Genome-Wide Association Mapping of Seed Shape-Related Traits in Cotton Using SSR Markers

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Comprehending the genetic foundation of traits associated with seed shape is crucial for improving the seed and fiber quality in cotton. Results: A total of 238 cotton accessions were evaluated in four different environments over a period of two years. Traits including thousand grain weight (TGW), aspect ratio (AR), seed length, seed width, diameter, and roundness demonstrated high heritability and significant genetic variation, as indicated by phenotypic analysis. The association analysis involved 145 SSR markers and identified 50 loci significantly associated with six traits related to seed shape. The markers MON_DPL0504aa and BNL2535ba were identified as influencing multiple traits, including aspect ratio and thousand grain weight. Notably, markers such as HAU2588a and MUSS422aa had considerable influence on seed diameter and roundness. The identified markers represented an average phenotypic variance between 3.92% for seed length and 16.54% for thousand grain weight (TGW). Conclusions: The research finds key loci for seed shape-related traits in cotton, providing significant potential for marker-assisted breeding. These findings establish a framework for breeding initiatives focused on enhancing seed quality, hence advancing the cotton production. QTL Seed Shape Marker-Assisted Breeding Cotton SSR Markers Genome-wide association analysis Genetic Improvement Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Background Cotton is China's principal fiber crop and the foremost contributor to fiber production in the nation, representing 35% of the country's fiber output. China, as a significant global producer and consumer of cotton, is pivotal in the international cotton market (Zhang et al., 2018; Sanogo et al., 2016). Cotton seeds exhibit significant agricultural traits and resources, essential for enhancing the productivity of crops. The seed industry, essential for food security, has been a longstanding priority in China, guaranteeing a promising future for the agricultural sector. However, although cotton research has predominantly emphasized fiber quality, studies on cotton seeds are a handful. Traditional breeding methods prioritize fiber quality and yield, which frequently results in decreased seed quality, emphasizing the importance for deeper research into cotton seed traits (Hamid et al., 2024). Seed shape is a key determinant of cotton productivity, influencing seedling vigor, fiber quality, and yield. Studies have shown that seed morphology affects germination, early-stage growth, and crop establishment, making it an essential trait for breeding programs (Kandasamy et al., 2020). Additionally, seed shape is linked to seed weight, impacting fiber quality and overall yield stability (ZHAO et al., 2019). Genetic research has identified specific loci controlling seed shape, providing opportunities for marker-assisted selection to enhance cotton production (Li et al., 2023). Optimizing seed shape has also been shown to improve planting efficiency and resilience under varying environmental conditions (Rathinavel et al., 2021). These findings highlight the importance of seed shape in future cotton breeding and crop management strategies. Small seeds generally demonstrate reduced vitality due to limited seed reserves, which can hinder the development of fiber germplasm (Zhao et al., 2015). Obtaining high-quality cotton requires the cultivation of high-quality cotton seeds. Modern seed breeding and associated cultivation techniques are essential tools to further enhance cotton quality and yield. A key barrier to mechanical sowing is the shape of the cotton seed. Recent research indicates that seed shape is a quantitative trait regulated by many genes (Nelson et al., 1989). QTL mapping studies have found seed shape-related QTLs by SSR markers, revealing significant insights for breeding high-yield, high-quality cotton via molecular markers. Conversely, studies on seed shape-related traits in rice have been significantly more comprehensive (Fan et al., 2006; Song et al., 2007; Wan et al., 2006; Zhang et al., 2012; Sun et al., 2013). The GS3 gene, which regulates seed length, has been cloned, and multiple linked QTLs related to seed weight have been found. Furthermore, genes like GW2 and qSW5, which regulate seed width and weight, have been cloned (Song et al., 2007; Wan et al., 2006). Research on QTL mapping related to the shape of cotton seeds is limited. Research on cotton seeds mostly investigates the effects of soil, temperature, and hormones on seed traits including germination, vigor, and growth. Factors including seed volume, fullness, and particle size affect seedling emergence and overall seedling development. The quality of cottonseed is integrally associated with cotton yield and fiber quality, since higher-quality seeds leads to healthier plants and higher yields. The major objective of cotton breeding is the production of excellent seeds (Maeda et al., 2021). The quality of cotton is dependent upon the quality of the seeds, which determines both fiber yield and development of seedlings. Studies indicate that large, healthy seeds yield healthy seedlings, consistent emergence, and enhanced development of seedlings. (Snider et al., 2014) examined the effect of seed particle size on seedling emergence and productivity. It was shown that larger seeds acquired higher dry matter and produced a higher yield per unit area (mu, about 666.7 m²) than smaller seeds after 30 days of growth. Seed shape is a complex quantitative trait regulated by multiple genes in agricultural crops. QTL mapping studies on the traits related to seed size and shape have been conducted on various crops, including peanut (Zhang et al., 2019), soybean (Hina et al., 2020), and rice (Ying et al., 2018). (Xie et al., 2014) conducted fine mapping of quantitative trait loci for soybean seed size traits. (Hina et al., 2020) discovered 88 quantitative trait loci (QTLs) exhibiting both main and epistatic effects for six traits associated with soybean seed size and shape. (Li et al., 2020) similarly identified 42 QTLs demonstrating additive effects for seed traits. (Sakamoto et al., 2019) examined 329 sorghum germplasm samples from multiple origin and discovered SNPs potentially associated to seed shape, including SNP loci S01_50413644, S04_59021202, and S05_9112888, based on GWAS-associated polymorphisms. (Zhang et al., 2019) identified 73 QTLs linked to seed color and tannin content in Chinese sorghum and uncovered a novel recessive allelic variant in Tanin2 (gene). (Fonceka et al., 2012) identified several QTLs for pod and seed size that differentiate cultivated peanuts from their wild relatives through an advanced backcross population. (Pandey et al., 2014) conducted genome-wide association research using 300 peanut genotypes, identifying 9 loci related to seed length, 3 loci linked to seed width, and 5 loci related to 100-seed weight. Upland cotton's narrow genetic base complicates the identification of key loci for quantitative traits using traditional mapping. Association mapping, leveraging genetic diversity, enables high-resolution identification of multiple QTLs (Zhu et al., 2024). This study aims to identify allelic variants associated with seed shape traits in Xinjiang cotton using SSR markers. A genome-wide association study (GWAS) across four environments was conducted to identify significant allelic variations, which were compared with known cotton QTLs related to yield and fiber quality (Kushanov et al., 2021). These findings contribute to understanding the genetic basis of seed shape and support marker-assisted breeding in cotton. 2. Materials and Methods 2.1 Plant materials and field experiments A total of 238 cotton accessions with stable genetic traits, sourced from breeding units in Xinjiang, were used in this study, including varieties from Xinhai, Xinluzhong, Xinluzao, and Xincaimian as shown in Table S1 . The test materials were selected, purified, and propagated over several generations. The cotton varieties were planted in experimental fields at Shihezi City (SHZ) and Korla City (KEL), located in northern and southern Xinjiang, respectively, during 2018 and 2019, covering four environments: 2018SHZ, 2018KEL, 2019SHZ, and 2019KEL. The experimental design employed a Randomized Complete Block Design (RCBD), with each variety randomly assigned to plots within blocks to control for spatial variation. Two replications per variety were used to enhance reliability. The experimental units consisted of plots with row spacing of 66 cm + 10 cm and a plant spacing of 9.5 cm. Drip irrigation, mulching film, and standard field management practices (fertilization and chemical control) were used, following local agricultural protocols. The RCBD ensured that treatment effects were accurately assessed while accounting for environmental variations across the field. 2.2 Statistical analysis Variance analysis, normality tests, MANOVA, and fundamental statistical evaluations, including extreme values, means, standard deviation, coefficient of variation, heritability, skewness and kurtosis, and trait correlations, were conducted using SPSS v22.0 software (IBM Corp., Armonk, NY, USA). Correlation studies and boxplots for different environments were generated utilizing R software (Shui et al., 2024 ; Guo et al., 2021 ). The Best Linear Unbiased Prediction (BLUP) model was used, selecting a Mixed Linear Model (MLM) to evaluate genetic and environmental influences on the variation of six traits: TGW, AR, Length, Width, Diameter, and Roundness. Environmental impacts were modeled as fixed, while genetic effects were considered random through the utilization of a kinship matrix. Coefficient of Variation (CV) $$\:CV=\frac{\sigma\:}{\mu\:}\times\:100$$ Where: σ = Standard deviation of the observed phenotypic data µ = Mean of the observed phenotypic data 2. Broad-Sense Heritability ( H² ) $$\:{H}^{2}=\frac{{\sigma\:}_{g}^{2}}{{\sigma\:}_{p}^{2}}$$ Where: \(\:{\sigma\:}_{g}^{2}\:\) = Genetic variance, which includes both additive variance \(\:{({\sigma\:}}_{A}^{2}\) ) dominance variance \(\:{({\sigma\:}}_{D}^{2})\) , and interaction variance \(\:{({\sigma\:}}_{I}^{2})\) \(\:{\sigma\:}_{p}^{2}\) = Total phenotypic variance (the observed variance in your cotton traits, including both genetic and environmental effects). 