Bioinformatics Strategies for Genome Wide Association Studies

精准医学的承诺是编辑患者的DNA和/或针对病因学给予治疗。 使用定制的设计来预防或逆转疾病过程的分子。所有这些都发生在 并且需要对患者的生物学有精确的了解。与此形成鲜明对比的是， 我们所掌握的关于基因组风险因素的知识来自于人类基因组相关性的统计测量， 人口。我们所研究的人群和我们所研究的个体之间在概念上和实践上的脱节， 在一个由基因组驱动的时代， 技术.该提案的主要目标是开发新的信息学方法和软件， 通过连接群体和个体基因组现象促进精准医疗。我们在此建议， 虚拟基因组医学（VGMed）工作台，临床医生可以在其中进行关于 使用来自人群水平研究的疾病风险模型治疗个体患者。这将是 首先开发一种新的基因组学指导的自动机器学习（GAML）算法， 从临床医生可访问的真实的数据导出风险模型（AIM 1）。然后我们会写一部小说 一种模拟方法，能够生成保留遗传效应分布的人工数据 观察到的真实的数据，同时保持其他特征，如基因型频率（AIM 2）。这 将生成开放数据，允许任何人对来自人群的患者进行虚拟干预- 水平风险分布。工作台将允许编辑个体基因型并模拟给药 通过编辑模拟模型中的机器学习参数来实现药物的自动识别（AIM 3）。风险的变化， 将真实的跟踪特定患者的疾病状态。最后，我们在工作台中提供了一个特性 这将允许临床医生产生关于个体遗传变异的特定假设， 使用包括PubMed和ClinVar等数据库在内的综合知识源进行验证， 用户即时反馈（AIM 4）。所有方法和软件都将作为开源（AIM 5）提供。

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).