A Proteogenomic Approach to Understanding AD GWAS Results

总结/摘要：项目2 众所周知，阿尔茨海默病（AD）具有很强的遗传基础。最近全基因组 AD的关联研究（GWAS）已经确定了人类基因组的至少20个区域， 与发展中的AD。目前尚不清楚为什么这些区域与AD相关。填补 在这一知识空白中，我们建议对20个GWAS信号中及其周围的基因进行测序，以确定 所有潜在的AD风险变体。我们建议在发现数据集（n= 1，052）和复制数据集中进行此操作 数据集（n=504），分别和联合收割机结合我们的分析在一起的荟萃分析。这些独特 数据集完全由经历了表型和病理学改变的个体组成。 来自三个大型社区和临床研究的特征和来自四个 阿尔茨海默病研究中心（ADRC），包括埃默里ADRC。我们的总体 一个假设是，一些AD GWAS信号是由于一个或多个编码变体，使 翻译的蛋白质更容易聚集。我们是唯一准备解决这个假设， 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的质谱分析相结合， 基因组序列编码的蛋白质产物来自经历遗传学治疗的个体， 测序这将使我们能够询问哪些遗传变异与AD相关，以及这些变异是否与AD相关。 遗传变异会影响大脑中蛋白质的丰度或聚集潜力， AD患者。此外，我们还开发了一种新的方法来检测和测量新的 由基因序列变异引起的蛋白质，这些变异导致一级氨基酸的改变， 顺序这使我们能够直接测试含有蛋白质的变体是否更多或更少， 载波与非载波中的丰富或聚合倾向，并确定这是否有助于 AD风险。总的来说，这项工作很有可能有助于解开为什么20个GWAS目标地区中的一些地区 与AD相关，并为遗传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.