2.3 Phenotype investigation Individual cotton plants with typical boll sizes were selected for seed collection in mid-September of both 2018 and 2019. From the natural population, ten plants were selected, and from each plant, ten bolls were harvested for seed collection. After collection, the seed cotton was delinted, packed, and tied. Ultimately, 100 de-linted cotton seeds were collected per sample for each variety, with three replicates for each variety. Six trait indicators, including thousand grain Weight (TGW), aspect ratio (AR), seed length, seed width, diameter, and roundness, were measured using the SC-G automatic seed variety test analyzer (Doolan et al., 2024 ). The instrument was operated at a temperature of (25 ± 2) °C and a relative humidity of (30 ± 5) %. 2.4 Population structure analysis The population structure analysis was conducted using Structure v2.3.4 employing an admixture model. The optimal K value was determined by maximizing the ΔK statistic, as reported by (Pritchard et al., 2000 ). The number of clusters (K) was evaluated from 1 to 10, with 10 independent runs performed for each K value. The parameters used comprised a burn-in period of 10,000 steps, 100,000 MCMC steps after burn-in, with the number of populations (K) assumed ranging from 2 to 10. 2.5 Genetic diversity and association analysis SSR primers used in this study were obtained from interspecific maps of G. barbadense and G. hirsutum cultivars, which were constructed by the State Key Laboratory of Crop Genetic Improvement at Huazhong Agricultural University, Wuhan, China (Li et al., 2012 ). From these primers, 73 primer pairs exhibiting polymorphism among the different varieties were selected for the association analysis. Polymorphic information content (PIC) was calculated using the method described by (Muktar et al., 2019 ) with Power Marker v3.25 ( http://statgen.ncsu.edu/power_marker/downloads.htm ). Association between phenotypic and genotypic traits was analyzed using TASSEL v2.1 software, employing mixed linear models (MLM: G + P + Q + K) as described by (Bradbury et al., 2007 ). To determines the rate at which marker sites contribute to phenotypic variance, the FDR method (Zhang et al., 2020 ) were performed based on the P value at threshold level (FDR < 0.05). 3. Results 3.1. Descriptive statistics and phenotypic analysis 3.1.1 Normality analysis The normality results demonstrate that all traits assessed across the four environments adhere to a normal distribution. The p-values for all six traits (TGW, AR, Length, Width, Diameter, and Roundness) above the significance threshold of 0.05, indicating no significant deviation from normality. The histograms further illustrate this, showing symmetric distributions across all environments. 3.1.2 Multivariate analysis of variance (MANOVA) A MANOVA was used to assess the impact of environmental factors on the normalized traits (TGW, AR, Length, Width, Diameter, Roundness). The findings demonstrated significant multivariate effects of the environment on the combined traits (Wilks' Lambda = 0.0055, p < 0.001) demonstrating that the environment markedly affects trait variability. Boxplots indicated significant changes in trait distribution across environments, whereas the PCA plot supported the significant distinction between environments, reinforcing the MANOVA results. These results emphasize the significant impact of environmental factors on the examined traits as shown in Fig. 1. 3.1.3 Brief descriptive statistics Over two years (four environments), six traits were analyzed, including TGW, AR, seed length, seed width, seed diameter, and roundness. The traits exhibited continuous distribution, indicating the polygenic and quantitative nature of their inheritance as shown in Table 1 , 2 . The coefficient of variation (CV) for the six traits ranged from 4.40% for AR to 16.84% for TGW. Specifically, TGW had the highest CV at 16.84%, while AR had the lowest CV at 4.40%. Heritability estimates for these traits ranged from 0.968 for AR, TGW had the highest heritability values of 0.919, while seed roundness exhibited the highest heritability at 0.967. Traits with relatively higher heritability, such as roundness, diameter, aspect ratio, seed length and seed width were highlighted as key traits for selection in cotton breeding programs Table 2 . Table 1 Statistical analysis of phenotypic traits of 238 Cotton Varieties in each environment *Standard Deviation; **Coefficient of Variation; ***Heritability Environment Trait Minimum Maximum Mean *SD **CV (%) ***H 2 2018_KEL TGW 64.1 172.8 89.86 16.9 18.86 0.950 AR 1.6 2.0 1.84 0.08 4.48 0.940 Length 8.0 10.7 9.17 0.3 4.24 0.861 Width 4.5 5.5 5.01 0.2 4.66 0.820 Diameter 5.9 7.3 6.52 0.2 4.03 0.820 Roundness 0.5 0.6 0.55 0.02 4.88 0.948 2018_SHZ TGW 59.0 120.4 92.15 13.2 14.40 0.880 AR 1.6 2.0 1.81 0.09 5.02 0.990 Length 8.0 10.1 9.03 0.3 4.25 0.892 Width 4.5 5.4 5.00 0.2 4.06 0.960 Diameter 5.9 7.0 6.50 0.2 3.55 0.841 Roundness 0.4 0.6 0.56 0.02 5.28 0.952 2019_KEL TGW 54.7 141.8 85.35 15.9 18.64 0.970 AR 1.6 2.0 1.81 0.06 3.85 0.980 Length 7.9 10.3 9.16 0.3 4.20 0.901 Width 4.5 5.6 5.10 0.2 5.16 0.951 Diameter 5.8 7.1 6.52 0.2 4.55 0.901 Roundness 0.5 0.6 0.56 0.02 3.94 0.988 2019_SHZ TGW 61.4 119.0 86.66 11.5 13.30 0.820 AR 1.5 2.0 1.80 0.08 4.71 0.952 Length 7.9 10.7 9.09 0.3 4.38 0.918 Width 4.6 5.5 5.08 0.1 3.83 0.942 Diameter 5.8 7.4 6.52 0.2 3.64 0.696 Roundness 0.5 0.6 0.57 0.02 4.83 0.996 *Standard Deviation; **Coefficient of Variation; ***Heritability Table 2 Combined statistical analysis of phenotypic traits of 238 cotton varieties in four environments Traits Min Max Mean *SD Skewness Kurtosis **CV ***H 2 TGW 54.7 172.8 88.46 14.9 0.9 1.3 16.84% 0.919 AR 1.59 2.03 1.82 0.08 0.8 2.1 4.40% 0.968 Length 7.92 10.72 9.12 0.39 -0.1 2 4.28% 0.892 Width 4.55 5.68 5.05 0.23 -0.6 -1.6 4.55% 0.930 Diameter 5.83 7.42 6.52 0.26 -0.3 -1.8 3.99% 0.863 Roundness 0.49 0.64 0.56 0.03 1.69 5.5 5.36% 0.967 *Standard Deviation; **Coefficient of Variation; ***Heritability The stability of six phenotypic traits TGW, AR, seed length, seed width, diameter, and roundness were evaluated across different environmental conditions using box plots Fig. 2 . Among these traits, seed length, seed width, and diameter exhibited relatively consistent trends across the four environments. Seed length distribution was concentrated, with the 2019KEL environment showing a slightly higher average than SHZ. The 2019SHZ environment exhibited the greatest dispersion, with an average seed length ranging between 9- and 10-mm. Seed width displayed a discernible increasing trend in both average value and variability in 2019. Diameter values were relatively stable, although the dispersion in 2019 showed increased variation. Trends in (TGW), aspect ratio (AR), and roundness also demonstrated notable changes. TGW exhibited an increasing trend moving from the 2018SHZ environment to the 2019KEL environment, with a concentrated distribution. In contrast, AR showed considerable variability, with both average values and trend displaying a marked decrease. Roundness displayed broad dispersion, with the 2019KEL environment showing more concentrated values. Notably, roundness exhibited a significant increasing trend in 2019. 3.1.4 Best linear unbiased prediction model (BLUP) results The BLUP analysis revealed significant environmental effects for all traits (TGW, AR, Length, Width, Diameter, and Roundness), with p -values consistently below 0.05 as shown in Table S4. The analysis unveiled significant genetic variability, with trait like TGW, AR and Roundness showing high heritability (H² > 0.90) values across all accessions. This indicates these traits are less influenced by the environmental factors and are ideal for breeding programs. 3.2 Correlation Analysis of Phenotypic Traits Phenotypic values of the studied traits varied across environments, as shown in the frequency distribution map as shown in Fig. 3 . A strong negative correlation was found between TGW and AR, while TGW positively correlated with seed length, width, diameter, and roundness. Additionally, seed roundness and width were positively correlated with AR. In the 2019SHZ environment, roundness showed a strong positive correlation with seed length, a trend also observed in other environments. However, in the 2018SHZ and 2018KEL environments, no significant correlation was found between roundness and diameter, whereas the 2019SHZ and 2019KEL environments exhibited strong and moderate positive correlations, respectively. Seed width was positively correlated with both diameter and roundness, with a strong positive correlation between width and roundness in the 2019SHZ environment, contrasting with a negative correlation in other environments. These results highlight the environment-specific nature of trait correlations, which can inform future phenotypic selection strategies. 3.3 Diversity analysis of molecular markers The efficiency and polymorphism of 73 SSR primer pairs, targeting 145 loci (Li et al., 2012 ), were evaluated across 238 cotton accessions as shown in Table S2 . The results indicated that all 145 SSR loci demonstrated clear, consistent amplification in 238 cotton accessions, with high reproducibility and substantial polymorphism across the 26 cotton chromosomes (pairs) as shown by the polymorphic information content (PIC) values in Table S3. Markers were selected based on polymorphism and genetic coverage, resulting in varying densities across chromosomes. This variability reflects differences in genetic diversity and the availability of polymorphic SSR markers and does not affect the analysis. On average, 2.81 SSR primer pairs were mapped to each chromosome, with the number of markers per chromosome ranging from 1 to 13 as shown in Fig. 4 . The average genetic distance between markers was estimated to be 36.97 cM (centimorgan). The Polymorphism Information Content (PIC) at each locus ranged from 0.327–0.666, with an average of 0.420, indicating substantial genetic variation among the markers, as shown in Table S3. These results suggest that the SSR markers used in this study exhibit considerable polymorphism, making them highly suitable for genetic analysis and breeding application. 