Comprehensive evaluation of compound saline-alkali tolerance and gene mining by GWAS in Vicia faba L.

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Xiaoxing Peng, Xianli Zhou, Zhi hao Sun, Xuexia Wu, Changcai Teng, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6519550/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Sep, 2025 Read the published version in BMC Plant Biology → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Faba bean ( Vicia faba L.) is a crucial cool-season legume crop, which is highly valued for its high protein content and key role in crop rotation systems. Considering the increasing threat of soil salinity and alkalinity globally, it is critical to screen germplasm resources with salt and alkali tolerance in faba bean and to identify the underlying genes. Results: In this study, 12 morphological and physiological traits under compound saline-alkali stress were measured to evaluate saline-alkali tolerance of 240 germplasm based on principal component analysis. The results showed that biomass-related traits such as fresh weight of shoot and leaf number had relatively high weights in the evaluation of salt and alkali tolerance at the seedling stage, and 38 highly saline-alkali tolerant materials were identified. A total of 242 SNPs affecting seedling saline-alkali tolerance were identified in a genome-wide association study of 240 faba bean accessions, with 57 SNPs significantly associated with 7 traits and identified by GLM and MLM models. It was found that 10 genes (such as L-GalLDH , ZAT4 , GA20ox2 ) overlapped with the reported genes related to salt-alkali tolerance or stress resistance by functional annotation of candidate genes. Conclusions: Which enhanced our understanding of the regulatory network of saline-alkali tolerance in faba beans and provided genetic resources and potential targets for molecular design breeding of saline-alkali tolerance in faba bean. Vicia faba L. Compound saline-alkali tolerance Comprehensive evaluation GWAS Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Faba bean, an important temperate legume crop, ranks sixth globally in production[ 1 ]. It exhibits high yield potential with an average crude protein content of approximately 29%, making it a significant source of plant-based protein, and its amino acid composition meets human dietary requirements[ 2 – 4 ]. Furthermore, faba bean is one of the crops with the highest nitrogen fixation efficiency, reducing the need for nitrogen fertilizers, which provides major benefits for rotation systems and supports sustainable agricultural practices[ 4 – 5 ]. Its adaptability to a variety of climatic and soil conditions presents obvious advantages over other legume crops. Despite these benefits, the area harvested and production of faba bean still remain lower than those of legumes such as peas and chickpeas, mainly due to unstable yields resulting from biotic and abiotic stresses, highlighting the necessary for further research to enhance its productivity and stress resistance[ 6 – 7 ]. Soil salinization and alkalization pose a substantial threat to global food production and severely impede the sustainable utilization of cultivated lands[ 8 – 9 ]. It is estimated that over 20% of irrigated lands around the world are impacted by excessive salt accumulation. This predicament is exacerbating at an alarming rate, primarily driven by a confluence of factors including unsustainable irrigation practices, inadequate drainage systems, and the far-reaching impacts of climate change[ 10 ]. The expansion of saline-alkali soils forces farmers to reduce cultivation areas or even abandon farming altogether, further exacerbating food security issues, particularly in developing countries with limited agricultural resources[ 8 , 10 – 11 ]. Exploring the salt-alkali tolerance characteristics of crops and breeding superior varieties with salt-alkali resistance are important methods for the utilization and improvement of saline-alkali lands. Faba bean, despite its sensitivity to salt-alkali stress conditions, exhibits a certain degree of tolerance within a specific range of salinity-alkalinity conditions. Furthermore, there exists significant variation in salinity-alkalinity resistance among different genotypes of faba bean[ 12 – 13 ]. Given the potential of faba bean as a resilient crop, exploring of its salt-alkali tolerance characteristics and developing a superior genotype with enhanced resistance could represent a strategic approach for the effective utilization of saline-alkali lands. A limited number of studies have investigated the harmful effects of saline conditions containing sodium chloride (NaCl), calcium chloride (CaCl 2 ), and magnesium chloride (MgCl 2 ) on various genotype in faba bean, evaluating genotype-specific responses to salinity through emergence rates and early growth characteristics[ 12 – 16 ]. However, these studies utilized a few materials and did not incorporate a comprehensive assessment of trait contributions to salt-alkali tolerance for a robust evaluation of salinity tolerance. While quantitative trait locus (QTL) analysis and genome-wide association studies (GWAS) are commonly employed to identify genes associated with stress tolerance traits such as cold and drought resistance in faba bean[ 1 , 17 – 19 ], there is currently no report on QTLs or GWAS related to salt-alkali tolerance. High concentrations of Na + and Cl - ions, coupled with elevated soil pH, pose significant challenges to plant ion homeostasis and metabolic balance, ultimately leading to impaired growth and reduced productivity[ 20 ]. Plants, particularly during the germination stage, exhibit heightened sensitivity to abiotic stress conditions. Even a modest increase in salinity can significantly impair seedling biomass accumulation, highlighting the critical importance of understanding salt-alkali tolerance mechanisms during early development[ 21 ]. Comprehensive studies have been conducted to evaluate salt-alkali tolerance