Assessing the phenotypic and molecular selection indices for grain yield improvement in maize (Zea mays L.)

Document Type : Research Paper

Authors

1 Graduate PhD, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran

2 Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran

3 Associate Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran

Abstract

Introduction
Maize as a tropical cereal is a main source of food for humans and livestock, as well as biofuels and fiber in some regions of the world. Increasing maize production is one of the main priorities of the country, Iran. One of the columns of increasing production is development of new high-yielding cultivars. To improve a complex trait such as grain yield that has low heritability, indirect selection by other traits or developing a suitable index based on several traits can be used. In this study, linear phenotypic selection index (LPSI) and linear molecular selection index (LMSI) were prepared using the combination of morphological traits and informative ISSR molecular markers. The objective of the present study was to prepare appropriate selection indices in maize to improve grain yield.
Materials and methods
The plant materials of this research were 97 maize genotypes that were cultivated in a randomized complete block design with six replications in the research field of the Faculty of Agriculture, Urmia University, Urmia, Iran. Morphological traits were measured from the tasseling stage to the physiological maturity. Sixty ISSR primer combinations were also used to prepare the molecular profile of the studied maize genotypes. To select the suitable genotypes, two indices including linear phenotypic selection index and linear molecular selection index were used, and the efficiency of the indices was compared with the estimation of different parameters such as the rate of genetic gain and response to selection.
Research findings
The results of the linear phenotypic selection index showed that the highest rate of genetic gain based on the index (DG) was observed for chlorophyll content (99.15) and the lowest one for number of ears per plant (0.01). The expected genetic gain for all studied traits (DH) and response to selection was estimated at 163.2234 and 0.774, respectively. Based on the linear molecular selection index, the highest rate of genetic gain (DG) was observed for leaf area (99.31) and the lowest one was observed for number of ears per plant (0.02). The expected genetic gain for all studied traits (DH) and response to selection was also estimated at 50.972 and 0.774, respectively. The results showed that the correlation between index and breeding value (rHI) in the LPSI index was relatively favorable (less than one), and in the LMSI index was optimal (one), but both correlations were significant at 0.05 probability level according to the t-test. However, the efficiency of selection based on the index (ΔH) was 163.22 for the LPSI index and 50.97 for the LMSI index. On the other hand, the degree of genetic gain of trait (DG) was different depending on the type of index. For example, the ratio of genetic gain (DG) derived from molecular to phenotypic index for the number of ears per plant and grain yield (2.00 and 1.28, respectively) was higher than the other traits. Also, the best genotype based on both indices was genotype number of 61.
Conclusion
According to the results obtained from the present study and the review of sources in this field, it seems that it is possible to benefit from the advantages of development of the LMSI index in the breeding programs in early generations, but in advanced generations, it is better to select genotypes using the LPSI index, in which case the cost of molecular evaluations will be reduced.

