Principal Component Analysis Of Morphological Traits In Cotton Genotypes Under Heat Stress

http://amresearchreview.com/index.php/Journal/about Volume 3, Issue 12 (2025) Online ISSN Print ISSN . . 3007-3197 3007-3189 http://amresearchreview.com/index.php/Journal/about Page 166 Principal Component Analysis Of Morphological Traits In Cotton Genotypes Under Heat Stress Kamran Nazir Department of Organic Chemistry, University of Education, Lahore, Pakistan Abid Hussain Department of Agriculture Entomology, The Islamia University of Bahawalpur (IUB), Pakistan Muhammad Sufyan Khalid Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad (UAF), Pakistan Muqadas Shaikh Department of Plant Breeding and Genetics, Sindh Agriculture University, Tandojam, Pakistan Muhammad Ishtiaq* Department of Botany, Azad Jammu and Kashmir University of Bhimber (AJ&KUoB), Bhimber-10040, AJK, Pakistan Email: [email protected] Noor-Ul-Ain Keerio Department of Plant Breeding and Genetics, Sindh Agriculture University, Tandojam, Pakistan Heat stress is among the primary abiotic factors that suppress cotton productivity globally, especially during the initial seedling growth stage. To evaluate morphological responses and determine the main characteristics of heat tolerance, six cotton genotypes were studied under controlled high‑temperature conditions. Morphological parameters including shoot length, root length, number of leaves, fresh weight, and dry weight were measured and subjected to principal component analysis (PCA). The findings indicated that the first principal component (PC1) accounted for 97.97% of the total variance, with uniformly negative loadings across all traits, suggesting that PC1 represents overall growth and biomass reduction under heat stress. PC2 explained 1.75% of the variance and was dominated by the number of leaves, separating leaf production from biomass traits. Later components (PC3–PC5) contributed less than 0.2% each, reflecting minor trait‑specific variations. The scree plot revealed that the dominant stress response was captured in PC1, whereas the biplot showed clear separation of genotypes along Dim1 and Dim2, with shoot length and fresh weight exerting the strongest influence. These results imply that biomass A B S T R A C T http://amresearchreview.com/index.php/Journal/about Volume 3, Issue 12 (2025) Online ISSN Print ISSN . . 3007-3197 3007-3189 http://amresearchreview.com/index.php/Journal/about Page 167 reduction and leaf production are the most important indicators of heat stress resistance in cotton seedlings. PCA therefore serves as a robust multivariate model for differentiating tolerant and susceptible genotypes, providing crucial input for breeding programs aimed at enhancing cotton resilience under rising global temperatures. Keywords: Cotton Genotypes; Heat Stress; Principal Component Analysis; Morphological Traits; Biomass Reduction; Multivariate Analysis; Genotype Differentiation; Stress Tolerance Introduction Cotton (Gossypium hirsutum L.) is a widely cultivated fiber crop of global importance, but its production is increasingly impacted by abiotic stresses, particularly heat stress, which has been exacerbated by climate change. Elevated temperatures during seedling development disrupt photosynthesis, impair enzyme activity, increase respiration, and reduce biomass, ultimately resulting in yield losses (Singh et al., 2018; Abro et al., 2016). Seedlings are especially vulnerable to thermal variations because their tissues are immature, and morphological characteristics such as shoot length, root length, number of leaves, fresh weight, and dry weight are reliable predictors of stress response (Yousefi et al., 2023; Luqman et al., 2025). Single-trait assessments used in the past are not sufficient to measure stress responses, as multiple traits interact to determine tolerance. Principal component analysis (PCA) is a powerful multivariate method that reduces dimensionality and detects leading axes of variation (Ahmad et al., 2022; Kanwal et al., 2021). PCA transforms correlated variables into uncorrelated components, enabling researchers to estimate the proportion of variance explained by each component and to visualize genotype distribution in reduced dimensional space (Rajkai, 2019). In cotton, PCA has been applied to examine genetic diversity, morphological clustering, and stress tolerance, with biomass reduction and leaf production identified as key indicators of genotype differentiation (Arshad et al., 2025; Ghorbanzadeh et al., 2025). Recent innovations in genomics and molecular breeding highlight the importance of combining morphological data with statistical modeling to accelerate the development of stress-tolerant cultivars (Heat Stress in Cotton, 2021; Springer, 2022). Under stress conditions, studies consistently report that PC1 captures most of the variance, representing general growth reduction, while subsequent components capture trait-specific variations such as leaf production or root–shoot balance (Cotton Seedling Growth and Development, 2018; Yousaf et al., 2023). The application of PCA in cotton research therefore provides valuable information on genotype performance under heat stress, offering breeders a practical tool to select resilient lines and guide future molecular interventions (Ahmad et al., 2022; Kanwal et al., 2021). This experiment was designed to evaluate the morphological responses of six cotton genotypes under controlled heat stress conditions using PCA. The analysis of genotype–environment relationships identifies key traits associated with heat tolerance by quantifying variance explained, defining trait loadings, and visualizing genotype distribution through scree plots and biplots. These findings are relevant to breeding programs, as they help distinguish tolerant and susceptible genotypes, http://amresearchreview.com/index.php/Journal/about Volume 3, Issue 12 (2025) Online ISSN Print ISSN . . 