Efficient electronic phenotyping using APHRODITE in the Million Veteran Program

百万退伍军人计划(MVP)是目前世界上最大的生物库研究。该资源提供 这是一个前所未有的机会，可以确定各种人类疾病的遗传原因 不成比例地影响我们的退伍军人，包括影响神经、心血管、肺部、 胃肠道、内分泌和肌肉骨骼器官。过去10年的快节奏技术进步 几年来，我们现在可以可靠地、密集地分析整个基因组中的个体。这样的数据已经 已经产生，并与广泛的人类疾病和生理特征有关。然而， 还有更多的联系有待建立，这将为科学界提供更多的重要线索 关于许多危及生命的疾病的根本原因以及如何开发新药的宝贵见解 来治疗或预防这些相同的疾病。目前进行这些额外发现的挑战是没有 更长时间生成大量高质量的基因数据，而是组织和查询 这些大型生物库研究正在利用非常庞大和复杂的电子健康记录(EHR)。直到 现在，已经花费了大量的精力和时间来艰苦地开发和验证基于规则的定义，以 跨各种电子病历平台识别患有特定疾病、综合症或状态的个人。然而， 退伍军人管理局公司数据仓库与观察性医疗结果伙伴关系的最新映射 公共数据模型(OMOP-CDM)为我们提供了前所未有的机会来应用新的电子化 表型“工具，可以识别个人的特定疾病，综合症，或状态更多 比基于规则的方法更有效的方式。这项建议的目标是全面测试 这些新工具之一被称为Aphrodite(用于观测定义的自动表型例程， 识别、培训和评估)，以确定MVP参与者之间已建立的遗传联系。 Aphrodite是在斯坦福大学由我们的一名合作研究人员开发的，使用了最先进的机器 学习算法识别患有某种疾病的人，所需时间仅为识别时间的一小部分 通过基于规则的定义。该算法在斯坦福大学的临床数据中显示出了巨大的前景 仓库，但需要在其他EHR队列中进行验证。在目标1中，我们将测试阿芙罗狄蒂的准确性 使用退伍军人管理局中的黄金标准集对至少5种疾病使用基于规则的分类器的分类器。在目标2中， 我们将测试目标1中的Aphrodite分类器是否可以应用于MVP参与者 已经建立了遗传联系。如果Aphrodite中的自动化方法执行得与Aphrodite相同或更好 基于规则的方法用于多种疾病，自动化方法可用于表型，其中规则 基于MVP的方法可能不存在，从而最大限度地提高MVP中的基因发现效率，并促进快速 将MVP内的发现复制到映射到OMOP-CDM的其他EHR中。

The Million Veteran Program (MVP) is currently the largest biobank study in the world. The resource provides an unprecedented opportunity to identify the genetic causes of a variety of human diseases that disproportionally affect our veterans including diseases that affect the neurological, cardiovascular, pulmonary, gastrointestinal, endocrine, and musculoskeletal organs. Fast-paced technological progress over the last 10 years now allows us to reliably and densely profile individuals across their entire genome. Such data has already been generated and linked to a wide spectrum of human diseases and physiologic traits. However, many more links remain to be made which will provide the scientific community with additional important clues on the root causes of many life-threatening diseases as well as valuable insights on how to develop new drugs to treat or prevent these same diseases. The current challenge in making these additional discoveries is no longer the generation of high quality genetic data in large numbers but rather the organization and querying of very large and complex electronic health records (EHR) being leveraged by these large biobank studies. Until now, much effort and time has been expended to painstakingly develop and validate rules-based definitions to identify individuals with a specific disease, syndrome, or state across a variety of EHR platforms. However, the recent mapping of the VA corporate data warehouse to the Observational Medical Outcomes Partnership common data model (OMOP-CDM) provides us with unprecedented opportunities to apply new “electronic phenotyping” tools that can identify individuals with a specific disease, syndrome, or state in a much more efficient manner than rules-based methods. The goal of this proposal is to comprehensively test the ability of one of these new tools named APHRODITE (Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation) to identify established genetic links among MVP participants. APHRODITE was developed at Stanford by one of our co-investigators and uses state of the art machine learning algorithms to identify individuals with a condition in a fraction of the time it takes to identify them through rules-based definitions. The algorithm has shown great promise within the Stanford clinical data warehouse but requires validation in other EHR cohorts. In aim 1, we will test the accuracy of an APHRODITE classifier to that of a rules-based classifier for at least 5 diseases using gold-standard sets in the VA. In aim 2, we will test whether APHRODITE classifiers from aim 1 can be applied to MVP participants to replicate established genetic associations. If automated methods in APHRODITE perform equally well or better than rules-based methods for multiple diseases, automated methods may be leveraged for phenotypes where rules based methods may not exist, maximizing the efficiency of genetic discovery in MVP and facilitating rapid replication of findings within MVP in other EHRs mapped to the OMOP-CDM.