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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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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha : |
11/05/2018 |
Actualizado : |
28/05/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
MONTEVERDE, E.; ROSAS, J.E.; BLANCO, P.H.; PÉREZ DE VIDA, F.; BONNECARRERE, V.; QUERO, G.; GUTIERREZ, L.; MCCOUCH, S. |
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. |
Título : |
Multienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa). |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
Crop Science, 2018, 58:1519-1530. |
DOI : |
10.2135/cropsci2017.09.0564 |
Idioma : |
Inglés |
Notas : |
Article history: Accepted on May 09, 2018. Published online June 21, 2018. |
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 |
Palabras claves : |
GENOTYPE X ENVIRONMENT INTERACTION; INTERACCIONES GENOTIPO-AMBIENTE. |
Thesagro : |
ARROZ; GENOTIPOS; RICE. |
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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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3. | | GUTIERREZ, L.; BORGES, A.; QUERO, G.; GONZALEZ-REYMUNDEZ, A.; BERRO, I.; LADO, B.; CASTRO, A. Biostatistical tools for plant breeding in the genomics era. 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.46-57. (INIA Serie Técnica; 241).Tipo: Capítulo en Libro Técnico-Científico |
Biblioteca(s): INIA La Estanzuela. |
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6. | | QUERO, G.; BONNECARRERE, V.; FERNÁNDEZ, S.; SILVA, P.; SIMONDI, S.; BORSANI, O. La eficiencia del uso de luz y la partición energética en arroz es genotipo dependiente. [resumen] In: INIA (Instituto Nacional de Investigación Agropecuaria); INIA Las Brujas; Biotecnología. Jornada de Agrobiotecnología, XI. Encuentro Nacional de REDBIO, III. Jornada técnica. Las Brujas, Canelones (UY): INIA, 2018. p.8-9 (Serie Actividades de Difusión; 786)Biblioteca(s): INIA Las Brujas. |
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7. | | LARZABAL, J.; BONNECARRERE, V.; QUERO, G.; BENTOS, D.; RODRIGUEZ, M.; STEWART, S. P13. Agresividad de aislamientos de Diaporthe caulivora, principal agente causal del cancro del tallo de la soja en Uruguay. [Poster]. Posters. In: Sociedad Uruguaya de Fitopatología (SUFIT). Jornada Uruguaya de Fitopatología, 7., Jornada Uruguaya de Protección Vegetal, 5., 10 noviembre 2023, Montevideo, Uruguay. Libro de resúmenes. 30 años SUFIT, 1993-2023. Montevideo (UY): Sociedad Uruguay de Fitopatología (SUFIT), 2023. p. 40. Fuente de financiamiento: ANII Beca de Doctorado POS_NAC_2020_1_164264. -- Autor correspondencia: e-mail: larzabaljc@gmail.comTipo: Abstracts/Resúmenes |
Biblioteca(s): INIA Las Brujas. |
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8. | | QUERO, G.; SIMONDI, S.; CERETTA, S.; OTERO, A.; GARAYCOCHEA, S.; FERNANDEZ, S.; BORSANI, O.; BONNECARRERE, V. An integrative analysis of yield stability for a GWAS in a small soybean breeding population. Crop Science, May 2021, volume 61, issue 3, pages 19003-1914. Doi: https://doi.org/10.1002/csc2.20490 Article history: Received, 3 November 2020; Accepted, 11 February 2021; Published online, 14 April 2021.
Associate Editor: Junping Chen.
The authors thank Edgardo Rey and Wanda Iriarte for technical assistance in field experiment and...Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
Biblioteca(s): INIA Las Brujas. |
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11. | | 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.Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : -- - -- |
Biblioteca(s): INIA Treinta y Tres. |
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12. | | 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/g3.7685636Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : -- - -- |
Biblioteca(s): INIA Treinta y Tres. |
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13. | | REBUFFO, M.; MONZA, J.; CASTILLO, A.; SANJUÁN, J.; REYNO, R.; BATISTA, L.; QUERO, G.; CUITIÑO, M.J. Multidisciplinary approaches to improve forage legume species for stressing environments in South America. In: INTERNATIONAL SYMPOSIUM ON THE MOLECULAR BREEDING OF FORAGE AND TURF, 7., 2012, Salt Lake City, UT, US. Proceedings: other invited oral presentation abstracts. Salt Lake City, UT: MBFT, 2012. p. 62.Tipo: Abstracts/Resúmenes |
Biblioteca(s): INIA La Estanzuela. |
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14. | | 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.Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
Biblioteca(s): INIA Treinta y Tres. |
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16. | | BONNECARRERE, V.; QUERO, G.; MONTEVERDE, E.; ROSAS, J.E.; PÉREZ DE VIDA, F.; CRUZ, M.; CORREDOR, E.; GARAYCOCHEA, S.; MONZA, J.; BORSANI, O. Candidate gene markers associated with cold tolerance in vegetative stage of rice (Oryza sativa L.). Euphytica, 2015, v. 203 no. 2, p. 385-398. p. 385-398. Received: 17 June 2014 / Accepted: 23 October 2014 / Published online: 2 November 2014Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
Biblioteca(s): INIA Las Brujas; INIA Treinta y Tres. |
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17. | | 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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18. | | QUERO, G.; GUTIÉRREZ, L.; FERNANDEZ, S.; BLANCO, P.H.; PÉREZ DE VIDA, F.; GARAYCOCHEA, S.; MONTEVERDE, E.; MCCOUCH, M.; ROSAS, J.E.; BERBERIAN, N.; SIMONDIS, S.; BONNECARRERE, V. Genome wide association (GWAS) discovers rice granin quality genes in the starch metabolism, grain size and cell wall synthesis pathways. MV 24 - COMUNICACIONES LIBRES - MV. MEJORAMIENTO VEGETAL 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. 292Tipo: Trabajos en Congresos/Conferencias |
Biblioteca(s): INIA Las Brujas. |
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19. | | MONTEVERDE, E.; BLANCO, P.H.; BONNECARRERE, V.; GUTIÉRREZ, L.; ROSAS, J.E.; QUERO, G.; BARBERIÁN, N.; GARAYCOCHEA, S.; FERNANDEZ, S.; MCCOUCH, S. Implementing Genomic Selection in a temperate Rice Breeding Program. [P0716] In: International Plant & Animal Genome, Conference PAG XXIV, "The largest Ag-genomics Meeting in the World San Diego, CA, USA; January 9-13, 2016. [Abstract]Tipo: Abstracts/Resúmenes |
Biblioteca(s): INIA Las Brujas. |
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20. | | ROSAS, J.E.; BONNECARRERE, V.; MARTÍNEZ, S.; PÉREZ DE VIDA, F.; BLANCO, P.H.; QUERO, G.; FERNANDEZ, S.; GARAYCOCHEA, S.; JANNINK, J.; GUTIERREZ, L. Mapeo asociativo de la resistencia a enfermedades del tallo y la vaina en germoplasma avanzado de arroz. 2 - SIMPOSIOS "MEJORAMIENTO GENÉTICO POR RESISTENCIA A ENFERMEDADES E INTERACCIONES PLANTA-PATÓGENO" 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. 61Tipo: Trabajos en Congresos/Conferencias |
Biblioteca(s): INIA Las Brujas. |
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Registros recuperados : 25 | |
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