Abstract
Key message
A breeding strategy with moderate nursery selection followed by genomic selection and one-stage phenotypic selection maximizes annual selection gain for grain yield across a wide range of hybrid breeding scenarios.
Abstract
Genomic selection (GS) is a promising method for the selection of quantitatively inherited traits but its most effective implementation in routine hybrid breeding schemes requires further research. We compared five breeding strategies and varied their available budget, the costs for doubled haploid (DH) line and hybrid seed production as well as variance components for grain yield in a wide range. In contrast to previous studies, we included a nursery selection for disease resistance just before GS on grain yield. The breeding strategy GSrapid with moderate nursery selection followed by one stage GS and one final stage with phenotypic selection on grain yield had the highest annual selection gain across all strategies, budgets, costs and variance components considered and we, therefore, highly recommend its use in hybrid breeding of cereals. Although selecting on traits not correlated with grain yield in the observation nursery, this selection reduced the selection gain of grain yield, especially in the breeding schemes with GS and for selected fractions smaller than 0.3. Owing to the very high number of test candidates entering breeding strategies with GS, the costs for DH line production had a larger impact on the annual selection gain than the hybrid seed production costs. The optimum allocation of test resources maximizing annual selection gain in classical two-stage phenotypic selection on grain yield and for the recommended breeding strategy GSrapid is finally explored for maize, wheat, rye, barley, rice and triticale.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Albrecht T, Auinger H-J, Wimmer V et al (2014) Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years. Theor Appl Genet 127:1375–1386. doi:10.1007/s00122-014-2305-z
Bassi FM, Bentley AR, Charmet G et al (2015) Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). Plant Sci 242:23–36. doi:10.1016/j.plantsci.2015.08.021
Becker H (2011) Pflanzenzüchtung (in German). Eugen Ulmer, Stuttgart
Bernal-Vasquez A-M, Möhring J, Schmidt M et al (2014) The importance of phenotypic data analysis for genomic prediction—a case study comparing different spatial models in rye. BMC Genom 15:646. doi:10.1186/1471-2164-15-646
Bernal-Vazquez A, Gordillo GA, Schmidt M, Piepho H-P (2015) Genomic selection in a hybrid rye breeding program using historical data: An approach considering genotype by year interaction. In: Poster presented at the XVIth meeting of the EUCARPIA section biometrics in plant breeding. Wageningen
Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090. doi:10.2135/cropsci2006.11.0690
Boeven PHG, Würschum T, Weissmann S et al (2016) Prediction of hybrid performance for Fusarium head blight resistance in triticale (×Triticosecale Wittmack). Euphytica 207:475–490. doi:10.1007/s10681-015-1498-9
Chaikam V, Martinez L, Melchinger A et al (2016) Development and validation of red root marker-based haploid inducers that effectively complement R1-nj (navajo) marker-based in vivo haploid identification in maize. Crop Sci. doi:10.2135/cropsci2015.10.0653
Cochran WG (1951) Improvement by means of selection. In: Proceedings of the second Berkeley symposium on mathematical statistics and probability. University of California, pp 449–470
Cooper M, Messina CD, Podlich D et al (2014) Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction. Crop Pasture Sci 65:311–336. doi:10.1071/CP14007
Geiger HH, Miedaner T (2009) Rye breeding. In: Carena MJ (ed) Cereals. Springer, Berlin, pp 157–181
Gordillo GA, Geiger HH (2008) Alternative recurrent selection strategies using doubled haploid lines in hybrid maize breeding. Crop Sci 48:911–922. doi:10.2135/cropsci2007.04.0223
He S, Schulthess AW, Mirdita V et al (2016) Genomic selection in a commercial winter wheat population. Theor Appl Genet 129:641–651. doi:10.1007/s00122-015-2655-1
Heffner EL, Sorrells ME, Jannink J-L (2009) Genomic selection for crop improvement. Crop Sci 49:1–12. doi:10.2135/cropsci2008.08.0512
Heffner EL, Lorenz AJ, Jannink J-L, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690. doi:10.2135/cropsci2009.11.0662