and associated genetic determinants in crops such as wheat[ 22 – 23 ], rice[ 24 – 25 ], and maize[ 26 – 27 ]. Given the polygenic and multifactorial nature of salt-alkali tolerance, researchers have employed a suite of phenotypic indices, including relative germination percentage (RGP), relative germination index (RGI), relative shoot length (RSL), relative shoot dry weight per plant (RSDW), relative root dry weight per plant (RRDW), relative dry weight per plant (RDW), and relative root-to-shoot ratio (RRSR), to evaluate salt-alkali tolerance and identify key quantitative trait loci (QTLs). Meanwhile, the assessment of crop tolerance based on comprehensive traits requires the application of multivariate statistical methods. These methods include tolerance coefficients, multiple linear regression analysis[ 28 ], principal component analysis (PCA)[ 29 ], membership function values, comprehensive evaluation (D) values, and cluster analysis, which were widely used to evaluate crop tolerance under stress conditions[ 30 – 31 ]. In the context of faba bean, while the large genome size (~ 13 Gb) and limited molecular marker resources have historically hindered the application of GWAS, recent advancements in high-throughput sequencing technologies and the release of the faba bean reference genome have made it possible to conduct a comprehensive analysis of the genetic basis of stress response in faba bean[ 32 – 33 ]. In this study, we applied saline-alkali stress and control conditions(CK) to a faba bean panel containing 240 accessions and 12 morphological and physiological traits were measured to evaluate the saline-alkali tolerance based on principal component analysis (PCA) and membership function values. A 130K gene chip was selected, and combined with GWAS, the target loci and genes were mapped, and the candidate genes for alkaline tolerance were predicted. The results provided genetic resources and potential targets for molecular design breeding of saline-alkali tolerance in faba bean, which are significant for promoting the process of faba bean saline-alkali resistance breeding. Results Statistical analysis of 12 phenotype traits under saline-alkali stress Phenotypic responses to salt-alkali stress exhibited significant differences ( P < 0.01) compared to the control condition across all measured traits (Table 1 , Fig. 1 ). Under salt-alkali stress, most growth and biomass traits showed notable reductions in mean values relative to the control. For instance, the average fresh weight of shoots (FWS) decreased from 4.28 g in the control group to 2.30 g under salt-alkali stress, representing a 46% reduction. Shoot dry weight (DWS) decreased by 60% (from 0.40 g to 0.16 g), root fresh weight (FWR) decreased by 33% (from 5.70 g to 3.83 g), and leaf area (LA) decreased by 35% (from 8.67 cm² to 5.63 cm²). Additionally, leaf number (LN) and stem diameter (SD) also decreased significantly under salt-alkali stress. A few traits showed little change or even increased under stress. There was no significant difference in dry weight of shoot(DWS) between the stress and control conditions. The relative chlorophyll content (RCC) of leaves slightly increased under stress (from an average of 38.1 SPAD units to 41.1 SPAD units). The germination rate (GR) under stress conditions only decreased slightly and seedling time (ST) increased slightly compared to the control group. Table 1 Statistical analysis of 12 traits in controlled and saline-alkali stress conditions. Traits Treatment Mean SD CV(%) Skewness Min Max FWS(g) Control 4.28 1.30 30.37 0.30 1.48 9.31 Stress 2.30 0.93 40.43 0.23 0.00 5.52 FWR(g) Control 5.70 1.83 32.11 1.85 2.59 18.29 Stress 3.83 1.25 32.64 0.46 0.00 8.16 DWS(g) Control 0.40 0.14 35.00 0.40 0.08 0.97 Stress 0.16 0.09 56.25 0.45 0.00 0.46 DWR(g) Control 0.60 0.17 28.33 2.04 0.29 1.68 Stress 0.62 0.19 30.65 0.51 0.00 1.53 RCC(SPAD) Control 38.14 5.08 13.32 0.22 24.47 54.60 Stress 41.13 7.03 17.09 -2.29 0.00 59.03 SL(cm) Control 27.62 4.28 15.50 -0.23 14.47 40.20 Stress 20.05 5.39 26.88 -0.52 0.00 34.87 RL(cm) Control 24.70 5.47 22.15 0.59 10.20 43.63 Stress 18.87 5.88 31.16 0.43 0.00 45.70 SD(cm) Control 0.13 0.02 15.38 0.50 0.07 0.20 Stress 0.10 0.02 20.00 -0.81 0.00 0.17 LN Control 9.65 1.64 16.99 -0.20 4.00 14.00 Stress 6.79 1.73 25.48 -0.81 0.00 11.33 ST(d) Control 7.00 2.02 28.86 1.02 4.00 15.00 Stress 7.31 2.36 32.28 0.61 0.00 16.00 GR(%) Control 0.48 0.32 66.67 0.10 0.00 1.00 Stress 0.44 0.33 75.00 0.20 0.00 1.00 LA(cm 2 ) Control 8.67 2.37 27.34 0.69 3.84 18.71 Stress 5.63 1.83 32.50 -0.15 0.00 10.96 a FWS, fresh weight of shoot (g); FWR, fresh weight of root (g); DWS, dry weight of shoot (g); DWR, dry weight of root (g); RCC, relative chlorophyll content (SPAD); SL, shoot length (cm); RL, root length (cm); SD, stem diameter (cm); LN, leaf number; ST, seedling time (days); GR, germination rate (%); LA, leaf area (cm²). The boxplot in Fig. 1 showed that the coefficient of variation (CV) of all traits under stress was higher than that in the control group, indicating that various genotypes had significant differences in responses to salt-alkali stress. The CV of the fresh weight of shoots was 40.4%. The CV of the germination rate and the dry weight of shoot under stress reached 75.0% and 56.3% respectively. These traits demonstrated substantial genetic variability, which offers a wide range for selecting salt-alkali tolerance. Comprehensive evaluation (D) of salt-alkali tolerance Salt-alkali resistance is a complex polygenic trait influenced by multiple genetic and environmental factors, poorly reflected by a single trait. Tavakoli et al.[ 39 ] and Benabderrahim et al.