Single - step genomic prediction of Eucalyptus dunnii using different identity - by - descent and identity - by - state relationship matrices

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Genomic selection based on the single-step genomic best linear unbiased prediction (ssGBLUP) approach is becoming an important tool in forest tree breeding. The quality of the variance components and the predictive ability of the estimated breeding values (GEBV) depends on how well marker-based geno...

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Detalles Bibliográficos
Otros Autores: Jurcic, Esteban, Villalba, Pamela V., Pathauer, Pablo Santiago, Palazzini, Dino A., Oberschelp, Gustavo Pedro Javier, Harrand, Leonel, Garcia, Martín N., Munilla Leguizamón, Sebastián
Formato: Artículo
Lenguaje:Inglés
Materias:
EUCALYPTUS DUNNII
FOREST TREE BREEDING
GBLUP MODELS
GENOMIC PREDICTION
Acceso en línea:http://ri.agro.uba.ar/files/intranet/articulo/2021jurcic.pdf
LINK AL EDITOR
Aporte de:Registro referencial: Solicitar el recurso aquí
Biblioteca Central (FAUBA) de Universidad de Buenos Aires
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024 |a 10.1038/s41437-021-00450-9 
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245 1 0 |a Single - step genomic prediction of Eucalyptus dunnii using different identity - by - descent and identity - by - state relationship matrices 
520 |a Genomic selection based on the single-step genomic best linear unbiased prediction (ssGBLUP) approach is becoming an important tool in forest tree breeding. The quality of the variance components and the predictive ability of the estimated breeding values (GEBV) depends on how well marker-based genomic relationships describe the actual genetic relationships at unobserved causal loci. We investigated the performance of GEBV obtained when fitting models with genomic covariance matrices based on two identity-by-descent (IBD) and two identity-by-state (IBS) relationship measures. Multiple-trait multiple-site ssGBLUP models were fitted to diameter and stem straightness in five open-pollinated progeny trials of Eucalyptus dunnii, genotyped using the EUChip60K. We also fitted the conventional ABLUP model with a pedigree-based covariance matrix. Estimated relationships from the IBD estimators displayed consistently lower standard deviations than those from the IBS approaches. Although ssGBLUP based in IBS estimators resulted in higher trait-site heritabilities, the gain in accuracy of the relationships using IBD estimators has resulted in higher predictive ability and lower bias of GEBV, especially for low-heritability trait-site. ssGBLUP based on IBS and IBD approaches performed considerably better than the traditional ABLUP. In summary, our results advocate the use of the ssGBLUP approach jointly with the IBD relationship matrix in open-pollinated forest tree evaluation. 
650 |2 Agrovoc  |9 26 
653 |a EUCALYPTUS DUNNII 
653 |a FOREST TREE BREEDING 
653 |a GBLUP MODELS 
653 |a GENOMIC PREDICTION 
700 1 |a Jurcic, Esteban  |u Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos (INTA-CIRN). Hurlingham, Buenos Aires, Argentina.  |u CONICET. Buenos Aires, Argentina.  |9 38553 
700 1 |a Villalba, Pamela V.  |u CONICET. Buenos Aires, Argentina.  |u Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular (IABIMO). Hurlingham, Buenos Aires, Argentina.  |u CONICET - Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular (IABIMO). Hurlingham, Buenos Aires, Argentina.  |9 73911 
700 1 |a Pathauer, Pablo Santiago  |u Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos (INTA-CIRN). Hurlingham, Buenos Aires, Argentina.  |9 51040 
700 1 |a Palazzini, Dino A.  |u Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos (INTA-CIRN). Hurlingham, Buenos Aires, Argentina.  |9 73912 
700 1 |a Oberschelp, Gustavo Pedro Javier  |u Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia (EEA Concordia). Entre Ríos. Argentina.  |9 47835 
700 1 |a Harrand, Leonel  |u Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia (EEA Concordia). Entre Ríos. Argentina.  |9 41115 
700 1 |a Garcia, Martín N.  |u CONICET. Buenos Aires, Argentina.  |u Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular (IABIMO). Hurlingham, Buenos Aires, Argentina.  |u CONICET - Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular (IABIMO). Hurlingham, Buenos Aires, Argentina.  |9 73913 
700 1 |a Munilla Leguizamón, Sebastián  |u CONICET. Buenos Aires, Argentina.  |u Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Buenos Aires, Argentina.  |9 13019 
773 |t Heredity  |g Vol.127, no.2 (2021), p.176–189, tbls., grafs. 
856 |f 2021jurcic  |i En reservorio  |q application/pdf  |u http://ri.agro.uba.ar/files/intranet/articulo/2021jurcic.pdf  |x ARTI202206 
856 |u http://www.nature.com/  |z LINK AL EDITOR 
942 |c ARTICULO 
942 |c ENLINEA 
976 |a AAG 

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