Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach
The COVID-19 pandemic has led to over 2.26 million deaths for almost 104 million confirmed cases worldwide, as of 4 February 2021 (WHO). Risk factors include pre-existing conditions such as cancer, cardiovascular disease, diabetes, and obesity. Although several vaccines have been deployed, there are...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/10/8/1666 |
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| author | Micaela F. Beckman Farah Bahrani Mougeot Jean-Luc C. Mougeot |
| author_facet | Micaela F. Beckman Farah Bahrani Mougeot Jean-Luc C. Mougeot |
| author_sort | Micaela F. Beckman |
| collection | DOAJ |
| description | The COVID-19 pandemic has led to over 2.26 million deaths for almost 104 million confirmed cases worldwide, as of 4 February 2021 (WHO). Risk factors include pre-existing conditions such as cancer, cardiovascular disease, diabetes, and obesity. Although several vaccines have been deployed, there are few alternative anti-viral treatments available in the case of reduced or non-existent vaccine protection. Adopting a long-term holistic approach to cope with the COVID-19 pandemic appears critical with the emergence of novel and more infectious SARS-CoV-2 variants. Our objective was to identify comorbidity-associated single nucleotide polymorphisms (SNPs), potentially conferring increased susceptibility to SARS-CoV-2 infection using a computational meta-analysis approach. SNP datasets were downloaded from a publicly available genome-wide association studies (GWAS) catalog for 141 of 258 candidate COVID-19 comorbidities. Gene-level SNP analysis was performed to identify significant pathways by using the program MAGMA. An SNP annotation program was used to analyze MAGMA-identified genes. Differential gene expression was determined for significant genes across 30 general tissue types using the Functional and Annotation Mapping of GWAS online tool GENE2FUNC. COVID-19 comorbidities (<i>n</i> = 22) from six disease categories were found to have significant associated pathways, validated by Q–Q plots (<i>p</i> < 0.05). Protein–protein interactions of significant (<i>p</i> < 0.05) differentially expressed genes were visualized with the STRING program. Gene interaction networks were found to be relevant to SARS and influenza pathogenesis. In conclusion, we were able to identify the pathways potentially affected by or affecting SARS-CoV-2 infection in underlying medical conditions likely to confer susceptibility and/or the severity of COVID-19. Our findings have implications in future COVID-19 experimental research and treatment development. |
| first_indexed | 2024-03-10T12:21:20Z |
| format | Article |
| id | doaj.art-883c03b09ebf4a20a23ba73368924fe7 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T12:21:20Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-883c03b09ebf4a20a23ba73368924fe72023-11-21T15:26:23ZengMDPI AGJournal of Clinical Medicine2077-03832021-04-01108166610.3390/jcm10081666Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining ApproachMicaela F. Beckman0Farah Bahrani Mougeot1Jean-Luc C. Mougeot2Department of Oral Medicine, Carolinas Medical Center, Atrium Health, Charlotte, NC 28203, USADepartment of Oral Medicine, Carolinas Medical Center, Atrium Health, Charlotte, NC 28203, USADepartment of Oral Medicine, Carolinas Medical Center, Atrium Health, Charlotte, NC 28203, USAThe COVID-19 pandemic has led to over 2.26 million deaths for almost 104 million confirmed cases worldwide, as of 4 February 2021 (WHO). Risk factors include pre-existing conditions such as cancer, cardiovascular disease, diabetes, and obesity. Although several vaccines have been deployed, there are few alternative anti-viral treatments available in the case of reduced or non-existent vaccine protection. Adopting a long-term holistic approach to cope with the COVID-19 pandemic appears critical with the emergence of novel and more infectious SARS-CoV-2 variants. Our objective was to identify comorbidity-associated single nucleotide polymorphisms (SNPs), potentially conferring increased susceptibility to SARS-CoV-2 infection using a computational meta-analysis approach. SNP datasets were downloaded from a publicly available genome-wide association studies (GWAS) catalog for 141 of 258 candidate COVID-19 comorbidities. Gene-level SNP analysis was performed to identify significant pathways by using the program MAGMA. An SNP annotation program was used to analyze MAGMA-identified genes. Differential gene expression was determined for significant genes across 30 general tissue types using the Functional and Annotation Mapping of GWAS online tool GENE2FUNC. COVID-19 comorbidities (<i>n</i> = 22) from six disease categories were found to have significant associated pathways, validated by Q–Q plots (<i>p</i> < 0.05). Protein–protein interactions of significant (<i>p</i> < 0.05) differentially expressed genes were visualized with the STRING program. Gene interaction networks were found to be relevant to SARS and influenza pathogenesis. In conclusion, we were able to identify the pathways potentially affected by or affecting SARS-CoV-2 infection in underlying medical conditions likely to confer susceptibility and/or the severity of COVID-19. Our findings have implications in future COVID-19 experimental research and treatment development.https://www.mdpi.com/2077-0383/10/8/1666SARS-CoV-2COVID-19comorbiditySNPsusceptibilityseverity |
| spellingShingle | Micaela F. Beckman Farah Bahrani Mougeot Jean-Luc C. Mougeot Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach Journal of Clinical Medicine SARS-CoV-2 COVID-19 comorbidity SNP susceptibility severity |
| title | Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach |
| title_full | Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach |
| title_fullStr | Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach |
| title_full_unstemmed | Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach |
| title_short | Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach |
| title_sort | comorbidities and susceptibility to covid 19 a generalized gene set data mining approach |
| topic | SARS-CoV-2 COVID-19 comorbidity SNP susceptibility severity |
| url | https://www.mdpi.com/2077-0383/10/8/1666 |
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