Turbocharging introgression breeding of perennial fruit crops: a case study on apple

Abstract

The allelic diversity of primitive germplasm of fruit crops provides a useful resource for introgressing novel genes to meet consumer preferences and environmental challenges. Pre-breeding facilitates the identification of novel genetic variation in the primitive germplasm and expedite its utilisation in cultivar breeding programmes. Several generations of pre-breeding could be required to minimise linkage drag from the donor parent and to maximise the genomic content of the recipient parent. In this study we investigated the potential of genomic selection (GS) as a tool for rapid background selection of parents for the successive generation. A diverse set of 274 accessions was genotyped using random-tag genotyping-by-sequencing, and phenotyped for eight fruit quality traits. The relationship between ‘own phenotypes’ of 274 accessions and their general combining ability (GCA) was also examined. Trait heritability influenced the strength of correspondence between own phenotype and the GCA. The average (across eight traits) accuracy of predicting own phenotype was 0.70, and the correlations between genomic-predicted own phenotype and GCA were similar to the observed correlations. Our results suggest that genome-assisted parental selection (GAPS) is a credible alternative to phenotypic parental selection, so could help reduce the generation interval to allow faster accumulation of favourable alleles from donor and recipient parents.

References

[1] Combs, E. & Bernardo, R. Genomewide selection to introgress semidwarf maize germplasm into US Corn Belt inbreds. Crop Sci. 53, 1427-1436 (2013).

[2] 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 Genom. 17, 30 (2016).

[3] Kumar, S., Volz, R. K., Alspach, P. A. & Bus, V. G. M. Development of a recurrent apple-breeding programme in New Zealand: a synthesis of results, and a proposed revised breeding strategy. Euphytica 173, 207-222 (2010).

[4] Bus, V. G. M., Esmenjaud, D., Buck, E. & Laurens, F. in Genetics and Genomics of the Rosaceae, Vol. 6 (eds Folta, K. M. & Gardiner, S. E.) Ch. 27 (Springer, 2009).

[5] Flachowsky, H. et al. Applying a high-speed breeding technology to apple (Malus × domestica) based on transgenic early flowering plants and marker-assisted selection. N. Phytol. 192, 364-377 (2011).

[6] Hillel, J. et al. DNA fingerprints applied to gene introgression in breeding programs. Genetics 124, 783-789 (1990).

[7] Visscher, P. M. Speed congenics: accelerated genome recovery using genetic markers. Genet. Res. 74, 81-85 (1999).

[8] Volz, R. K., Rikkerink, E., Austin, P., Lawrence, T. & Bus, V. G. M. “Fast Breeding” in apple: a strategy to accelerate introgression of new traits into elite germplasm. Acta Hort. 814, 163-168 (2009).

[9] Schlathölter, I. et al. Generation of advanced fire blight-resistant apple (Malus × domestica) selections of the fifth generation within 7 years of applying the early flowering approach. Planta 247, 1475-1488 (2018).

[10] Baumgartner, I. O., Patocchi, A., Franck, L. & Kellerhals, M. Fire blight resistance from ‘Evereste’ and Malus sieversii used in breeding for new high quality apple cultivars: strategies and results. Acta Hort. 895, 391-397 (2011).

[11] Bernardo, R. Genomewide selection for rapid introgression of exotic germplasm in maize. Crop Sci. 49, 419-425 (2009).

[12] Ødegård, J., Sonesson, A. K., Yazdi, M. H. & Meuwissen, T. H. E. Introgression of a major QTL from an inferior into a superior population using genomic selection. Genet. Sel. Evol. 41, 38 (2009).

[13] Kumar, S. et al. Genomic selection for fruit quality traits in apple (Malus × domestica Borkh.). PLoS ONE 7, e36674 (2012).

[14] Yu, X. et al. Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nat. Plants 2, 16150 (2016).

