, Volume 195, Issue 3, pp 331–344 | Cite as

Genetic control of the performance of maize hybrids using complex pedigrees and microsatellite markers

  • Wagner Mateus Costa Melo
  • Marcio Balestre
  • Renzo Garcia Von Pinho
  • Júlio Sílvio de Sousa Bueno Filho


This study seeks to quantify the importance of epistatic effects on the heterosis of maize using complex pedigrees in a single model of the so-called bi-, tri-, and tetra-alleles in an analysis with and without information from microsatellite markers. To this end, 51 inbred lines were sampled from different backgrounds, obtaining 6 double cross hybrids, 14 triple cross hybrids, and 58 single cross hybrids, for a total of 78 hybrids. Seventy-nine microsatellite markers were used in the genotyping of the 51 lines. These markers were distributed throughout the 10 linkage groups in maize. This information was used to construct an information matrix on kinship. The mixed models and restricted maximum likelihood approaches were used to estimate additive, dominant and epistatic effects. It was observed that the dominant by dominant epistasis was the most important effect related to genetic control of the heterosis in maize. Also, our study demonstrated that it is possible to exploit a large amount of information when we jointly analyze simple, double, and three-way cross hybrids under the same model. Using this approach, it is possible to dissect heterosis into several components and to adopt the best crossbreeding strategy based on the importance of each component. Additionally, it was possible to verify that the use of molecular markers improves the accuracy of calculating the epistatic and dominance effects. Thus, using the current state-of-art in quantitative genetics and statistical methods the concept of crossbreeding can be expanded to frontiers that are far beyond the traditional general and specific combining ability.


Heterosis Mixed models SSR Identity by state 



To Advanta Seeds for supporting this research and to the Brazilian funding agency (FAPEMIG) for conceding a research grant for the last author. Comments of both reviewers were very insightful and made this a better paper.


