Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments

dc.creatorOrtiz, R.
dc.creatorReslow, F.
dc.creatorMontesinos-Lopez, A.
dc.creatorHuicho, J.
dc.creatorPérez-Rodríguez, P.
dc.creatorMontesinos-Lopez, O.A.
dc.creatorCrossa, J.
dc.date2023-06-29T20:10:11Z
dc.date2023-06-29T20:10:11Z
dc.date2023
dc.date.accessioned2023-07-17T20:10:41Z
dc.date.available2023-07-17T20:10:41Z
dc.identifierhttps://hdl.handle.net/10883/22633
dc.identifier10.1038/s41598-023-37169-y
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514375
dc.descriptionIt is of paramount importance in plant breeding to have methods dealing with large numbers of predictor variables and few sample observations, as well as efficient methods for dealing with high correlation in predictors and measured traits. This paper explores in terms of prediction performance the partial least squares (PLS) method under single-trait (ST) and multi-trait (MT) prediction of potato traits. The first prediction was for tested lines in tested environments under a five-fold cross-validation (5FCV) strategy and the second prediction was for tested lines in untested environments (herein denoted as leave one environment out cross validation, LOEO). There was a good performance in terms of predictions (with accuracy mostly > 0.5 for Pearson’s correlation) the accuracy of 5FCV was better than LOEO. Hence, we have empirical evidence that the ST and MT PLS framework is a very valuable tool for prediction in the context of potato breeding data.
dc.languageEnglish
dc.publisherNature Publishing Group
dc.relationhttps://hdl.handle.net/11529/10548617
dc.relationhttps://hdl.handle.net/11529/10548784
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source1
dc.source13
dc.source2045-2322
dc.sourceScientific Reports
dc.source9947
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectPotato Traits
dc.subjectCross-Validation
dc.subjectBreeding Data
dc.subjectLEAST SQUARES METHOD
dc.subjectPOTATOES
dc.subjectENVIRONMENT
dc.subjectPLANT BREEDING
dc.subjectGenetic Resources
dc.titlePartial least squares enhance multi-trait genomic prediction of potato cultivars in new environments
dc.typeArticle
dc.typePublished Version
dc.coverageLondon (United Kingdom)


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