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

阿尔茨海默病(AD)以外的脑部疾病是导致痴呆症的常见原因，但研究不足。一个 特别流行的非阿尔茨海默氏症的亚型称为海马硬化性痴呆症，或 脑部年龄相关的TDP-43硬化症(CARTS)。这种神经病理学(NP)定义了疾病，这种疾病通常是 临床误诊为阿尔茨海默病，影响约20%的老年人，对认知有很大影响。长期的 目标是解决调节CART严重程度和异质性的基因组因素。为了实现这一目标，我们 将建立、测试和应用一个强大的管道来阐明受遗传风险因素影响的机制 考虑到其他非阿尔茨海默病的大脑病变。这需要一支经验丰富的多学科团队， 精通NP、分子生物学、神经成像、“大数据”分析，特别是统计基因组学。 基于大量初步数据的中心假设是，修改购物车的等位基因存在风险 通过候选基因和全基因组关联研究(GWAS)发现的更多现象 直接参与疾病的发病机制。为了验证这一假设，该团队将执行以下特定的 目的：1.建立并验证用于分析购物车遗传驱动因素的分类框架。这个 拟议的优化用于基因分型的购物车分类的努力将测试和验证一套修订的 区分CART的病理学标准、AD相关的TDP-43病理学和脑动脉硬化(B- ASC)，以完善对定义疾病的“边界地带”的理解。疾病严重性将被操作以供使用 作为一种数量性状，疾病亚型的遗传图谱将与基因组研究相关联。 2.构建稳健、协调的组学数据库，定位影响CART的遗传区域。 用丰富的NP内表型扩充的遗传学数据将使新见解的发现和提炼成为可能 关于导致手推车痴呆症的机制。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的购物车的遗传驱动因素 内表型，确定特定亚型候选遗传区域的优先顺序。 3.开发超越DNA变异的系统生物学分析管道，以建立和测试 与之相关的特定基因变异/区域的候选功能分子结果 CARTS病理学。Gwas的大多数发现不是因果关系，而是真正潜在的遗传影响的替代 通过以下机制表现的疾病：(A)表达数量性状基因座(EQTL)，(B) 差异异构体剪接QTL(SQTL)，(C)脑成像QTL(IQTL)，和(D)蛋白质QTL(PQTL)。这些遗嘱 用最新开发的统计方法进行检测。这些目标的圆满完成将产生 对购物车的机械洞察力，可能导致新的治疗方法。拟议的研究不同于 之前的努力，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(大车)。我们将通过NIAGADS将结果上传到网上，供研究社区使用。

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.