3.4 Population structure analysis The K value increases with the value of LnP (D), with no obvious upward inflection point and no very clear peak as shown in Fig. 5A. Although Δ K is rapidly decreasing from K value 3 to 4, showing a significant upward inflection point as shown in Fig. 5B. At this time, it is determined that K value 3 is the number of subgroups divided by the population. Subgroup-1 comprises 30 varieties (12.6%), predominantly Xinluzao; subgroup-2 comprises 32 (13.4%), predominantly Xinluzhong; subgroup-3 comprises 176 varieties (73.9%), principally the Xinhai (Sea Island cotton), Xinluzhong and Xinluzhao (backbone of Xinjiang) and Xincaimin as shown in Fig. 5C and D. 3.5 Association Analysis of Seed Shape-related Traits Association analysis was conducted using the TASSEL software with the (MLM: G + P + Q + K) model, based on 145 SSR loci and six seed shape-related traits. After excluding gene sites with p -values ≥ 0.05, a total of 50 markers loci were identified as significantly associated with seed shape traits, all at a stringent p -value threshold of < 0.01 as shown in Table 3 . Among them, 18 loci were associated with TGW, 10 loci with AR, 8 loci with seed roundness, 5 loci with seed length, 5 with seed width, while only 4 loci were associated with seed diameter. Multiple markers were identified as associated with seed shape-related traits, often linked to multiple traits simultaneously. For example, HAU2588a was associated with seed width and diameter. HAU2846a showed associations with AR and TGW, while HAU4022b was associated with AR, seed length, and TGW. Similarly, HAU4483a was associated with seed width, diameter, and roundness. MUSS422aa was associated with seed width, and diameter, while MUSS422ab was associated with diameter, and roundness. Among the six seed shape-related traits analyzed, TGW had the highest number of associated marker loci (18 loci, p < 0.001). The average phenotypic variation explained (PVE) for TGW was 16.71%, ranging from 12.75% (BNL2535ba) to 20.67% (MON_DPL0504aa), with MON_DPL0504aa contributing most significantly to the phenotype. Other significant loci included CCRI596aa (19.49%), Gh330c (17.33%), HAU1496 (18.03%), HAU2846a (19.58%), HAU4022b (18.98%), MON_SHIN-1494b (15.48%), MON_SHIN-1585a (20.50%), NAU2240b (15.98%), NAU3298b (16.49%), NAU3827b (16.87%), and NAU5323 (16.01%). A total of ten loci were identified as significantly associated with AR ( p < 0.001), explaining an average of 13.25% of the phenotypic variation. The contribution of individual loci ranged from 20.5% (MON_SHIN-1585a) to 3.24% (NAU5172b). Key markers contributing most to the phenotype included MON_SHIN-1585a (20.5%), HAU2846a (19.58%), CCRI596aa (19.49%), HAU4022b (18.98%), HAU1496 (18.03%), MON_SHIN-1585a (17.49%), Gh330c (17.33%), NAU3827b (16.87%), NAU3298b (16.49%), NAU5323 (16.01%), BNL2535ba (15.59%), MON_SHIN-1494b (15.48%) and so on as shown in Table 3 . For seed length, five marker loci were identified with a p-value < 0.005. These loci explained an average of 3.92% of the phenotypic variation, with individual loci contributing between 4.47% (HAU1952bc) and 3.24% (NAU5172b). The most significant marker for seed length was HAU1952bc (4.47%). Diameter exhibited the fewest marker loci (four) associated with the trait at p < 0.005, explaining an average of 9.23% of the phenotypic variation. The contribution ranged from 10.42% (HAU2588a) to 7.31% (HAU4483a). The markers MUSS422ab (9.71%) and MUSS422aa (9.48%) were the primary contributors to the variation in diameter as shown in Table 3 . Table 3 Association analysis results of seed shape-related traits showing loci related each trait Trait Locus P_FDR *R 2 % TGW BNL2535ba 0.000 12.75 BNL2535bb 0.000 13.11 CCRI596aa 0.000 19.49 Gh330c 0.000 17.33 HAU1496 0.000 18.03 HAU2846a 0.000 19.58 HAU4022b 0.000 18.98 MGHES31a 0.000 14.69 MON_DPL0504aa 0.000 20.67 MON_DPL0893b 0.000 13.88 MON_SHIN-1481a 0.000 14.08 MON_SHIN-1494b 0.000 15.48 MON_SHIN-1585a 0.000 20.50 NAU2240a 0.000 13.81 NAU2240b 0.000 15.98 NAU3298b 0.000 16.49 NAU3827b 0.000 16.87 NAU5323 0.000 16.01 Aspect Ratio BNL2535ba 0.000 15.59 Gh330c 0.000 10.87 HAU1496 0.000 14.25 HAU2846a 0.000 11.35 HAU4022b 0.000 10.65 MON_DPL0504aa 0.000 15.10 MON_SHIN-1481a 0.000 11.57 MON_SHIN-1494b 0.000 14.98 MON_SHIN-1585a 0.000 17.49 NAU2631b 0.000 10.65 Seed Length HAU1952bc 0.007 4.47 HAU4022b 0.003 4.21 NAU2126b 0.000 3.37 NAU3346bb 0.001 4.31 NAU5172b 0.009 3.24 Seed Width HAU2588a 0.000 9.18 HAU4483a 0.006 8.39 MUSS422aa 0.003 8.51 MUSS422ab 0.000 8.6 BNL2535ba 0.004 7.44 Diameter HAU2588a 0.004 10.42 HAU4483a 0.003 7.31 MUSS422ab 0.001 9.71 MUSS422aa 0.003 9.48 Roundness NAU2126a 0.001 4.72 NAU2126b 0.001 4.27 NAU3346bb 0.004 4.51 HAU2588b 0.001 7.7 HAU4483a 0.000 9.1 HAU4483b 0.009 8.6 MUSS422ab 0.006 7.1 MON_CGR5447b 0.008 5.13 * R²/%: The proportion of phenotypic variance explained by the marker-trait association. 4. Discussion 4.1 Heritability and phenotypic variation in cotton seed traits across environments The phenotypic analysis of six cotton seed shape-related traits across four environments revealed significant variation, indicating a polygenic inheritance pattern. Traits with H² ≥ 0.85 were considered suitable for selection, following established genetic principles that prioritize traits with high genetic control for efficient breeding. Previous studies have demonstrated that traits with H² ≥ 0.80–0.85 exhibit strong genetic influence, minimizing environmental effects and ensuring selection efficiency (Zhang et al., 2020 ). All traits showed high heritability but the traits like TGW, AR, width and roundness showed high heritability in all four environments (H² values above 0.80 are considered less influenced by environmental factors and more genetically controlled) suggesting these traits are genetically controlled and less affected by environmental factors, consistent with previous studies (Li et al., 2020 ). The heritability estimates for TGW ( H² = 0.919), AR ( H² = 0.968), roundness ( H² = 0.967) and seed width ( H² = 0.930) suggest that these traits are suitable for targeted selection in breeding programs aiming to improve seed quality. The broad phenotypic distribution observed in the traits highlights the potential for selecting extreme phenotypes for breeding, which aligns with findings by (Xie et al., 2014 ) on cotton seed morphology. The phenotypic analysis revealed that roundness and thousand-grain weight (TGW) had high heritability and remained stable across environments, confirming strong genetic control. In contrast, seed length and width exhibited greater variability, indicating a higher sensitivity to environmental influences. These findings suggest that while TGW, AR, Width and Roundness are primarily governed by genetic factors, seed length and seed diameter are somewhat more influenced by environmental conditions as compared to other traits. 4.2 Genetic subgroup differentiation and its influence on cotton seed shape mapping Understanding population structure is a critical step in association studies, as it helps to account for the potential confounding effects of genetic relatedness among individuals. The population structure analysis, based on SSR markers, revealed three distinct subgroups, which were consistent with the breeding history and geographic origin of the cotton accessions. These subgroups, primarily composed of varieties from Xinluzao, Xinluzhong, and Xinhai, displayed different levels of genetic differentiation. The identification of these subgroups is crucial, as it informs the association analysis by minimizing false positives due to population structure. Previous studies (Huang et al., 2017 ) have shown that accurately accounting for population structure improves the precision of association mapping, and our study corroborates these findings. 4.3 QTLs for Traits Related to Seed Shape The identification of 50 significant loci associated with seed shape-related traits represents a significant advancement in our understanding of the genetic architecture underlying cotton seed morphology. These loci are of particular interest because they not only contribute to seed shape traits but may also have pleiotropic effects on agronomically important traits such as fiber quality and yield. For example, the locus HAU2588b, which showed significant association with roundness, also exhibited pleiotropic effects on fiber length and elongation, as reported by (Jiejie et al., 2020 ). This suggests that these loci may serve as potential targets for multi-trait selection in cotton breeding. Furthermore, MON_DPL0504aa emerged as a novel locus significantly associated with both AR and TGW. Its contribution to AR (15.10%)) and TGW (20.67%)) is particularly noteworthy, as these are key traits influencing cotton seed quality. The identification of these loci is significant for improving seed quality, a key determinant of cotton productivity, as both AR (Aspect Ratio) and TGW (Thousand Grain Weight) directly influence seedling establishment and overall plant growth. Given the significant heritability estimates for traits such as roundness and seed width, these traits can be targeted for selection in breeding programs aimed at improving seed quality. As previously demonstrated by (Qi et al., 2017 ) optimal seed shape traits such as larger, healthy seeds contribute to better seedling emergence, enhanced vigor which ultimately leads to higher yields. These findings are supported by subsequent studies (ZHAO et al., 2019 ) 4.4 Implications for Cotton Breeding and Future Research Directions The identification of important genetic loci associated to seed shape traits presents considerable potential for cotton breeding. These loci can be incorporated into breeding programs via marker-assisted selection (MAS) to produce cotton varieties with preferred seed shapes and related agronomic traits. Subsequent research must prioritize the validation of these loci, particularly those exhibiting pleiotropic effects such as MON_DPL0504aa and HAU2588b, through functional genomics and gene expression analyses. Expanding genome-wide association studies (GWAS) with high-density markers may reveal more loci, thus enhancing the understanding of the genetic pathways regulating seed shape. A multi-trait selection strategy, integrated with genomic selection, can expedite the advancement of cotton varieties with enhanced fiber output, seed weight, and quality. 