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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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).
The authors declare no competing financial interests.
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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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