ArticleFrontiers in genetics2023
Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?
Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Impact of environmental covariates summarization on predictive ability in genomic selection.The plant genome · 2026Article
- Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Penalized factorial regression as a flexible and computationally attractive reaction norm model for prediction in the presence of GxE.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large-scale soybean dataset.The plant genome · 2025Article
- Obscured-ensemble models for genomic prediction.PloS one · 2025Article
- ACVPICPred: Inhibitory activity prediction of anti-coronavirus peptides based on artificial neural network.Computational and structural biotechnology journal · 2024Article
- Enhancing winter wheat prediction with genomics, phenomics and environmental data.BMC genomics · 2024Article
- A marker weighting approach for enhancing within-family accuracy in genomic prediction.G3 (Bethesda, Md.) · 2024Article
- Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.Frontiers in plant science · 2024Article
- Feature engineering of environmental covariates improves plant genomic-enabled prediction.Frontiers in plant science · 2024Article
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10 authors.
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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.
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