Keywords

Main Subjects

References

Bernardo, R. 2010. Breeding for Quantitative Traits in Plants, 2nd Edition. Stemma Press, Woodbury, MN, USA.##Branlard, G.; Pierre, J.; Rousset, M. 1992. Selection indices for quality evaluation in wheat breeding. Theoretical and Applied Genetics, 84, pp. 57-64. https://doi.org.10.1007/BF00223981.##Brim, C. A., Johnson, H. W. and Cockerham, C. C. 1959. Multiple selection criteria in soybeans. Agronomy Journal, 51, pp. 42–46. https://doi.org/10.2134/agronj1959.00021962005100010015x.##Cerón-Rojas, J. J., and Crossa, J. 2018. Linear selection indices in modern plant breeding. Springer. https://doi.org/10.1007/978-3-319-91223-3. (eBook).##Cerón-Rojas, J. J. and Crossa, J. 2020. Combined multi-stage linear genomic selection indices to predict the net genetic merit in plant breeding. G3: Genes, Genomes, Genetics, 10, pp. 2087- 2101. doi: https://doi.org/10.1534/g3.120.401171.##Cerón-Rojas J. J. and Crossa J. 2022. The statistical theory of linear selection indices from phenotypic to genomic selection. Crop Science, 62, pp. 537–563. doi: 10.1002/csc2.20676.##Cerón-Rojas, J. J., Gowda, M., Toledo, F., Beyene, Y., Bentley, A. R. Crespo-Herrera, L. Gardner, K. and Crossa, J.  2023. A linear profit function for economic weights of linear phenotypic selection indices in plant breeding. Crop Science, pp. 1–13. https://doi.org/10.1002/csc2.20882.##Crossa, J.,   Jesús Cerón-Rojas, J.,  Martini, J. W. R.,  Covarrubias-Pazaran, G., Alvarado, G., Toledo, F. H. and Govindan, V. 2022. Theory and practice of phenotypic and genomic selection indices. Wheat Improvement, 593–616. https://doi.org/10.1007/978-3-030-90673-3_32.##Dekkers, J. C. M. 2007. Prediction of response to marker-assisted and genomic selection using selection index theory. Journal of Animal Breeding and Genetics, 124, pp. 331–341. https://doi.org/10.1111/j. 1439 0388.2007.00701.x.##Dekkers, J. C. M. and Settar, P. 2004. Long-term selection with known quantitative trait loci. Plant Breeding Reviews, 24, pp. 311–335.##Dekkers, J. C. M., and Dentine, M. R. 1991. Quantitative genetic variation associated with chromosomal markers in segregating populations. Theoretical and Applied Genetics, 81, pp. 212–220. https://doi.org/ 10.1007/BF00215725.f.##Eagles, H. A. and Frey, K. J. 1974. Expected and actual gains in economic value of oat lines from five selection methods. Crop Science, 14, pp. 861–864. https://doi.org/10.2135/cropsci1974.0011183X001400060026x.##Emrani, S., Rezai, A. and Arzani A. 2008. Comparison of Selection Indices for Yield and Related Traits of Barley under Nitrogen Stress and Non-Stress Conditions. Journal of Crop Production and Processing, 11 (42): pp.183-194. (In Persian). http://jstnar.iut.ac.ir/article-1-779-en.html##Esheghi, R., Javid, O. and Samira, S. 2011. Genetic gain through selection indices in hulless barley. International Journal of Agriculture and Biology, 13, pp. 191-197. 10–431/AWB/2011/13–2–191–197.##Falconer, D.S. and Mackay, T.F.C. 1996. Introduction to Quantitative Genetics, fourth ed. Longman, New York, 464.##Gazal, A., Nehvi, F.A., Lone, A.A., Dar, Z.A. and Wani, M.A. 2017. Smith Hazel Selection Index for the Improvement of Maize Inbred Lines under Water Stress Conditions. International Journal of Pure and Applied Bioscience, 5 (1), pp. 72-81 doi: http://dx.doi.org/10.18782/2320-7051.2444.##Gill, H.S., Halder, J., Zhang, J., Brar, N. K., Rai, T.S., Hall, C., Bernardo, A., Amand, P. S., Bai, G., Olson, E., Ali, S., Turnipseed, B. and Sehga, S.K. 2021. Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat. Frontiers of Plant Science, 12, 709545. doi: 10.3389/fpls.2021.709545.##Gimelfarb, A., and Lande, R. 1994. Simulation of marker-assisted selection in hybrid populations. Genetic Research, 63, pp. 39-47. https://doi. org/10.1017/S0016672300032067.##Gimelfarb, A., and Lande, R. 1995. Marker-assisted selection and marker-QTL associations in hybrid populations. Theoretical and Applied Genetics, 91, pp. 522-528. https://doi.org/10.1007/BF0022298.##Ghaffari Azar, A., Darvishzadeh, R., Hatami Maleki, H., Kahrizi, D., Darvishi, B. and Bernoosi, I. 2018. Identification of Inter simple sequence repeat regions associated with agro-morphological traits in maize genome. Cereal Research, 8 (1), pp. 97-109. [In Persian]. Doi: 10.22124/c.2018.8211.1322.