Integrated Phenotypic, Nutritional, and SSR Marker Analyses Reveal Genetic Diversity and Guide Germplasm Utilization in Lotus corniculatus

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However, the limited number of improved cultivars hampers its broader utilization. In this study, we characterized the genetic variation of 23 germplasm accessions from diverse geographic origins using 12 quantitative traits and 29 simple sequence repeat (SSR) markers. Quantitative trait analysis revealed substantial variation, particularly in plant height (CV = 31.76%), leaf area (30.09%), ether extract (38.53%), and crude fiber (31.79%). Significant correlations were observed between nutritional quality and morphological traits, indicating that phenotypic selection can indirectly improve forage quality. Cluster analysis based on phenotypic and nutritional data grouped the accessions into five categories, identifying germplasms with high crude protein and ether extract (Q1), superior leaf morphology (Q2), and high total sugar content with thick stems (Q5), each offering distinct breeding advantages. Genome-wide SSR mining identified 53,364 loci, dominated by dinucleotide repeats (52.5%), with (AT/AT)ₙ as the most frequent motif. Twenty-nine highly polymorphic SSR primers generated 299 alleles (mean = 10.17 per locus; PIC = 0.740). SSR-based clustering separated the accessions into three groups broadly aligned with geographic origin. Analysis of molecular variance (AMOVA) indicated that most genetic variation resided within populations, underscoring the potential for intra-population selection. These findings provide a germplasm classification framework that integrates phenotypic performance, nutritional quality, and genetic background, enabling breeders to select complementary parental combinations for developing L. corniculatus cultivars with improved yield, quality, and adaptability. Lotus corniculatus germplasm resources genetic diversity phenotypic and nutritional traits SSR markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Lotus corniculatus is a perennial, allopolyploid, and cross-pollinated legume species (Chen, 2023) widely cultivated for forage, soil and water conservation, and ornamental landscaping (Ünlüsoy et al., 2023 ). It is characterized by lush vegetative growth, high crude protein and tannin content, and stable nutritional quality across harvest periods (Shen et al., 1992 ), making it highly palatable to livestock without causing bloating in ruminants (Kostopoulou and Karatassiou, 2017 ). In addition to strong stress resistance and nitrogen fixation capacity, its extensive root system and creeping growth habit enable adaptation to poor, saline, and acidic soils, thereby preventing erosion and improving soil fertility (Hymes-Fecht et al., 2013 ; Wang et al., 2022 ; Zhao et al., 2023 ). With a long flowering period and abundant floral display, L. corniculatus also offers high ornamental value (Hao et al., 2024 ). Elite cultivars are the cornerstone for the sustainable utilization of plant genetic resources, and comprehensive germplasm characterization is a prerequisite for targeted breeding programs (Zhao et al., 2024 ; Zhou et al., 2024 ). Despite its agronomic potential, the number of forage-type L. corniculatus cultivars remains limited, and existing varieties exhibit suboptimal yield and quality performance. Systematic assessments of genetic diversity and phenotypic variation across representative germplasm collections are therefore essential to establish the genetic foundation for cultivar improvement, facilitate the identification of elite parental lines, and enable the design of efficient hybridization strategies. Forage legume germplasm has undergone long-term adaptation to diverse environments, resulting in rich genetic diversity that underpins population stability and evolutionary potential (Daniel et al., 2020 ). Traditional assessments using morphological and biochemical markers provide valuable but often environmentally influenced insights (Noohi et al., 2020 ). With advances in biotechnology, DNA-based molecular markers have become indispensable tools in genetic diversity research owing to their high efficiency, reproducibility, and independence from environmental variation (Ming et al., 2009; Noohi et al., 2020 ), and have been successfully applied across a wide range of plant species (Garcia-Mas et al., 2000 ; Luo et al., 2020 ). Among these, simple sequence repeats (SSRs) are particularly advantageous due to their high polymorphism, co-dominant inheritance, and robust reproducibility, making them suitable for resolving population structure, assessing gene flow, and elucidating genetic relationships (Calderón et al., 2019; Hu et al., 2024 ). Their broad applicability has been demonstrated in diverse taxa; for instance, SSRs outperformed ISSRs in assessing the genetic diversity of Pandanus odorifer (Noohi et al., 2020 ). However, for L. corniculatus , integrative studies that combine morphological characterization, nutritional quality assessment, and SSR-based molecular analysis across a representative and diverse germplasm panel remain scarce. This gap in knowledge constrains our ability to identify trait–marker associations and to elucidate the genetic architecture underlying key agronomic traits. To address this, we evaluated 23 germplasm accessions of L. corniculatus using a combination of phenotypic characterization, nutritional quality analysis, and SSR-based genotyping. Our objectives were to (i) quantify genetic diversity within and among accessions, (ii) assess the relationships between morphological and nutritional traits, and (iii) identify high-performing germplasm with superior yield and forage quality traits. These results are intended to provide both the material basis and the theoretical framework for targeted breeding strategies to develop high-yield, high-quality L. corniculatus cultivars. Materials and Methods Plant Materials and Trait Assessment A total of 23 germplasms of Lotus corniculatus L. were obtained from the National Medium-Term Forage Germplasm Bank, Inner Mongolia, China (Table 1 ). All L. corniculatus accessions were cultivated in the experimental greenhouse of the Department of Grassland Science, Guizhou University (Guiyang, China; 26°25′N, 106°40′E), which has a subtropical monsoon climate. The determination indexes of Lotus corniculatus include phenotypic traits and quality traits. Phenotypic traits measured included plant height, stem diameter, branch number, and leaf morphology; nutritional quality traits included crude protein (CP), crude fat (EE), crude fiber (CF), dry matter (DM), and total sugar (TS) content. Plant height and stem diameter were recorded using a ruler and vernier caliper, respectively. Leaves were scanned with an Epson Perfection V800 photo scanner, and leaf area, circumference, length, and width were quantified using WinRHIZO software (Regent Instruments Inc., Quebec, Canada). Nutritional quality was determined according to Chinese national standards: CP (GB/T 6432 − 2018), EE (GB/T 6433 − 2006), CF (GB/T 6434 − 2006, filtration method), DM (GB/T 6435 − 1986), and TS (anthrone method). Table 1 Information of 23 Lotus corniculatus germplasms Code Collection Number Species Country of origin/source 1 00114 Lotus corniculatus L. Wild, China 2 00115 Lotus corniculatus L. Japan 3 00116 Lotus corniculatus L. Guizhou, China 4 00420 Lotus corniculatus L. Canada 5 01549 Lotus corniculatus L. United States 6 01885 Lotus corniculatus L. Canada 7 01886 Lotus corniculatus L. Germany 8 01887 Lotus corniculatus L. New Zealand 9 01888 Lotus corniculatus L. United States 10 03046 Lotus corniculatus L. Institute of Animal Science of CAAS, China 11 04670 Lotus corniculatus subsp. frondosus Freyn . Tarbagatay Prefecture 12 04679 Lotus corniculatus subsp. frondosus Freyn Tarbagatay Prefecture 13 05689 Lotus corniculatus L. Xinjiang, China 14 05690 Lotus corniculatus L. Gansu, China 15 07880 Lotus corniculatus L. Stavropol Krai, Russian 16 08515 Lotus corniculatus L. Meixian County, China 17 08516 Lotus corniculatus L. Zhouzhi County, China 18 08517 Lotus corniculatus L. Taibai County, China 19 08518 Lotus corniculatus L. Fengxiang County, China 20 08519 Lotus corniculatus L. Baoji County, China 21 08520 Lotus corniculatus L. Long County, China 22 08521 Lotus corniculatus L. Qianyang County, China 23 B08 Lotus corniculatus L. Shibing County, China DNA extraction and primer design Genomic DNA was extracted from 100 mg of fresh leaf tissue using a Tiangen Plant Genomic DNA Kit (Tiangen Biotech Co., Beijing, China), following the manufacturer’s instructions with minor modifications. Briefly, leaf samples were ground in liquid nitrogen, lysed with LP1 buffer and RNase A, followed by LP2 addition and centrifugation. The supernatant was mixed with LP3 buffer, transferred to an adsorption column (CB3), and washed twice. DNA was eluted with TE buffer after incubation at room temperature and quantified using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA) with TE buffer as the blank control. Samples with OD260/280 ratios between 1.7 and 1.9 were considered high-quality and suitable for downstream analysis. DNA integrity was confirmed by 1% agarose gel electrophoresis, which showed bright, intact, and tail-free bands (Fig. S1 ). SSR loci were identified from transcriptome sequencing data of a low phosphorus-tolerant accession (01549) and a low phosphorus-sensitive accession (08518) (Zhao et al., 2023 ). Lotus japonicus genome annotations were obtained from http://www.kazusa.or.jp/lotus/summary3.0.html . SSR detection was performed using MISA, and corresponding primers were designed using Primer3 (v1.1.4). PCR Optimization and Primer Screening One hundred primer pairs were synthesized (Shanghai Biological Engineering Co., Ltd., China) and optimized via an L16(4 5 ) orthogonal design to determine the optimal concentrations of Taq DNA polymerase, primers, Mg²⁺, dNTPs, and template DNA (Table S1 , Fig. S2 ). The optimized PCR system comprised 1.25 U Taq polymerase, 0.4 mM dNTPs, 0.2 µM primers, 3 mM Mg²⁺, and 60 ng template DNA. Four germplasms (00114, 00115, 01886, and 08518) with contrasting phenotypes were used for primer screening. PCR products were resolved on 1% agarose gels, and primers generating clear, polymorphic bands were selected. A total of 29 highly polymorphic primer pairs were retained for subsequent analysis (Table S2 ). PCR Amplification and Capillary Electrophoresis The 29 selected primer pairs were re-synthesized with fluorescent labels (FAM, HEX, ROX, and TAMRA) attached to the 5' end of the forward primers, while the reverse primers remained unlabeled (Fig. S3). All primers were synthesized by Shanghai Sangon Biotechnology Co., Ltd. PCR reactions were carried out using the optimized reaction system described above. The thermal cycling profile consisted of an initial denaturation at 94 ℃ for 4 minutes, followed by 35 cycles of denaturation at 94 ℃ for 30 s, annealing at 58–61 ℃ for 30 s (depending on the primer), and extension at 72 ℃ for 20 s. A final extension was performed at 72°C for 10 min, and reactions were held at 4 ℃ until further analysis. PCR products were analyzed by capillary fluorescence electrophoresis using an ABI 3730XL automated DNA sequencer (Applied Biosystems, Foster City, CA, USA). Statistical analysis Phenotypic data were analyzed via ANOVA, Coefficients of variation and Shannon–Weaver diversity indices, correlation analysis, and principal component analysis (PCA) in SPSS 20.0, and visualized in Origin 2022. Shannon–Weaver diversity indices ( H ′) were calculated as: where H ′ is Gene Diversity Index, P i is the frequency of the i-th phenotypic category. SSR allele sizes were scored using GeneMarker HID v2.9.0. Genetic diversity parameters—number of alleles (Na), observed heterozygosity (Ho), expected heterozygosity (He), and Shannon’s information index (I)—were computed in GenAlEx 6.51b2. Polymorphic information content (PIC) was calculated in Cervus 3.0. Binary data matrices (presence/absence) were used for similarity coefficient calculation, UPGMA clustering, and genetic relationship analysis in NTSYSpc 2.11. Results Genetic variation in major phenotypic traits among L. corniculatus germplasm A comprehensive statistical analysis of seven key agronomic traits across 23 L. corniculatus germplasms from diverse geographic origins revealed substantial phenotypic variability (Table 2 ; Table S3). Among the evaluated traits, plant height exhibited the highest coefficient of variation (CV = 31.76%), followed closely by leaf area (CV = 30.09%) and branch number (CV = 27.65%), indicating considerable morphological divergence among accessions. In contrast, leaf circumference (CV = 18.80%), leaf length (CV = 18.89%), and leaf width (CV = 18.50%) showed comparatively lower variability, suggesting more conserved leaf morphological proportions. Table 2 Genetic variation in major morphological agronomic traits of Lotus corniculatus Trait Max Min Average SD CV(%) GDI Leaf area (cm 2 ) 3.33 0.60 1.77 0.53 30.09 1.13 Leaf circumference (cm) 13.04 5.12 9.24 1.74 18.80 1.07 Leaf length (cm) 2.14 0.84 1.54 0.29 18.89 1.06 Leaf width (cm) 3.22 1.21 2.22 0.41 18.50 1.11 Stem diameter (mm) 1.38 0.41 0.69 0.17 24.83 1.01 Number of branches 7.00 3.00 4.91 1.36 27.65 1.03 Plant height (cm) 23.01 4.45 11.92 3.78 31.76 1.05 Note: SD, Standard Deviation; CV, Coefficient of Variation; GDI, Gene Diversity Index. Across germplasms, the mean plant height was 11.92 cm, ranging from 6.403 cm in accession 04679 to 18.963 cm in accession 08516. Leaf area averaged 1.77 cm², with accession 01887 showing the largest mean leaf area (2.860 cm²) and accession 03046 the smallest (0.617 cm²). The number of branches varied between 3.333 in accession 01888 and 6.333 in accession 03046, while stem diameter ranged from 0.457 mm in accession 03046 to 1.117 mm in accession 08519. These findings highlight the presence of both high-performing and low-performing germplasms in specific traits, providing valuable material for targeted breeding programs. Genetic variation in major nutritional quality traits among L. corniculatus germplasm Analysis of five key nutritional quality traits revealed pronounced variation among the 23 L. corniculatus germplasms (Table 3 ; Table S4). Across traits, ether extract (EE) exhibited the greatest variability (CV = 38.53%), followed by crude fiber (CF, CV = 31.79%), total sugar (TS, CV = 14.81%), crude protein (CP, CV = 10.21%), and dry matter (DM, CV = 8.15%). The mean EE content was 3.019%, with the highest values observed in accessions 08518 and 01887, whereas accession 08515, had the lowest EE content. CF content averaged 15.238%, ranging from a maximum in accession 08519 to minima in 01887 and 00116. CP content averaged 242.462 g/kg, with accession 01886 showing the highest value; the lowest CP levels occurred in accessions 08521and 03046. DM content averaged 22.348%, with accession 08519 showing the highest proportion, while 08515 had the lowest. TS content averaged 15.801%, peaking in accession 08517 and reaching its minimum in 00116. These patterns indicate that certain germplasms, such as 01886, 08517, and 08518, exhibit superior nutritional profiles in specific traits, making them valuable candidates for targeted breeding programs aimed at enhancing forage quality. Table 3 Genetic variation in key quality traits of Lotus corniculatus Trait Max Min Average SD CV(%) GDI Ether extract(EE, %) 5.11 0.38 3.019 1.15 38.53 1.12 Crude fiber (CF, %) 30.47 10.21 15.238 4.84 31.79 0.75 Crude protein (CP, g/kg) 28.74 19.38 242.462 2.49 10.21 1.06 Dry matter (DM, %) 26.67 18.10 22.348 1.81 8.15 1.11 Total sugar (%) 22.18 11.86 15.801 2.34 14.81 1.04 Note: SD, Standard Deviation; CV, Coefficient of Variation; GDI, Gene Diversity Index. Principal component analysis based on quantitative traits Correlation analysis among the 12 quantitative traits of the 23 L. corniculatus germplasms revealed several significant relationships (Fig. 1 ). The four leaf morphological traits—leaf circumference, leaf length, leaf width, and leaf area—were highly and positively correlated with each other. Plant height exhibited significant or highly significant positive