Genetic parameters and multiple-trait selection in winter barley genotypes
Boryana Dyulgerova
, Darina Valcheva
Abstract: This study aimed to assess the genetic potential of advanced barley genotypes using REML/BLUP and identify top-performing breeding lines by employing the multi-trait genotype–idiotype distance index (MGIDI). Twenty-one two-rowed winter barley genotypes, including six varieties and 15 advanced breeding lines, were evaluated over two growing years (2020/2021 and 2021/2022) at the Institute of Agriculture-Karnobat, Southeastern Bulgaria. Key traits such as number of spikes per m2, plant height, dry weight of plant, spike length, number of spikelets per spike, number of grains per spike, grain weight per spike, grain weight per plant, harvest index, 1000-grain weight, and grain yield were studied. The combined analysis of variance revealed highly significant variations in genotype, year, and their interaction for all traits except harvest index, which showed no significant variation between growing seasons. Grain yield demonstrated diverse associations with different traits in each season, underscoring the impact of specific environmental conditions. Throughout both growing years, grain yield exhibited a significant correlation with ²the number of spikes per m², emphasizing the crucial role of this trait in selecting high-yielding genotypes of winter barley. In 2021, the multi-trait genotype-ideotype distance index (MGIDI) identified 12 genotypes that outperformed the standard variety; however, only two maintained their excellence in 2022, highlighting the importance of genotype adaptability. Among the selected genotypes, the breeding line 671D-3/10 consistently demonstrated superior performance in both growing seasons, showcasing its adaptability and breeding value. The study contributes valuable insights for selecting high-yielding winter barley genotypes, providing a foundation for future breeding efforts.
Keywords: grain yield; Hordeum vulgare L.; multi-trait genotype-idiotype distance index; REML/BLUP; yield-related traits
Citation: Dyulgerova, B. & Valcheva, D. (2025). Genetic parameters and multiple-trait selection in winter barley genotypes. Bulg. J. Agric. Sci., 31(6), 1137–1148
| References: (click to open/close) | Addisu, A. & Shumet, T. (2015). Variability, heritability and genetic advance for some yield and yield related traits in barley (Hordeum vulgare L.) landraces in Ethiopia. International Journal of Plant Breeding and Genetics, 9(2), 68 - 76. http://dx.doi.org/10.3923/ijpbg.2015.68.76.
Ahmadi, J., Vaezi, B. & Pour Aboughadareh, A. (2016). Analysis of variability, heritability, and interrelationships among grain yield and related characters in barley advanced lines. Genetika, 48(1), 73 - 85. http://dx.doi.org/10.2298/GENSR1601073A.
Akgün, N. (2017). Genetic variability and correlation studies in yield and yield related characters of barley (Hordeum vulgare L.) genotypes. Selcuk Journal of Agriculture and Food Sciences, 30(2), 88 - 95.
Bizari, E. H., Uneda-Trevisoli, S. H., Val, B. H. P., Pereira, E. d. M. & Mauro, A. O. D. (2017) Selection indices for agronomic traits in segregating populations of soybean. Rev. Cienc. Agron, 48, 110 - 117. http://dx.doi.org/10.5935/1806-6690.20170012.
Cooper, M., van Eeuwijk, F. A., Hammer, G. L., Podlich, D. W. & Messina, C. (2009). Modeling QTL for complex traits: detection and context for plant breeding. Current opinion in plant biology, 12(2), 231 - 240. http://dx.doi.org/10.1016/j.pbi.2009.01.006.
Debsharma, S. K., Syed, M. A., Ali, M. H., Maniruzzaman, S., Roy, P. R., Brestic, M., Gaber, A. & Hossain, A. (2022). Harnessing on Genetic Variability and Diversity of Rice (Oryza sativa L.) Genotypes Based on Quantitative and Qualitative Traits for Desirable Crossing Materials. Genes, 14(1), 10. https://doi.org/10.3390/genes14010010.
Devı, S., Kumar, Y., Bhuker, A. & Niwas, R. (2020). Assessment of genetic variability for metric traits in barley under different sowing conditions. Forage Res., 45, 277 - 280.
Dyulgerov, N. & Dyulgerova, B. (2020). Variability, heritability, and correlations among grain yield and related traits in hulless barley accessions. Trakia Journal of Sciences, 18(4), 285 - 293. http://dx.doi.org/10.15547/tjs.2020.04.002.
Dyulgerova, B. & Dyulgerov, N. (2023). Evaluation of genetic parameters of winter barley varieties under water deficit conditions. Journal of Central European Agriculture, 24(4), 862 - 872. http://dx.doi.org/10.5513/JCEA01/24.4.3884.
Elsen, J. M. (2016). Prediction of genetic gain in finite populations with heterogeneous predicted breeding values accuracies. Journal of Animal Breeding and Genetics, 133(6), 493 - 502. http://dx.doi.org/10.1111/jbg.12222.
Hailu, A., Alamerew, S., Nigussie, M. & Assefa, E. (2016). Genetic variability, heritability and genetic advance for yield and yield related traits in barley (Hordeum vulgare L.) germplasm. World Journal of Agricultural Sciences, 12(1), 36 – 44.
Jahufer, M. Z. Z. & Casler, M. D. (2015). Application of the Smith‐Hazel selection index for improving biomass yield and quality of switchgrass. Crop Science, 55(3), 1212 - 1222. http://dx.doi.org/10.2135/cropsci2014.08.0575.
