Genomic prediction of maize yield across European environmental conditions

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

*Corresponding author for this work

Research output: Contribution to journalLetter (Academic Journal)

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)952-956
Number of pages5
JournalNature Genetics
Volume51
Issue number6
Early online date20 May 2019
DOIs
Publication statusPublished - 1 Jun 2019

Fingerprint Dive into the research topics of 'Genomic prediction of maize yield across European environmental conditions'. Together they form a unique fingerprint.

  • Cite this

    Millet, E. J., Kruijer, W., Coupel-Ledru, A., Alvarez Prado, S., Cabrera-Bosquet, L., Lacube, S., Charcosset, A., Welcker, C., van Eeuwijk, F., & Tardieu, F. (2019). Genomic prediction of maize yield across European environmental conditions. Nature Genetics, 51(6), 952-956. https://doi.org/10.1038/s41588-019-0414-y