correlations with leaf area, leaf circumference, leaf width, and stem thickness, but a significant negative correlation with branch number. Ether extract (EE) content was highly and negatively correlated with stem thickness. Crude protein (CP) showed a highly significant positive correlation with crude fiber (CF) and significant negative correlations with leaf width and stem thickness. Dry matter (DM) content was significantly negatively correlated with all four leaf morphological traits but positively correlated with CP. Total sugar (TS) content was significantly positively correlated with leaf area and leaf width, and significantly negatively correlated with CF, CP, and DM. Principal component analysis (PCA) further indicated that all phenotypic and nutritional quality traits could be condensed into four principal components, collectively explaining 79.002% of the total variance (Table 4 ). PC1, representing a leaf morphology factor, was driven by leaf area, circumference, length, and width; PC2, associated with biological yield and nutritional quality, was dominated by DM and CP; PC3, representing a digestive limitation factor, was determined by CF and stem thickness; and PC4, corresponding to a yield composition factor, was mainly influenced by branch number. These patterns suggest that phenotypic variation in L. corniculatus is organized around distinct functional trait groups, including morphological, nutritional, and structural characteristics, which may be jointly targeted in breeding programs to optimize both forage quality and yield potential. Table 4 Principal component analysis of quantitative traits in Lotus corniculatus germplasms Trait PC1 PC2 PC3 PC4 Leaf area 0.208 0.100 0.021 0.071 Leaf circumference 0.206 0.109 0.102 0.103 Leaf length 0.200 0.125 0.065 0.099 Leaf width 0.209 0.065 0.055 0.114 Stem thickness -0.004 -0.241 0.399 0.167 Number of branches -0.021 0.147 -0.143 0.660 Plant height 0.085 -0.144 0.293 -0.356 Ether extract(EE) 0.010 0.223 -0.346 -0.336 Crude fiber (CF) -0.031 0.272 0.398 -0.093 Crude protein(CP) 0.099 -0.286 -0.221 0.150 Dry matter(DM) -0.087 0.294 0.106 -0.068 Total sugar -0.153 0.047 0.188 0.362 Eigenvalues 4.566 2.293 1.521 1.101 Contribution rate(%) 38.046 19.106 12.677 9.173 Cumulative contribution rate(%) 38.046 57.153 69.830 79.002 Genetic cluster analysis of L. corniculatus germplasm based on quantitative traits UPGMA clustering based on seven morphological traits (plant height, leaf area, leaf circumference, leaf length, leaf width, stem diameter, and branch number) and five nutritional quality traits (ether extract, crude fiber, crude protein, dry matter, and total sugar) resolved the 23 L. corniculatus germplasms from diverse geographic origins into five distinct groups (Q1–Q5) at a dissimilarity coefficient of 10 (Fig. 2 ). Cluster Q1 comprised 15 accessions, representing the largest group and encompassing germplasms with intermediate morphological and nutritional profiles. Q2 contained two accessions—08517 and 01887—characterized by the largest leaf area and circumference, coupled with the lowest CF content, making them promising candidates for improving forage nutritional quality. Q3 consisted solely of 08520, which exhibited high CF content, low CP content, and high DM content, indicating a need for targeted improvement in nutritional composition. Q4 included 08521, 00114, and 03046, displaying comparatively weaker leaf morphology and nutritional traits but with higher branch numbers, suggesting potential for yield enhancement through branching. Finally, Q5 grouped 04670 and 08519, which shared high TS content and greater stem thickness, traits that could contribute to improved energy content and structural robustness in forage. Distribution of SSRs in the L. corniculatus genome A total of 53,364 SSR loci were identified using the MISA software, with repeat motif lengths ranging from di- to hexa-nucleotides. Di-nucleotide repeats were the most abundant, comprising 28,063 loci (52.52%), followed by tri-nucleotide (29.42%) and tetra-nucleotide repeats (7.25%) (Fig. 3 A). Among di-nucleotide motifs, (AT/AT)n was the most prevalent, representing 53.18% of all di-nucleotide repeats. For tri-nucleotide motif, (AAG/CTT)n was the dominant type (27.02%), whereas (AAAT/ATTT)n was the most frequent among tetra-nucleotide repeats, accounting for 41.24% of that category. After removing mononucleotide repeats, the abundance of SSR loci showed a clear inverse relationship with repeat motif length, with longer motifs occurring less frequently. Additionally, PCR amplification artifacts, such as the propensity of Taq polymerase to add adenine residues to 3′ termini, occasionally affected the discrimination of single-nucleotide differences in capillary electrophoresis profiles (Fig. 3 B). Genetic diversity analysis of SSR markers in L. corniculatus To assess the molecular diversity of L. corniculatus , 69 accessions from 23 germplasm resources were genotyped using 29 highly polymorphic SSR loci (Table 5 ). A total of 299 alleles were detected, with an average of 10.31 alleles per locus, reflecting substantial allelic richness within the collection. Allele numbers varied markedly across loci, from only 2 at chr0_18003 to 24 at chr0_15876. The effective number of alleles (Ne) showed a similar pattern, averaging 10.168 and ranging from 2.471 (chr0_18003) to 23.534 (chr0_15876). The Shannon’s information index (I) varied from 0.500 to 5.160 (mean = 3.167), indicating a high level of genetic complexity. Observed heterozygosity (Ho) ranged between 0 and 1.900 (mean = 1.064), while expected heterozygosity (He) ranged from 0.320 to 1.829 (mean = 1.425), suggesting moderate-to-high heterogeneity across loci. Polymorphism information content (PIC) values spanned from 0.178 to 0.934, with a mean of 0.740, confirming that the selected SSR markers possess high discriminatory power for genetic diversity assessment. Notably, chr0_15876 exhibited the highest allele number, Ne, and PIC, making it a particularly informative locus for population genetic studies. These results collectively highlight the broad genetic base of the tested L. corniculatus germplasm and provide robust molecular tools for future breeding, conservation, and association mapping efforts. Table 5 Summary of diversity statistics based on 29 SSR markers in 69 samples from 23 Lotus corniculatus Primer name Number of alleles(Na) Effective number of alleles(Ne) Shannon Diversity Information Index(I) Observation of heterozygosity(Ho) Expected heterozygosity(He) Polymorphism Information Content(PIC) chr0_14344 16 18.418 4.603 0.929 1.775 0.912 chr5_724 21 15.500 4.671 1.608 1.742 0.871 chr5_3797 21 19.379 4.816 1.324 1.790 0.908 chr0_17962 11 14.091 3.946 1.900 1.690 0.878 chr0_17429 7 6.950 2.626 0.667 1.383 0.751 chr0_15414 15 9.499 3.788 1.518 1.572 0.822 chr0_4872 17 17.728 4.638 1.571 1.773 0.904 chr2_4161 12 11.409 3.629 0.582 1.608 0.865 chr0_15876 24 23.534 5.160 1.857 1.829 0.934 chr3_2976 9 8.575 3.304 1.800 1.522 0.752 chr0_7339 18 18.093 4.573 1.600 1.779 0.908 chr0_18003 2 2.471 0.500 0.000 0.320 0.178 chr4_868 3 4.488 1.674 0.286 1.094 0.486 chr5_3453 16 16.052 4.533 1.590 1.748 0.875 chr0_9069 9 10.777 3.638 1.429 1.617 0.808 chr3_3963 2 2.600 0.562 0.000 0.375 0.365 chr0_6664 9 10.542 3.550 1.750 1.620 0.810 chr6_1459 4 4.119 1.666 0.286 1.028 0.511 chr0_14998 8 8.609 3.207 1.714 1.528 0.750 chr4_4327 7 10.003 3.427 1.786 1.597 0.780 chr5_2279 9 7.092 2.938 0.736 1.429 0.755 chr6_130 4 4.712 1.955 1.467 1.150 0.523 chr4_2109 6 4.375 1.767 0.881 0.877 0.368 chr1_2455 11 9.067 3.357 0.686 1.556 0.859 chr5_3017 6 6.013 2.553 1.095 1.334 0.697 chr1_83 14 12.00 3.699 0.833 1.653 0.907 chr0_12234 5 5.133 1.916 0.200 1.144 0.701 chr4_2393 6 6.509 2.543 0.250 1.378 0.789 chr5_3457 7 7.121 2.590 0.500 1.401 0.798 Population structure of L. corniculatus based on SSR markers The population genetic structure of L. corniculatus germplasm was inferred using 299 allelic loci generated from 29 highly polymorphic SSR markers. Genetic similarity coefficients, calculated with NTSYSpc 2.11, ranged from 0.276 to 0.983, with an average of 0.773, indicating a broad spectrum of genetic relatedness among accessions. UPGMA cluster analysis yielded a high cophenetic correlation coefficient ( r = 0.98355), confirming the robustness of the clustering results. As illustrated in Fig. 4 , at a genetic similarity threshold of 0.65, the 23 germplasms were resolved into three major clusters (Class I–III). Class I and Class III each comprised a single, genetically distinct accession (00114 and 01886, respectively), suggesting unique genetic backgrounds. Class II contained the majority of accessions (n = 21) and represented the core genetic group within the panel. Further subdivision of Class II at a similarity coefficient of 0.85 revealed two subgroups corresponding to geographical origin: group a, consisting primarily of Chinese accessions, and group b, containing accessions from other countries. This geographic stratification indicates a degree of regional differentiation within L. corniculatus germplasm, which may reflect historical selection pressures, adaptation to local environments, or breeding history. Principal coordinate analysis (PCoA) based on SSR marker data was performed in GenAlEx 6.51b2 to visualize patterns of genetic differentiation among the 69 L. corniculatus accessions. The first three principal coordinates explained 25.65%, 14.42%, and 11.88% of the total genetic variation, respectively, accounting for a cumulative variance of 51.95% (Table 6 ). In the PCoA plot (Fig. 5 ), each point represents an individual accession, and points of the same color indicate samples belonging to the same group identified by UPGMA clustering. Spatial proximity among points reflects genetic similarity, whereas greater separation indicates stronger genetic divergence. Notably, most group a accessions (domestic germplasm) clustered predominantly in the fourth quadrant, whereas group b accessions (foreign germplasm) were distributed mainly across the second and third quadrants, highlighting marked genetic differentiation between Chinese and non-Chinese germplasm. Table 6 Contribution rates of principal components derived from SSR marker analysis in Lotus corniculatus Ingredients PC1 PC2 PC3 Contribution rate 25.65 14.42 11.88 Cumulative contribution rate 25.65 40.07 51.95 Analysis of molecular variance (AMOVA) further quantified the genetic partitioning, revealing that 56% of the total variation resided within germplasm, while 44% was attributable to among-germplasm differences (Table 7 ). Table 7 Analysis of molecular variance (AMOVA) within the Lotus corniculatus germplasm population Source of variation df Sum of squares Variance components Percentage of variation(%) P Value Among germplasms 22 340.804 4.962 44 <0.001 Within germplasms 46 145.000 6.304 56 <0.001 Total 68 485.804 11.266 100 Discussion Genetic diversity of phenotypic and nutritional quality traits in L. corniculatus germplasm Genetic diversity within germplasm resources constitutes a critical reservoir for the improvement of agronomic traits, enabling the introduction of favorable alleles to enhance yield, quality, and stress resistance through hybridization or introgression lines (Angessa et al., 2016). In the present study, coefficients of variation (CV) for phenotypic traits revealed substantial heterogeneity, with plant height and leaf area exhibiting the highest variability (31.76% and 30.09%, respectively), followed by branch number, stem diameter, and leaf morphology traits. Such levels of variation are indicative of broad selection potential, particularly for traits directly linked to forage biomass and harvest index. Stems and leaves are central determinants of forage yield, and previous studies have highlighted the significance of plant height, stem thickness, leaf area, and branching density in determining productivity in L. corniculatus and related forage legumes (Zhang et al., 2024 ). Accessions such as 08516 and 01887, which exhibit superior plant height and leaf area, respectively, represent promising parental candidates for breeding programs targeting these yield-associated traits. Nutritional quality traits displayed equally notable diversity, particularly ether extract (EE) and crude fiber (CF), which recorded CV values of 38.53% and 31.79%, respectively. EE is not only a concentrated energy source but also provides essential fatty acids such as linoleic, α-linolenic, and arachidonic acids that are critical for growth, reproduction, and immune function in livestock (Valentina et al., 2016 ; Jančík et al., 2022 ). However, excessive EE (> 5% of dry matter) can impair rumen microbial activity and digestive efficiency in ruminants (Vahmani et al., 2020 ), underscoring the importance of balanced selection. In contrast, high CF can limit digestibility, making the concurrent increase of crude protein (CP) and reduction of CF a key breeding objective for enhancing forage nutritional value (Powell et al., 2003 ). In this context, 08518 and 01887 emerged as elite germplasm with high EE and low CF, while 01886 possessed the highest CP, making these accessions valuable for quality-oriented improvement. Correlation analyses revealed significant associations between phenotypic and nutritional traits, consistent with findings in wild oats ( Avena fatua ) by Onyśk and Boczkowska ( 2017 ). For example, CP was negatively correlated with leaf width and stem diameter, suggesting possible trade-offs between structural and biochemical traits. Such correlations imply that unidirectional selection for a single trait may inadvertently affect others, reinforcing the necessity of multi-trait selection strategies in L. corniculatus breeding. Principal component analysis (PCA), as a multivariate statistical tool, offers an effective means of integrating complex phenotypic and nutritional data, thereby enhancing the precision of parent selection in multi-objective breeding programs (Liu et al., 2022 ). In this study, PCA revealed that the measured traits could be grouped into four distinct functional dimensions: leaf morphology, biological yield and nutritional quality, digestive constraints, and yield composition. This dimensionality not only clarifies the underlying structure of trait variation but also provides a practical framework for matching complementary parental types in breeding plans. Compared with earlier findings (Drobná, 2010 ), which emphasized stem and internode traits, the present results highlight a stronger influence of leaf-related and nutritional factors, likely due to the broader geographical origins and higher genetic diversity of the germplasm set. These insights reinforce the need for breeding objectives that simultaneously address both morphological and nutritional targets, ensuring balanced improvements in yield potential, feed quality, and adaptability. The phenotypic clustering of L. corniculatus germplasm revealed distinct groupings based on combinations of morphological and nutritional quality traits, yet the substantial within-group heterogeneity highlights that cluster membership does not necessarily equate to uniform breeding potential. This suggests that phenotypic clustering should serve as an initial germplasm organization tool, with final selection guided by trait-specific performance and genetic background. Q1, the largest group (15 accessions), encompassed both high-CP/low-CF accessions suitable for improving forage quality and morphologically superior but nutritionally average types, indicating its value as a broad donor pool rather than a coherent breeding block. Q2 demonstrated the pitfalls of interpreting cluster means in breeding contexts: while both members grouped together, 01887 combined the largest leaf area with the lowest CF, whereas 08517 exhibited higher CF and average leaf morphology, underscoring the need for accession-level evaluation within clusters. Q3 (08520) exemplified a biomass–digestibility trade-off, with high dry matter and CF but low CP, while Q4’s consistently high branch number suggests potential for yield enhancement through increased tiller density when integrated with high-nutrition backgrounds. Q5’s elevated total sugar and stem