Genomic prediction of maize yield across European environmental conditions

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 flow...

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Detalles Bibliográficos
Otros Autores: Millet, Emilie J., Kruijer, Willem, Coupel Ledru, Aude, Alvarez Prado, Santiago, Cabrera Bosquet, Llorenç, Lacube, Sébastien, Charcosset, Alain, Welcker, Claude, Eeuwijk, Fred van, Tardieu, François
Formato: Artículo
Lenguaje:Inglés
Materias:
GENETICS
PHYSIOLOGY
PLANT SCIENCES
Acceso en línea:http://ri.agro.uba.ar/files/intranet/articulo/2019millet.pdf
LINK AL EDITOR
Aporte de:Registro referencial: Solicitar el recurso aquí
Biblioteca Central (FAUBA) de Universidad de Buenos Aires
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245 1 0 |a Genomic prediction of maize yield across European environmental conditions 
520 |a 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. 
653 |a GENETICS 
653 |a PHYSIOLOGY 
653 |a PLANT SCIENCES 
700 1 |a Millet, Emilie J.  |u Biometris. WUR. Wageningen, the Netherlands.  |u Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France.  |9 69497 
700 1 |a Kruijer, Willem  |u Biometris. WUR. Wageningen, the Netherlands.  |9 69499 
700 1 |a Coupel Ledru, Aude  |u Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France.  |u University of Bristol. School of Biological Sciences. Bristol.  |9 69500 
700 1 |9 29122  |a Alvarez Prado, Santiago  |u Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France.  |u Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina.  |u CONICET – Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. 
700 1 |a Cabrera Bosquet, Llorenç  |u Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France.  |9 69501 
700 1 |a Lacube, Sébastien  |u Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France.  |9 69502 
700 1 |a Charcosset, Alain  |u Université Paris - Sud. CNRS. AgroParisTech. INRA. Gif-sur-Yvette, Paris, France.  |u Université Paris-Saclay. GQE-Le Moulon, Paris, France.  |9 69503 
700 1 |a Welcker, Claude  |u Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France.  |9 69504 
700 1 |a Eeuwijk, Fred van  |u Biometris. WUR. Wageningen, the Netherlands.  |9 69685 
700 1 |a Tardieu, François  |u Université Montpellier. LEPSE. INRA. SupAgro. Montpellier, France.  |9 69686 
773 |t Nature genetics  |w SECS001666  |g vol.51, no.6 (2019), p.952-956, tbls., grafs. 
856 |f 2019millet  |i en reservorio  |q application/pdf  |u http://ri.agro.uba.ar/files/intranet/articulo/2019millet.pdf  |x ARTI201911 
856 |z LINK AL EDITOR  |u http://www.springer.com/ 
942 |c ARTICULO 
942 |c ENLINEA 
976 |a AAG 

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