[ 40 ] used a comprehensive analysis of multi-index results to better reflect the salt tolerance of alfalfa seedlings. Therefore, we performed correlation and PCA on 12 phenotypic traits to develop a salt-alkali comprehensive evaluation index (D). Correlation analysis of 12 traits We constructed a correlation matrix for 12 traits under salt-alkali stress (Fig. 2 ) and identified strong positive correlations( r > 0.70, P < 0.001) among biomass-related indices, particularly between fresh weight of shoots (FWS) and fresh weight of roots (FWR). This indicated that genotypes capable of maintaining higher shoot biomass under stress typically exhibited higher root biomass as well. Additionally, plant height (SL), leaf area (LA), and leaf number (LN) displayed highly significant correlations ( r ranged from 0.50 to 0.75, P < 0.01). In contrast, relative chlorophyll content (RCC) exhibited weak correlations with most other traits (| r |< 0.33). Principal component analysis PCA was used to reduce the dimensionality of the traits. We performed PCA on the standardized values. This analysis grouped the 12 estimated variables into 6 principal comprehensive indices, namely, PC1, PC2, PC3, PC4, PC5, and PC6, which contributed 44.267%, 14.415%, 9.832%, 6.217%, 5.703% and 4.623%, respectively. The cumulative contribution reached 85.057 (Table 2 ). PC1 was related to shoot biomass, PC2 represented germination-related indicators, PC3 was related to RCC and FWR, PC4 was related to root-related indicators, PC5 was related to RL, and PC6 was related to SD. The phenotypic interpretation rates of PC1 and PC2 were relatively high, and the cumulative contribution reached 58.682%. Under stress, PC1 and PC2 played a major role in the overall phenotypic variation. Table 2 Eigen vectors and percentage of accumulated contribution of principal components. Component PC1 PC2 PC3 PC4 PC5 PC6 Explained Variance (%) 44.267 14.415 9.832 6.217 5.703 4.623 Cumulative Variance (%) 44.267 58.682 68.514 74.731 80.434 85.057 FWS 0.924 -0.122 -0.103 -0.073 -0.146 -0.029 FWR 0.716 0.261 -0.503 0.007 0.166 0.019 DWS 0.807 -0.075 -0.162 0.063 -0.218 -0.268 DWR 0.345 0.655 -0.416 0.403 0.215 0.011 RCC 0.349 0.427 0.68 0.222 -0.04 -0.324 SL 0.859 -0.05 0.109 -0.049 -0.081 -0.088 RL 0.564 0.231 0.168 -0.559 0.502 -0.065 SD 0.706 0.068 0.277 0.188 -0.065 0.467 LN 0.823 -0.095 0.124 0.109 0.019 -0.088 ST -0.261 0.814 0.231 -0.118 -0.125 0.224 GR 0.497 -0.544 0.249 0.235 0.357 0.22 LA 0.712 0.052 -0.081 -0.306 -0.367 0.201 Comprehensive evaluation of salt-alkali tolerance and hierarchical cluster Subordinate function µ values of each accession were calculated using Eq. ( 2 ). For the same comprehensive index, the salt-alkali tolerance of materials could be evaluated according to the subordinate function µ value. For PC1, the µ( x 1) values of 8 accessions (T298, T307, T351, T590, T780, T781, T790, and T1060) exceeded 0.9, indicating the highest level of salt-alkali tolerance. Conversely, 3 materials including T1020, T1099, and T1130 had the lowest µ( x 1) value of 0.000, suggesting the poorest salt-alkali tolerance. The comprehensive weights for the comprehensive indices were calculated using Eq. ( 3 ). The comprehensive weights of the 5 indices ( W1 , W2 , W3 , W4 , W5 ) were 52.04%, 16.95%, 11.56%, 7.31%, 6.71%, and 5.44%, respectively. The comprehensive evaluation D value for salt-alkali tolerance at the seedling stage was calculated using Eq. ( 4 ). There were a total of 33 accessions with a D value greater than 0.7. Hierarchical clustering based on the D values classified the examined 240 materials into five groups (Fig. 3 ). Cluster Ⅴ, the highly salt-alkali-tolerant group, included 38 materials, such as T255. These materials exhibited significantly higher biomass and chlorophyll content compared to the other groups. Cluster Ⅳ, the salt-alkali-tolerant group, included 76 materials such as T123. Cluster Ⅲ, the moderately salt-alkali-tolerant group, included 59 materials such as T119. Cluster Ⅱ, the low salt-alkali-tolerant group, included 55 materials such as T109. The Cluster Ⅰ, the salt-alkali-sensitive group, included 12 materials such as T163. We analyzed the correlation between 12 indicators and the D value. With the exception of seeding time, the other 11 indices exhibited extremely significant positive correlations with the D value. Notably, the correlation coefficient between stem diameter and D reached 0.8, indicating a strong positive relationship. Population genetic structure We genotyped the association population using SNPs derived from the faba bean 130K SNP array. A total of 30,338 filtered polymorphic SNP markers were retained for further analyses. The population structure of 240 faba bean resources was analyzed using the Admixture software, and the CV error was employed to determine the optimal number of subgroups. The results indicated that the CV error reached its minimum value at K = 4(Fig. 4 b), suggesting that the materials could be partitioned into four distinct subgroups(Fig. 4 a): Subpop I, Subpop II, Subpop III, and Subpop IV. Accessions with membership probabilities ≥ 0.50 were considered to belong to the same group. Among the 240 accessions, 51 were assigned to Subpop I, 81 to Subpop II, 42 to Subpop III, and 31 to Subpop IV, while the remaining materials were classified as a mixed group. Notably, materials from diverse geographical origins or ecological types were grouped within the same subgroups, which implies the occurrence of gene flow or introgression among the faba bean resources. To better elucidate the genetic relationships among the materials, PCA was performed using PLINK software on the 240 accessions, and the results were visualized as a three-dimensional scatter plot (Fig. 4 c). The first three principal components, PC1 (35.22%), PC2 (14.52%), and PC3 (10.96%), collectively captured 60.70% of the total genetic variation. The results revealed that the 240 materials were clearly divided into four distinct clusters, with some overlap observed among certain accessions. These findings aligned with the results of the population structure analysis, thereby demonstrating the reliability of the groups classification. GWAS for salt-alkali tolerance indexes To elucidate the genetic basis of salt-alkali tolerance in faba bean seedlings, we conducted a GWAS that combined phenotypic