Genomic prediction of maize yield across European environmental conditions

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Millet, Emilie | Kruijer, Willem | Coupel-Ledru, Aude | Alvarez Prado, Santiago | Cabrera Bosquet, Llorenç | Lacube, Sébastien | Charcosset, Alain | Welcker, Claude | van Eeuwijk, Fred | Tardieu, Francois

Edité par CCSD ; Nature Publishing Group -

EU project FP7-244374 (DROPS), the and. International audience. 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 scenarios3,4,5,6,7 (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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