Gene-based model to predict heading date in wheat based on allelic characterization and environmental drivers

Autores
Jardón, Mariana; Alvarez Prado, Santiago; Vanzetti, Leonardo Sebastian; Gonzalez, Fernanda Gabriela; Pérez Gianmarco, Thomas; Gomez, Dionisio Tomas; Serrago, Román A.; Dubcovsky, Jorge; Fernandez Long, Maria Elena; Miralles, Daniel Julio
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
While numerous wheat phenology prediction models are available, most of them are constrained to using variety-dependent coefficients. The overarching objective of this study was to calibrate a gene-based model to predict wheat heading date that allows breeders to select specific gene combinations that would head within the optimal window for a given environment independently of varietal genetic background. A dataset with a total of 49 Argentine wheat cultivars and two recombinant inbred lines was chosen to cover a wide range of allelic combinations for major vernalization, photoperiod, and earliness per se genes. The model was validated using independent data from an Argentine wheat trial network that includes sites from a wide latitudinal range. Ultimately, using this gene-based model, simulations were made to identify optimal gene combinations (ideotypes) × site combinations in contrasting locations. The selected model accurately predicted heading date with an overall median error of 4.6 d. This gene-based crop model for wheat phenology allowed the identification of groups of gene combinations predicted to produce heads within a low-risk window and can be adapted to predict other phenological stages based on accessible climatic information and publicly available molecular markers, facilitating its adoption in wheat-growing regions worldwide.
EEA Marcos Juárez
Fil: Jardón, Mariana. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Alvarez Prado, Santiago. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Alvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Alvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Sistemas de Cultivos Extensivos-GIMUCE; Argentina
Fil: Vanzetti, Leonardo Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Vanzetti, Leonardo Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; Argentina
Fil: González, Fernanda G. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Departamento de Ecofisiología; Argentina
Fil: González, Fernanda G. Universidad Nacional del Noroeste de la Provincia de Buenos Aires (UNNOBA). Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); Argentina
Fil: González, Fernanda G. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); Argentina
Fil: Pérez Gianmarco, Thomas. Universidad Nacional del Noroeste de la Provincia de Buenos Aires (UNNOBA). Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); Argentina
Fil: Pérez Gianmarco, Thomas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); Argentina
Fil: Gómez, Dionisio Tomás. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; Argentina
Fil: Serrago, Román A. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Serrago, Román A. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Dubcovsky, Jorge. University of California-Davis. Department of Plant Sciences; Estados Unidos
Fil: Dubcovsky, Jorge. Howard Hughes Medical Institute; Estados Unidos
Fil: Fernández Long, M.E. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Fernández Long, M.E. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Miralles, Daniel J. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Miralles, Daniel J. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fuente
Journal of Experimental Botany : eraf049. (Published: 07 February 2025)
Materia
Trigo
Espigueo
Interacción Genotipo Ambiente
Fotoperiodismo
Fisiología Vegetal
Vernalización
Wheat
Heading
Genotype-environment Interaction
Photoperiodicity
Plant Physiology
Vernalization
Nivel de accesibilidad
acceso restringido
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/21626
Acceder
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oai_identifier_str oai:localhost:20.500.12123/21626
network_acronym_str INTADig
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network_name_str INTA Digital (INTA)
spelling Gene-based model to predict heading date in wheat based on allelic characterization and environmental driversJardón, MarianaAlvarez Prado, SantiagoVanzetti, Leonardo SebastianGonzalez, Fernanda GabrielaPérez Gianmarco, ThomasGomez, Dionisio TomasSerrago, Román A.Dubcovsky, JorgeFernandez Long, Maria ElenaMiralles, Daniel JulioTrigoEspigueoInteracción Genotipo AmbienteFotoperiodismoFisiología VegetalVernalizaciónWheatHeadingGenotype-environment InteractionPhotoperiodicityPlant PhysiologyVernalizationWhile numerous wheat phenology prediction models are available, most of them are constrained to using variety-dependent coefficients. The overarching objective of this study was to calibrate a gene-based model to predict wheat heading date that allows breeders to select specific gene combinations that would head within the optimal window for a given environment independently of varietal genetic background. A dataset with a total of 49 Argentine wheat