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Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?

Genomic selection (GS) is transforming plant and animal breeding, but its practical implementation for complex traits and multi-environmental trials remains challenging. To address this issue, this study investigates the integration of environmental information with genotypic information in GS. The...

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Autores principales: Montesinos-López, Osval A., Crespo-Herrera, Leonardo, Saint Pierre, Carolina, Bentley, Alison R., de la Rosa-Santamaria, Roberto, Ascencio-Laguna, José Alejandro, Agbona, Afolabi, Gerard, Guillermo S., Montesinos-López, Abelardo, Crossa, José
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10405933/
https://www.ncbi.nlm.nih.gov/pubmed/37554404
http://dx.doi.org/10.3389/fgene.2023.1209275
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author Montesinos-López, Osval A.
Crespo-Herrera, Leonardo
Saint Pierre, Carolina
Bentley, Alison R.
de la Rosa-Santamaria, Roberto
Ascencio-Laguna, José Alejandro
Agbona, Afolabi
Gerard, Guillermo S.
Montesinos-López, Abelardo
Crossa, José
author_facet Montesinos-López, Osval A.
Crespo-Herrera, Leonardo
Saint Pierre, Carolina
Bentley, Alison R.
de la Rosa-Santamaria, Roberto
Ascencio-Laguna, José Alejandro
Agbona, Afolabi
Gerard, Guillermo S.
Montesinos-López, Abelardo
Crossa, José
author_sort Montesinos-López, Osval A.
collection PubMed
description Genomic selection (GS) is transforming plant and animal breeding, but its practical implementation for complex traits and multi-environmental trials remains challenging. To address this issue, this study investigates the integration of environmental information with genotypic information in GS. The study proposes the use of two feature selection methods (Pearson’s correlation and Boruta) for the integration of environmental information. Results indicate that the simple incorporation of environmental covariates may increase or decrease prediction accuracy depending on the case. However, optimal incorporation of environmental covariates using feature selection significantly improves prediction accuracy in four out of six datasets between 14.25% and 218.71% under a leave one environment out cross validation scenario in terms of Normalized Root Mean Squared Error, but not relevant gain was observed in terms of Pearson´s correlation. In two datasets where environmental covariates are unrelated to the response variable, feature selection is unable to enhance prediction accuracy. Therefore, the study provides empirical evidence supporting the use of feature selection to improve the prediction power of GS.
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spelling pubmed-104059332023-08-08 Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy? Montesinos-López, Osval A. Crespo-Herrera, Leonardo Saint Pierre, Carolina Bentley, Alison R. de la Rosa-Santamaria, Roberto Ascencio-Laguna, José Alejandro Agbona, Afolabi Gerard, Guillermo S. Montesinos-López, Abelardo Crossa, José Front Genet Genetics Genomic selection (GS) is transforming plant and animal breeding, but its practical implementation for complex traits and multi-environmental trials remains challenging. To address this issue, this study investigates the integration of environmental information with genotypic information in GS. The study proposes the use of two feature selection methods (Pearson’s correlation and Boruta) for the integration of environmental information. Results indicate that the simple incorporation of environmental covariates may increase or decrease prediction accuracy depending on the case. However, optimal incorporation of environmental covariates using feature selection significantly improves prediction accuracy in four out of six datasets between 14.25% and 218.71% under a leave one environment out cross validation scenario in terms of Normalized Root Mean Squared Error, but not relevant gain was observed in terms of Pearson´s correlation. In two datasets where environmental covariates are unrelated to the response variable, feature selection is unable to enhance prediction accuracy. Therefore, the study provides empirical evidence supporting the use of feature selection to improve the prediction power of GS. Frontiers Media S.A. 2023-07-24 /pmc/articles/PMC10405933/ /pubmed/37554404 http://dx.doi.org/10.3389/fgene.2023.1209275 Text en Copyright © 2023 Montesinos-López, Crespo-Herrera, Saint Pierre, Bentley, de la Rosa-Santamaria, Ascencio-Laguna, Agbona, Gerard, Montesinos-López and Crossa. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Montesinos-López, Osval A.
Crespo-Herrera, Leonardo
Saint Pierre, Carolina
Bentley, Alison R.
de la Rosa-Santamaria, Roberto
Ascencio-Laguna, José Alejandro
Agbona, Afolabi
Gerard, Guillermo S.
Montesinos-López, Abelardo
Crossa, José
Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?
title Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?
title_full Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?
title_fullStr Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?
title_full_unstemmed Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?
title_short Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?
title_sort do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10405933/
https://www.ncbi.nlm.nih.gov/pubmed/37554404
http://dx.doi.org/10.3389/fgene.2023.1209275
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