Bayesian phylogeography finds its roots.

As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic app...

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Autores principales:	Philippe Lemey, Andrew Rambaut, Alexei J Drummond, Marc A Suchard
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/2fd5ed0c3dfb45ada4e6f0ebded7b301
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spelling	oai:doaj.org-article:2fd5ed0c3dfb45ada4e6f0ebded7b3012021-11-25T05:42:08ZBayesian phylogeography finds its roots.1553-734X1553-735810.1371/journal.pcbi.1000520https://doaj.org/article/2fd5ed0c3dfb45ada4e6f0ebded7b3012009-09-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19779555/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.Philippe LemeyAndrew RambautAlexei J DrummondMarc A SuchardPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 9, p e1000520 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Philippe Lemey Andrew Rambaut Alexei J Drummond Marc A Suchard Bayesian phylogeography finds its roots.
description	As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.
format	article
author	Philippe Lemey Andrew Rambaut Alexei J Drummond Marc A Suchard
author_facet	Philippe Lemey Andrew Rambaut Alexei J Drummond Marc A Suchard
author_sort	Philippe Lemey
title	Bayesian phylogeography finds its roots.
title_short	Bayesian phylogeography finds its roots.
title_full	Bayesian phylogeography finds its roots.
title_fullStr	Bayesian phylogeography finds its roots.
title_full_unstemmed	Bayesian phylogeography finds its roots.
title_sort	bayesian phylogeography finds its roots.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/2fd5ed0c3dfb45ada4e6f0ebded7b301
work_keys_str_mv	AT philippelemey bayesianphylogeographyfindsitsroots AT andrewrambaut bayesianphylogeographyfindsitsroots AT alexeijdrummond bayesianphylogeographyfindsitsroots AT marcasuchard bayesianphylogeographyfindsitsroots
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Bayesian phylogeography finds its roots.

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