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...
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Statistics - Methodology Quantitative Biology - Populations and Evolution Regular Articles stat demo |
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Statistics - Methodology Quantitative Biology - Populations and Evolution Regular Articles stat demo Shannon N. Bennett Mandev S. Gill Philippe Lemey Roman Biek Marc A. Suchard Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates. |
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Statistics - Methodology Quantitative Biology - Populations and Evolution Regular Articles stat demo |
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. Comment: 31 pages, 6 figures |
format |
Article in Journal/Newspaper |
author |
Shannon N. Bennett Mandev S. Gill Philippe Lemey Roman Biek Marc A. Suchard |
author_facet |
Shannon N. Bennett Mandev S. Gill Philippe Lemey Roman Biek Marc A. Suchard |
author_sort |
Shannon N. Bennett |
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. |
publishDate |
2016 |
url |
http://academic.oup.com/sysbio/article-pdf/65/6/1041/17841030/syw050.pdf https://academic.oup.com/sysbio/article-pdf/65/6/1041/17841030/syw050.pdf http://eprints.gla.ac.uk/119885/19/119885.pdf https://doi.org/10.1093/sysbio/syw050 https://pubmed.ncbi.nlm.nih.gov/27368344/ https://academic.oup.com/sysbio/article/65/6/1041/2281638 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066065 http://academic.oup.com/sysbio/article-abstract/65/6/1041/2281638/ https://ui.adsabs.harvard.edu/abs/2016arXiv160105078G/abstract https://core.ac.uk/display/80803040 http://eprints.gla.ac.uk/119885/ http://sysbio.oxfordjournals.org/lookup/doi/10.1093/sysbio/syw050 https://academic.microsoft.com/#/detail/2276895023 http://arxiv.org/abs/1601.05078 https://europepmc.org/articles/PMC5066065/ http://sysbio.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=27368344 |
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musk ox |
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Systematic Biology |
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fttriple:oai:gotriple.eu:50|dedup_wf_001::2f41cf0d6a6365c794ae9fde59bec53d 2023-05-15T17:13:37+02:00 Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates. Shannon N. Bennett Mandev S. Gill Philippe Lemey Roman Biek Marc A. Suchard 2016-07-01 http://academic.oup.com/sysbio/article-pdf/65/6/1041/17841030/syw050.pdf https://academic.oup.com/sysbio/article-pdf/65/6/1041/17841030/syw050.pdf http://eprints.gla.ac.uk/119885/19/119885.pdf https://doi.org/10.1093/sysbio/syw050 https://pubmed.ncbi.nlm.nih.gov/27368344/ https://academic.oup.com/sysbio/article/65/6/1041/2281638 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066065 http://academic.oup.com/sysbio/article-abstract/65/6/1041/2281638/ https://ui.adsabs.harvard.edu/abs/2016arXiv160105078G/abstract https://core.ac.uk/display/80803040 http://eprints.gla.ac.uk/119885/ http://sysbio.oxfordjournals.org/lookup/doi/10.1093/sysbio/syw050 https://academic.microsoft.com/#/detail/2276895023 http://arxiv.org/abs/1601.05078 https://europepmc.org/articles/PMC5066065/ http://sysbio.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=27368344 undefined unknown http://academic.oup.com/sysbio/article-pdf/65/6/1041/17841030/syw050.pdf https://academic.oup.com/sysbio/article-pdf/65/6/1041/17841030/syw050.pdf http://eprints.gla.ac.uk/119885/19/119885.pdf https://dx.doi.org/10.1093/sysbio/syw050 http://dx.doi.org/10.1093/sysbio/syw050 https://pubmed.ncbi.nlm.nih.gov/27368344/ https://academic.oup.com/sysbio/article/65/6/1041/2281638 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066065 http://academic.oup.com/sysbio/article-abstract/65/6/1041/2281638/ https://ui.adsabs.harvard.edu/abs/2016arXiv160105078G/abstract https://core.ac.uk/display/80803040 http://eprints.gla.ac.uk/119885/ http://sysbio.oxfordjournals.org/lookup/doi/10.1093/sysbio/syw050 https://academic.microsoft.com/#/detail/2276895023 http://arxiv.org/abs/1601.05078 https://europepmc.org/articles/PMC5066065/ http://sysbio.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=27368344 undefined 27368344 10.1093/sysbio/syw050 2276895023 oai:arXiv.org:1601.05078 oai:pubmedcentral.nih.gov:5066065 oai:eprints.gla.ac.uk:119885 oai:lirias2repo.kuleuven.be:123456789/562896 10|opendoar____::8b6dd7db9af49e67306feb59a8bdc52c 10|openaire____::55045bd2a65019fd8e6741a755395c8c 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 10|issn___print::3fd34a0452e3795cfdf1db35c5400fc6 10|openaire____::8ac8380272269217cb09a928c8caa993 10|openaire____::5f532a3fc4f1ea403f37070f59a7a53a 10|opendoar____::6f4922f45568161a8cdf4ad2299f6d23 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|opendoar____::eda80a3d5b344bc40f3bc04f65b7a357 10|openaire____::0a836ef43dcb67bb7cbd4dd509b11b73 10|opendoar____::82aa4b0af34c2313a562076992e50aa3 10|opendoar____::fe709c654eac84d5239d1a12a4f71877 10|openaire____::806360c771262b4d6770e7cdf04b5c5a Statistics - Methodology Quantitative Biology - Populations and Evolution Regular Articles stat demo Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ Preprint https://vocabularies.coar-repositories.org/resource_types/c_816b/ 2016 fttriple https://doi.org/10.1093/sysbio/syw050 2023-01-22T17:22:11Z 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. Comment: 31 pages, 6 figures Article in Journal/Newspaper musk ox Unknown Systematic Biology 65 6 1041 1056 |