Identification and characterization of AD risk networks using multi-dimensional "omics" data

总结 全基因组关联、全基因组/外显子组测序和基因网络研究已经使 研究人员确定了20个影响阿尔茨海默病（AD）风险的基因座和另外6个基因 携带影响疾病风险的特殊罕见变异。随着新的全基因组序列（WGS）和 来自ADSP的10，000 + AD病例和对照的全外显子组序列（WES）数据， AMP的3，500多个个体的mRNA表达数据，现在可以开发更全面的 AD的遗传结构和相关风险的图片。除了完善AD遗传结构，我们的目标是 是通过识别功能性驱动或保护AD的基因来识别和验证AD的治疗靶点， AD和询问它们各自的基因网络以寻找治疗靶点。我们将使用最大的， 最全面的数据集。基于遗传和途径的分析强烈暗示了一个小的 许多网络包括免疫反应、吞噬作用、脂质代谢和内吞作用。我们将 整合遗传学研究和基因表达/调控研究的数据，以确定风险和弹性基因 以确定在功能上推动AD发展和进步的关键网络。我们将采取两 互补的方法来确定风险和弹性AD基因：（1）我们将使用以家庭为基础的方法， 使用公开数据和我们自己的WGS/WES数据， NIALOAD和犹他州家庭;和（2）我们将使用公开可用的高维分子数据，从AD 案例和控制，以构建全球互动和因果网络。然后我们将重点分析 ADSP案件控制序列数据的最引人注目的网络，从而减少了我们的搜索空间， 增加力量。为了确定治疗靶点，我们将使用网络分析来测试已知的靶向药物， 在我们对基于家庭和病例对照数据的序列分析中确定的网络。然后我们将验证 我们的发现是通过基于我们的计算机观察进行体外实验，并确定功能性 从AD序列数据鉴定的风险/弹性等位基因的后果。总之，这项研究的结果 这项研究将查明在功能上驱动AD的关键网络，并将为治疗AD提供重要的见解。 干预

Summary Genome-wide association, whole genome/exome sequencing and gene network studies have already enabled researchers to identify twenty loci influencing Alzheimer's disease (AD) risk and another half dozen genes carrying specific rare variants that influence disease risk. With the new whole-genome sequence (WGS) and whole-exome sequence (WES) data from 10,000+ AD cases and controls from the ADSP, combined with mRNA expression data from 3,500+ individuals from AMP, it is now possible to develop a more comprehensive picture of the genetic architecture of AD and associated risk. Beyond refining AD genetic architecture, our goal is to identify and validate therapeutic targets for AD by identifying genes that functionally drive or protect from AD and interrogating their respective gene networks for therapeutic targets. We will do this using the largest, most comprehensive data set, to date. Genetic and pathway-based analyses have strongly implicated a small number of networks including immune response, phagocytosis, lipid metabolism and endocytosis. We will integrate data from genetic studies and gene expression/regulation studies to identify risk and resilience genes to pinpoint key networks that functionally drive AD development and progression. We will take two complementary approaches to identify risk and resilience AD genes: (1) we will use a family-based approach to identify both risk and protective alleles using publicly available data and our own WGS/WES data from both NIALOAD and Utah families; and (2) we will use publicly available high-dimensional molecular data from AD cases and controls to construct global interaction and causal networks. We will then focus our analysis of ADSP case control sequence data on the most compelling networks, thereby reducing our search space and increasing power. To identify therapeutic targets, we will use network analysis to test known drugs that target networks identified in our sequence analysis of both family-based and case control data. We will then validate our findings by performing in vitro experiments based our in silico observations and determine the functional consequences of risk/resilience alleles identified from the AD sequence data. Together, the findings from this study will pinpoint key networks that functionally drive AD and will provide critical insight into therapeutic intervention.