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

电子健康记录中随时可用的表型数据是一种未得到充分利用的资源，具有极大的潜力为诊断和治疗决策提供信息。使用数据科学方法，我们可以聚合、浓缩和提炼海量的健康数据，以最大限度地提高其预测价值，以支持医疗决策。这个项目寻求从电子健康记录中开发临床分级表型定义，这些电子健康记录派生出表型可能性或诊断确定性的量化测量。这些表型风险的定量测量在研究和临床实践中都有应用；例如，为专家测试的优先顺序和解释提供信息，如基因或成像测试。这个项目最初的重点是心血管疾病，复杂的和家族遗传都有描述，包括心力衰竭和心肌病。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)来解决医学现象。该项目的具体目标是根据Bastarache等人报告的一种方法，在英国生物库中得出孟德尔疾病和后来的常见疾病的基于EHR的表型风险分数。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的数量。成功的结果包括新的基因发现，例如与正在调查的疾病有关的未知变异的发现，发现误诊患者的可能性，并在临床上实现变化，使更早的诊断和改进的治疗成为可能，同时支持医生的决策。

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.