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<p>A set of 121 intersubspecific-derived rice lines was evaluated during Kharif 2024 and Summer 2025 at the Department of Rice, Tamil Nadu Agricultural University, Coimbatore, to assess genetic variability, genotype × environment interaction, inter-trait associations, and phenotypic diversity. The experiment was conducted in an alpha lattice design with three replications, and observations were recorded for twelve quantitative traits. Combined ANOVA across seasons revealed highly significant variation among the genotypes for all traits, demonstrating substantial variability. Higher genotypic and phenotypic coefficients of variation were observed for single plant yield, number of filled grains per panicle, flag leaf length, grain breadth, and productive tillers. High broad-sense heritability coupled with substantial genetic advance as a percentage of the mean was recorded for GB, GL, L/B ratio, NFP, and SPY. Correlation analysis revealed a positive relationship of SPY with FL and NFP. Principal component analysis extracted five principal components with eigenvalues exceeding unity, accounting for 75.35% of the total variability. The application of t-distributed stochastic neighbor embedding (t-SNE) illustrated broad phenotypic dispersion, whereas silhouette analysis indicated weak overall cluster structure. Mahalanobis D² analysis grouped the genotypes into six clusters, and hierarchical clustering provided a complementary assessment of phenotypic diversity. GGE biplot analysis evaluated genotype performance and seasonal stability, whereas the Multi-trait Genotype–Ideotype Distance Index (MGIDI) ranked the genotypes based on their combined multi-trait performance. Collectively, the integrated analytical framework facilitated a comprehensive characterization of genetic variability, phenotypic diversity, and seasonal performance, providing a useful basis for the identification of promising breeding materials for rice improvement programmes.</p>

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Integrated multivariate and nonlinear analyses reveal genetic variability and phenotypic diversity in intersubspecific-derived rice lines

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Aishwarya Saravanan,
  • Kalaimagal Thiyagarajan,
  • Manonmani Swaminathan,
  • Thiyageshwari Subramanium,
  • Anita Bellie,
  • Senthilkumar Govindan,
  • Saraswathi Ramaswamy

摘要

A set of 121 intersubspecific-derived rice lines was evaluated during Kharif 2024 and Summer 2025 at the Department of Rice, Tamil Nadu Agricultural University, Coimbatore, to assess genetic variability, genotype × environment interaction, inter-trait associations, and phenotypic diversity. The experiment was conducted in an alpha lattice design with three replications, and observations were recorded for twelve quantitative traits. Combined ANOVA across seasons revealed highly significant variation among the genotypes for all traits, demonstrating substantial variability. Higher genotypic and phenotypic coefficients of variation were observed for single plant yield, number of filled grains per panicle, flag leaf length, grain breadth, and productive tillers. High broad-sense heritability coupled with substantial genetic advance as a percentage of the mean was recorded for GB, GL, L/B ratio, NFP, and SPY. Correlation analysis revealed a positive relationship of SPY with FL and NFP. Principal component analysis extracted five principal components with eigenvalues exceeding unity, accounting for 75.35% of the total variability. The application of t-distributed stochastic neighbor embedding (t-SNE) illustrated broad phenotypic dispersion, whereas silhouette analysis indicated weak overall cluster structure. Mahalanobis D² analysis grouped the genotypes into six clusters, and hierarchical clustering provided a complementary assessment of phenotypic diversity. GGE biplot analysis evaluated genotype performance and seasonal stability, whereas the Multi-trait Genotype–Ideotype Distance Index (MGIDI) ranked the genotypes based on their combined multi-trait performance. Collectively, the integrated analytical framework facilitated a comprehensive characterization of genetic variability, phenotypic diversity, and seasonal performance, providing a useful basis for the identification of promising breeding materials for rice improvement programmes.