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

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...

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Autores principales: Cappa, Eduardo Pablo, de Lima, Bruno Marco, Silva-Junior, Orzenil B. da, García, Carla C., Mansfield, Shawn D., Grattapaglia, Dario
Formato: info:ar-repo/semantics/artículo
Lenguaje:Inglés
Publicado: Elsevier 2019
Materias:
Eucalyptus
Evaluación
Información Fenotípico
Genomic Features
Evaluation
Phenotypic Information
Genetics
Genética
Accuracy Bias
Sesgo de Precisión
Características Genómicas
Acceso en línea: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
Sumario: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.

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