Identification of Risk Genes by Comparing Whole Genome Sequences of Alzheimer's Disease Patients and Cognitively Healthy Centenarians

项目摘要/摘要 晚发性阿尔茨海默病(AD)是一种遗传性很高的常见病和致命性疾病。身份识别 阿尔茨海默病的危险基因有可能加深我们对疾病机制和修饰因素的理解，从而 导致有效治疗方法的发展。尽管在超过50%的人群中发现了常见的遗传风险变异 20个与阿尔茨海默病相关的基因，其中大部分遗传性尚不清楚。这可能是由于存在 许多罕见的风险变异，在全基因组关联研究中无法确定。因此，评估 需要罕见的遗传变异的影响。与异质群体相比，在中国进行基因研究 相同的创始人群体，如德系犹太人(AJ)，减少了统计噪音，从而增加了 统计学上的力量。此外，与年龄匹配的对照组相比，后者在较晚的年龄仍可能患上阿尔茨海默病， 认知健康的百岁老人可能被视为AD的真正控制因素。下一代的飞速发展 测序技术现在使全面分析全基因组数据成为可能。在一个正在进行的 另一个项目，400名AD患者和200名认知健康的百岁老人的全基因组测序数据 正在确定200名年龄匹配的对照，均为AJ血统。 云·弗洛登贝格博士是一名内科科学家，在临床老年精神病学方面拥有专业知识，他拥有 对推进阿尔茨海默病遗传学的浓厚兴趣。这一奖项将为她提供受保护的研究时间以获得 专门知识(1)基因调控和表观遗传学，(2)开发候选途径和基因集 AD生物学，以及(3)罕见变异负担和风险等位基因交互作用的新计算和统计方法。 她将通过一个跨学科的导师团队来实现这些目标。 这项提议的目标是检验这一假设，即罕见的功能变体在特定的途径或 这些罕见的变异效应取决于共同的遗传背景。 变种。除了注释编码变体之外，我们还将根据非编码变体的潜力来确定它们的优先顺序 调控对基因表达和表观遗传重塑的影响。我们将确定路径和基因集是 在我们的AJ病例/对照队列的整个基因组数据集中，丰富了AD的编码和非编码变体。 首先，我们将在基于生物学的预定义候选基因集上执行罕见的变异负担分析 关注免疫系统途径的网络(Aim1)；接下来，我们将研究 常见风险变量跨特定基因集预测AD的多基因风险分数和罕见的变异负担 (AIM2)；最后，我们将通过将结果与其他公开获得的数据相结合来复制重要发现 测序项目(Aim3)。在特定的途径中阐明AD的罕见遗传风险变异将产生 这些知识可以转化为AD诊断的改进和治疗剂的开发。这个 在这个项目中产生的数据和基础设施应该允许Freudenberg-hua博士竞争R01资金 将翻译基因组学应用于痴呆的临床诊断和治疗。

Project Summary/Abstract Late onset Alzheimer’s Disease (AD) is a common and devastating disease with a high heritability. Identification of risk genes for AD has the potential to further our understanding of disease mechanism and modifying factors, thus lead to development of effective treatments. Despite the identification of common genetic risk variants in more than 20 genes associated with AD, most of the heritability remains unexplained. This may be due to the presence of many rare risks variants, which cannot be identified in genome wide association studies. Therefore evaluating the impact of rare genetic variants is required. Compared to a heterogeneous population, conducting genetic studies in a homogeneous founder population such as the Ashkenazi Jews (AJ) reduces statistical noise, thereby increases statistical power. Furthermore, compared to age-matched controls who may still develop AD at a later age, cognitively healthy centenarians may be viewed as true controls for AD. The rapid advance of next generation sequencing technologies now makes it possible to comprehensively analyze whole genome data. In an ongoing separate project, whole genome sequencing data of 400 AD patients and 200 cognitively healthy centenarians and 200 age-matched controls, all of AJ ancestry, are being ascertained. Dr. Yun Freudenberg-Hua is a physician-scientist with expert knowledge in clinical geriatric psychiatry who has a keen interest in advancing genetics for AD. This award will provide her with protected research time to gain expertise on (1) gene regulation and epigenetics, (2) developing candidate pathways and gene sets informed by AD biology, and (3) novel computational and statistical methods for rare variants burden and risk allele interaction. She will accomplish these goals with a cross-disciplinary team of mentors. The goal of this proposal is to test the hypothesis that rare functional variants are enriched in specific pathways or gene sets among AD patients, and that these rare variant effects depend on the genetic background of common variants. In addition to annotating coding variants, we will prioritize non-coding variants according to their potential regulatory impact on gene expression and epigenetic remodeling. We will identify pathways and gene sets that are enriched for coding and non-coding variants for AD in the whole genome data set of our AJ case/control cohort. First, we will perform rare variant burden analysis across pre-defined candidate gene sets based on biological networks with focus on immune system pathways (Aim1); next, we will investigate the interaction between polygenic risk scores predicted by common risk variants across specific gene sets and rare variant burden for AD (Aim2); finally, we will replicate significant findings by integrating the results with data from other publicly available sequencing projects (Aim3). Elucidating rare genetic risk variants for AD in specific pathways will generate knowledge that can be translated into improvement of AD diagnosis and development of therapeutic agents. The data and infrastructure generated in this project should allow Dr. Freudenberg-Hua to compete for R01 funding to implement translational genomics into clinical diagnosis and management of dementia.