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<p>This study evaluated genetic variability, trait associations, principal component analysis (PCA), and the Multitrait Genotype–Ideotype Distance Index (MGIDI) to support multi-trait rice (<i>Oryza sativa</i> L.) breeding for improved yield and nutritional quality. Thirty rice genotypes were assessed using key agronomic traits—days to flowering (DTF), plant height (PH), number of productive tillers (NPT), filled grains per panicle (FGPP), thousand-seed weight (TSW), and grain yield per plant (GYP)—along with grain iron (Fe) and zinc (Zn) concentrations. Substantial genetic variability was observed, with very high heritability estimates for Fe (0.979) and Zn (0.995), indicating strong prospects for effective phenotypic selection. Correlation analysis revealed a positive association between grain Fe and Zn, suggesting the feasibility of simultaneous micronutrient improvement. PCA and factor analysis explained 93.47–97.30% of the total variation and identified distinct trait clusters, facilitating dimensionality reduction and interpretation of complex relationships. MGIDI proved highly effective for ideotype-based selection, resulting in notable genetic gains for GYP (17.6%), Fe (13.9%), and FGPP (13.8%). Genotypes G18 and G13 consistently ranked closest to the ideotype, highlighting their potential as donors for biofortified, high-yielding rice. Overall, the integrated multivariate–MGIDI framework provides an efficient strategy for preliminary identification of superior genotypes for sustainable rice improvement and addressing hidden hunger.</p>

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Multi-trait index-based delineation and selection of promising rice genotypes for yield and nutritional quality

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Dubbala Prshanthi Divya Vani,
  • Narkhede Gopal Wasudeo

摘要

This study evaluated genetic variability, trait associations, principal component analysis (PCA), and the Multitrait Genotype–Ideotype Distance Index (MGIDI) to support multi-trait rice (Oryza sativa L.) breeding for improved yield and nutritional quality. Thirty rice genotypes were assessed using key agronomic traits—days to flowering (DTF), plant height (PH), number of productive tillers (NPT), filled grains per panicle (FGPP), thousand-seed weight (TSW), and grain yield per plant (GYP)—along with grain iron (Fe) and zinc (Zn) concentrations. Substantial genetic variability was observed, with very high heritability estimates for Fe (0.979) and Zn (0.995), indicating strong prospects for effective phenotypic selection. Correlation analysis revealed a positive association between grain Fe and Zn, suggesting the feasibility of simultaneous micronutrient improvement. PCA and factor analysis explained 93.47–97.30% of the total variation and identified distinct trait clusters, facilitating dimensionality reduction and interpretation of complex relationships. MGIDI proved highly effective for ideotype-based selection, resulting in notable genetic gains for GYP (17.6%), Fe (13.9%), and FGPP (13.8%). Genotypes G18 and G13 consistently ranked closest to the ideotype, highlighting their potential as donors for biofortified, high-yielding rice. Overall, the integrated multivariate–MGIDI framework provides an efficient strategy for preliminary identification of superior genotypes for sustainable rice improvement and addressing hidden hunger.