data with genotypic data through GLM and MLM. After referring to Bonferroni correction, a stringent significance threshold of P < 1.0×10 − 4 was determined. A total of 242 significant SNP loci were detected across the genome. These included 107 loci consistently identified by both GLM and MLM, as well as 18 loci that exhibited significant associations with four or more traits simultaneously. These multi-trait-associated loci likely play critical roles in the salt-alkali stress response in faba bean and warrant further investigation as key candidate loci. Among these, the greatest number of loci (145) were associated with germination rate (GR), whereas FWR and RL were associated with fewer than 10 loci. The Manhattan plot and QQ plot were provided in Figure S1 and Figure S2 . Candidate Gene Analysis Based on the Hedin/2 reference genome in faba bean, a total of 103 genes were predicted, which are distributed across multiple chromosomes. Among them, 10 candidate genes closely related to salt-alkali tolerance traits were identified. The gene Vfaba.Hedin2.R1.3g023640 , located on chromosome 3, was annotated as L-GalLDH . This gene encodes L-galactono-1,4-lactone dehydrogenase, an enzyme that is of critical importance in the synthesis pathway related to maintaining redox balance[ 41 – 42 ]. Vfaba.Hedin2.R1.2g026040 was annotated as ZAT4 , a member of the C2H2 zinc finger TF family. Previous studies have reported that ZAT4 can enhance plant tolerance to osmotic stress and salt stress via the abscisic acid(ABA) signaling pathway[ 43 – 44 ]. Vfaba.Hedin2.R1.2g148560 was annotated as NAC domain-containing protein 82 . NAC transcription factors are one of the largest families of plant transcription factors and have been widely proven to be involved in abiotic stress adaptation[ 45 – 47 ]. The remaining candidate genes were predominantly involved in ion transport, signal protein synthesis, and the synthesis of protective enzymes associated with the antioxidant pathway. These functions are all integral to the plant’s ability to cope with salt-alkali stress, as they help maintain cellular ion balance, transmit stress signals, and detoxify ROS. In summary, these 10 candidate genes(Table 3 ) provide valuable insights into the genetic basis of salt-alkali tolerance in faba bean and serve as potential targets for further genetic improvement and functional studies. Table 3 The information of significant association loci related to 12 candidate genes ID Trait Marker Model Type Chr p Gene Name Gene Description 1 ST dou_TRINITY_DN47983_c0_g1_503 GLM chr3 3.92E-05 Vfaba.Hedin2.R1.3g023640 L-galactono-1,4-lactone dehydrogenase, mitochondrial 2 ST dou_TRINITY_DN50263_c0_g1_698 GLM chr2 2.35E-05 Vfaba.Hedin2.R1.2g026040 Zinc finger protein ZAT4 MLM 8.27E-05 3 FWS hua_TRINITY_DN149364_c1_g1_436 GLM chr1 5.15E-05 Vfaba.Hedin2.R1.1g188000 1-aminocyclopropane-1-carboxylate oxidase homolog 1 4 GR ye_TRINITY_DN135934_c6_g2_1000 GLM chr3 4.85E-05 Vfaba.Hedin2.R1.3g108200 NAC domain containing protein 50 5 GR ye_TRINITY_DN125437_c2_g1_365 GLM chr3 1.63E-05 Vfaba.Hedin2.R1.3g108200 NAC domain containing protein 50 6 GR ye_TRINITY_DN128763_c0_g1_87 GLM chr4 6.23E-05 Vfaba.Hedin2.R1.4g007000 Sucrose transport protein SUC3 MLM 8.91E-05 7 GR ye_TRINITY_DN142350_c3_g1_1444 GLM chr4 6.74E-05 Vfaba.Hedin2.R1.4g007000 Sucrose transport protein SUC3 MLM 9.00E-05 8 GR ye_TRINITY_DN129792_c1_g2_262 GLM chr3 1.55E-05 Vfaba.Hedin2.R1.3g084000 Gibberellin 20 oxidase 2 MLM 7.82E-05 9 GR ye_TRINITY_DN129792_c1_g2_293 GLM chr3 9.00E-06 Vfaba.Hedin2.R1.3g084000 Gibberellin 20 oxidase 2 MLM 7.94E-05 10 GR ye_TRINITY_DN130469_c4_g4_600 GLM chr3 1.17E-07 Vfaba.Hedin2.R1.3g214960 K(+) efflux antiporter 6 MLM 3.39E-06 11 GR ye_TRINITY_DN136702_c1_g3_568 GLM chr2 2.96E-05 Vfaba.Hedin2.R1.2g082800 Mitogen-activated protein kinase kinase kinase 1 12 BL ye_TRINITY_DN146501_c1_g2_1391 GLM chr3 1.37E-05 Vfaba.Hedin2.R1.3g036920 E3 ubiquitin-protein ligase PUB22 MLM 3.82E-05 DWR GLM 6.00E-05 LN GLM 7.88E-05 RCC GLM 1.69E-09 MLM 1.70E-07 SD GLM 7.34E-06 MLM 2.76E-05 ST GLM 3.03E-05 13 GR ye_TRINITY_DN150763_c2_g1_517 GLM chr1 1.99E-05 Vfaba.Hedin2.R1.1g093640 Chloride channel protein CLC-c MLM 7.22E-05 Discussion In China, saline-alkali land accounts for 25% of agricultural cultivated areas, particularly in the northwest, north, and parts of the northeast regions [ 48 ]. These lands remain underutilized due to soil constraints. With continuous changes in land use patterns, the area of saline-alkali land has been increasing annually [ 49 ]. Under such circumstances, investigating crop responses to saline-alkali stress becomes critically important. As a vital temperate legume crop [ 1 ], the salt-alkali tolerance of faba bean directly determines its cultivation feasibility in saline-alkali regions. The seedling stage represents the most vulnerable phase in the crop lifecycle, during which environmental stresses exert the most pronounced impacts [ 50 – 52 ]. Therefore, analyzing phenotypic changes at this stage under abiotic stress can effectively reflect crop stress tolerance. Previous studies have demonstrated that biomass-related traits of faba bean seedlings, including fresh seedling weight, plant height, and leaf area, are significantly reduced under saline-alkali stress [ 53 – 54 ]. Investigating the phenotypic responses of faba bean seedlings to saline-alkali stress will contribute to elucidating the underlying tolerance mechanisms and provide theoretical support for breeding salt-alkali tolerant faba bean cultivars. Response of faba bean seedling phenotypes to saline-alkali stress In this study, saline-alkali stress was applied to faba bean materials during the seedling stage, and significant phenotypic alterations were observed. Comparative analysis between the control and stress groups further revealed the negative effects of saline-alkali stress on seedling growth, manifested by reductions in biomass-related traits, including fresh seedling weight, root fresh weight, and