Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates
Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics and evolutionary biology. Kingman’s coalescent process enables inference of past population dynamics directly from molecular sequence data, and researcher...
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ftpubmed:oai:pubmedcentral.nih.gov:5066065 2023-05-15T17:13:37+02:00 Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates Gill, Mandev S. Lemey, Philippe Bennett, Shannon N. Biek, Roman Suchard, Marc A. 2016-11 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066065/ http://www.ncbi.nlm.nih.gov/pubmed/27368344 https://doi.org/10.1093/sysbio/syw050 en eng Oxford University Press http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066065/ http://www.ncbi.nlm.nih.gov/pubmed/27368344 http://dx.doi.org/10.1093/sysbio/syw050 © The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com Regular Articles Text 2016 ftpubmed https://doi.org/10.1093/sysbio/syw050 2017-11-05T01:04:59Z Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics and evolutionary biology. Kingman’s coalescent process enables inference of past population dynamics directly from molecular sequence data, and researchers have developed a number of flexible coalescent-based models for Bayesian nonparametric estimation of the effective population size as a function of time. Major goals of demographic reconstruction include identifying driving factors of effective population size, and understanding the association between the effective population size and such factors. Building upon Bayesian nonparametric coalescent-based approaches, we introduce a flexible framework that incorporates time-varying covariates that exploit Gaussian Markov random fields to achieve temporal smoothing of effective population size trajectories. To approximate the posterior distribution, we adapt efficient Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. Incorporating covariates into the demographic inference framework enables the modeling of associations between the effective population size and covariates while accounting for uncertainty in population histories. Furthermore, it can lead to more precise estimates of population dynamics. We apply our model to four examples. We reconstruct the demographic history of raccoon rabies in North America and find a significant association with the spatiotemporal spread of the outbreak. Next, we examine the effective population size trajectory of the DENV-4 virus in Puerto Rico along with viral isolate count data and find similar cyclic patterns. We compare the population history of the HIV-1 CRF02_AG clade in Cameroon with HIV incidence and prevalence data and find that the effective population size is more reflective of incidence rate. Finally, we explore the hypothesis that the population dynamics of musk ox during the Late Quaternary period were related to climate change. ... Text musk ox PubMed Central (PMC) Systematic Biology 65 6 1041 1056 |
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Regular Articles Gill, Mandev S. Lemey, Philippe Bennett, Shannon N. Biek, Roman Suchard, Marc A. Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates |
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Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics and evolutionary biology. Kingman’s coalescent process enables inference of past population dynamics directly from molecular sequence data, and researchers have developed a number of flexible coalescent-based models for Bayesian nonparametric estimation of the effective population size as a function of time. Major goals of demographic reconstruction include identifying driving factors of effective population size, and understanding the association between the effective population size and such factors. Building upon Bayesian nonparametric coalescent-based approaches, we introduce a flexible framework that incorporates time-varying covariates that exploit Gaussian Markov random fields to achieve temporal smoothing of effective population size trajectories. To approximate the posterior distribution, we adapt efficient Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. Incorporating covariates into the demographic inference framework enables the modeling of associations between the effective population size and covariates while accounting for uncertainty in population histories. Furthermore, it can lead to more precise estimates of population dynamics. We apply our model to four examples. We reconstruct the demographic history of raccoon rabies in North America and find a significant association with the spatiotemporal spread of the outbreak. Next, we examine the effective population size trajectory of the DENV-4 virus in Puerto Rico along with viral isolate count data and find similar cyclic patterns. We compare the population history of the HIV-1 CRF02_AG clade in Cameroon with HIV incidence and prevalence data and find that the effective population size is more reflective of incidence rate. Finally, we explore the hypothesis that the population dynamics of musk ox during the Late Quaternary period were related to climate change. ... |
format |
Text |
author |
Gill, Mandev S. Lemey, Philippe Bennett, Shannon N. Biek, Roman Suchard, Marc A. |
author_facet |
Gill, Mandev S. Lemey, Philippe Bennett, Shannon N. Biek, Roman Suchard, Marc A. |
author_sort |
Gill, Mandev S. |
title |
Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates |
title_short |
Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates |
title_full |
Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates |
title_fullStr |
Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates |
title_full_unstemmed |
Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates |
title_sort |
understanding past population dynamics: bayesian coalescent-based modeling with covariates |
publisher |
Oxford University Press |
publishDate |
2016 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066065/ http://www.ncbi.nlm.nih.gov/pubmed/27368344 https://doi.org/10.1093/sysbio/syw050 |
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musk ox |
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musk ox |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066065/ http://www.ncbi.nlm.nih.gov/pubmed/27368344 http://dx.doi.org/10.1093/sysbio/syw050 |
op_rights |
© The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com |
op_doi |
https://doi.org/10.1093/sysbio/syw050 |
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Systematic Biology |
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65 |
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6 |
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1041 |
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1056 |
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1766070789255200768 |