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Omics-based hybrid prediction in maize

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Complementing genomic data with other “omics” predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits.

Abstract

Accurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream “omics” data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of “omics” data. Here, we evaluate several “omics” predictors—genomic, transcriptomic and metabolic data—measured on parent lines at early developmental stages and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream “omics” data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.

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Acknowledgements

We thank the staff of the Agricultural Experimental Research station, University of Hohenheim, for excellent technical assistance in conducting the field experiments. We are indebted to the group of R. Fries from Technische Universität München for the SNP genotyping of the parent inbred lines, to X. Mi for his assistance in preparing auxiliary figures based on the Mathematica software, to C. Zenke for advice on the computation of transcriptomic BLUEs and to P. Schopp for advice on prediction models.

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Authors and Affiliations

  1. Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany

    Matthias Westhues, Tobias A. Schrag, H. Friedrich Utz, Wolfgang Schipprack & Albrecht E. Melchinger

  2. Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, 24098, Kiel, Germany

    Claas Heuer, Georg Thaller & Anita Ehret

  3. Inguran LLC dba STGenetics, 22575 SH6 South, Navasota, TX, 77868, USA

    Claas Heuer

  4. Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, 22609, Hamburg, Germany

    Alexander Thiemann, Felix Seifert & Stefan Scholten

  5. Max-Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany

    Armin Schlereth, Mark Stitt, Zoran Nikoloski & Lothar Willmitzer

  6. Plant Breeding, Technische Universität München, 85354, Freising, Germany

    Chris C. Schön

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Corresponding authors

Correspondence to Stefan Scholten or Albrecht E. Melchinger.

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The authors declare that they have no conflict of interest.

Additional information

Communicated by Matthias Frisch.

The authors acknowledge support by the state of Baden–Württemberg through bwHPC. This project was funded by the German Federal Ministry of Education and Research (BMBF) within the projects OPTIMAL (FKZ: 0315958B,0315958F), SYNBREED (FKZ: 0315528D) and by the German Research Foundation (DFG, Grants No. ME 2260/5-1 and SCHO 764/6-1). Financial support for M.W. was provided by the Fiat Panis foundation, Ulm, Germany.

M. Westhues and Tobias A. Schrag contributed equally to this work.

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Westhues, M., Schrag, T.A., Heuer, C. et al. Omics-based hybrid prediction in maize. Theor Appl Genet 130, 1927–1939 (2017). https://doi.org/10.1007/s00122-017-2934-0

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