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| Registros recuperados : 62 | |
| 21. |  | LADO, B.; VÁZQUEZ, D.; QUINCKE, M.; SILVA, P.; AGUILAR, I.; GUTIÉRREZ, L. Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. Theoretical and Applied Genetics, 1 December 2018, Volume 131, Issue 12, pp. 2719-2731. OPEN ACCESS. Article history: Received: 29 January 2018 / Accepted: 10 September 2018 / Published online: 19 September 2018.
Supplementary materials.
Acknowledgements: We express our appreciation for the effort of the technical personnel of INIA La...| Biblioteca(s): INIA Las Brujas. |
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| 23. | | ROSAS, J.E.; ESCOBAR, M.; MARTÍNEZ, S.; BLANCO, P.H.; PÉREZ DE VIDA, F.; QUERO, G.; GUTIÉRREZ, L.; BONNECARRERE, V. Epistasis and quantitative resistance to Pyricularia oryzae revealed by GWAS in advanced rice breeding populations. Agriculture 2020, 10(12), 622. Open Access. DOI: https://doi.org/10.3390/agriculture10120622 Article history: Received: 30 October 2020 / Revised: 23 November 2020 / Accepted: 24 November 2020 / Published: 11 December 2020.| Biblioteca(s): INIA Treinta y Tres. |
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| 24. | | SILVA, P.; LADO, B.; BRANDARIZ, S.; PEREYRA, S.; GERMAN, S.; VON ZITZEWITZ, J.; GUTIÉRREZ, L.; QUINCKE, M. Herramientas utilizadas y avances en mejoramiento molecular en el Programa de Mejoramiento Genético de Trigo de INIA Uruguay.[Presentación oral]. 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. 81.| Biblioteca(s): INIA La Estanzuela. |
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| 25. | | SILVA, P.; LADO, B.; BRANDARIZ, S.; BERRO, I.; GUTIÉRREZ, L.; PEREYRA, S.; GERMAN, S.; VON ZITZEWITZ, J.; QUINCKE, M. Herramientas utilizadas y avances en mejoramiento molecular en el programa de mejoramiento genético de trigo de Inia Uruguay. 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. 277-285. (INIA Serie Técnica; 241).| Biblioteca(s): INIA La Estanzuela. |
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| 26. | | VEROCAI, M.; BARAIBAR, S.; CAMMAROTA, L.; CARDOZO, F.; GERMAN, S.; GUTIÉRREZ, L.; LOCATELLI, A.; CASTRO, F.; CASTRO, A. Genome-wide association mapping in a nested population representative of elite breeding in Uruguay. 160. (resúmen) Áreas temáticas: Genética. In: Physiological Mini Reviews, 2022, volume 15, Special Issue: III (3er) Congreso Nacional de Biociencias Octubre 2022, Montevideo, Uruguay. p.152-153. Resumen publicado en las jornadas de BIOCIENCIAS: II Jornadas Binacionales Argentina-Uruguay; III Congreso Nacional 2022 "Ciencia para el desarrollo sustentable".| Biblioteca(s): INIA Las Brujas. |
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| 27. | | BERBERIAN, N.; BONNECARRERE, V.; BLANCO, P.H.; PÉREZ DE VIDA, F.; ROSAS, J.E.; MARTÍNEZ, S.; GUTIÉRREZ, L. Model comparison and experimental design simulation including natural field variability in rice crop (Oryza sativa L.). In: UNIVERSIDAD DE LA REPÚBLICA (UDELAR). FACULTAD DE AGRONOMÍA. Resúmenes. Jornadas de Investigación, 8-9 nov., 2018, Montevideo, Uruguay. Montevideo; FAGRO, 2019. p. 14 Trabajo originalmente presentado en: Berberian, N.; Bonecarrere, V.; Blaco, P.; Pérez de Vida, F.; Rosas, J.; Martínez, S.; Gutíerrez, L. 2018. International Biometric Conference, 29. Model comparison and experimental design...| Biblioteca(s): INIA Treinta y Tres. |