Heslot N, Yang HP, Sorrells ME, Jannink JL (2012) Genomic selection in plant breeding: a comparison of Models. Crop Sci 52:146–160. doi:10.2135/cropsci2011.06.0297
Heslot N, Jannink J-L, Sorrells ME (2015) Perspectives for genomic selection applications and research in plants. Crop Sci 55:1–12. doi:10.2135/cropsci2014.03.0249
Jannink J-L, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genom 9:166–177. doi:10.1093/bfgp/elq001
Jonas E, de Koning D-J (2013) Does genomic selection have a future in plant breeding? Trends Biotechnol 31:497–504. doi:10.1016/j.tibtech.2013.06.003
Knapp SJ (1998) Marker-assisted selection as a strategy for increasing the probability of selecting superior genotypes. Crop Sci 38:1164–1174. doi:10.2135/cropsci1998.0011183X003800050009x
Krchov L-M, Bernardo R (2015) Relative efficiency of genomewide selection for testcross performance of doubled haploid lines in a maize breeding program. Crop Sci 55:2091–2099. doi:10.2135/cropsci2015.01.0064
Lehermeier C, Kramer N, Bauer E et al (2014) Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction. Genetics 198:3–16. doi:10.1534/genetics.114.161943
Longin CFH, Utz HF, Reif JC et al (2006) Hybrid maize breeding with doubled haploids: I. One-stage versus two-stage selection for testcross performance. Theor Appl Genet 112:903–912. doi:10.1007/s00122-005-0192-z
Longin CFH, Utz HF, Melchinger AE, Reif JC (2007) Hybrid maize breeding with doubled haploids: II. Optimum type and number of testers in two-stage selection for general combining ability. Theor Appl Genet 114:393–402. doi:10.1007/s00122-006-0422-z
Longin CFH, Mühleisen J, Maurer HP et al (2012) Hybrid breeding in autogamous cereals. Theor Appl Genet 125:1087–1096. doi:10.1007/s00122-012-1967-7
Longin CFH, Gowda M, Mühleisen J et al (2013) Hybrid wheat: quantitative genetic parameters and consequences for the design of breeding programs. Theor Appl Genet 126:2791–2801. doi:10.1007/s00122-013-2172-z
Longin CFH, Mi X, Melchinger AE et al (2014a) Optimum allocation of test resources and comparison of breeding strategies for hybrid wheat. Theor Appl Genet 127:2117–2126. doi:10.1007/s00122-014-2365-0
Longin CFH, Reif JC, Würschum T (2014b) Long-term perspective of hybrid versus line breeding in wheat based on quantitative genetic theory. Theor Appl Genet 127:1635–1641. doi:10.1007/s00122-014-2325-8
Longin CFH, Mi X, Würschum T (2015) Genomic selection in wheat: optimum allocation of test resources and comparison of breeding strategies for line and hybrid breeding. Theor Appl Genet 128:1297–1306. doi:10.1007/s00122-015-2505-1
Lorenz AJ (2013) Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: a simulation experiment. G3(3):481–491. doi:10.1534/g3.112.004911
Melchinger AE, Longin CFH, Utz HF, Reif JC (2005) Hybrid maize breeding with doubled haploid lines: quantitative genetic and selection theory for optimum allocation of resources. In: Proceedings of the 41st annual Illinois corn breeders school. Urbana-Champaign, pp 8–21
Melchinger AE, Schipprack W, Utz HF, Mirdita V (2014) In vivo haploid induction in maize: identification of haploid seeds by their oil content. Crop Sci 54:1497–1504. doi:10.2135/cropsci2013.12.0851
Melchinger AE, Correa Brauner P, Böhm J, Schipprack W (2016a) In vivo haploid induction in maize: comparison of different testing regimes for measuring haploid induction rates. Crop Sci. doi:10.2135/cropsci2015.11.0668
Melchinger AE, Molenaar WS, Mirdita V, Schipprack W (2016b) Colchicine alternatives for chromosome doubling in maize haploids for doubled-haploid production. Crop Sci 56:1–11. doi:10.2135/cropsci2015.06.0383
Meng L, Zhao X, Ponce K et al (2016) QTL mapping for agronomic traits using multi-parent advanced generation inter-cross (MAGIC) populations derived from diverse elite indica rice lines. F Crop Res 189:19–42. doi:10.1016/j.fcr.2016.02.004
Mi X, Utz HF, Technow F, Melchinger AE (2014) Optimizing resource allocation for multistage selection in plant breeding with R package selectiongain. Crop Sci 54:1413–1418. doi:10.2135/cropsci2013.10.0699
Mi X, Utz HF, Melchinger AE (2015) Selectiongain: an R package for optimizing multi-stage selection. Comput Stat. doi:10.1007/s00180-015-0583-9
Michel S, Ametz C, Gungor H et al (2016) Genomic selection across multiple breeding cycles in applied bread wheat breeding. Theor Appl Genet. doi:10.1007/s00122-016-2694-2
