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PLoS Genet. 2014 Feb 27;10(2):e1004185. doi: 10.1371/journal.pgen.1004185. eCollection 2014.
Influenza virus drug resistance: a time-sampled population genetics perspective.
Foll M1, Poh YP2, Renzette N3, Ferrer-Admetlla A4, Bank C1, Shim H1, Malaspinas AS5, Ewing G1, Liu P6, Wegmann D7, Caffrey DR6, Zeldovich KB8, Bolon DN9, Wang JP6, Kowalik TF3, Schiffer CA9, Finberg RW6, Jensen JD1.
Author information
Abstract
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.
PMID:
24586206
[PubMed - in process]
Free full text
Influenza virus drug resistance: a time-sampled population genetics perspective.
Foll M1, Poh YP2, Renzette N3, Ferrer-Admetlla A4, Bank C1, Shim H1, Malaspinas AS5, Ewing G1, Liu P6, Wegmann D7, Caffrey DR6, Zeldovich KB8, Bolon DN9, Wang JP6, Kowalik TF3, Schiffer CA9, Finberg RW6, Jensen JD1.
Author information
Abstract
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.
PMID:
24586206
[PubMed - in process]
Free full text
