A Proteogenomic Approach to Understanding AD GWAS Results

摘要/摘要：项目2 众所周知，阿尔茨海默病(AD)有很强的遗传基础。最新基因组范围 阿尔茨海默病的关联研究发现，人类基因组中至少有20个区域是 与开发AD联系在一起。目前尚不清楚为什么这些区域与AD有关。填满 在这一知识缺口中，我们建议对20个Gwas信号及其周围的基因进行测序以识别 所有潜在的AD风险变种。我们建议在发现数据集中(n=1,052)和复制中执行此操作 数据集(n=504)，并在荟萃分析中将我们的分析结合在一起。这些独特的 数据集完全由经历了表型和病理的个体组成 来自三个大型社区和临床研究的特征和来自四个 阿尔茨海默氏症研究中心(ADRC)，包括埃默里ADRC。我们最重要的是 假设一些AD Gwas信号是由于一个或多个编码变体导致的 翻译后的蛋白质更容易聚集。我们独一无二地准备解决这一假设 独特的脑组织数据集和我们在大规模和靶向蛋白质组学方面的专业知识 分析。通过将我们的基因测序数据与尖端质谱学相结合，我们将 来自经历了基因突变的个体的相同基因的编码蛋白质产物的序列 测序。这将使我们能够问，哪些基因变异与阿尔茨海默病有关，以及这些 遗传变异会影响大脑中蛋白质的丰度或聚集潜力 阿尔茨海默病患者。此外，我们还开发了一种新的方法来检测和测量新奇 由引起主要氨基酸变化的遗传序列变异所产生的蛋白质 序列。这使我们能够直接测试含有蛋白质的变体是多还是少 运营商与非运营商之间的丰富性或聚集性，并确定这是否对 到AD风险。总体而言，这项工作很有可能有助于揭开为什么20个全球气候变化网络的目标区域中的一些 与AD相关，并提供了可测试的模型，说明基因变异如何影响编码的 蛋白质的生命周期，并有助于AD患者的疾病。

SUMMARY/ABSTRACT: PROJECT 2 Alzheimer's disease (AD) is well known to have a strong genetic basis. Recent genome wide association studies (GWAS) of AD have identified at least 20 regions of the human genome that are linked with developing AD. Currently it is not known why these regions are associated with AD. To fill in this knowledge gap, we propose to sequence genes in and around the 20 GWAS signals to identify all potential AD-risk variants. We propose to do this in a discovery dataset (n=1,052) and replication dataset (n=504), separately and combine our analysis together in a meta-analysis. These unique datasets are entirely comprised of individuals who have undergone phenotypic and pathologic characterization from three large community- and clinic-based studies and resources from four Alzhiemer's Disease Research Centers (ADRCs), including the Emory ADRC. Our overarching hypothesis is that some AD GWAS signals are due to one or more coding variants that makes the translated protein more likely to aggregate. We are uniquely poised to address this hypothesis given the unique datasets of brain tissue and our expertise in both large-scale and targeted proteomic analyses. By combining our genetic sequencing data with cutting-edge mass spectrometry we will sequence encoded protein products of the same genes from individuals that underwent genetic sequencing. This will allow us to ask which genetic variants associate with AD and whether those genetic variants influence the abundance or aggregation potential of proteins in the brains of individuals with AD. Furthermore, we have developed a new method to detect and measure novel proteins that result from genetic sequence variants that cause a change the primary amino acid sequence. This gives us the ability to directly test whether variant containing proteins are more or less abundant or aggregation prone in carriers versus non-carriers and determine whether that contributes to AD risk. Overall, this work is highly likely to help unravel why some of the 20 GWAS target regions are associated with AD and provide testable models for how genetic variants influence the encoded protein's lifecycle and contribute to disease in individuals with AD.