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

随着2019冠状病毒病大流行于2020年初出现并迅速在美国蔓延， 需要发展，以提高我们目前的理解是什么因素增加感染风险， 严重疾病或不良后果的可能性。早期的报告显示，基因，个人健康 历史，社会经济因素和一个人的环境增加了感染或差异的风险 结果，但很少有人知道高信心。由于目前没有疫苗或 其他预防性治疗，了解临床和遗传风险因素， 提高我们在人群中管理大流行病的能力， 床边电子医疗记录和基因组学（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）和基因挖掘记录中快速编译数据， 疾病协会。为了做好这项工作，我们必须了解新冠肺炎的病程特点， COVID-19患者和作为对照的患者的特征必须精确 在不同的记录系统中定义（“ePhenotyped”）。我们的经验与表型和 在大规模人群中输入基因组和EHR数据将使我们能够快速合并 大量COVID-19患者用于未来的基因组和表型组广泛关联研究， 多基因风险评估和候选基因研究。我们的具体目标包括： 创建和部署ePhenotypes，用于建立COVID病例的即时研究 定义、严重程度量表和与结局相关的合并症。第二，我们建议 集中收集COVID EHR和基因组数据，用于未来的转化研究。这些 资源将有利于科学界，有必要预测综合风险 在整个生命周期中的疾病，并有可能影响下游的病人护理。

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.