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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Online Access: | https://doi.org/10.5061/dryad.mj0hn |
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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 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 |
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 |
_version_ |
1766070791594573824 |