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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
14/09/2023 |
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Actualizado : |
14/09/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
REBOLLO, I.; AGUILAR, I.; PÉREZ DE VIDA, F.; MOLINA, F.; GUTIÉRREZ, L.; ROSAS, J.E. |
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Afiliación : |
MARÍA INÉS REBOLLO PANUNCIO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO MOLINA CASELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZEPARTMENT OF STATISTICS, UNIVERSITY DE LA REPÚBLICA, COLLEGE OF AGRICULTURE, GARZÓN 780, MONTEVIDEO, MONTEVIDEO, URUGUAY DEPARTMENT OF AGRONOMY, UNIVERSITY OF WISCONSIN–MADISON, 1575 LINDEN DRIVE, MADISON, WI, UNITED STATES, Department of Statistics, University de la República, College of Agriculture, Montevideo, Uruguay; Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Drive, Madison, WI, United States; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay. |
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Título : |
Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations. |
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Complemento del título : |
Original article. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Crop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS. |
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ISSN : |
0011-183X (print); 1435-0653 (electronic). |
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DOI : |
10.1002/csc2.21029 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ ) |
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Contenido : |
ABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America. MenosABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiati... Presentar Todo |
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Palabras claves : |
Genotype by environment interaction (GEI); Random regression models (RRMs); Rice (Oryza sativa L.). |
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Asunto categoría : |
-- |
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URL : |
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21029
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Marc : |
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022 $a0011-183X (print); 1435-0653 (electronic).
024 7 $a10.1002/csc2.21029$2DOI
100 1 $aREBOLLO, I.
245 $aGenotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ )
520 $aABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America.
653 $aGenotype by environment interaction (GEI)
653 $aRandom regression models (RRMs)
653 $aRice (Oryza sativa L.)
700 1 $aAGUILAR, I.
700 1 $aPÉREZ DE VIDA, F.
700 1 $aMOLINA, F.
700 1 $aGUTIÉRREZ, L.
700 1 $aROSAS, J.E.
773 $tCrop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS.
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Registro original : |
INIA Las Brujas (LB) |
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| Registros recuperados : 14 | |
| 1. |  | REBOLLO, I.; AGUILAR, I.; ROSAS, J.E. Comparison of conventional and GBLUP Genome-Wide Association mapping of cold tolerance in advanced rice breeding populations. [Abstract] + [Poster]. In: International Temperate Rice Conference (7., 2020, Pelotas, RS), Science & Innovation: feeding a world of 10 billion people: proceedings. Pelotas RS, Brasil, February 9-12, 2020. Brasília, DF : Embrapa, 2020.| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Treinta y Tres. |
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| 4. | | SCHEFFEL, S.; REBOLLO, I.; PÉREZ DE VIDA, F.; ROSAS, J.E. Consolidating INIA's Rice Breeding Program Database, phase I: historical índica trials.[Abstract] + [Poster]. In: International Temperate Rice Conference (7., 2020, Pelotas, RS), Science & Innovation: feeding a world of 10 billion people: proceedings. Pelotas RS, Brasil, February 9-12, 2020. Brasília, DF : Embrapa, 2020.| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Treinta y Tres. |
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| 5. | | SANDRO, P.; REBOLLO, I.; GAIERO, P.; VAIO, M.; VILARO, F.; SPERANZA, P. Diseño de microsatélites para Solanum commersonii a partir de información genómica. GGM 44 - COMUNICACIONES LIBRES - GGM. GENÓMICA Y GENÉTICA MOLECULAR 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. 276| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Las Brujas. |
