The Electronic Medical Records and Genomics (eMERGE) Network Phase III - Coordinating Center (U01)

随着新冠肺炎疫情于2020年初出现并迅速蔓延至全美，一个紧迫的问题 需要改进我们目前对哪些因素会增加感染风险的理解， 罹患重病或预后不良的可能性。早期的报告表明，基因，个人健康 病史、社会经济因素和环境会增加感染或差异的风险 在结果上，但几乎没有人高度自信地知道。由于目前没有接种疫苗或 其他预防性治疗，了解临床和遗传风险因素将立即 提高我们管理跨人群大流行的能力，并在 在床边。电子病历和基因组学(Emerge)网络拥有专业知识 和资源，以调查导致COVID疾病易感性增加的因素 从电子健康记录(EHR)快速编译数据并挖掘基因和 疾病协会。要做好这项工作，必须根据冠状病毒的病程特点和 新冠肺炎患者和对照组的特征必须准确 在不同的记录系统中定义(“ePhenotype”)。我们在表型鉴定方面的经验和 在大量人群中输入基因组和EHR数据将使我们能够快速合并 大量新冠肺炎患者用于未来基因组和表型组的广泛关联研究， 多基因风险评估和候选基因研究。我们的具体目标包括：第一 创建和部署ePhenotype以供立即研究使用建立COVID病例 定义、严重程度分级和与结果相关的并发症。第二，我们建议 集中收集COVID、EHR和基因组数据，用于未来的翻译研究。这些 资源将有益于科学界，是预测综合风险所必需的 疾病在整个生命周期内的发病率，并有可能影响下游患者的护理。

As the COVID-19 pandemic emerged in early 2020 and rapidly spread across the US, an urgent need developed to improve our current understanding of what factors increase infection risk, likelihood of severe illness, or poor outcomes. Early reports suggest genetics, personal health history, socioeconomic factors, and one's environment increases risk of infection or differences in outcomes, but little is known with high confidence. As there are currently no vaccinations or other preventative treatment, understanding clinical and genetic risk factors would immediately improve our ability to manage the pandemic across populations and deliver precision care at the bedside. The Electronic Medical Records and Genomics (eMERGE) Network has the expertise and resources to investigate the factors leading to increased COVID disease susceptibility by rapidly compiling data from electronic health records (EHRs) and mining records for gene and disease associations. To perform this task well, the features of COVID disease course and characteristics of patients with COVID-19 and those who serve as controls must be precisely defined (“ePhenotyped”) across different record system. Our experience with phenotyping and imputing genomic and EHR data across large populations will enable us to quickly merge a large number of COVID-19 patients for future genome and phenome wide association studies, polygenic risk assessments, and candidate gene studies. Our specific aims include first to create and deploy ePhenotypes for immediate research use establishing a COVID case definition, severity scale, and comorbidities with relation to outcomes. Secondly, we propose to collect COVID EHR and genomic data centrally for future translational research. These resources will be beneficial to the scientific community, necessary to predict comprehensive risk of disease across the lifespan, and have the potential to impact downstream patient care.