seedling length. These results indicate that saline-alkali stress impairs plant water uptake and developmental processes, which aligns with findings reported by Ziche et al. [ 55 ] and Rajhi et al. [ 56 ]. However, a slight increase in the relative chlorophyll content of leaf was detected, suggesting a potential adaptive mechanism by which faba beans enhance photosynthetic efficiency to mitigate stress impacts. This hypothesis has been previously proposed in studies by Zhuang J et al. [ 57 ] and Xu L et al. [ 58 ]. Furthermore, saline-alkali stress exhibited inhibitory effects on seed germination, characterized by reduced germination rates and prolonged germination periods, consistent with observations by Diab et al. [ 59 ]. Notably, significant differences in the coefficient of variation (CV) among genotypes under stress conditions highlight phenotypic variability, providing a basis for screening salt-alkali tolerant cultivars. Comprehensive evaluation for salt-alkali tolerance of faba bean accession at the seedling stage Salt-alkali tolerance in crops is a complex polygenic trait, and single phenotypic traits often fail to comprehensively evaluate this capability [ 60 – 62 ]. In this study, a multi-indicator integrated evaluation approach was implemented, combining principal component analysis (PCA) and membership function values to systematically assess the salt-alkali tolerance of 240 faba bean accessions. PCA revealed that the first two principal components accounted for 58.68% of the total variation, primarily associated with biomass traits (fresh seedling weight, root fresh weight, seedling length) and germination characteristics (emergence time), indicating their pivotal roles in salt-alkali tolerance. Based on the comprehensive evaluation D-value calculated for each trait, the 240 accessions were classified into five clusters. Notably, Cluster Ⅴ exhibited the strongest salt-alkali tolerance, containing 38 accessions (e.g., T255, T289), However, evaluating only the seedling stage of faba bean does not necessarily represent the overall performance of these germplasms throughout the entire growth period. Therefore, further comprehensive assessment across the full growth period is required to screen and validate these genetic resources. Identification of salt-alkali tolerance-related loci and candidate genes in faba bean This study identified 242 significant SNP loci associated with salt-alkali tolerance in faba bean through genome-wide association study, among which 57 SNPs were consistently detected by both the Generalized Linear Model (GLM) and Mixed Linear Model (MLM). Functional annotation identified 10 candidate genes strongly linked to salt-alkali tolerance traits. Notably, the L-GalLDH gene ( Vfaba.Hedin2.R1.3g023640 ) is involved in antioxidant responses. Zhang G-Y et al. [ 63 ] demonstrated that overexpression of L-GalLDH in rice ( Oryza sativa ) via transgenic methods significantly increased ascorbate content and enhanced salt stress tolerance. The ZAT4 gene ( Vfaba.Hedin2.R1.2g026040 ) plays a critical role in osmotic stress regulation in faba bean. Sun L et al. [ 64 ] reported that the overexpression of soybean GmZAT4 in Arabidopsis thaliana markedly improved salt tolerance, which was accompanied by elevated activities of ascorbate peroxidase (APX) and superoxide dismutase (SOD). Additionally, the NAC domain-containing protein 82 gene ( Vfaba.Hedin2.R1.2g148560 ) enhances faba bean tolerance to saline-alkali stress by maintaining cellular osmotic homeostasis and strengthening antioxidant capacity. Bokolia et al. [ 65 ] conducted a genome-wide identification of NAC transcription factors in oat ( Avena sativa ), revealing their expression patterns under salt stress. In addition to the aforementioned genes, other candidate genes also play certain roles in the mechanisms underlying crop tolerance to saline-alkali stress. These candidate genes not only deepen the understanding of salt-alkali tolerance mechanisms in faba bean. The application of these genes in faba bean requires further experimental validation. Conclusions This study evaluated the salt-alkali tolerance of 240 faba bean germplasm accessions at the seedling stage, employing a multidimensional analytical framework that integrated principal component analysis and the comprehensive evaluation D-value. The comprehensive assessment identified 38 accessions (e.g., T255, T289) exhibiting strong tolerance under saline-alkali stress, which represent critical genetic resources for breeding salt-alkali tolerant cultivars. Genome-wide association study revealed 242 significant single nucleotide polymorphism (SNP) loci associated with salt-alkali tolerance, including 57 loci consistently detected by both the Generalized Linear Model and Mixed Linear Model, thereby elucidating the genetic architecture underlying this trait. Functional annotation further prioritized 10 candidate genes (e.g., L-GalLDH , ZAT4 , NAC82 ) involved in faba bean salt-alkali tolerance. These genes are functionally enriched in antioxidant responses, osmotic regulation, and signal transduction pathways, providing actionable targets for molecular design breeding. In summary, this study advances the understanding of the genetic mechanisms governing salt-alkali tolerance in faba bean and provides valuable gene resources for breeding programs. Future efforts focusing on functional validation of these candidate genes and the application of gene editing technologies are expected to enhance faba bean performance in saline-alkali regions, ultimately facilitating its widespread cultivation in marginal lands. Materials and methods Plant Materials A panel of 240 faba bean accessions from different countries was phenotyped for seedling-stage saline-alkali tolerance. Of these, 183 were from China, and 57 were from other countries. These 240 accessions of faba been germplasm resources used in this research come from Qinghai Academy of Agricultural and Forestry Sciences, and all germplasm resources