cultivars and two recombinant inbred lines was chosen to cover a wide range of allelic combinations for major vernalization, photoperiod, and earliness per se genes. The model was validated using independent data from an Argentine wheat trial network that includes sites from a wide latitudinal range. Ultimately, using this gene-based model, simulations were made to identify optimal gene combinations (ideotypes) × site combinations in contrasting locations. The selected model accurately predicted heading date with an overall median error of 4.6 d. This gene-based crop model for wheat phenology allowed the identification of groups of gene combinations predicted to produce heads within a low-risk window and can be adapted to predict other phenological stages based on accessible climatic information and publicly available molecular markers, facilitating its adoption in wheat-growing regions worldwide.EEA Marcos JuárezFil: Jardón, Mariana. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Alvarez Prado, Santiago. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Alvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Alvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Sistemas de Cultivos Extensivos-GIMUCE; ArgentinaFil: Vanzetti, Leonardo Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Vanzetti, Leonardo Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; ArgentinaFil: González, Fernanda G. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Departamento de Ecofisiología; ArgentinaFil: González, Fernanda G. Universidad Nacional del Noroeste de la Provincia de Buenos Aires (UNNOBA). Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); ArgentinaFil: González, Fernanda G. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); ArgentinaFil: Pérez Gianmarco, Thomas. Universidad Nacional del Noroeste de la Provincia de Buenos Aires (UNNOBA). Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); ArgentinaFil: Pérez Gianmarco, Thomas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); ArgentinaFil: Gómez, Dionisio Tomás. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; ArgentinaFil: Serrago, Román A. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Serrago, Román A. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Dubcovsky, Jorge. University of California-Davis. Department of Plant Sciences; Estados UnidosFil: Dubcovsky, Jorge. Howard Hughes Medical Institute; Estados UnidosFil: Fernández Long, M.E. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Fernández Long, M.E. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Miralles, Daniel J. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Miralles, Daniel J. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaOxford University Pressinfo:eu-repo/date/embargoEnd/2026-03-122025-03-12T11:56:27Z2025-03-12T11:56:27Z2025-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/21626https://academic.oup.com/jxb/advance-article-abstract/doi/10.1093/jxb/eraf049/80050220022-09571460-2431https://doi.org/10.1093/jxb/eraf049Journal of Experimental Botany : eraf049. (Published: 07 February 2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:47:11Zoai:localhost:20.500.12123/21626instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:47:12.137INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Gene-based model to predict heading date in wheat based on allelic characterization and environmental drivers
title Gene-based model to predict heading date in wheat based on allelic characterization and environmental drivers
spellingShingle Gene-based model to predict heading date in wheat based on allelic characterization and environmental drivers
Jardón, Mariana
Trigo
Espigueo
Interacción Genotipo Ambiente
Fotoperiodismo
Fisiología Vegetal
Vernalización
Wheat
Heading
Genotype-environment Interaction
Photoperiodicity
Plant Physiology
Vernalization
title_short Gene-based model to predict heading date in wheat based on allelic characterization and environmental drivers
title_full Gene-based model to predict heading date in wheat based on allelic characterization and environmental drivers
title_fullStr Gene-based model to predict heading date in wheat based on allelic characterization and environmental drivers
title_full_unstemmed Gene-based model to predict heading date in wheat based on allelic characterization and environmental drivers
title_sort Gene-based model to predict heading date in wheat based on allelic characterization and environmental drivers
dc.creator.none.fl_str_mv Jardón, Mariana
Alvarez Prado, Santiago
Vanzetti, Leonardo Sebastian
Gonzalez, Fernanda Gabriela
Pérez Gianmarco, Thomas
Gomez, Dionisio Tomas
Serrago, Román A.
Dubcovsky, Jorge
Fernandez Long, Maria Elena
Miralles, Daniel Julio
author Jardón, Mariana
author_facet Jardón, Mariana
Alvarez Prado, Santiago
Vanzetti, Leonardo Sebastian
Gonzalez, Fernanda Gabriela
Pérez Gianmarco, Thomas
Gomez, Dionisio Tomas
Serrago, Román A.
Dubcovsky, Jorge
Fernandez Long, Maria Elena
Miralles, Daniel Julio
author_role author
author2 Alvarez Prado, Santiago
Vanzetti, Leonardo Sebastian
Gonzalez, Fernanda Gabriela
Pérez Gianmarco, Thomas
Gomez, Dionisio Tomas
Serrago, Román A.
