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: 9955052
• 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/9955052
• 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; United States Department of Veterans Affairs; 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）是目前世界上最大的生物库研究。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， 严重影响我们的退伍军人，包括影响神经系统，心血管，肺， 胃肠道、内分泌和肌肉骨骼器官。在过去10年里， 多年的研究使我们能够可靠而密集地分析个体的整个基因组。这些数据有 已经产生并与广泛的人类疾病和生理特征有关。然而，在这方面， 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。 对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。目前在这些额外的发现中面临的挑战是， 更长的时间产生大量高质量的遗传数据，而是组织和查询 这些大型生物库研究利用了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精心开发和验证基于规则的定义， 在各种EHR平台上识别患有特定疾病、综合征或状态的个体。但 最近将VA公司数据仓库映射到观察性医学结局伙伴关系 公共数据模型（OMOP-CDM）为我们应用新的“电子”技术提供了前所未有的机会 表型分析”工具，可以识别个体与特定的疾病，综合征，或状态，在一个更 比基于规则的方法更有效。本提案的目的是全面测试 其中一个新工具名为APHRODITE（用于观察定义的自动化Phenotype例程， 鉴定、培训和评估），以确定MVP参与者之间已建立的遗传联系。 APHRODITE是由我们的合作研究者之一在斯坦福大学开发的，使用最先进的机器 学习算法，以识别个人的条件，在一小部分的时间，它需要确定他们 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很大的前景 但需要在其他EHR队列中进行验证。在目标1中，我们将测试一个APHRODITE的准确性 在VA中，将基于规则的分类器与使用金标准集的至少5种疾病的基于规则的分类器进行比较。在目标2中， 我们将测试aim 1中的APHRODITE分类器是否可以应用于MVP参与者， 建立了基因关联。如果APHRODITE中的自动化方法与 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 的方法可能不存在，最大限度地提高MVP中基因发现的效率， 在映射到OMOP-CDM的其他EHR中复制MVP中的调查结果。