thickness could contribute to energy-rich forage but may require balancing against digestibility constraints. Similar patterns of within-group variability have been reported in Medicago sativa and Coffea arabica , where phenotypic clustering captured broad adaptation types but failed to fully resolve functional trait variation critical for breeding (Annicchiarico et al., 2015 ; Andini et al., 2025 ). Therefore, phenotypic grouping should be combined with molecular data to identify complementary crosses and avoid overlooking high-value outliers. Genetic diversity of phenotypic traits in L. corniculatus germplasm revealed by SSR markers Molecular markers, particularly Simple Sequence Repeats (SSRs), have proven to be indispensable tools in the characterization of genetic diversity across a wide range of plant species (Yin et al., 2023 ). Due to their codominant inheritance, high polymorphism, and genome-wide distribution, SSRs are especially well suited for applications such as genetic diversity assessment, QTL mapping, DNA fingerprinting, and marker-assisted selection (Misiukevičius et al., 2023 ; Tyagi et al., 2021 ). Over the past decade, their utility has been demonstrated in both model and non-model species. For example, Carvalho et al. ( 2020 ) employed SSR markers to assess the genetic diversity of common bean (Phaseolus vulgaris L.), identifying 172 allelic variants, while Hemasai et al. ( 2024 ) characterized 13 miRNA-SSR markers and their corresponding target gene SSRs derived from yield-responsive miRNAs in rice In the present study, analysis with GeneMarker HID V2.9.0 revealed a total of 299 alleles across 29 SSR loci, with an average of 10.310 alleles per locus—substantially exceeding the 4.8 alleles per locus reported in white clover ( Trifolium repens L.) by Kölliker et al. ( 2001 ). The polymorphism information content (PIC) ranged from 0.178 to 0.934, with a mean of 0.740, which is notably higher than the values reported for L. corniculatus by Daudi et al. ( 2021 ) (0.34) and Belalia et al. ( 2019 ) (0.575). While PIC variation can be influenced by the marker set and genotypes under investigation, the consistently high PIC values observed here suggest substantial allelic richness and differentiation, indicating that the tested germplasm harbors broad genetic variation suitable for breeding improvement. Furthermore, the average effective number of alleles (Ne) was 10.168, with a total Ne of 294.859, reflecting a high degree of allelic evenness. Diversity indices also supported this conclusion: the mean Shannon information index (I) was 3.167, and observed (Ho) and expected heterozygosities (He) averaged 1.064 and 1.521, respectively. These values exceed those reported in analogous SSR-based studies of Spinacia oleracea (Göl et al., 2017 ), Papaver somniferum (György et al., 2022 ), and guava ( Psidium guajava ) (Ma et al., 2020 ), further reinforcing the premise that L. corniculatus germplasm exhibits a higher-than-average reservoir of genetic diversity. Such diversity not only reflects the wide geographical origins of the accessions but also signals a rich pool of alleles that can be strategically combined to enhance both yield and forage quality. Cluster analysis remains a cornerstone method for elucidating genetic relationships among crop germplasm resources (Bakayoko et al., 2021 ; Souza et al., 2023 ; Chikh-Rouhou et al., 2021 ). In the present study, UPGMA clustering based on SSR data at a genetic similarity coefficient threshold of 0.65 partitioned the 23 L. corniculatus accessions into three major groups (Groups I–III). Within Group II, a clear subdivision was observed that largely corresponded to geographical origin: subgroup a comprised predominantly Chinese germplasm, whereas subgroup b was enriched in accessions from other countries. This geographic pattern is consistent with the separation trends detected by principal coordinate analysis (PCoA), suggesting that domestically adapted L. corniculatus lines in China have diverged from foreign counterparts through prolonged selection under distinct agroecological conditions. When compared with the clustering based on quantitative phenotypic traits, both approaches could broadly distinguish germplasm within China from that outside China, yet the detailed grouping patterns were not entirely congruent. Certain accessions that were phenotypically aligned with germplasm within China clustered genetically with foreign accessions, and vice versa. Such inconsistencies have been reported in other species, including Camellia oleifera (Zhu et al., 2022 ) and Indian Mustard (Sharma et al., 2022 ), and may arise from several factors: (i) phenotypic traits are often under polygenic control and strongly influenced by environmental conditions; (ii) SSR markers capture genome-wide polymorphisms, including variation in non-coding regions, which may not directly translate into morphological differences. These factors together highlight the complementary value of integrating phenotypic and molecular data in germplasm evaluation. From a breeding perspective, this integrated clustering framework serves as a roadmap for parental selection: genetically divergent accessions with complementary phenotypic and nutritional traits can be used to maximize heterosis and generate novel trait combinations, whereas genetically similar accessions with stable performance can be employed to fix desirable traits in uniform cultivars. Such a combined strategy is particularly pertinent to L. corniculatus , where the simultaneous improvement of yield, nutritional quality, and environmental adaptation remains a core breeding objective. Molecular analysis of variance (AMOVA) indicated that most of the genetic variation in L. corniculatus occurs within populations rather than between them, a pattern also reported for other predominantly outcrossing forage legumes such as alfalfa and white clover (Annicchiarico et al., 2020 ; Kölliker et al., 2001 ). This high intra-population diversity is likely a consequence of its predominantly outcrossing reproductive system, which promotes heterozygosity and maintains a broad genetic base through pollen-mediated gene flow. The relatively low genetic differentiation among populations may reflect both historical and ongoing germplasm exchange across regions, facilitated by formal breeding programs and informal farmer-to-farmer seed exchange (Loskutov et al., 2011; Ustariz et al., 2022 ). For breeders, this means that a large proportion of exploitable genetic variability is accessible within existing local germplasm, reducing reliance solely on exotic introductions. Nonetheless, targeted crosses between phenotypically divergent and genetically distinct accessions remain the most effective route to expand adaptive potential and achieve multi-trait improvement. Conclusions By integrating phenotypic, nutritional, and SSR marker data, we comprehensively assessed the genetic diversity of 23 Lotus corniculatus germplasm accessions. The large variation in key agronomic and nutritional traits, together with high SSR polymorphism (PIC = 0.740), revealed substantial diversity at both morphological and molecular levels. Differences between phenotypic and molecular clustering underscored the value of multi-dimensional analyses for germplasm characterization. Importantly, several accessions combined superior forage quality with distinct genetic backgrounds, making them promising parents for breeding programs targeting yield, quality, and adaptability. These findings provide a practical framework for germplasm classification and the strategic selection of complementary parental combinations, thereby facilitating the genetic improvement and sustainable utilization of L. corniculatus . Declarations Acknowledgments Not applicable. Author contribution statement LLZ and PCW conceived and designed research. WWZ and YW conducted experiments. LLZ, WWZ and JYZ analysed the data. LLZ, JYZ and PCW wrote the manuscript. All authors read and approved the manuscript. Funding This work was supported by the National Natural Science Foundation of China (32260340, 32060391) and Guizhou Provincial Science and Technology Projects (QKHPTRC-GCC[2022]022-1). Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate There are no ethical issues involved in this paper. Consent for publication The manuscript is approved by all authors for publication. Availability of data and materials All data supporting the findings of this study are available within the paper and its supplementary information. simple sequence repeat (SSR) primer sequences are provided in supplementary Table 2. Competing interests The authors declare no competing interests. References Chen C, Zhang K, Liu F, et al. Resequencing of global Lotus corniculatus accessions reveals population distribution and genetic loci, associated with cyanogenic glycosides accumulation and growth traits. BMC Biol. 2023;21:176. https://doi.org/10.1186/s12915-023-01670-7 . Ünlüsoy AG, Yolcu S, Bor M, et al. Activation of Photorespiration Facilitates Drought Stress Tolerance in Lotus corniculatus . J Plant Growth Regul. 2023;42:2088–101. Shen YX, Liang DZ, Wang HS, Chen ZF. Effects of planting density, mowing height and mowing frequency on grass production performance of Lotus corniculatus . Pratacultural Sci. 1992;3:37–40. Kostopoulou P, Karatassiou M. Lotus corniculatus L. response to carbon dioxide concentration and radiation level variations. Photosynthetica. 2017;55(3):522–31. Hymes-Fecht UC, Broderick GA, Muck RE, Grabber JH. Replacing alfalfa or red clover silage with birdsfoot trefoil silage in total mixed rations increases production of lactating dairy cows. J Dairy Sci. 2013;96:460–9. Wang LT, Jian ZL, Wang PC, Zhao LL, Chen KK. Combined physiological responses and differential expression of drought-responsive genes preliminarily explain the drought resistance mechanism of Lotus corniculatus . Funct Plant Biol. 2022. https://doi.org/10.1071/FP22051 . Zhao X, Chen KK, Wang LT, Zhao LL, Wang PC. Transcriptome analysis provides insights into the response of Lotus corniculatus roots to low-phosphorus stress. Front Plant Sci. 2023. https://doi.org/10.3389/fpls.2023.1089380 . Hao H, Li Q, Lyu S, et al. High-efficiency Agrobacterium -mediated transformation of Lotus corniculatus using DsRed2 positive selection and vermiculite-based rooting method. Plant Cell Tissue Organ Cult. 2024;156:58. https://doi.org/10.1007/s11240-024-02685-w . Zhao LL, Xie WH, Huang L, Long SS, Wang PC. Characterization of the gibberellic oxidase gene SdGA20ox1 in Sophora davidii (Franch.) Skeels and interaction protein screening. Front Plant Sci. 2024. https://doi.org/10.3389/fpls.2024.1478854 . Zhou ZJ, Li JQ, Gao Y, Wang R, Hang HY, Zhang Y, Zhao LL, Wang PC. Research on drought stress in Medicago sativa L. from 1998 to 2023: A Bibliometric Analysis. Front Plant Sci. 2024. https://doi.org/10.3389/fpls.2024.1406256 . von Daniel D, Lehnert H, Berner T, Klocke E, Junghanns W, et al. On genetic diversity in caraway: Genotyping of a large germplasm collection. PLoS ONE. 2020;15(12):e0244666. https://doi.org/10.1371/journal.pone.0244666 . Noohi N, Sriram IS, Sahoo A, Das S, Panda MK, Acharya L, RamaRao VV, Nayak S, Mohanty S. Population genetic structure and diversity analysis in economically important Pandanus odorifer (Forssk.) Kuntze accessions employing ISSR and SSR markers. Ind Crops Prod . 2020; 143: 111894. https://doi.org/10.1016/j.indcrop.2019.111894 Mingxiong P, GP R, Jinfa HE. Promoter anchored amplified polymorphism based on random amplified polymorphic DNA (PAAP-RAPD) in cotton. Euphytica. 2009;167(3):289–91. Garcia-Mas J, Oliver M, Gómez-Paniagua H, et al. Comparing AFLP, RAPD and RFLP markers for measuring genetic diversity in melon. Theor Appl Genet. 2000;101:860–4. https://doi.org/10.1007/s001220051553 . Luo Y, Zhang X, Xu J, et al. Phenotypic and molecular marker analysis uncovers the genetic diversity of the grass Stenotaphrum secundatum . BMC Genet. 2020;21:86. https://doi.org/10.1186/s12863-020-00892-w . Calderón-González Á, Pouilly N, Muños S, Grand X, Coque M, Velasco L, Pérez-Vich B. An SSR-SNP Linkage Map of the Parasitic Weed Orobanche cumana Wallr. Including a Gene for Plant Pigmentation. Front Plant Sci. 2019;10:797. https://doi.org/10.3389/fpls.2019.00797 . Hu L, Wang J, Wang X, Zhang D, Sun Y, Lu T, Shi W. Development of SSR Markers and Evaluation of Genetic Diversity of Endangered Plant Saussurea involucrata . Biomolecules. 2024;14:1010. https://doi.org/10.3390/biom14081010 . Angessa TT, Li C. Exploration and Utilization of Genetic Diversity Exotic Germplasm for Barley Improvement. Exploration, Identification and Utilization of Barley Germplasm. Academic; 2016. pp. 223–40. https://doi.org/10.1016/B978-0-12-802922-0.00009-1 . Zhang Q, Li S, Guo S, et al. Morphology and SSR markers reveal the genetic diversity of Elymus species germplasm in Northwestern China. Genet Resour Crop Evol. 2024;71:2091–103. https://doi.org/10.1007/s10722-023-01768-5 . Valentina F, Fernando G, João CM, Marleen DT, Gonçalves AMM. Biochemical and toxicological effects of organic (herbicide Primextra® Gold TZ) and inorganic (copper) compounds on zooplankton and phytoplankton species. Aquat Toxicol. 2016;177:33–43. https://doi.org/10.1016/j.aquatox.2016.05.008 . Jančík F, Kubelková P, Loučka R, Jambor V, Kumprechtová D, Homolka P, Koukolová V, Tyrolová Y, Výborná A. Shredlage Processing Affects the Digestibility of Maize Silage. Agronomy. 2022;12:1164. https://doi.org/10.3390/agronomy12051164 . Vahmani P, Ponnampalam EN, Kraft J, Mapiye C, Bermingham EN, Watkins PJ, Proctor SD, Dugan MER. Bioactivity and health effects of ruminant meat lipids. Meat Sci. 2020;165:108114. https://doi.org/10.1016/j.meatsci.2020.108114 . Powell MC, Muntifering RB, Lin JC, Chappelka AH. Yield and nutritive quality of sericea lespedeza ( Lespedeza cuneata ) and little bluestem ( Schizachyrium scoparium ) exposed to ground-level ozone. Environ Pollut. 2003;122(3):313–22. https://doi.org/10.1016/S0269-7491(02)00331-7 . Onyśk A, Boczkowska M. M13-Tailed Simple Sequence Repeat (SSR) Markers in Studies of Genetic Diversity and Population Structure of Common Oat Germplasm. In: Gasparis S, ed. Oat . Methods Mol Biol . 2017; 1536: 179–193. https://doi.org/10.1007/978-1-4939-6682-0_12 Liu D, Wang X, Li W, et al. Genetic Diversity Analysis of the Phenotypic Traits of 215 Sugar Beet Germplasm Resources. Sugar Tech. 2022;24:1790–800. https://doi.org/10.1007/s12355-022-01120-8 . Drobná J. Morphological variation in natural populations of Lotus corniculatus in association to geographical parameters of collecting sites. Biologia. 2010;65(2):213–8. 10.2478/s11756-010-0011-0 . Annicchiarico P, Barrett B, Brummer EC, Julier B, Marshall A. Achievements and Challenges in Improving Temperate Perennial Forage Legumes. Crit Rev Plant Sci. 2015;34(1–3):327–80. 10.1080/07352689.2014.898462 . Andini R, Zaelani A, Sulaiman MI, et al. Genetic diversity of cultivated Gayo Arabica Coffee ( Coffea arabica L.) based on morphological and microsatellite markers. BMC Plant Biol. 2025;25:990. https://doi.org/10.1186/s12870-025-06768-4 . Yin J, Zhao H, Wu X, et al. SSR marker based analysis for identification and of genetic diversity of non-heading Chinese cabbage varieties. Front Plant Sci. 2023;14:1112748. https://doi.org/10.3389/fpls.2023.1112748 . Misiukevičius E, Frercks B, Šikšnianienė JB, et al. Assessing the Genetic Diversity of Daylily Germplasm Using SSR Markers: Implications for Daylily Breeding. Plants (Basel). 2023;12:1752. https://doi.org/10.3390/plants12091752 . Tyagi S, Kumar A, Gautam T, et al. Development and use of miRNA-derived SSR markers for the study of genetic diversity, population structure, and characterization of genotypes for breeding heat tolerant wheat varieties. PLoS ONE. 2021;16(2):e0231063. https://doi.org/10.1371/journal.pone.0231063 . Carvalho MS, de Oliveira Moulin Carias CM, Silva MA, et al. Genetic diversity and structure of landrace accessions, elite lineages and cultivars of common bean estimated with SSR and SNP markers. Mol Biol Rep. 2020;47:6705–15. https://doi.org/10.1007/s11033-020-05726-7 . Hemasai B, Kumbha DK, Modem VN, et al. Development of miRNA-SSR and target-SSR markers from yield-associate genes and their applicability in the assessment of genetic diversity and association mapping in rice ( Oryza sativa L). Mol Breed. 