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Evaluating genetic drift in time-series evolutionary analysis

The Wright–Fisher model is the most popular population model for describing the behaviour of evolutionary systems with a finite population size. Approximations have commonly been used but the model itself has rarely been tested against time-resolved genomic data. Here, we evaluate the extent to whic...

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Detalles Bibliográficos
Autores principales: R. Nené, Nuno, Mustonen, Ville, J. R. Illingworth, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703635/
https://www.ncbi.nlm.nih.gov/pubmed/28958783
http://dx.doi.org/10.1016/j.jtbi.2017.09.021
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author R. Nené, Nuno
Mustonen, Ville
J. R. Illingworth, Christopher
author_facet R. Nené, Nuno
Mustonen, Ville
J. R. Illingworth, Christopher
author_sort R. Nené, Nuno
collection PubMed
description The Wright–Fisher model is the most popular population model for describing the behaviour of evolutionary systems with a finite population size. Approximations have commonly been used but the model itself has rarely been tested against time-resolved genomic data. Here, we evaluate the extent to which it can be inferred as the correct model under a likelihood framework. Given genome-wide data from an evolutionary experiment, we validate the Wright–Fisher drift model as the better option for describing evolutionary trajectories in a finite population. This was found by evaluating its performance against a Gaussian model of allele frequency propagation. However, we note a range of circumstances under which standard Wright–Fisher drift cannot be correctly identified.
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spelling pubmed-57036352018-01-21 Evaluating genetic drift in time-series evolutionary analysis R. Nené, Nuno Mustonen, Ville J. R. Illingworth, Christopher J Theor Biol Article The Wright–Fisher model is the most popular population model for describing the behaviour of evolutionary systems with a finite population size. Approximations have commonly been used but the model itself has rarely been tested against time-resolved genomic data. Here, we evaluate the extent to which it can be inferred as the correct model under a likelihood framework. Given genome-wide data from an evolutionary experiment, we validate the Wright–Fisher drift model as the better option for describing evolutionary trajectories in a finite population. This was found by evaluating its performance against a Gaussian model of allele frequency propagation. However, we note a range of circumstances under which standard Wright–Fisher drift cannot be correctly identified. Elsevier 2018-01-21 /pmc/articles/PMC5703635/ /pubmed/28958783 http://dx.doi.org/10.1016/j.jtbi.2017.09.021 Text en © 2017 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
R. Nené, Nuno
Mustonen, Ville
J. R. Illingworth, Christopher
Evaluating genetic drift in time-series evolutionary analysis
title Evaluating genetic drift in time-series evolutionary analysis
title_full Evaluating genetic drift in time-series evolutionary analysis
title_fullStr Evaluating genetic drift in time-series evolutionary analysis
title_full_unstemmed Evaluating genetic drift in time-series evolutionary analysis
title_short Evaluating genetic drift in time-series evolutionary analysis
title_sort evaluating genetic drift in time-series evolutionary analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703635/
https://www.ncbi.nlm.nih.gov/pubmed/28958783
http://dx.doi.org/10.1016/j.jtbi.2017.09.021
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