Unraveling Risk Genes of COVID-19 by Multi-Omics Integrative Analyses

Objectives: Uncovering the genetic basis of COVID-19 may shed insight into its pathogenesis and help to improve treatment measures. We aimed to investigate the host genetic variants associated with COVID-19. Methods: The summary result of a COVID-19 GWAS (9,373 hospitalized COVID-19 cases and 1,197,256 controls) was obtained from the COVID-19 Host Genetic Initiative GWAS meta-analyses. We tested colocalization of the GWAS signals of COVID-19 with expression and methylation quantitative traits loci (eQTL and mQTL, respectively) using the summary data-based Mendelian randomization (SMR) analysis. Four eQTL and two mQTL datasets were utilized in the SMR analysis, including CAGE blood eQTL data ( n = 2,765), GTEx v7 blood ( n = 338) and lung ( n = 278) eQTL data, Geuvadis lymphoblastoid cells eQTL data, LBC-BSGS blood mQTL data ( n = 1,980), and Hannon blood mQTL summary data ( n = 1,175). We conducted a transcriptome-wide association study (TWAS) on COVID-19 with precomputed prediction models of GTEx v8 eQTL in lung and blood using S-PrediXcan. Results: Our SMR analyses identified seven protein-coding genes ( TYK2, IFNAR2, OAS1, OAS3, XCR1, CCR5 , and MAPT ) associated with COVID-19, including two novel risk genes, CCR5 and tau-encoding MAPT . The TWAS revealed four genes for COVID-19 ( CXCR6, CCR5, CCR9 , and PIGN ), including two novel risk genes, CCR5 and PIGN . Conclusion: Our study highlighted the functional relevance of some known genome-wide risk genes of COVID-19 and revealed novel genes contributing to differential outcomes of COVID-19 disease.