Bioinformatics Strategies for Genome-Wide Association Studies

治疗阿尔茨海默病的精准医学的一个承诺是编辑患者的DNA和/或给药 针对病因分子的治疗方法，使用量身定制的设计来预防或逆转疾病过程。 所有这一切都发生在个人层面上，需要对患者的生物学有准确的了解。在……里面 与之形成鲜明对比的是，我们拥有的关于基因组风险因素的大部分知识都来自于统计方法 阿尔茨海默病患者和非阿尔茨海默病患者的关联性。概念和实践的脱节 我们研究的人群和我们想要治疗的个体之间的关系是造成困惑的主要原因 如何在基因组技术驱动的时代前进。这项提议的主要目标是发展 通过连接促进阿尔茨海默病精准医学的新信息学方法和软件 群体和个体基因组现象。我们提出了一种虚拟基因组医学(VGMed) 临床医生可以进行个体化阿尔茨海默氏症治疗的思维实验的工作台 患者使用从人群水平研究得出的疾病风险模型。这将首先完成 开发一种新的基因组学指导的自动机器学习(GAML)算法来推导风险模型 来自阿尔茨海默病临床医生可访问的真实数据(目标1)。然后我们将开发一种新的模拟 一种能够生成保存遗传效应分布的人工阿尔茨海默氏症数据的方法 在真实数据中观察，同时保持其他特征，如基因频率(AIM 2)。这 将生成开放数据，允许任何人对阿尔茨海默病患者进行虚拟干预，这些患者来自 人口层面的风险分布。工作台将允许编辑单个基因类型并模拟 通过在模拟模型中编辑机器学习参数来给药(目标3)。中国经济的变化 将实时跟踪特定患者的风险和阿尔茨海默氏症状态。最后，我们提供了一个 工作台中的功能，将允许阿尔茨海默氏症临床医生生成关于 单个遗传变异，然后可以使用集成的阿尔茨海默氏症知识来源进行验证 包括PubMed和ClinVar等数据库，从而为用户提供即时反馈(AIM 4)。所有方法 软件将作为开源软件提供给阿尔茨海默病研究社区(AIM 5)。

One promise of precision medicine for Alzheimer’s disease is to edit a patient’s DNA and/or administer therapeutics targeting etiologic molecules that prevent or reverse the disease process using a tailored design. All of this happens at the level of the individual and requires precision knowledge of that patient’s biology. In stark contrast, much of the knowledge we possess about genomic risk factors comes from statistical measures of association in subjects ascertained with and without Alzheimer’s. The conceptual and practical disconnect between the populations we study and the individuals we want to treat is a major source of confusion about how to move forward in an era driven by genome technology. The primary goal of this proposal is to develop novel informatics methodology and software to facilitate precision medicine for Alzheimer’s by connecting population and individual genomic phenomena. We propose here a Virtual Genomic Medicine (VGMed) workbench where clinicians can carry out thought experiments about the treatment of individual Alzheimer’s patients using models of disease risk derived from population-level studies. This will be accomplished by first developing a novel Genomics-guided Automated Machine Learning (GAML) algorithm for deriving risk models from real data that is accessible to Alzheimer’s clinicians (AIM 1). We will then develop a novel simulation approach that is able to generate artificial Alzheimer’s data that preserves the distribution of genetic effects observed in the real data while maintaining other characteristics such as genotype frequencies (AIM 2). This will generate open data allowing anyone to perform virtual interventions on Alzheimer’s patients derived from a population-level risk distribution. The workbench will allow editing of individual genotypes and simulate the administration of drugs by editing machine learning parameters in the simulation model (AIM 3). The change in risk and Alzheimer’s disease status for the specific patient will be tracked in real time. Finally, we provide a feature in the workbench that will allow the Alzheimer’s clinician to generate specific hypotheses about individual genetic variants that can then be validated using integrated Alzheimer’s knowledge sources that include databases such as PubMed and ClinVar thus giving the user immediate feedback (AIM 4). All methods and software will be provided as open-source to the Alzheimer’s disease research community (AIM 5).