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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
12/08/2016 |
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Actualizado : |
02/01/2017 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
LADO, B.; GONZÁLEZ BARRIOS, P.; QUINCKE, M.; SILVA, P.; GUTIÉRREZ, L. |
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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. |
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Título : |
Modeling genotype x environment interaction for genomic selection with unbalanced data from a wheat breeding program. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Crop Science, 2016, v. 56, p. 1-15. OPEN ACCESS. |
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DOI : |
http://dx.doi.org/10.2135/cropsci2015.04.0207 |
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Idioma : |
Inglés |
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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. MenosABSTRACT.
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 ab... Presentar Todo |
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Palabras claves : |
GENOMIC SELECTION; WHEAT. |
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Thesagro : |
TRIGO. |
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Asunto categoría : |
-- |
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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
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Marc : |
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024 7 $ahttp://dx.doi.org/10.2135/cropsci2015.04.0207$2DOI
100 1 $aLADO, B.
245 $aModeling genotype x environment interaction for genomic selection with unbalanced data from a wheat breeding program.$h[electronic resource]
260 $c2016
520 $aABSTRACT. 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.
650 $aTRIGO
653 $aGENOMIC SELECTION
653 $aWHEAT
700 1 $aGONZÁLEZ BARRIOS, P.
700 1 $aQUINCKE, M.
700 1 $aSILVA, P.
700 1 $aGUTIÉRREZ, L.
773 $tCrop Science, 2016$gv. 56, p. 1-15. OPEN ACCESS.
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INIA Las Brujas (LB) |
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| Registros recuperados : 633 | |
| 186. |  | VÁZQUEZ, D. El trigo nuestro de cada día. [Presentación Oral]. In Seminario Latinoamericano y del Caribe de Ciencia y Tecnología de los Alimentos, 19º.; Jornadas Uruguayas de Ciencia y Tecnología de Alimentos,11º.,7-10 de agosto de 2016 , Pocitos, Montevideo, Uruguay.| Tipo: Presentaciones Orales |
| Biblioteca(s): INIA La Estanzuela. |
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| 190. | | CAFFAREL, J. usar ln: INIA LA ESTANZUEL. Jornada de cultivos de invierno, 1999. p. 1-20 Actividades de difusión Instituto Nacional de Investigación Agropecuaria (Uruguay)| Biblioteca(s): INIA La Estanzuela. |
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| 195. | | RUSSO, M.; VÁZQUEZ, D. Caracterización nutricional y en compuestos bioactivos de trigo en Uruguay: variabilidad en genotipos y ambiente. [Poster]. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. p. 258. (INIA Serie Técnica; 241).| Biblioteca(s): INIA La Estanzuela. |
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| 196. | | SEMINARIO INTERNACIONAL DE TRIGO, 2014, LA ESTANZUELA, COLONIA, UY; GERMAN, S.; QUINCKE, M.; VÁZQUEZ, D.; CASTRO, M.; GARCIA-LAMOTHE, A.; PEREYRA, S.; SILVA, P.; RESTAINO, E.; SILVERA, A. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014. 89 p. Encuadernado con espiral doble. Habrá edición posterior de los trabajos completos.| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA La Estanzuela. |
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| 197. | | PEREYRA, S. Aportes al manejo agroecológico de enfermedades en trigo. (Capítulo 3). Primera sección: Transitando hacia la protección agroecológica de los cultivos. Editora: Carolina Leoni. In: Georgina Paula García-Inza; José María Paruelo; Roberto Zoppolo. (eds). Aportes científicos y tecnológicos del Instituto Nacional de Investigación Agropecuaria (INIA) del Uruguay a las trayectorias agroecológicas. Ciudad Autónoma de Buenos Aires : Fundación CICCUS, 2023. p.67-81. p.67-81.| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Las Brujas. |
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| 198. | | LUIZZI, D.; PEREYRA, S.; QUINCKE, M.; ABADIE, T.; GATTI, I.; DÍAZ DE ACKERMANN, M.; VÁZQUEZ, D.; CONDON, F.; GERMAN, S. Cien años de mejoramiento genético de trigo en La Estanzuela, Uruguay. In: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro: presentaciones; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014. p. 1-2.| Tipo: Presentaciones Orales |
| Biblioteca(s): INIA La Estanzuela. |
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| 199. | | RUBIO, V.; CASTRO, M.; DIAZ, R. Efecto de la variabilidad climática en el rendimiento de trigo en Uruguay. [Poster]. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. p. 131. (INIA Serie Técnica; 241).| Biblioteca(s): INIA La Estanzuela. |
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| 200. | | VÁZQUEZ, D.; CASTRO, M. Mejoramiento para calidad estable en ambientes variables. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. p. 212-220. (INIA Serie Técnica; 241).| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA La Estanzuela. |
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| Registros recuperados : 633 | |
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