Influenza Virus Drug Resistance: A Time-Sampled Population Genetics Perspective

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.

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BibTeX RIS

BibTeX

@article{foll2026influenza,
  title = {{Influenza Virus Drug Resistance: A Time-Sampled Population Genetics Perspective}},
  author = {Matthieu Foll and Yu-Ping Poh and Nicholas Renzette and Anna Ferrer-Admetlla and Claudia Bank and Hyunjin Shim and Anna-Sapfo Malaspinas and Gregory Ewing and Ping Liu and Daniel Wegmann and Daniel R. Caffrey and Konstantin B. Zeldovich and Daniel N. Bolon and Jennifer P. Wang and Timothy F. Kowalik and Celia A. Schiffer and Robert W. Finberg and Jeffrey D. Jensen},
  journal = {PLoS Genetics},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pgen.1004185},
  url = {https://hal.science/hal-05842785v1},
  note = {CC BY},
}

RIS

TY  - JOUR
TI  - Influenza Virus Drug Resistance: A Time-Sampled Population Genetics Perspective
AU  - Matthieu Foll
AU  - Yu-Ping Poh
AU  - Nicholas Renzette
AU  - Anna Ferrer-Admetlla
AU  - Claudia Bank
AU  - Hyunjin Shim
AU  - Anna-Sapfo Malaspinas
AU  - Gregory Ewing
AU  - Ping Liu
AU  - Daniel Wegmann
AU  - Daniel R. Caffrey
AU  - Konstantin B. Zeldovich
AU  - Daniel N. Bolon
AU  - Jennifer P. Wang
AU  - Timothy F. Kowalik
AU  - Celia A. Schiffer
AU  - Robert W. Finberg
AU  - Jeffrey D. Jensen
PY  - 2026
DA  - 2026/10/28
JO  - PLoS Genetics
DO  - 10.1371/journal.pgen.1004185
UR  - https://hal.science/hal-05842785v1
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.
SN  - 1553-7390
SN  - 1553-7404
ER  -

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