TY - JOUR
T1 - Influenza Virus Drug Resistance
T2 - A Time-Sampled Population Genetics Perspective
AU - Foll, Matthieu
AU - Poh, Yu Ping
AU - Renzette, Nicholas
AU - Ferrer-Admetlla, Anna
AU - Bank, Claudia
AU - Shim, Hyunjin
AU - Malaspinas, Anna Sapfo
AU - Ewing, Gregory
AU - Liu, Ping
AU - Wegmann, Daniel
AU - Caffrey, Daniel R.
AU - Zeldovich, Konstantin B.
AU - Bolon, Daniel N.
AU - Wang, Jennifer P.
AU - Kowalik, Timothy F.
AU - Schiffer, Celia A.
AU - Finberg, Robert W.
AU - Jensen, Jeffrey D.
PY - 2014/2
Y1 - 2014/2
N2 - 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.
AB - 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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U2 - 10.1371/journal.pgen.1004185
DO - 10.1371/journal.pgen.1004185
M3 - Article
C2 - 24586206
AN - SCOPUS:84901729595
SN - 1553-7390
VL - 10
JO - PLoS genetics
JF - PLoS genetics
IS - 2
M1 - e1004185
ER -