Introduction
Cotton (Gossypium hirsutum L.) is a globally significant natural fiber and oil source. Seeds are particularly sensitive to environmental stressors during the germination stage. Among these stressors, soil drought and salinity are critical factors that affect seed germination (Ibrahim, Reference Ibrahim2016). Xinjiang, located in northwestern China, accounts for approximately 90% of the nation’s cotton production. Spring cultivation in this region faces significant challenges, particularly during nighttime when temperatures exhibit instability and frequently drop below optimal levels for crop development. Spring sowing season low temperatures impair cotton seed germination, reducing germination percentage and seedling vigor, ultimately lowering yield and quality (Bradford and Nonogaki, Reference Bradford and Nonogaki2013; Maeda et al., Reference Maeda, Wells, Sheehan and Dever2021). These abiotic stresses frequently cause uneven emergence and missing seedlings in fields (Reddy et al., Reference Reddy, Seepaul, Gajanayake, Lokhande, Oosterhuis and Loka2020). With the continuous advancement of seeding technology and agricultural mechanization, mechanical seeding needs precise placement of one seed per hole to avoid artificial colonization. This has led to increasingly stringent requirements for cotton seed germination-related quality. Therefore, the evaluation and cultivation of cotton varieties with strong stress tolerance in the comprehensive germination stage are crucial for ensuring uniform emergence and strong growth of cotton (Li et al., Reference Li, Liu, Huang, Liu, Si, Kong, Wang, Zhao, Mei and Yu2024).
Current studies on cotton stress tolerance primarily focus on later developmental stages, such as seedling growth, flowering and boll formation, with drought resistance often assessed through controlled irrigation. Methods to evaluate germination-stage stress tolerance, such as simulating drought using polyethylene glycol (PEG) solutions, are generally adopted (Wang et al., Reference Wang, Ye, Wang, Fan and Wang2011). Cotton is recognized as a salt-tolerant crop and has been effectively utilized for saline-alkali land improvement (Abdelraheem et al., Reference Abdelraheem, Esmaeili, O Connell and Zhang2019). Research indicates that 34 mmol/L soil salt is a critical threshold for cotton cultivation; values below this benefit seedling growth, while higher concentrations lead to osmotic stress and ion toxicity (Moussouraki et al., Reference Moussouraki, Tani, Velliou, Goufa, Psychogiou, Papadakis and Abraham2019). Studies employing 150 mmol/L NaCl stress treatments have successfully differentiated genotypes based on germination percentage under salinity stress (Li et al., Reference Li, Liu, Huang, Liu, Si, Kong, Wang, Zhao, Mei and Yu2024). Temperature also significantly impacts seed germination. Low temperatures (5–12℃) during sowing extend germination periods and reduce seedling emergence percentage, posing a severe threat to cotton productivity (Bolek et al., 2013; Çokkizgin and Bölek, Reference Çokkizgin and Bölek2015; Reddy et al., Reference Reddy, Seepaul, Gajanayake, Lokhande, Oosterhuis and Loka2020). The optimal temperature for cotton seed germination is 26–28℃, with germination failing below 10℃ (Wang et al., Reference Wang, Xie, Li, Wei and Lin2014; Gao et al., Reference Gao, Deng, Han, Kong, Shen, Li and Yin2018). Interestingly, low-temperature tolerance during germination varies widely among different cotton genotypes.
Abiotic stress is not the only factor influencing germination; the chemical composition of seeds also plays a pivotal role. Nutrients stored within seeds, including proteins, oils and gossypol, provide the energy required for germination (Rifna et al., Reference Rifna, Ratish Ramanan and Mahendran2019). The content ratios and where it is distributed in the seed are closely tied to germination ability and seed vigor (Pelc and Linder, Reference Pelc and Linder2015). The effects of seed chemical composition appear to differ across cotton genotypes (Sanchez et al., Reference Sanchez, Mangat and Angeles-Shim2019). For instance, a high proportion of saturated fatty acids in genetically modified cotton has been shown to hinder germination under low temperatures significantly (Liu et al., Reference Liu, Singh and Green2002). Similarly, sweet corn seeds with high sugar content exhibit reduced vigor, potentially due to inefficient nutrient allocation and energy expenditure during germination (Landry et al., Reference Landry, Damerval, Azevedo and Delhaye2005).
