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Nature Genetics | Letter

日本語要約

Genomic and metabolic prediction of complex heterotic traits in hybrid maize

Journal name:
Nature Genetics
Volume:
44,
Pages:
217–220
Year published:
DOI:
doi:10.1038/ng.1033
Received
Accepted
Published online

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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  1. Genealogy of the population.
    Figure 1: Genealogy of the population.

    The tree was reconstructed from SNP data with the balanced minimum evolution (BME) algorithm47 on genetic distances. The breeding subgroups are distinguished by their color. T, tropical lines.

  2. Structure of whole-genome LD.
    Figure 2: Structure of whole-genome LD.

    (a) Decay of LD (r2) with distance between pairs of SNPs. LD averaged over chromosomes is given in distance bins of 50 kb. The value of r2 = 0.1 was reached at approximately 500 kb. (b) Probability distribution of LD between adjacent SNPs (bin size = 0.01).

  3. Observed versus whole-genome-predicted GCA for dry matter yield.
    Figure 3: Observed versus whole-genome–predicted GCA for dry matter yield.

    Results were averaged over all cross-validation runs. Dividing the set of genotypes based solely on their SNPs would result in 203 (74.9%) correct classifications (blue), 36 (13.3%) false negatives (red) and 32 (11.8%) false positives (yellow).

  4. Analysis of the core set of 124 lines.
    Figure 4: Analysis of the core set of 124 lines.

    (a) Results of a principal-component analysis on the SNP data. (b) Decay of LD (r2) with distance between pairs of SNPs. LD averaged over chromosomes is given in distance bins of 50 kb. The value of r2 = 0.1 was reached at approximately 275 kb. (c) Genealogy of the core set, estimated with the BME algorithm on genetic distances. (d) Observed versus whole-genome–predicted GCA for dry matter yield with the breeding subgroups distinguished by color. (e) Observed versus whole-genome–predicted GCA for dry matter yield, with different geographical origins distinguished by color. EU, Europe; NA, North America.

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Author information

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

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.

Competing financial interests

The authors declare no competing financial interests.

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