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| 28. | | MONTEVERDE, E.; ROSAS, J.E.; BLANCO, P.H.; PÉREZ DE VIDA, F.; BONNECARRERE, V.; QUERO, G.; GUTIERREZ, L.; MCCOUCH, S. Multienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa). Crop Science, 2018, 58:1519-1530. Article history: Accepted on May 09, 2018. Published online June 21, 2018.| Biblioteca(s): INIA Treinta y Tres. |
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| 29. | | MONTEVERDE, E.; GUTIERREZ, L.; BLANCO, P.H.; PÉREZ DE VIDA, F.; ROSAS, J.E.; BONNECARRERE, V.; QUERO, G.; MCCOUCH, SUSAN Integrating molecular markers and environmental covariates to interpret genotype by environment interaction in rice (Oryza sativa L.) grown in subtropical areas. G3: GENES, GENOMES, GENETICS May 1, 2019, v.9 (5), p. 1519-1531. OPEN ACCESS. Article history: Manuscript received February 6, 2019 // Accepted for publication March 5, 2019// Published Early Online March 15, 2019.
Supplemental material available at Figshare: https://doi.org/10.25387/g3.7685636| Biblioteca(s): INIA Treinta y Tres. |
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| 30. | | GUTIÉRREZ, L.; BERBERIAN, N.; CAPETTINI, F.; FROS, D.; GERMAN, S.; PEREYRA, S.; PEREZ, C.; SANDOVAL-ISLAS, S.; CASTRO, A. Spot blotch QTLs in barley germplasm from Latin America: D3. In: INTERNATIONAL WORKSHOP ON BARLEY LEAF BLIGHTS, 4., 2011, Dundee, Scotland, UK. Resistant breeding: poster abstracts. Dundee, James Hutton Institute/BSPP, 2011. p. 43.| Biblioteca(s): INIA La Estanzuela. |
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| 31. | | BONNECARRERE, V.; GARAYCOCHEA, S.; GUTIERREZ, L.; ROSAS, J.E.; BERBERIAN, N.; FERNÁNDEZ, S.; MARTÍNEZ, S.; PÉREZ DE VIDA, F.; BLANCO, P.H. Avances de resultados del proyecto mapeo asociativo para la identificación de marcadores asociados a rendimiento, calidad y resistencia a enfermedades In: PROGRAMA NACIONAL PRODUCCIÓN DE ARROZ; JORNADA ANUAL ARROZ-SOJA, 2013, INIA TREINTA Y TRES, UY. Arroz-soja: resultados experimentales 2012-2013. Treinta y Tres: INIA, 2013. "cap. 6; p. 22-24" (INIA Serie Actividades de Difusión ; 713)| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 32. | | LADO, B.; BATTENFIELD, S.; SILVA, P.; QUINCKE, M.; GUZMAN, C.; SINGH, R.P.; DREISIGACKER, S.; PEÑA, J.; FRITZ, A.; POLAND, J.; GUTIERREZ, L. Comparing strategies to select crosses using genomic prediction in two wheat breeding programs. In: International Wheat Genetics Symposium, 12, Tulln, Austria; April 23-28, 2017; BOKU: University of Natural Resources and Life Sciences, Vienna, Austria. p.88-90.| Biblioteca(s): INIA La Estanzuela. |
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| 33. | | ROSAS, J.E.; MARTÍNEZ, S.; BONNECARRERE, M.; PÉREZ DE VIDA, F.; BLANCO, P.H.; MALOSETTI, M.; JANNINK, J.L.; GUTIÉRREZ, L. Comparison of phenotyping methods for resistance to stem rot and aggregated sheath spot in rice. Crop Science, 2016, v. 56, no. 4, p. 1619-1627. Open Access Article history: Published June 15, 2016.| Biblioteca(s): INIA Las Brujas. |
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| 34. | | BRANDARIZ, S.P.; GONZÁELZ-REYMÚNDEZ, A.; LADO, B.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS, I.; DEL POZO, A.; GUTIÉRREZ, L. Effect of using imputed missing data on QTL detection on a wheat GWAS panel. 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: posters; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014. p. 86.| Biblioteca(s): INIA La Estanzuela. |
