Genomic Architecture of a Key Alzheimer's Disease Mimic: CARTS

阿尔茨海默病（AD）以外的脑部疾病是常见的，但研究不足的痴呆症的原因。一 非阿尔茨海默氏痴呆的特别普遍的亚型被称为海马硬化痴呆，或 脑老化相关TDP-43伴硬化症（CARTS）。这种神经病理学（NP）定义的疾病，通常是 在临床上被误诊为AD，影响约20%的老年人，对认知有实质性影响。长期 目标是解决调节CARTS严重性和异质性的基因组因素。为了做到这一点，我们 将建立，测试和应用一个强大的管道，以阐明遗传风险因素影响的机制， CARTS，考虑其他非AD脑病理。这需要一个经验丰富的多学科团队， 在NP，分子生物学，神经影像学，“大数据”分析，特别是统计基因组学方面的专业知识。 基于大量初步数据的中心假设是， 通过候选基因和全基因组关联研究（GWAS）发现的这些基因是更多现象的代理， 直接参与疾病的发病机制。为了验证这一假设，团队将执行以下具体 目的：1.开发并验证分类框架，以分析CARTS的遗传驱动因素。的 一项旨在优化基因分型CARTS分类的提议将测试和验证一组修订后的 基于病理学的标准来区分CARTS、AD相关TDP-43病理学和脑小动脉硬化（B- ASC）以加深对疾病定义"边界区"的理解。疾病严重程度将可操作化以供使用 作为一个数量性状，疾病亚型的标题将与基因组研究的相关性。 2.构建一个强大的和协调的组学数据库和定位影响CARTS的遗传区域。 用丰富的NP内表型增强的遗传学数据将能够发现和改进新的见解 关于导致CARTS痴呆症的机制大规模数据集（NACC、ADGC、ADNI、ADSP、AMP- AD）将被汇总和协调，以测试临床和基于NP的CARTS的遗传驱动因素 内表型，优先考虑亚型特异性候选遗传区域。 3.开发一个系统生物学分析管道，扩展到DNA变异之外，以建立和测试 特定基因变体/区域的候选功能分子结果， CARTS病理学。大多数GWAS发现不是因果关系，而是真正潜在的遗传影响的代理 通过包括（a）表达数量性状基因座（eQTL），（B） 差异同种型剪接QTL（sQTL），（c）脑成像QTL（iQTL），和（d）蛋白质QTL（pQTL）。这些将 用最近开发的统计方法检测。成功完成目标将产生 对CARTS的机械见解，可能导致新的治疗方法。拟议的研究不同于 先前的努力，利用下一代测序，专注于一个共同的，但最近表征的大脑 疾病（CARTS）。我们将通过NIAGADS在线存款结果，供研究界使用。

Brain diseases other than Alzheimer’s disease (AD) are common but understudied causes of dementia. A particularly prevalent subtype of non-Alzheimer’s dementia is termed hippocampal sclerosis dementia, or cerebral age-related TDP-43 with sclerosis (CARTS). This neuropathology (NP) defined disease, which is often misdiagnosed clinically as AD, affects ~20% of the elderly, with substantial impact on cognition. The long-term goal is to resolve the genomic factors that modulate CARTS severity and heterogeneity. To accomplish this, we will establish, test, and apply a robust pipeline to elucidate the mechanisms influenced by genetic risk factors for CARTS, factoring in other non-AD brain pathologies. This requires a seasoned, multidisciplinary team with expertise in NP, molecular biology, neuroimaging, “large data” analyses, and, in particular, statistical genomics. The central hypothesis, based on considerable preliminary data, is that alleles modifying CARTS risk that were discovered via candidate gene and genome-wide association studies (GWAS) are proxies for phenomena more directly involved in disease pathogenesis. To test this hypothesis, the team will execute the following Specific Aims: 1. Develop and validate a classification framework to analyze the genetic drivers of CARTS. The proposed effort to optimize classification of CARTS for genotyping will test and validate a revised set of pathology-based criteria to differentiate CARTS, AD-related TDP-43 pathology, and brain arteriolosclerosis (B- ASC) to refine understanding of disease-defining “border zones.” Disease severity will be operationalized for use as a quantitative trait, and rubrics for disease subtypes will be developed for correlation with genomic studies. 2. Construct a robust and harmonized ‘omics database and localize genetic regions influencing CARTS. Genetics data augmented with rich NP endophenotypes will enable discovery and refinement of novel insights regarding the mechanisms driving CARTS dementia. Large-scale datasets (NACC, ADGC, ADNI, ADSP, AMP- AD) will be aggregated and harmonized to test the genetic drivers of clinical and NP-based CARTS endophenotypes, prioritizing subtype-specific candidate genetic regions. 3. Develop a systems biology analytic pipeline that extends beyond DNA variation to establish and test candidate functional molecular outcomes of specific gene variants/regions that are associated with CARTS pathology. Most GWAS findings are not causal but rather proxies for true underlying genetic influences of disease manifested through mechanisms that include (a) expression quantitative trait loci (eQTL), (b) differential isoform splicing QTL (sQTL), (c) brain imaging QTL (iQTL), and (d) protein QTL (pQTL). These will be detected with recently developed statistical methodologies. Successful completion of the aims will produce mechanistic insights into CARTS, potentially leading to new therapeutics. The proposed studies are distinct from prior efforts, exploiting next-generation sequencing, focusing on a common, yet recently characterized brain disease (CARTS). We will deposit results online via NIAGADS for use by the research community.