通过收集和管理来自AD影响或未影响的人脑的丰富组学数据，例如， 国家老龄化研究所的加速阿尔茨海默病药物伙伴关系（AMP-AD）项目。 大部分组学数据，如基因表达，DNA甲基化和蛋白质组学，都是在组织中收集的。 级别，并且因此小区类型特定（CTS）信号被掩蔽。最近，随着新兴的单细胞 通过这些技术，已经生成了单细胞RNA-seq和DNA甲基化数据。然而，考虑到 由于量化少量分子和相关的高成本，单细胞数据受到高技术的限制， 变化，并被限制到少量的样本，缺乏代表性。解决这些问题 在AD研究和加速我们对AD的细胞多组学机制的理解方面，我们的目标是： 1)- 通过集合改进对脑组织样品中的细胞分数的估计，超过现有方法， 考虑细胞类型层次结构。2)鉴定与AD相关的CTS差异甲基化区域（DMR）。我们 将考虑CpG位点的空间相关性和细胞类型特异性。3)我们将进一步建立统计模型 通过组学范围内的关联研究和因果中介，系统地整合这些CTS组学估计 分析。通过对AMP-AD数据集中几个大型队列的广泛分析，我们将在统计学上产生 一个重要的和有生物学意义的组学结果在前所未有的人口规模和细胞类型 分辨率，这将提高我们对复杂AD生物学的理解。我们将验证我们的发现， AMP-AD项目内外的其他可用数据，包括单细胞多组学数据。的 由此产生的方法将通过容易获得的公共软件作为有效的计算算法来实现 给研究界。该项目的成功完成将为细胞- 类型解卷积和综合多组学分析，并推进我们对基因/蛋白质的了解 在选择性脆弱的大脑区域和细胞类型中导致AD。

PROJECT SUMMARY/ABSTRACT Alzheimer's disease (AD) accounts for 60-80% of dementia cases and causes progressive neurodegeneration that ultimately leads to death. While the number of US people with late-onset AD is expected to reach 13.8 million by 2050, its prevention and treatment remain only modestly effective. Many efforts have been made to study AD pathophysiology by collecting and curating rich omics data from AD-affected or unaffected human brains, e.g., the National Institute on Aging's Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) project. Most of those omics data, such as gene expression, DNA methylation, and proteomics, are collected at the tissue level, and thus the cell-type-specific (CTS) signals are masked. Recently, with the emerging single-cell techniques, single-cell RNA-seq and DNA methylation data have been generated. However, given the difficulty of quantifying a small number of molecules and associated high costs, single-cell data suffer from high technical variation and are constrained to a small number of samples that lack representativity. To address these issues in AD research and accelerate our understanding of cellular multi-omics mechanisms underlying AD, we aim to: 1) Improve estimation of cellular fractions in brain tissue samples by the ensemble over existing methods and considering cell-type hierarchy. 2) Identify CTS differentially methylated regions (DMR) associated with AD. We will consider the spatial correlation of CpG sites and cell-type specificity. 3) We will further build statistical models to systematically integrate those CTS omics estimates via omics-wide association studies and causal mediation analyses. Through extensive analyses of several large cohorts in AMP-AD datasets, we will produce statistically significant and biologically meaningful omics results at an unprecedented population-scale and cell-type resolution, which will improve our understanding of complex AD biology. We will validate our findings using additional data available within and outside the AMP-AD project, including single-cell multi-omics data. The resulting methods will be implemented as efficient computational algorithms via public software readily available to the research community. Successful completion of this project will provide state-of-the-art methods for cell- type deconvolution and integrative multi-omics analyses and advance our knowledge of genes/proteins contributing to AD in selectively vulnerable brain regions and cell types.