[15] Mascher, M. et al. Genebank genomics bridges the gap between the conservation of crop diversity and plant breeding. Nat. Genet. 51, 1076-1081 (2019).

[16] Bernardo, R. Genomewide predictions for backcrossing a quantitative trait from an exotic to an adapted line. Crop Sci. 56, 1067-1075 (2016).

[17] Kumar, S. Correlation between clonal means and open-pollinated seedling progeny means and its implications for radiata pine breeding strategy. Can. J. For. Res. 36, 1968-1975 (2006).

[18] Noiton, D. & Shelbourne, C. J. A. Quantitative genetics in apple breeding strategy. Euphytica 60, 213-219 (1992).

[19] Hilario, E. et al. Random tagging genotyping by sequencing (rtGBS), an unbiased approach to locate restriction enzyme sites across the target genome. PLoS ONE 10, e0143193 (2015).

[20] Bradbury, P. J. et al. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633-2635 (2007).

[21] Velasco, R. et al. The genome of the domesticated apple (Malus × domestica Borkh). Nat. Genet. 42, 833-839 (2010).

[22] Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357-359 (2012).

[23] Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing data inference for whole genome association studies using localized haplotype clustering. Am. J. Hum. Genet. 81, 1084-1097 (2007).

[24] Wimmer, V., Albrecht, T., Auinger, H. J. & Schön, C. C. Synbreed: a framework for the analysis of genomic prediction data using R. Bioinformatics 28, 2086-2087 (2012).

[25] Mrode, R. A. Linear models for the prediction of animal breeding values. (CAB Int., 1996).

[26] Gilmour, A. R., Cullis, B. R., Harding, S. A. & Thompson, R. ASReml Update: what’s new in Release 2.00. (VSN Int. Ltd, Hemel Hempstead, 2006).

[27] Van Raden, P. M. Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414-4423 (2008).

[28] Pérez, P. & de Los Campos, G. Genome-wide regression & prediction with the BGLR statistical package. Genetics 206, 114 (2014).

[29] Herzog, E. & Frisch, M. Selection strategies for marker-assisted backcrossing with high-throughput marker systems. Theor. Appl. Genet. 123, 251-260 (2011).

[30] Jun-Yan, B. A., Qin, Z. H. & Xiao-Ping, J. I. Comparison of different foreground and background selection methods in marker-assisted introgression. Acta Genet. Sin. 33, 1073-1080 (2006).

[31] Muranty, H. et al. Accuracy and responses of genomic selection on key traits in apple breeding. Hortic. Res. 2, 15060 (2015).

[32] Minamikawa, M. F. et al. Genome-wide association study and genomic prediction using parental and breeding populations of Japanese pear (Pyrus pyrifolia Nakai). Sci. Rep. 8, 11994 (2018).

[33] Kumar, S. et al. Marker-trait associations and genomic predictions of interspecific pear (Pyrus) fruit characteristics. Sci. Rep. 9, 9072 (2019).

[34] Legarra, A., Aguilar, I. & Misztal, I. A relationship matrix including full pedigree and genomic information. J. Dairy Sci. 92, 4656-4663 (2009).

[35] Hayes, B. J., Pryce, J., Chamberlain, A. J., Bowman, P. J. & Goddard, M. E. Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits. PLoS Genet. 6, e1001139 (2010).

[36] Kumar, S., Bink, M. C. A. M., Volz, R. K., Bus, V. G. M. & Chagné, D. Towards genomic selection in apple (Malus × domestica Borkh.) breeding programmes: prospects, challenges and strategies. Tree Genet. Genomes 8, 1-14 (2012).

[37] Jenko, J. et al. Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs. Gen. Sel. Evol. 47, 55 (2015).

[38] Varshney, R. K., Singh, V. K., Kumar, A., Powell, W. & Sorrells, M. E. Can genomics deliver climate-change ready crops? Curr. Opin. Plant Biol. 45, 205-211 (2018).