5. Conclusion This study identified 50 allelic variation loci associated with six seed shape-related traits using association analysis of 238 Xinjiang cotton accessions examined across four different environments based on 145 SSR markers. These loci provide promising targets for marker-assisted breeding and molecular design breeding, which can be employed to enhance both seed and fiber quality in cotton. The novel loci identified, including MON_DPL0504aa, which influences both aspect ratio and thousand-grain weight, provide essential insights into the cotton seed shape genetics, facilitating more targeted breeding approaches. This study identified key loci associated with seed shape traits, providing a strong basis for future research, validation and genome wide analysis to unlock more precise breeding strategies to enhance seed quality and fiber yield in cotton. Declarations Acknowledgments We would like to thank the anonymous reviewers for their valuable comments and helpful suggestions; these have greatly helped us improve the quality of the manuscript. Authors’ contributions Nie XH designed the experiments. Siddho IA, Zixin Z, Peng H performed the experiments, Siddho IA wrote the main manuscript and prepared all the figures. Ding S performed the data analysis. Wu Y, Xu L, Abudurezike A, Muhammad A, Li Z, Lin H, revised and polished the manuscript. All authors contributed to the interpretation of results and have read and approved the final manuscript. Funding This work was supported by the Fund for BTNYGG (NYHXGG, 2023AA102), the National Natural Science Foundation of China (32260510), the Key Project for Science, Technology Development of Shihezi city, Xinjiang Production and Construction Crops (2022NY01), Shihezi University high-level talent research project (RCZK202337), Science and Technology Major Project of the Department of Science and Technology of Xinjiang Uygur Autonomous region (2022A03004-1) and the Key Programs for Science and Technology Development in Agricultural Field of Xinjiang Production and Construction Corps. Availability of data and materials The datasets used and analyzed in the current study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Bradbury PJ, Zhang Z, Kroon DE, et al. 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Molecular breeding . 2014;34 : 2165-2178. https://doi.org/10.1007/s11032-014-0171-7 Ying J-Z, Ma M, Bai C, et al. TGW3, a major QTL that negatively modulates grain length and weight in rice. Molecular Plant . 2018;11 (5) : 750-753. https://doi.org/10.1016/j.molp.2018.03.007 Zhang S, Hu X, Miao H, et al. QTL identification for seed weight and size based on a high-density SLAF-seq genetic map in peanut (Arachis hypogaea L.). BMC plant biology . 2019;19 : 1-15. https://doi.org/10.1186/s12870-019-2164-5 Zhang W, Zhang L, Li Y, et al. Neglected environmental health impacts of China's supply-side structural reform. Environment international . 2018;115 : 97-103. https://doi.org/10.1016/j.envint.2018.03.006 Zhang X, Wang J, Huang J, et al. Rare allele of OsPPKL1 associated with grain length causes extra-large grain and a significant yield increase in rice. Proceedings of the National Academy of Sciences . 2012;109 (52) : 21534-21539. https://doi.org/10.1073/pnas.1219776110 Zhang Z, Li J, Jamshed M, et al. Genome‐wide quantitative trait loci reveal the genetic basis of cotton fibre quality and yield‐related traits in a Gossypium hirsutum recombinant inbred line population. biotechnology journal . 2020;18 (1) : 239-253. https://doi.org/10.1111/pbi.13191 Zhao J, Bai W, Zeng Q, et al. Moderately enhancing cytokinin level by down-regulation of GhCKX expression in cotton concurrently increases fiber and seed yield. Molecular Breeding . 2015;35 (60) : 1-11. https://doi.org/10.1007/s11032-015-0232-6 ZHAO W, YAN Q, YANG H, et al. Effects of mepiquat chloride on yield and main properties of cottonseed under different plant densities. Journal of Cotton Research . 2019;2 : 1-10. https://doi.org/10.1186/s42397-019-0026-1 Zhu H, Xu J, Yu K, et al. Genome-wide identification of the key Kinesin genes during fiber and boll development in upland cotton (Gossypium hirsutum L). Molecular Genetics and Genomics . 2024;299 (1) : 2. https://doi.org/10.1007/s00438-023-02087-1 Supplementary Files SupplementaryData.xlsx Cite Share Download PDF Status: Published Journal Publication published 25 Aug, 2025 Read the published version in Journal of Cotton Research → Version 1 posted Editorial decision: Accept 25 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers invited by journal 21 Apr, 2025 Editor assigned by journal 21 Apr, 2025 First submitted to journal 17 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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2","display":"","copyAsset":false,"role":"figure","size":286141,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic distribution box-plot diagram.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5635782/v1/dbe8e411dfd89e22958acab6.png"},{"id":81091518,"identity":"e22c4269-876f-472f-8257-5cc92d068a36","added_by":"auto","created_at":"2025-04-22 07:13:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171495,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation among 6 traits in 4 environments \u003cstrong\u003eA\u003c/strong\u003e：2018SHZ；\u003cstrong\u003eB\u003c/strong\u003e：2018KEL；\u003cstrong\u003eC\u003c/strong\u003e：2019SHZ；\u003cstrong\u003eD\u003c/strong\u003e：2019KEL\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5635782/v1/978d5d0e62331b6af2d8e546.png"},{"id":81093011,"identity":"bc721746-9dd3-4dd0-87f0-746705e68665","added_by":"auto","created_at":"2025-04-22 07:29:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":99886,"visible":true,"origin":"","legend":"\u003cp\u003eBar graph showing number of markers on each chromosome\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5635782/v1/38e1901f1bf67a41e47da8ff.png"},{"id":81092121,"identity":"e1c2a62a-1961-44a9-b005-ff442f0f9f42","added_by":"auto","created_at":"2025-04-22 07:21:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":169207,"visible":true,"origin":"","legend":"\u003cp\u003eLines Graphs of K Value with ln(P(D)) Value and ΔK Value Based on Population Structure Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e：Line Chart of K Value and lnP(D)；\u003cstrong\u003eB\u003c/strong\u003e: Line Chart of ΔK Value Based on Population Structure Analysis \u003cstrong\u003eC:\u003c/strong\u003ePopulation Genetic Structure of 238 Cotton Varieties Based on SSR Markers; \u003cstrong\u003eD:\u003c/strong\u003ePopulation subgroups\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5635782/v1/3e1f0786f43f0dbe70c96ef0.png"},{"id":90345003,"identity":"4f218a9a-3f12-4083-a905-994a915039a9","added_by":"auto","created_at":"2025-09-01 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Background","content":"\u003cp\u003eCotton is China\u0026apos;s principal fiber crop and the foremost contributor to fiber production in the nation, representing 35% of the country\u0026apos;s fiber output. China, as a significant global producer and consumer of cotton, is pivotal in the international cotton market (Zhang et al., 2018; Sanogo et al., 2016). Cotton seeds exhibit significant agricultural traits and resources, essential for enhancing the productivity of crops. The seed industry, essential for food security, has been a longstanding priority in China, guaranteeing a promising future for the agricultural sector. However, although cotton research has predominantly emphasized fiber quality, studies on cotton seeds are a handful. Traditional breeding methods prioritize fiber quality and yield, which frequently results in decreased seed quality, emphasizing the importance for deeper research into cotton seed traits (Hamid et al., 2024).\u003c/p\u003e\n\u003cp\u003eSeed shape is a key determinant of cotton productivity, influencing seedling vigor, fiber quality, and yield. Studies have shown that seed morphology affects germination, early-stage growth, and crop establishment, making it an essential trait for breeding programs (Kandasamy et al., 2020). Additionally, seed shape is linked to seed weight, impacting fiber quality and overall yield stability (ZHAO et al., 2019). Genetic research has identified specific loci controlling seed shape, providing opportunities for marker-assisted selection to enhance cotton production (Li et al., 2023). Optimizing seed shape has also been shown to improve planting efficiency and resilience under varying environmental conditions (Rathinavel et al., 2021). These findings highlight the importance of seed shape in future cotton breeding and crop management strategies.