Data from: 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: Dataset
Language:English
Published: Dryad 2016
Subjects:
Online Access:https://doi.org/10.5061/dryad.mj0hn
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spelling fttriple:oai:gotriple.eu:50|dedup_wf_001::dfaae436adebf0d5780c9886993fccc3 2023-05-15T17:13:37+02:00 Data from: Understanding past population dynamics: Bayesian coalescent-based modeling with covariates Gill, Mandev S. Lemey, Philippe Bennett, Shannon N. Biek, Roman Suchard, Marc A. 2016-01-01 https://doi.org/10.5061/dryad.mj0hn en eng Dryad http://dx.doi.org/10.5061/dryad.mj0hn https://dx.doi.org/10.5061/dryad.mj0hn lic_creative-commons 10.5061/dryad.mj0hn oai:easy.dans.knaw.nl:easy-dataset:93129 oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:93129 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 re3data_____::r3d100000044 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|opendoar____::8b6dd7db9af49e67306feb59a8bdc52c population genetics Gaussian Markov Random Fields phylogenetics coalescent Bayesian inference Phylodynamics Evolutionary Biology FOS: Biological sciences effective population size Life sciences medicine and health care stat envir Dataset https://vocabularies.coar-repositories.org/resource_types/c_ddb1/ 2016 fttriple https://doi.org/10.5061/dryad.mj0hn 2023-01-22T16:52:31Z 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. BEAST ... Dataset musk ox Unknown
institution Open Polar
collection Unknown
op_collection_id fttriple
language English
topic population genetics
Gaussian Markov Random Fields
phylogenetics
coalescent
Bayesian inference
Phylodynamics
Evolutionary Biology
FOS: Biological sciences
effective population size
Life sciences
medicine and health care
stat
envir
spellingShingle population genetics
Gaussian Markov Random Fields
phylogenetics
coalescent
Bayesian inference
Phylodynamics
Evolutionary Biology
FOS: Biological sciences
effective population size
Life sciences
medicine and health care
stat
envir
Gill, Mandev S.
Lemey, Philippe
Bennett, Shannon N.
Biek, Roman
Suchard, Marc A.
Data from: Understanding past population dynamics: Bayesian coalescent-based modeling with covariates
topic_facet population genetics
Gaussian Markov Random Fields
phylogenetics
coalescent
Bayesian inference
Phylodynamics
Evolutionary Biology
FOS: Biological sciences
effective population size
Life sciences
medicine and health care
stat
envir
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. BEAST ...
format Dataset
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 Data from: Understanding past population dynamics: Bayesian coalescent-based modeling with covariates
title_short Data from: Understanding past population dynamics: Bayesian coalescent-based modeling with covariates
title_full Data from: Understanding past population dynamics: Bayesian coalescent-based modeling with covariates
title_fullStr Data from: Understanding past population dynamics: Bayesian coalescent-based modeling with covariates
title_full_unstemmed Data from: Understanding past population dynamics: Bayesian coalescent-based modeling with covariates
title_sort data from: understanding past population dynamics: bayesian coalescent-based modeling with covariates
publisher Dryad
publishDate 2016
url https://doi.org/10.5061/dryad.mj0hn
genre musk ox
genre_facet musk ox
op_source 10.5061/dryad.mj0hn
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op_relation http://dx.doi.org/10.5061/dryad.mj0hn
https://dx.doi.org/10.5061/dryad.mj0hn
op_rights lic_creative-commons
op_doi https://doi.org/10.5061/dryad.mj0hn
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