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Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts
Birth-death processes have given biologists a model-based framework to answer questions about changes in the birth and death rates of lineages in a phylogenetic tree. Therefore birth-death models are central to macroevolutionary as well as phylodynamic analyses. Early approaches to studying temporal...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7652323/ https://www.ncbi.nlm.nih.gov/pubmed/33112848 http://dx.doi.org/10.1371/journal.pcbi.1007999 |
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author | Magee, Andrew F. Höhna, Sebastian Vasylyeva, Tetyana I. Leaché, Adam D. Minin, Vladimir N. |
author_facet | Magee, Andrew F. Höhna, Sebastian Vasylyeva, Tetyana I. Leaché, Adam D. Minin, Vladimir N. |
author_sort | Magee, Andrew F. |
collection | PubMed |
description | Birth-death processes have given biologists a model-based framework to answer questions about changes in the birth and death rates of lineages in a phylogenetic tree. Therefore birth-death models are central to macroevolutionary as well as phylodynamic analyses. Early approaches to studying temporal variation in birth and death rates using birth-death models faced difficulties due to the restrictive choices of birth and death rate curves through time. Sufficiently flexible time-varying birth-death models are still lacking. We use a piecewise-constant birth-death model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov random field (HSMRF) prior distributions, to approximate arbitrary changes in birth rate through time. We implement these models in the widely used statistical phylogenetic software platform RevBayes, allowing us to jointly estimate birth-death process parameters, phylogeny, and nuisance parameters in a Bayesian framework. We test both GMRF-based and HSMRF-based models on a variety of simulated diversification scenarios, and then apply them to both a macroevolutionary and an epidemiological dataset. We find that both models are capable of inferring variable birth rates and correctly rejecting variable models in favor of effectively constant models. In general the HSMRF-based model has higher precision than its GMRF counterpart, with little to no loss of accuracy. Applied to a macroevolutionary dataset of the Australian gecko family Pygopodidae (where birth rates are interpretable as speciation rates), the GMRF-based model detects a slow decrease whereas the HSMRF-based model detects a rapid speciation-rate decrease in the last 12 million years. Applied to an infectious disease phylodynamic dataset of sequences from HIV subtype A in Russia and Ukraine (where birth rates are interpretable as the rate of accumulation of new infections), our models detect a strongly elevated rate of infection in the 1990s. |
format | Online Article Text |
id | pubmed-7652323 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-76523232020-11-18 Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts Magee, Andrew F. Höhna, Sebastian Vasylyeva, Tetyana I. Leaché, Adam D. Minin, Vladimir N. PLoS Comput Biol Research Article Birth-death processes have given biologists a model-based framework to answer questions about changes in the birth and death rates of lineages in a phylogenetic tree. Therefore birth-death models are central to macroevolutionary as well as phylodynamic analyses. Early approaches to studying temporal variation in birth and death rates using birth-death models faced difficulties due to the restrictive choices of birth and death rate curves through time. Sufficiently flexible time-varying birth-death models are still lacking. We use a piecewise-constant birth-death model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov random field (HSMRF) prior distributions, to approximate arbitrary changes in birth rate through time. We implement these models in the widely used statistical phylogenetic software platform RevBayes, allowing us to jointly estimate birth-death process parameters, phylogeny, and nuisance parameters in a Bayesian framework. We test both GMRF-based and HSMRF-based models on a variety of simulated diversification scenarios, and then apply them to both a macroevolutionary and an epidemiological dataset. We find that both models are capable of inferring variable birth rates and correctly rejecting variable models in favor of effectively constant models. In general the HSMRF-based model has higher precision than its GMRF counterpart, with little to no loss of accuracy. Applied to a macroevolutionary dataset of the Australian gecko family Pygopodidae (where birth rates are interpretable as speciation rates), the GMRF-based model detects a slow decrease whereas the HSMRF-based model detects a rapid speciation-rate decrease in the last 12 million years. Applied to an infectious disease phylodynamic dataset of sequences from HIV subtype A in Russia and Ukraine (where birth rates are interpretable as the rate of accumulation of new infections), our models detect a strongly elevated rate of infection in the 1990s. Public Library of Science 2020-10-28 /pmc/articles/PMC7652323/ /pubmed/33112848 http://dx.doi.org/10.1371/journal.pcbi.1007999 Text en © 2020 Magee et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Magee, Andrew F. Höhna, Sebastian Vasylyeva, Tetyana I. Leaché, Adam D. Minin, Vladimir N. Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts |
title | Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts |
title_full | Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts |
title_fullStr | Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts |
title_full_unstemmed | Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts |
title_short | Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts |
title_sort | locally adaptive bayesian birth-death model successfully detects slow and rapid rate shifts |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7652323/ https://www.ncbi.nlm.nih.gov/pubmed/33112848 http://dx.doi.org/10.1371/journal.pcbi.1007999 |
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