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Estimate of effective recombination rate and average selection coefficient for HIV in chronic infection.

TitleEstimate of effective recombination rate and average selection coefficient for HIV in chronic infection.
Publication TypeJournal Article
Year of Publication2011
AuthorsBatorsky, R, Kearney, MF, Palmer, SE, Maldarelli, F, IM Rouzine, Coffin, JM
JournalProc Natl Acad Sci U S A
Volume108
Issue14
Pagination5661-6
Date Published2011 Apr 05
ISSN1091-6490
KeywordsAdaptation, Biological, Algorithms, Computer Simulation, Genetic Drift, Haplotypes, HIV Infections, HIV-1, Humans, Linkage Disequilibrium, Models, Genetic, Monte Carlo Method, Recombination, Genetic, Selection, Genetic
Abstract

HIV adaptation to a host in chronic infection is simulated by means of a Monte-Carlo algorithm that includes the evolutionary factors of mutation, positive selection with varying strength among sites, random genetic drift, linkage, and recombination. By comparing two sensitive measures of linkage disequilibrium (LD) and the number of diverse sites measured in simulation to patient data from one-time samples of pol gene obtained by single-genome sequencing from representative untreated patients, we estimate the effective recombination rate and the average selection coefficient to be on the order of 1% per genome per generation (10(-5) per base per generation) and 0.5%, respectively. The adaptation rate is twofold higher and fourfold lower than predicted in the absence of recombination and in the limit of very frequent recombination, respectively. The level of LD and the number of diverse sites observed in data also range between the values predicted in simulation for these two limiting cases. These results demonstrate the critical importance of finite population size, linkage, and recombination in HIV evolution.

DOI10.1073/pnas.1102036108
Alternate JournalProc. Natl. Acad. Sci. U.S.A.
PubMed ID21436045
PubMed Central IDPMC3078368
Grant ListR01 AI063926 / AI / NIAID NIH HHS / United States
R37 CA089441 / CA / NCI NIH HHS / United States
R01AI063926 / AI / NIAID NIH HHS / United States
R37CA 089441 / CA / NCI NIH HHS / United States