##Gravois, K. A. and McNew, R. W. 1993. Genetic relationships among and selection for rice yield and yield components. Crop Science, 33, pp. 249–252. https://doi.org/10.2135/cropsci1993.0011183X003300020006x.##Hazel, L.N. 1943.The genetic basis for constructing selection indexes. Genetics, 28, pp. 476–490. https://doi.org/10.1093/genetics/28.6.476.##Hazel, L. N., and Lush, J. L. 1942. The efficiency of three methods of selection. The Journal of Heredity, 33, pp. 393–399. https://doi.org/10.1093/oxfordjournals.jhered.a105102.##Hazel, L. N., Dickerson, G. E., and Freeman, A. E. 1994. The selection index: Then, now, and for the future. Journal of Dairy Science, 77, pp. 3236-3251. https://doi.org/10.3168/jds.S0022-0302 (94)77265-9.##Hidalgo-Contreras, J.V., Salinas-Ruiz, J. and Eskridge, K.M. 2021. Molecular Markers and Causal Structure among Traits Using a Smith-Hazel Index and Structural Equation Models. Agronomy, 11, 1953. https://doi.org/10.3390/ agronomy11101953.##Htwe, N. M., Aye, M. and Thu, C. N. 2020. Selection Index for Yield and Yield Contributing Traits in Improved Rice Genotypes. International Journal of Engineering Research and Development, 11(2), pp. 86- 91.##Iqbal, M., Semagn, K., Céron-Rojas, J.J., Crossa, J., Jarquin, D., Howard, R., Beres, B.L., Strenzke, K., Ciechanowska, I. and Spaner, D. 2022. Identification of Spring Wheat with Superior Agronomic Performance under Contrasting Nitrogen Managements Using Linear Phenotypic Selection Indices. Plants, 11, 1887. https://doi.org/10.3390/plants11141887.##Jannatdoust, M., Darvishzadeh, R. and Ebrahimi, M. A. 2014. Studying genetic diversity in confectionary sunflower (Helianthus annuus L.) by using microsatellit markers. Crop Biotechnology, 6, pp. 61-72. [In Persian]. 20.1001.1.22520783.1393.4.6.6.0.##Juliana, P., He, X., Poland, J., Roy, K. K., Malaker, P. K., Mishra, V. K., Chand, R., Shrestha, S., Kumar, U., Roy, C., Gahtyari, N. C., Joshi, A. K., Singh, R. P. and Singh, P. K. 2022. Genomic selection for spot blotch in bread wheat breeding panels, full-sibs and half- sibs and index-based selection for spot blotch, heading and plant height. Theoretical and Applied Genetics, 135, pp. 1965–1983. https://doi.org/10.1007/ s00122-022-04087-y.##Karthikeya Reddy, S.G.P. and Babariya, C. A. 2020. Selection indices for yield improvement in bread wheat (Triticum aestivum L.). Electronic Journal of Plant Breeding, 11 (1), pp. 314-317. https://doi.org/10.37992/2020.1101.056.##Kempthorne, O., and Nordskog, A. W. 1959. Restricted selection indices. Biometrics, 15(1), pp. 10–19. https://doi.org/10.2307/2527598.##Khavari Khorasani, S. and Mahdi Poor A. 2018. Genetic improvement of Grain Yield by Determination of Selection Index in Single Cross Hybrids of Maize (Zea mays L.). Plant Genetic Reseearch, 5 (1), pp. 1-18. http://pgr.lu.ac.ir/article-1-119-en.html##Lande, R., and Thompson, R. 1990. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics, 124, pp. 743–756. https://doi.org/10.1093/genetics/124.3.743.##Li, Z. 1998. Molecular analysis of epistasis affecting complex traits. In A. H. Paterson (Ed.), Molecular dissection of complex traits. CRC Press LLC, pp.119–130.##Mahdy, R.E., Althagafi, Z.M.A., Al-Zahrani, R.M., Aloufi, H.H.K., Alsalmi, R.A., Abeed, A.H.A.; Mahdy, E.E. and Tammam, S.A. 2022. Comparison of Desired-Genetic-Gain Selection Indices in Late Generations as an Insight on Superior-Family Formation in Bread Wheat (Triticum aestivum L.). Agronomy, 12, 1738. https://doi.org/10.3390/ agronomy12081738.##Mather, K., and Jinks, J. L. 1971. Biometrical genetics: the study of continuous variation. Chapman and
