A method to predict the response to directional selection using a Kalman filter
Milocco, Lisandro (University of Helsinki)
Salazar Ciudad, Isaac (Universitat Autònoma de Barcelona. Departament de Genètica i de Microbiologia)

Date:	2022
Abstract:	Evolution is a historical process with contingency, where outcomes are sensitive to past events. Due to this, predicting evolution remains challenging. In this paper, we propose a method to predict the response to selection that incorporates history. The method uses tools from quantitative genetics and combines them with information from past evolution that is extracted through a combination of signal processing and learning techniques. This information is underexploited by existing methods to predict evolution but is of great value since it reflects singularities of the evolutionary system. We show that this combination of information coming from the time series and quantitative genetics methods outperforms classical methods in predicting the response to selection. 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.
Grants:	Agencia Estatal de Investigación PGC2018-096802-B-I00
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Quantitative genetics ; Evolutionary prediction ; Kalman filter ; Breeder's equation ; G matrix
Published in:	Proceedings of the National Academy of Sciences of the United States of America, Vol. 119, Num. 28 (July 2022) , art. e2117916119, ISSN 1091-6490

DOI: 10.1073/pnas.2117916119
PMID: 35867739

11 p, 1.7 MB

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