Abstract
The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying approach to deliver innovative 'step changes' in the rate of genetic gain at scale.
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References
Godfray, H.C.J. et al. Special issue: 'Food security: feeding the world in 2050'. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 365, 2765–3097 (2010).
Nelson, G.C. et al. Food Security, Farming, and Climate Change to 2050 (International Food Policy Research Institute (IFPRI), 2010).
Alexandratos, N. & Bruinsma, J. World agriculture towards 2030/2050: the 2012 revision (ESA Working paper no. 12-03) (Food and Agriculture Organization of the United Nations, 2012).
The Economist Intelligence Unit. Global food security index 2015. An annual measure of the state of global food security (The Economist Intelligence Unit, 2015).
Schön, C.C. & Simianer, H. Resemblance between two relatives—animal and plant breeding. J. Anim. Breed. Genet. 132, 1–2 (2015).
Kingsbury, N. Hybrid: The History and Science of Plant Breeding (University of Chicago Press, 2009).
Marshall, F.H.A. & Hammond, J. The Science of Animal Breeding in Britain: A Short History (Longmans Green & Co. for the British Council, 1946).
Lush, J.L. Family merit and individual merit as bases for selection, Part I. Am. Nat. 81, 241–261 (1947).
Henderson, C.R. Estimation of genetic parameters. Ann. Math. Stat. 21, 309–310 (1950).
Patterson, H.D. & Williams, E.R. A new class of resolvable incomplete block designs. Biometrika 63, 83–92 (1976).
Galton, F. Regression towards mediocrity in hereditary stature. J. R. Anthropol. Inst. 15, 246–263 (1886).
Fisher, R. The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinb. 52, 399–433 (1919).
Biffen, R. Mendel's laws of inheritance and wheat breeding. J. Agric. Sci. 1, 4–48 (1905).
Hill, W.G. Quantitative genetics in the genomics era. Curr. Genomics 13, 196–206 (2012).
Lush, J.L. Animal Breeding Plans 2nd edn. (The Iowa State College Press, 1943).
Sprague, G.F. & Tatum, L.A. General vs. specific combining ability in single crosses of corn. Agron. J. 34, 923–932 (1942).
Robertson, A. in Fifty Years of Genetics: Proceedings of a Symposium Held at the 160th Meeting of the Genetical Society of Great Britain on the 50th Anniversary of its Foundation, Held on 9th, 10th and 11th July 1969 in Reading (ed. Jinks, J.L.) 27–69 (Oliver & Boyd, 1970).
Meuwissen, T.H., Hayes, B.J. & Goddard, M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001).
Lande, R. & Thompson, R. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124, 743–756 (1990).
Nejati-Javaremi, A., Smith, C. & Gibson, J.P. Effect of total allelic relationship on accuracy of evaluation and response to selection. J. Anim. Sci. 75, 1738–1745 (1997).
Whittaker, J.C., Thompson, R. & Denham, M.C. Marker-assisted selection using ridge regression. Ann. Hum. Genet. 63, 366 (1999).
Lynch, M. Estimation of relatedness by DNA fingerprinting. Mol. Biol. Evol. 5, 584–599 (1988).
Bernardo, R. A model for marker-assisted selection among single crosses with multiple genetic markers. Theor. Appl. Genet. 97, 473–478 (1998).
Haley, C.S. & Visscher, P.M. Strategies to utilize marker–quantitative trait loci associations. J. Dairy Sci. 81 (Suppl. 2), 85–97 (1998).
Schaeffer, L.R. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet. 123, 218–223 (2006).
Wolfe, M.D. et al. Prospects for genomic selection in cassava breeding. Preprint at bioRxiv http://dx.doi.org/10.1101/108662 (2017).
Dwivedi, S.L. et al. Haploids: constraints and opportunities in plant breeding. Biotechnol. Adv. 33, 812–829 (2015).
Falconer, D.S. & Mackay, T.F.C. Introduction to Quantitative Genetics 4th edn. (Pearson, 1996).
Meuwissen, T. in Genetics (eds. Donini, P., Lanteri, S. & Sonnino, A.) 54–59 (FAO Biotechnology Forum, 2003).
Gorjanc, G., Jenko, J., Hearne, S.J. & Hickey, J.M. Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations. BMC Genomics 17, 30 (2016).
Hill, W.G. Understanding and using quantitative genetic variation. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 365, 73–85 (2010).
Mackay, I. & Powell, W. Methods for linkage disequilibrium mapping in crops. Trends Plant Sci. 12, 57–63 (2007).
Noman, A., Aqeel, M. & He, S. CRISPR–Cas9: tool for qualitative and quantitative plant genome editing. Front. Plant Sci. 7, 1740 (2016).
Jenko, J. et al. Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs. Genet. Sel. Evol. 47, 55 (2015).
Hickey, J.M., Bruce, C., Whitelaw, A. & Gorjanc, G. Promotion of alleles by genome editing in livestock breeding programmes. J. Anim. Breed. Genet. 133, 83–84 (2016).
Gaynor, R.C. et al. A two-part strategy for using genomic selection to develop inbred lines. Crop Sci. http://dx.doi.org/10.2135/cropsci2016.09.0742 (2017).
Fahlgren, N., Gehan, M.A. & Baxter, I. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr. Opin. Plant Biol. 24, 93–99 (2015).
Cooper, M. et al. Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction. Crop Pasture Sci. 65, 311–336 (2014).
Acknowledgements
The Implementing Genomic Selection in CGIAR Breeding Programs Workshop was funded by the CGIAR Consortium and the UK Biotechnology and Biological Sciences Research Council (BBSRC); it was held at the CGIAR Consortium offices in Montpellier, France.
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Hickey, J., Chiurugwi, T., Mackay, I. et al. Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery. Nat Genet 49, 1297–1303 (2017). https://doi.org/10.1038/ng.3920
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DOI: https://doi.org/10.1038/ng.3920
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