Both abiotic stress and the chemical composition of cotton seeds influence germination to varying degrees. In this study, 120 cotton materials were selected, which represent the main genotypes from China’s three major cotton-growing regions and imported varieties suitable for domestic cultivation, providing comprehensive coverage (Table S1). The germination performance of 120 cotton genotypes under drought, salinity and low-temperature stress, compared with under standard (control) conditions to identify genotypes with superior abiotic stress resistance to one or more of these abiotic stresses. In addition, seed composition (protein, oil and gossypol) was measured to examine its correlation with germination characteristics. Finally, we developed Kompetitive Allele-Specific PCR (KASP) molecular markers based on previously reported loci associated with abiotic stress to distinguish genotypes with significant differences in germination-stage stress resistance. This study aims to advance understanding of stress tolerance mechanisms during germination and facilitate breeding resilient cotton varieties. By analysing genotypic differences in germination percentage under stress, determining seed components and developing stress-resistance molecular markers, we seek to identify resistant genotypes and markers for breeding, and elucidate the mechanisms by which seed components affect germination under stress.
Materials and methods
Cotton materials and sources
(1) Genetic resource composition (Table S1)
The experimental panel comprised 120 upland cotton genotypes (Table S1), including 54 accessions from the Yellow River Basin, 26 from the Yangtze River Basin, 11 from the Northwest Inland Region, 2 early-maturing cultivars from North China’s cotton belt and 27 international introductions. Germplasm acquisition occurred through collaborative exchanges and institutional collections.
(2) Agronomic protocol
All genotypes were cultivated under plastic-mulched drip irrigation in Shawan County, Xinjiang (44ºN, 85ºE). Field plots (1.25 × 2.0 m per genotype) followed regional phenological patterns: spring sowing (April 15th) and autumn harvest (September 30th). Thermal regimes during cultivation showed monthly means of 17–9℃ (April), 23–13℃ (May), 28–15℃ (June), 30–15℃ (July), 30–14℃ (August) and 24–8℃ (September). The seeds of each genotype (3.0–4.0 kg) harvested from the field were reserved for germination tests.
(3) Seed processing methodology
Post-harvest fuzzy seeds underwent mechanical delinting (MT-180 mini gin). Acid delinting: 15% (v/v) H2SO4 immersion (25 ± 2℃, 15 min, continuous agitation). Neutralization: 3% (w/v) Na2CO3 bath (25 min, intermittent stirring). Rinsing: Triple distilled-water washes until surface hydrophobicity elimination (pH 7.0 confirmed via indicator strips). Air-dried seeds on nylon mesh (24 h shaded ventilation), subsequently stored at 16℃ in desiccators.
Cottonseed germination test
Cotton seeds with uniform in size and full mature were selected for the tests. Seeds were disinfected with a 0.1% mercury chloride solution for 15 min, rinsed three times with distilled water and air-dried in a cool, ventilated area (Bai et al., Reference Bai, Xiao, Zhang, Zhang, Sun, Zhang, Wang, Bai, Li and Liu2020; Chang et al., Reference Chang, Gu, Luke, Wang, Wang and Deng2022).
Germination tests were carried out for each of the 120 cotton genotypes under four different stress treatments (outlined below). For each treatment, 3 replicates of 25 seeds were germinated per genotype. Seeds were sown in germination boxes – I box per replicate – lined with two layers of filter paper. Thus, for each genotype, 3 boxes were transferred to each of the following treatments:
Treatment 1: Refer to GB/ T3543.4-1995 for standard germination tests. The two layers of filter paper were moistened with 12 mL of double distilled water (ddH2O) and incubated in a light chamber at 26℃ with a 16-h photoperiod for 7 days. To maintain moisture, 10 mL of ddH2O was added daily, and germination percentage were recorded on the seventh day.
