Coalescent Estimates of HIV-1 Generation Time in vivo

The generation time of HIV Type 1 (HIV-1) in vivo has previously been estimated using a mathematical model of viral dynamics and was found to be on the order of one to two days per generation. Here, we describe a new method based on coalescence theory that allows the estimate of generation times to...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 1999-03, Vol.96 (5), p.2187-2191
Main Authors: Rodrigo, Allen G., Shpaer, Eugene G., Delwart, Eric L., Astrid K. N. Iversen, Gallo, Michael V., Brojatsch, Jurgen, Hirsch, Martin S., Walker, Bruce D., Mullins, James I.
Format: Article
Language:English
Subjects:
Biological Sciences
DNA, Viral - genetics
Estimation methods
Evolution
Evolution, Molecular
Evolutionary genetics
Genealogy
Genes, env
Genetics
HIV
HIV 1
HIV Seropositivity - virology
HIV-1 - genetics
HIV-1 - physiology
Homosexuality, Male
Human genetics
Human immunodeficiency virus
Humans
Male
Mathematical models
Models, Genetic
Models, Statistical
Molecular Sequence Data
Phylogeny
Population estimates
Population size
Time Factors
Virus Replication
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Summary:The generation time of HIV Type 1 (HIV-1) in vivo has previously been estimated using a mathematical model of viral dynamics and was found to be on the order of one to two days per generation. Here, we describe a new method based on coalescence theory that allows the estimate of generation times to be derived by using nucleotide sequence data and a reconstructed genealogy of sequences obtained over time. The method is applied to sequences obtained from a long-term nonprogressing individual at five sampling occasions. The estimate of viral generation time using the coalescent method is 1.2 days per generation and is close to that obtained by mathematical modeling (1.8 days per generation), thus strengthening confidence in estimates of a short viral generation time. Apart from the estimation of relevant parameters relating to viral dynamics, coalescent modeling also allows us to simulate the evolutionary behavior of samples of sequences obtained over time.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.96.5.2187