Combining quantitative estimates of phenotype likelihood from EHR with genomic information for genetic discovery and clinical risk prediction

将 EHR 对表型可能性的定量估计与基因组信息相结合，以进行遗传发现和临床风险预测

• 批准号: 2089124
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2089124
• 关键词: Combining; quantitative; estimates; phenotype; likelihood

Readily available phenotypic data from electronic health records is an underused resource that has significant potential to inform diagnostic and therapeutic decisions. Using data science methods, we can aggregate, condense and refine vast amounts of health data to maximise its predictive value to support medical decision-making. This project seeks to develop clinical grade phenotype definitions from electronic health records that derive quantitative measures of phenotype likelihood or diagnostic certainty. These quantitative measures of phenotype risk have applications for both research and clinical practice; for example, to inform the prioritisation and interpretation of specialist tests, such as genetic or imaging tests. The initial focus of this project is on cardiovascular disease for which both complex and familial inheritance is described, including heart failure and cardiomyopathies. However, the aim is to develop methods and tools (such as software packages or online applications) that are scalable to address the medical phenome. Specific aims of the project are to derive EHR-based phenotypic risk scores for Mendelian disorders and later common disease in the UK Biobank, based on a method reported by Bastarache et al. These disease-specific scores are based on the number of relevant phenotypes found in a patient's health records. Successful outcomes include new genetic discovery e.g. of unknown variants related to the diseases under investigation, potential to find misdiagnosed patients and to effect change in clinics and enable earlier diagnoses and improved treatment, whilst supporting physician's decision-making.

来自电子健康记录的容易获得的表型数据是一种未充分利用的资源，具有为诊断和治疗决策提供信息的巨大潜力。使用数据科学方法，我们可以汇总、精简和细化大量健康数据，以最大限度地提高其预测价值，以支持医疗决策。该项目旨在从电子健康记录中开发临床级表型定义，这些表型定义可以定量测量表型可能性或诊断确定性。这些表型风险的定量测量方法可用于研究和临床实践;例如，为遗传或成像测试等专业测试的优先级和解释提供信息。该项目的最初重点是心血管疾病，包括心力衰竭和心肌病，描述了复杂和家族遗传。然而，目标是开发可扩展的方法和工具（如软件包或在线应用程序），以解决医学表型。该项目的具体目标是根据Bastarache等人报告的方法，在英国生物库中推导出孟德尔疾病和后来常见疾病的基于EHR的表型风险评分。这些疾病特异性评分是基于患者健康记录中发现的相关表型数量。成功的结果包括新的遗传发现，例如与正在调查的疾病相关的未知变异，发现误诊患者的潜力，以及影响诊所的变化，并使早期诊断和改善治疗成为可能，同时支持医生的决策。