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Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown
Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, optimal linear estimation methods are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more chal...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377634/ https://www.ncbi.nlm.nih.gov/pubmed/35921376 http://dx.doi.org/10.1371/journal.pcbi.1010407 |
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author | Burger, Klara Elisabeth Pfaffelhuber, Peter Baumdicker, Franz |
author_facet | Burger, Klara Elisabeth Pfaffelhuber, Peter Baumdicker, Franz |
author_sort | Burger, Klara Elisabeth |
collection | PubMed |
description | Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, optimal linear estimation methods are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more challenging. As an alternative to model-based estimation, neural networks and other machine learning tools could help to develop good estimators in these involved scenarios. However, if no benchmark is available it is difficult to assess how well suited these tools are for different applications in population genetics. Here we investigate feedforward neural networks for the estimation of the mutation rate based on the site frequency spectrum and compare their performance with model-based estimators. For this we use the model-based estimators introduced by Fu, Futschik et al., and Watterson that minimize the variance or mean squared error for no and free recombination. We find that neural networks reproduce these estimators if provided with the appropriate features and training sets. Remarkably, using the model-based estimators to adjust the weights of the training data, only one hidden layer is necessary to obtain a single estimator that performs almost as well as model-based estimators for low and high recombination rates, and at the same time provides a superior estimation method for intermediate recombination rates. We apply the method to simulated data based on the human chromosome 2 recombination map, highlighting its robustness in a realistic setting where local recombination rates vary and/or are unknown. |
format | Online Article Text |
id | pubmed-9377634 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-93776342022-08-16 Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown Burger, Klara Elisabeth Pfaffelhuber, Peter Baumdicker, Franz PLoS Comput Biol Research Article Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, optimal linear estimation methods are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more challenging. As an alternative to model-based estimation, neural networks and other machine learning tools could help to develop good estimators in these involved scenarios. However, if no benchmark is available it is difficult to assess how well suited these tools are for different applications in population genetics. Here we investigate feedforward neural networks for the estimation of the mutation rate based on the site frequency spectrum and compare their performance with model-based estimators. For this we use the model-based estimators introduced by Fu, Futschik et al., and Watterson that minimize the variance or mean squared error for no and free recombination. We find that neural networks reproduce these estimators if provided with the appropriate features and training sets. Remarkably, using the model-based estimators to adjust the weights of the training data, only one hidden layer is necessary to obtain a single estimator that performs almost as well as model-based estimators for low and high recombination rates, and at the same time provides a superior estimation method for intermediate recombination rates. We apply the method to simulated data based on the human chromosome 2 recombination map, highlighting its robustness in a realistic setting where local recombination rates vary and/or are unknown. Public Library of Science 2022-08-03 /pmc/articles/PMC9377634/ /pubmed/35921376 http://dx.doi.org/10.1371/journal.pcbi.1010407 Text en © 2022 Burger et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 Burger, Klara Elisabeth Pfaffelhuber, Peter Baumdicker, Franz Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown |
title | Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown |
title_full | Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown |
title_fullStr | Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown |
title_full_unstemmed | Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown |
title_short | Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown |
title_sort | neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377634/ https://www.ncbi.nlm.nih.gov/pubmed/35921376 http://dx.doi.org/10.1371/journal.pcbi.1010407 |
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