Genomic Basis of Susceptibility to COVID-19 Infection and its Complications

项目摘要 除了造成数百万病例和数十万人死亡外， 2019冠状病毒病（COVID-19）大流行使生活和经济活动几乎停顿， 世界上许多地方。协调的科学努力是必要的，以减轻广泛的 这一流行病造成的痛苦、发病率和死亡率。本次增补的目的 应用是有助于信息学和基因组学的努力，以确定基因组的基础， 对COVID-19的易感性和并发症。广泛的疾病严重程度， COVID-19仅部分由年龄和医学合并症解释，遗传因素 可能会发挥关键作用。确定影响COVID-19病例状态的基因组因素， 并发症对于危险分层很重要，确定新的病理生理途径， 药物开发/再利用，并提高对SARS-CoV-2生物学的认识 感染及其并发症。 作为电子病历和基因组学（eMERGE）的一部分，自2007年成立以来， 马约研究人员在使用电子健康记录（EHR）方面有相当丰富的经验， 基因组学研究我们将开发电子表型分析算法，以确定COVID-19 病例状态、并发症和死亡率，以确定与不良反应相关的基因组变异 结果。使用与EHR相关的DNA样本，我们将进行基因组分析， 与病例状态、病例严重程度和病例死亡率相关的常见和罕见变异。我们 将与美国和世界各地的卫生系统和财团合作， 基因组研究的力量和速度。我们的具体目标是：具体目标1：发展 并验证电子表型分析算法，以确定COVID-19相关表型 包括病例控制状态，即，个人测试和那些被确定为积极的 COVID-19和疾病严重程度，特别是心血管并发症，包括心肌梗死 损伤/梗塞、心律失常、凝血病以及大血管血栓形成。具体目标二： 进行基因组关联分析，以确定与对以下疾病易感性相关的变异： SARS-CoV-2感染及其并发症。我们将比较test +ve和test -ve 个人，轻度vs住院病例COVID-19，后者中发展为 严重的疾病或死亡。除了全基因组关联研究（GWAS），我们还将进行 HLA区域的关联研究和使用序列数据的负荷测试。

PROJECT SUMMARY In addition to causing millions of cases and hundreds of thousands of deaths, the Coronavirus disease 2019 (COVID-19) pandemic has brought life and economic activity to a near standstill in many parts of the world. A coordinated scientific effort is necessary to mitigate the widespread misery, morbidity and mortality inflicted by the pandemic. The goal of this supplemental application is to contribute to informatics and genomics efforts to identify the genomic basis of susceptibility to and complications of COVID-19. The wide spectrum of disease severity with COVID-19 is only partially explained by age and medical comorbidities and genetic factors are likely to play a key role. Identifying genomic factors impacting COVID-19 case status and complications is important for risk stratification, identifying new pathophysiologic pathways for drug development/repurposing, and improved understanding of the biology of SARS-CoV-2 infection and its complications. As part of the electronic Medical Records and Genomics (eMERGE) since its inception in 2007, Mayo investigators have considerable experience in using the electronic health record (EHR) for genomics research. We will develop electronic phenotyping algorithms to ascertain COVID-19 case status, complications and fatality, to identify genomic variants associated with adverse outcomes. Using DNA samples linked to the EHR, we will perform genomic analyses to identify common and rare variants associated with case status, case severity and case mortality. We will collaborate with health systems and consortia in the US and around the world to increase the power and rapidity of the genomic studies. Our specific aims are: Specific Aim 1: Develop and validate electronic phenotyping algorithms to ascertain COVID-19 related phenotypes including case control status, i.e., individuals tested and those were identified to be positive for COVID-19, and disease severity, in particular cardiovascular complications including myocardial injury/infarction, arrhythmias, coagulopathy as well as large vessel thrombosis. Specific Aim 2: Perform genomic association analyses to identify variants associated with susceptibility to infection with SARS-CoV-2 and its complications. We will compare test +ve vs test -ve individuals, mild vs hospitalized cases of COVID-19 and among the latter those who develop severe disease or die. In addition to genome-wide association studies (GWAS), we will conduct association studies of the HLA region and burden tests using sequence data.