Identification of Risk Genes Supplement

摘要 迟发性阿尔茨海默病(AD)是一种常见病、致命性疾病 估计遗传力。阿尔茨海默病危险基因的识别有可能进一步 对疾病机制和致病因素的认识，从而导致 有效的治疗方法。尽管在40多个国家中发现了常见的遗传风险变异 与阿尔茨海默病相关的基因，大部分遗传性仍未解释。这可能是由于 存在许多罕见的风险变异，无法在全基因组关联中识别 学习。因此，需要通过基因组来评估稀有遗传变异的影响。 测序。与异质群体相比，在一个 基因同质的德系犹太人(AJ)祖先的创始人数量减少 统计噪声，从而增加统计能力。此外，与典型的控制相比， 年龄在60到80岁之间的人，他们可能在晚年患上阿尔茨海默病，认知健康的百岁老人代表 真正的AD控制。因此，将AD病例的全基因组测序数据与 百岁老人对AJ血统的控制可以为AD中高影响力的罕见变异提供洞察力。 Yun Freudenberg-hua博士是一位内科数据科学家，在临床老年病方面具有专业知识。 精神病学和临床痴呆表型。她对阿尔茨海默病和 分析基因数据的能力。K08奖项的延期将为她提供保护 研究时间来完成她正在进行的全基因组测序数据的分析，这是 被新冠肺炎大流行扰乱了。在延期期间，她将确定推定的 通过集成1)功能编码变体的知识，2)选择 3)应用新的生物信息学研究阿尔茨海默病相关基因 和统计方法。她将在艾莉森博士的指导下实现这些目标 并继续她的计划，申请独立的NIH资金。 其目的是验证稀有功能变异在特定基因集中富含的假设 在AD患者中。将包括假定的功能编码和非编码变体 应用一种新的机器学习方法REGENIE进行聚集分析。我们将扩大规模 从阿尔茨海默病测序中纳入AJ全基因组亚组的分析 项目。在特定途径中突出显示的罕见遗传风险变异可以转化为 阿尔茨海默病的预测和治疗药物的发展。在此项目中生成的数据 应该允许弗洛登贝格-华博士竞争R01资金来调查 遗传风险、生物标志物和复杂的临床表型之间的多维相互作用 以确定哪些因素是可以改变的。

Abstract Late onset Alzheimer’s Disease (AD) is a common and devastating disease with a high estimated heritability. Identification of risk genes for AD has the potential to further our understanding of disease mechanism and modifying factors, thus leading to development of effective treatments. Despite the identification of common genetic risk variants in more than 40 genes associated with AD, much of the heritability remains unexplained. This may be due to the presence of many rare risk variants, which cannot be identified in genome wide association studies. Therefore, evaluating the impact of rare genetic variants is required through genome sequencing. Compared to a heterogeneous population, conducting genetic studies in a genetically homogeneous founder population of Ashkenazi Jewish (AJ) ancestry reduces statistical noise, thereby increasing statistical power. Furthermore, compared to typical controls aged 60s to 80s, who may develop AD at a later age, cognitively healthy centenarians represent true controls for AD. Thus, comparing whole genome sequencing data of AD cases and centenarian controls of AJ ancestry could offer insights on high impact rare variants in AD. Dr. Yun Freudenberg-Hua is a physician data scientist with expert knowledge in clinical geriatric psychiatry and clinical dementia phenotypes. She has a keen interest in genetics for AD and the ability to analyze genetic data. The extension of the K08 award will provide her with protected research time to complete her ongoing analysis of whole genome sequencing data, which was disrupted by the COVID-19 pandemic. During the extension period, she will identify putative functional variants by integrating 1) knowledge of functional coding variants, 2) selection of functional non-coding variants, and 3) gene sets involved in AD by applying novel bioinformatics and statistical methods. She will accomplish these goals under the mentorship of Dr. Alison Goate and continue her plan to apply for independent NIH funding. The goal is to test the hypothesis that rare functional variants are enriched in specific gene sets among AD patients. Putative functional coding and non-coding variants will be included for aggregation analysis by applying a novel machine-learning method REGENIE. We will expand the analysis to include AJ whole genome subsets from the Alzheimer’s Disease Sequencing Project. The rare genetic risk variants highlighted in specific pathways can be translated into both AD prediction and development of therapeutic agents. The data generated in this project should allow Dr. Freudenberg-Hua to compete for R01 funding to investigate the multidimensional interplay between genetic risks, biomarkers, and complex clinical phenotypes of dementia and to identify factors that are modifiable.