Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Hybrid wheat: quantitative genetic parameters and consequences for the design of breeding programs

  • 2155 Accesses

  • 67 Citations

Abstract

Key message

Commercial heterosis for grain yield is present in hybrid wheat but long-term competiveness of hybrid versus line breeding depends on the development of heterotic groups to improve hybrid prediction.

Abstract

Detailed knowledge of the amount of heterosis and quantitative genetic parameters are of paramount importance to assess the potential of hybrid breeding. Our objectives were to (1) examine the extent of midparent, better-parent and commercial heterosis in a vast population of 1,604 wheat (Triticum aestivum L.) hybrids and their parental elite inbred lines and (2) discuss the consequences of relevant quantitative parameters for the design of hybrid wheat breeding programs. Fifteen male lines were crossed in a factorial mating design with 120 female lines, resulting in 1,604 of the 1,800 potential single-cross hybrid combinations. The hybrids, their parents, and ten commercial wheat varieties were evaluated in multi-location field experiments for grain yield, plant height, heading time and susceptibility to frost, lodging, septoria tritici blotch, yellow rust, leaf rust, and powdery mildew at up to five locations. We observed that hybrids were superior to the mean of their parents for grain yield (10.7 %) and susceptibility to frost (−7.2 %), leaf rust (−8.4 %) and septoria tritici blotch (−9.3 %). Moreover, 69 hybrids significantly (P < 0.05) outyielded the best commercial inbred line variety underlining the potential of hybrid wheat breeding. The estimated quantitative genetic parameters suggest that the establishment of reciprocal recurrent selection programs is pivotal for a successful long-term hybrid wheat breeding.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

References

  1. Baker RJ (1986) Selection indices in plant breeding. University of Michigan, CRC Press, Boca Raton

  2. Barbosa-Neto JF, Sorrels ME, Cisar G (1996) Prediction of heterosis in wheat using coefficient of parentage and RFLP-based estimates of genetic relationship. Genome 39:1142–1149

  3. Bernardo R (2002) Breeding for quantitative traits in plants. Stemma Press, Woodbury

  4. Borghi B, Perenzin M (1994) Diallel analysis to predict heterosis and combining ability for grain yield, yield components and bread-making quality in bread wheat (T. aestivum). Theor Appl Genet 89:975–981

  5. Butler D, BR Cullis, AR Gilmour, Gogel BJ (2009) ASREML-R, reference manual. Version 3. Queensland Department of Primary Industries and Fisheries, Brisbane, Queensland, Australia

  6. Corbellini M, Perenzin M, Accerbi M, Vaccino P, Borghi B (2002) Genetic diversity in bread wheat, as revealed by coefficient of parentage and molecular markers, and its relationship to hybrid performance. Euphytica 123:273–285

  7. Edwards IB (2001) Origin of cultivated wheat. In: Bonjean AP, Angus WJ (eds) The world wheat book-a history of wheat breeding, vol 1. Lavoisier Publishing, Paris, pp 1019–1045

  8. Falconer DS, Mackay TF (1996) Introduction to quantitative genetics, 4th edn. Longmans Green, Harlow

  9. Fischer S, Möhring J, Schön CC, Piepho H-P, Klein D, Schipprack W, Utz HF, Melchinger AE, Reif JC (2008) Trends in genetic variance components during 30 years of hybrid maize breeding at the University of Hohenheim. Plant Breed 127:446–451

  10. Fisher RA (1921) On the “probable error” of a coefficient of correlation deduced from a small sample. Metron 1:1–32

  11. Gordillo AG, Geiger HH (2008) Alternative recurrent selection strategies using doubled haploid lines in hybrid maize breeding. Crop Sci 48:911–922

  12. Gowda M, Longin CFH, Lein V, Reif JC (2012) Relevance of specific versus general combining ability effects in wheat. Crop Sci 52:2494–2500

  13. Hallauer AR, Miranda JB (1981) Quantitative genetics in maize breeding. Iowa State University Press, Ames, pp 267–298

  14. Hallauer AR, Russell WA, Lamkey KR (1988) Corn breeding. In: Sprague GF, Dudley JW (eds) Corn and corn improvement, 3rd edn. Agron Monogr 18 ASA, CSSA, SSSA, Madison, WI, pp 469–565

  15. Labate JA, Lamkey KR, Lee M, Woodman WL (1997) Molecular genetic diversity after reciprocal recurrent selection in BSSS and BSCB1 maize populations. Crop Sci 37:416–423

  16. 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

  17. Longin CFH, Mühleisen J, Maurer HP, Zhang H, Gowda M, Reif JC (2012) Hybrid breeding in autogamous cereals. Theor Appl Genet 125:1087–1096