were cultivated at the Academy of Agriculture and Forestry Sciences of Qinghai University. The panel had been genotyped with the faba bean 130 K targeted next-generation sequencing SNP genotyping platform[ 34 ]. From this SNP dataset, 30,338 SNPs were selected as high-quality SNPs with minor-allele frequency (MAF) greater than 0.05 and maximum missing rate less than 20%. Field trial and phenotyping Five seeds were used for each accession to assess seedling saline-alkali tolerance. Seeds were sown in pots and grown in an artificial climate chamber with an 18 h/6 h light/dark photoperiod, day/night temperatures of 22°C/17°C, and approximately 50% relative humidity. A saline-alkaline solution composed of NaCl, sodium carbonate (Na 2 CO 3 ), and sodium bicarbonate (NaHCO 3 ) with a 9:1:1 ratio was used to irrigate the stress group. The final concentration of this solution was 100 mmol/L and the pH was adjusted to 9.2. Control plants were irrigated with ddH 2 O. A total of 12 morphological and physiological traits of faba been plants were measured after being irrigated with a saline-alkaline solution for 21 days. These traits include shoot fresh weight (FWS), root fresh weight (FWR), shoot dry weight (DWS), root dry weight (DWR), relative chlorophyll content (RCC) in leaves, root length (RL), shoot length (SL), stem diameter (SD), leaf number (LN), germination rate (GR), germination time (ST), and leaf area (LA). Standard protocols were used for all measurements. Statistical analysis Refer to the comprehensive evaluation (D) value of crop stress tolerance evaluation to conduct statistical analysis on the data[ 31 , 35 – 36 ]. The saline-alkaline tolerance coefficient (SATC) of each of the above indices was calculated. $$\:{SATC}_{ij}=\frac{{\text{X}}_{\text{i}\text{j}}\left(\text{s}\text{t}\text{r}\text{e}\text{s}\text{s}\right)}{{\text{X}}_{\text{i}\text{j}}\left(\text{c}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\right)}$$ 1 SATC ij represents the saline-alkaline tolerance coefficient of index (j) for accessions (i); X ij (control) and X ij (stress) denote the values of the index for the accessions evaluated under ddH 2 O and saline-alkaline treatments, respectively. The membership function value µ was calculated using Eq. ( 2 ). $$\:\mu\:\left(\text{X}i\right)=\frac{(\text{X}i-\text{X}i\text{m}\text{i}\text{n})}{(\text{X}imax-\text{X}imin)}$$ 2 The X i is the ith comprehensive index; X imax and X imin represent the maximum and minimum values for the ith comprehensive index of each material, respectively. In Eq. ( 3 ) the weight function W i was calculated and represents the relative importance of the ith comprehensive index for a accession. $$\:{W}_{i}=\frac{{P}_{\text{i}}}{\sum\:_{i-1}^{n}{P}_{i}}\:i=\text{1,2},3\cdots\:,n$$ 3 P i represents the contribution to the ith comprehensive index. In Eq. ( 4 ) the comprehensive evaluation parameter for saline-alkaline tolerance resilience (D) for each cultivar was calculated to identify the saline-alkaline tolerance capacity of the different accessions. Then, hierarchical cluster analysis was also used to evaluate salt tolerance. $$\:D={\sum\:}_{i-1}^{n}\left[\text{μ(}{X}_{i}\text{)×}{W}_{i}\right]\:i=\text{1,2},3\cdots\:,n\:\:\:\:\:$$ 4 µ(X i ) is the membership function value of ith comprehensive index; W i represents the relative importance of the ith comprehensive index. SPSS26.0 and R4.3.2 software were used to conduct correlation analysis, principal component analysis, membership function analysis, and cluster analysis. R4.1.3, Origin 2022, and GraphPad Prism 9 were used to draw the images required in this study. Population Structure Analysis To estimate the population structure and phylogenetic relationships within the panel of faba bean, the Admixture software was run from 2 to 10 to analyze 30,338 markers. A tenfold cross-validation (CV) scheme was repeated 10 times for each value of K. The optimal grouping was determined by identifying the K-value associated with the minimum CV error, which was visualized through a curve plotting the CV error against the K-value. Accessions with membership probabilities ≥ 0.50 were considered to belong to the same group. We then utilized R4.1.3 to visually represent the admixture proportions. For PCA, the PLINK v.1.9 software was applied, setting the number of principal components (PCs) equal to the number of samples[ 37 ]. The resulting eigenvector plot was generated using the ggplot2 package in R, following the methodology described by Wickham[ 38 ]. Genome-wide association analysis The Generalized Linear Model (GLM) and Mixed Linear Model (MLM) in Tassel 5 software were used to conduct GWAS for traits related to salt-alkali tolerance. The significance of associated markers was evaluated using P-values, with a stringent threshold of -log10( P ) > 4.0 applied to identify significant associations. This threshold was determined based on Bonferroni correction to account for multiple testing. Finally, the CMplot package in R software was used to visualize the results of the GWAS through a Manhattan plot and a quantile-quantile (Q-Q) plot. Functional annotations of candidate genes within the identified regions were retrieved using the Hedin/2 reference genome ( https://projects.au.dk/fabagenome/genomics-data ). Abbreviations FWS: shoot fresh weight FWR: root fresh weight DWS: shoot dry weight DWR: root dry weight RCC: relative chlorophyll content RL: root length SL: shoot length SD: stem diameter LN: leaf number GR: germination rate ST: germination time LA: leaf area GWAS: Genome - Wide Association Study SNP: Single Nucleotide Polymorphism GLM: Generalized Linear Model MLM: Mixed Linear Model Declarations Ethics approval and consent to participate All procedures were conducted following the guidelines. Consent for publication Not applicable. Availability of data and materials All the materials were provided by the Legume Research Group of the Qinghai Academy of Agricultural and Forestry Sciences. All the data generated were