Genetic variability, correlation and diversity analysis for yield and related traits among advanced breeding lines of rice (Oryza sativa L.)

Introduction

Rice (Oryza sativa L.) belongs to the Poaceae family, tribe Oryzae, and genus Oryza. It is a diploid species (2n = 24) with a genome size of 430 Mb and is widely recognized as the most important cereal crop (Devi et al., 2025) [9] . As a predominant staple food, more than half of the world's population consumes rice daily (Ngangkham et al., 2018) [19] . In developing countries, rice contributes 27% of dietary calories, 20% of dietary protein, and 3% of dietary fats. Rice is prone to a range of abiotic stresses like drought, salinity, flooding, temperature extremes and heavy metal stress; to name a few and all of these have an impact on plant growth and yield (Bindu et al., 2022) [4] . Climate change and abiotic stresses are further reshaping agricultural landscapes (Kylash et al., 2023) [15] and even conversion of highly productive rice lands for industrial and residential purposes has forced cultivation into less favorable areas such as saline, droughtprone, and flood-prone regions (Kylash et al., 2025) [14] . To meet the challenges of population growth, climate change, and limited arable land, rice production must be expanded by increasing yield potential. Yield in rice is a complex quantitative trait governed by genotype, environment, management practices, and their interactions (Chandramohan et al., 2025) [7] . Genetic variation is important for any breeding material, as it not only provides selection, but also valuable information on the selection of different parents for hybridization programs. Phenotypic variability refers to the observed differences among individuals for a trait, arising from both genetic variation and environmental factors. In breeding programs, the genotypic coefficient of variation and phenotypic coefficient of variation quantify this variability: GCV reflects the genetic portion, while PCV captures the total observed variation, including environmental influences. Understanding grain yield's heritability and genetic variability is a prerequisite for successful breeding. High heritability suggests that genetic factors strongly influence the trait, making selection more reliable; conversely, low heritability indicates that environmental effects have a greater impact (Chacko et al., 2023) [6] . Traits that exhibit high heritability coupled with substantial genetic advance are most likely governed by additive gene action and are strong candidates for effective selection. Such knowledge broadens the gene pool and guides breeders in efficiently transferring desirable traits to future generations (Islam et al., 2025) [13] . To accelerate rice improvement, it is crucial to examine phenotypic variation in exotic and native germplasms. Identifying new genes and assessing morphological traits are essential for evaluating genetic variability and trait associations, particularly for developing varieties suited to coastal and stress-prone regions (Alam et al., 2025) [2] . The presence of sufficient genetic variability, coupled with knowledge of character associations and relative contributions to yield, forms the foundation of any breeding program. Heritability and genetic advance are key selection parameters, offering predictive value for genetic gain under effective selection (Bisne et al., 2009) [5] . Therefore, the present investigation was undertaken to estimate genetic parameters, character associations, in a set of 126 genotypes with 6 checks, with the goal of developing high-yielding, stress-tolerant rice varieties.

Materials and Methods

This study evaluated 126 promising rice lines, with 6 checks KPS-2874, DRR Dhan 44, US 312, Sambha mahsuri, IR 64, and KNM-118 under irrigated conditions at Agricultural Research Station, Kampasagar during Rabi, 2024-25. The experiment was conducted using an Augmented Block Design (ABD) with a plant spacing of 20 × 15 cm. Check varieties were replicated across blocks to control for environmental variation, while test genotypes were unreplicated. Standard crop management practices and necessary precautions were followed to ensure optimal crop growth. Observations were taken on traits such as days to 50% flowering, plant height, panicle length, number of productive tillers, and grain yield. Phenotypic and genotypic correlation coefficients among traits were calculated following Falconer (1981) [11] . Direct and indirect effects of yield components on single plant yield were estimated through path coefficient analysis based on methods by Wright (1921) [28] and Dewey and Lu (1959) [8] , using grain yield as the dependent variable. Genetic divergence was estimated by Mahalanobis' D2 statistics (1936) on 10 quantitative traits. The genotypes were grouped into a number of clusters by Tocher's method described by Rao (1952) [23] . All statistical analyses were performed in R statistical software (Posit Team, 2025) utilizing packages such as ' augmented RCBD ' for analysis of variance, 'metan' for correlation, and path coefficient calculations.

