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Genomic and metabolic prediction of complex heterotic traits in hybrid maize

Nature Genetics volume 44, pages 217–220 (2012)Cite this article

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Abstract

Maize is both an exciting model organism in plant genetics and also the most important crop worldwide for food, animal feed and bioenergy production. Recent genome-wide association and metabolic profiling studies aimed to resolve quantitative traits to their causal genetic loci and key metabolic regulators. Here we present a complementary approach that exploits large-scale genomic and metabolic information to predict complex, highly polygenic traits in hybrid testcrosses. We crossed 285 diverse Dent inbred lines from worldwide sources with two testers and predicted their combining abilities for seven biomass- and bioenergy-related traits using 56,110 SNPs and 130 metabolites. Whole-genome and metabolic prediction models were built by fitting effects for all SNPs or metabolites. Prediction accuracies ranged from 0.72 to 0.81 for SNPs and from 0.60 to 0.80 for metabolites, allowing a reliable screening of large collections of diverse inbred lines for their potential to create superior hybrids.

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Figure 1: Genealogy of the population.
Figure 2: Structure of whole-genome LD.
Figure 3: Observed versus whole-genome–predicted GCA for dry matter yield.
Figure 4: Analysis of the core set of 124 lines.

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Acknowledgements

We thank the staff of the experimental research stations of the University of Hohenheim for assistance in conducting the field experiments. This research was funded by the Max-Planck Society and the German Federal Ministry of Education and Research (BMBF) within the project GABI-Energy (FKZ: 0315045) and the AgroClustEr 'Synbreed—Synergistic plant and animal breeding' (FKZ: 0315528D).

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Authors and Affiliations

  1. Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany

    Christian Riedelsheimer, Christoph Grieder, Frank Technow & Albrecht E Melchinger

  2. Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany

    Angelika Czedik-Eysenberg, Jan Lisec, Ronan Sulpice, Mark Stitt & Lothar Willmitzer

  3. Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany

    Thomas Altmann

  4. King Abdulaziz University, Jeddah, Saudi Arabia

    Lothar Willmitzer

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  1. Christian Riedelsheimer
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Contributions

T.A., M.S., L.W. and A.E.M. designed the experiments and supervised research. C.R. and C.G. conducted the field experiments. C.R., A.C.-E., R.S. and J.L. performed the metabolic profiling. C.G. analyzed the phenotypic data. C.R. analyzed genomic and metabolic data and developed software. F.T. contributed to the statistical analysis. C.R. and A.E.M. wrote the manuscript.

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Correspondence to Albrecht E Melchinger.

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The authors declare no competing financial interests.

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Supplementary Figures 1–12, Supplementary Tables 1–4 and Supplementary Note (PDF 3802 kb)

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Riedelsheimer, C., Czedik-Eysenberg, A., Grieder, C. et al. Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet 44, 217–220 (2012). https://doi.org/10.1038/ng.1033

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