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Genomic prediction of maize yield across European environmental conditions

Colaborador(es): Millet, Emilie J. Biometris. WUR. Wageningen, the Netherlands. Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France | Kruijer, Willem. Biometris. WUR. Wageningen, the Netherlands | Coupel Ledru, Aude. Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France. University of Bristol. School of Biological Sciences. Bristol | Alvarez Prado, Santiago. Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. CONICET – Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina | Cabrera Bosquet, Llorenç. Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France | Lacube, Sébastien. Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France | Charcosset, Alain. Université Paris - Sud. CNRS. AgroParisTech. INRA. Gif-sur-Yvette, Paris, France. Université Paris-Saclay. GQE-Le Moulon, Paris, France | Welcker, Claude. Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France | Eeuwijk, Fred van. Biometris. WUR. Wageningen, the Netherlands | Tardieu, François. Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France.
ISSN: 1061-4036.Tipo de material: Artículos y capítulos. Recurso electrónico.Tema(s): GENETICS | PHYSIOLOGY | PLANT SCIENCES | Recursos en línea: Haga clic para acceso en línea | LINK AL EDITOR En: Nature genetics vol.51, no.6 (2019), p.952-956, tbls., grafs.Resumen: The development of germplasm adapted to changing climate is required to ensure food security1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios3–7 (genotype × environment interaction), in spite of promising results for flowering time8. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields9,10. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype - specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.
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The development of germplasm adapted to changing climate is required to ensure food security1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios3–7 (genotype × environment interaction), in spite of promising results for flowering time8. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields9,10. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype - specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.

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