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 scenarios3,4,5,6,7 (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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Fig. 1: Progression of leaf phenological stages in a phenotyping platform and in two field experiments.
Fig. 2: The calculation of environmental indices over phenological phases for three hybrids in one experiment.
Fig. 3: Variability of genotype-specific response curves of grain number to environmental indices and of genotypic means of grain number and individual grain weight.
Fig. 4: Yield prediction: method and results for each dataset.

Data availability

The field data, the accessions list and the genotypic information associated with this study are stored in GnpIS (ref. 47) and can be downloaded at (ref. 48). Phenological data in the phenotyping platform can be downloaded at after logging as guest into the PHIS (ref. 49) information system (


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We are grateful to A. Grau, B. Suard, C. Check, P. Sidawy and T. Laisné for technical assistance in the Phenoarch experiments, as well as to key persons from the 2014 field experiments at Arvalis, Euralis and KWS. We are also grateful to S. Nicolas and S. Negro for the genotyping (imputation and quality check) and to R. Rincent for advice on the use of the BGLR package. This work was supported by the EU project FP7-244374 (DROPS), the Agence Nationale de la Recherche projects ANR-10-BTBR-01 (Amaizing) and ANR-11-INBS-0012 (Phenome), the Netherlands Scientific Organisation for Research NWO-STW project 11145 Learning from Nature and the EU project H2020 731013 (EPPN2020).

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E.J.M., F.T., C.W. and F.v.E. designed the research and analyzed the field experiments. E.J.M., F.T and F.v.E. wrote the paper with the contributions of W.K., S.A.P., L.C.B., A.C.L., C.W., S.L., and A.C. F.v.E., E.J.M. and W.K. performed the genomic prediction. L.C.B. and S.A.P. performed and analyzed the platform experiments.

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Correspondence to François Tardieu.

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Millet, E.J., Kruijer, W., Coupel-Ledru, A. et al. Genomic prediction of maize yield across European environmental conditions. Nat Genet 51, 952–956 (2019).

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