Purpose

It is critical to identify putative causal targets for SARS coronavirus 2, which may guide drug repurposing options to reduce the public health burden of COVID-19.

Methods

We applied complementary methods and multiphased design to pinpoint the most likely causal genes for COVID-19 severity. First, we applied cross-methylome omnibus (CMO) test and leveraged data from the COVID-19 Host Genetics Initiative (HGI) comparing 9,986 hospitalized COVID-19 patients and 1,877,672 population controls. Second, we evaluated associations using the complementary S-PrediXcan method and leveraging blood and lung tissue gene expression prediction models. Third, we assessed associations of the identified genes with another COVID-19 phenotype, comparing very severe respiratory confirmed COVID versus population controls. Finally, we applied a fine-mapping method, fine-mapping of gene sets (FOGS), to prioritize putative causal genes.

Results

Through analyses of the COVID-19 HGI using complementary CMO and S-PrediXcan methods along with fine-mapping, XCR1, CCR2, SACM1L, OAS3, NSF, WNT3, NAPSA, and IFNAR2 are identified as putative causal genes for COVID-19 severity.

Conclusion

We identified eight genes at five genomic loci as putative causal genes for COVID-19 severity.

中文翻译：

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综合多组学分析确定了 COVID-19 严重程度的假定因果基因

目的

确定 SARS 冠状病毒 2 的推定因果目标至关重要，这可能会指导药物再利用的选择，以减轻 COVID-19 的公共卫生负担。

方法

我们应用互补方法和多阶段设计来确定 COVID-19 严重程度最可能的致病基因。首先，我们应用了交叉甲基化组 (CMO) 测试，并利用了来自 COVID-19 宿主遗传学倡议 (HGI) 的数据，比较了 9,986 名住院 COVID-19 患者和 1,877,672 名人群对照。其次，我们使用互补 S-PrediXcan 方法并利用血液和肺组织基因表达预测模型评估关联。第三，我们评估了已识别基因与另一种 COVID-19 表型的关联，比较了非常严重的呼吸道确诊 COVID 与人群对照。最后，我们应用了一种精细映射方法，即基因集精细映射 (FOGS)，以优先考虑假定的因果基因。

结果

通过使用互补的 CMO 和 S-PrediXcan 方法以及精细映射分析 COVID-19 HGI，XCR1、CCR2、SACM1L、OAS3、NSF、WNT3、NAPSA和IFNAR2被确定为 COVID-19 严重程度的推定因果基因。

结论

我们在五个基因组位点确定了八个基因作为 COVID-19 严重程度的假定因果基因。