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 1,197,2...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-09-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2021.738687/full |
| _version_ | 1818595107293626368 |
|---|---|
| author | Ancha Baranova Ancha Baranova Hongbao Cao Fuquan Zhang Fuquan Zhang |
| author_facet | Ancha Baranova Ancha Baranova Hongbao Cao Fuquan Zhang Fuquan Zhang |
| author_sort | Ancha Baranova |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T11:10:45Z |
| format | Article |
| id | doaj.art-4538c9edcc974c44a3c7ee144a1b62a9 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-12-16T11:10:45Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-4538c9edcc974c44a3c7ee144a1b62a92022-12-21T22:33:45ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2021-09-01810.3389/fmed.2021.738687738687Unraveling Risk Genes of COVID-19 by Multi-Omics Integrative AnalysesAncha Baranova0Ancha Baranova1Hongbao Cao2Fuquan Zhang3Fuquan Zhang4School of Systems Biology, George Mason University, Manassas, VA, United StatesResearch Centre for Medical Genetics, Moscow, RussiaSchool of Systems Biology, George Mason University, Manassas, VA, United StatesInstitute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaObjectives: 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.https://www.frontiersin.org/articles/10.3389/fmed.2021.738687/fullGWASCOVID-19TWASeQTLmQTL |
| spellingShingle | Ancha Baranova Ancha Baranova Hongbao Cao Fuquan Zhang Fuquan Zhang Unraveling Risk Genes of COVID-19 by Multi-Omics Integrative Analyses Frontiers in Medicine GWAS COVID-19 TWAS eQTL mQTL |
| title | Unraveling Risk Genes of COVID-19 by Multi-Omics Integrative Analyses |
| title_full | Unraveling Risk Genes of COVID-19 by Multi-Omics Integrative Analyses |
| title_fullStr | Unraveling Risk Genes of COVID-19 by Multi-Omics Integrative Analyses |
| title_full_unstemmed | Unraveling Risk Genes of COVID-19 by Multi-Omics Integrative Analyses |
| title_short | Unraveling Risk Genes of COVID-19 by Multi-Omics Integrative Analyses |
| title_sort | unraveling risk genes of covid 19 by multi omics integrative analyses |
| topic | GWAS COVID-19 TWAS eQTL mQTL |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2021.738687/full |
| work_keys_str_mv | AT anchabaranova unravelingriskgenesofcovid19bymultiomicsintegrativeanalyses AT anchabaranova unravelingriskgenesofcovid19bymultiomicsintegrativeanalyses AT hongbaocao unravelingriskgenesofcovid19bymultiomicsintegrativeanalyses AT fuquanzhang unravelingriskgenesofcovid19bymultiomicsintegrativeanalyses AT fuquanzhang unravelingriskgenesofcovid19bymultiomicsintegrativeanalyses |