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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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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. |
format | Online Article Text |
id | pubmed-5703635 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT rnenenuno evaluatinggeneticdriftintimeseriesevolutionaryanalysis AT mustonenville evaluatinggeneticdriftintimeseriesevolutionaryanalysis AT jrillingworthchristopher evaluatinggeneticdriftintimeseriesevolutionaryanalysis |