Euphytica

, 213:128 | Cite as

Use of genomic and phenotypic selection in lines for prediction of testcrosses in maize II: grain yield and plant traits

  • Gustavo Vitti Môro
  • Mateus Figueiredo Santos
  • Cláudio Lopes de SouzaJr.
Article
  • 298 Downloads

Abstract

Plant breeders have been trying to predict the performance of hybrids based on their parental performance. One application of molecular markers is its use in selection. The objectives were to map quantitative trait loci (QTL) and verify its congruence in maize lines and in their testcrosses and verify the possibility to select testcrosses from the predicted means of the lines by using information from markers. Two-hundred and fifty six lines and the testcrosses of these lines with two testers were evaluated in six environments, considering grain yield, plant lodging, days to anthesis and silking, anthesis-silking interval, plant and ear height and ear placement. QTL were mapped in the lines and in testcrosses and the predicted means of the lines were computed based on QTL effects and in all markers of the genome. The congruence of QTL detected in the lines and testcrosses were small for all traits. The correlations between the predicted means of the lines and the phenotypic means of the testcrosses ranged from low for grain yield to moderate for cycle and stature traits. The highest coincidences of the lines and selected testcrosses were observed for cycle and stature traits and the lowest for grain yield. Even by using molecular markers information, it is only possible to predict the testcrosses performance from the lines information to less complex traits and with reduced dominance effect. For complex traits and with pronounced dominance effect, information of markers must be obtained directly in the testcrosses, so they can be used for selection.

Keywords

Correlation Endogamy Hybrids QTL Marker assisted selection Tropical maize 

Notes

Acknowledgements

This research was supported by “Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq-140964/2006-1)” and by the Department of Genetics at the Agriculture College “Luiz de Queiroz”-University of São Paulo. C. L. Souza Jr. and G. V. Môro are recipient of a research fellowship from CNPq. The authors are grateful to Dr. Anete Pereira de Souza, from the University of Campinas for the genetic mapping of the population, and to A.J. Desidério, A.S. Oliveira, C.R. Segatelli, and for their assistance with the field experiments.

