Genetic Architecture of Pure Alzheimer's Disease and Mixed Pathology

纯阿尔茨海默病和混合病理学的遗传结构

• 批准号: 10712591
• 负责人: David William Fardo
• 金额: $ 100.15万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712591
• 关键词: Address; Aging; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease pathology; Alzheimer' s disease related dementia; Atlases; Autopsy; Bioinformatics; Biological; Brain Pathology; Brain region; Cerebrovascular Trauma; Chromatin; Clinical; Clinical Data; Clinical Trials; Clinical Trials Design; Cognitive; Data; Dementia; Development; Disease Progression; Drug Targeting; Exhibits; Future; Genetic; Genetic study; Genomic Segment; Genotype; Goals; Hand; Heterogeneity; Human; Individual; Infarction; Knowledge; Lead; Lewy Bodies; Machine Learning; Memory; Mendelian randomization; Molecular; Multiomic Data; Participant; Pathology; Pathway Analysis; Pattern; Persons; Phenotype; Research; Resolution; Risk Factors; Sample Size; Stratification; Tissue-Specific Gene Expression; Variant; biobank; brain cell; case control; cell type; clinical risk; data resource; disorder control; epidemiology study; follow-up; gene network; genetic analysis; genetic architecture; genome wide association study; genome-wide; hippocampal sclerosis; improved; insight; neuropathology; novel; novel strategies; pleiotropism; prevent; protein TDP-43; religious order study; risk variant; single nucleus RNA-sequencing; trait; transcriptome

Co-occurring neuropathologies such as Lewy bodies, hippocampal sclerosis, and microinfarcts likely influence heterogeneity in genetic studies of Alzheimer’s disease (AD). The genetic architecture of these co-occurring neuropathologies is not clear. Most AD genetic analyses use clinical AD dementia phenotypes and are limited by phenotype misclassification: many clinical AD “controls” harbor AD pathology while clinical AD “cases” have no or minimal AD pathology. Only 20% of pathology confirmed AD dementia cases have only AD pathology (“pure AD”); 80% have co-occurring neuropathologies (“mixed AD”). A complementary strategy is to perform genome-wide association studies (GWAS) specifically distinguishing between pure and mixed AD. The overarching goal of this proposal is to further scientific understanding of pure AD and mixed AD by integrating neuropathology together with advanced statistical approaches and extensive multi-omics data. Aim 1: Distinguish the genetic architecture of pure AD vs. mixed AD vs. pathology-free controls and determine how associations are driven by particular neuropathological patterns. 1a. Perform GWAS of pathology-confirmed AD controls vs. pure AD vs. mixed AD using a multinomial regression framework. We will also examine how associations are driven by particular neuropathological features furthering our understanding of underlying mechanisms. 1b. Infer possible mechanisms by integrating biological knowledge. We will perform transcriptome- wide analyses and gene-network analyses of pure AD and mixed AD, integrating human PPI data. Aim 2: Identify factors specific to pure AD or mixed AD. 2a. Use machine learning and harmonized clinical data to isolate factors associated with pure or mixed AD. Identified clinical risk factors could be used for future clinical trial stratification. 2b. Perform genetic correlation analysis and Mendelian randomization to estimate correlated genetic effects and potential causal effects between candidate risk factor traits and pure or mixed AD. Genetic correlation analysis will gain novel insights into the shared genetic basis between biobank-scale GWAS traits and pure AD and mixed AD. We will follow-up significant correlations with local genetic correlation and Mendelian randomization (MR) to identify specific genomic regions contributing to the correlation and quantify causal effects of these traits on pure AD and mixed AD. Aim 3. Characterize the cellular and molecular consequences of pure AD vs mixed AD. We will harness the data generated by the SEA-AD and the ROS-MAP studies to characterize the molecular changes occurring for our phenotypes at cell type resolution. We will perform abundance analyses (3a), differential gene expression (3b), and differential chromatin accessibility (3c) in each cell type across multiple brain regions. We will investigate loci from recent GWASs of AD dementia and candidates from Aim 1.

路易小体、海马硬化和微梗塞等共同发生的神经病变可能会影响 阿尔茨海默病(AD)遗传学研究中的异质性这些共生现象的遗传结构 神经病理学尚不清楚。大多数AD基因分析使用的是临床AD痴呆表型，而且是有限的 按表型错误分类：许多临床AD“对照”有AD病理，而临床AD“病例”有 无或仅有轻微的AD病理改变。只有20%的病理确诊的AD痴呆患者只有AD病理 (“纯性阿尔茨海默病”)；80%有共病神经病变(“混合性阿尔茨海默病”)。相辅相成的战略是 全基因组关联研究(GWAS)，明确区分单纯AD和混合性AD。这个 这一建议的总体目标是通过整合 神经病理学以及先进的统计方法和广泛的多组学数据。 目的1：区分单纯阿尔茨海默病和混合性阿尔茨海默病的遗传结构 确定特定的神经病理模式是如何驱动关联的。 1A.对病理证实的AD对照、纯AD和混合型AD使用 多项回归框架。我们还将研究关联是如何由特定的 神经病理特征加深了我们对潜在机制的理解。 1B.通过整合生物学知识来推断可能的机制。我们将执行转录组- 对纯AD和混合型AD的广泛分析和基因网络分析，整合了人类PPI数据。 目的2：确定单纯阿尔茨海默病或混合性阿尔茨海默病的特异性因子。 2A。使用机器学习和协调的临床数据来分离与纯或 混合公元。已确定的临床危险因素可用于未来的临床试验分层。 2B。进行遗传相关分析和孟德尔随机化以估计相关遗传 候选危险因素特征与单纯或混合性AD之间的影响和潜在的因果关系。 遗传相关分析将对生物库尺度之间共享的遗传基础提供新的见解 单纯性AD和混合性AD的遗传特征。我们将追踪与当地基因的显著相关性 相关和孟德尔随机化(MR)，以确定特定的基因组区域有助于 将这些性状对单纯AD和混合性AD的因果效应进行相关和量化。 目的3.研究单纯AD与混合性AD的细胞和分子后果。我们会驾驭 由SEA-AD和ROS-MAP研究产生的数据以表征发生的分子变化 我们在细胞类型分辨率上的表型。我们将进行丰度分析(3a)，差异基因 每种细胞类型跨多个脑区的表达(3b)和差异染色质可及性(3c)。我们 将调查最近AD痴呆症GWAS和AIM 1候选人的基因座。