Cited by 292Genetic and phenotypic traits in livestockGenetics and Plant BreedingGenetic Mapping and Diversity in Plants and Animals

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Genomic prediction of maize yield across European environmental conditions

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Millet, Emilie J., Kruijer, Willem, Coupel-Ledru, Aude, Alvarez Prado, Santiago, Cabrera-Bosquet, Llorenç, Lacube, Sébastien, Charcosset, Alain, Welcker, Claude, van Eeuwijk, Fred, Tardieu, François·2019·Nature Genetics

DOI 10.1038/s41588-019-0414-y

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Abstract

The development of germplasm adapted to changing climate is required to ensure food security 1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios 3-7 (genotype × environment interaction), in spite of promising results for flowering time 8. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields 9,10. 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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Authors: 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.
Affiliations: Institut Agro Montpellier (FR)
Wageningen University & Research (NL)
Université Paris-Sud (FR)
Corresponding author: Tardieu, François
ORCID: Millet, Emilie J.
Kruijer, Willem
Coupel-Ledru, Aude
Alvarez Prado, Santiago
Cabrera-Bosquet, Llorenç
Lacube, Sébastien
Charcosset, Alain
Welcker, Claude
van Eeuwijk, Fred
Tardieu, François
Venue: Nature Genetics
Publisher: Springer Science and Business Media LLC
Published: 2019-05-20
Volume: 51
Issue: 6
Pages: 952-956
ISSN: 1061-4036 (ISSN-L)
1546-1718
DOI: 10.1038/s41588-019-0414-y
OpenAlex: W2945720445
Cited by: 280
References: 46
Citations by year: 2026: 30 · 2025: 38 · 2024: 42 · 2023: 43 · 2022: 49 · 2021: 49 · 2020: 25 · 2019: 4
FWCI: 19.90
Type: journal article
Language: en
Open access: green
research.wur.nl
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Genetics (publishedVersion)
PubMed (publishedVersion)
Socio-Environmental Systems Modeling (acceptedVersion)
HAL (submittedVersion)
Explore Bristol Research (submittedVersion)
CONICET Digital (CONICET) (submittedVersion, cc-by-nc-sa)
Data Archiving and Networked Services (DANS) (submittedVersion)
Funders: Agence Nationale de la Recherche (ANR-11-INBS-0012)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords: maize, grain yield prediction, gene-environment interaction, environmental drivers, sensor networks, phenotyping platforms, germplasm evaluation, climate change adaptation
MeSH terms: Agriculture, Edible Grain, Zea mays, Environment, Europe, Geography, Phenotype, Genome, Plant, Genomics, Genetic Association Studies, Gene-Environment Interaction

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Genomic prediction of maize yield across European environmental conditions

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