Mühleisen J, Maurer HP, Stiewe G et al (2013) Hybrid breeding in barley. Crop Sci 53:819–824. doi:10.2135/cropsci2012.07.0411
Mühleisen J, Piepho H-P, Maurer HP, Reif JC (2015) Yield performance and stability of CMS-based triticale hybrids. Theor Appl Genet 128:291–301. doi:10.1007/s00122-014-2429-1
Oettler G, Tams SH, Utz HF et al (2005) Prospects for hybrid breeding in winter triticale: I. Heterosis and combining ability for agronomic traits in European elite germplasm. Crop Sci 45(4):1476–1482. doi:10.2135/cropsci2004.0462
Riedelsheimer C, Melchinger AE (2013) Optimizing the allocation of resources for genomic selection in one breeding cycle. Theor Appl Genet 126:2835–2848. doi:10.1007/s00122-013-2175-9
Robson DS, Powers L, Urquhart NS (1967) The proportion of genetic deviates in the tails of a normal population. Theor Appl Genet 37:205–216. doi:10.1007/BF00329530
R Development Core Team (2016) R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.r-project.org. Accessed March 2016
Technow F, Schrag TA, Schipprack W et al (2014) Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize. Genetics 197:1343–1355. doi:10.1534/genetics.114.165860
Tenhola-Roininen T, Immonen S, Tanhuanpää P (2006) Rye doubled haploids as a research and breeding tool—a practical point of view. Plant Breed 125:584–590. doi:10.1111/j.1439-0523.2006.01296.x
Tomerius A-M (2001) Optimizing the development of seed-parent lines in hybrid rye breeding. Dissertation. University of Hohenheim
Tribout T, Larzul C, Phocas F (2013) Economic aspects of implementing genomic evaluations in a pig sire line breeding scheme. Genet Sel Evol 45:40. doi:10.1186/1297-9686-45-40
Utz HF (1969) Mehrstufenselektion in der Pflanzenzüchtung (In German). Eugen Ulmer, Stuttgart
Verstegen H, Köneke O, Korzun V, von Broock R (2014) The world importance of Barley and challenges to further improvements. In: Kumlehn J, Stein N (eds) Biotechnological approaches to Barley improvement. Springer, Berlin, pp 3–19
Virmani SS, Sun ZX, Mou TM et al (2003) Two-line hybrid rice breeding manual. International Rice Research Institute, Los Baños
Windhausen VS, Atlin GN, Hickey JM et al (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3(2):1427–1436. doi:10.1534/g3.112.003699
Würschum T, Tucker MR, Reif JC, Maurer HP (2012) Improved efficiency of doubled haploid generation in hexaploid triticale by in vitro chromosome doubling. BMC Plant Biol 12:109. doi:10.1186/1471-2229-12-109
Würschum T, Tucker MR, Maurer HP, Leiser WL (2015) Ethylene inhibitors improve efficiency of microspore embryogenesis in hexaploid triticale. Plant Cell Tissue Organ Cult 122:751–757. doi:10.1007/s11240-015-0808-1
Xu S, Zhu D, Zhang Q (2014) Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proc Natl Acad Sci 111:12456–12461. doi:10.1073/pnas.1413750111
Zhang X, Pérez-Rodríguez P, Semagn K et al (2015) Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity 114:291–299. doi:10.1038/hdy.2014.99
Zhao Y, Zeng J, Fernando R, Reif JC (2013) Genomic prediction of hybrid wheat performance. Crop Sci 53:802–810. doi:10.2135/cropsci2012.08.0463
Zhao Y, Mette MF, Reif JC (2015) Genomic selection in hybrid breeding. Plant Breed 134:1–10. doi:10.1111/pbr.12231
Acknowledgments
This research was conducted with the financial support provided by the German Academic Exchange Service (DAAD) to J. J. Marulanda in the frame of the program “PhD Scholarships for international students”. Model calculations for rice in this paper were supported by National High Technology Research and Development Program of China (863 Program: 2014AA10A601) and Shenzhen Peacock Plan. The authors thank Prof. T. Miedaner and Dr. H. P. Maurer to provide valuable information about costs and budget estimates for rye and triticale. We thank two anonymous reviewers for their useful and constructive comments on the manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical standard
The authors declare that the experiments comply with the current laws of Germany.
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by H. Iwata.
Rights and permissions
About this article
Cite this article
Marulanda, J.J., Mi, X., Melchinger, A.E. et al. Optimum breeding strategies using genomic selection for hybrid breeding in wheat, maize, rye, barley, rice and triticale. Theor Appl Genet 129, 1901–1913 (2016). https://doi.org/10.1007/s00122-016-2748-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00122-016-2748-5