Treatment 2: Low-temperature stress was simulated by setting the incubator to 18℃, as opposed to 26℃ under control conditions (treatment 1). The filter paper was moistened with 12 mL of ddH2O as per the control.”
Treatment 3. Drought stress was induced using 12 mL 10% PEG6000 solution instead of ddH2O.
Treatment 4. Salt stress was applied using 12 mL 150 mmol/L NaCl solution instead of ddH2O.
Detection of storage components in cottonseeds
The chemical composition of cottonseeds was determined using classical methods (Bolek et al., Reference Bolek, Tekerek, Hayat and Bardak2016). Oil content was quantified via Soxhlet extraction, crude protein content was measured using the Kjeldahl method, and gossypol content was assessed using a modified phloroglucinol method (Lei, Reference Lei2013).
Development of KASP markers in cotton
Genomic DNA was extracted from 120 cotton genotypes using the CTAB method. Each genotype was repeated with 3 seeds, 1 per seed, for a total of 360 seeds. Eight KASP primers targeting specific SNP loci were designed with Primer5 software (Table S2). Each marker contained two PCR forward primers: the 3′ end of each primer corresponded to a specific SNP allele, with FAM (5 ′GAAGGTGACCAAGTTCATGCT 3′) and HEX (5′ GAAGGTCGGAGTCAACGGATT 3′) sequences added to the 5′ ends, respectively. The PCR amplification system and protocol followed a previously described method (Guo et al., Reference Guo, Chen, Qu, Deng, Zheng, Wang, Shi, Zhang, Chen and Yan2023). No additional extension step was required as the amplicons were typically less than 120 bp. The KASP assays were performed using a high-throughput genotyping system (GeneMatrix, China) on genomic DNA extracted from the 120 cotton genotypes.
Statistical analysis
The experimental datasets underwent systematic collation via Microsoft® Excel 2016 (Redmond, WA, USA) for preliminary data structuring. Multivariate statistical analyses were conducted using IBM® SPSS Statistics 27.0 for Windows (Armonk, NY, USA), with one-way analysis of variance (ANOVA) followed by Duncan’s multiple-range test to delineate statistically significant differences (P < 0.05). Parametric assumptions for ANOVA were validated through Shapiro–Wilk normality tests and Levene’s homogeneity of variance analysis.
Data visualization was implemented through a dual-platform approach: exploratory graphical representations were generated using R Programming Language version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria) with the ggplot2 package, while publication-quality figures, including histograms and box plots, were refined using GraphPad Prism 9.4.1 (San Diego, CA, USA). Quantitative results are expressed as mean values ± standard deviation (SD) derived from triplicate biological replicates.
The comprehensive resistance index (D-value) was quantified through the fuzzy mathematics membership function method (Wang et al., Reference Wang, Xie, Li, Wei and Lin2014), with weighted normalization of phenotypic traits based on their contribution coefficients. Allelic effects associated with KASP markers were statistically evaluated using two-tailed Student’s t-tests, incorporating Benjamini–Hochberg false-discovery rate (FDR) correction (q < 0.05) to account for multiple comparison artifacts.
Results
Differences in germination percentage of 120 cotton genotypes under different stress conditions
The germination percentage of 120 cotton genotypes were measured under drought, salt, low-temperature stress and control conditions (Table S3). The results revealed that the coefficient of variation (CV) of germination percentage differed significantly among treatments compared with the control (Table 1, Fig. 1). Germination percentage ranged from 53.33% (No. 24, FeizhoumianE40) to 100% (No. 27) under control conditions, 4% (No. 63, Nandanlihudamian) to 82.66% (No. 17, Daihongdai) under drought stress, 2.67% (No. 64, Ning523) to 92% (4133Bt) under salt stress, and 9.33% (No. 49, Jinzimianking) to 100% (No. 10) under low-temperature stress. The mean values were 93.81%, 36.88%, 33.84% and 82.42%, respectively, Among them, 150 mmol/L NaCl stress significantly inhibited seed germination (Fig. 1). The coefficients of variation were 9.74%, 55.85%, 67.33% and 24.00%, respectively (Table 1). These results demonstrate that susceptibility to abiotic stress varies significantly among genotypes.
