Multi-trait genome prediction of new environments with partial least squares

dc.creatorMontesinos-Lopez, O.A.
dc.creatorMontesinos-López, A.
dc.creatorBernal Sandoval, D.A.
dc.creatorMosqueda-Gonzalez, B.A.
dc.creatorValenzo-Jiménez, M.A.
dc.creatorCrossa, J.
dc.date2022-12-07T20:29:46Z
dc.date2022-12-07T20:29:46Z
dc.date2022
dc.date.accessioned2023-07-17T20:09:45Z
dc.date.available2023-07-17T20:09:45Z
dc.identifierhttps://hdl.handle.net/10883/22290
dc.identifier10.3389/fgene.2022.966775
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514041
dc.descriptionThe genomic selection (GS) methodology proposed over 20 years ago by Meuwissen et al. (Genetics, 2001) has revolutionized plant breeding. A predictive methodology that trains statistical machine learning algorithms with phenotypic and genotypic data of a reference population and makes predictions for genotyped candidate lines, GS saves significant resources in the selection of candidate individuals. However, its practical implementation is still challenging when the plant breeder is interested in the prediction of future seasons or new locations and/or environments, which is called the “leave one environment out” issue. Furthermore, because the distributions of the training and testing set do not match, most statistical machine learning methods struggle to produce moderate or reasonable prediction accuracies. For this reason, the main objective of this study was to explore the use of the multi-trait partial least square (MT-PLS) regression methodology for this specific task, benchmarking its performance with the Bayesian Multi-trait Genomic Best Linear Unbiased Predictor (MT-GBLUP) method. The benchmarking process was performed with five actual data sets. We found that in all data sets the MT-PLS method outperformed the popular MT-GBLUP method by 349.8% (under predictor E + G), 484.4% (under predictor E + G + GE; where E denotes environments, G genotypes and GE the genotype by environment interaction) and 15.9% (under predictor G + GE) across traits. Our results provide empirical evidence of the power of the MT-PLS methodology for the prediction of future seasons or new environments. Furthermore, the comparison between single univariate-trait (UT) versus MT for GBLUP and PLS gave an increase in prediction accuracy of MT-GBLUP versus UT-GBLUP, but not for MT-PLS versus UT-PLS.
dc.languageEnglish
dc.publisherFrontiers
dc.relationhttps://hdl.handle.net/11529/10548705
dc.relationhttps://figshare.com/collections/Multi-trait_genome_prediction_of_new_environments_with_partial_least_squares/6181645
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dc.rightsOpen Access
dc.source13
dc.source1664-8021
dc.sourceFrontiers in Genetics
dc.source966775
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectGenomic Prediction
dc.subjectMulti-Trait Partial Least Squares
dc.subjectSingle-Trait Partial Least Squares
dc.subjectPrediction of One Complete Environment
dc.subjectGENOTYPES
dc.subjectGENOTYPE ENVIRONMENT INTERACTION
dc.subjectMACHINE LEARNING
dc.subjectFORECASTING
dc.subjectMARKER-ASSISTED SELECTION
dc.titleMulti-trait genome prediction of new environments with partial least squares
dc.typeArticle
dc.typePublished Version
dc.coverageSwitzerland


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