Hall, London.##Modarresi, M., Assad, M. T. and Kheradnam, M. 2004. Determining Selection Indices in Corn Hybrids (Zea mays L.) to Increase Grain. Journal of Water and Soil Science, 7 (4); pp.71-82. 20.1001.1.24763594.1382.7.4.7.7.##Moreau, L., Lemarié, S., Charcosset, A., and Gallais, A. 2000. Economic efficiency of one cycle of marker-assisted selection efficiency. Crop Science, 40, pp. 329–337. https://doi.org/10.2135/cropsci2000.402329x.##Moreau, L., Hospital, F., and Whittaker, J. 2007. Marker-assisted selection and introgression. In: Balding D.J., Bishop M, Cannings C. (eds), Handbook of statistical genetics, (1, 3rd ed.). Wiley, New York, pp. 718–751.##Moreira, S. O., Kuhlcamp, Barros, K. T., F. L. S. Zucoloto, M. and Godinho, T. O. 2019. Selection index based on phenotypic and genotypic values predicted by REML/BLUP in Papaya. Revista Brasileira de Fruticultura, 41(1), e-079. doi.org /10.1590/0100-29452019079.##Pacheco, A., Pérez, S., Alvarado, G., Ceron, J., Rodríguez, F., Crossa J, Burgueño J. 2017. RIndSel: selection indices for plant breeding. hdl:11529/10854, CIMMYT Research Data & Software Repository Network, V1.##Pesek, J. and Baker, R. J. 1970. An application of index selection to the improvement of self.pollinated species. Canadian Journal of Plant Science, 50, pp. 267-276. https://doi.org/10.4141/cjps70-051.##Rabiei, B., Valizadeh, M., Ghareyazie, B. and Moghaddam, M. 2004. Evaluation of selection indices for improving rice grain shape. Field Crops Research, 89, pp. 359–367. https://doi.org/10.1016/j.fcr.2004.02.016.##Randhawa, H. S., Asif, M., Pozniak, C., Clarke, J. M., Graf, R. J., Fox, S. L., et al. 2013. Application of molecular markers to wheat breeding in Canada. Plant Breeding, 132, pp. 458–471. doi: 10.1111/pbr.12057.##Robinson, H. F., Comstock, R. E. and Harvey, P. H. 1951. Genotypic and phenotypic correlations in corn and their implications in selection. Agronomy Journal, 43, pp. 282-287.##Sabouri, H., Rabiei, B. and Fazlalipour, M. 2008. Use of selection indices based on multivariate analysis for improving grain yield in rice. Rice Science, 15, pp. 303–310. https://doi.org/10.1016/S1672-6308(09)60008-1.##Shah, S., Mehta, D. R. and Raval, L. 2016. Selection indices in bread wheat (Triticum aestivum L.). Electronic Journal of Plant Breeding, 7(2), pp. 459-463. DOI:10.5958/0975-928X.2016.00059.4.##Smiderle, É. C., Furtini, I.V., da Silva, C. S.C., Botelho, F. B. S. et al. 2019. Index selection for multiple traits in upland rice progenies. Revista de Ciências Agrárias, 42(1): pp. 4-12. https://doi.org/10.19084/RCA18059.##Smith, H.F. 1936. A discriminant function for plant selection. Annals of Eugenics, 7, pp. 240–250. https://doi.org/10.1111/j.1469-1809.1936. tb02143.x.##Smith, O., Hallauer, A. R. and Russell, W. A. 1981. Use of index selection in recurrent selection programs in maize. Euphytica, 30, pp. 611–618. https://doi.org/10.1007/BF00038788.##Suwantaradon, K., Eberhart, S. A., Mock, J. J., Owens, J. C. and Guthrie, W. D. 1975. Index selection for several agronomic traits in the BSSS2 maize population. Crop science, 15(6), pp. 827–833. https://doi.org/10.2135/cropsci1975.0011183X001500060025x.##Smiderle, E. C., Furtini, I. V., Silva, C. S. C. da, Botelho, F. B. S., Resende, M. P. M., Botelho, R. T. C., Colombari Filho, J. M., Castro, A. P. de, and Utumi, M. M. 2019. Index selection for multiple traits in upland rice progenies. Security Content Automation Protocol, 42(1), pp. 4-12. Doi: 10.19084/RCA18059.##Strefeler, M.S. and Wehner, T.C. 1986. Estimates of heritabilities and genetic variances of three yield and five quality traits in three fresh-market cucumber populations. The Journal of the American Society for Horticultural Science, 111, pp. 599-605.##Tahmasbali, M., Darvishzadeh, R. and Fayaz Moghaddam, A. 2021. Evaluation of oriental tobacco (Nicotiana tabacum L.) genotypes using selection indices under the presence and absence of broomrape. Iranian Journal of Field Crops Research, 52 (3), pp. 189-207. doi: 10.22059/IJFCS.2020.300277.654707. [In Persian].##Zhang, W., and Smith, C. 1992. Computer simulation of marker-assisted selection utilizing linkage disequilibrium. Theoretical and Applied Genetics, 83, pp. 813–820. https://doi.org/10.1007/BF00226702.##Zhang, W., and Smith, C. 1993. Simulation of marker-assisted selection utilizing linkage disequilibrium: The effects of several additional factors. Theoretical and Applied Genetics, 86, pp. 492–496. https://doi.org/10.1007/BF00838565.
Cereal Research
Volume 13, Issue 2 - Serial Number 47
September 2023
Pages 145-161

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