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 具体治疗。全基因组相关研究（GWAS）阐明了遗传标记， 提高对许多疾病和病症的风险和机制的临床理解， 这可能最终在患者特异性的基础上指导诊断和治疗。前两 这一努力的周期（2011-2014年和2014-2018年）引入了全表型关联研究 （PheWAS）作为一种系统和有效的方法，以确定新的疾病变异的关联 并利用电子健康记录（EHR）发现多效性。该提案将开发新的 使用表型风险评分基于表型模式鉴定关联的方法 （PheRS）方法系统地搜索孟德尔疾病变体的影响 关于常见病通过这样做，它也创造了一种评估罕见变异致病性的方法， 并将识别出患有未确诊的孟德尔氏病的风险最高的患者。该项目 通过大型DNA生物库与去识别的EHR副本相结合。该项目有四个 具体目标。首先，我们将开发和验证PheRS用于评估变异致病性 通过在其预测算法中利用计费代码、实验室数据和NLP功能。的 第二个目标是将PheRS应用于大量人群，以创建一个强大的稀有变异库 不同人群中的协会（eMERGE网络和大型国家队列， 接近200万人的基因型数据）。第三个目标是评估孟德尔疾病 筛选和评估PheRS作为识别未确诊孟德尔遗传病风险患者的工具 疾病第四个目标是广泛提供这些工具和资源， 解释和促进其他人运行PheRS。该项目产生的工具将 验证新的方法来解释罕见变异的功能，改善基本的 对孟德尔疾病的了解，大大提高了我们的认识， 孟德尔疾病的变异，以共同的疾病和性状，并提供了一个潜在的方法， 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.