Efficient electronic phenotyping using APHRODITE in the Million Veteran Program

在百万退伍军人计划中使用 APHRODITE 进行高效电子表型分析

• 批准号: 9485175
• 负责人: THEMISTOCLES LEONARD ASSIMES
• 金额: --
• 依托单位: VETERANS ADMIN PALO ALTO HEALTH CARE SYS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9485175
• 关键词: Acute myocardial infarction; Affect; Algorithms; Cardiovascular system; Catalogs; Chronic; Classification; Clinical Research; Code; Communities; Complex; Computational algorithm; Computer software; DNA; Data; Data Set; Development; Disease; Electronic Health Record; Endocrine; Evaluation; Generations; Genetic; Genetic Determinism; Genotype; Goals; Gold; Health; Human; Individual; Inherited; Knowledge; Label; Learning; Life; Link; Lung; Manuals; Maps; Medical; Medical Genetics; Methodology; Methods; Modeling; Musculoskeletal; Names; Neurologic; Non-Insulin-Dependent Diabetes Mellitus; Organ; Outcome; Participant; Patients; Performance; Phenotype; Physiological; Plant Roots; Play; Process; Publishing; Reporting; Research Personnel; Resources; Role; Signal Transduction; Source; Supervision; Syndrome; Testing; Time; Training; Universities; Validation; Veterans; base; biobank; case control; clinical data warehouse; clinical phenotype; cohort; data modeling; data warehouse; gastrointestinal; genetic association; genetic profiling; genome wide association study; genome-wide; human disease; improved; insight; interest; machine learning algorithm; novel; novel therapeutics; open source; prevent; programs; supervised learning; tool; trait; whole genome

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.

百万退伍军人计划（MVP）目前是世界上最大的生物库研究。资源提供的 识别各种人类疾病的遗传原因的前所未有的机会 不成比例地影响我们的退伍军人，包括影响神经系统，心血管，肺部的疾病 胃肠道，内分泌和肌肉骨骼器官。在过去的10个中快节奏的技术进步 几年现在使我们能够可靠而愚蠢的人在整个基因组中介绍个人。这样的数据具有 已经生成并与广泛的人类疾病和生理特征有关。然而， 还有更多的链接有待建立，这将为科学界提供其他重要集群 关于许多威胁生命的疾病的根本原因，以及如何开发新药的宝贵见解 治疗或预防这些相同的疾病。当前有其他发现的挑战是没有 大量的高质量遗传数据的产生更长，而是组织和查询 这些大型生物库研究利用了非常大而复杂的电子健康记录（EHR）。直到 现在，已经探索了很多精力和时间，以艰苦地开发和验证基于规则的定义 在各种EHR平台中识别患有特定疾病，综合征或状态的人。但是， VA公司数据仓库的最新映射到观察性医疗成果合作伙伴关系 常见数据模型（OMOP-CDM）为我们提供了前所未有的机会，可以应用新的“电子” 表型的工具可以识别患有特定疾病，综合征或状态的人 高效方式比基于规则的方法。该建议的目的是全面测试 这些新工具之一称为阿芙罗狄蒂（观察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。 阿芙罗狄蒂是由我们的一位共同投资者在斯坦福大学开发的，并使用了最先进的机器 学习算法以识别有条件的人在很短的时间内识别他们 通过基于规则的定义。该算法在斯坦福大学的临床数据中表现出巨大的希望 仓库，但需要在其他EHR队列中进行验证。在AIM 1中，我们将测试阿芙罗狄蒂的准确性 使用VA中的金标准集的基于规则的分类器的分类器至少5种疾病。在AIM 2中， 我们将测试AIM 1的阿芙罗狄蒂分类器是否可以应用于MVP参与者以复制 建立的遗传关联。如果阿芙罗狄蒂中的自动化方法的表现同样好或更好 用于多种疾病的基于规则的方法，可以利用自动化方法来利用规则的表型 可能不存在基于基于的方法，最大化MVP遗传发现的效率并支持快速 在其他EHR中映射到OMOP-CDM中MVP中发现的复制。