ArticleFrontiers in genetics2022
Partial Least Squares Enhances Genomic Prediction of New Environments.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Multi-variant GWAS and genomic prediction dissect the genetic architecture underlying tomato fruit weight.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- A benchmarking of genomic selection models for predicting grain-yield related traits using haplotype-based and genome-wide association study-based markers in rice.The plant genome · 2026Article
- Impact of environmental covariates summarization on predictive ability in genomic selection.The plant genome · 2026Article
- Surprising regulatory plasticity for the conserved HOG pathway in diversebioRxiv : the preprint server for biology · 2025Article
- Comparing wMAS, GWAS, and genomic prediction for selecting powdery mildew-resistant spring barley genotypes.BMC genomics · 2025Article
- Integration of machine learning and genome-wide association study to explore the genomic prediction accuracy of agronomic trait in oats (Avena sativa L.).The plant genome · 2025Article
- Accuracy of prediction from multi-environment trials for new locations using pedigree information and environmental covariates: the case of sorghum (Sorghum bicolor (L.) Moench) breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Climate change impacts on crop breeding: Targeting interacting biotic and abiotic stresses for wheat improvement.The plant genome · 2024Article
- Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments.Scientific reports · 2023Article
- Article
- Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data.G3 (Bethesda, Md.) · 2023Article
- Article
- Multi-trait genome prediction of new environments with partial least squares.Frontiers in genetics · 2022Article
- Crop genomic selection with deep learning and environmental data: A survey.Frontiers in artificial intelligence · 2022Review
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8 authors.
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Abstract
In plant breeding, the need to improve the prediction of future seasons or new locations and/or environments, also denoted as "leave one environment out," is of paramount importance to increase the genetic gain in breeding programs and contribute to food and nutrition security worldwide. Genomic selection (GS) has the potential to increase the accuracy of future seasons or new locations because it is a predictive methodology. However, most statistical machine learning methods used for the task of predicting a new environment or season struggle to produce moderate or high prediction accuracies. For this reason, in this study we explore the use of the partial least squares (PLS) regression methodology for this specific task, and we benchmark its performance with the Bayesian Genomic Best Linear Unbiased Predictor (GBLUP) method. The benchmarking process was done with 14 real datasets. We found that in all datasets the PLS method outperformed the popular GBLUP method by margins between 0% (in the Indica data) and 228.28% (in the Disease data) across traits, environments, and types of predictors. Our results show great empirical evidence of the power of the PLS methodology for the prediction of future seasons or new environments.
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