2024;44:30. https://doi.org/10.1007/s11032-024-01462-z . Kölliker R, Jones E, Drayton M, et al. Development and characterisation of simple sequence repeat (SSR) markers for white clover ( Trifolium repens L). Theor Appl Genet. 2001;102:416–24. https://doi.org/10.1007/s001220051662 . Daudi H, Shimelis H, Mathew I, et al. Genetic diversity and population structure of groundnut ( Arachis hypogaea L.) accessions using phenotypic traits and SSR markers: implications for rust resistance breeding. Genet Resour Crop Evol. 2021;68:581–604. https://doi.org/10.1007/s10722-020-01007-1 . Belalia N, Lupini A, Djemel A, et al. Analysis of genetic diversity and population structure in Saharan maize ( Zea mays L.) populations using phenotypic traits and SSR markers. Genet Resour Crop Evol. 2019;66:243–57. https://doi.org/10.1007/s10722-018-0709-3 . Göl Ş, Göktay M, Allmer J, et al. Newly developed SSR markers reveal genetic diversity and geographical clustering in spinach ( Spinacia oleracea ). Mol Genet Genomics. 2017;292:847–55. https://doi.org/10.1007/s00438-017-1314-4 . György Z, Alam S, Priyanka P, Németh ÉZ. Genetic Diversity and Relationships of Opium Poppy Accessions Based on SSR Markers. Agric (Basel). 2022;12:1343. https://doi.org/10.3390/agriculture12091343 . Ma Z, Liu S, Liang Z, Xu S, Hu W. Analysis of Genetic Diversity of 45 Guava Germplasm Evaluated Using SSR Markers. Int J Fruit Sci. 2020;20(3):385–93. https://doi.org/10.1080/15538362.2019.1640168 . Bakayoko L, Pokou DN, Kouassi AB, et al. Diversity of Water Yam ( Dioscorea alata L.) Accessions from Côte d’Ivoire Based on SNP Markers and Agronomic Traits. Plants (Basel). 2021;10:2562. https://doi.org/10.3390/plants10122562 . Souza PNS, Andrade FHP, Azevedo AM, et al. Morphoagronomic diversity in Butia capitata progenies (Arecaceae). Euphytica. 2023;219:81. https://doi.org/10.1007/s10681-023-03203-3 . Chikh-Rouhou H, Mezghani N, Mnasri S, Mezghani N, Garcés-Claver A. Assessing the Genetic Diversity and Population Structure of a Tunisian Melon ( Cucumis melo L.) Collection Using Phenotypic Traits and SSR Molecular Markers. Agronomy. 2021;11:1121. https://doi.org/10.3390/agronomy11061121 . Zhu Y, Liang D, Song Z, Tan Y, Guo X, Wang D. Genetic Diversity Analysis and Core Germplasm Collection Construction of Camellia oleifera Based on Fruit Phenotype and SSR Data. Genes (Basel). 2022;13:2351. https://doi.org/10.3390/genes13122351 . Sharma D, Nanjundan J, Singh L, et al. Genetic Diversity and Population Structure Analysis in Indian Mustard Germplasm Using Phenotypic Traits and SSR Markers. Plant Mol Biol Rep. 2022;40:579–94. https://doi.org/10.1007/s11105-022-01339-5 . Annicchiarico P, Brummer EC, Carelli M, et al. Strategies of molecular diversity assessment to infer morphophysiological and adaptive diversity of germplasm accessions: an alfalfa case study. Euphytica. 2020;216:98. https://doi.org/10.1007/s10681-020-02637-3 . Loskutov IG, Novikova LY, Belskaya GV, Blinova EV. Long-Term Analysis of the Variability of Agronomic Characters in the VIR Oat Germplasm Collection in Central Black Soil Region of Russia. Agronomy. 2021;11:423. https://doi.org/10.3390/agronomy11030423 . Ustariz K, Geleta M, Persson Hovmalm H, Ortiz R. Analysis of Genetic Diversity of Fescue Populations from the Highlands of Bolivia Using EST-SSR Markers. Genes (Basel). 2022;13:2311. https://doi.org/10.3390/genes13122311 . Additional Declarations No competing interests reported. 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16:53:49","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":176521,"visible":true,"origin":"","legend":"","description":"","filename":"85fdf43c036149db905a91a09c9c42b01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7373284/v1/440d82a50eb948423bfb567d.xml"},{"id":92738653,"identity":"cce20a7e-6a7b-4e41-9c6b-d8b4eeb26ec3","added_by":"auto","created_at":"2025-10-03 16:53:49","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187493,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7373284/v1/d718e612856beb57ef132cc1.html"},{"id":92738627,"identity":"4f407030-8798-4f51-9442-69dc26620b38","added_by":"auto","created_at":"2025-10-03 16:53:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":647058,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix of agronomic traits in \u003cem\u003eLotus corniculatus\u003c/em\u003e germplasm resources\u003cbr\u003e\n \u003cem\u003eNote:\u003c/em\u003e \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 (**), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 (*).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7373284/v1/8fc8d51403470e1ec5fa0501.png"},{"id":92739154,"identity":"2320f43a-468b-4c04-8bf4-f1e08b4d041d","added_by":"auto","created_at":"2025-10-03 17:01:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162392,"visible":true,"origin":"","legend":"\u003cp\u003eCluster dendrogram of 23 \u003cem\u003eLotus corniculatus\u003c/em\u003egermplasms based on quantitative trait data\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7373284/v1/36133532e58d30ec9ebe3fe3.png"},{"id":92739704,"identity":"10e0dd38-0c2d-4ea6-9574-00e32af027a4","added_by":"auto","created_at":"2025-10-03 17:09:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129375,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The percentage of different SSR repeat motifs in \u003cem\u003eL. corniculatus\u003c/em\u003e; (B) The distribution of repeat motif occurrences in SSR loci within the genome.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7373284/v1/44447d7b6e575d158043803a.png"},{"id":92739158,"identity":"b902653d-d99c-417b-b47a-5b5d399c9a32","added_by":"auto","created_at":"2025-10-03 17:01:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":107493,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis of 23 \u003cem\u003eLotus corniculatus\u003c/em\u003egermplasms based on SSR marker profiles\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7373284/v1/7b051b5809b7689fb014a3f0.png"},{"id":92739155,"identity":"1ff8d672-ff75-4884-b08d-5333dfb2b09c","added_by":"auto","created_at":"2025-10-03 17:01:48","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":95120,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinates analysis (PCoA) of 23 \u003cem\u003eLotus corniculatus\u003c/em\u003e germplasms based on SSR marker data\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7373284/v1/bf4bb14a165d7cfd14dcf1f7.jpeg"},{"id":98814947,"identity":"11203cdd-160b-4783-81ef-8bbdf1dbe8ae","added_by":"auto","created_at":"2025-12-22 16:13:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2380566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7373284/v1/c3f87f5f-3ac6-4ba5-9c3e-b64eb3804328.pdf"},{"id":92738632,"identity":"e8293e95-aa34-45c6-ba4c-95513c692fa9","added_by":"auto","created_at":"2025-10-03 16:53:48","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":168655,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1S3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7373284/v1/f6482ff460053cfd7cd0858a.docx"},{"id":92738630,"identity":"03c0f703-dd07-442d-9cce-7d3871dc2db2","added_by":"auto","created_at":"2025-10-03 16:53:48","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22272,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1S4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7373284/v1/ad8e3432b48f13cc9547474c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Phenotypic, Nutritional, and SSR Marker Analyses Reveal Genetic Diversity and Guide Germplasm Utilization in Lotus corniculatus","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e is a perennial, allopolyploid, and cross-pollinated legume species (Chen, 2023) widely cultivated for forage, soil and water conservation, and ornamental landscaping (\u0026Uuml;nl\u0026uuml;soy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is characterized by lush vegetative growth, high crude protein and tannin content, and stable nutritional quality across harvest periods (Shen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), making it highly palatable to livestock without causing bloating in ruminants (Kostopoulou and Karatassiou, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition to strong stress resistance and nitrogen fixation capacity, its extensive root system and creeping growth habit enable adaptation to poor, saline, and acidic soils, thereby preventing erosion and improving soil fertility (Hymes-Fecht et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). With a long flowering period and abundant floral display, \u003cem\u003eL. corniculatus\u003c/em\u003e also offers high ornamental value (Hao et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eElite cultivars are the cornerstone for the sustainable utilization of plant genetic resources, and comprehensive germplasm characterization is a prerequisite for targeted breeding programs (Zhao et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite its agronomic potential, the number of forage-type \u003cem\u003eL. corniculatus\u003c/em\u003e cultivars remains limited, and existing varieties exhibit suboptimal yield and quality performance. Systematic assessments of genetic diversity and phenotypic variation across representative germplasm collections are therefore essential to establish the genetic foundation for cultivar improvement, facilitate the identification of elite parental lines, and enable the design of efficient hybridization strategies.\u003c/p\u003e\u003cp\u003eForage legume germplasm has undergone long-term adaptation to diverse environments, resulting in rich genetic diversity that underpins population stability and evolutionary potential (Daniel et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Traditional assessments using morphological and biochemical markers provide valuable but often environmentally influenced insights (Noohi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With advances in biotechnology, DNA-based molecular markers have become indispensable tools in genetic diversity research owing to their high efficiency, reproducibility, and independence from environmental variation (Ming et al., 2009; Noohi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and have been successfully applied across a wide range of plant species (Garcia-Mas et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among these, simple sequence repeats (SSRs) are particularly advantageous due to their high polymorphism, co-dominant inheritance, and robust reproducibility, making them suitable for resolving population structure, assessing gene flow, and elucidating genetic relationships (Calder\u0026oacute;n et al., 2019; Hu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Their broad applicability has been demonstrated in diverse taxa; for instance, SSRs outperformed ISSRs in assessing the genetic diversity of \u003cem\u003ePandanus odorifer\u003c/em\u003e (Noohi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, for \u003cem\u003eL. corniculatus\u003c/em\u003e, integrative studies that combine morphological characterization, nutritional quality assessment, and SSR-based molecular analysis across a representative and diverse germplasm panel remain scarce. This gap in knowledge constrains our ability to identify trait\u0026ndash;marker associations and to elucidate the genetic architecture underlying key agronomic traits. To address this, we evaluated 23 germplasm accessions of \u003cem\u003eL. corniculatus\u003c/em\u003e using a combination of phenotypic characterization, nutritional quality analysis, and SSR-based genotyping. Our objectives were to (i) quantify genetic diversity within and among accessions, (ii) assess the relationships between morphological and nutritional traits, and (iii) identify high-performing germplasm with superior yield and forage quality traits. These results are intended to provide both the material basis and the theoretical framework for targeted breeding strategies to develop high-yield, high-quality \u003cem\u003eL. corniculatus\u003c/em\u003e cultivars.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant Materials and Trait Assessment\u003c/h2\u003e\u003cp\u003eA total of 23 germplasms of \u003cem\u003eLotus corniculatus\u003c/em\u003e L. were obtained from the National Medium-Term Forage Germplasm Bank, Inner Mongolia, China (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All \u003cem\u003eL. corniculatus\u003c/em\u003e accessions were cultivated in the experimental greenhouse of the Department of Grassland Science, Guizhou University (Guiyang, China; 26\u0026deg;25\u0026prime;N, 106\u0026deg;40\u0026prime;E), which has a subtropical monsoon climate. The determination indexes of \u003cem\u003eLotus corniculatus\u003c/em\u003e include phenotypic traits and quality traits.\u003c/p\u003e\u003cp\u003ePhenotypic traits measured included plant height, stem diameter, branch number, and leaf morphology; nutritional quality traits included crude protein (CP), crude fat (EE), crude fiber (CF), dry matter (DM), and total sugar (TS) content. Plant height and stem diameter were recorded using a ruler and vernier caliper, respectively. Leaves were scanned with an Epson Perfection V800 photo scanner, and leaf area, circumference, length, and width were quantified using WinRHIZO software (Regent Instruments Inc., Quebec, Canada). Nutritional quality was determined according to Chinese national standards: CP (GB/T 6432\u0026thinsp;\u0026minus;\u0026thinsp;2018), EE (GB/T 6433\u0026thinsp;\u0026minus;\u0026thinsp;2006), CF (GB/T 6434\u0026thinsp;\u0026minus;\u0026thinsp;2006, filtration method), DM (GB/T 6435\u0026thinsp;\u0026minus;\u0026thinsp;1986), and TS (anthrone method).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInformation of 23 \u003cem\u003eLotus corniculatus\u003c/em\u003e germplasms\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollection Number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCountry of origin/source\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e00114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWild, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e00115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e00116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGuizhou, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e00420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCanada\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e01549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnited States\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e01885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCanada\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e01886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGermany\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e01887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew Zealand\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e01888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnited States\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e03046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInstitute of Animal Science of CAAS, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e04670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus subsp. frondosus Freyn\u003c/em\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTarbagatay Prefecture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e04679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus subsp. frondosus Freyn\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTarbagatay Prefecture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e05689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eXinjiang, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e05690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGansu, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e07880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStavropol Krai, Russian\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e08515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMeixian County, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e08516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZhouzhi County, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e08517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTaibai County, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e08518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFengxiang County, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e08519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBaoji County, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e08520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLong County, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e08521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQianyang County, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eShibing County, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA extraction and primer design\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from 100 mg of fresh leaf tissue using a Tiangen Plant Genomic DNA Kit (Tiangen Biotech Co., Beijing, China), following the manufacturer\u0026rsquo;s instructions with minor modifications. Briefly, leaf samples were ground in liquid nitrogen, lysed with LP1 buffer and RNase A, followed by LP2 addition and centrifugation. The supernatant was mixed with LP3 buffer, transferred to an adsorption column (CB3), and washed twice. DNA was eluted with TE buffer after incubation at room temperature and quantified using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA) with TE buffer as the blank control. Samples with OD260/280 ratios between 1.7 and 1.9 were considered high-quality and suitable for downstream analysis. DNA integrity was confirmed by 1% agarose gel electrophoresis, which showed bright, intact, and tail-free bands (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSSR loci were identified from transcriptome sequencing data of a low phosphorus-tolerant accession (01549) and a low phosphorus-sensitive accession (08518) (Zhao et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eLotus japonicus\u003c/em\u003e genome annotations were obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kazusa.or.jp/lotus/summary3.0.html\u003c/span\u003e\u003cspan address=\"http://www.kazusa.or.jp/lotus/summary3.0.