\u003c/p\u003e\n\u003cp\u003eSmall seeds generally demonstrate reduced vitality due to limited seed reserves, which can hinder the development of fiber germplasm (Zhao et al., 2015). Obtaining high-quality cotton requires the cultivation of high-quality cotton seeds. Modern seed breeding and associated cultivation techniques are essential tools to further enhance cotton quality and yield. A key barrier to mechanical sowing is the shape of the cotton seed. Recent research indicates that seed shape is a quantitative trait regulated by many genes (Nelson et al., 1989). QTL mapping studies have found seed shape-related QTLs by SSR markers, revealing significant insights for breeding high-yield, high-quality cotton via molecular markers. Conversely, studies on seed shape-related traits in rice have been significantly more comprehensive (Fan et al., 2006; Song et al., 2007; Wan et al., 2006; Zhang et al., 2012; Sun et al., 2013). The GS3 gene, which regulates seed length, has been cloned, and multiple linked QTLs related to seed weight have been found. Furthermore, genes like GW2 and qSW5, which regulate seed width and weight, have been cloned (Song et al., 2007; Wan et al., 2006).\u003c/p\u003e\n\u003cp\u003eResearch on QTL mapping related to the shape of cotton seeds is limited. Research on cotton seeds mostly investigates the effects of soil, temperature, and hormones on seed traits including germination, vigor, and growth. Factors including seed volume, fullness, and particle size affect seedling emergence and overall seedling development. The quality of cottonseed is integrally associated with cotton yield and fiber quality, since higher-quality seeds leads to healthier plants and higher yields. The major objective of cotton breeding is the production of excellent seeds (Maeda et al., 2021). The quality of cotton is dependent upon the quality of the seeds, which determines both fiber yield and development of seedlings. Studies indicate that large, healthy seeds yield healthy seedlings, consistent emergence, and enhanced development of seedlings. (Snider et al., 2014) examined the effect of seed particle size on seedling emergence and productivity. It was shown that larger seeds acquired higher dry matter and produced a higher yield per unit area (mu, about 666.7 m\u0026sup2;) than smaller seeds after 30 days of growth.\u003c/p\u003e\n\u003cp\u003eSeed shape is a complex quantitative trait regulated by multiple genes in agricultural crops. QTL mapping studies on the traits related to seed size and shape have been conducted on various crops, including peanut (Zhang et al., 2019), soybean (Hina et al., 2020), and rice (Ying et al., 2018). (Xie et al., 2014) conducted fine mapping of quantitative trait loci for soybean seed size traits. (Hina et al., 2020) discovered 88 quantitative trait loci (QTLs) exhibiting both main and epistatic effects for six traits associated with soybean seed size and shape. (Li et al., 2020) similarly identified 42 QTLs demonstrating additive effects for seed traits. (Sakamoto et al., 2019) examined 329 sorghum germplasm samples from multiple origin and discovered SNPs potentially associated to seed shape, including SNP loci S01_50413644, S04_59021202, and S05_9112888, based on GWAS-associated polymorphisms. (Zhang et al., 2019) identified 73 QTLs linked to seed color and tannin content in Chinese sorghum and uncovered a novel recessive allelic variant in Tanin2 (gene). (Fonceka et al., 2012) identified several QTLs for pod and seed size that differentiate cultivated peanuts from their wild relatives through an advanced backcross population. (Pandey et al., 2014) conducted genome-wide association research using 300 peanut genotypes, identifying 9 loci related to seed length, 3 loci linked to seed width, and 5 loci related to 100-seed weight.\u003c/p\u003e\n\u003cp\u003eUpland cotton\u0026apos;s narrow genetic base complicates the identification of key loci for quantitative traits using traditional mapping. Association mapping, leveraging genetic diversity, enables high-resolution identification of multiple QTLs (Zhu et al., 2024). This study aims to identify allelic variants associated with seed shape traits in Xinjiang cotton using SSR markers. A genome-wide association study (GWAS) across four environments was conducted to identify significant allelic variations, which were compared with known cotton QTLs related to yield and fiber quality (Kushanov et al., 2021). These findings contribute to understanding the genetic basis of seed shape and support marker-assisted breeding in cotton.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Plant materials and field experiments\u003c/h2\u003e\n \u003cp\u003eA total of 238 cotton accessions with stable genetic traits, sourced from breeding units in Xinjiang, were used in this study, including varieties from Xinhai, Xinluzhong, Xinluzao, and Xincaimian as shown in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. The test materials were selected, purified, and propagated over several generations. The cotton varieties were planted in experimental fields at Shihezi City (SHZ) and Korla City (KEL), located in northern and southern Xinjiang, respectively, during 2018 and 2019, covering four environments: 2018SHZ, 2018KEL, 2019SHZ, and 2019KEL. The experimental design employed a Randomized Complete Block Design (RCBD), with each variety randomly assigned to plots within blocks to control for spatial variation. Two replications per variety were used to enhance reliability. The experimental units consisted of plots with row spacing of 66 cm\u0026thinsp;+\u0026thinsp;10 cm and a plant spacing of 9.5 cm. Drip irrigation, mulching film, and standard field management practices (fertilization and chemical control) were used, following local agricultural protocols. The RCBD ensured that treatment effects were accurately assessed while accounting for environmental variations across the field.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eVariance analysis, normality tests, MANOVA, and fundamental statistical evaluations, including extreme values, means, standard deviation, coefficient of variation, heritability, skewness and kurtosis, and trait correlations, were conducted using SPSS v22.0 software (IBM Corp., Armonk, NY, USA). Correlation studies and boxplots for different environments were generated utilizing R software (Shui et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Guo et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Best Linear Unbiased Prediction (BLUP) model was used, selecting a Mixed Linear Model (MLM) to evaluate genetic and environmental influences on the variation of six traits: TGW, AR, Length, Width, Diameter, and Roundness. Environmental impacts were modeled as fixed, while genetic effects were considered random through the utilization of a kinship matrix.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient of Variation (CV)\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:CV=\\frac{\\sigma\\:}{\\mu\\:}\\times\\:100$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003e\u0026sigma;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Standard deviation of the observed phenotypic data\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003e\u0026micro;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Mean of the observed phenotypic data\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cstrong\u003e2. Broad-Sense Heritability (\u003cem\u003eH\u0026sup2;\u003c/em\u003e)\u003c/strong\u003e\u003c/div\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:{H}^{2}=\\frac{{\\sigma\\:}_{g}^{2}}{{\\sigma\\:}_{p}^{2}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e= Genetic variance, which includes both additive variance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{({\\sigma\\:}}_{A}^{2}\\)\u003c/span\u003e\u003c/span\u003e) dominance variance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{({\\sigma\\:}}_{D}^{2})\\)\u003c/span\u003e\u003c/span\u003e, and interaction variance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{({\\sigma\\:}}_{I}^{2})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{p}^{2}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e = Total phenotypic variance (the observed variance in your cotton traits, including both genetic and environmental effects).\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Phenotype investigation\u003c/h2\u003e\n \u003cp\u003eIndividual cotton plants with typical boll sizes were selected for seed collection in mid-September of both 2018 and 2019. From the natural population, ten plants were selected, and from each plant, ten bolls were harvested for seed collection. After collection, the seed cotton was delinted, packed, and tied. Ultimately, 100 de-linted cotton seeds were collected per sample for each variety, with three replicates for each variety. Six trait indicators, including thousand grain Weight (TGW), aspect ratio (AR), seed length, seed width, diameter, and roundness, were measured using the SC-G automatic seed variety test analyzer (Doolan et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The instrument was operated at a temperature of (25\u0026thinsp;\u0026plusmn;\u0026thinsp;2) \u0026deg;C and a relative humidity of (30\u0026thinsp;\u0026plusmn;\u0026thinsp;5) %.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Population structure analysis\u003c/h2\u003e\n \u003cp\u003eThe population structure analysis was conducted using Structure v2.3.4 employing an admixture model. The optimal K value was determined by maximizing the \u0026Delta;K statistic, as reported by (Pritchard et al., \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). The number of clusters (K) was evaluated from 1 to 10, with 10 independent runs performed for each K value. The parameters used comprised a burn-in period of 10,000 steps, 100,000 MCMC steps after burn-in, with the number of populations (K) assumed ranging from 2 to 10.