  1. Balestre M, Machado JC, Lima JL, Souza JC, Filho LN (2008) Genetics distances estimates among single cross hybrids and correlation with specific combining ability and yield in maize double cross hybrids. Genet Mol Res 7:65–73PubMedCrossRefGoogle Scholar
  2. Balestre M, Von Pinho RG, Souza CL Jr, Bueno Filho JSS (2012) Bayesian mapping of multiple traits in maize: the importance of pleiotropic effects in studying the inheritance of quantitative traits. Theor Appl Genet 125:479–493PubMedCrossRefGoogle Scholar
  3. Bueno Filho JSS, Gilmour SG (2003) Planning incomplete block experiments when treatments are genetically related. Biometrics 59:375–381PubMedCrossRefGoogle Scholar
  4. Carlborg O, Haley CS (2004) Epistasis: too often neglected in complex trait studies? Nat Rev Genet 5:618–625PubMedCrossRefGoogle Scholar
  5. Cockerham CC (1961) Implications of genetic variances in a hybrid breeding program. Crop Sci 1:47–52CrossRefGoogle Scholar
  6. Cockerham CC (1980) Random and fixed effects in plant genetics. Theor Appl Genet 56:119–131PubMedCrossRefGoogle Scholar
  7. Cockerham CC, Zeng ZB (1996) Design III with marker loci. Genetics 143:437–1456Google Scholar
  8. Ferreira DV, Von Pinho RG, Balestre M, Oliveira RL (2010) Prediction of maize hybrid performance using similarity in state and similarity by descent information. Genet Mol Res 9:2381–2394PubMedCrossRefGoogle Scholar
  9. Frascaroli E, Canè MA, Landi P, Pea G, Gianfranceschi L, Villa M, Morgante M, Pè ME (2007) Classical genetic and quantitative trait loci analyses of heterosis in a maize hybrid between two elite inbred lines. Genetics 176:625–644PubMedCrossRefPubMedCentralGoogle Scholar
  10. Garcia AA, Wang S, Melchinger AE, Zeng ZB (2008) Quantitative trait mapping and the genetic basis of heterosis in maize and rice. Genetics 180:1707–1724PubMedCrossRefPubMedCentralGoogle Scholar
  11. Henderson CR (2008) Applications of liner models in animal breeding. University of Guelph Press, GuelphGoogle Scholar
  12. Hill WG, Goddard ME, Visscher PM (2008) Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet 4:1–10CrossRefGoogle Scholar
  13. Hochholdinger F, Hoecker N (2007) Towards the molecular basis of Heterosis. Trends Plant Sci 12:427–432PubMedCrossRefGoogle Scholar
  14. Hua J, Xing Y, Wu W et al (2003) Single-locus heterotic effects and dominance by dominance interactions can adequately explain the genetic basis of heterosis in an elite rice hybrid. PNAS 100:2574–2579PubMedCrossRefPubMedCentralGoogle Scholar
  15. Jordan DR, Tao YZ, Godwin ID, Henzell RG, Cooper M, McIntyre CL (2004) Comparison of identity by descent and identity by state for detecting genetic regions under selection in a sorghum pedigree breeding program. Mol Breed 14:441–454CrossRefGoogle Scholar
  16. Larièpe A, Mangin B, Jasson S et al (2012) The genetic basis of heterosis: multiparental quantitative trait loci mapping reveals contrasted levels of apparent overdominance among traits of agronomical interest in maize (Zea mays L.). Genetics 190:795–811PubMedCrossRefPubMedCentralGoogle Scholar
  17. Lu H, Romero-Severson J, Bernardo R (2003) Genetics basis of heterosis explored by simple sequence repeat markers in a random-mated maize population. Theor Appl Genet 107:494–502PubMedCrossRefGoogle Scholar
  18. Mackay TF, Stone EA, Ayroles JF (2009) The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 10:565–577PubMedCrossRefGoogle Scholar
  19. Melchinger AE, Utz HF, Piepho HP, Zeng ZB, Schön CC (2007) The role of epistasis in the manifestation of heterosis: a systems-oriented approach. Genetics 177:1815–1825PubMedCrossRefPubMedCentralGoogle Scholar
  20. Mrode RA, Thompson R (2005) Linear models for the prediction of animal breeding values, 2nd edn. CABI Publishing, New YorkCrossRefGoogle Scholar
  21. Powers L (1944) An expansion of Jones’ theory for the explanation of heterosis. Am Nat 78:275–280CrossRefGoogle Scholar
  22. Rawlings J, Cockerham CC (1962a) Analysis of double cross hybrid populations. Biometrics 18(229):244Google Scholar
  23. Rawlings J, Cockerham CC (1962b) Triallel analysis. Crop Sci 2:228–231CrossRefGoogle Scholar
  24. Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R, Altmann T, Stitt M, Willmitzer L, Melchinger AE (2012) Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet 44:217–220PubMedCrossRefGoogle Scholar
  25. Roso VM, Schenkel FS, Miller SP, Schaeffer LR (2005) Estimation of genetic effects in the presence of multicollinearity in multibreed beef cattle evaluation. J Anim Sci 83:1788–1800PubMedGoogle Scholar
  26. Schnell FW, Cockerham CC (1992) Multiplicative vs. arbitrary gene action in heterosis. Genetics 131:461–469PubMedPubMedCentralGoogle Scholar
  27. Schön CC, Dhillon BS, Utz HF, Melchinger AE (2010) High congruency of QTL positions for heterosis of grain yield in three crosses of maize. Theor Appl Genet 120:321–332PubMedCrossRefGoogle Scholar
  28. Troyer AF (2006) Adaptedness and heterosis in maize and mule hybrids. Crop Sci 46:528–543CrossRefGoogle Scholar
  29. Van Der Veen JH (1959) Tests of non-allelic interaction and linkage for quantitative characters in generations derived from two diploid pure lines. Genetica 30:201–232CrossRefGoogle Scholar
  30. Zeng ZB, Wang T, Zou W (2005) Modeling quantitative trait loci and interpretation of models. Genetics 169:1711–1725PubMedCrossRefPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Wagner Mateus Costa Melo
    • 1
  • Marcio Balestre
    • 2
  • Renzo Garcia Von Pinho
    • 1
  • Júlio Sílvio de Sousa Bueno Filho
    • 2
  1. 1.Department of AgricultureFederal University of LavrasLavrasBrazil
  2. 2.Department of Exact SciencesFederal University of LavrasLavrasBrazil

Personalised recommendations