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 TITLE=Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy? JOURNAL=Frontiers in Genetics VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1209275 DOI=10.3389/fgene.2023.1209275 ISSN=1664-8021 ABSTRACT=

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.