Germination percentage of 120 cotton genotypes under different treatments. ***Correlation is significant at the 0.001 level; **correlation is significant at the 0.01 level, *correlation at the 0.05 level.
Analysis of variation in germination percentage of 120 upland cotton genotypes under different treatments
Differences in seed storage components among 120 genotypes
The germination of seeds is closely related to the content of stored chemical components in many plants (Penfield, Reference Penfield2017). In this study, the oil, protein and gossypol contents of the 120 genotypes were analyzed, revealing considerable variability (Fig. 2) (Table S4). Oil content ranged from 15.00% to 28.36%, averaging 21.17% and a CV of 15.01%. Protein content ranged from 6.89% to 32.73%, with an average of 20.33% and a CV of 27.15%. Gossypol content ranged from 0.63% to 1.60%, with an average of 1.40% and a CV of 12.76%. Oil content exhibited the highest variability among genotypes (Table 2).
Variations in seed storage contents among 120 cotton genotypes.
Analysis of variation in cottonseed storage components of 120 upland cotton genotypes
Clustering analysis of germination percentage under abiotic stress
To evaluate the comprehensive stress resistance of 120 genotypes at the germination stage, PCA was used to cluster germination percentage under control conditions, drought, salt and low temperature. The interpretation percentage of the first and the second principal components were 50.6% and 31.9%, respectively, with the evaluation accuracy reaching 82.5% (Fig. 3). Cluster analysis classified the 120 into three categories: high resistance (35 genotypes), including 41B006, 4133Bt, Daihongdai, Wanzao686, Hunanmayangyangmianhua, Xinluzao10, Zhongmiansuo49, Dalingmian, Xinluzao28 and Yumian8; Medium resistance (74 genotypes), sensitive S (11 genotypes), including Si168, Yumian18, Jianyang303, Jinzimianking, Ganmian12, FeizhoumianE40, Dai4554, Liao4853, St213RNR, Zhongyuan0114 and Jinmian36.
Clustering analysis of comprehensive germination resistance in 120 cotton genotypes. Different coloured areas represent high resistance (blue), medium resistance (red) and sensitivity (green) to germination, respectively.
Using the membership function method, the weight of germination percentage of 120 genotypes under different conditions was calculated, and the comprehensive resistance D value of germination rate of each genotype was obtained and ranked (Table 3). The D value for comprehensive stress resistance at the germination stage ranged from 0.20 to 1.12. The top 10 genotypes are 41B006, 4133Bt, Daihongdai, Wanzao686, Hunanmayangyangmianhua, Xinluzao10, Zhongmiansuo49, Dalingmian, Xinluzao28 and Yumian8. The bottom 10 genotypes are Nandanlihudamian, Changkangmian, Ning523, Jinmian36, Ganmian12, St213RNR, Zhongyuan0114, Jinzimianking, FeizhoumianE40 and Dai4554.
The D-value clustering of comprehensive resistance to germination in 120 cotton genotypes
No. = Genotype number, D = D Value, R = Ranking of D Value; Bule: Top 10 of D Value, Green: Min. 10 of D Value.
Correlation of gossypol, oil and protein content with germination percentage under abiotic stress
To investigate the relationship between chemical components and germination characteristics, correlation analysis was performed between germination percentage and the oil, protein and gossypol contents of the 120 genotypes (Fig. 4) (Table S5). Results revealed a significant negative correlation between oil content and germination percentage under drought (R = −0.27**) and salt stress (R = −0.24**). Germination percentage under standard and low-temperature conditions showed a significant positive correlation (R = 0.71***). Additionally, drought and salt stress germination percentage were positively correlated (R = 0.58***).
