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.
Similar content being viewed by others
References
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 40:30–39
Basten CJ, Weir BS, Zeng Z-B (2003) QTL cartographer: version 1.17
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 34:882–896
Berke TG, Rocheford TR (1995) Quantitative trait loci for flowering, plant and ear height, and kernel traits in maize. Crop Sci 35:1542–1549
Bernardo R (2002) Breeding for quantitative traits in plants. Stemma Press, Woodbury
Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664
Bernardo R (2009) Genome wide selection for rapid introgression of exotic germplasm in maize. Crop Sci 49:419–425
Bernardo R, Yu J (2007) Prospects for genome wide selection for quantitative traits in maize. Crop Sci 47:1082–1090
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 162:1945–1959
Cochran WG, Cox GM (1966) Experimental design. John Wiley, New York
East EM (1908) Inbreeding in corn. Conn Agric Expt Sta Rept For 1907:419–428
Eathington SR, Crosbie TM, Edwards MD, Reiter RS, Bull JK (2007) Molecular markers in a commercial breeding program. Crop Sci 47:S154–S163
Goddard ME, Hayes BJ (2007) Genomic selection. J Anim Breed Genet 124:323–330
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 117:193–202
Hallauer AR, Miranda Filho JB (1988) Quantitative genetics in maize breeding. Iowa State University Press, Ames
Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12
Henderson CR (1984) Applications of linear models in animal breeding. University of Guelph, Ontario
Hospital F, Moreau L, Charcosset A, Gallais A (1997) More the efficiency of marker assisted selection. Theor Appl Genet 95:1181–1189
Jacobson A, Lian L, Zhong S, Bernardo R (2014) General combining ability model for genome wide selection in a biparental cross. Crop Sci 54:895–905
Jiang C, Zeng Z (1995) Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140:1111–1127
Johnson R (2004) Marker-assisted selection. Plant Breed 24:293–309
Jones DF (1918) The effects of inbreeding and crossbreeding upon development. Conn Agric Expt Sta Bull 207:5–100
Kolbehdari D, Schaeffer LR, Robinson JAB (2007) Estimation of genome-wide haplotype effect in half-sib designs. J Anim Breed Genet 124:356–361
Lander ES, Botstein D (1989) Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199
Legarra A, Misztal I (2008) Computing strategies in genome-wide selection. J Dairy Sci 91:360–366
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 17:227–239
Lincoln SE, Daly MJ, Lander ES (1992) Constructing genetic maps with Mapmaker Exp 3.0. Whitehead Institute for Biometrical Research, Cambridge
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 91:378–387
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 124:377–389
Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161
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 107:494–502
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 53:58–66
Mendes MP, Souza Júnior CL (2016) Genome wide prediction of tropical maize single-crosses. Euphytica 209:651–663
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829
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 45:114–122
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 119:1361–1369
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 185:419–428
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 126:773–789
Sabadin PK, Souza CL Jr, Souza PH, Garcia AAF (2008) QTL mapping for yield components in a tropical maize population using microsatellite markers. Hereditas 145:194–203
SAS Institute Inc (2001) SAS/STAT User’s guide, v.6.03. SAS Institute, Cary
Shull GH (1910) Hybridization methods in corn breeding. Am Breed Mag 6:63–72
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 139:107–115
Smith OS (1986) Covariance between line per se and heterosis performance. Crop Sci 26:540–543
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 104–113
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 154:213–227
Zeng Z-B (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1466
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Môro, G.V., Santos, M.F. & de Souza, C.L. Use of genomic and phenotypic selection in lines for prediction of testcrosses in maize II: grain yield and plant traits. Euphytica 213, 128 (2017). https://doi.org/10.1007/s10681-017-1915-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10681-017-1915-3