Theoretical and Applied Genetics

, Volume 126, Issue 11, pp 2791–2801 | Cite as

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

  • Carl Friedrich Horst Longin
  • Manje Gowda
  • Jonathan Mühleisen
  • Erhard Ebmeyer
  • Ebrahim Kazman
  • Ralf Schachschneider
  • Johannes Schacht
  • Martin Kirchhoff
  • Yusheng Zhao
  • Jochen Christoph ReifEmail author
Original Paper


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.


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.


Powdery Mildew Leaf Rust Specific Combine Ability Hybrid Performance Yellow Rust 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.

Supplementary material

122_2013_2172_MOESM1_ESM.eps (435 kb)
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)
122_2013_2172_MOESM2_ESM.eps (1.5 mb)
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)
122_2013_2172_MOESM3_ESM.eps (466 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)


  1. Baker RJ (1986) Selection indices in plant breeding. University of Michigan, CRC Press, Boca RatonGoogle Scholar
  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 39:1142–1149PubMedCrossRefGoogle Scholar
  3. Bernardo R (2002) Breeding for quantitative traits in plants. Stemma Press, WoodburyGoogle Scholar
  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 89:975–981Google Scholar
  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, AustraliaGoogle Scholar
  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 123:273–285CrossRefGoogle Scholar
  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 1019–1045Google Scholar
  8. Falconer DS, Mackay TF (1996) Introduction to quantitative genetics, 4th edn. Longmans Green, HarlowGoogle Scholar
  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 127:446–451CrossRefGoogle Scholar
  10. Fisher RA (1921) On the “probable error” of a coefficient of correlation deduced from a small sample. Metron 1:1–32Google Scholar
  11. Gordillo AG, Geiger HH (2008) Alternative recurrent selection strategies using doubled haploid lines in hybrid maize breeding. Crop Sci 48:911–922CrossRefGoogle Scholar
  12. Gowda M, Longin CFH, Lein V, Reif JC (2012) Relevance of specific versus general combining ability effects in wheat. Crop Sci 52:2494–2500CrossRefGoogle Scholar
  13. Hallauer AR, Miranda JB (1981) Quantitative genetics in maize breeding. Iowa State University Press, Ames, pp 267–298Google Scholar
  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 469–565Google Scholar
  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 37:416–423CrossRefGoogle Scholar
  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 114:393–402PubMedCrossRefGoogle Scholar
  17. Longin CFH, Mühleisen J, Maurer HP, Zhang H, Gowda M, Reif JC (2012) Hybrid breeding in autogamous cereals. Theor Appl Genet 125:1087–1096PubMedCrossRefGoogle Scholar
  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 28:647–655CrossRefGoogle Scholar
  19. Möhring J, Piepho H-P (2009) Comparison of weighting in two-stage analysis of plant breeding trials. Crop Sci 49:1977–1988CrossRefGoogle Scholar
  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 45:1476–1482CrossRefGoogle Scholar
  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 100:96–104CrossRefGoogle Scholar
  22. Payne RW (2006) New and traditional methods for the analysis of unreplicated experiments. Crop Sci 46:2476–2481CrossRefGoogle Scholar
  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 100:273–279CrossRefGoogle Scholar
  24. Reif JC, Gumpert F, Fischer S, Melchinger AE (2007) Impact of genetic divergence on additive and dominance variance in hybrid populations. Genetics 176:1931–1934PubMedCrossRefGoogle Scholar
  25. Schachschneider R (1997) Hybridweizen-Stand und Erfahrungen. In: Bericht 48. Arbeitstagung österreichischer Pflanzenzüchter, Gumpenstein, Österreich, pp 27–32 (in German)Google Scholar
  26. Schnell FW (1965) Die Covarianz zwischen Verwandten in einer genorthogonalen Population. I. Allgemeine Theorie. Biometrische Z 7:1–49 (in German)CrossRefGoogle Scholar
  27. Schnell FW (1982) A synoptic study of the methods and categories of plant breeding. Z Pflanzenzüchtg 89:1–18Google Scholar
  28. Schrag TA, Frisch M, Dhillon BS, Melchinger AE (2009) Marker-based prediction of hybrid performance in maize single-crosses involving doubled haploids. Maydica 54:353–362Google Scholar
  29. Singh SK, Chatrath R, Mishra B (2010) Perspective of hybrid wheat research: a review. Indian J Agric Sci 80:1013–1027Google Scholar
  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 115:59–66PubMedCrossRefGoogle Scholar
  31. Stram DO, Lee JW (1994) Variance components testing in longitudinal mixed effects model. Biometrics 50:1171–1177PubMedCrossRefGoogle Scholar
  32. Tomerius AM (2001) Optimizing the development of seed-parent lines in hybrid rye breeding. PhD thesis, University of Hohenheim.
  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 117:251–260PubMedCrossRefGoogle Scholar
  34. Weißmann S, Weißmann AE (2002) Hybrid triticale-prospects for research and breeding. In: Proceedings of the 5th International Triticale Symposium, Radzikow, Poland, pp 188–191Google Scholar
  35. Williams ER, Piepho H-P, Whitaker D (2010) Augmented p-rep designs. Biom J 53:19–27PubMedCrossRefGoogle Scholar
  36. Wricke G, Weber WE (1986) Quantitative genetics and selection in plant breeding. Walter de Gruyter, Berlin, pp 172–194CrossRefGoogle Scholar
  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 Google Scholar
  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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Carl Friedrich Horst Longin
    • 1
  • Manje Gowda
    • 1
  • Jonathan Mühleisen
    • 1
  • Erhard Ebmeyer
    • 3
  • Ebrahim Kazman
    • 4
  • Ralf Schachschneider
    • 5
  • Johannes Schacht
    • 6
  • Martin Kirchhoff
    • 5
  • Yusheng Zhao
    • 2
  • Jochen Christoph Reif
    • 2
    Email author
  1. 1.State Plant Breeding InstituteUniversity of HohenheimStuttgartGermany
  2. 2.Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)GaterslebenGermany
  3. 3.KWS LOCHOW GmbHBergenGermany
  4. 4.Lantmännen SW Seed Hadmersleben GmbHHadmerslebenGermany
  5. 5.Nordsaat Saatzuchtgesellschaft mbHLangensteinGermany
  6. 6.Limagrain GmbHPeine-RosenthalGermany

Personalised recommendations