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 | Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha : |
11/05/2018 |
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Actualizado : |
28/05/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
MONTEVERDE, E.; ROSAS, J.E.; BLANCO, P.H.; PÉREZ DE VIDA, F.; BONNECARRERE, V.; QUERO, G.; GUTIERREZ, L.; MCCOUCH, S. |
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Afiliación : |
ELIANA MONTEVERDE, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, USA.; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PEDRO HORACIO BLANCO BARRAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA VICTORIA BONNECARRERE MARTINEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GASTÓN QUERO CORRALLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIERREZ, Department of Agronomy, University of Wisconsin, WI, USA.; SUSAN MCCOUCH, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, USA. |
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Título : |
Multienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa). |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Crop Science, 2018, 58:1519-1530. |
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DOI : |
10.2135/cropsci2017.09.0564 |
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Idioma : |
Inglés |
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Notas : |
Article history: Accepted on May 09, 2018. Published online June 21, 2018. |
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Contenido : |
ABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to
accommodate correlation among environments.
We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when predicting the performance of lines across years. We also show that, for some traits, high prediction accuracies can be obtained in untested years, which is important for resource allocation in small breeding programs. MenosABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to
accommodate correlation among environments.
We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when pr... Presentar Todo |
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Palabras claves : |
GENOTYPE X ENVIRONMENT INTERACTION; INTERACCIONES GENOTIPO-AMBIENTE. |
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Thesagro : |
ARROZ; GENOTIPOS; RICE. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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Marc : |
LEADER 02635naa a2200289 a 4500
001 1058574
005 2019-05-28
008 2018 bl uuuu u00u1 u #d
024 7 $a10.2135/cropsci2017.09.0564$2DOI
100 1 $aMONTEVERDE, E.
245 $aMultienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa).$h[electronic resource]
260 $c2018
500 $aArticle history: Accepted on May 09, 2018. Published online June 21, 2018.
520 $aABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to accommodate correlation among environments. We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when predicting the performance of lines across years. We also show that, for some traits, high prediction accuracies can be obtained in untested years, which is important for resource allocation in small breeding programs.
650 $aARROZ
650 $aGENOTIPOS
650 $aRICE
653 $aGENOTYPE X ENVIRONMENT INTERACTION
653 $aINTERACCIONES GENOTIPO-AMBIENTE
700 1 $aROSAS, J.E.
700 1 $aBLANCO, P.H.
700 1 $aPÉREZ DE VIDA, F.
700 1 $aBONNECARRERE, V.
700 1 $aQUERO, G.
700 1 $aGUTIERREZ, L.
700 1 $aMCCOUCH, S.
773 $tCrop Science, 2018, 58:1519-1530.
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| 1. |  | Aguilar, I.; Pravia, M.I.; Ravagnolo, O.; Chiappesoni, G.; Mattos, M.; Ahlig, I.; Urioste, J.; Naya, H. Servicio de evaluación de reproductores Aberdeen Angus Las Brujas, Canelones (Uruguay): INIA, 2004. 23 p| Biblioteca(s): INIA La Estanzuela. |
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| 3. | | AGUILAR, I.; RAVAGNOLO, O.; PRAVIA, M.I. Evaluación genética poblacional raza Braford. Unidad Experimental La Magnolia, 3 abril, 2009. ln: INIA TACUAREMBÓ. Soluciones tecnológicas para la raza Braford: gira técnica. En el marco del CONGRESO MUNDIAL BRAFORD, 4., Punta del Este, Uruguay, 2009. Tacuarembó: INIA, 2009. p. 42-44.| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Tacuarembó. |
