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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2009-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19779555/?tool=EBI |
| _version_ | 1819198393959841792 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-23T02:59:44Z |
| format | Article |
| id | doaj.art-2fd5ed0c3dfb45ada4e6f0ebded7b301 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-23T02:59:44Z |
| publishDate | 2009-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-2fd5ed0c3dfb45ada4e6f0ebded7b3012022-12-21T18:02:27ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582009-09-0159e100052010.1371/journal.pcbi.1000520Bayesian phylogeography finds its roots.Philippe LemeyAndrew RambautAlexei J DrummondMarc A SuchardAs 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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19779555/?tool=EBI |
| spellingShingle | Philippe Lemey Andrew Rambaut Alexei J Drummond Marc A Suchard Bayesian phylogeography finds its roots. PLoS Computational Biology |
| title | 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_short | Bayesian phylogeography finds its roots. |
| title_sort | bayesian phylogeography finds its roots |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19779555/?tool=EBI |
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