A multi-trait gaussian kernel genomic prediction model under three tunning strategies

dc.creatorKismiantini
dc.creatorMontesinos-López, A.
dc.creatorCano-Paez, B.
dc.creatorMontesinos-Lopez, J.C.
dc.creatorChavira-Flores, M.
dc.creatorMontesinos-Lopez, O.A.
dc.creatorCrossa, J.
dc.date2023-01-14T01:10:13Z
dc.date2023-01-14T01:10:13Z
dc.date2022
dc.date.accessioned2023-07-17T20:10:03Z
dc.date.available2023-07-17T20:10:03Z
dc.identifierhttps://hdl.handle.net/10883/22401
dc.identifier10.3390/genes13122279
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514148
dc.descriptionWhile genomic selection (GS) began revolutionizing plant breeding when it was proposed around 20 years ago, its practical implementation is still challenging as many factors affect its accuracy. One such factor is the choice of the statistical machine learning method. For this reason, we explore the tuning process under a multi-trait framework using the Gaussian kernel with a multi-trait Bayesian Best Linear Unbiased Predictor (GBLUP) model. We explored three methods of tuning (manual, grid search and Bayesian optimization) using 5 real datasets of breeding programs. We found that using grid search and Bayesian optimization improve between 1.9 and 6.8% the prediction accuracy regarding of using manual tuning. While the improvement in prediction accuracy in some cases can be marginal, it is very important to carry out the tuning process carefully to improve the accuracy of the GS methodology, even though this entails greater computational resources.
dc.languageEnglish
dc.publisherMDPI
dc.relationhttps://github.com/osval78/Multivariate_Tuning_Kernel_Method
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.source12
dc.source13
dc.source2073-4425
dc.sourceGenes
dc.source2279
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectMulti-Trait
dc.subjectBayesian Optimization
dc.subjectGrid Search
dc.subjectGenomic Selection
dc.subjectBREEDING
dc.subjectKERNELS
dc.subjectFORECASTING
dc.subjectMARKER-ASSISTED SELECTION
dc.subjectGenetic Resources
dc.titleA multi-trait gaussian kernel genomic prediction model under three tunning strategies
dc.typeArticle
dc.typePublished Version
dc.coverageBasel (Switzerland)


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