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A method to predict the response to directional selection using a Kalman filter

Predicting evolution remains challenging. The field of quantitative genetics provides predictions for the response to directional selection through the breeder’s equation, but these predictions can have errors. The sources of these errors include omission of traits under selection, inaccurate estima...

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Detalles Bibliográficos
Autores principales: Milocco, Lisandro, Salazar-Ciudad, Isaac
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9282428/
https://www.ncbi.nlm.nih.gov/pubmed/35867739
http://dx.doi.org/10.1073/pnas.2117916119
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author Milocco, Lisandro
Salazar-Ciudad, Isaac
author_facet Milocco, Lisandro
Salazar-Ciudad, Isaac
author_sort Milocco, Lisandro
collection PubMed
description Predicting evolution remains challenging. The field of quantitative genetics provides predictions for the response to directional selection through the breeder’s equation, but these predictions can have errors. The sources of these errors include omission of traits under selection, inaccurate estimates of genetic variance, and nonlinearities in the relationship between genetic and phenotypic variation. Previous research showed that the expected value of these prediction errors is often not zero, so predictions are systematically biased. Here, we propose that this bias, rather than being a nuisance, can be used to improve the predictions. We use this to develop a method to predict evolution, which is built on three key innovations. First, the method predicts change as the breeder’s equation plus a bias term. Second, the method combines information from the breeder’s equation and from the record of past changes in the mean to predict change using a Kalman filter. Third, the parameters of the filter are fitted in each generation using a learning algorithm on the record of past changes. We compare the method to the breeder’s equation in two artificial selection experiments, one using the wing of the fruit fly and another using simulations that include a complex mapping of genotypes to phenotypes. The proposed method outperforms the breeder’s equation, particularly when traits under selection are omitted from the analysis, when data are noisy, and when additive genetic variance is estimated inaccurately or not estimated at all. The proposed method is easy to apply, requiring only the trait means over past generations.
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spelling pubmed-92824282023-01-06 A method to predict the response to directional selection using a Kalman filter Milocco, Lisandro Salazar-Ciudad, Isaac Proc Natl Acad Sci U S A Biological Sciences Predicting evolution remains challenging. The field of quantitative genetics provides predictions for the response to directional selection through the breeder’s equation, but these predictions can have errors. The sources of these errors include omission of traits under selection, inaccurate estimates of genetic variance, and nonlinearities in the relationship between genetic and phenotypic variation. Previous research showed that the expected value of these prediction errors is often not zero, so predictions are systematically biased. Here, we propose that this bias, rather than being a nuisance, can be used to improve the predictions. We use this to develop a method to predict evolution, which is built on three key innovations. First, the method predicts change as the breeder’s equation plus a bias term. Second, the method combines information from the breeder’s equation and from the record of past changes in the mean to predict change using a Kalman filter. Third, the parameters of the filter are fitted in each generation using a learning algorithm on the record of past changes. We compare the method to the breeder’s equation in two artificial selection experiments, one using the wing of the fruit fly and another using simulations that include a complex mapping of genotypes to phenotypes. The proposed method outperforms the breeder’s equation, particularly when traits under selection are omitted from the analysis, when data are noisy, and when additive genetic variance is estimated inaccurately or not estimated at all. The proposed method is easy to apply, requiring only the trait means over past generations. National Academy of Sciences 2022-07-06 2022-07-12 /pmc/articles/PMC9282428/ /pubmed/35867739 http://dx.doi.org/10.1073/pnas.2117916119 Text en Copyright © 2022 the Author(s). Published by PNAS https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Biological Sciences
Milocco, Lisandro
Salazar-Ciudad, Isaac
A method to predict the response to directional selection using a Kalman filter
title A method to predict the response to directional selection using a Kalman filter
title_full A method to predict the response to directional selection using a Kalman filter
title_fullStr A method to predict the response to directional selection using a Kalman filter
title_full_unstemmed A method to predict the response to directional selection using a Kalman filter
title_short A method to predict the response to directional selection using a Kalman filter
title_sort method to predict the response to directional selection using a kalman filter
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9282428/
https://www.ncbi.nlm.nih.gov/pubmed/35867739
http://dx.doi.org/10.1073/pnas.2117916119
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