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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
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dc.contributor.author | Cappa, Eduardo Pablo | |
dc.contributor.author | de Lima, Bruno Marco | |
dc.contributor.author | Silva-Junior, Orzenil B. da | |
dc.contributor.author | García, Carla C. | |
dc.contributor.author | Mansfield, Shawn D. | |
dc.contributor.author | Grattapaglia, Dario | |
dc.date.accessioned | 2019-10-29T13:54:32Z | |
dc.date.available | 2019-10-29T13:54:32Z | |
dc.date.issued | 2019-03-28 | |
dc.identifier.issn | 0168-9452 | |
dc.identifier.other | https://doi.org/10.1016/j.plantsci.2019.03.017 | |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0168945218314134 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12123/6227 | |
dc.description.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 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.format | application/pdf | es_AR |
dc.language.iso | eng | es_AR |
dc.publisher | Elsevier | |
dc.rights | info:eu-repo/semantics/restrictedAccess | es_AR |
dc.source | Plant Science 284 : 9-15 (July 2019) | es_AR |
dc.subject | Eucalyptus | es_AR |
dc.subject | Genomic Features | eng |
dc.subject | Evaluation | eng |
dc.subject | Evaluación | es_AR |
dc.subject | Phenotypic Information | eng |
dc.subject | Información Fenotípico | es_AR |
dc.subject | Genética | |
dc.subject | Genetics | eng |
dc.subject.other | Accuracy Bias | es_AR |
dc.subject.other | Sesgo de Precisión | es_AR |
dc.subject.other | Características Genómicas | |
dc.title | Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP | es_AR |
dc.type | info:ar-repo/semantics/artículo | es_AR |
dc.type | info:eu-repo/semantics/article | es_AR |
dc.type | info:eu-repo/semantics/publishedVersion | es_AR |
dc.description.fil | Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina | es_AR |
dc.description.fil | Fil: de Lima, Bruno Marco. FIBRIA S.A. Technology Center; Brasil | es_AR |
dc.description.fil | Fil: Silva-Junior, Orzenil B. da. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; Brasil | es_AR |
dc.description.fil | Fil: García, Carla C. International Paper of Brazil; Brasil | es_AR |
dc.description.fil | Fil: Mansfield, Shawn D. University of British Columbia. Faculty of Forestry. Department of Wood Science; Canadá | es_AR |
dc.description.fil | Fil: Grattapaglia, Dario. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; Brasil | es_AR |
dc.subtype | cientifico |
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