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An association transcriptomics approach to the prediction of hybrid performance

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Abstract

Many animal and plant species exhibit increased growth rates, reach larger sizes and, in the cases of crops and farm animals, produce higher yields when bred as hybrids between genetically differing strains, a phenomenon known as hybrid vigour or heterosis. Despite the importance of heterosis, and its extensive genetic analysis, little understanding exists of its molecular basis. We aimed to determine whether characteristics of the leaf transcriptome, as an indicator of the innate functional genetic architecture of a plant line, could be used as markers to predict heterosis and the performance of hybrids, a methodology we term Association Transcriptomics. Relationships between transcript abundance of specific genes and the values of heterosis and heterosis-dependent traits were identified and mathematical models were constructed that relate gene expression characteristics in inbred lines of Arabidopsis thaliana and maize with vegetative biomass and grain yield, respectively, in corresponding hybrids. These models were used to predict, using gene expression data, the performance of additional hybrids. The success of the application in a monocot crop of a methodology developed in a dicot model species indicates that transcriptional markers may have widespread applicability in hybrid breeding.

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Acknowledgments

We would like to express out gratitude to Eddie Arthur for his support, advice and encouragement throughout this project. This work was supported by the John Innes Centre Competitive Support Grant, UK Biotechnology and Biological Sciences Research Council PhD studentship 01/A1/G/07546 to D.S., and by Plant Bioscience Limited. GeneChip hybridisations were conducted in the JIC Genome Laboratory by James Hadfield and we are grateful for the help of Gawain Bennett for collation of GeneChip data.

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Authors and Affiliations

  1. John Innes Centre, Norwich Research Park, Colney, Norwich, NR4 7UH, UK

    David Stokes, Fiona Fraser, Colin Morgan, Carmel M. O’Neill, Rene Dreos, Andreas Magusin & Ian Bancroft

  2. North Carolina State University, Raleigh, NC, 27695-7614, USA

    Stephen Szalma

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  1. David Stokes
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  2. Fiona Fraser
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  3. Colin Morgan
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  4. Carmel M. O’Neill
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  8. Ian Bancroft
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Correspondence to Ian Bancroft.

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Stokes, D., Fraser, F., Morgan, C. et al. An association transcriptomics approach to the prediction of hybrid performance. Mol Breeding 26, 91–106 (2010). https://doi.org/10.1007/s11032-009-9379-3

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