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Genetic diversity and association analysis\u003c/h2\u003e\n \u003cp\u003eSSR primers used in this study were obtained from interspecific maps of \u003cem\u003eG. barbadense\u003c/em\u003e and \u003cem\u003eG. hirsutum\u003c/em\u003e cultivars, which were constructed by the State Key Laboratory of Crop Genetic Improvement at Huazhong Agricultural University, Wuhan, China (Li et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). From these primers, 73 primer pairs exhibiting polymorphism among the different varieties were selected for the association analysis. Polymorphic information content (PIC) was calculated using the method described by (Muktar et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) with Power Marker v3.25 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://statgen.ncsu.edu/power_marker/downloads.htm\u003c/span\u003e\u003c/span\u003e). Association between phenotypic and genotypic traits was analyzed using TASSEL v2.1 software, employing mixed linear models (MLM: G\u0026thinsp;+\u0026thinsp;P\u0026thinsp;+\u0026thinsp;Q\u0026thinsp;+\u0026thinsp;K) as described by (Bradbury et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). To determines the rate at which marker sites contribute to phenotypic variance, the FDR method (Zhang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) were performed based on the P value at threshold level (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Descriptive statistics and phenotypic analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Normality analysis\u003c/h2\u003e\n \u003cp\u003eThe normality results demonstrate that all traits assessed across the four environments adhere to a normal distribution. The p-values for all six traits (TGW, AR, Length, Width, Diameter, and Roundness) above the significance threshold of 0.05, indicating no significant deviation from normality. The histograms further illustrate this, showing symmetric distributions across all environments.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 Multivariate analysis of variance (MANOVA)\u003c/h2\u003e\n \u003cp\u003eA MANOVA was used to assess the impact of environmental factors on the normalized traits (TGW, AR, Length, Width, Diameter, Roundness). The findings demonstrated significant multivariate effects of the environment on the combined traits (Wilks\u0026apos; Lambda\u0026thinsp;=\u0026thinsp;0.0055, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) demonstrating that the environment markedly affects trait variability. Boxplots indicated significant changes in trait distribution across environments, whereas the PCA plot supported the significant distinction between environments, reinforcing the MANOVA results. These results emphasize the significant impact of environmental factors on the examined traits as shown in Fig. 1.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3 Brief descriptive statistics\u003c/h2\u003e\n \u003cp\u003eOver two years (four environments), six traits were analyzed, including TGW, AR, seed length, seed width, seed diameter, and roundness. The traits exhibited continuous distribution, indicating the polygenic and quantitative nature of their inheritance as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The coefficient of variation (CV) for the six traits ranged from 4.40% for AR to 16.84% for TGW. Specifically, TGW had the highest CV at 16.84%, while AR had the lowest CV at 4.40%. Heritability estimates for these traits ranged from 0.968 for AR, TGW had the highest heritability values of 0.919, while seed roundness exhibited the highest heritability at 0.967. Traits with relatively higher heritability, such as roundness, diameter, aspect ratio, seed length and seed width were highlighted as key traits for selection in cotton breeding programs Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistical analysis of phenotypic traits of 238 Cotton Varieties in each environment *Standard Deviation; **Coefficient of Variation; ***Heritability\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnvironment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e*SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e**CV (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e***H\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e2018_KEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoundness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e2018_SHZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoundness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e2019_KEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoundness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e2019_SHZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoundness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e*Standard Deviation; **Coefficient of Variation; ***Heritability\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCombined statistical analysis of phenotypic traits of 238 cotton varieties in four environments\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e*SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e**CV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e***H\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoundness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*Standard Deviation; **Coefficient of Variation; ***Heritability\u003c/p\u003e\n \u003cp\u003eThe stability of six phenotypic traits TGW, AR, seed length, seed width, diameter, and roundness were evaluated across different environmental conditions using box plots Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Among these traits, seed length, seed width, and diameter exhibited relatively consistent trends across the four environments. Seed length distribution was concentrated, with the 2019KEL environment showing a slightly higher average than SHZ. The 2019SHZ environment exhibited the greatest dispersion, with an average seed length ranging between 9- and 10-mm. Seed width displayed a discernible increasing trend in both average value and variability in 2019. Diameter values were relatively stable, although the dispersion in 2019 showed increased variation. Trends in (TGW), aspect ratio (AR), and roundness also demonstrated notable changes. TGW exhibited an increasing trend moving from the 2018SHZ environment to the 2019KEL environment, with a concentrated distribution. In contrast, AR showed considerable variability, with both average values and trend displaying a marked decrease. Roundness displayed broad dispersion, with the 2019KEL environment showing more concentrated values. Notably, roundness exhibited a significant increasing trend in 2019.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.4 Best linear unbiased prediction model (BLUP) results\u003c/h2\u003e\n \u003cp\u003eThe BLUP analysis revealed significant environmental effects for all traits (TGW, AR, Length, Width, Diameter, and Roundness), with \u003cem\u003ep\u003c/em\u003e-values consistently below 0.05 as shown in Table S4. The analysis unveiled significant genetic variability, with trait like TGW, AR and Roundness showing high heritability (H\u0026sup2; \u0026gt; 0.90) values across all accessions. This indicates these traits are less influenced by the environmental factors and are ideal for breeding programs.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Correlation Analysis of Phenotypic Traits\u003c/h2\u003e\n \u003cp\u003ePhenotypic values of the studied traits varied across environments, as shown in the frequency distribution map as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. A strong negative correlation was found between TGW and AR, while TGW positively correlated with seed length, width, diameter, and roundness. Additionally, seed roundness and width were positively correlated with AR. In the 2019SHZ environment, roundness showed a strong positive correlation with seed length, a trend also observed in other environments. However, in the 2018SHZ and 2018KEL environments, no significant correlation was found between roundness and diameter, whereas the 2019SHZ and 2019KEL environments exhibited strong and moderate positive correlations, respectively. Seed width was positively correlated with both diameter and roundness, with a strong positive correlation between width and roundness in the 2019SHZ environment, contrasting with a negative correlation in other environments. These results highlight the environment-specific nature of trait correlations, which can inform future phenotypic selection strategies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Diversity analysis of molecular markers\u003c/h2\u003e\n \u003cp\u003eThe efficiency and polymorphism of 73 SSR primer pairs, targeting 145 loci (Li et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), were evaluated across 238 cotton accessions as shown in Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e. The results indicated that all 145 SSR loci demonstrated clear, consistent amplification in 238 cotton accessions, with high reproducibility and substantial polymorphism across the 26 cotton chromosomes (pairs) as shown by the polymorphic information content (PIC) values in Table S3. Markers were selected based on polymorphism and genetic coverage, resulting in varying densities across chromosomes. This variability reflects differences in genetic diversity and the availability of polymorphic SSR markers and does not affect the analysis.\u003c/p\u003e\n \u003cp\u003eOn average, 2.81 SSR primer pairs were mapped to each chromosome, with the number of markers per chromosome ranging from 1 to 13 as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The average genetic distance between markers was estimated to be 36.97 cM (centimorgan). The Polymorphism Information Content (PIC) at each locus ranged from 0.327\u0026ndash;0.666, with an average of 0.420, indicating substantial genetic variation among the markers, as shown in Table S3. These results suggest that the SSR markers used in this study exhibit considerable polymorphism, making them highly suitable for genetic analysis and breeding application.