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. SSR detection was performed using MISA, and corresponding primers were designed using Primer3 (v1.1.4).\u003c/p\u003e\n\u003ch3\u003ePCR Optimization and Primer Screening\u003c/h3\u003e\n\u003cp\u003eOne hundred primer pairs were synthesized (Shanghai Biological Engineering Co., Ltd., China) and optimized via an L16(4\u003csup\u003e5\u003c/sup\u003e) orthogonal design to determine the optimal concentrations of Taq DNA polymerase, primers, Mg\u0026sup2;⁺, dNTPs, and template DNA (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The optimized PCR system comprised 1.25 U Taq polymerase, 0.4 mM dNTPs, 0.2 \u0026micro;M primers, 3 mM Mg\u0026sup2;⁺, and 60 ng template DNA.\u003c/p\u003e\u003cp\u003eFour germplasms (00114, 00115, 01886, and 08518) with contrasting phenotypes were used for primer screening. PCR products were resolved on 1% agarose gels, and primers generating clear, polymorphic bands were selected. A total of 29 highly polymorphic primer pairs were retained for subsequent analysis (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePCR Amplification and Capillary Electrophoresis\u003c/h3\u003e\n\u003cp\u003eThe 29 selected primer pairs were re-synthesized with fluorescent labels (FAM, HEX, ROX, and TAMRA) attached to the 5' end of the forward primers, while the reverse primers remained unlabeled (Fig. S3). All primers were synthesized by Shanghai Sangon Biotechnology Co., Ltd.\u003c/p\u003e\u003cp\u003ePCR reactions were carried out using the optimized reaction system described above. The thermal cycling profile consisted of an initial denaturation at 94 ℃ for 4 minutes, followed by 35 cycles of denaturation at 94 ℃ for 30 s, annealing at 58\u0026ndash;61 ℃ for 30 s (depending on the primer), and extension at 72 ℃ for 20 s. A final extension was performed at 72\u0026deg;C for 10 min, and reactions were held at 4 ℃ until further analysis. PCR products were analyzed by capillary fluorescence electrophoresis using an ABI 3730XL automated DNA sequencer (Applied Biosystems, Foster City, CA, USA).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003ePhenotypic data were analyzed via ANOVA, Coefficients of variation and Shannon\u0026ndash;Weaver diversity indices, correlation analysis, and principal component analysis (PCA) in SPSS 20.0, and visualized in Origin 2022. Shannon\u0026ndash;Weaver diversity indices (\u003cem\u003eH\u003c/em\u003e\u0026prime;) were calculated as:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"120\" height=\"32\"\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eH\u003c/em\u003e\u0026prime; is Gene Diversity Index, P\u003csub\u003ei\u003c/sub\u003e is the frequency of the i-th phenotypic category.\u003c/p\u003e\u003cp\u003eSSR allele sizes were scored using GeneMarker HID v2.9.0. Genetic diversity parameters\u0026mdash;number of alleles (Na), observed heterozygosity (Ho), expected heterozygosity (He), and Shannon\u0026rsquo;s information index (I)\u0026mdash;were computed in GenAlEx 6.51b2. Polymorphic information content (PIC) was calculated in Cervus 3.0. Binary data matrices (presence/absence) were used for similarity coefficient calculation, UPGMA clustering, and genetic relationship analysis in NTSYSpc 2.11.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eGenetic variation in major phenotypic traits among\u003c/b\u003e \u003cb\u003eL. corniculatus\u003c/b\u003e \u003cb\u003egermplasm\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA comprehensive statistical analysis of seven key agronomic traits across 23 \u003cem\u003eL. corniculatus\u003c/em\u003e germplasms from diverse geographic origins revealed substantial phenotypic variability (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table S3). Among the evaluated traits, plant height exhibited the highest coefficient of variation (CV\u0026thinsp;=\u0026thinsp;31.76%), followed closely by leaf area (CV\u0026thinsp;=\u0026thinsp;30.09%) and branch number (CV\u0026thinsp;=\u0026thinsp;27.65%), indicating considerable morphological divergence among accessions. In contrast, leaf circumference (CV\u0026thinsp;=\u0026thinsp;18.80%), leaf length (CV\u0026thinsp;=\u0026thinsp;18.89%), and leaf width (CV\u0026thinsp;=\u0026thinsp;18.50%) showed comparatively lower variability, suggesting more conserved leaf morphological proportions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenetic variation in major morphological agronomic traits of \u003cem\u003eLotus corniculatus\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCV(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGDI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf area (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf circumference (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf length (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf width (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStem diameter (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of branches\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant height (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: SD, Standard Deviation; CV, Coefficient of Variation; GDI, Gene Diversity Index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAcross germplasms, the mean plant height was 11.92 cm, ranging from 6.403 cm in accession 04679 to 18.963 cm in accession 08516. Leaf area averaged 1.77 cm\u0026sup2;, with accession 01887 showing the largest mean leaf area (2.860 cm\u0026sup2;) and accession 03046 the smallest (0.617 cm\u0026sup2;). The number of branches varied between 3.333 in accession 01888 and 6.333 in accession 03046, while stem diameter ranged from 0.457 mm in accession 03046 to 1.117 mm in accession 08519. These findings highlight the presence of both high-performing and low-performing germplasms in specific traits, providing valuable material for targeted breeding programs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic variation in major nutritional quality traits among\u003c/b\u003e \u003cb\u003eL. corniculatus\u003c/b\u003e \u003cb\u003egermplasm\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAnalysis of five key nutritional quality traits revealed pronounced variation among the 23 \u003cem\u003eL. corniculatus\u003c/em\u003e germplasms (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table S4). Across traits, ether extract (EE) exhibited the greatest variability (CV\u0026thinsp;=\u0026thinsp;38.53%), followed by crude fiber (CF, CV\u0026thinsp;=\u0026thinsp;31.79%), total sugar (TS, CV\u0026thinsp;=\u0026thinsp;14.81%), crude protein (CP, CV\u0026thinsp;=\u0026thinsp;10.21%), and dry matter (DM, CV\u0026thinsp;=\u0026thinsp;8.15%).\u003c/p\u003e\u003cp\u003eThe mean EE content was 3.019%, with the highest values observed in accessions 08518 and 01887, whereas accession 08515, had the lowest EE content. CF content averaged 15.238%, ranging from a maximum in accession 08519 to minima in 01887 and 00116. CP content averaged 242.462 g/kg, with accession 01886 showing the highest value; the lowest CP levels occurred in accessions 08521and 03046. DM content averaged 22.348%, with accession 08519 showing the highest proportion, while 08515 had the lowest. TS content averaged 15.801%, peaking in accession 08517 and reaching its minimum in 00116.\u003c/p\u003e\u003cp\u003eThese patterns indicate that certain germplasms, such as 01886, 08517, and 08518, exhibit superior nutritional profiles in specific traits, making them valuable candidates for targeted breeding programs aimed at enhancing forage quality.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenetic variation in key quality traits of \u003cem\u003eLotus corniculatus\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCV(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGDI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEther extract(EE, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e38.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrude fiber (CF, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrude protein (CP, g/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e242.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry matter (DM, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal sugar (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: SD, Standard Deviation; CV, Coefficient of Variation; GDI, Gene Diversity Index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003ePrincipal component analysis based on quantitative traits\u003c/h3\u003e\n\u003cp\u003eCorrelation analysis among the 12 quantitative traits of the 23 \u003cem\u003eL. corniculatus\u003c/em\u003e germplasms revealed several significant relationships (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The four leaf morphological traits\u0026mdash;leaf circumference, leaf length, leaf width, and leaf area\u0026mdash;were highly and positively correlated with each other. Plant height exhibited significant or highly significant positive correlations with leaf area, leaf circumference, leaf width, and stem thickness, but a significant negative correlation with branch number. Ether extract (EE) content was highly and negatively correlated with stem thickness. Crude protein (CP) showed a highly significant positive correlation with crude fiber (CF) and significant negative correlations with leaf width and stem thickness. Dry matter (DM) content was significantly negatively correlated with all four leaf morphological traits but positively correlated with CP. Total sugar (TS) content was significantly positively correlated with leaf area and leaf width, and significantly negatively correlated with CF, CP, and DM.\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) further indicated that all phenotypic and nutritional quality traits could be condensed into four principal components, collectively explaining 79.002% of the total variance (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). PC1, representing a leaf morphology factor, was driven by leaf area, circumference, length, and width; PC2, associated with biological yield and nutritional quality, was dominated by DM and CP; PC3, representing a digestive limitation factor, was determined by CF and stem thickness; and PC4, corresponding to a yield composition factor, was mainly influenced by branch number. These patterns suggest that phenotypic variation in \u003cem\u003eL. corniculatus\u003c/em\u003e is organized around distinct functional trait groups, including morphological, nutritional, and structural characteristics, which may be jointly targeted in breeding programs to optimize both forage quality and yield potential.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrincipal component analysis of quantitative traits in \u003cem\u003eLotus corniculatus\u003c/em\u003e germplasms\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePC2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePC3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePC4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf circumference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf width\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStem thickness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of branches\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.660\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.356\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEther extract(EE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.336\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrude fiber (CF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrude protein(CP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry matter(DM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal sugar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.362\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEigenvalues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContribution rate(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative contribution rate(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic cluster analysis of\u003c/b\u003e \u003cb\u003eL. corniculatus\u003c/b\u003e \u003cb\u003egermplasm based on quantitative traits\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUPGMA clustering based on seven morphological traits (plant height, leaf area, leaf circumference, leaf length, leaf width, stem diameter, and branch number) and five nutritional quality traits (ether extract, crude fiber, crude protein, dry matter, and total sugar) resolved the 23 \u003cem\u003eL. corniculatus\u003c/em\u003e germplasms from diverse geographic origins into five distinct groups (Q1\u0026ndash;Q5) at a dissimilarity coefficient of 10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCluster Q1 comprised 15 accessions, representing the largest group and encompassing germplasms with intermediate morphological and nutritional profiles. Q2 contained two accessions\u0026mdash;08517 and 01887\u0026mdash;characterized by the largest leaf area and circumference, coupled with the lowest CF content, making them promising candidates for improving forage nutritional quality. Q3 consisted solely of 08520, which exhibited high CF content, low CP content, and high DM content, indicating a need for targeted improvement in nutritional composition. Q4 included 08521, 00114, and 03046, displaying comparatively weaker leaf morphology and nutritional traits but with higher branch numbers, suggesting potential for yield enhancement through branching. Finally, Q5 grouped 04670 and 08519, which shared high TS content and greater stem thickness, traits that could contribute to improved energy content and structural robustness in forage.