Genetic Architecture of Pure Alzheimer's Disease and Mixed Pathology

同时发生的神经病理学如路易体、海马硬化和微梗塞可能影响 阿尔茨海默病（AD）遗传学研究中的异质性。这些共同发生的基因结构 神经病理学尚不清楚。大多数AD遗传分析使用临床AD痴呆表型， 表型错误分类：许多临床AD“对照”具有AD病理学，而临床AD“病例”具有 无或轻微AD病理学。只有20%的病理证实的AD痴呆病例只有AD病理 （“纯AD”）; 80%具有共同发生的神经病理学（“混合AD”）。一个补充策略是执行 全基因组关联研究（GWAS）专门区分纯AD和混合AD。的 该提案的总体目标是通过整合，进一步科学地了解纯AD和混合AD 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：区分纯AD与混合AD与无病理对照的遗传结构， 确定特定的神经病理模式如何驱动关联。 1a.使用GWAS对病理学证实的AD对照、纯AD和混合AD进行GWAS， 多项式回归框架我们还将研究关联是如何由特定的 神经病理学特征促进了我们对潜在机制的理解。 1b.通过整合生物学知识推断可能的机制。我们将进行转录组- 整合人类PPI数据，对纯AD和混合AD进行广泛分析和基因网络分析。 目的2：确定单纯AD或混合AD的特异性因素。 2a.使用机器学习和协调的临床数据来隔离与单纯或 混合AD确定的临床风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，以估计相关的遗传 候选风险因素特征与纯粹或混合AD之间的影响和潜在因果关系。 遗传相关性分析将获得新的见解生物库规模之间共享的遗传基础 GWAS性状与纯AD和混合AD。我们将跟踪与当地遗传学的显著相关性， 相关性和孟德尔随机化（MR）来鉴定有助于基因表达的特定基因组区域。 相关和量化这些性状对纯AD和混合AD的因果影响。 目标3：表征纯AD与混合AD的细胞和分子后果。我们将利用 SEA-AD和ROS-MAP研究生成的数据，用于表征发生的分子变化 在细胞类型分辨率下的表型。我们将进行丰度分析（3a），差异基因 表达（3b）和跨多个脑区域的每种细胞类型中的差异染色质可及性（3c）。我们 将研究最近AD痴呆的GWAS和Aim 1的候选人的基因座。

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.