3007-3197 3007-3189 http://amresearchreview.com/index.php/Journal/about Page 168 ultimately contributing to the development of heat-resistant cotton cultivars in the face of rising global temperatures. Materials and Methods Plant Material and Experimental Design: Six cotton genotypes (COT1–COT6) were evaluated under controlled heat stress conditions during the seedling stage. Seeds were sown in plastic pots containing a standardized soil mixture and maintained in a growth chamber with day/night temperatures of 40 °C/30 °C to simulate heat stress. Each genotype was replicated three times in a completely randomized design (CRD). Trait Measurement: After 21 days of growth, five morphological traits were recorded: shoot length (cm), root length (cm), number of leaves, fresh weight (g), and dry weight (g). Measurements were taken using a ruler and digital balance, and all data were averaged across replicates. Statistical Analysis: Principal component analysis (PCA) was performed using standardized trait data to reduce dimensionality and identify patterns of variation among genotypes. Eigenvalues, proportion of variance, and cumulative variance were extracted to determine the significance of each principal component (Table 1). Trait loadings on the first five components were calculated to assess their contribution to genotype separation (Table 2). A scree plot was generated to visualize the variance explained by each component, and a biplot was constructed to display genotype distribution and trait influence (Figure 1 and Figure 2). Results PCA Results of Morphological Traits in Cotton Genotypes under Heat Stress Table 1 shows that PC1 explains 97.97% of the total variance, representing overall growth reduction under heat stress. PC2 adds 1.75%, mainly separating leaf traits, while the remaining components (PC3–PC5) contribute less than 0.2% combined. This indicates that cotton genotypes are primarily distinguished by PC1 and PC2. Table 1. Principal Component Analysis (PCA) Results Component PC1 PC2 PC3 PC4 PC5 Standard deviation 2.2133 0.29618 0.08580 0.07476 0.02819 Proportion of Variance 0.9797 0.01754 0.00147 0.00112 0.00016 Cumulative Proportion 0.9797 0.99725 0.99872 0.99984 Principal Component Analysis of Cotton Genotypes under Heat Stress Principal component analysis (PCA) revealed that PC1 explained 97.97% of the total variance, with uniform negative loadings across shoot length (-0.45), root length (- 0.45), number of leaves (-0.44), fresh weight (-0.45), and dry weight (-0.45). This indicates that PC1 represents overall growth and biomass reduction under heat stress. PC2 accounted for 1.75% of variance, dominated by number of leaves (-0.87), http://amresearchreview.com/index.php/Journal/about Volume 3, Issue 12 (2025) Online ISSN Print ISSN . . 3007-3197 3007-3189 http://amresearchreview.com/index.php/Journal/about Page 169 separating leaf production from biomass traits. PC3 (0.15%) highlighted root length (- 0.76) and dry weight (0.63), reflecting variation in below‑ground development and biomass allocation. PC4 (0.11%) was associated with shoot length (-0.77) and dry weight (0.46), while PC5 (0.02%) was mainly influenced by fresh weight (-0.80). Overall, these results demonstrate that cotton genotypes under heat stress are primarily differentiated by biomass reduction (PC1), leaf production (PC2), and root– shoot balance (PC3–PC4), with PC1 capturing the dominant stress response pattern. Table 2. PCA Loadings for Cotton Genotypes under Heat Stress Trait PC1 PC2 PC3 PC4 PC5 Shoot length (cm) -0.45 0.13 -0.05 -0.77 0.44 Root length (cm) -0.45 0.05 -0.76 0.43 0.18 Number of leaves -0.44 -0.87 0.17 0.01 -0.16 Fresh weight (g) -0.45 0.37 0.03 -0.13 -0.80 Dry weight (g) -0.45 0.29 0.63 0.46 Scree Plot of Principal Component Variance in Cotton Genotypes under Heat Stress Principal component analysis revealed that PC1 explains the majority of variance (~4.9 units), as shown in the scree plot. The remaining components (PC2–PC5) contribute minimal variance, each below 0.3 units. This steep drop confirms that PC1 captures the dominant morphological variation among cotton genotypes under heat stress, and additional components offer limited explanatory value (see scree plot). Figure .1 PCA results http://amresearchreview.com/index.php/Journal/about Volume 3, Issue 12 (2025) Online ISSN Print ISSN . . 3007-3197 3007-3189 http://amresearchreview.com/index.php/Journal/about Page 170 PCA Biplot of Cotton Seedling Traits under Heat Stress This biplot displays the distribution of cotton seedling genotypes (COT1–COT6) along the first two principal components: Dim1 (98%) and Dim2 (1.8%). Trait vectors (FreshWt, Shoot, DryWt, Root, Leaves) indicate the direction and magnitude of each trait’s contribution. Genotypes are separated primarily along Dim1, reflecting variation in overall growth and biomass traits under heat stress. Traits with longer arrows, such as Shoot and FreshWt, show stronger influence on genotype differentiation. Figure .2 Biplot Discussion The current research showed that heat stress had a far-reaching effect on cotton seedling morphology, and PCA identified that the first principal component (PC1) contributed 97.97% of the overall variance. The negative loadings of all indicators— shoot length, root length, number of leaves, fresh weight, and dry weight— demonstrate that heat stress generally retards growth and biomass accumulation. This result aligns with previous studies which found that high temperatures affect photosynthesis, enzyme activities, and respiration, thereby reducing growth and inducing premature senescence in cotton seedlings (Singh et al., 2018; Abro et al., 2016). The prevalence of PC1 emphasizes biomass reduction as the major axis of variation under heat stress, a tendency also observed in other crops such as wheat and soybean when exposed to abiotic stress (Mubushar et al., 2022; Kokebiea et al., http://amresearchreview.com/index.php/Journal/about Volume 3, Issue 12 (2025) Online ISSN Print ISSN . . 