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

 描述(申请人提供)：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能够确定20个影响阿尔茨海默病(AD)风险的基因座，以及另外6个携带影响疾病风险的特定稀有变异的基因。有了来自10,000多例AD患者和ADSP对照的新的全基因组序列(WGS)和全外显子序列(WES)数据，结合AMP 3,500多名个体的mRNA表达数据，现在有可能开发出更全面的AD遗传结构和相关风险的图景。除了完善AD的遗传结构，我们的目标是识别和验证AD b的治疗靶点，识别功能上驱动或保护AD的基因，并询问它们各自的基因网络以寻找治疗靶点。我们将使用迄今为止最大、最全面的数据集来完成这项工作。遗传和基于途径的分析强烈地涉及到少量的网络，包括免疫反应、吞噬作用、脂代谢和内吞作用。我们将整合来自基因研究和基因表达/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发展和进展的关键网络。我们将采取两种互补的方法来识别风险和韧性AD基因：(1)我们将使用基于家庭的方法来识别风险和保护性等位基因，使用公开可用的数据和我们自己的来自NIALOAD和犹他州家族的WGS/WES数据；以及(2)我们将使用来自AD病例和对照的公开可用的高维分子数据来构建全球相互作用和因果网络。然后，我们将把对ADSP病例控制序列数据的分析集中在最引人注目的网络上，从而减少我们的搜索空间并增加能力。为了确定治疗靶点，我们将使用网络分析来测试已知药物，这些药物针对的是我们基于家庭和病例对照数据的序列分析中确定的网络。然后，我们将通过在计算机观察的基础上进行体外实验来验证我们的发现，并确定从AD序列数据中识别的风险/韧性等位基因的功能后果。总而言之，这项研究的发现将确定在功能上推动AD的关键网络，并将为治疗干预提供关键的见解

 DESCRIPTION (provided by applicant): 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 b 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