Results and Discussion

The augmented design ANOVA revealed highly significant differences across most sources of variation. Block effects were highly significant for all traits, indicating strong environmental heterogeneity across blocks. Treatment effects were also highly significant, confirming substantial genetic variability among the entries. The checks showed very high significance across traits, reflecting consistent differences among the standard checks (Table 1). Test entries and their comparison with checks were significant for most traits, with particularly strong effects for days to flowering (DFF), plant height (PH), panicle length (PL), and grain yield (GY), while number of productive tillers (NPT) showed moderate significance. The significance of test entries and their comparison with checks indicates that several test genotypes performed differently from the standard checks, especially for yield-related traits, suggesting potential for selection Residual variance was very low across traits, suggesting high precision of the experiment and reliable estimates of treatment differences. [20] and Akhil et al., (2025) [1] for plant height. Akhil et al., (2025) [1] and Saha et al., (2019) [24] for panicle length, Sharmila et al., (2025) [25] for number of productive tillers and days to 50% flowering. High heritability coupled with high genetic advance as percent of mean was reported by Dhruv et al., (2025) [10] for plant height, Shashidhara et al., (2025) [26] for plant height, and number of productive tillers and grain yield, high heritability with moderate genetic advance as percent of mean by Gupta et al., (2021) [12] for panicle length, and High heritability coupled with moderate genetic advance as percent of mean for days to 50% flowering by Shashidhara et al., (2025) [26] .

The high heritability coupled with high GAM observed for grain yield, number of productive tillers, and plant height suggests that these traits are predominantly governed by additive gene effects, making them highly amenable to direct selection in breeding programs. Grain yield, in particular, demonstrated substantial genetic variability, indicating that significant genetic gains can be achieved through selection. The medium GAM values for panicle length and days to flowering, despite their high heritability, imply that non-additive gene action may play a role, and selection for these traits may require larger populations or multiple cycles to achieve meaningful improvement. The correlation analysis revealed varying degrees of association among the studied traits. Days to flowering (DFF) showed a significant positive correlation with plant height (PH, r = 0.209) and panicle length (PL, r = 0.182), suggesting that later flowering genotypes tend to be taller and have longer panicles (table 3; Figure 1). However, DFF had no significant relationship with grain yield (GY) or number of productive tillers (NPT). The significant positive associations of DFF with PH and PL suggest that genotypes with delayed flowering may allocate more time to vegetative growth, resulting in taller plants and longer panicles.

Muthuvijayaragavan and Murugan (2020) [17] Shrivatsav et al. (2020) [27] reported a positive significant association of days to flowering with plant height, while Narayanan et al.

(2019) [18] Shrivatsav et al. (2020) [27] reported it with panicle length. This relationship can be useful in breeding programs where plant architecture traits are considered alongside flowering behavior. However, the absence of significant correlation between GY and other traits indicates that yield is influenced by complex interactions beyond these individual parameters, possibly involving physiological efficiency, stress tolerance, or other yield components not captured here. The weak and nonsignificant correlations among NPT, PH, and PL imply that these traits vary independently, offering opportunities for simultaneous improvement without strong trade-offs. This study revealed substantial variation across all the traits examined in the advanced breeding lines of rice. The presence of wide genetic diversity indicates that these lines hold considerable potential for improvement. Importantly, most traits exhibited high heritability coupled with a high genetic advance as a percentage of the mean, suggesting that the observed differences are largely due to additive genetic effects rather than environmental influence. Such a genetic pattern implies that these traits are stable and can be effectively fixed through selection. In other words, the inheritance of these characteristics is reliable, and breeders can expect consistent improvement when selecting for them. Therefore, focusing on these traits in breeding programs would be highly beneficial, as it would accelerate genetic gain and contribute to the development of superior rice varieties.

Acknowledgement

AcknowledgementThe author(s) gratefully acknowledge the Professor Jayashankar Telangana State Agricultural University (PJTAU), Hyderabad, India for providing the research facilities for this research. Conflicts of Interest. None.

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