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

阿尔茨海默病组织组学数据的群体水平细胞类型特异性分析的统计方法

基本信息

批准号：
10589254
负责人：
Christopher McKennan
金额：
$ 37.83万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-02-01 至 2027-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10589254
关键词：
Acceleration Address Affect Alzheimer&apos s Disease Behavior Biology Brain Brain region Cell Lineage Cells Cessation of life Child Clinical Communities Complement Complex Computational algorithm Computer software Computing Methodologies DNA DNA Methylation Data Data Analyses Data Set Dementia Dependence Development Functional disorder Future Gene Expression Genes Genotype Goals Human Intervention Knowledge Late Onset Alzheimer Disease Learning Masks Mediation Medicine Methods Methylation Multiomic Data National Institute on Aging Parents Pathogenesis Pathogenicity Pathway interactions Persons Population Prevention Prevention strategy Problem Solving Proteins Proteome Proteomics RNA Research Resolution Role Sample Size Sampling Signal Transduction Site Solid Specificity Statistical Methods Statistical Models Techniques Technology Testing Tissue Sample Tissues Variant aging population brain cell brain tissue cell type cohort cost cost efficient epigenome genome-wide high dimensionality improved innovation insight multiple omics novel progressive neurodegeneration single cell technology single nucleus RNA-sequencing single-cell RNA sequencing statistics tool transcriptome treatment strategy

项目摘要

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.

项目概要/摘要阿尔茨海默病 (AD) 占痴呆病例的 60-80%，并导致进行性神经退行性变最终导致死亡。而美国患有迟发性AD的人数预计将达到1380万到 2050 年，其预防和治疗效果仍然有限。人们为研究 AD 做出了许多努力通过收集和整理来自受 AD 影响或未受影响的人类大脑的丰富组学数据来进行病理生理学研究，例如美国国家老龄化研究所的阿尔茨海默病加速药物合作伙伴关系 (AMP-AD) 项目。大多数组学数据，例如基因表达、DNA 甲基化和蛋白质组学，都是在组织中收集的水平，因此细胞类型特异性（CTS）信号被掩盖。近年来，随着单细胞的兴起技术，单细胞RNA-seq和DNA甲基化数据已经生成。但考虑到难度由于量化少量分子和相关的高成本，单细胞数据受到高科技的困扰变化，并且仅限于缺乏代表性的少量样本。为了解决这些问题在 AD 研究中并加速我们对 AD 背后的细胞多组学机制的理解，我们的目标是： 1）通过现有方法改进集成对脑组织样本中细胞分数的估计考虑细胞类型层次结构。 2) 识别与 AD 相关的 CTS 差异甲基化区域 (DMR)。我们将考虑 CpG 位点和细胞类型特异性的空间相关性。 3）我们将进一步建立统计模型通过全组学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 数据集中的几个大型队列进行广泛分析，我们将生成统计数据在前所未有的群体规模和细胞类型中获得重要且具有生物学意义的组学结果分辨率，这将提高我们对复杂 AD 生物学的理解。我们将使用以下方法验证我们的发现 AMP-AD 项目内外可用的其他数据，包括单细胞多组学数据。这由此产生的方法将通过现成的公共软件实现为有效的计算算法到研究界。该项目的成功完成将为细胞提供最先进的方法类型反卷积和综合多组学分析并增进我们对基因/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。