Influenza virus drug resistance: a time-sampled population genetics perspective.
The challenge of distinguishing genetic drift from selection remains a central focus of population genetics. Time-sampled data may provide a powerful tool for distinguishing these processes, and we here propose approximate Bayesian, maximum likelihood, and analytical methods for the inference of dem...
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-02-01
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| Series: | PLoS Genetics |
| Online Access: | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1004185&type=printable |
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| author | Matthieu Foll Yu-Ping Poh Nicholas Renzette Anna Ferrer-Admetlla Claudia Bank Hyunjin Shim Anna-Sapfo Malaspinas Gregory Ewing Ping Liu Daniel Wegmann Daniel R Caffrey Konstantin B Zeldovich Daniel N Bolon Jennifer P Wang Timothy F Kowalik Celia A Schiffer Robert W Finberg Jeffrey D Jensen |
| author_facet | Matthieu Foll Yu-Ping Poh Nicholas Renzette Anna Ferrer-Admetlla Claudia Bank Hyunjin Shim Anna-Sapfo Malaspinas Gregory Ewing Ping Liu Daniel Wegmann Daniel R Caffrey Konstantin B Zeldovich Daniel N Bolon Jennifer P Wang Timothy F Kowalik Celia A Schiffer Robert W Finberg Jeffrey D Jensen |
| author_sort | Matthieu Foll |
| collection | DOAJ |
| description | The challenge of distinguishing genetic drift from selection remains a central focus of population genetics. Time-sampled data may provide a powerful tool for distinguishing these processes, and we here propose approximate Bayesian, maximum likelihood, and analytical methods for the inference of demography and selection from time course data. Utilizing these novel statistical and computational tools, we evaluate whole-genome datasets of an influenza A H1N1 strain in the presence and absence of oseltamivir (an inhibitor of neuraminidase) collected at thirteen time points. Results reveal a striking consistency amongst the three estimation procedures developed, showing strongly increased selection pressure in the presence of drug treatment. Importantly, these approaches re-identify the known oseltamivir resistance site, successfully validating the approaches used. Enticingly, a number of previously unknown variants have also been identified as being positively selected. Results are interpreted in the light of Fisher's Geometric Model, allowing for a quantification of the increased distance to optimum exerted by the presence of drug, and theoretical predictions regarding the distribution of beneficial fitness effects of contending mutations are empirically tested. Further, given the fit to expectations of the Geometric Model, results suggest the ability to predict certain aspects of viral evolution in response to changing host environments and novel selective pressures. |
| format | Article |
| id | doaj-art-c83d55bae4ba41bfac40d6f845e6e97e |
| institution | DOAJ |
| issn | 1553-7390 1553-7404 |
| language | English |
| publishDate | 2014-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj-art-c83d55bae4ba41bfac40d6f845e6e97e2025-08-20T03:01:14ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042014-02-01102e100418510.1371/journal.pgen.1004185Influenza virus drug resistance: a time-sampled population genetics perspective.Matthieu FollYu-Ping PohNicholas RenzetteAnna Ferrer-AdmetllaClaudia BankHyunjin ShimAnna-Sapfo MalaspinasGregory EwingPing LiuDaniel WegmannDaniel R CaffreyKonstantin B ZeldovichDaniel N BolonJennifer P WangTimothy F KowalikCelia A SchifferRobert W FinbergJeffrey D JensenThe challenge of distinguishing genetic drift from selection remains a central focus of population genetics. Time-sampled data may provide a powerful tool for distinguishing these processes, and we here propose approximate Bayesian, maximum likelihood, and analytical methods for the inference of demography and selection from time course data. Utilizing these novel statistical and computational tools, we evaluate whole-genome datasets of an influenza A H1N1 strain in the presence and absence of oseltamivir (an inhibitor of neuraminidase) collected at thirteen time points. Results reveal a striking consistency amongst the three estimation procedures developed, showing strongly increased selection pressure in the presence of drug treatment. Importantly, these approaches re-identify the known oseltamivir resistance site, successfully validating the approaches used. Enticingly, a number of previously unknown variants have also been identified as being positively selected. Results are interpreted in the light of Fisher's Geometric Model, allowing for a quantification of the increased distance to optimum exerted by the presence of drug, and theoretical predictions regarding the distribution of beneficial fitness effects of contending mutations are empirically tested. Further, given the fit to expectations of the Geometric Model, results suggest the ability to predict certain aspects of viral evolution in response to changing host environments and novel selective pressures.https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1004185&type=printable |
| spellingShingle | Matthieu Foll Yu-Ping Poh Nicholas Renzette Anna Ferrer-Admetlla Claudia Bank Hyunjin Shim Anna-Sapfo Malaspinas Gregory Ewing Ping Liu Daniel Wegmann Daniel R Caffrey Konstantin B Zeldovich Daniel N Bolon Jennifer P Wang Timothy F Kowalik Celia A Schiffer Robert W Finberg Jeffrey D Jensen Influenza virus drug resistance: a time-sampled population genetics perspective. PLoS Genetics |
| title | Influenza virus drug resistance: a time-sampled population genetics perspective. |
| title_full | Influenza virus drug resistance: a time-sampled population genetics perspective. |
| title_fullStr | Influenza virus drug resistance: a time-sampled population genetics perspective. |
| title_full_unstemmed | Influenza virus drug resistance: a time-sampled population genetics perspective. |
| title_short | Influenza virus drug resistance: a time-sampled population genetics perspective. |
| title_sort | influenza virus drug resistance a time sampled population genetics perspective |
| url | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1004185&type=printable |
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