3007-3197 3007-3189 http://amresearchreview.com/index.php/Journal/about Page 171 2025). PC2 explained 1.75% of the variance and was highly dependent on the number of leaves, which separated leaf production from biomass traits. Leaf number is a morphological marker of vital importance under stress conditions because when leaf initiation is limited, photosynthesis and assimilate production are directly restricted. Genotypes that retain higher leaf production under heat stress may therefore be more resilient, even when overall biomass is reduced (Yousefi et al., 2023; Luqman et al., 2025). This disaggregation of leaf characteristics from biomass in PC2 highlights the need to consider multiple axes of variation when assessing tolerance. The following components (PC3–PC5) each described less than 0.2% of the variance, representing minor trait-specific differences. PC3 emphasized root length and dry weight, implying variation in below-ground development and biomass allocation. Root traits have often been overlooked in cotton stress research, yet they play a significant role in water and nutrient uptake under adverse conditions (Arshad et al., 2025). PC4 and PC5 were linked to shoot length and dry weight, and fresh weight, respectively. Although these components contributed little to total variance, they provide hints of fine-tuned genotype differences that may be useful when breeding for specific traits. The scree plot confirmed that PC1 captured the strongest stress response, while the biplot showed clear genotype segregation along the two dimensions (Dim1 and Dim2). Genotypes separated in varied ways based on their morphological performance, with shoot length and fresh weight exerting the greatest influence on separation. Similarly, PCA biplots have been used to distinguish tolerant and susceptible lines exposed to abiotic stress in cotton and other crops (Ghorbanzadeh et al., 2025; Ahmad et al., 2022). The strong impact of shoot and fresh weight vectors highlights their importance as selection criteria in breeding programs. Beyond morphological interpretation, this study underscores the extended importance of combining PCA with physiological, biochemical, and molecular methods. Recent proteomic studies have shown that heat-tolerant cotton genotypes express different stress-responsive proteins, such as heat shock proteins and antioxidant enzymes, which stabilize membranes and reduce oxidative stress (Zhang et al., 2024; PLOS One, 2025). Integrating such molecular insights with PCA-based trait analysis provides a holistic approach to detecting resilient genotypes. Furthermore, the growing importance of genomics and molecular breeding points to the necessity of integrating statistical modeling with multi-omics data to accelerate the development of stress-resilient cultivars (Luqman et al., 2025; Patil et al. 2024) Conclusively, PCA has proven useful in differentiating tolerant and susceptible cotton genotypes under heat stress. PC1 was significant in capturing the primary stress response, namely total biomass reduction, while PC2 was important in identifying leaf production as an independent axis of variation. The scree plot and biplots effectively depicted genotype separation, with shoot length and fresh weight exerting the strongest influence. These results have significant implications for breeding programs, providing practical selection criteria and informing molecular interventions aimed at improving cotton resilience to rising global temperatures. http://amresearchreview.com/index.php/Journal/about Volume 3, Issue 12 (2025) Online ISSN Print ISSN . . 3007-3197 3007-3189 http://amresearchreview.com/index.php/Journal/about Page 172 Conclusion This study demonstrated that heat stress exerts a significant impact on cotton seedling morphology, with PCA effectively distinguishing tolerant and susceptible genotypes. PC1 captured the dominant stress response, representing overall biomass reduction, while PC2 highlighted leaf production as an independent axis of variation. The scree plot and biplots provided clear visualization of genotype separation, with shoot length and fresh weight exerting the strongest influence. These findings emphasize that biomass reduction and leaf production are critical indicators of heat stress tolerance in cotton seedlings. The integration of PCA with physiological and molecular approaches offers a robust framework for identifying resilient genotypes. Ultimately, this research provides practical selection criteria for breeding programs and contributes to the development of cotton cultivars capable of withstanding rising global temperatures. Conflict of Interest: The authors declare no conflict of interest regarding the publication of this study. References Abro, A. A., Anwar, M., Javwada, M. U., Zhang, M., Liu, F., Jiménez-Ballesta, R., Salama, E. A. A., & Ahmed, M. A. A. (2016). Morphological and physio-biochemical responses under heat stress in cotton: Overview. Environmental and Experimental Botany, 131, 118–125. Ahmad, M., Khan, M. A., & Hussain, A. (2022). Stability assessment of wheat genotypes under heat stress using PCA. 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