Correlation coefficients of germination percentage under different treatments and seed storage components of 120 cotton genotypes. Standard germination rate; (b) drought germination rate; (c) salt germination rate; (d) low-temperature germination rate; (e) protein content; (f) oil content; (g) gossypol content. ***Correlation is significant at the 0.001 level; **correlation is significant at the 0.01 level, *correlation at the 0.05 level.
Development of molecular markers for stress resistance in upland cotton
Eight previously reported SNP markers associated with stress resistance were evaluated to identify genotype-phenotype correlations among the 120 genotypes (Table S2). SNP_A07_90682411 and SNP_A13_1869056 effectively classified genotypes, while two markers yielded no signal, and four markers exhibited a single genotype across all samples.
SNP_A07_90682411 classified genotypes into three groups: AA (18), GG (71) and AG (12) (Fig. 5a). Germination percentage under drought and salt stress, comprehensive stress resistance D values, and chemical compositions differed significantly among these groups. AA genotypes exhibited significantly higher germination percentage and D values than GG genotypes under drought (P = 0.010) and salt stress (P = 0.0004) (Fig. 5b). Oil content was significantly lower in AA and AG genotypes compared to GG (P = 0.034 and P = 0.023, respectively), while protein and gossypol content showed no significant differences (Fig. 5c-e). The combined germinating resistance D value of AA-type materials was significantly higher than that of GG and AG (P = 0.0003 and P = 0.0007) (Fig. 5f).
The A/G types of SNP_A07_90682411 show significant differences in germination percentage and seed storage components. Scatter plots for selected A/G assays showing clustering of types on HEX (AA, blue), FAM (GG, red) and middle (AG, green) axes. Black dots represent ‘no call’; and red x represents ‘untyped’. (b) Analysis of germination percentage of A/G types under different treatments. (c–e) Significance analysis of differences in seed storage components (oil, protein and gossypol content) among A/G types. (f) Significance analysis of differences in d-values of seed viability among A/G types. ***Correlation is significant at the 0.001 level; **Correlation is significant at the 0.01 level, *correlation at the 0.05 level.
SNP_A13_1869056 categorized genotypes into CC (75) and GG (36), with eight untyped samples (Fig. 6a). Significant differences were observed only in gossypol content (P = 0.036), suggesting the limited utility of this marker for germination-stage stress resistance analysis (Fig. 6).
The C/G types of SNP_A13_1869056 show significant differences in germination percentage and seed storage components. Scatter plots for selected C/G assays showing clustering of types on HEX (CC, blue) and FAM (GG, red) axes. Black dots represent ‘no call’ (b) Analysis of germination percentage of C/G types under different treatments.(C, d, e) Significance analysis of differences in seed storage components (oil, protein and gossypol content) among C/G types. (f) Significance analysis of differences in d-values of seed viability among C/G types. ***Correlation is significant at the 0.001 level; **correlation is significant at the 0.01 level, *Correlation at the 0.05 level.
Discussion
This investigation employed 10% PEG and 150 mM NaCl to simulate drought and salinity stress, respectively, aligning with the multi-stress environment (drought, salinity and suboptimal temperatures) characteristic of spring cotton sowing in Xinjiang, China. Under these simulated conditions, the germination responses of 120 cotton genotypes were effectively discriminated, with CV reaching 55.85% for drought and 67.33% for salinity (Table 1). While the selected stress intensities for drought, salinity and low temperature (16℃) were based on previously validated thresholds for evaluating germination tolerance in upland cotton (Sanchez et al., Reference Sanchez, Mangat and Angeles-Shim2019; Ko et al., 2018b), the observed CV for low-temperature stress (24.00%) suggests limited discriminatory power compared to other stressors. Notably, cotton germination exhibits strong thermosensitivity: optimal germination occurs at 26–28℃, with complete inhibition below 10.0℃ and minimum germination thresholds at 10.5–12.0℃ (Wang et al., Reference Wang, Xie, Li, Wei and Lin2014b; Zhang et al., Reference Zhang, Cheng, Wei, Wang, Lu, Ma and Yu2021). Hypocotyl elongation and vascular differentiation further require temperatures above 16.0℃ (Sanchez et al., Reference Sanchez, Mangat and Angeles-Shim2019a).