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Population structure analysis\u003c/h2\u003e\n \u003cp\u003eThe K value increases with the value of LnP (D), with no obvious upward inflection point and no very clear peak as shown in Fig. 5A. Although \u0026Delta; K is rapidly decreasing from K value 3 to 4, showing a significant upward inflection point as shown in Fig. 5B. At this time, it is determined that K value 3 is the number of subgroups divided by the population. Subgroup-1 comprises 30 varieties (12.6%), predominantly Xinluzao; subgroup-2 comprises 32 (13.4%), predominantly Xinluzhong; subgroup-3 comprises 176 varieties (73.9%), principally the Xinhai (Sea Island cotton), Xinluzhong and Xinluzhao (backbone of Xinjiang) and Xincaimin as shown in Fig. 5C and D.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Association Analysis of Seed Shape-related Traits\u003c/h2\u003e\n \u003cp\u003eAssociation analysis was conducted using the TASSEL software with the (MLM: G\u0026thinsp;+\u0026thinsp;P\u0026thinsp;+\u0026thinsp;Q\u0026thinsp;+\u0026thinsp;K) model, based on 145 SSR loci and six seed shape-related traits. After excluding gene sites with \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026ge;\u0026thinsp;0.05, a total of 50 markers loci were identified as significantly associated with seed shape traits, all at a stringent \u003cem\u003ep\u003c/em\u003e-value threshold of \u0026lt;\u0026thinsp;0.01 as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Among them, 18 loci were associated with TGW, 10 loci with AR, 8 loci with seed roundness, 5 loci with seed length, 5 with seed width, while only 4 loci were associated with seed diameter.\u003c/p\u003e\n \u003cp\u003eMultiple markers were identified as associated with seed shape-related traits, often linked to multiple traits simultaneously. For example, HAU2588a was associated with seed width and diameter. HAU2846a showed associations with AR and TGW, while HAU4022b was associated with AR, seed length, and TGW. Similarly, HAU4483a was associated with seed width, diameter, and roundness. MUSS422aa was associated with seed width, and diameter, while MUSS422ab was associated with diameter, and roundness. Among the six seed shape-related traits analyzed, TGW had the highest number of associated marker loci (18 loci, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The average phenotypic variation explained (PVE) for TGW was 16.71%, ranging from 12.75% (BNL2535ba) to 20.67% (MON_DPL0504aa), with MON_DPL0504aa contributing most significantly to the phenotype. Other significant loci included CCRI596aa (19.49%), Gh330c (17.33%), HAU1496 (18.03%), HAU2846a (19.58%), HAU4022b (18.98%), MON_SHIN-1494b (15.48%), MON_SHIN-1585a (20.50%), NAU2240b (15.98%), NAU3298b (16.49%), NAU3827b (16.87%), and NAU5323 (16.01%).\u003c/p\u003e\n \u003cp\u003eA total of ten loci were identified as significantly associated with AR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining an average of 13.25% of the phenotypic variation. The contribution of individual loci ranged from 20.5% (MON_SHIN-1585a) to 3.24% (NAU5172b). Key markers contributing most to the phenotype included MON_SHIN-1585a (20.5%), HAU2846a (19.58%), CCRI596aa (19.49%), HAU4022b (18.98%), HAU1496 (18.03%), MON_SHIN-1585a (17.49%), Gh330c (17.33%), NAU3827b (16.87%), NAU3298b (16.49%), NAU5323 (16.01%), BNL2535ba (15.59%), MON_SHIN-1494b (15.48%) and so on as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. For seed length, five marker loci were identified with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005. These loci explained an average of 3.92% of the phenotypic variation, with individual loci contributing between 4.47% (HAU1952bc) and 3.24% (NAU5172b). The most significant marker for seed length was HAU1952bc (4.47%). Diameter exhibited the fewest marker loci (four) associated with the trait at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005, explaining an average of 9.23% of the phenotypic variation. The contribution ranged from 10.42% (HAU2588a) to 7.31% (HAU4483a). The markers MUSS422ab (9.71%) and MUSS422aa (9.48%) were the primary contributors to the variation in diameter as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation analysis results of seed shape-related traits showing loci related each trait\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLocus\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP_FDR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e*R\u003csup\u003e2\u003c/sup\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"18\"\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNL2535ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNL2535bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCRI596aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGh330c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU1496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU2846a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU4022b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMGHES31a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMON_DPL0504aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMON_DPL0893b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMON_SHIN-1481a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMON_SHIN-1494b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMON_SHIN-1585a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU2240a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU2240b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU3298b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU3827b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU5323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eAspect Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNL2535ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGh330c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU1496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU2846a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU4022b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMON_DPL0504aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMON_SHIN-1481a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMON_SHIN-1494b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMON_SHIN-1585a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU2631b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eSeed Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU1952bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU4022b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU2126b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU3346bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU5172b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eSeed Width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU2588a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU4483a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMUSS422aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMUSS422ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNL2535ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU2588a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU4483a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMUSS422ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMUSS422aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eRoundness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU2126a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU2126b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNAU3346bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU2588b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU4483a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAU4483b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMUSS422ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMON_CGR5447b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e* R\u0026sup2;/%: The proportion of phenotypic variance explained by the marker-trait association.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Heritability and phenotypic variation in cotton seed traits across environments\u003c/h2\u003e \u003cp\u003eThe phenotypic analysis of six cotton seed shape-related traits across four environments revealed significant variation, indicating a polygenic inheritance pattern. Traits with \u003cem\u003eH\u0026sup2;\u003c/em\u003e \u0026ge; 0.85 were considered suitable for selection, following established genetic principles that prioritize traits with high genetic control for efficient breeding. Previous studies have demonstrated that traits with \u003cem\u003eH\u0026sup2;\u003c/em\u003e \u0026ge; 0.80\u0026ndash;0.85 exhibit strong genetic influence, minimizing environmental effects and ensuring selection efficiency (Zhang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). All traits showed high heritability but the traits like TGW, AR, width and roundness showed high heritability in all four environments (H\u0026sup2; values above 0.80 are considered less influenced by environmental factors and more genetically controlled) suggesting these traits are genetically controlled and less affected by environmental factors, consistent with previous studies (Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The heritability estimates for TGW (\u003cem\u003eH\u0026sup2;\u003c/em\u003e = 0.919), AR (\u003cem\u003eH\u0026sup2;\u003c/em\u003e = 0.968), roundness (\u003cem\u003eH\u0026sup2;\u003c/em\u003e = 0.967) and seed width (\u003cem\u003eH\u0026sup2;\u003c/em\u003e = 0.930) suggest that these traits are suitable for targeted selection in breeding programs aiming to improve seed quality. The broad phenotypic distribution observed in the traits highlights the potential for selecting extreme phenotypes for breeding, which aligns with findings by (Xie et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) on cotton seed morphology. The phenotypic analysis revealed that roundness and thousand-grain weight (TGW) had high heritability and remained stable across environments, confirming strong genetic control. In contrast, seed length and width exhibited greater variability, indicating a higher sensitivity to environmental influences. These findings suggest that while TGW, AR, Width and Roundness are primarily governed by genetic factors, seed length and seed diameter are somewhat more influenced by environmental