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    Modeling HIV-1 drug resistance as episodic directional selection

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    Modeling HIV-1 drug resistance ...
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    Genre
    Journal Article
    Date
    2012-05-01
    Author
    Murrell, B
    de Oliveira, T
    Seebregts, C
    Kosakovsky Pond, SL
    Scheffler, K
    Subject
    Anti-HIV Agents
    Biological Evolution
    Computer Simulation
    Drug Resistance, Viral
    Evolution, Molecular
    Genetic Variation
    HIV-1
    Models, Genetic
    Selection, Genetic
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/5463
    
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    DOI
    10.1371/journal.pcbi.1002507
    Abstract
    The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance. © 2012 Murrell et al.
    Citation to related work
    Public Library of Science (PLoS)
    Has part
    PLoS Computational Biology
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    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/5445
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