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Abstract
Genomic Best Linear Unbiased Prediction (GBLUP) in tree breeding typically only uses information from genotyped trees. However, information from phenotyped but non-genotyped trees can also be highly valuable. The single-step GBLUP approach(ssGBLUP) allows genomic prediction to takeinto account both genotyped and nongenotyped trees simultaneously in a single evaluation. In this study, we investigated the advantage, in terms of breeding value accuracy and [ver mas...]
dc.contributor.authorCappa, Eduardo Pablo
dc.contributor.authorde Lima, Bruno Marco
dc.contributor.authorSilva-Junior, Orzenil B. da
dc.contributor.authorGarcía, Carla C.
dc.contributor.authorMansfield, Shawn D.
dc.contributor.authorGrattapaglia, Dario
dc.date.accessioned2019-10-29T13:54:32Z
dc.date.available2019-10-29T13:54:32Z
dc.date.issued2019-03-28
dc.identifier.issn0168-9452
dc.identifier.otherhttps://doi.org/10.1016/j.plantsci.2019.03.017
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0168945218314134
dc.identifier.urihttp://hdl.handle.net/20.500.12123/6227
dc.description.abstractGenomic Best Linear Unbiased Prediction (GBLUP) in tree breeding typically only uses information from genotyped trees. However, information from phenotyped but non-genotyped trees can also be highly valuable. The single-step GBLUP approach(ssGBLUP) allows genomic prediction to takeinto account both genotyped and nongenotyped trees simultaneously in a single evaluation. In this study, we investigated the advantage, in terms of breeding value accuracy and bias, of including phenotypic observation from non-genotyped trees in a standard tree GBLUP evaluation. We compared the efficiency of the conventional pedigree-based (ABLUP), GBLUP and ssGBLUP approaches to evaluate eight growth and wood quality traits in a Eucalyptus hybrid population, genotyped with 33,398 single nucleotide polymorphisms (SNPs) using the EucHIP60k. Theoretical accuracies, predictive ability and bias were calculated by ten-fold cross validation on all traits. The use of additional phenotypic information from non-genotyped trees by means of ssGBLUP provided higher predictive ability (from 37% to 75%) and lower prediction bias (from 21% to 73%) for the genetic component of non-phenotyped but genotyped trees when compared to GBLUP. The increase (decrease) in the prediction accuracy (bias) became stronger as trait heritability decreased. We concluded that ssGBLUP is a promising breeding tool to improve accuracies and bias over classical GBLUP for genomic evaluation in Eucalyptus breeding practice.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherElsevier
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.sourcePlant Science 284 : 9-15 (July 2019)es_AR
dc.subjectEucalyptuses_AR
dc.subjectGenomic Featureseng
dc.subjectEvaluationeng
dc.subjectEvaluaciónes_AR
dc.subjectPhenotypic Informationeng
dc.subjectInformación Fenotípicoes_AR
dc.subjectGenética
dc.subjectGeneticseng
dc.subject.otherAccuracy Biases_AR
dc.subject.otherSesgo de Precisiónes_AR
dc.subject.otherCaracterísticas Genómicas
dc.titleImproving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUPes_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.description.filFil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentinaes_AR
dc.description.filFil: de Lima, Bruno Marco. FIBRIA S.A. Technology Center; Brasiles_AR
dc.description.filFil: Silva-Junior, Orzenil B. da. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; Brasiles_AR
dc.description.filFil: García, Carla C. International Paper of Brazil; Brasiles_AR
dc.description.filFil: Mansfield, Shawn D. University of British Columbia. Faculty of Forestry. Department of Wood Science; Canadáes_AR
dc.description.filFil: Grattapaglia, Dario. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; Brasiles_AR
dc.subtypecientifico


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