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| 35. | | BRANDARIZ, S.P.; GONZÁLEZ-REYMÚNDEZ, A.; LADO, B.; QUINCKE, M.; VON ZITZEWITZ, J.; CASTRO, M.; MATUS, I.; DEL POZO, A.; GUTIÉRREZ , L. Effect of using imputed missing data on QTL detection on a wheat GWAS panel. [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. 304. (INIA Serie Técnica; 241).| Biblioteca(s): INIA La Estanzuela. |
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| 36. | | SILVA, P.; CALVO-SALAZAR, V.; CONDON, F.; QUINCKE, M.; PRITSCH, C.; GUTIÉRREZ, L.; CASTRO, A.; HERRERA-FOESSEL, S.; VON ZITZEWITZ, J.; GERMAN, S. Effects and interactions of genes Lr34, Lr68 and Sr2 on wheat leaf rust adult plant resistance in Uruguay Euphytica, 2015, v. 204, p. 599?608.| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas. |
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| 37. | | ROSAS, J.E.; MARTÍNEZ, S.; BONNECARRERE, V.; BLANCO, P.H.; PÉREZ DE VIDA, F.; GERMAN, S.; JANNINK, J.L.; GUTIÉRREZ, L. Evaluación de nuevos métodos de selección para resistencia a enfermedades del tallo y la vaina en arroz. In: Zorrilla, G.; Martínez, S.; Saravia, H. (Eds.) Arroz 2017. Montevideo (UY): INIA, 2017. p. 31-34. (INIA Serie Técnica; 233)| Biblioteca(s): INIA Treinta y Tres. |
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| 38. | | ROSAS, J.E.; BONNECARRERE, V.; MARTÍNEZ, S.; PÉREZ DE VIDA, F.; BLANCO, P.H.; QUERO, G.; FERNANDEZ, S.; GARAYCOCHEA, S.; JANNINK, J.L.; GUTIÉRREZ, L. GWAS for resistance to stem rot and aggregated sheath spot in advanced temperate rice (Oryza sativa L.) germplasm. [Poster]. In: International Conference on Quantitative Genetics, (5o., 2016, Madison)| Biblioteca(s): INIA Treinta y Tres. |
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| 39. | | ROSAS, J.E.; MARTÍNEZ, S.; BONNECARRERE, V.; PÉREZ DE VIDA, F.; BLANCO, P.H.; FERNANDEZ, S.; GARAYCOCHEA, S.; JANNINK, J.L.; GUTIERREZ, L. GWAS for resistance to stem rot and aggregated sheath spot of rice advanced breeding lines. [Poster]. In: International Symposium on Rice Functional Genomics, (14o., 2016, Montpellier),| Biblioteca(s): INIA Treinta y Tres. |
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| 40. | | ROSAS, J.E.; MARTÍNEZ, S.; BONNECARRERE, V.; BLANCO, P.H.; PÉREZ DE VIDA, F.; GERMAN, S.; JANNINK, J.L.; GUTIÉRREZ, L. Herramientas bioestadísticas para mejoramiento de la resistencia genética a enfermedades del tallo en arroz. In: INIA (Instituto Nacional de Investigación Agropecuaria); INIA Las Brujas; Biotecnología. Jornada de Agrobiotecnología, X. Encuentro Nacional de REDBIO, II. Jornada técnica. Las Brujas, Canelones (UY): INIA, 2017. p. 9-12. (Serie Actividades de Difusión; 780)| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 62 | |
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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 actual : |
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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Circulación / Nivel : |
Internacional - -- |
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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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