Bioinformatics Strategies for Genome Wide Association Studies

全基因组关联研究的生物信息学策略

• 批准号: 10616262
• 负责人: Jason H. Moore
• 金额: $ 36.95万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2024-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10616262
• 关键词: Algorithmic Software; Algorithms; Bioinformatics; Biology; Characteristics; ClinVar; Community Medicine; Computational algorithm; Computer software; Confusion; DNA; Data; Databases; Disease; Disease model; Etiology; Feedback; Frequencies; Genetic; Genetic study; Genome; Genomic medicine; Genomics; Genotype; Goals; Health; Human; Individual; Informatics; Information Resources; Knowledge; Machine Learning; Measures; Methodology; Methods; Modeling; Online Systems; Patients; Pattern; Pharmaceutical Preparations; Population; Population Analysis; Population Study; Process; PubMed; Pythons; Research Personnel; Risk; Risk Factors; Source; Technology; Time; Validation; base; data preservation; design; disorder risk; experimental study; genetic makeup; genetic variant; genome wide association study; human disease; individual patient; machine learning algorithm; machine learning model; models and simulation; novel; open data; open source; phenotypic data; precision medicine; prevent; simulation; statistics; therapeutic target; virtual; virtual intervention

The promise of precision medicine 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 from human populations. 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 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 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 clinicians (AIM 1). We will then develop a novel simulation approach that is able to generate artificial 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 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 disease status for the specific patient will be tracked in real time. Finally, we provide a feature in the workbench that will allow the clinician to generate specific hypotheses about individual genetic variants that can then be validated using integrated 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 (AIM 5).

精确医学的承诺是编辑患者的DNA和/或靶向病因的治疗 使用量身定制的设计预防或扭转疾病过程的分子。所有这些都发生在级别 个人需要精确了解该患者的生物学。与之形成鲜明对比的是 我们对基因组风险因素的知识来自人类的统计量度 人群。我们研究的人群与我们的个人之间的概念性和实际断开连接 想要对待是关于如何在基因组驱动的时代前进的主要困惑的主要来源 技术。该提案的主要目标是开发新颖的信息方法和软件 通过连接人群和个体基因组现象来促进精确医学。我们在这里提出 虚拟基因组医学（VGMED）工作台，临床医生可以进行有关该工作的思维实验 使用人口水平研究得出的疾病风险模型对个别患者的治疗。这将是 通过首先开发一种新型的基因组指导自动化机器学习（GAML）算法来完成 从临床医生可以访问的真实数据中得出风险模型（AIM 1）。然后，我们将发展一本小说 能够生成人工数据的仿真方法，以保留遗传效应的分布 在实际数据中观察到，同时保持其他特征，例如基因型频率（AIM 2）。 将产生开放数据，使任何人都可以对来自人群的患者进行虚拟干预措施 - 水平风险分布。工作台将允许编辑单个基因型并模拟给药 通过在模拟模型中编辑机器学习参数（AIM 3）中的药物。风险变化和 特定患者的疾病状况将实时跟踪。最后，我们在工作台中提供了一个功能 这将使临床可以产生有关单个遗传变异的特定假设 使用包括PubMed和Clinvar等数据库的集成知识来源进行了验证 用户立即反馈（AIM 4）。所有方法和软件将作为开源（AIM 5）提供。