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| 6. | | REBOLLO, I.; AGUILAR, I.; PÉREZ DE VIDA, F.; MOLINA, F.; GUTIÉRREZ, L.; ROSAS, J.E. Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations. Original article. Crop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS. Article history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 7. | | MONTEVERDE, E.; SCHEFFEL, S.; REBOLLO, I.; MOLINA, F.; PÉREZ DE VIDA, F.; ROSAS, J.E. Ganancia genética del Programa de Mejoramiento Genético de Arroz de INIA. In: Terra, J. A.; Martínez, S.; Saravia, H.; Mesones, B. (Eds.) Arroz 2021. Montevideo (UY): INIA, 2022. p. 68-70. (INIA Serie Técnica; 262)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 8. | | REBOLLO, I.; SCHEFFEL, S.; IRIARTE, W.; BLANCO, P.H.; MOLINA, F.; PÉREZ DE VIDA, F.; ROSAS, J.E. Consolidación de los datos históricos del Programa de Mejoramiento Genético de Arroz en una base de datos. In: Terra, J. A.; Martínez, S.; Saravia, H.; Mesones, B.; Álvarez, O. (Eds.) Arroz 2020. Montevideo (UY): INIA, 2020. p. 5-8. (INIA Serie Técnica; 257)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 9. | | ROSAS, J.E.; ALE, L.; REBOLLO, I.; SCHEFFEL, S.; AGUILAR, I.; MOLINA, F.; PÉREZ DE VIDA, F. Boosting INIA's Rice Breeding Program with molecular quantitative genetics approaches. [Abstract]. In: International Temperate Rice Conference (7., 2020, Pelotas, RS), Science & Innovation: feeding a world of 10 billion people: proceedings. Pelotas RS, Brasil, February 9-12, 2020. Brasília, DF : Embrapa, 2020.| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Treinta y Tres. |
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| 10. | | REBOLLO, I.; PÉREZ DE VIDA, F.; BLANCO, P.H.; MOLINA, F.; CRUZ, M.; BONNECARRERE, V.; GARAYCOCHEA, S.; ROSAS, J.E. Mapeo asociado de tolerancia a bajas temperaturas en germoplasma avanzado de arroz. [Poster]. En: Jornadas de Investigación, Facultad de Agronomía (UdelaR), 8-9, nov. 2018, Montevideo, UY.| Biblioteca(s): INIA Treinta y Tres. |
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| 11. | | REBOLLO, I.; CRUZ, M.; PÉREZ DE VIDA, F.; BLANCO, P.H.; MOLINA, F.; BONNECARRERE, V.; GARAYCOCHEA, S.; ROSAS, J.E. Mapeo asociativo de tolerancia a bajas temperaturas en germoplasma avanzado de arroz. In: UNIVERSIDAD DE LA REPÚBLICA (UDELAR). FACULTAD DE AGRONOMÍA. Resúmenes. Jornadas de Investigación, 8-9 nov., 2018, Montevideo, Uruguay. Montevideo; FAGRO, 2019. p. 18| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Treinta y Tres. |
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| 12. | | REBOLLO, I.; SCHEFFEL, S.; BLANCO, P.H.; MOLINA, F.; MARTÍNEZ, S.; CARRACELAS, G.; PÉREZ DE VIDA, F.; ROSAS, J.E. Instituto Nacional de Investigación Agropecuaria (INIA) Rice Breeding Program Historical Dataset. [Dataset]. DRYAD Dataset, 2024. https://doi.org/10.5061/dryad.x69p8czn8 Correspondence author: Juan E. Rosas, email: jrosas@inia.org.uy -- Publication date: February 16, 2024. -- This dataset is embargoed and will be released when the associated article is published. Lists of files and downloads will become...| Tipo: Artículos Indexados |
| Biblioteca(s): INIA Las Brujas. |
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| 13. | | ROSAS, J.E.; SPRUNCK, B.; IRIARTE, W.; REBOLLO, I.; BONNECARRERE, V.; MOLINA, F.; BLANCO, P.H.; PÉREZ DE VIDA, F. Validación de SNP asociados a variables de interés en germoplasma Japónica tropical de INIA. In: Terra, J. A.; Martínez, S.; Saravia, H. (Eds.) Arroz 2019. Montevideo (UY): INIA, 2019. p. 89-92. (INIA Serie Técnica; 250)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 14. | | REBOLLO, I.; SCHEFFEL, S.; BLANCO, P.H.; MOLINA, F.; MARTÍNEZ, S.; CARRACELAS, G.; AGUILAR, I.; PÉREZ DE VIDA, F.; ROSAS, J.E. Consolidating twenty-three years of historical data from an irrigated subtropical rice breeding program in Uruguay. Crop Science, 2023. https://doi.org/10.1002/csc2.20955 - [Article in Press]. Article history: First published 15 March 2023. -- Corresponding author: jrosas@inia.org.uy --| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 14 | |
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