  18. Miedaner T, Würschum T, Maurer HP, Korzun V, Ebmeyer E, Reif JC (2010) Association mapping for Fusarium head blight resistance in European soft winter wheat. Mol Breed 28:647–655

  19. Möhring J, Piepho H-P (2009) Comparison of weighting in two-stage analysis of plant breeding trials. Crop Sci 49:1977–1988

  20. Oettler G, Tams SH, Utz HF, Bauer E, Melchinger AE (2005) Prospects for hybrid breeding in winter triticale: I. heterosis and combining ability for agronomic traits in European elite germplasm. Crop Sci 45:1476–1482

  21. Oury F-X, Brabant P, Berard P, Pluchard P (2000) Predicting hybrid value in bread wheat: biometric modeling based on a top-cross design. Theor Appl Genet 100:96–104

  22. Payne RW (2006) New and traditional methods for the analysis of unreplicated experiments. Crop Sci 46:2476–2481

  23. Perenzin M, Corbellini M, Accerbi M, Vaccion P, Borghi B (1998) Bread wheat: F1 hybrid performance and parental diversity estimates using molecular markers. Euphytica 100:273–279

  24. Reif JC, Gumpert F, Fischer S, Melchinger AE (2007) Impact of genetic divergence on additive and dominance variance in hybrid populations. Genetics 176:1931–1934

  25. Schachschneider R (1997) Hybridweizen-Stand und Erfahrungen. In: Bericht 48. Arbeitstagung österreichischer Pflanzenzüchter, Gumpenstein, Österreich, pp 27–32 (in German)

  26. Schnell FW (1965) Die Covarianz zwischen Verwandten in einer genorthogonalen Population. I. Allgemeine Theorie. Biometrische Z 7:1–49 (in German)

  27. Schnell FW (1982) A synoptic study of the methods and categories of plant breeding. Z Pflanzenzüchtg 89:1–18

  28. Schrag TA, Frisch M, Dhillon BS, Melchinger AE (2009) Marker-based prediction of hybrid performance in maize single-crosses involving doubled haploids. Maydica 54:353–362

  29. Singh SK, Chatrath R, Mishra B (2010) Perspective of hybrid wheat research: a review. Indian J Agric Sci 80:1013–1027

  30. Spielmeyer W, Hyles J, Joaquim P, Azanza F, Bonnet B, Ellis ME, Moore C, Richards RA (2007) A QTL on chromosome 6A in bread wheat (Triticum aestivum L.) is associated with longer coleoptiles, greater seedling vigour and final plant height. Theor Appl Genet 115:59–66

  31. Stram DO, Lee JW (1994) Variance components testing in longitudinal mixed effects model. Biometrics 50:1171–1177

  32. Tomerius AM (2001) Optimizing the development of seed-parent lines in hybrid rye breeding. PhD thesis, University of Hohenheim. http://opus.ub.uni-hohenheim.de/volltexte/2001/10/pdf/tomerius.pdf

  33. Wegenast T, Longin CFH, Utz HF, Melchinger AE, Maurer HP, Reif JC (2008) Hybrid maize breeding with doubled haploids IV Number versus size of crosses and importance of parental selection in two-stage selection for testcross performance. Theor Appl Genet 117:251–260

  34. Weißmann S, Weißmann AE (2002) Hybrid triticale-prospects for research and breeding. In: Proceedings of the 5th International Triticale Symposium, Radzikow, Poland, pp 188–191

  35. Williams ER, Piepho H-P, Whitaker D (2010) Augmented p-rep designs. Biom J 53:19–27

  36. Wricke G, Weber WE (1986) Quantitative genetics and selection in plant breeding. Walter de Gruyter, Berlin, pp 172–194

  37. Würschum T, Langer S, Longin CFH, Korzun V, Akhunov E, Ebmeyer E, Schachschneider R, Kazman E, Schacht J, Reif JC (2013) Population structure, genetic diversity and linkage disequilibrium in elite winter wheat assessed with SNP and SSR markers. Theor Appl Genet. doi:10.1007/s00122-013-2065-1

  38. Zhao Y, Gowda M, Würschum T, Longin CFH, Korzun V, Kollers S, Schachschneider R, Zeng J, Fernando R, Dubkovsky J, Reif JC (2013) Genetic architecture of frost tolerance in wheat. J Exp Bot (in press)

Download references

Acknowledgments

M. Gowda, J. Mühleisen and Y. Zhao were supported by BMBF within the HYWHEAT project (Grant ID: FKZ0315945D).

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Correspondence to Jochen Christoph Reif.