Dubcovsky, Jorge
Fernandez Long, Maria Elena
Miralles, Daniel Julio
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Trigo
Espigueo
Interacción Genotipo Ambiente
Fotoperiodismo
Fisiología Vegetal
Vernalización
Wheat
Heading
Genotype-environment Interaction
Photoperiodicity
Plant Physiology
Vernalization
topic Trigo
Espigueo
Interacción Genotipo Ambiente
Fotoperiodismo
Fisiología Vegetal
Vernalización
Wheat
Heading
Genotype-environment Interaction
Photoperiodicity
Plant Physiology
Vernalization
dc.description.none.fl_txt_mv While numerous wheat phenology prediction models are available, most of them are constrained to using variety-dependent coefficients. The overarching objective of this study was to calibrate a gene-based model to predict wheat heading date that allows breeders to select specific gene combinations that would head within the optimal window for a given environment independently of varietal genetic background. A dataset with a total of 49 Argentine wheat cultivars and two recombinant inbred lines was chosen to cover a wide range of allelic combinations for major vernalization, photoperiod, and earliness per se genes. The model was validated using independent data from an Argentine wheat trial network that includes sites from a wide latitudinal range. Ultimately, using this gene-based model, simulations were made to identify optimal gene combinations (ideotypes) × site combinations in contrasting locations. The selected model accurately predicted heading date with an overall median error of 4.6 d. This gene-based crop model for wheat phenology allowed the identification of groups of gene combinations predicted to produce heads within a low-risk window and can be adapted to predict other phenological stages based on accessible climatic information and publicly available molecular markers, facilitating its adoption in wheat-growing regions worldwide.
EEA Marcos Juárez
Fil: Jardón, Mariana. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Alvarez Prado, Santiago. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Alvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Alvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Sistemas de Cultivos Extensivos-GIMUCE; Argentina
Fil: Vanzetti, Leonardo Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Vanzetti, Leonardo Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; Argentina
Fil: González, Fernanda G. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Departamento de Ecofisiología; Argentina
Fil: González, Fernanda G. Universidad Nacional del Noroeste de la Provincia de Buenos Aires (UNNOBA). Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); Argentina
Fil: González, Fernanda G. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); Argentina
Fil: Pérez Gianmarco, Thomas. Universidad Nacional del Noroeste de la Provincia de Buenos Aires (UNNOBA). Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); Argentina
Fil: Pérez Gianmarco, Thomas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires (CITNOBA, CONICET-UNNOBA); Argentina
Fil: Gómez, Dionisio Tomás. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; Argentina
Fil: Serrago, Román A. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Serrago, Román A. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Dubcovsky, Jorge. University of California-Davis. Department of Plant Sciences; Estados Unidos
Fil: Dubcovsky, Jorge. Howard Hughes Medical Institute; Estados Unidos
Fil: Fernández Long, M.E. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Fernández Long, M.E. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Miralles, Daniel J. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Miralles, Daniel J. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description While numerous wheat phenology prediction models are available, most of them are constrained to using variety-dependent coefficients. The overarching objective of this study was to calibrate a gene-based model to predict wheat heading date that allows breeders to select specific gene combinations that would head within the optimal window for a given environment independently of varietal genetic background. A dataset with a total of 49 Argentine wheat cultivars and two recombinant inbred lines was chosen to cover a wide range of allelic combinations for major vernalization, photoperiod, and earliness per se genes. The model was validated using independent data from an Argentine wheat trial network that includes sites from a wide latitudinal range. Ultimately, using this gene-based model, simulations were made to identify optimal gene combinations (ideotypes) × site combinations in contrasting locations. The selected model accurately predicted heading date with an overall median error of 4.6 d. This gene-based crop model for wheat phenology allowed the identification of groups of gene combinations predicted to produce heads within a low-risk window and can be adapted to predict other phenological stages based on accessible climatic information and publicly available molecular markers, facilitating its adoption in wheat-growing regions worldwide.
publishDate 2025
dc.date.none.fl_str_mv 2025-03-12T11:56:27Z
2025-03-12T11:56:27Z
2025-02
info:eu-repo/date/embargoEnd/2026-03-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/21626
https://academic.oup.com/jxb/advance-article-abstract/doi/10.1093/jxb/eraf049/8005022
0022-0957
1460-2431
https://doi.org/10.1093/jxb/eraf049
url http://hdl.handle.net/20.500.12123/21626
https://academic.oup.com/jxb/advance-article-abstract/doi/10.1093/jxb/eraf049/8005022
https://doi.org/10.1093/jxb/eraf049
identifier_str_mv 0022-0957
1460-2431
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv Journal of Experimental Botany : eraf049. (Published: 07 February 2025)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.559606

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