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDistribution of SSRs in the\u003c/b\u003e \u003cb\u003eL. corniculatus\u003c/b\u003e \u003cb\u003egenome\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 53,364 SSR loci were identified using the MISA software, with repeat motif lengths ranging from di- to hexa-nucleotides. Di-nucleotide repeats were the most abundant, comprising 28,063 loci (52.52%), followed by tri-nucleotide (29.42%) and tetra-nucleotide repeats (7.25%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Among di-nucleotide motifs, (AT/AT)n was the most prevalent, representing 53.18% of all di-nucleotide repeats. For tri-nucleotide motif, (AAG/CTT)n was the dominant type (27.02%), whereas (AAAT/ATTT)n was the most frequent among tetra-nucleotide repeats, accounting for 41.24% of that category.\u003c/p\u003e\u003cp\u003eAfter removing mononucleotide repeats, the abundance of SSR loci showed a clear inverse relationship with repeat motif length, with longer motifs occurring less frequently. Additionally, PCR amplification artifacts, such as the propensity of Taq polymerase to add adenine residues to 3\u0026prime; termini, occasionally affected the discrimination of single-nucleotide differences in capillary electrophoresis profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic diversity analysis of SSR markers in\u003c/b\u003e \u003cb\u003eL. corniculatus\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess the molecular diversity of \u003cem\u003eL. corniculatus\u003c/em\u003e, 69 accessions from 23 germplasm resources were genotyped using 29 highly polymorphic SSR loci (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A total of 299 alleles were detected, with an average of 10.31 alleles per locus, reflecting substantial allelic richness within the collection. Allele numbers varied markedly across loci, from only 2 at chr0_18003 to 24 at chr0_15876. The effective number of alleles (Ne) showed a similar pattern, averaging 10.168 and ranging from 2.471 (chr0_18003) to 23.534 (chr0_15876). The Shannon\u0026rsquo;s information index (I) varied from 0.500 to 5.160 (mean\u0026thinsp;=\u0026thinsp;3.167), indicating a high level of genetic complexity. Observed heterozygosity (Ho) ranged between 0 and 1.900 (mean\u0026thinsp;=\u0026thinsp;1.064), while expected heterozygosity (He) ranged from 0.320 to 1.829 (mean\u0026thinsp;=\u0026thinsp;1.425), suggesting moderate-to-high heterogeneity across loci.\u003c/p\u003e\u003cp\u003ePolymorphism information content (PIC) values spanned from 0.178 to 0.934, with a mean of 0.740, confirming that the selected SSR markers possess high discriminatory power for genetic diversity assessment. Notably, chr0_15876 exhibited the highest allele number, Ne, and PIC, making it a particularly informative locus for population genetic studies. These results collectively highlight the broad genetic base of the tested \u003cem\u003eL. corniculatus\u003c/em\u003e germplasm and provide robust molecular tools for future breeding, conservation, and association mapping efforts.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of diversity statistics based on 29 SSR markers in 69 samples from 23 \u003cem\u003eLotus corniculatus\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimer name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of alleles(Na)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEffective number of alleles(Ne)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eShannon Diversity Information Index(I)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eObservation of heterozygosity(Ho)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExpected heterozygosity(He)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePolymorphism Information Content(PIC)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_14344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr5_724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.871\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr5_3797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_17962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_17429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_15414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_4872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr2_4161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_15876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr3_2976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_7339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_18003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr4_868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr5_3453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_9069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr3_3963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_6664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr6_1459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_14998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr4_4327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr5_2279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.755\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr6_130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr4_2109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.368\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr1_2455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr5_3017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr1_83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr0_12234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.701\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr4_2393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echr5_3457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePopulation structure of\u003c/b\u003e \u003cb\u003eL. corniculatus\u003c/b\u003e \u003cb\u003ebased on SSR markers\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe population genetic structure of \u003cem\u003eL. corniculatus\u003c/em\u003e germplasm was inferred using 299 allelic loci generated from 29 highly polymorphic SSR markers. Genetic similarity coefficients, calculated with NTSYSpc 2.11, ranged from 0.276 to 0.983, with an average of 0.773, indicating a broad spectrum of genetic relatedness among accessions. UPGMA cluster analysis yielded a high cophenetic correlation coefficient (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98355), confirming the robustness of the clustering results. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, at a genetic similarity threshold of 0.65, the 23 germplasms were resolved into three major clusters (Class I\u0026ndash;III). Class I and Class III each comprised a single, genetically distinct accession (00114 and 01886, respectively), suggesting unique genetic backgrounds. Class II contained the majority of accessions (n\u0026thinsp;=\u0026thinsp;21) and represented the core genetic group within the panel. Further subdivision of Class II at a similarity coefficient of 0.85 revealed two subgroups corresponding to geographical origin: group a, consisting primarily of Chinese accessions, and group b, containing accessions from other countries. This geographic stratification indicates a degree of regional differentiation within \u003cem\u003eL. corniculatus\u003c/em\u003e germplasm, which may reflect historical selection pressures, adaptation to local environments, or breeding history.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrincipal coordinate analysis (PCoA) based on SSR marker data was performed in GenAlEx 6.51b2 to visualize patterns of genetic differentiation among the 69 \u003cem\u003eL. corniculatus\u003c/em\u003e accessions. The first three principal coordinates explained 25.65%, 14.42%, and 11.88% of the total genetic variation, respectively, accounting for a cumulative variance of 51.95% (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In the PCoA plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), each point represents an individual accession, and points of the same color indicate samples belonging to the same group identified by UPGMA clustering. Spatial proximity among points reflects genetic similarity, whereas greater separation indicates stronger genetic divergence. Notably, most group a accessions (domestic germplasm) clustered predominantly in the fourth quadrant, whereas group b accessions (foreign germplasm) were distributed mainly across the second and third quadrants, highlighting marked genetic differentiation between Chinese and non-Chinese germplasm.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eContribution rates of principal components derived from SSR marker analysis in \u003cem\u003eLotus corniculatus\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIngredients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePC2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePC3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContribution rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative contribution rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnalysis of molecular variance (AMOVA) further quantified the genetic partitioning, revealing that 56% of the total variation resided within germplasm, while 44% was attributable to among-germplasm differences (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of molecular variance (AMOVA) within the \u003cem\u003eLotus corniculatus\u003c/em\u003e germplasm population\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of variation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of squares\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariance components\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage of variation(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmong germplasms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e340.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin germplasms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e145.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e485.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eGenetic diversity of phenotypic and nutritional quality traits in\u003c/b\u003e \u003cb\u003eL. corniculatus\u003c/b\u003e \u003cb\u003egermplasm\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenetic diversity within germplasm resources constitutes a critical reservoir for the improvement of agronomic traits, enabling the introduction of favorable alleles to enhance yield, quality, and stress resistance through hybridization or introgression lines (Angessa et al., 2016). In the present study, coefficients of variation (CV) for phenotypic traits revealed substantial heterogeneity, with plant height and leaf area exhibiting the highest variability (31.76% and 30.09%, respectively), followed by branch number, stem diameter, and leaf morphology traits. Such levels of variation are indicative of broad selection potential, particularly for traits directly linked to forage biomass and harvest index. Stems and leaves are central determinants of forage yield, and previous studies have highlighted the significance of plant height, stem thickness, leaf area, and branching density in determining productivity in \u003cem\u003eL. corniculatus\u003c/em\u003e and related forage legumes (Zhang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Accessions such as 08516 and 01887, which exhibit superior plant height and leaf area, respectively, represent promising parental candidates for breeding programs targeting these yield-associated traits.\u003c/p\u003e\u003cp\u003eNutritional quality traits displayed equally notable diversity, particularly ether extract (EE) and crude fiber (CF), which recorded CV values of 38.53% and 31.79%, respectively. EE is not only a concentrated energy source but also provides essential fatty acids such as linoleic, α-linolenic, and arachidonic acids that are critical for growth, reproduction, and immune function in livestock (Valentina et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Janč\u0026iacute;k et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, excessive EE (\u0026gt;\u0026thinsp;5% of dry matter) can impair rumen microbial activity and digestive efficiency in ruminants (Vahmani et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), underscoring the importance of balanced selection. In contrast, high CF can limit digestibility, making the concurrent increase of crude protein (CP) and reduction of CF a key breeding objective for enhancing forage nutritional value (Powell et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In this context, 08518 and 01887 emerged as elite germplasm with high EE and low CF, while 01886 possessed the highest CP, making these accessions valuable for quality-oriented improvement.\u003c/p\u003e\u003cp\u003eCorrelation analyses revealed significant associations between phenotypic and nutritional traits, consistent with findings in wild oats (\u003cem\u003eAvena fatua\u003c/em\u003e) by Onyśk and Boczkowska (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, CP was negatively correlated with leaf width and stem diameter, suggesting possible trade-offs between structural and biochemical traits. Such correlations imply that unidirectional selection for a single trait may inadvertently affect others, reinforcing the necessity of multi-trait selection strategies in \u003cem\u003eL. corniculatus\u003c/em\u003e breeding.\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA), as a multivariate statistical tool, offers an effective means of integrating complex phenotypic and nutritional data, thereby enhancing the precision of parent selection in multi-objective breeding programs (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, PCA revealed that the measured traits could be grouped into four distinct functional dimensions: leaf morphology, biological yield and nutritional quality, digestive constraints, and yield composition. This dimensionality not only clarifies the underlying structure of trait variation but also provides a practical framework for matching complementary parental types in breeding plans. Compared with earlier findings (Drobn\u0026aacute;, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which emphasized stem and internode traits, the present results highlight a stronger influence of leaf-related and nutritional factors, likely due to the broader geographical origins and higher genetic diversity of the germplasm set. These insights reinforce the need for breeding objectives that simultaneously address both morphological and nutritional targets, ensuring balanced improvements in yield potential, feed quality, and adaptability.