Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approaches

项目摘要/摘要： 阿尔茨海默病测序项目(ADSP)寻求识别有助于 在多种族人群中增加阿尔茨海默氏病的风险和保护，并确定新的 疾病治疗和预防的途径。全基因组和全外显子组测序数据(WGS和 WES)可从ADSP发现和发现-扩展阶段和不同种族的WGS中获得 将为后续研究(FUS)生成。这项提案的调查者有不同的但 生物信息学、应用统计学和方法论领域的互补专业知识 发展、混合和种族多样性、罕见变异关联、网络建模、临床前验证 目标和AD的临床专业知识，并自ADSP成立以来一直参与其中。更进一步，他们 通过他们在Charge(队列)中的作用，带来关于内表型和其他WGS数据的专业知识 用于基因组流行病学中的心脏和衰老研究)和TOPMed(用于精密医学的跨基因组学) 财团。我们现在提出以下目标，以实现减贫战略文件的目标。目标1：充分描述已知的AD基因座 并通过利用全范围的遗传变异来识别AD的新的保护性和风险变异 WGS揭示包括单核苷酸多态、小插入/缺失和结构变异。 分析将包括AD和内表型的扩展关联分析，新的 通过精心挑选的“Wellderly”样品，以及分析结果的整合，实现保护性变异。目标 2：利用种族多元化和混杂的人口来确定AD和AD的新变种 内表型。这将通过估计和核算#年全球规模的人口结构来实现。 对ADSP的三个阶段进行关联分析。此外，我们还建议执行混合材 绘制混合血统样本的图谱，并进行种族特异性分析和跨种族元分析。 分析。目的2对AD、AD内表型和健康状态进行分析。目标3：实现 通过整合生物信息学和组学，从功能上表征基因、基因网络和系统 分析以确定假定的治疗靶点。调查人员将与加速器密切合作 药品伙伴关系(AMP)项目。我们建议结合使用生物信息学工具和分析 “组学”数据，包括DNA甲基化、基因表达、miRNA和AMP和代谢组学数据 在收费队列中预测特定变种或变种组的功能，应用网络 跨基因集的方法，利用系统方法来理解ADSP测序数据 更大的人类生物学背景，并确定假定的治疗靶点。我们的提案提供了一个 全面、综合的计划，我们将在现有的ADSP基础设施内和在 与ADSP调查人员协调。

Project Summary/Abstract: The Alzheimer Disease Sequencing Project (ADSP) seeks to identify new genomic variants contributing to increased risk for and protection from Alzheimer's Disease in multi-ethnic populations, and to identify new pathways for disease treatment and prevention. Whole genome and whole exome sequencing data (WGS and WES) are available from ADSP Discovery and Discovery-Extension Phases and WGS in diverse ethnic groups will be generated for the Follow-up Study (FUS). The investigators of this proposal have diverse but complementary expertise across the range of bioinformatics, applied statistics and methodological development, admixture and ethnic diversity, rare variant association, network modeling, preclinical validation of targets, and clinical expertise in AD and have been involved in the ADSP since its inception. Further they bring expertise on endophenotypes and additional WGS data through their role within the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) and TOPMed (Trans-Omics for Precision Medicine) consortia. We now propose the following aims to meet ADSP goals. Aim 1: To fully characterize known AD loci and to identify novel protective and risk variants for AD by exploiting the full range of genetic variability revealed by WGS including single nucleotide polymorphisms, small insertion/deletions, and structural variants. Analyses will include expanded association analyses of AD and endophenotypes, identification of novel protective variants via carefully selected “Wellderly” samples, and integration of findings across analyses. Aim 2: To leverage ethnically-diverse and admixed populations to identify novel variants for AD and endophenotypes. This will be achieved by estimating and accounting for global-scale population structure in association analyses across the three phases of ADSP. We additionally propose to perform admixture mapping in samples of admixed ancestry and to perform ethnic-specific analyses and trans-ethnic meta- analyses. Aim 2 analyses will be performed for AD, AD endophenotypes, and Wellderly status. Aim 3: To functionally characterize genes, gene networks, and systems, via bioinformatics and omics integrative analyses to identify putative therapeutic targets. The investigators will work closely with the Accelerating Medicines Partnership (AMP) projects. We propose to use a combination of bioinformatics tools and analysis of “omics” data, including DNA methylation, gene expression, miRNA and metabolomics data within AMP and in the CHARGE cohorts to predict function of specific variants or groups of variants, to apply network approaches across gene sets, to utilize a systems approach to understand the ADSP sequencing data in the larger context of human biology and to identify putative therapeutic targets. Our proposal provides a comprehensive, integrated plan, which we will implement within the existing ADSP infrastructure and in coordination with ADSP investigators.