References

  1. Austin DF, Lee M, Veldboom LR, Hallauer AR (2000) Genetic mapping in maize with hybrid progeny across testers and generations: grain yield and grain moisture. Crop Sci 40:30–39CrossRefGoogle Scholar
  2. Basten CJ, Weir BS, Zeng Z-B (2003) QTL cartographer: version 1.17Google Scholar
  3. Beavis WD, Smith OS, Grant D, Fincher RR (1994) Identification of quantitative trait loci using a small sample of top crossed and F4 progeny from maize. Crop Sci 34:882–896CrossRefGoogle Scholar
  4. Berke TG, Rocheford TR (1995) Quantitative trait loci for flowering, plant and ear height, and kernel traits in maize. Crop Sci 35:1542–1549CrossRefGoogle Scholar
  5. Bernardo R (2002) Breeding for quantitative traits in plants. Stemma Press, WoodburyGoogle Scholar
  6. Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664CrossRefGoogle Scholar
  7. Bernardo R (2009) Genome wide selection for rapid introgression of exotic germplasm in maize. Crop Sci 49:419–425CrossRefGoogle Scholar
  8. Bernardo R, Yu J (2007) Prospects for genome wide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar
  9. Bouchez A, Hospital F, Causse M, Gallais A, Charcosset A (2002) Marker-assisted introgression of favorable alleles at quantitative trait loci between maize elite lines. Genetics 162:1945–1959PubMedPubMedCentralGoogle Scholar
  10. Cochran WG, Cox GM (1966) Experimental design. John Wiley, New YorkGoogle Scholar
  11. East EM (1908) Inbreeding in corn. Conn Agric Expt Sta Rept For 1907:419–428Google Scholar
  12. Eathington SR, Crosbie TM, Edwards MD, Reiter RS, Bull JK (2007) Molecular markers in a commercial breeding program. Crop Sci 47:S154–S163CrossRefGoogle Scholar
  13. Goddard ME, Hayes BJ (2007) Genomic selection. J Anim Breed Genet 124:323–330CrossRefPubMedGoogle Scholar
  14. Groh S, Khairallah MM, González-de-Leon D, Willcox M, Jiang C, Hoisington DA, Melchinger EH (1998) Comparison of QTL mapped in RILs and their test-cross progenies of tropical maize for insect resistance and agronomic traits. Plant Breed 117:193–202CrossRefGoogle Scholar
  15. Hallauer AR, Miranda Filho JB (1988) Quantitative genetics in maize breeding. Iowa State University Press, AmesGoogle Scholar
  16. Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12CrossRefGoogle Scholar
  17. Henderson CR (1984) Applications of linear models in animal breeding. University of Guelph, OntarioGoogle Scholar
  18. Hospital F, Moreau L, Charcosset A, Gallais A (1997) More the efficiency of marker assisted selection. Theor Appl Genet 95:1181–1189CrossRefGoogle Scholar
  19. Jacobson A, Lian L, Zhong S, Bernardo R (2014) General combining ability model for genome wide selection in a biparental cross. Crop Sci 54:895–905CrossRefGoogle Scholar
  20. Jiang C, Zeng Z (1995) Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140:1111–1127PubMedPubMedCentralGoogle Scholar
  21. Johnson R (2004) Marker-assisted selection. Plant Breed 24:293–309Google Scholar
  22. Jones DF (1918) The effects of inbreeding and crossbreeding upon development. Conn Agric Expt Sta Bull 207:5–100Google Scholar
  23. Kolbehdari D, Schaeffer LR, Robinson JAB (2007) Estimation of genome-wide haplotype effect in half-sib designs. J Anim Breed Genet 124:356–361CrossRefPubMedGoogle Scholar
  24. Lander ES, Botstein D (1989) Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199PubMedPubMedCentralGoogle Scholar
  25. Legarra A, Misztal I (2008) Computing strategies in genome-wide selection. J Dairy Sci 91:360–366CrossRefPubMedGoogle Scholar
  26. Lima MLA, Souza CL Jr, Vieira DA, Souza PH, Garcia LC (2006) Mapping QTL for grain yield and plant traits in a tropical maize population. Mol Breed 17:227–239CrossRefGoogle Scholar
  27. Lincoln SE, Daly MJ, Lander ES (1992) Constructing genetic maps with Mapmaker Exp 3.0. Whitehead Institute for Biometrical Research, CambridgeGoogle Scholar
  28. Liu X, Fu J, Gu D, Liu W, Liu T, Peng Y, Wang J, Wang G (2008) Genome-wide analysis of gene expression profiles during the kernel development of maize (Zea mays L.). Genomics 91:378–387CrossRefPubMedGoogle Scholar
  29. Long N, Gianola D, Rosa GJM, Weigel KA, Avendaño S (2007) Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. J Anim Breed Genet 124:377–389CrossRefPubMedGoogle Scholar
  30. Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161CrossRefPubMedGoogle Scholar
  31. Lu H, Romero-Severson J, Bernardo R (2003) Genetic basis of heterosis explored by simple sequence repeat markers in a random-mated maize population. Theor Appl Genet 107:494–502CrossRefPubMedGoogle Scholar
  32. Massman JM, Jung H-JG, Bernardo R (2013) Genome wide selection versus marker-assisted recurrent selection to improve grain yield and stover-quality traits for cellulosic ethanol in maize. Crop Sci 53:58–66CrossRefGoogle Scholar
  33. Mendes MP, Souza Júnior CL (2016) Genome wide prediction of tropical maize single-crosses. Euphytica 209:651–663CrossRefGoogle Scholar
  34. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  35. Mihaljevic R, Schon CC, Utz HF, Melchinger EH (2005) Correlations and QTL correspondence between line per se and testcross performance for agronomic traits in four populations of European maize. Crop Sci 45:114–122CrossRefGoogle Scholar
  36. Moreira JUV, Bento DAV, Souza PH, Souza CL Jr (2009) QTL mapping for reaction to Phaeosphaeria leaf spot in a tropical maize population. Theor Appl Genet 119:1361–1369CrossRefPubMedGoogle Scholar
  37. Môro GV, Santos MF, Bento DAV, Aguiar AM, Souza CL Jr (2012) Genetic analysis of kernel oil content in tropical maize with design III and QTL mapping. Euphytica 185:419–428CrossRefGoogle Scholar
  38. Peng B, Li Y, Wang Y, Liu C, Liu Z, Zhang Y, Tan W, Wang D, Shi Y, Sun B, Song Y, Wang T, Li Y (2013) Correlations and comparisons of quantitative trait loci with family per se and testcross performance for grain yield and related traits in maize. Theor Appl Genet 126:773–789CrossRefPubMedGoogle Scholar
  39. Sabadin PK, Souza CL Jr, Souza PH, Garcia AAF (2008) QTL mapping for yield components in a tropical maize population using microsatellite markers. Hereditas 145:194–203CrossRefGoogle Scholar
  40. SAS Institute Inc (2001) SAS/STAT User’s guide, v.6.03. SAS Institute, CaryGoogle Scholar
  41. Shull GH (1910) Hybridization methods in corn breeding. Am Breed Mag 6:63–72Google Scholar
  42. Sibov ST, Souza CL Jr, Garcia AAF, Silva AR, Mangolin CA, Benchimol LL, Souza PH (2003) Molecular mapping in tropical maize using microsatellite markers. 2. Quantitative trait loci (QTL) for grain yield, ear height, and grain moisture. Hereditas 139:107–115CrossRefPubMedGoogle Scholar
  43. Smith OS (1986) Covariance between line per se and heterosis performance. Crop Sci 26:540–543CrossRefGoogle Scholar
  44. Stuber CW, Sisco P (1992) Marker-facilitated transfer of QTL alleles between inbred lines and responses in hybrids. In: Proceedings of 46th Ann Corn Sorghum Res. Conference. ASTA, Washington pp 104–113Google Scholar
  45. Vieira C, Pasyukova EG, Zeng ZB, Hackette JB, Lyman RF, Mackay TFC (2000) Genotype-environment interaction for quantitative trait loci affecting life span in Drosophila melanogaster. Genetics 154:213–227PubMedPubMedCentralGoogle Scholar
  46. Zeng Z-B (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1466PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Gustavo Vitti Môro
    • 1
  • Mateus Figueiredo Santos
    • 2
  • Cláudio Lopes de SouzaJr.
    • 3
  1. 1.Department of Plant Production, School of Agricultural and Veterinarian SciencesSão Paulo State University (Unesp)JaboticabalBrazil
  2. 2.Embrapa Beef CattlePlant Production GroupCampo GrandeBrazil
  3. 3.Department of GeneticsUniversity of São PauloPiracicabaBrazil

Personalised recommendations