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dc.contributor.authorFoll, Matthieu
dc.contributor.authorPoh, Yu Ping
dc.contributor.authorRenzette, Nicholas
dc.contributor.authorAdmetlla, Anna Ferrer
dc.contributor.authorBank, Claudia
dc.contributor.authorShim, Hyunjin
dc.contributor.authorMalaspinas, Anna Sapfo
dc.contributor.authorEwing, Gregory
dc.contributor.authorLiu, Ping
dc.contributor.authorWegmann, Daniel
dc.contributor.authorCaffrey, Daniel R.
dc.contributor.authorZeldovich, Konstantin B.
dc.contributor.authorBolon, Daniel N A
dc.contributor.authorWang, Jennifer
dc.contributor.authorKowalik, Timothy F.
dc.contributor.authorSchiffer, Celia A.
dc.contributor.authorFinberg, Robert W.
dc.contributor.authorJensen, Jeffrey D.
dc.date2022-08-11T08:08:30.000
dc.date.accessioned2022-08-23T15:57:21Z
dc.date.available2022-08-23T15:57:21Z
dc.date.issued2014-02-27
dc.date.submitted2014-10-20
dc.identifier.citationPLoS Genet. 2014 Feb 27;10(2):e1004185. doi: 10.1371/journal.pgen.1004185. eCollection 2014. <a href="http://dx.doi.org/10.1371/journal.pgen.1004185">Link to article on publisher's site</a>
dc.identifier.issn1553-7390 (Linking)
dc.identifier.doi10.1371/journal.pgen.1004185
dc.identifier.pmid24586206
dc.identifier.urihttp://hdl.handle.net/20.500.14038/30176
dc.description.abstractThe 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.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=24586206&dopt=Abstract">Link to Article in PubMed</a>
dc.rights© 2014 Foll et al. This is an open-access article distributed under the terms of the <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectComputational Biology
dc.subjectGenetics
dc.subjectImmunity
dc.subjectImmunoprophylaxis and Therapy
dc.subjectPopulation Biology
dc.titleInfluenza virus drug resistance: a time-sampled population genetics perspective
dc.typeJournal Article
dc.source.journaltitlePLoS genetics
dc.source.volume10
dc.source.issue2
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1418&amp;context=faculty_pubs&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/419
dc.identifier.contextkey6251697
refterms.dateFOA2022-08-23T15:57:21Z
html.description.abstract<p>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.</p>
dc.identifier.submissionpathfaculty_pubs/419
dc.contributor.departmentDepartment of Medicine
dc.contributor.departmentDepartment of Biochemistry and Molecular Pharmacology
dc.contributor.departmentDepartment of Medicine
dc.contributor.departmentDepartment of Microbiology and Physiological Systems
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology
dc.source.pagese1004185


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© 2014 Foll et al. This is an open-access article distributed under the terms of the <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as © 2014 Foll et al. This is an open-access article distributed under the terms of the <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.