listed in the supplementary tables and figures. Conflict statement The authors declare no competing interests. Funding The Key Research and Development and Transformation Plan of Qinghai Province (2022-NK-109); National Natural Science Foundation of China (NSFC,42267008) and the China Agriculture Research System of MOF and MARA (CARS-08-G06). Data availability Data is provided within the manuscript or supplementary information files. Authors’ contributions X.P. contributed to software, data analysis, writing, and editing. Z.S. contributed to software, data analysis, and editing. X.Z. contributed to software, supervision, data analysis, and writing. X.W. contributed to the investigation, review, and editing. D.Z. contributed to software, investigation, and writing. C.T. contributed to software, data analysis, and writing. H.Z. contributed to experimental design, methodology, software, investigation, data analysis, and writing the original draft. W.H contributed to software, data analysis. L.Y. contributed to supervision, funding acquisition, writing, review, and editing. 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Supplementary Files FigureS1.docx FigureS2.docx SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3GermplasmOverview.xlsx Cite Share Download PDF Status: Published Journal Publication published 30 Sep, 2025 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 11 Jun, 2025 Reviews received at journal 30 May, 2025 Reviewers agreed at journal 22 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviews received at journal 20 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers invited by journal 12 May, 2025 Editor assigned by journal 12 May, 2025 Editor invited by journal 29 Apr, 2025 Submission checks completed at journal 29 Apr, 2025 First submitted to journal 29 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6519550","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456771559,"identity":"73248c09-0222-4d73-9281-9ed28b01f516","order_by":0,"name":"Xiaoxing Peng","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxing","middleName":"","lastName":"Peng","suffix":""},{"id":456771560,"identity":"c9475f08-d1f1-49db-bbf0-b2755764cfee","order_by":1,"name":"Xianli Zhou","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Xianli","middleName":"","lastName":"Zhou","suffix":""},{"id":456771561,"identity":"0e14a533-dc4f-451d-b68b-4390ab74f315","order_by":2,"name":"Zhi hao Sun","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"hao","lastName":"Sun","suffix":""},{"id":456771562,"identity":"ff7d1926-c305-4af8-b980-1a0436901bd3","order_by":3,"name":"Xuexia Wu","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Xuexia","middleName":"","lastName":"Wu","suffix":""},{"id":456771563,"identity":"6ae0f918-e32a-44fd-a97d-7ff487b00239","order_by":4,"name":"Changcai Teng","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Changcai","middleName":"","lastName":"Teng","suffix":""},{"id":456771564,"identity":"cff6d9b1-539c-4bf5-be41-3393b69063be","order_by":5,"name":"Wanwei Hou","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Wanwei","middleName":"","lastName":"Hou","suffix":""},{"id":456771565,"identity":"00712f80-b69f-4bb5-8465-8ff6ccfd213a","order_by":6,"name":"Ping Li","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Li","suffix":""},{"id":456771566,"identity":"f5d78118-76eb-4391-b5ce-e30fd894c449","order_by":7,"name":"Dong Zheng","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Zheng","suffix":""},{"id":456771567,"identity":"d25a5599-8646-4bed-92fc-7b2a7eac292d","order_by":8,"name":"Hongyan Zhang","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Zhang","suffix":""},{"id":456771568,"identity":"1aee2f29-e958-41ca-ba85-9a4c07c08a17","order_by":9,"name":"Huiling Fan","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Huiling","middleName":"","lastName":"Fan","suffix":""},{"id":456771569,"identity":"bb952846-5c62-4877-a01e-4532134e9e39","order_by":10,"name":"Yujiao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBACNv7mg4//GPyTk5c/fPABYwNILAG/Fj6JY8kGPBUHjA1nsCUbEKVFjiHHTILnzIHEhhs8ahJEaWFjOGMmIdl2x5hxdg9bxdsdhxn42XMMGH7uwKOFua3YwrDtmRy7zNljN+eeOcwg2fPGgLH3DD5bDm+8kdjGbMzYkJd2m7ftMIPBjRwDZsY2fFoSDCQOtjEnNhzIMSsGabEnrCXFSLLhzGGg93PMmMG2SBDSAgxkY4aKNGPDnmPJknPb0nkkzjwrONiLR4t8PzAqGQxs5OTZmw9+eNtmLcffnrzxwU88WlABDxgxMBwgVgNU/SgYBaNgFIwCNAAASI1WiboYEM8AAAAASUVORK5CYII=","orcid":"","institution":"Qinghai University","correspondingAuthor":true,"prefix":"","firstName":"Yujiao","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-04-24 09:53:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6519550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6519550/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-07249-4","type":"published","date":"2025-09-30T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82798750,"identity":"3d6075f4-80d8-4945-8b76-32ffe07b88dc","added_by":"auto","created_at":"2025-05-15 10:52:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4110857,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of the 12 traits under control and salt-alkali stress conditions. Abbreviations wer provided in Table 1.\u003c/p\u003e","description":"","filename":"Fig.1.Boxplotsofthe12traitsundercontrolandsaltalkalistressconditions.AbbreviationswerprovidedinTable1.png","url":"https://assets-eu.researchsquare.com/files/rs-6519550/v1/469690de2011d5c7f14b06f1.png"},{"id":82799627,"identity":"aef9141a-c737-4620-8a32-52a9b6976d73","added_by":"auto","created_at":"2025-05-15 11:00:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":866691,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Matrix of 12 traits and D-value.\u003c/p\u003e","description":"","filename":"Fig.2.CorrelationMatrixof12traitsandDvalue..png","url":"https://assets-eu.researchsquare.com/files/rs-6519550/v1/f5049b3ada2c947ff80433d9.png"},{"id":82798752,"identity":"31fa6579-3abb-4b29-8191-2253d5003bc0","added_by":"auto","created_at":"2025-05-15 10:52:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10526581,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis for salt-alkali tolerance in 240 faba bean accessions based on D values.