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Identifying predictors of time-inhomogeneous viral evolutionary processes
Various factors determine the rate at which mutations are generated and fixed in viral genomes. Viral evolutionary rates may vary over the course of a single persistent infection and can reflect changes in replication rates and selective dynamics. Dedicated statistical inference approaches are requi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5072463/ https://www.ncbi.nlm.nih.gov/pubmed/27774306 http://dx.doi.org/10.1093/ve/vew023 |
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author | Bielejec, Filip Baele, Guy Rodrigo, Allen G Suchard, Marc A Lemey, Philippe |
author_facet | Bielejec, Filip Baele, Guy Rodrigo, Allen G Suchard, Marc A Lemey, Philippe |
author_sort | Bielejec, Filip |
collection | PubMed |
description | Various factors determine the rate at which mutations are generated and fixed in viral genomes. Viral evolutionary rates may vary over the course of a single persistent infection and can reflect changes in replication rates and selective dynamics. Dedicated statistical inference approaches are required to understand how the complex interplay of these processes shapes the genetic diversity and divergence in viral populations. Although evolutionary models accommodating a high degree of complexity can now be formalized, adequately informing these models by potentially sparse data, and assessing the association of the resulting estimates with external predictors, remains a major challenge. In this article, we present a novel Bayesian evolutionary inference method, which integrates multiple potential predictors and tests their association with variation in the absolute rates of synonymous and non-synonymous substitutions along the evolutionary history. We consider clinical and virological measures as predictors, but also changes in population size trajectories that are simultaneously inferred using coalescent modelling. We demonstrate the potential of our method in an application to within-host HIV-1 sequence data sampled throughout the infection of multiple patients. While analyses of individual patient populations lack statistical power, we detect significant evidence for an abrupt drop in non-synonymous rates in late stage infection and a more gradual increase in synonymous rates over the course of infection in a joint analysis across all patients. The former is predicted by the immune relaxation hypothesis while the latter may be in line with increasing replicative fitness during the asymptomatic stage. |
format | Online Article Text |
id | pubmed-5072463 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-50724632016-10-21 Identifying predictors of time-inhomogeneous viral evolutionary processes Bielejec, Filip Baele, Guy Rodrigo, Allen G Suchard, Marc A Lemey, Philippe Virus Evol Research Article Various factors determine the rate at which mutations are generated and fixed in viral genomes. Viral evolutionary rates may vary over the course of a single persistent infection and can reflect changes in replication rates and selective dynamics. Dedicated statistical inference approaches are required to understand how the complex interplay of these processes shapes the genetic diversity and divergence in viral populations. Although evolutionary models accommodating a high degree of complexity can now be formalized, adequately informing these models by potentially sparse data, and assessing the association of the resulting estimates with external predictors, remains a major challenge. In this article, we present a novel Bayesian evolutionary inference method, which integrates multiple potential predictors and tests their association with variation in the absolute rates of synonymous and non-synonymous substitutions along the evolutionary history. We consider clinical and virological measures as predictors, but also changes in population size trajectories that are simultaneously inferred using coalescent modelling. We demonstrate the potential of our method in an application to within-host HIV-1 sequence data sampled throughout the infection of multiple patients. While analyses of individual patient populations lack statistical power, we detect significant evidence for an abrupt drop in non-synonymous rates in late stage infection and a more gradual increase in synonymous rates over the course of infection in a joint analysis across all patients. The former is predicted by the immune relaxation hypothesis while the latter may be in line with increasing replicative fitness during the asymptomatic stage. Oxford University Press 2016-09-06 /pmc/articles/PMC5072463/ /pubmed/27774306 http://dx.doi.org/10.1093/ve/vew023 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Research Article Bielejec, Filip Baele, Guy Rodrigo, Allen G Suchard, Marc A Lemey, Philippe Identifying predictors of time-inhomogeneous viral evolutionary processes |
title | Identifying predictors of time-inhomogeneous viral evolutionary processes |
title_full | Identifying predictors of time-inhomogeneous viral evolutionary processes |
title_fullStr | Identifying predictors of time-inhomogeneous viral evolutionary processes |
title_full_unstemmed | Identifying predictors of time-inhomogeneous viral evolutionary processes |
title_short | Identifying predictors of time-inhomogeneous viral evolutionary processes |
title_sort | identifying predictors of time-inhomogeneous viral evolutionary processes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5072463/ https://www.ncbi.nlm.nih.gov/pubmed/27774306 http://dx.doi.org/10.1093/ve/vew023 |
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