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 resea...

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Main Authors: Gill, Mandev S, Lemey, Philippe, Bennett, Shannon N, Biek, Roman, Suchard, Marc A
Format: Article in Journal/Newspaper
Language:English
Published: Society of Systematic Biologists 2016
Subjects:
Online Access:https://lirias.kuleuven.be/handle/123456789/562896
http://sysbio.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=27368344
https://lirias.kuleuven.be/bitstream/123456789/562896/3//2016237.pdf
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spelling ftunivleuven:oai:lirias.kuleuven.be:123456789/562896 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 https://lirias.kuleuven.be/handle/123456789/562896 http://sysbio.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=27368344 https://lirias.kuleuven.be/bitstream/123456789/562896/3//2016237.pdf en eng Society of Systematic Biologists Systematic Biology vol:65 issue:6 pages:1041-1056 https://lirias.kuleuven.be/handle/123456789/562896 1063-5157 http://sysbio.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=27368344 1076-836X https://lirias.kuleuven.be/bitstream/123456789/562896/3//2016237.pdf 448797;intranet Article IT 448797;Article 2016 ftunivleuven 2017-06-02T19:43:58Z 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. [Coalescent; effective population size; Gaussian Markov random fields; phylodynamics; phylogenetics; population genetics. status: published Article in Journal/Newspaper musk ox KU Leuven: Lirias
institution Open Polar
collection KU Leuven: Lirias
op_collection_id ftunivleuven
language English
description 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. [Coalescent; effective population size; Gaussian Markov random fields; phylodynamics; phylogenetics; population genetics. status: published
format Article in Journal/Newspaper
author Gill, Mandev S
Lemey, Philippe
Bennett, Shannon N
Biek, Roman
Suchard, Marc A
spellingShingle Gill, Mandev S
Lemey, Philippe
Bennett, Shannon N
Biek, Roman
Suchard, Marc A
Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates
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 Society of Systematic Biologists
publishDate 2016
url https://lirias.kuleuven.be/handle/123456789/562896
http://sysbio.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=27368344
https://lirias.kuleuven.be/bitstream/123456789/562896/3//2016237.pdf
genre musk ox
genre_facet musk ox
op_relation Systematic Biology vol:65 issue:6 pages:1041-1056
https://lirias.kuleuven.be/handle/123456789/562896
1063-5157
http://sysbio.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=27368344
1076-836X
https://lirias.kuleuven.be/bitstream/123456789/562896/3//2016237.pdf
op_rights 448797;intranet
_version_ 1766070789779488768