Beyond PheWAS: Recognition of Phenotype Patterns for Discovery and Translation

项目摘要 基因组医学为改进诊断方法和更有效的患者提供了希望 特定的疗法。全基因组相关研究(GWAS)阐明了 提高临床对许多疾病和状况的风险和机制的了解 这最终可能会根据患者的具体情况指导诊断和治疗。前两个 这项工作的周期(2011-2014年和2014-2018年)介绍了整个物候群的关联研究 (Phewas)作为一种系统和有效的方法来识别新的疾病变异关联 并使用电子健康记录(EHR)发现多效性。这项提议将使小说 使用表型风险分数根据表型模式识别关联性的方法 (PHERS)方法系统地寻找孟德尔病变异的影响 关于常见病。通过这样做，它还创造了一种评估罕见变异的致病性的方法， 并将确定患有未诊断孟德尔病风险最高的患者。该项目是 通过与未识别的EHR副本连接的大型DNA生物库实现。这个项目有四个 明确的目标。首先，我们将开发和验证用于评估变异致病性的phers。 通过在其预测算法中利用帐单代码、实验室数据和NLP功能。这个 第二个目标是在庞大的种群中应用Phers来创建一个强大的稀有变异库 不同人群中的协会(Emerge Network和大型国家队列，这可能 接近200万人的基因数据)。第三个目标是评估孟德尔病 外显率和评估PHERS作为一种工具来识别未诊断的孟德尔病风险患者 疾病。第四个目标是使这些工具和资源广泛可用，以帮助变种 翻译和方便其他人运行phers。此项目生成的工具将 验证解释稀有变体功能的新方法，改进基本 对孟德尔病的认识，大大提高了我们对孟德尔病贡献的认识 孟德尔病变异为常见疾病和特征，并提供了一种潜在的方法 确定新疗法可能带来益处的患者亚群。

Project Summary Genomic medicine offers hope for improved diagnostic methods and for more effective, patient specific therapies. Genome-wide associated studies (GWAS) elucidate genetic markers that improve clinical understanding of risks and mechanisms for many diseases and conditions and that may ultimately guide diagnosis and therapy on a patient-specific basis. The previous two cycles of this effort (2011-2014 and 2014-2018) introduced the phenome-wide association study (PheWAS) as a systematic and efficient approach to identify novel disease-variant associations and discover pleiotropy using electronic health records (EHRs). This proposal will develop novel methods to identify associations based on patterns of phenotypes using a phenotype risk score (PheRS) methodology to systematically search for the influence of Mendelian disease variants on common disease. By doing so, it also creates a way to assess pathogenicity for rare variants, and will identify patients at highest risk of having undiagnosed Mendelian disease. The project is enabled by large DNA biobanks coupled to de-identified copies of EHR. This project has four specific aims. First, we will develop and validate PheRS for assessment of variant pathogenicity by leveraging billing codes, laboratory data, and NLP features in its predictive algorithms. The second aim is to apply PheRS in huge populations to create a robust repository of rare variant associations in diverse populations (eMERGE Network and large national cohorts, which could approach 2 million people with genotype data). The third aim is to assess Mendelian disease penetrance and evaluate PheRS as a tool to identify patients at risk for undiagnosed Mendelian disease. The fourth aim is make these tools and resources broadly available to aid in variant interpretation and facilitate others running PheRS. The tools generated from this project will validate new approaches to interpreting the function of rare variants, improve basic understanding of Mendelian disease, greatly enhance our understanding of the contribution of Mendelian disease variants to common disease and traits, and offers a potential approach to identify subpopulations of patients for whom new therapies may offer benefit.