Seed germination capacity represents a polygenic trait governed by intrinsic genetic regulation and modulated by extrinsic factors, including seed maturation status, nutrient reserves and environmental conditions (Carrera-Castaño et al., Reference Carrera-Castaño, Calleja-Cabrera, Pernas, Gómez and Oñate-Sánchez2020). Notably, genotypes such as AC321 and Zhong1421 exhibited germination percentage under optimal conditions but demonstrated compromised germination efficiency under multi-stress scenarios.
The membership function method was employed to address multicollinearity among stress-response parameters through principal component dimensionality reduction. This approach enables weighted integration of normalized membership values, facilitating quantitative evaluation of complex stress-resistance indices – a well-established methodology in agricultural stress phenotyping (Yan et al., Reference Yan, Song, Wang, Wang, Li, Wang, Li and Sun2020; Bangari et al., Reference Bangari, Sastya and Yarna2024). Our preliminary validation confirmed the efficacy of PCA-coupled membership function analysis in discriminating drought and salinity tolerance among cotton genotypes at the seedling stage (Fan et al., Reference Fan, Su, Guo, Sun, Qu and Chen2021).
In this study, the seed germination percentage was significantly negatively correlated with oil content after drought and salt stress (R = −0.27** and R = −0.24**) (Fig. 4), indicating that higher oil content in cottonseeds was not conducive to germination under drought and saline conditions. This finding highlights a contradiction between the desire for seed resistance during germination and the increased cottonseed oil content. Lipids are the primary energy source during the early stages of seed embryo germination and consist of saturated and unsaturated fatty acids (Herman, Reference Herman2017). At average or warm temperatures, seeds with a higher proportion of saturated fatty acids possess higher total chemical energy, promoting growth and, ultimately, the germination of seed embryos. However, at lower temperatures, saturated fatty acids become more viscous, which slows down the chemical reactions required for fatty acid catabolism and significantly delays germination (Linder, Reference Linder2000; Pelc and Linder, Reference Pelc and Linder2015; Sanchez et al., Reference Sanchez, Mangat and Angeles-Shim2019).
Under drought stress, lipase activity in Mongolian almond (Prunus mongolica Maxim) seeds was significantly inhibited, limiting the efficiency of fat metabolism. Consequently, the vigor and germination rate of the seeds decreased significantly (Wang et al., Reference Wang, Zheng, Bai, Gao, Liu and Yan2015). Similarly, in this study, the high oil content in cottonseeds under drought and salt stress was associated with lower germination percentage, possibly due to the obstruction of fatty acid catabolism under these conditions. Therefore, genotypes with a higher proportion of unsaturated fatty acids were preferred in germplasm resources, as they are more conducive to improving the germination ability of seeds under stress conditions.
