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

项目总结/摘要 晚发性阿尔茨海默病（AD）是一种常见的、具有高度遗传性的疾病。鉴定 AD的危险基因有可能进一步加深我们对疾病机制和修饰因素的理解， 导致有效治疗的发展。尽管在超过100个国家中发现了常见的遗传风险变异， 20个与AD相关的基因中，大部分的遗传性仍然无法解释。这可能是由于存在 许多罕见的风险变异，这在全基因组关联研究中无法识别。因此，评估 罕见的遗传变异的影响是必要的。与异质性人群相比， 一个同质的创始人人口，如德系犹太人（AJ），减少了统计噪音，从而增加了 统计力量此外，与年龄匹配的对照组相比，这些对照组在较晚的年龄仍可能发展为AD， 认知健康的百岁老人可被视为AD的真正对照。下一代的快速发展 测序技术现在使全面分析整个基因组数据成为可能。以不间断 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， 200名年龄匹配的对照，都是AJ血统，正在确定中。 博士Yun Freudenberg-Hua是一位拥有临床老年精神病学专业知识的内科医生兼科学家， 对推进AD遗传学的浓厚兴趣。该奖项将为她提供受保护的研究时间，以获得 专业知识（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，和（3）罕见变异负担和风险等位基因相互作用的新计算和统计方法。 她将与一个跨学科的导师团队一起实现这些目标。 该提案的目标是检验以下假设：罕见的功能变体在特定途径中富集， 这些罕见的变异效应取决于AD患者常见的遗传背景， 变体。除了注释编码变体外，我们还将根据非编码变体的潜力对其进行优先排序 对基因表达和表观遗传重塑的调节影响。我们将识别出 在我们的AJ病例/对照组群的全基因组数据集中富集AD的编码和非编码变体。 首先，我们将基于生物学特性在预定义的候选基因集上进行罕见变异负荷分析。 网络，重点是免疫系统途径（Aim1）;接下来，我们将研究 通过特定基因集的常见风险变异和AD的罕见变异负荷预测的多基因风险评分 （目标2）;最后，我们将通过将结果与其他公开可用的数据相结合， 排序项目（Aim3）。阐明特定途径中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。的 该项目产生的数据和基础设施应允许Freudenberg-Hua博士竞争R01资金， 将翻译基因组学应用于痴呆症临床诊断和管理。

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.