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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
005 2017-01-02
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 Las Brujas; INIA Tacuarembó; INIA Treinta y Tres. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
14/10/2019 |
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Tipo de producción científica : |
Boletín de Divulgación |
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Autor : |
DELUCCHI, M.I.; LAMAS, D.; VIÑOLES, F.; DE TORRES, E.; RÍOS, C.; CARRO, S. |
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Afiliación : |
MARIA INES DELUCCHI ZAPARRART, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; D. LAMAS, Estudiantes parte del Trabajo Final para obtención del título de Dr. en Ciencias Veterinarias; F. VIÑOLES, Estudiante: parte del Trabajo Final para obtención del título de Dr. en Ciencias Veterinarias; E. DE TORRES, Universidad de la República (UdelaR)/ Facultad de Veterinaria; C. RÍOS, Universidad de la República (UdelaR)/ Facultad de Veterinaria; S. CARRO, Universidad de la República (UdelaR)/ Facultad de Veterinaria. |
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Título : |
Guía de buenas prácticas agrícolas (BPA) para la producción de leche de calidad |
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Fecha de publicación : |
2008 |
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Fuente / Imprenta : |
Montevideo (Uruguay): INIA, 2008. |
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Páginas : |
50 p |
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Serie : |
(INIA Boletín de Divulgación ; 93) |
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ISBN : |
978-9974-38-248-0 |
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ISSN : |
1510-7396 |
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Idioma : |
Español |
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Contenido : |
Las BPA para la producción de leche de calidad se definen como \"las acciones involucradas en la producción de leche orientadas a asegurar la protección de la higiene y la salud humana y del medio ambiente, mediante métodos ecológicamente más seguros, higiénicamente aceptables y económicamente factibles\". Constituyen una herramienta que persigue la sostenibilidad ambiental, económica y social de los establecimientos lecheros, a la vez de estar orientada a la obtención de un producto inocuo y saludable para el consumidor. Esta publicación es una guía con especificaciones y fundamentos técnicos para que pueda realizarse el manual correspondiente en cada establecimiento lechero sin importar su tamaño ni ubicación. |
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Thesagro : |
ALIMENTACION; BIENESTAR ANIMAL; BIENESTAR SOCIAL; BIOSEGURIDAD; BUENAS PRACTICAS AGRICOLAS; CALIDAD; HIGIENE; HIGIENE DE LA LECHE; HIGIENE DE LOS ALIMENTOS; LACTEOS; LECHE; LEGISLACION; MEDIO AMBIENTE; PRODUCCION LECHERA; REGISTRO; SANIDAD ANIMAL; URUGUAY. |
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Asunto categoría : |
A50 Investigación agraria
L70 Ciencias veterinarias e higiene - Aspectos generales |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/2791/1/18429230309132208.pdf
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Marc : |
LEADER 01840nam a2200421 a 4500
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008 2008 bl uuuu u00u1 u #d
020 $a978-9974-38-248-0
022 $a1510-7396
100 1 $aDELUCCHI, M.I.
245 $aGuía de buenas prácticas agrícolas (BPA) para la producción de leche de calidad
260 $aMontevideo (Uruguay): INIA$c2008
300 $a50 p
490 $a(INIA Boletín de Divulgación ; 93)
520 $aLas BPA para la producción de leche de calidad se definen como \"las acciones involucradas en la producción de leche orientadas a asegurar la protección de la higiene y la salud humana y del medio ambiente, mediante métodos ecológicamente más seguros, higiénicamente aceptables y económicamente factibles\". Constituyen una herramienta que persigue la sostenibilidad ambiental, económica y social de los establecimientos lecheros, a la vez de estar orientada a la obtención de un producto inocuo y saludable para el consumidor. Esta publicación es una guía con especificaciones y fundamentos técnicos para que pueda realizarse el manual correspondiente en cada establecimiento lechero sin importar su tamaño ni ubicación.
650 $aALIMENTACION
650 $aBIENESTAR ANIMAL
650 $aBIENESTAR SOCIAL
650 $aBIOSEGURIDAD
650 $aBUENAS PRACTICAS AGRICOLAS
650 $aCALIDAD
650 $aHIGIENE
650 $aHIGIENE DE LA LECHE
650 $aHIGIENE DE LOS ALIMENTOS
650 $aLACTEOS
650 $aLECHE
650 $aLEGISLACION
650 $aMEDIO AMBIENTE
650 $aPRODUCCION LECHERA
650 $aREGISTRO
650 $aSANIDAD ANIMAL
650 $aURUGUAY
700 1 $aLAMAS, D.
700 1 $aVIÑOLES, F.
700 1 $aDE TORRES, E.
700 1 $aRÍOS, C.
700 1 $aCARRO, S.
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