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| 6. | | CIAPPESONI, G.; PRAVIA, M.; RAVAGNOLO, O.; AGUILAR, I. Objetivos de selección y progreso genético. ln: INIA Tacuarembó. Sociedad Criadores Merino Australiano del Uruguay. SUL. Proyecto Merino Fino del Uruguay: quinta distribución de carneros generados en el núcleo fundacional de merino fino de la Unidad Experimental Glencoe, INIA Tacuarembó, 1999 - 2004. Glencoe, Paysandú, 10 diciembre, 2004. Tacuarembó (Uruguay): INIA, 2004. p. 55-62 (INIA Serie Actividades de Difusión ; 392)| Biblioteca(s): INIA Tacuarembó. |
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| 9. | | DINI, Y.; CAJARVILLE, C.; GERE, J.I.; FERNANDEZ. S.; FRAGA, M.; PRAVIA, M.I.; NAVAJAS, E.; CIGANDA, V. Association between residual feed intake and enteric methane emissions in Hereford steers. Translational Animal Science, v. 3, Issue 1, 1 January 2019, Pages 65-72. Doi: https://doi.10.1093/tas/txy111. Article history: Published: 01 October 2018 // Received: 12 September 2018.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA La Estanzuela. |
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| 12. | | MONTOSSI, F.; GUTIERREZ, D.; PRAVIA, M.I.; PORCILE, M.; PORCILE, V.; JAURENA, M.; AYALA, W. Caracterización del componente pasturas y forrajes en predios del GIPROCAR II: disponibilidad, crecimiento, composición botánica y valor nutritivo. In: MONTOSSI, F. (Ed.). Invernada de precisión: Pasturas, Calidad de Carne, Genética, Gestión Empresarial e Impacto Ambiental (GIPROCAR II) Montevideo (UY): INIA, 2013. p. 69-107 (Serie Técnica; 211)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 14. | | NAVAJAS, E.; MACEDO, F.; RAVAGNOLO, O.; AGUILAR, I.; CLARIGET, J.; LEMA, O.M.; PERAZA, P.; PRAVIA, M.I.; DALLA RIZZA, M.; CIAPPESONI, G. Herramientas genómicas para mejorar la eficiencia de alimentación y la calidad de canal de la raza Hereford. 3 - SIMPOSIOS "MEJORA GENÉTICA EN PRODUCCIÓN Y CALIDAD DE CARNE EN ESPECIES DE INTERÉS ECONÓMICO" In: JOURNAL OF BASIC & APPLIED GENETICS, 2016, Vol.27, Iss. 1 (Supp.). XVI LATIN AMERICAN CONGRESS OF GENETICS, IV CONGRESS OF THE URUGUAYAN SOCIETY OF GENETICS, XLIX ANNUAL MEETING OF THE GENETICS SOCIETY OF CHILE, XLV ARGENTINE CONGRESS OF GENETICS, 9-12 October 2016. PROCEEDINGS. Montevideo (Uruguay): SAG, 2016. p. 28| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Las Brujas. |
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| 16. | | NAVAJAS, E.; PRAVIA, M.I.; AGUIRRE, L.; MACEDO, F.; DE LA FUENTE, J.; MENDIOLA, B.; DEL PINO, M.L.; RAVAGNOLO, O.; LEMA, O.M.; PERAZA, P.; AGUILAR, I.; CARRAU, J.; CIAPPESONI, G. Tercer año de la evaluación de eficiencia de conversión de kiyú. Anuario Hereford (Montevideo), p. 182-186, 2016.| Biblioteca(s): INIA La Estanzuela; INIA Treinta y Tres. |
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| 18. | | NAVAJAS, E.; PRAVIA, M.I.; LEMA, O.M.; CLARIGET, J.M.; AGUILAR, I.; RAVAGNOLO, O.; BRITO, G.; PERAZA, P.; DALLA RIZZA, M.; MONTOSSI, F. Genetic improvement of feed efficiency and carcass and meat quality of hereford cattle by genomics. In: INTERNATIONAL CONGRESS OF MEAT SCIENCE AND TECHNOLOGY, 60., 2014, Punta del Este, Uruguay: ICOMST. Oral Poster Presentation :Sessions I and II: 2 Posters.| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA La Estanzuela; INIA Tacuarembó. |
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| 19. | | NAVAJAS, E.; PRAVIA, M.I.; LEMA, O.M.; RAVAGNOLO, O.; AGUILAR, I.; BRITO, G.; CLARIGET, J.; DALLA RIZZA, M.; MONTOSSI, F. Selección genómica en eficiencia de conversión y calidad de canal de la raza Hereford en Uruguay. Anuario Hereford (Montevideo), p. 160-172, 2014.| Biblioteca(s): INIA La Estanzuela. |
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| Registros recuperados : 58 | |
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