Comparison of traditional selection indices and the Multi-Trait Genotype Ideotype Distance Index (MGIDI) for improving rice (Oryza sativa L.) cultivars

Authors

DOI:

https://doi.org/10.14719/pst.5475

Keywords:

multicollinearity, MGIDI, Selection gains, selection index, smith hazel

Abstract

Rice is a vital cereal crop that is crucial to ensuring global food security. Developing high-yield varieties is essential to feed the growing world population. Selecting the best-performing genotypes based on genetic gain is critical for improving rice production. In this process, various selection indices help identify superior genotypes. Thus, the current study aimed to select superior genotypes through a multi-trait selection index. Among the multivariate indices, the widely used classical Smith-Hazel selection index (SI) and the multi-trait genotype ideotype index (MGIDI) were used to evaluate the studied genotypes for eleven quantitative traits simultaneously. Three scenarios were formulated namely, retaining multicollinearity (SI-1), removing multi-collinearity (SI-2) and using a path analysis-based index (SI-3) for estimating the Smith-Hazel selection index. Using these indices, maximum selection gains of 9.2% with the Smith-Hazel index and 18.5% with the MGIDI index for grain yield were predicted. Notably, genotypes K5 and H7 were selected by the Smith (SI-1, SI-2, SI-3) and MGIDI selection index at a selection intensity of 15%. This shows that these genotypes exhibited strong performance across various traits and were classified as elite, highlighting their potential to contribute significantly to future breeding programs aimed at improving grain yield along with multiple traits in rice. The selection of these genotypes makes a valuable resource for developing productive rice varieties.

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References

Zhao M, Lin Y, Chen H. Improving nutritional quality of rice for human health. Theoretical Appli Genet. 2020;133:1397-413. https://doi.org/10.1007/s00122-019-03530-x

Verma V, Vishal B, Kohli A, Kumar PP. Systems-based rice improvement approaches for sustainable food and nutritional security. Plant Cell Reports. 2021;40(11):2021-36. https://doi.org/10.1007/s00299-021-02790-6

Donald CT. The breeding of crop ideotypes. Euphytica. 1968;17:385-403. https://doi.org/10.1007/BF00056241

Smith HF. A discriminant function for plant selection. Annals of Eugenics. 1936;7(3):240-50. https://doi.org/10.1111/j.1469-1809.1936.tb02143.x

Graham MH. Confronting multicollinearity in ecological multiple regression. Ecology. 2003;84(11):2809-15. https://doi.org/10.1890/02-3114

Olivoto T, de Souza VQ, Nardino M, Carvalho IR, Ferrari M, de Pelegrin AJ, et al. Multicollinearity in path analysis: a simple method to reduce its effects. Agronomy Journal. 2017;109(1):131-42. https://doi.org/10.2134/agronj2016.04.0196

Olivoto T, Nardino M. MGIDI: Toward an effective multivariate selection in biological experiments. Bioinformatics. 2021;37(10):1383-89. https://doi.org/10.1093/bioinformatics/btaa981

Bizari EH, Val BH, Pereira ED, Mauro AO, Unêda-Trevisoli SH. Selection indices for agronomic traits in segregating populations of soybean. Revista Ciência Agronômica. 2017;48(1):110-17. https://doi.org/10.5935/1806-6690.20170012

Hazel LN. The genetic basis for constructing selection indexes. Genetics. 1943;28(6):476-90. https://doi.org/10.1093/genetics/28.6.476

Cheng J, Sun J, Yao K, Xu M, Cao Y. A variable selection method based on mutual information and variance inflation factor. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2022;268:120652. https://doi.org/10.1016/j.saa.2021.120652

Kim JH. Multicollinearity and misleading statistical results. Korean Journal of Anesthesiology. 2019;72(6):558-69. https://doi.org/10.4097/kja.19087

Palaniyappan S, Arunachalam P, Banumathy S, Muthuramu S. Introspection of discriminate function analysis and MGIDI selection index for selection to improve yield in rice (Oryza sativa L.). Indian J Genet Plant Breed. 2024;84(02):202-28. https://doi.org/10.31742/ISGPB.84.2.7

Gepts P, Janick J. Plant breeding reviews. Crop Domestication as a Long-Term Selection Experiment. 2010;24(2):1-44.