Additional information

C. F. H. Longin and M. Gowda contributed equally to this work.

Communicated by P. Langridge.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Figure S1: Efficiency of reducing specific combining ability effects by increasing the number of different gametes in a tester for fT1T2 = 0 (O), fT1T2 = 0.25 (Δ), fT1T2 = 0.5 (+). Number of gametes = 2 means either 2 inbred testers or 1 single cross, tester lines = 4 means either 4 inbreds, or 2 single crosses, or 1 double cross tester and so on.(EPS 435 kb)

Supplementary Figure S2: Association between performance of the 1604 wheat hybrids and the hybrid performance predicted based on general combining ability (GCA) effects. **P < 0.01. (EPS 1552 kb)

Supplementary Figure S3: GCA effects for grain yield of the 135 parents plotted against their line per se performance for an index combining per se data on grain yield, plant height, heading time, and susceptibility to frost, lodging, yellow rust, leaf rust, powdery mildew and septoria tritici blotch with equal weight (○), which are commonly subject to early generation selection. Filled circles (●) represent lines with frost susceptibility < 6.5, disease susceptibility < 5, and belonging to the 70 % best lines regarding per se performance for grain yield, i.e. selection on independent culling levels. (EPS 465 kb)

Appendix

Appendix

Assume two unrelated base populations π1 (females) and π2 (males) with two alleles, no epistasis, no linkage and equilibrium within and among loci in the base populations. For hybrid breeding, the total genetic variance is then defined after Schnell (1965, 1982) as \(\sigma_{\text{G}}^{2} ({\text{hybrid}}) = \varphi^{\prime}\sigma_{{GCA^{\prime}}}^{2} + \varphi^{\prime\prime}\sigma_{{GCA^{\prime\prime}}}^{2} + \varphi^{\prime}\varphi^{\prime\prime}\sigma_{\text{SCA}}^{2}\), where \(\varphi^{\prime} = 1/2\left( {1 + F_{\pi 1} } \right)\) and \(\varphi^{\prime\prime} = 1/2\left( {1 + F_{\pi 2} } \right)\) refer to the probability that testcross lines received alleles identical by descent from π1 and π2, and F is the inbreeding coefficient of the respective population. Regarding the reciprocal recurrent selection of GCA, each heterotic group is tested with few elite testers from the opposite pool, e.g., numerous female parental lines with few male lines. Thus, for φ″, we need to consider the number of tester lines. Imagine the homozygous lines J and K, which will be combined in a single-cross tester. Consequently, φ″ = 1/2 + 1/2f jk , where f jk refers to the coefficient of coancestry among lines J and K. For three lines J, K, and L assuming theoretically an equal contribution of gametes to the tester, we get φ″ = 1/3 + 2/9(f jk  + f jl  + f lk ) and for arbitrary numbers of n lines, \(\varphi^{\prime\prime} = \frac{1}{n} + \frac{2}{{n^{2} }}\mathop \sum \nolimits_{j = 1}^{n - 1} \mathop \sum \nolimits_{k > 1}^{n} f_{jk}\). The value n represents the product of the number of testers multiplied by the number of tester lines (gametes) used to build up each tester, e.g., n = 4 for either using four inbred testers or two single-cross testers. The efficiency of type and number of tester lines with different relatedness [f jk  = 0 (○), 0.25 (Δ) and 0.5 (×)] on the reduction of SCA is then determined relative to the use of one inbred tester as \({\text{Eff}} = 100 - \left[ {{{100 \times \left( {\varphi^{\prime}\varphi^{\prime\prime}\sigma_{\text{SCA}}^{2} + \varphi^{\prime}\varphi^{\prime\prime}\sigma_{{{\text{SCA}} \times {\text{E}}}}^{2} } \right)} \mathord{\left/ {\vphantom {{100 \times \left( {\varphi^{\prime}\varphi^{\prime\prime}\sigma_{\text{SCA}}^{2} + \varphi^{\prime}\varphi^{\prime\prime}\sigma_{{{\text{SCA}} \times {\text{E}}}}^{2} } \right)} {\left( {\varphi^{\prime}\sigma_{\text{SCA}}^{2} + \varphi^{\prime}\sigma_{{{\text{SCA}} \times {\text{E}}}}^{2} } \right)}}} \right. \kern-0pt} {\left( {\varphi^{\prime}\sigma_{\text{SCA}}^{2} + \varphi^{\prime}\sigma_{{{\text{SCA}} \times {\text{E}}}}^{2} } \right)}}} \right] = 100 - 100 \times \varphi^{\prime\prime}.\)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Longin, C.F.H., Gowda, M., Mühleisen, J. et al. Hybrid wheat: quantitative genetic parameters and consequences for the design of breeding programs. Theor Appl Genet 126, 2791–2801 (2013). https://doi.org/10.1007/s00122-013-2172-z

Download citation

Keywords

  • Powdery Mildew
  • Leaf Rust
  • Specific Combine Ability
  • Hybrid Performance
  • Yellow Rust