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. Kingman's coalescent process enables inference of past population dynamics directly from molecular sequence data, and researchers have developed a n...

Full description

Bibliographic Details
Published in:Systematic Biology
Main Authors: Gill, Mandev S., Lemey, Philippe, Bennett, Shannon N., Biek, Roman, Suchard, Marc A.
Format: Article in Journal/Newspaper
Language:English
Published: Oxford University Press 2016
Subjects:
Online Access:https://eprints.gla.ac.uk/119885/
https://eprints.gla.ac.uk/119885/19/119885.pdf
id ftuglasgow:oai:eprints.gla.ac.uk:119885
record_format openpolar
spelling ftuglasgow:oai:eprints.gla.ac.uk:119885 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 text https://eprints.gla.ac.uk/119885/ https://eprints.gla.ac.uk/119885/19/119885.pdf en eng Oxford University Press https://eprints.gla.ac.uk/119885/19/119885.pdf Gill, M. S., Lemey, P., Bennett, S. N., Biek, R. <http://eprints.gla.ac.uk/view/author/10097.html> and Suchard, M. A. (2016) Understanding past population dynamics: Bayesian coalescent-based modeling with covariates. Systematic Biology <https://eprints.gla.ac.uk/view/journal_volume/Systematic_Biology.html>, 65(6), pp. 1041-1056. (doi:10.1093/sysbio/syw050 <https://doi.org/10.1093/sysbio/syw050>) (PMID:27368344) Articles PeerReviewed 2016 ftuglasgow https://doi.org/10.1093/sysbio/syw050 2022-09-22T22:12:59Z Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics. 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. A major goal of demographic reconstruction is understanding the association between the effective population size and potential explanatory factors. Building upon Bayesian nonparametric coalescent-based approaches, we introduce a flexible framework that incorporates time-varying covariates through Gaussian Markov random fields. 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. Article in Journal/Newspaper musk ox University of Glasgow: Enlighten - Publications Systematic Biology 65 6 1041 1056
institution Open Polar
collection University of Glasgow: Enlighten - Publications
op_collection_id ftuglasgow
language English
description Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics. 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. A major goal of demographic reconstruction is understanding the association between the effective population size and potential explanatory factors. Building upon Bayesian nonparametric coalescent-based approaches, we introduce a flexible framework that incorporates time-varying covariates through Gaussian Markov random fields. 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 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 Oxford University Press
publishDate 2016
url https://eprints.gla.ac.uk/119885/
https://eprints.gla.ac.uk/119885/19/119885.pdf
genre musk ox
genre_facet musk ox
op_relation https://eprints.gla.ac.uk/119885/19/119885.pdf
Gill, M. S., Lemey, P., Bennett, S. N., Biek, R. <http://eprints.gla.ac.uk/view/author/10097.html> and Suchard, M. A. (2016) Understanding past population dynamics: Bayesian coalescent-based modeling with covariates. Systematic Biology <https://eprints.gla.ac.uk/view/journal_volume/Systematic_Biology.html>, 65(6), pp. 1041-1056. (doi:10.1093/sysbio/syw050 <https://doi.org/10.1093/sysbio/syw050>) (PMID:27368344)
op_doi https://doi.org/10.1093/sysbio/syw050
container_title Systematic Biology
container_volume 65
container_issue 6
container_start_page 1041
op_container_end_page 1056
_version_ 1766070788553703424