Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP

Autores
Cappa, Eduardo Pablo; de Lima, Bruno Marco; Silva-Junior, Orzenil B. da; García, Carla C.; Mansfield, Shawn D.; Grattapaglia, Dario
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: de Lima, Bruno Marco. FIBRIA S.A. Technology Center; Brasil
Fil: Silva-Junior, Orzenil B. da. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; Brasil
Fil: García, Carla C. International Paper of Brazil; Brasil
Fil: Mansfield, Shawn D. University of British Columbia. Faculty of Forestry. Department of Wood Science; Canadá
Fil: Grattapaglia, Dario. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; Brasil
Fuente
Plant Science 284 : 9-15 (July 2019)
Materia
Eucalyptus
Evaluación
Información Fenotípico
Genomic Features
Evaluation
Phenotypic Information
Genetics
Genética
Accuracy Bias
Sesgo de Precisión
Características Genómicas
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/6227
Acceder
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oai_identifier_str oai:localhost:20.500.12123/6227
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network_name_str INTA Digital (INTA)
spelling Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUPCappa, Eduardo Pablode Lima, Bruno MarcoSilva-Junior, Orzenil B. daGarcía, Carla C.Mansfield, Shawn D.Grattapaglia, DarioEucalyptusEvaluaciónInformación FenotípicoGenomic FeaturesEvaluationPhenotypic InformationGeneticsGenéticaAccuracy BiasSesgo de PrecisiónCaracterísticas GenómicasGenomic 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.Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Lima, Bruno Marco. FIBRIA S.A. Technology Center; BrasilFil: Silva-Junior, Orzenil B. da. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; BrasilFil: García, Carla C. International Paper of Brazil; BrasilFil: Mansfield, Shawn D. University of British Columbia. Faculty of Forestry. Department of Wood Science; CanadáFil: Grattapaglia, Dario. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; BrasilElsevier2019-10-29T13:54:32Z2019-10-29T13:54:32Z2019-03-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://www.sciencedirect.com/science/article/pii/S0168945218314134http://hdl.handle.net/20.500.12123/62270168-9452https://doi.org/10.1016/j.plantsci.2019.03.017Plant Science 284 : 9-15 (July 2019)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-29T13:44:48Zoai:localhost:20.500.12123/6227instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:44:49.162INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP
title Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP
spellingShingle Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP
Cappa, Eduardo Pablo
Eucalyptus
Evaluación
Información Fenotípico
Genomic Features
Evaluation
Phenotypic Information
Genetics
Genética
Accuracy Bias
Sesgo de Precisión
Características Genómicas
title_short Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP
title_full Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP
title_fullStr Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP
title_full_unstemmed Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP
title_sort Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP
dc.creator.none.fl_str_mv Cappa, Eduardo Pablo
de Lima, Bruno Marco
Silva-Junior, Orzenil B. da
García, Carla C.
Mansfield, Shawn D.
Grattapaglia, Dario
author Cappa, Eduardo Pablo
author_facet Cappa, Eduardo Pablo
de Lima, Bruno Marco
Silva-Junior, Orzenil B. da
García, Carla C.
Mansfield, Shawn D.
Grattapaglia, Dario
author_role author
author2 de Lima, Bruno Marco
Silva-Junior, Orzenil B. da
García, Carla C.
Mansfield, Shawn D.
Grattapaglia, Dario
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Eucalyptus
Evaluación
Información Fenotípico
Genomic Features
Evaluation
Phenotypic Information
Genetics
Genética
Accuracy Bias
Sesgo de Precisión
Características Genómicas
topic Eucalyptus
Evaluación
Información Fenotípico
Genomic Features
Evaluation
Phenotypic Information
Genetics
Genética
Accuracy Bias
Sesgo de Precisión
Características Genómicas
dc.description.none.fl_txt_mv 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.
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: de Lima, Bruno Marco. FIBRIA S.A. Technology Center; Brasil
Fil: Silva-Junior, Orzenil B. da. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; Brasil
Fil: García, Carla C. International Paper of Brazil; Brasil
Fil: Mansfield, Shawn D. University of British Columbia. Faculty of Forestry. Department of Wood Science; Canadá
Fil: Grattapaglia, Dario. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; Brasil
description 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.
publishDate 2019
dc.date.none.fl_str_mv 2019-10-29T13:54:32Z
2019-10-29T13:54:32Z
2019-03-28
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S0168945218314134
http://hdl.handle.net/20.500.12123/6227
0168-9452
https://doi.org/10.1016/j.plantsci.2019.03.017
url https://www.sciencedirect.com/science/article/pii/S0168945218314134
http://hdl.handle.net/20.500.12123/6227
https://doi.org/10.1016/j.plantsci.2019.03.017
identifier_str_mv 0168-9452
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Plant Science 284 : 9-15 (July 2019)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.559606

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