Genomic prediction of maize yield across European environmental conditions

KRUIJER, WILLEM; CABRERA-BOSQUET, LLORENÇ; WELCKER, CLAUDE; MILLET, EMILIE J. ; ALVAREZ PRADO, SANTIAGO; CHARCOSSET, ALAIN; TARDIEU, FRANÇOIS; COUPEL-LEDRU, AUDE; LACUBE, SÉBASTIEN; VAN EEUWIJK, FRED

NATURE GENETICS

NATURE PUBLISHING GROUP

Lugar: Londres; Año: 2019

1061-4036

The development of germplasm adapted to changing climateis required to ensure food security1,2. Genomic predictionis a powerful tool to evaluate many genotypes butperforms poorly in contrasting environmental scenarios3?7(genotype × environment interaction), in spite of promisingresults for flowering time8. New avenues are opened by thedevelopment of sensor networks for environmental characterizationin thousands of fields9,10. We present a new strategyfor germplasm evaluation under genotype × environmentinteraction. Yield was dissected in grain weight and numberand genotype × environment interaction in these componentswas modeled as genotypic sensitivity to environmental drivers.Environments were characterized using genotype-specificindices computed from sensor data in each field and theprogression of phenology calibrated for each genotype on aphenotyping platform. A whole-genome regression approachfor the genotypic sensitivities led to accurate prediction ofyield under genotype × environment interaction in a widerange of environmental scenarios, outperforming a benchmarkapproach.