ArticlePLoS computational biology2010
Recombination rate and selection strength in HIV intra-patient evolution.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers.
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Who cites it
115 citing papers in PubMed, 195 citations in OpenAlex.
- HIV RNA Rebound in Seminal Plasma after Antiretroviral Treatment Interruption.Journal of virology · 2020Trial
- Relationship between latent and rebound viruses in a clinical trial of anti-HIV-1 antibody 3BNC117.The Journal of experimental medicine · 2018Trial
- Single cell analysis of lymph node tissue from HIV-1 infected patients reveals that the majority of CD4+ T-cells contain one HIV-1 DNA molecule.PLoS pathogens · 2013Trial
- Haplotype and diversity signatures of ultra-soft selective sweeps in HIV-1.bioRxiv : the preprint server for biology · 2026Article
- HIV broadly neutralizing antibody escape dynamics drive the outcome of AAV-vectored immunotherapy in humanized mice.Immunity · 2026Article
- Variations and evolution of HIV-1 provirus in peripheral blood under the pressure of ART.Virus evolution · 2026Article
- Article
- wavess: An R package for simulation of adaptive within-host virus sequence evolution.PLoS computational biology · 2025Article
- Article
- Tradeoffs between proliferation and transmission in virus evolution- insights from evolutionary and functional analyses of SARS-CoV-2.Virology journal · 2025Article
- A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Review
- Estimates of HIV-1 within-host recombination rates across the whole genome.Virus evolution · 2025Article
- Comparative Evaluation of Open-Source Bioinformatics Pipelines for Full-Length Viral Genome Assembly.Viruses · 2024Article
- Linkage equilibrium between rare mutations.Genetics · 2024Article
- Efficient epistasis inference via higher-order covariance matrix factorization.bioRxiv : the preprint server for biology · 2024Article
- A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses.bioRxiv : the preprint server for biology · 2024Article
- Incorporating Intracellular Processes in Virus Dynamics Models.Microorganisms · 2024Review
- Linkage equilibrium between rare mutations.bioRxiv : the preprint server for biology · 2024Article
- Elevated HIV Viral Load is Associated with Higher Recombination Rate In Vivo.Molecular biology and evolution · 2024Article
55 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The evolutionary dynamics of HIV during the chronic phase of infection is driven by the host immune response and by selective pressures exerted through drug treatment. To understand and model the evolution of HIV quantitatively, the parameters governing genetic diversification and the strength of selection need to be known. While mutation rates can be measured in single replication cycles, the relevant effective recombination rate depends on the probability of coinfection of a cell with more than one virus and can only be inferred from population data. However, most population genetic estimators for recombination rates assume absence of selection and are hence of limited applicability to HIV, since positive and purifying selection are important in HIV evolution. Yet, little is known about the distribution of selection differentials between individual viruses and the impact of single polymorphisms on viral fitness. Here, we estimate the rate of recombination and the distribution of selection coefficients from time series sequence data tracking the evolution of HIV within single patients. By examining temporal changes in the genetic composition of the population, we estimate the effective recombination to be rho = 1.4+/-0.6 x 10(-5) recombinations per site and generation. Furthermore, we provide evidence that the selection coefficients of at least 15% of the observed non-synonymous polymorphisms exceed 0.8% per generation. These results provide a basis for a more detailed understanding of the evolution of HIV. A particularly interesting case is evolution in response to drug treatment, where recombination can facilitate the rapid acquisition of multiple resistance mutations. With the methods developed here, more precise and more detailed studies will be possible as soon as data with higher time resolution and greater sample sizes are available.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.