Jalalifar, R., Sabouri, A., Mousanejad, S. & Dadras, A. R. (2023). Estimation of Genetic Parameters and Identification of Leaf Blast-Resistant Rice RILs Using Cluster Analysis and MGIDI. Agronomy, 13(11), 2730. http://dx.doi.org/10.3390/agronomy13112730.
Klein, L. A., Marchioro, V. S., Toebe, M., Olivoto, T., Meira, D., Meier, C., Benin, G., Busatto, A. C., Garafini, D. C., Alberti, J. V. & Finatto, J. L. B. (2023). Selection of superior black oat lines using the MGIDI index. Crop Breeding and Applied Biotechnology, 23, e45112332. http://dx.doi.org/10.1590/1984-70332023v23n3a25.
Kolde, R. & Kolde, M. R. (2015). Package ‘pheatmap’. R package, 2022. https://cran-r.c3sl.ufpr.br/web/packages/pheatmap/pheatmap.pdf.
Kumar, M., Vishwakarma, S. R., Bhushan, B. & Kumar, A. (2013). Estimation of genetic parameters and character association in barley (Hordeum vulgare L.). Journal of Cereal Research, 5(2), 76 - 78.
Malosetti, M., Ribaut, J. M. & van Eeuwijk, F. A. (2013). The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Frontiers in physiology, 4, 44. https://doi.org/10.3389/fphys.2013.00044.
Olivoto, T. & Lúcio, A. D. C. (2020). Metan: An R package for multi environment trial analysis. Methods in Ecology and Evolution, 11, 783 - 789. http://dx.doi.org/10.1111/2041-210X.13384.
Olivoto, T. & Nardino, M. (2021). MGIDI: Toward an effective multivariate selection in biological experiments. Bioinformatics, 37(10), 1383 - 1389. http://dx.doi.org/10.1093/bioinformatics/btaa981.
Olivoto, T., Nardino, M., Meira, D., Meier, C., Follmann, D. N., de Souza, V. Q., Konflanz, D. B. & Baretta, D. (2021). Multi‐trait selection for mean performance and stability in maize. Agronomy Journal, 113(5), 3968 - 3974. http://dx.doi.org/10.1002/agj2.20741.
Piepho, H. P., Möhring, J., Melchinger, A. E. & Büchse, A. (2008). BLUP for phenotypic selection in plant breeding and variety testing. Euphytica, 161(1-2), 209 - 228. http://dx.doi.org/10.1007/s10681-007-9449-8.
Pour-Aboughadareh, A. & Poczai, P. (2021). Dataset on the use of MGIDI index in screening drought-tolerant wild wheat accessions at the early growth stage. Data in Brief, 36, 107096. http://dx.doi.org/10.1016/j.dib.2021.107096.
Ramalho, M. A. P. & Araújo, L. C. A. D. (2011) Breeding self-pollinated plants. Crop Breeding and Applied Biotechnology, 11(1), 1 - 7. http://dx.doi.org/10.1007/s10681-007-9449-8.
Resende, M. D. V. & Duarte, J. B. (2007) Precisão e controle de qualidade em experimentos de avaliação de cultivares. Pesquisa Agropecuária Tropical, 37, 182 - 194 (in Portuguese).
Rocha, J. R. D. A. S. D. C., Machado, J. C. & Carneiro, P. C. S. (2018). Multitrait index based on factor analysis and ideotype‐design: Proposal and application on elephant grass breeding for bioenergy. GCB Bioenergy, 10(1), 52 - 60. http://dx.doi.org/10.1111/gcbb.12443.
Sanhueza, R. P., White, T. L., Huber, D. A. & Griffin, A. R. (2002) Genetic parameter estimates, selection indices and predicted genetic gains from selection of Eucalyptus globulus in Chile. For. Genet., 9, 19 - 29.
Singamsetti, A., Zaidi, P. H., Seetharam, K., Vinayan, M. T., Olivoto, T., Mahato, A., Madankar, K., Kumar M. & Shikha, K. (2023). Genetic gains in tropical maize hybrids across moisture regimes with multi-trait-based index selection. Frontiers in Plant Science, 14, 1147424. http://dx.doi.org/10.3389/fpls.2023.1147424.
Singh, B. D. (1990) Plant Breeding: Principles and Methods (4th Edn.). Kalyani Publishers, New Delhi, 654.
Tawfiq, S. I., Abdulkhaleq, D. A., Hama, S. J. & Abdulqader, S. H. (2016). Correlation and path analysis in barley under rainfall conditions. Journal of Zankoy Sulaimani, 18(3-A), 1 - 7. http://dx.doi.org/10.17656/jzs.10538.
Uddin, M. S., Billah, M., Afroz, R., Rahman, S., Jahan, N., Hossain, M. G., Bagum S. A., Uddin M. S., Khaldun A. B. M., Azam M. G., Hossain N., Akanda M. A. L., Alhomrani M., Gaber A. & Hossain, A. (2021). Evaluation of 130 Eggplant (Solanum melongena L.) genotypes for future breeding program based on qualitative and quantitative traits, and various genetic parameters. Horticulturae, 7(10). 376. http://dx.doi.org/10.3390/horticulturae7100376.
Vieira, R. A., Rocha, R., Scapim, C. A., Amaral, A. T., Jr. & Vivas, M. (2016). Selection index based on the relative importance of traits and possibilities in breeding popcorn. Genet. Mol. Res., 15, 1 - 10. http://dx.doi.org/10.4238/gmr.1502771. |
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| Date published: 2025-12-16
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