Bayesian phylodynamic inference with complex models.

Population genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry. The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and m...

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Autores principales:	Erik M Volz, Igor Siveroni
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2018
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/107e5fd2343f4303b4e6f6dce23148f3
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spelling	oai:doaj.org-article:107e5fd2343f4303b4e6f6dce23148f32021-12-02T19:57:35ZBayesian phylodynamic inference with complex models.1553-734X1553-735810.1371/journal.pcbi.1006546https://doaj.org/article/107e5fd2343f4303b4e6f6dce23148f32018-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1006546https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Population genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry. The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and model parameters has enabled phylodynamic inference of population growth rates, reproduction numbers, and effective population size through time. Phylodynamic inference based on pathogen genetic sequence data has emerged as useful supplement to epidemic surveillance, however commonly-used mechanistic models that are typically fitted to non-genetic surveillance data are rarely fitted to pathogen genetic data due to a dearth of software tools, and the theory required to conduct such inference has been developed only recently. We present a framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source package PhyDyn for the BEAST2 phylogenetics platform.Erik M VolzIgor SiveroniPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 14, Iss 11, p e1006546 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Erik M Volz Igor Siveroni Bayesian phylodynamic inference with complex models.
description	Population genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry. The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and model parameters has enabled phylodynamic inference of population growth rates, reproduction numbers, and effective population size through time. Phylodynamic inference based on pathogen genetic sequence data has emerged as useful supplement to epidemic surveillance, however commonly-used mechanistic models that are typically fitted to non-genetic surveillance data are rarely fitted to pathogen genetic data due to a dearth of software tools, and the theory required to conduct such inference has been developed only recently. We present a framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source package PhyDyn for the BEAST2 phylogenetics platform.
format	article
author	Erik M Volz Igor Siveroni
author_facet	Erik M Volz Igor Siveroni
author_sort	Erik M Volz
title	Bayesian phylodynamic inference with complex models.
title_short	Bayesian phylodynamic inference with complex models.
title_full	Bayesian phylodynamic inference with complex models.
title_fullStr	Bayesian phylodynamic inference with complex models.
title_full_unstemmed	Bayesian phylodynamic inference with complex models.
title_sort	bayesian phylodynamic inference with complex models.
publisher	Public Library of Science (PLoS)
publishDate	2018
url	https://doaj.org/article/107e5fd2343f4303b4e6f6dce23148f3
work_keys_str_mv	AT erikmvolz bayesianphylodynamicinferencewithcomplexmodels AT igorsiveroni bayesianphylodynamicinferencewithcomplexmodels
_version_	1718375763921600512

Bayesian phylodynamic inference with complex models.

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