Genomic-enabled prediction Kernel models with random intercepts for multi-environment trials

dc.creatorCuevas, J.
dc.creatorGranato, I.
dc.creatorFritsche-Neto, R.
dc.creatorMontesinos-Lopez, O.A.
dc.creatorBurgueño, J.
dc.creatorBandeira e Sousa, M.
dc.creatorCrossa, J.
dc.date2018-05-30T17:01:03Z
dc.date2018-05-30T17:01:03Z
dc.date2018
dc.date.accessioned2023-07-17T20:02:44Z
dc.date.available2023-07-17T20:02:44Z
dc.identifierhttps://hdl.handle.net/10883/19499
dc.identifier10.1534/g3.117.300454
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7511390
dc.descriptionIn this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multienvironment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines (l) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy.
dc.description1347-1365
dc.formatPDF
dc.languageEnglish
dc.publisherGenetics Society of America
dc.relationhttp://hdl.handle.net/11529/10887
dc.relationhttp://hdl.handle.net/11529/10710
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dc.rightsOpen Access
dc.source4
dc.source8
dc.sourceG3: Genes, Genomes, Genetics
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectGenomic Enabled Prediction Accuracy
dc.subjectMain Genetic Effects
dc.subjectDeviations from Main Genetic Effects
dc.subjectRandom Intercepts
dc.subjectGenomic Selection
dc.subjectShared Data Resources
dc.subjectGenPred
dc.subjectGENOTYPE ENVIRONMENT INTERACTION
dc.subjectGENOMICS
dc.titleGenomic-enabled prediction Kernel models with random intercepts for multi-environment trials
dc.typeArticle
dc.coverageBethesda, Md, U.S.


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