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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 : |
LEADER 02297naa a2200217 a 4500
001 1055260
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008 2016 bl uuuu u00u1 u #d
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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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha actual : |
15/11/2017 |
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Actualizado : |
16/09/2019 |
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Tipo de producción científica : |
Documentos |
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Autor : |
NIGHBOR, D. |
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Afiliación : |
CEO. Asociación de Productos Forestales de Canadá. |
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Título : |
Bioeconomía en Canadá: experiencia del sector forestal. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
In: Bennadji, Z.; Ferreira, F. (Coord.). Simposiso Biomateriales Forestales, miércoles 11 de octubre, INIA Tacuarembó. Tacuarembó: INIA, 2017. |
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Páginas : |
p. 7 |
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Serie : |
(INIA Serie Actividades de Difusión ; 777) |
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ISSN : |
1688-9258 |
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Idioma : |
Español |
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Contenido : |
La Asociación de Productos Forestales de Canadá es el vocero a nivel nacional e internacional de los productores canadienses de madera, pulpa y papel en los ámbitos de gobierno, comercio y medio ambiente. La industria de productos forestales de Canadá
representa el 2% del PBI, con un monto de 67 billones de dólares canadienses por año. La industria forestal de Canadá involucra más de 600 comunidades y emplea directamente 230.000 personas en todo el país. |
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Palabras claves : |
BIOECONOMÍA. |
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Thesagro : |
FORESTACIÓN. |
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Asunto categoría : |
K10 Producción forestal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/8009/1/SAD-777-7.pdf
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Marc : |
LEADER 01056naa a2200181 a 4500
001 1057786
005 2019-09-16
008 2017 bl uuuu u00u1 u #d
022 $a1688-9258
100 1 $aNIGHBOR, D.
245 $aBioeconomía en Canadá$bexperiencia del sector forestal.
260 $c2017
300 $ap. 7
490 $a(INIA Serie Actividades de Difusión ; 777)
520 $aLa Asociación de Productos Forestales de Canadá es el vocero a nivel nacional e internacional de los productores canadienses de madera, pulpa y papel en los ámbitos de gobierno, comercio y medio ambiente. La industria de productos forestales de Canadá representa el 2% del PBI, con un monto de 67 billones de dólares canadienses por año. La industria forestal de Canadá involucra más de 600 comunidades y emplea directamente 230.000 personas en todo el país.
650 $aFORESTACIÓN
653 $aBIOECONOMÍA
773 $tIn: Bennadji, Z.; Ferreira, F. (Coord.). Simposiso Biomateriales Forestales, miércoles 11 de octubre, INIA Tacuarembó. Tacuarembó: INIA, 2017.
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