Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data
Abstract New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could improve our understanding of viral biology and highlight new variants that warrant further study. Here we develop a gene...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55593-0 |
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| _version_ | 1841544509983293440 |
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| author | Brian Lee Ahmed Abdul Quadeer Muhammad Saqib Sohail Elizabeth Finney Syed Faraz Ahmed Matthew R. McKay John P. Barton |
| author_facet | Brian Lee Ahmed Abdul Quadeer Muhammad Saqib Sohail Elizabeth Finney Syed Faraz Ahmed Matthew R. McKay John P. Barton |
| author_sort | Brian Lee |
| collection | DOAJ |
| description | Abstract New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could improve our understanding of viral biology and highlight new variants that warrant further study. Here we develop a generic, analytical epidemiological model to infer the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that substantially affect the transmission rate, both within and outside the Spike protein. The mutations that we infer to have the largest effects on transmission are strongly supported by experimental evidence from prior studies. Importantly, our model detects lineages with increased transmission even at low frequencies. As an example, we infer significant transmission advantages for the Alpha, Delta, and Omicron variants shortly after their appearances in regional data, when they comprised only around 1-2% of sample sequences. Our model thus facilitates the rapid identification of variants and mutations that affect transmission from genomic surveillance data. |
| format | Article |
| id | doaj-art-5ab0f878fac2476cb934989be98203af |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-5ab0f878fac2476cb934989be98203af2025-01-12T12:30:26ZengNature PortfolioNature Communications2041-17232025-01-0116111310.1038/s41467-024-55593-0Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance dataBrian Lee0Ahmed Abdul Quadeer1Muhammad Saqib Sohail2Elizabeth Finney3Syed Faraz Ahmed4Matthew R. McKay5John P. Barton6Department of Physics and Astronomy, University of California, RiversideDepartment of Electronic and Computer Engineering, Hong Kong University of Science and TechnologyDepartment of Electronic and Computer Engineering, Hong Kong University of Science and TechnologyDepartment of Physics and Astronomy, University of California, RiversideDepartment of Electronic and Computer Engineering, Hong Kong University of Science and TechnologyDepartment of Electronic and Computer Engineering, Hong Kong University of Science and TechnologyDepartment of Physics and Astronomy, University of California, RiversideAbstract New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could improve our understanding of viral biology and highlight new variants that warrant further study. Here we develop a generic, analytical epidemiological model to infer the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that substantially affect the transmission rate, both within and outside the Spike protein. The mutations that we infer to have the largest effects on transmission are strongly supported by experimental evidence from prior studies. Importantly, our model detects lineages with increased transmission even at low frequencies. As an example, we infer significant transmission advantages for the Alpha, Delta, and Omicron variants shortly after their appearances in regional data, when they comprised only around 1-2% of sample sequences. Our model thus facilitates the rapid identification of variants and mutations that affect transmission from genomic surveillance data.https://doi.org/10.1038/s41467-024-55593-0 |
| spellingShingle | Brian Lee Ahmed Abdul Quadeer Muhammad Saqib Sohail Elizabeth Finney Syed Faraz Ahmed Matthew R. McKay John P. Barton Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data Nature Communications |
| title | Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data |
| title_full | Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data |
| title_fullStr | Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data |
| title_full_unstemmed | Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data |
| title_short | Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data |
| title_sort | inferring effects of mutations on sars cov 2 transmission from genomic surveillance data |
| url | https://doi.org/10.1038/s41467-024-55593-0 |
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