Research on cotton abiotic stress resistance has primarily focused on QTL mapping for drought and salt tolerance. These traits are governed by multiple genes with pleiotropic effects, meaning they influence multiple phenotypes simultaneously; a phenomenon is reflected as QTL pleiotropy in trait mapping studies. This study selected eight reported markers (Table S5) based on their detection in multiple drought and salt stress environments and their close association with various agronomic traits, yield and quality indicators (Fang et al., Reference Fang, Wang, Hu, Jia, Chen, Liu, Zhang, Guan, Chen, Zhou, Mei, Sun, Pan, He, Xiao, Shi, Gong, Liu, Ma, Cai, Zhu, Guo, Du and Zhang2017; Huang et al., Reference Huang, Nie, Shen, You, Li, Zhao, Zhang and Lin2017; Ma et al., Reference Ma, He, Wang, Sun, Zhang, Zhang, Wu, Li, Liu, Sun, Yan, Jia, Yang, Pan, Gu, Li, Sun, Dai, Liu, Gong, Wu, Wang, Liu, Feng, Ke, Wang, Lan, Wang, Peng, Wang, Wang, Pang, Peng, Li, Tian and Du2018; Sun et al., Reference Sun, Wang, Liu, Gu, Zhang, Li, Ke, Yang, Wu, Wu, Zhang, Zhang and Ma2018; Guo et al., Reference Guo, Su, Huang, Wang, Nie, Zhao and Hua2021, Reference Guo, Li, Huang, Ma, Li, Du, Cui, Zhao and Hua2024; Xu et al., Reference Xu, Guo, Meng, Zhang, Xu, Guo and Shen2021; Zhu et al., Reference Zhu, Hou, Song, Wang, Wang, Chen and Guo2021). These eight SNPs were developed into KASP markers and used to analyse the genotypes of 120 cotton materials. Among them, two markers produced no signal, and four markers exhibited only one genotype across all samples, likely due to differences between the materials used in this study and those used in the references.
SNP_A07_90682411 was used to classify 120 materials into three types, revealing significant differences in germination percentage under drought and salt stress, comprehensive stress resistance D values, and oil content among the groups. The results indicated that the SNP_A07_90682411 marker could effectively distinguish comprehensive stress resistance, drought resistance and salt tolerance in cotton at the germination stage, with the AA genotype identified as a strong resistance type. These results indicate that SNP_A07_90682411 is a reliable marker for distinguishing comprehensive stress resistance, drought tolerance and salt tolerance during germination. Additionally, it could serve as a preliminary indicator of oil content. SNP_A07_90682411 KASP markers can be utilized for auxiliary selection in breeding programs and provide a reference for further screening and fine mapping of resistance traits in cotton germination.
Conclusion
This study evaluated the chemical composition and the ability to germination of 120 cotton under drought, salt and low-temperature stress. PCA divided the 120 cotton genotypes into three categories: 35 high-tolerance, 74 medium-tolerance and 11 medium-sensitivity genotypes. The D values of the samples were analysed using the membership function method, allowing the identification of materials with high resistance and sensitivity during germination.
Correlation analysis between germination rate and chemical composition revealed that higher oil content in cottonseed was associated with adverse effects on germination under drought and salt stress, with both stresses showing similar effects on seed germination. The developed SNP_A07_90682411 KASP marker effectively distinguished genotypes with levels of comprehensive stress resistance and salt tolerance during germination. The AA genotype was identified as a strong resistance type, laying a foundation for further fine mapping and marker-assisted breeding. These findings provide valuable insights into the comprehensive resistance mechanisms of cotton germination and offer a foundation for developing new stress-resistant cotton varieties.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S1479262125100129
Acknowledgements
This work was financially supported through grants from The Basic Research Funds projects for universities in Xinjiang Uygur Autonomous Region (Grant Nos. XJEDU2024J037 and XJEDU2024J038), the Science and Technology Major Project of Xinjiang Uygur Autonomous Region (Grant No: 2024A02003-3), Xinjiang Qiangnong Fenghe Agricultural Technology Co., LTD. (Grant No: 2523HXKT1), and the Support for Cotton Industry Technology System of Xinjiang Uygur Autonomous Region (XJARS 03-07).
Author contributions
G.Y. analysed experimental data and wrote the manuscript. C.Q. provided cotton materials and verified experimental methods. D.Z. determined the contents of oil, protein and gossypol. L.P. and C.Y. measured the germination percentage of cotton seeds under different conditions. D.X. designed the experimental scheme and revised the manuscript.
Competing interests
The authors declare no conflicts of interest.