Olivoto T, Lúcio AD. Metan: An R package for multi?environment trial analysis. Methods in Ecology and Evolution. 2020;11(6):783-89. https://doi.org/10.1111/2041-210X.13384

Resende MA, Freitas JA, Lanza MA, Resende MD, Azevedo CF. Genetic divergence and selection index via BLUP in cotton accessions for technological characteristics of the fiber. Trop Agri Res. 2014;44:334-640.https://doi.org/10.1590/S1983-40632014000300006

de Souza YP, Daher RF, Vander Pereira A, da Silva VB, Freitas RS, de Amaral Gravina G. Repeatability and minimum number of evaluations for morpho-agronomic characters of elephant-grass for energy purposes. Revista Brasileira de CiênciasAgrárias. 2017;12(3):391-97. https://doi.org/10.5039/agraria.v12i3a5456

Burdon RD, Li Y. Genotype-environment interaction involving site differences in expression of genetic variation along with genotypic rank changes: simulations of economic significance. Tree Genetics and Genomes. 2019;15(1):2. https://doi.org/10.1007/s11295-018-1308-3

Costello AB, Osborne J. Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment Research and Evaluation. 2019;10(1):7.

Debnath P, Chakma K, Bhuiyan MS, Thapa R, Pan R, Akhter D. A novel Multi trait genotype ideotype distance index (MGIDI) for genotype selection in plant breeding: Application, prospects and limitations. Crop Design. 2024;2:100074. https://doi.org/10.1016/j.cropd.2024.100074

Pallavi M, Prasad BM, Shanthi P, Reddy VL, Kumar AN. Multi trait genotype-ideotype distance index (MGIDI) for early seedling vigour and yield related traits to identify elite lines in rice (Oryza sativa L.). Electronic Journal of Plant Breeding. 2024;15(1):120-31. https://doi.org/10.37992/2024.1501.020

Al-Ashkar I, Sallam M, Ibrahim A, Ghazy A, Al-Suhaibani N, Ben Romdhane W, Al-Doss A. Identification of wheat ideotype under multiple abiotic stresses and complex environmental interplays by multivariate analysis techniques. Plants. 2023;12(20):3540. https://doi.org/10.3390/plants12203540

Olivoto T, Diel MI, Schmidt D, Lúcio AD. Multivariate analysis of strawberry experiments: where are we now and where can we go?. BioRxiv. 2021:2020-12. https://doi.org/10.1101/2020.12.30.424876

Palaniyappan S, Ganesan KN, Manivannan N, Ravichandran V, Senthil N. Multi trait genotype-ideotype distance index-A tool for identification of elite parental inbreds for developing heterotic hybrids of fodder maize (Zea mays L.). Electr J Plant Breed. 2023;14(3):841-49. https://doi.org/10.37992/2023.1403.098

Meier C, Marchioro VS, Meira D, Olivoto T, Klein LA. Genetic parameters and multiple-trait selection in wheat genotypes. Pesquisa Agropecuária Tropical. 2021;51:e67996. https://doi.org/10.1590/1983-40632021v5167996

Uddin MS, Billah M, Afroz R, Rahman S, Jahan N, Hossain MG, et al. Evaluation of 130 eggplant (Solanum melongena L.) genotypes for future breeding program based on qualitative and quantitative traits and various genetic parameters. Horticulturae. 2021;7(10):376. https://doi.org/10.3390/horticulturae7100376

Olivoto T, Lúcio AD, da Silva JA, Sari BG, Diel MI. Mean performance and stability in multi?environment trials II: Selection based on multiple traits. Agronomy Journal. 2019;111(6):2961-69. https://doi.org/10.2134/agronj2019.03.0221

Klein LA, Marchioro VS, Toebe M, Olivoto T, Meira D, Meier C, et al. Selection of superior black oat lines using the MGIDI index. Crop Breed Appli Biotechnol. 2023;23(3):e45112332. https://doi.org/10.1590/1984-70332023v23n3a25

Gabriel A, de Resende JT, Zeist AR, Resende LV, Resende NC, Zeist RA. Phenotypic stability of strawberry cultivars based on physicochemical traits of fruits. Horticultura Brasileira. 2019;37:75-81. https://doi.org/10.1590/s0102-053620190112

Mamun AA, Islam MM, Adhikary SK, Sultana MS. Resolution of genetic variability and selection of novel genotypes in EMS induced rice mutants based on quantitative traits through MGIDI.

Ambrósio M, Daher RF, Santos RM, Santana JG, Vidal AK, Nascimento MR, et al. Multi-trait index: selection and recommendation of superior black bean genotypes as new improved varieties. BMC Plant Biology. 2024;24(1):525. https://doi.org/10.1186/s12870-024-05248-5

Published

10-01-2025

How to Cite

1.
Vinoth PK, Manonmani S, Raveendran M, Sritharan N, Sudha M, Puja M, Bonipas A, Babu C. Comparison of traditional selection indices and the Multi-Trait Genotype Ideotype Distance Index (MGIDI) for improving rice (Oryza sativa L.) cultivars . Plant Sci. Today [Internet]. 2025 Jan. 10 [cited 2025 Apr. 17];12(sp1). Available from: https://horizonepublishing.com/journals/index.php/PST/article/view/5475

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