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Sampling bias and model choice in continuous phylogeography: Getting lost on a random walk

Phylogeographic inference allows reconstruction of past geographical spread of pathogens or living organisms by integrating genetic and geographic data. A popular model in continuous phylogeography—with location data provided in the form of latitude and longitude coordinates—describes spread as a Br...

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Autores principales: Kalkauskas, Antanas, Perron, Umberto, Sun, Yuxuan, Goldman, Nick, Baele, Guy, Guindon, Stephane, De Maio, Nicola
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7815209/
https://www.ncbi.nlm.nih.gov/pubmed/33406072
http://dx.doi.org/10.1371/journal.pcbi.1008561
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author Kalkauskas, Antanas
Perron, Umberto
Sun, Yuxuan
Goldman, Nick
Baele, Guy
Guindon, Stephane
De Maio, Nicola
author_facet Kalkauskas, Antanas
Perron, Umberto
Sun, Yuxuan
Goldman, Nick
Baele, Guy
Guindon, Stephane
De Maio, Nicola
author_sort Kalkauskas, Antanas
collection PubMed
description Phylogeographic inference allows reconstruction of past geographical spread of pathogens or living organisms by integrating genetic and geographic data. A popular model in continuous phylogeography—with location data provided in the form of latitude and longitude coordinates—describes spread as a Brownian motion (Brownian Motion Phylogeography, BMP) in continuous space and time, akin to similar models of continuous trait evolution. Here, we show that reconstructions using this model can be strongly affected by sampling biases, such as the lack of sampling from certain areas. As an attempt to reduce the effects of sampling bias on BMP, we consider the addition of sequence-free samples from under-sampled areas. While this approach alleviates the effects of sampling bias, in most scenarios this will not be a viable option due to the need for prior knowledge of an outbreak’s spatial distribution. We therefore consider an alternative model, the spatial Λ-Fleming-Viot process (ΛFV), which has recently gained popularity in population genetics. Despite the ΛFV’s robustness to sampling biases, we find that the different assumptions of the ΛFV and BMP models result in different applicabilities, with the ΛFV being more appropriate for scenarios of endemic spread, and BMP being more appropriate for recent outbreaks or colonizations.
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spelling pubmed-78152092021-01-27 Sampling bias and model choice in continuous phylogeography: Getting lost on a random walk Kalkauskas, Antanas Perron, Umberto Sun, Yuxuan Goldman, Nick Baele, Guy Guindon, Stephane De Maio, Nicola PLoS Comput Biol Research Article Phylogeographic inference allows reconstruction of past geographical spread of pathogens or living organisms by integrating genetic and geographic data. A popular model in continuous phylogeography—with location data provided in the form of latitude and longitude coordinates—describes spread as a Brownian motion (Brownian Motion Phylogeography, BMP) in continuous space and time, akin to similar models of continuous trait evolution. Here, we show that reconstructions using this model can be strongly affected by sampling biases, such as the lack of sampling from certain areas. As an attempt to reduce the effects of sampling bias on BMP, we consider the addition of sequence-free samples from under-sampled areas. While this approach alleviates the effects of sampling bias, in most scenarios this will not be a viable option due to the need for prior knowledge of an outbreak’s spatial distribution. We therefore consider an alternative model, the spatial Λ-Fleming-Viot process (ΛFV), which has recently gained popularity in population genetics. Despite the ΛFV’s robustness to sampling biases, we find that the different assumptions of the ΛFV and BMP models result in different applicabilities, with the ΛFV being more appropriate for scenarios of endemic spread, and BMP being more appropriate for recent outbreaks or colonizations. Public Library of Science 2021-01-06 /pmc/articles/PMC7815209/ /pubmed/33406072 http://dx.doi.org/10.1371/journal.pcbi.1008561 Text en © 2021 Kalkauskas et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kalkauskas, Antanas
Perron, Umberto
Sun, Yuxuan
Goldman, Nick
Baele, Guy
Guindon, Stephane
De Maio, Nicola
Sampling bias and model choice in continuous phylogeography: Getting lost on a random walk
title Sampling bias and model choice in continuous phylogeography: Getting lost on a random walk
title_full Sampling bias and model choice in continuous phylogeography: Getting lost on a random walk
title_fullStr Sampling bias and model choice in continuous phylogeography: Getting lost on a random walk
title_full_unstemmed Sampling bias and model choice in continuous phylogeography: Getting lost on a random walk
title_short Sampling bias and model choice in continuous phylogeography: Getting lost on a random walk
title_sort sampling bias and model choice in continuous phylogeography: getting lost on a random walk
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7815209/
https://www.ncbi.nlm.nih.gov/pubmed/33406072
http://dx.doi.org/10.1371/journal.pcbi.1008561
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