\u003c/p\u003e\u003cp\u003eThe phenotypic clustering of \u003cem\u003eL. corniculatus\u003c/em\u003e germplasm revealed distinct groupings based on combinations of morphological and nutritional quality traits, yet the substantial within-group heterogeneity highlights that cluster membership does not necessarily equate to uniform breeding potential. This suggests that phenotypic clustering should serve as an initial germplasm organization tool, with final selection guided by trait-specific performance and genetic background. Q1, the largest group (15 accessions), encompassed both high-CP/low-CF accessions suitable for improving forage quality and morphologically superior but nutritionally average types, indicating its value as a broad donor pool rather than a coherent breeding block. Q2 demonstrated the pitfalls of interpreting cluster means in breeding contexts: while both members grouped together, 01887 combined the largest leaf area with the lowest CF, whereas 08517 exhibited higher CF and average leaf morphology, underscoring the need for accession-level evaluation within clusters. Q3 (08520) exemplified a biomass\u0026ndash;digestibility trade-off, with high dry matter and CF but low CP, while Q4\u0026rsquo;s consistently high branch number suggests potential for yield enhancement through increased tiller density when integrated with high-nutrition backgrounds. Q5\u0026rsquo;s elevated total sugar and stem thickness could contribute to energy-rich forage but may require balancing against digestibility constraints. Similar patterns of within-group variability have been reported in \u003cem\u003eMedicago sativa\u003c/em\u003e and \u003cem\u003eCoffea arabica\u003c/em\u003e, where phenotypic clustering captured broad adaptation types but failed to fully resolve functional trait variation critical for breeding (Annicchiarico et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Andini et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, phenotypic grouping should be combined with molecular data to identify complementary crosses and avoid overlooking high-value outliers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic diversity of phenotypic traits in\u003c/b\u003e \u003cb\u003eL. corniculatus\u003c/b\u003e \u003cb\u003egermplasm revealed by SSR markers\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMolecular markers, particularly Simple Sequence Repeats (SSRs), have proven to be indispensable tools in the characterization of genetic diversity across a wide range of plant species (Yin et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Due to their codominant inheritance, high polymorphism, and genome-wide distribution, SSRs are especially well suited for applications such as genetic diversity assessment, QTL mapping, DNA fingerprinting, and marker-assisted selection (Misiukevičius et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tyagi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Over the past decade, their utility has been demonstrated in both model and non-model species. For example, Carvalho et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) employed SSR markers to assess the genetic diversity of common bean (Phaseolus vulgaris L.), identifying 172 allelic variants, while Hemasai et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) characterized 13 miRNA-SSR markers and their corresponding target gene SSRs derived from yield-responsive miRNAs in rice\u003c/p\u003e\u003cp\u003eIn the present study, analysis with GeneMarker HID V2.9.0 revealed a total of 299 alleles across 29 SSR loci, with an average of 10.310 alleles per locus\u0026mdash;substantially exceeding the 4.8 alleles per locus reported in white clover (\u003cem\u003eTrifolium repens\u003c/em\u003e L.) by K\u0026ouml;lliker et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The polymorphism information content (PIC) ranged from 0.178 to 0.934, with a mean of 0.740, which is notably higher than the values reported for \u003cem\u003eL. corniculatus\u003c/em\u003e by Daudi et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (0.34) and Belalia et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) (0.575). While PIC variation can be influenced by the marker set and genotypes under investigation, the consistently high PIC values observed here suggest substantial allelic richness and differentiation, indicating that the tested germplasm harbors broad genetic variation suitable for breeding improvement.\u003c/p\u003e\u003cp\u003eFurthermore, the average effective number of alleles (Ne) was 10.168, with a total Ne of 294.859, reflecting a high degree of allelic evenness. Diversity indices also supported this conclusion: the mean Shannon information index (I) was 3.167, and observed (Ho) and expected heterozygosities (He) averaged 1.064 and 1.521, respectively. These values exceed those reported in analogous SSR-based studies of \u003cem\u003eSpinacia oleracea\u003c/em\u003e (G\u0026ouml;l et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), \u003cem\u003ePapaver somniferum\u003c/em\u003e (Gy\u0026ouml;rgy et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and guava (\u003cem\u003ePsidium guajava\u003c/em\u003e) (Ma et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), further reinforcing the premise that \u003cem\u003eL. corniculatus\u003c/em\u003e germplasm exhibits a higher-than-average reservoir of genetic diversity. Such diversity not only reflects the wide geographical origins of the accessions but also signals a rich pool of alleles that can be strategically combined to enhance both yield and forage quality.\u003c/p\u003e\u003cp\u003eCluster analysis remains a cornerstone method for elucidating genetic relationships among crop germplasm resources (Bakayoko et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Souza et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chikh-Rouhou et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the present study, UPGMA clustering based on SSR data at a genetic similarity coefficient threshold of 0.65 partitioned the 23 \u003cem\u003eL. corniculatus\u003c/em\u003e accessions into three major groups (Groups I\u0026ndash;III). Within Group II, a clear subdivision was observed that largely corresponded to geographical origin: subgroup a comprised predominantly Chinese germplasm, whereas subgroup b was enriched in accessions from other countries. This geographic pattern is consistent with the separation trends detected by principal coordinate analysis (PCoA), suggesting that domestically adapted \u003cem\u003eL. corniculatus\u003c/em\u003e lines in China have diverged from foreign counterparts through prolonged selection under distinct agroecological conditions.\u003c/p\u003e\u003cp\u003eWhen compared with the clustering based on quantitative phenotypic traits, both approaches could broadly distinguish germplasm within China from that outside China, yet the detailed grouping patterns were not entirely congruent. Certain accessions that were phenotypically aligned with germplasm within China clustered genetically with foreign accessions, and vice versa. Such inconsistencies have been reported in other species, including \u003cem\u003eCamellia oleifera\u003c/em\u003e (Zhu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Indian Mustard (Sharma et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and may arise from several factors: (i) phenotypic traits are often under polygenic control and strongly influenced by environmental conditions; (ii) SSR markers capture genome-wide polymorphisms, including variation in non-coding regions, which may not directly translate into morphological differences. These factors together highlight the complementary value of integrating phenotypic and molecular data in germplasm evaluation.\u003c/p\u003e\u003cp\u003eFrom a breeding perspective, this integrated clustering framework serves as a roadmap for parental selection: genetically divergent accessions with complementary phenotypic and nutritional traits can be used to maximize heterosis and generate novel trait combinations, whereas genetically similar accessions with stable performance can be employed to fix desirable traits in uniform cultivars. Such a combined strategy is particularly pertinent to \u003cem\u003eL. corniculatus\u003c/em\u003e, where the simultaneous improvement of yield, nutritional quality, and environmental adaptation remains a core breeding objective.\u003c/p\u003e\u003cp\u003eMolecular analysis of variance (AMOVA) indicated that most of the genetic variation in \u003cem\u003eL. corniculatus\u003c/em\u003e occurs within populations rather than between them, a pattern also reported for other predominantly outcrossing forage legumes such as alfalfa and white clover (Annicchiarico et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; K\u0026ouml;lliker et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This high intra-population diversity is likely a consequence of its predominantly outcrossing reproductive system, which promotes heterozygosity and maintains a broad genetic base through pollen-mediated gene flow. The relatively low genetic differentiation among populations may reflect both historical and ongoing germplasm exchange across regions, facilitated by formal breeding programs and informal farmer-to-farmer seed exchange (Loskutov et al., 2011; Ustariz et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For breeders, this means that a large proportion of exploitable genetic variability is accessible within existing local germplasm, reducing reliance solely on exotic introductions. Nonetheless, targeted crosses between phenotypically divergent and genetically distinct accessions remain the most effective route to expand adaptive potential and achieve multi-trait improvement.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBy integrating phenotypic, nutritional, and SSR marker data, we comprehensively assessed the genetic diversity of 23 \u003cem\u003eLotus corniculatus\u003c/em\u003e germplasm accessions. The large variation in key agronomic and nutritional traits, together with high SSR polymorphism (PIC\u0026thinsp;=\u0026thinsp;0.740), revealed substantial diversity at both morphological and molecular levels. Differences between phenotypic and molecular clustering underscored the value of multi-dimensional analyses for germplasm characterization. Importantly, several accessions combined superior forage quality with distinct genetic backgrounds, making them promising parents for breeding programs targeting yield, quality, and adaptability. These findings provide a practical framework for germplasm classification and the strategic selection of complementary parental combinations, thereby facilitating the genetic improvement and sustainable utilization of \u003cem\u003eL. corniculatus\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLLZ and PCW conceived and designed research. WWZ and YW conducted experiments. LLZ, WWZ and JYZ analysed the data. LLZ, JYZ and PCW wrote the manuscript. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (32260340, 32060391) and Guizhou Provincial Science and Technology Projects (QKHPTRC-GCC[2022]022-1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no ethical issues involved in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe manuscript is approved by all authors for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and its supplementary information. simple sequence repeat (SSR) primer sequences are provided in supplementary Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen C, Zhang K, Liu F, et al. Resequencing of global \u003cem\u003eLotus corniculatus\u003c/em\u003e accessions reveals population distribution and genetic loci, associated with cyanogenic glycosides accumulation and growth traits. BMC Biol. 2023;21:176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12915-023-01670-7\u003c/span\u003e\u003cspan address=\"10.1186/s12915-023-01670-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026Uuml;nl\u0026uuml;soy AG, Yolcu S, Bor M, et al. Activation of Photorespiration Facilitates Drought Stress Tolerance in \u003cem\u003eLotus corniculatus\u003c/em\u003e. J Plant Growth Regul. 2023;42:2088\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShen YX, Liang DZ, Wang HS, Chen ZF. Effects of planting density, mowing height and mowing frequency on grass production performance of \u003cem\u003eLotus corniculatus\u003c/em\u003e. Pratacultural Sci. 1992;3:37\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKostopoulou P, Karatassiou M. \u003cem\u003eLotus corniculatus\u003c/em\u003e L. response to carbon dioxide concentration and radiation level variations. Photosynthetica. 2017;55(3):522\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHymes-Fecht UC, Broderick GA, Muck RE, Grabber JH. Replacing alfalfa or red clover silage with birdsfoot trefoil silage in total mixed rations increases production of lactating dairy cows. J Dairy Sci. 2013;96:460\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang LT, Jian ZL, Wang PC, Zhao LL, Chen KK. Combined physiological responses and differential expression of drought-responsive genes preliminarily explain the drought resistance mechanism of \u003cem\u003eLotus corniculatus\u003c/em\u003e. Funct Plant Biol. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/FP22051\u003c/span\u003e\u003cspan address=\"10.1071/FP22051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao X, Chen KK, Wang LT, Zhao LL, Wang PC. Transcriptome analysis provides insights into the response of \u003cem\u003eLotus corniculatus\u003c/em\u003e roots to low-phosphorus stress. Front Plant Sci. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2023.1089380\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2023.1089380\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHao H, Li Q, Lyu S, et al. High-efficiency \u003cem\u003eAgrobacterium\u003c/em\u003e-mediated transformation of \u003cem\u003eLotus corniculatus\u003c/em\u003e using DsRed2 positive selection and vermiculite-based rooting method. Plant Cell Tissue Organ Cult. 2024;156:58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11240-024-02685-w\u003c/span\u003e\u003cspan address=\"10.1007/s11240-024-02685-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao LL, Xie WH, Huang L, Long SS, Wang PC. Characterization of the gibberellic oxidase gene SdGA20ox1 in \u003cem\u003eSophora davidii\u003c/em\u003e (Franch.) Skeels and interaction protein screening. Front Plant Sci. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2024.1478854\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2024.1478854\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou ZJ, Li JQ, Gao Y, Wang R, Hang HY, Zhang Y, Zhao LL, Wang PC. Research on drought stress in Medicago sativa L. from 1998 to 2023: A Bibliometric Analysis. Front Plant Sci. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2024.1406256\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2024.1406256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evon Daniel D, Lehnert H, Berner T, Klocke E, Junghanns W, et al. On genetic diversity in caraway: Genotyping of a large germplasm collection. PLoS ONE. 2020;15(12):e0244666. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0244666\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0244666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNoohi N, Sriram IS, Sahoo A, Das S, Panda MK, Acharya L, RamaRao VV, Nayak S, Mohanty S. Population genetic structure and diversity analysis in economically important \u003cem\u003ePandanus odorifer\u003c/em\u003e (Forssk.) Kuntze accessions employing ISSR and SSR markers. \u003cem\u003eInd Crops Prod\u003c/em\u003e. 2020; 143: 111894. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.indcrop.2019.111894\u003c/span\u003e\u003cspan address=\"10.1016/j.indcrop.2019.111894\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMingxiong P, GP R, Jinfa HE. Promoter anchored amplified polymorphism based on random amplified polymorphic DNA (PAAP-RAPD) in cotton. Euphytica. 2009;167(3):289\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarcia-Mas J, Oliver M, G\u0026oacute;mez-Paniagua H, et al. Comparing AFLP, RAPD and RFLP markers for measuring genetic diversity in melon. Theor Appl Genet. 2000;101:860\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s001220051553\u003c/span\u003e\u003cspan address=\"10.1007/s001220051553\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo Y, Zhang X, Xu J, et al. Phenotypic and molecular marker analysis uncovers the genetic diversity of the grass \u003cem\u003eStenotaphrum secundatum\u003c/em\u003e. BMC Genet. 2020;21:86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12863-020-00892-w\u003c/span\u003e\u003cspan address=\"10.1186/s12863-020-00892-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalder\u0026oacute;n-Gonz\u0026aacute;lez \u0026Aacute;, Pouilly N, Mu\u0026ntilde;os S, Grand X, Coque M, Velasco L, P\u0026eacute;rez-Vich B. An SSR-SNP Linkage Map of the Parasitic Weed \u003cem\u003eOrobanche cumana\u003c/em\u003e Wallr. Including a Gene for Plant Pigmentation. Front Plant Sci. 2019;10:797. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2019.00797\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2019.00797\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu L, Wang J, Wang X, Zhang D, Sun Y, Lu T, Shi W. Development of SSR Markers and Evaluation of Genetic Diversity of Endangered Plant \u003cem\u003eSaussurea involucrata\u003c/em\u003e. Biomolecules. 2024;14:1010. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biom14081010\u003c/span\u003e\u003cspan address=\"10.3390/biom14081010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAngessa TT, Li C. Exploration and Utilization of Genetic Diversity Exotic Germplasm for Barley Improvement. Exploration, Identification and Utilization of Barley Germplasm. Academic; 2016. pp. 223\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12-802922-0.00009-1\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-802922-0.00009-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Q, Li S, Guo S, et al. Morphology and SSR markers reveal the genetic diversity of \u003cem\u003eElymus\u003c/em\u003e species germplasm in Northwestern China. Genet Resour Crop Evol. 2024;71:2091\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10722-023-01768-5\u003c/span\u003e\u003cspan address=\"10.1007/s10722-023-01768-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValentina F, Fernando G, Jo\u0026atilde;o CM, Marleen DT, Gon\u0026ccedil;alves AMM. Biochemical and toxicological effects of organic (herbicide Primextra\u0026reg; Gold TZ) and inorganic (copper) compounds on zooplankton and phytoplankton species. Aquat Toxicol. 