Fecha/hora: 09/06/2024 16:54:00

Biblioteca(s): | INIA Las Brujas.
Fecha corriente: | 12/08/2016
Fecha de la última actualización: | 02/01/2017
Autoria: | LADO, B.; GONZÁLEZ BARRIOS, P.; QUINCKE, M.; SILVA, P.; GUTIÉRREZ, L.
Afiliación: | BETTINA LADO, Universidad de la República (UdelaR)/ Facultad de Agronomía; PABLO GONZÁLEZ BARRIOS, Universidad de la República (UdelaR)/ Facultad de Agronomía; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA PAULA SILVA VILLELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCIA GUTIÉRREZ, Universidad de la República (UdelaR)/ Facultad de Agronomía.
Título: | Modeling genotype x environment interaction for genomic selection with unbalanced data from a wheat breeding program.
Año de publicación: | 2016
Fuente/Imprenta: | Crop Science, 2016, v. 56, p. 1-15. OPEN ACCESS.
DOI: | http://dx.doi.org/10.2135/cropsci2015.04.0207
Idioma: | Inglés

Contenido:

ABSTRACT.

Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype ? environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to
predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location?year combinations genotyped with genotyping-bysequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new
genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega-environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.

© 2016. Crop Science Society of America, Inc.

Palabras-Clave: | GENOMIC SELECTION; WHEAT.
Thesagro: | TRIGO.
Categoría del tema: | --

URL:

http://dx.doi.org/10.2135/cropsci2015.04.0207
http://www.ainfo.inia.uy/digital/bitstream/item/5875/1/Lado-B.-2016.-Crop-Science.pdf
http://www.ainfo.inia.uy/digital/bitstream/item/5876/1/Lado-B.-2016.-Crop-Science-supplement.pdf

Registro original: | INIA Las Brujas (LB)
Ejemplares: | LB - (101077-1) - PXI - AP - DD -  -  -  - DIS