Program in Bioinformatics and Integrative Biology; Department of Microbiology and Physiological Systems;Department of Medicine; Department of Biochemistry and Molecular Pharmacology; Department of Medicine
Computational Biology | Genetics | Immunity | Immunoprophylaxis and Therapy | Population Biology
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.
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© 2014 Foll et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI of Published Version
PLoS Genet. 2014 Feb 27;10(2):e1004185. doi: 10.1371/journal.pgen.1004185. eCollection 2014. Link to article on publisher's site
Foll M, Poh YP, Renzette N, Admetlla AF, Bank C, Shim H, Malaspinas AS, Ewing G, Liu P, Wegmann D, Caffrey DR, Zeldovich KB, Bolon DN, Wang J, Kowalik TF, Schiffer CA, Finberg RW, Jensen JD. (2014). Influenza virus drug resistance: a time-sampled population genetics perspective. UMass Chan Medical School Faculty Publications. https://doi.org/10.1371/journal.pgen.1004185. Retrieved from https://escholarship.umassmed.edu/faculty_pubs/419
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This work is licensed under a Creative Commons Attribution 4.0 License.