conditions as compared to other traits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Genetic subgroup differentiation and its influence on cotton seed shape mapping\u003c/h2\u003e \u003cp\u003eUnderstanding population structure is a critical step in association studies, as it helps to account for the potential confounding effects of genetic relatedness among individuals. The population structure analysis, based on SSR markers, revealed three distinct subgroups, which were consistent with the breeding history and geographic origin of the cotton accessions. These subgroups, primarily composed of varieties from Xinluzao, Xinluzhong, and Xinhai, displayed different levels of genetic differentiation. The identification of these subgroups is crucial, as it informs the association analysis by minimizing false positives due to population structure. Previous studies (Huang et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) have shown that accurately accounting for population structure improves the precision of association mapping, and our study corroborates these findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 QTLs for Traits Related to Seed Shape\u003c/h2\u003e \u003cp\u003eThe identification of 50 significant loci associated with seed shape-related traits represents a significant advancement in our understanding of the genetic architecture underlying cotton seed morphology. These loci are of particular interest because they not only contribute to seed shape traits but may also have pleiotropic effects on agronomically important traits such as fiber quality and yield. For example, the locus HAU2588b, which showed significant association with roundness, also exhibited pleiotropic effects on fiber length and elongation, as reported by (Jiejie et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This suggests that these loci may serve as potential targets for multi-trait selection in cotton breeding. Furthermore, MON_DPL0504aa emerged as a novel locus significantly associated with both AR and TGW. Its contribution to AR (15.10%)) and TGW (20.67%)) is particularly noteworthy, as these are key traits influencing cotton seed quality. The identification of these loci is significant for improving seed quality, a key determinant of cotton productivity, as both AR (Aspect Ratio) and TGW (Thousand Grain Weight) directly influence seedling establishment and overall plant growth. Given the significant heritability estimates for traits such as roundness and seed width, these traits can be targeted for selection in breeding programs aimed at improving seed quality. As previously demonstrated by (Qi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) optimal seed shape traits such as larger, healthy seeds contribute to better seedling emergence, enhanced vigor which ultimately leads to higher yields. These findings are supported by subsequent studies (ZHAO et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Implications for Cotton Breeding and Future Research Directions\u003c/h2\u003e \u003cp\u003eThe identification of important genetic loci associated to seed shape traits presents considerable potential for cotton breeding. These loci can be incorporated into breeding programs via marker-assisted selection (MAS) to produce cotton varieties with preferred seed shapes and related agronomic traits. Subsequent research must prioritize the validation of these loci, particularly those exhibiting pleiotropic effects such as MON_DPL0504aa and HAU2588b, through functional genomics and gene expression analyses. Expanding genome-wide association studies (GWAS) with high-density markers may reveal more loci, thus enhancing the understanding of the genetic pathways regulating seed shape. A multi-trait selection strategy, integrated with genomic selection, can expedite the advancement of cotton varieties with enhanced fiber output, seed weight, and quality.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study identified 50 allelic variation loci associated with six seed shape-related traits using association analysis of 238 Xinjiang cotton accessions examined across four different environments based on 145 SSR markers. These loci provide promising targets for marker-assisted breeding and molecular design breeding, which can be employed to enhance both seed and fiber quality in cotton. The novel loci identified, including MON_DPL0504aa, which influences both aspect ratio and thousand-grain weight, provide essential insights into the cotton seed shape genetics, facilitating more targeted breeding approaches. This study identified key loci associated with seed shape traits, providing a strong basis for future research, validation and genome wide analysis to unlock more precise breeding strategies to enhance seed quality and fiber yield in cotton.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the anonymous reviewers for their valuable comments and helpful suggestions; these have greatly helped us improve the quality of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNie XH designed the experiments. Siddho IA, Zixin Z, Peng H performed the experiments, Siddho IA wrote the main manuscript and prepared all the figures. Ding S performed the data analysis. Wu Y, Xu L, Abudurezike A, Muhammad A, Li Z, Lin H, revised and polished the manuscript. All authors contributed to the interpretation of results and have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Fund for BTNYGG (NYHXGG, 2023AA102), the National Natural Science Foundation of China (32260510), the Key Project for Science, Technology Development of Shihezi city, Xinjiang Production and Construction Crops (2022NY01), Shihezi University high-level talent research project (RCZK202337), Science and Technology Major Project of the Department of Science and Technology of Xinjiang Uygur Autonomous region (2022A03004-1) and the Key Programs for Science and Technology Development in Agricultural Field of Xinjiang Production and Construction Corps.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBradbury PJ, Zhang Z, Kroon DE, et al. 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Molecular Genetics and Genomics\u003cem\u003e.\u003c/em\u003e 2024;299 (1)\u003cstrong\u003e:\u003c/strong\u003e 2. https://doi.org/10.1007/s00438-023-02087-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cotton-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cotn","sideBox":"Learn more about [Journal of Cotton Research](https://jcottonres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cotn/default.aspx","title":"Journal of Cotton Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"QTL, Seed Shape, Marker-Assisted Breeding, Cotton, SSR Markers, Genome-wide association analysis, Genetic Improvement","lastPublishedDoi":"10.21203/rs.3.rs-5635782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5635782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Cotton is a significant crop for fiber production; however, seed shape-related traits have been less investigated in comparison to fiber quality. Comprehending the genetic foundation of traits associated with seed shape is crucial for improving the seed and fiber quality in cotton.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 238 cotton accessions were evaluated in four different environments over a period of two years. Traits including thousand grain weight (TGW), aspect ratio (AR), seed length, seed width, diameter, and roundness demonstrated high heritability and significant genetic variation, as indicated by phenotypic analysis. The association analysis involved 145 SSR markers and identified 50 loci significantly associated with six traits related to seed shape. The markers MON_DPL0504aa and BNL2535ba were identified as influencing multiple traits, including aspect ratio and thousand grain weight. Notably, markers such as HAU2588a and MUSS422aa had considerable influence on seed diameter and roundness. The identified markers represented an average phenotypic variance between 3.92% for seed length and 16.54% for thousand grain weight (TGW).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The research finds key loci for seed shape-related traits in cotton, providing significant potential for marker-assisted breeding. These findings establish a framework for breeding initiatives focused on enhancing seed quality, hence advancing the cotton production.\u003c/p\u003e","manuscriptTitle":"Genome-Wide Association Mapping of Seed Shape-Related Traits in Cotton Using SSR Markers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 07:13:12","doi":"10.21203/rs.3.rs-5635782/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2025-04-25T22:01:37+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-04-22T06:47:34+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-21T18:31:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-21T08:06:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cotton Research","date":"2025-04-17T13:53:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cotton-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cotn","sideBox":"Learn more about [Journal of Cotton Research](https://jcottonres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cotn/default.aspx","title":"Journal of Cotton Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2b7137f-e076-4a5b-bb33-068d1eceb0bc","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T16:04:52+00:00","versionOfRecord":{"articleIdentity":"rs-5635782","link":"https://doi.org/10.1186/s42397-025-00226-0","journal":{"identity":"journal-of-cotton-research","isVorOnly":false,"title":"Journal of Cotton Research"},"publishedOn":"2025-08-25 15:57:43","publishedOnDateReadable":"August 25th, 2025"},"versionCreatedAt":"2025-04-22 07:13:12","video":"","vorDoi":"10.1186/s42397-025-00226-0","vorDoiUrl":"https://doi.org/10.1186/s42397-025-00226-0","workflowStages":[]},"version":"v1","identity":"rs-5635782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5635782","identity":"rs-5635782","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}