2016;177:33\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aquatox.2016.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.aquatox.2016.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJanč\u0026iacute;k F, Kubelkov\u0026aacute; P, Loučka R, Jambor V, Kumprechtov\u0026aacute; D, Homolka P, Koukolov\u0026aacute; V, Tyrolov\u0026aacute; Y, V\u0026yacute;born\u0026aacute; A. Shredlage Processing Affects the Digestibility of Maize Silage. Agronomy. 2022;12:1164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy12051164\u003c/span\u003e\u003cspan address=\"10.3390/agronomy12051164\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVahmani P, Ponnampalam EN, Kraft J, Mapiye C, Bermingham EN, Watkins PJ, Proctor SD, Dugan MER. Bioactivity and health effects of ruminant meat lipids. Meat Sci. 2020;165:108114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.meatsci.2020.108114\u003c/span\u003e\u003cspan address=\"10.1016/j.meatsci.2020.108114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePowell MC, Muntifering RB, Lin JC, Chappelka AH. Yield and nutritive quality of sericea lespedeza (\u003cem\u003eLespedeza cuneata\u003c/em\u003e) and little bluestem (\u003cem\u003eSchizachyrium scoparium\u003c/em\u003e) exposed to ground-level ozone. Environ Pollut. 2003;122(3):313\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0269-7491(02)00331-7\u003c/span\u003e\u003cspan address=\"10.1016/S0269-7491(02)00331-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOnyśk A, Boczkowska M. M13-Tailed Simple Sequence Repeat (SSR) Markers in Studies of Genetic Diversity and Population Structure of Common Oat Germplasm. In: Gasparis S, ed. \u003cem\u003eOat\u003c/em\u003e. \u003cem\u003eMethods Mol Biol\u003c/em\u003e. 2017; 1536: 179\u0026ndash;193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4939-6682-0_12\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4939-6682-0_12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu D, Wang X, Li W, et al. Genetic Diversity Analysis of the Phenotypic Traits of 215 Sugar Beet Germplasm Resources. Sugar Tech. 2022;24:1790\u0026ndash;800. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12355-022-01120-8\u003c/span\u003e\u003cspan address=\"10.1007/s12355-022-01120-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDrobn\u0026aacute; J. Morphological variation in natural populations of \u003cem\u003eLotus corniculatus\u003c/em\u003e in association to geographical parameters of collecting sites. Biologia. 2010;65(2):213\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2478/s11756-010-0011-0\u003c/span\u003e\u003cspan address=\"10.2478/s11756-010-0011-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnnicchiarico P, Barrett B, Brummer EC, Julier B, Marshall A. Achievements and Challenges in Improving Temperate Perennial Forage Legumes. Crit Rev Plant Sci. 2015;34(1\u0026ndash;3):327\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/07352689.2014.898462\u003c/span\u003e\u003cspan address=\"10.1080/07352689.2014.898462\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndini R, Zaelani A, Sulaiman MI, et al. Genetic diversity of cultivated Gayo Arabica Coffee (\u003cem\u003eCoffea arabica\u003c/em\u003e L.) based on morphological and microsatellite markers. BMC Plant Biol. 2025;25:990. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12870-025-06768-4\u003c/span\u003e\u003cspan address=\"10.1186/s12870-025-06768-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYin J, Zhao H, Wu X, et al. SSR marker based analysis for identification and of genetic diversity of non-heading Chinese cabbage varieties. Front Plant Sci. 2023;14:1112748. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2023.1112748\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2023.1112748\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMisiukevičius E, Frercks B, Šikšnianienė JB, et al. Assessing the Genetic Diversity of Daylily Germplasm Using SSR Markers: Implications for Daylily Breeding. Plants (Basel). 2023;12:1752. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants12091752\u003c/span\u003e\u003cspan address=\"10.3390/plants12091752\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTyagi S, Kumar A, Gautam T, et al. Development and use of miRNA-derived SSR markers for the study of genetic diversity, population structure, and characterization of genotypes for breeding heat tolerant wheat varieties. PLoS ONE. 2021;16(2):e0231063. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0231063\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0231063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarvalho MS, de Oliveira Moulin Carias CM, Silva MA, et al. Genetic diversity and structure of landrace accessions, elite lineages and cultivars of common bean estimated with SSR and SNP markers. Mol Biol Rep. 2020;47:6705\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11033-020-05726-7\u003c/span\u003e\u003cspan address=\"10.1007/s11033-020-05726-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHemasai B, Kumbha DK, Modem VN, et al. Development of miRNA-SSR and target-SSR markers from yield-associate genes and their applicability in the assessment of genetic diversity and association mapping in rice (\u003cem\u003eOryza sativa\u003c/em\u003e L). Mol Breed. 2024;44:30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11032-024-01462-z\u003c/span\u003e\u003cspan address=\"10.1007/s11032-024-01462-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK\u0026ouml;lliker R, Jones E, Drayton M, et al. Development and characterisation of simple sequence repeat (SSR) markers for white clover (\u003cem\u003eTrifolium repens\u003c/em\u003e L). Theor Appl Genet. 2001;102:416\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s001220051662\u003c/span\u003e\u003cspan address=\"10.1007/s001220051662\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDaudi H, Shimelis H, Mathew I, et al. Genetic diversity and population structure of groundnut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) accessions using phenotypic traits and SSR markers: implications for rust resistance breeding. Genet Resour Crop Evol. 2021;68:581\u0026ndash;604. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10722-020-01007-1\u003c/span\u003e\u003cspan address=\"10.1007/s10722-020-01007-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBelalia N, Lupini A, Djemel A, et al. Analysis of genetic diversity and population structure in Saharan maize (\u003cem\u003eZea mays\u003c/em\u003e L.) populations using phenotypic traits and SSR markers. Genet Resour Crop Evol. 2019;66:243\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10722-018-0709-3\u003c/span\u003e\u003cspan address=\"10.1007/s10722-018-0709-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026ouml;l Ş, G\u0026ouml;ktay M, Allmer J, et al. Newly developed SSR markers reveal genetic diversity and geographical clustering in spinach (\u003cem\u003eSpinacia oleracea\u003c/em\u003e). Mol Genet Genomics. 2017;292:847\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00438-017-1314-4\u003c/span\u003e\u003cspan address=\"10.1007/s00438-017-1314-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGy\u0026ouml;rgy Z, Alam S, Priyanka P, N\u0026eacute;meth \u0026Eacute;Z. Genetic Diversity and Relationships of Opium Poppy Accessions Based on SSR Markers. Agric (Basel). 2022;12:1343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agriculture12091343\u003c/span\u003e\u003cspan address=\"10.3390/agriculture12091343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa Z, Liu S, Liang Z, Xu S, Hu W. Analysis of Genetic Diversity of 45 Guava Germplasm Evaluated Using SSR Markers. Int J Fruit Sci. 2020;20(3):385\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15538362.2019.1640168\u003c/span\u003e\u003cspan address=\"10.1080/15538362.2019.1640168\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBakayoko L, Pokou DN, Kouassi AB, et al. Diversity of Water Yam (\u003cem\u003eDioscorea alata\u003c/em\u003e L.) Accessions from C\u0026ocirc;te d\u0026rsquo;Ivoire Based on SNP Markers and Agronomic Traits. Plants (Basel). 2021;10:2562. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants10122562\u003c/span\u003e\u003cspan address=\"10.3390/plants10122562\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSouza PNS, Andrade FHP, Azevedo AM, et al. Morphoagronomic diversity in \u003cem\u003eButia capitata\u003c/em\u003e progenies (Arecaceae). Euphytica. 2023;219:81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10681-023-03203-3\u003c/span\u003e\u003cspan address=\"10.1007/s10681-023-03203-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChikh-Rouhou H, Mezghani N, Mnasri S, Mezghani N, Garc\u0026eacute;s-Claver A. Assessing the Genetic Diversity and Population Structure of a Tunisian Melon (\u003cem\u003eCucumis melo\u003c/em\u003e L.) Collection Using Phenotypic Traits and SSR Molecular Markers. Agronomy. 2021;11:1121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy11061121\u003c/span\u003e\u003cspan address=\"10.3390/agronomy11061121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu Y, Liang D, Song Z, Tan Y, Guo X, Wang D. Genetic Diversity Analysis and Core Germplasm Collection Construction of \u003cem\u003eCamellia oleifera\u003c/em\u003e Based on Fruit Phenotype and SSR Data. Genes (Basel). 2022;13:2351. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/genes13122351\u003c/span\u003e\u003cspan address=\"10.3390/genes13122351\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharma D, Nanjundan J, Singh L, et al. Genetic Diversity and Population Structure Analysis in Indian Mustard Germplasm Using Phenotypic Traits and SSR Markers. Plant Mol Biol Rep. 2022;40:579\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11105-022-01339-5\u003c/span\u003e\u003cspan address=\"10.1007/s11105-022-01339-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnnicchiarico P, Brummer EC, Carelli M, et al. Strategies of molecular diversity assessment to infer morphophysiological and adaptive diversity of germplasm accessions: an alfalfa case study. Euphytica. 2020;216:98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10681-020-02637-3\u003c/span\u003e\u003cspan address=\"10.1007/s10681-020-02637-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoskutov IG, Novikova LY, Belskaya GV, Blinova EV. Long-Term Analysis of the Variability of Agronomic Characters in the VIR Oat Germplasm Collection in Central Black Soil Region of Russia. Agronomy. 2021;11:423. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy11030423\u003c/span\u003e\u003cspan address=\"10.3390/agronomy11030423\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUstariz K, Geleta M, Persson Hovmalm H, Ortiz R. Analysis of Genetic Diversity of Fescue Populations from the Highlands of Bolivia Using EST-SSR Markers. Genes (Basel). 2022;13:2311. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/genes13122311\u003c/span\u003e\u003cspan address=\"10.3390/genes13122311\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lotus corniculatus, germplasm resources, genetic diversity, phenotypic and nutritional traits, SSR markers","lastPublishedDoi":"10.21203/rs.3.rs-7373284/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7373284/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eLotus corniculatus\u003c/em\u003e is a perennial legume valued for its roles in forage production, soil and water conservation, and landscaping. However, the limited number of improved cultivars hampers its broader utilization. In this study, we characterized the genetic variation of 23 germplasm accessions from diverse geographic origins using 12 quantitative traits and 29 simple sequence repeat (SSR) markers. Quantitative trait analysis revealed substantial variation, particularly in plant height (CV\u0026thinsp;=\u0026thinsp;31.76%), leaf area (30.09%), ether extract (38.53%), and crude fiber (31.79%). Significant correlations were observed between nutritional quality and morphological traits, indicating that phenotypic selection can indirectly improve forage quality. Cluster analysis based on phenotypic and nutritional data grouped the accessions into five categories, identifying germplasms with high crude protein and ether extract (Q1), superior leaf morphology (Q2), and high total sugar content with thick stems (Q5), each offering distinct breeding advantages. Genome-wide SSR mining identified 53,364 loci, dominated by dinucleotide repeats (52.5%), with (AT/AT)ₙ as the most frequent motif. Twenty-nine highly polymorphic SSR primers generated 299 alleles (mean\u0026thinsp;=\u0026thinsp;10.17 per locus; PIC\u0026thinsp;=\u0026thinsp;0.740). SSR-based clustering separated the accessions into three groups broadly aligned with geographic origin. Analysis of molecular variance (AMOVA) indicated that most genetic variation resided within populations, underscoring the potential for intra-population selection. These findings provide a germplasm classification framework that integrates phenotypic performance, nutritional quality, and genetic background, enabling breeders to select complementary parental combinations for developing \u003cem\u003eL. corniculatus\u003c/em\u003e cultivars with improved yield, quality, and adaptability.\u003c/p\u003e","manuscriptTitle":"Integrated Phenotypic, Nutritional, and SSR Marker Analyses Reveal Genetic Diversity and Guide Germplasm Utilization in Lotus corniculatus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 16:53:43","doi":"10.21203/rs.3.rs-7373284/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-08T15:50:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T12:24:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T06:41:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-27T07:39:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-27T01:02:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193251212154763566351208955063643024087","date":"2025-09-26T20:04:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T12:56:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299152937745634403843364712393258971830","date":"2025-09-25T05:28:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20004060618824573025191683369821380030","date":"2025-09-24T16:00:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132506853748519087061909660617742555194","date":"2025-09-24T14:12:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211229053059626024166474449195155774552","date":"2025-09-23T08:59:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298955227036865067031752305774144773848","date":"2025-09-22T16:34:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-22T15:05:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T06:25:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T08:59:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-04T02:55:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-09-04T02:52:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6862865b-18d3-4a11-adcd-72c8855e248d","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:08:49+00:00","versionOfRecord":{"articleIdentity":"rs-7373284","link":"https://doi.org/10.1186/s12870-025-07914-8","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-12-17 15:58:03","publishedOnDateReadable":"December 17th, 2025"},"versionCreatedAt":"2025-10-03 16:53:43","video":"","vorDoi":"10.1186/s12870-025-07914-8","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07914-8","workflowStages":[]},"version":"v1","identity":"rs-7373284","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7373284","identity":"rs-7373284","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}