\u003c/p\u003e","description":"","filename":"Fig.3.Clusteranalysisforsaltalkalitolerancein240fababeanaccessionsbasedonDvalues.png","url":"https://assets-eu.researchsquare.com/files/rs-6519550/v1/052737249864ff656c2eff62.png"},{"id":82798755,"identity":"c4c20a42-1120-4034-8b48-cc4d7b3e6857","added_by":"auto","created_at":"2025-05-15 10:52:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":102876,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation Genetic Structure Analysis Based on PCA and Admixture Results. (a) PCA of 240 accessions. (b) Relationship between K and CV error. (c) The structure of the 240 accessions based on Structure software when K=4.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6519550/v1/911c991428301fe55cf48e88.png"},{"id":92883944,"identity":"24193a4f-7dad-462a-a23d-3332c9e211ae","added_by":"auto","created_at":"2025-10-06 16:11:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16873400,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6519550/v1/17847aea-fa4c-44d9-a232-00f21b9d30a1.pdf"},{"id":82798756,"identity":"d0243e3d-3ed0-4245-a3e9-9a248a45b871","added_by":"auto","created_at":"2025-05-15 10:52:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":222119,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6519550/v1/3ae9c7b40489bd86ba3e03ec.docx"},{"id":82801780,"identity":"f2c434e9-5057-42f4-9f51-76fea0c51a20","added_by":"auto","created_at":"2025-05-15 11:24:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1114450,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6519550/v1/5c0a5428242222ab367770c5.docx"},{"id":82801009,"identity":"efdf3725-b466-4e55-a762-a599679be715","added_by":"auto","created_at":"2025-05-15 11:16:46","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":36881,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6519550/v1/821025f7f6b182342c724f76.docx"},{"id":82799642,"identity":"2b669b07-d7a4-4b0c-b9d6-f8d315434246","added_by":"auto","created_at":"2025-05-15 11:00:46","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":164818,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6519550/v1/2659a22a5178f717dd250d66.docx"},{"id":82801007,"identity":"777bfc5d-0926-4b2e-9b03-e76dc378a42e","added_by":"auto","created_at":"2025-05-15 11:16:45","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":25914,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3GermplasmOverview.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6519550/v1/5bf532a60aa72515de7c2012.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive evaluation of compound saline-alkali tolerance and gene mining by GWAS in Vicia faba L.","fulltext":[{"header":"Background","content":"\u003cp\u003eFaba bean, an important temperate legume crop, ranks sixth globally in production[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It exhibits high yield potential with an average crude protein content of approximately 29%, making it a significant source of plant-based protein, and its amino acid composition meets human dietary requirements[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, faba bean is one of the crops with the highest nitrogen fixation efficiency, reducing the need for nitrogen fertilizers, which provides major benefits for rotation systems and supports sustainable agricultural practices[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Its adaptability to a variety of climatic and soil conditions presents obvious advantages over other legume crops. Despite these benefits, the area harvested and production of faba bean still remain lower than those of legumes such as peas and chickpeas, mainly due to unstable yields resulting from biotic and abiotic stresses, highlighting the necessary for further research to enhance its productivity and stress resistance[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSoil salinization and alkalization pose a substantial threat to global food production and severely impede the sustainable utilization of cultivated lands[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It is estimated that over 20% of irrigated lands around the world are impacted by excessive salt accumulation. This predicament is exacerbating at an alarming rate, primarily driven by a confluence of factors including unsustainable irrigation practices, inadequate drainage systems, and the far-reaching impacts of climate change[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The expansion of saline-alkali soils forces farmers to reduce cultivation areas or even abandon farming altogether, further exacerbating food security issues, particularly in developing countries with limited agricultural resources[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Exploring the salt-alkali tolerance characteristics of crops and breeding superior varieties with salt-alkali resistance are important methods for the utilization and improvement of saline-alkali lands. Faba bean, despite its sensitivity to salt-alkali stress conditions, exhibits a certain degree of tolerance within a specific range of salinity-alkalinity conditions. Furthermore, there exists significant variation in salinity-alkalinity resistance among different genotypes of faba bean[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Given the potential of faba bean as a resilient crop, exploring of its salt-alkali tolerance characteristics and developing a superior genotype with enhanced resistance could represent a strategic approach for the effective utilization of saline-alkali lands.\u003c/p\u003e \u003cp\u003eA limited number of studies have investigated the harmful effects of saline conditions containing sodium chloride (NaCl), calcium chloride (CaCl\u003csub\u003e2\u003c/sub\u003e), and magnesium chloride (MgCl\u003csub\u003e2\u003c/sub\u003e) on various genotype in faba bean, evaluating genotype-specific responses to salinity through emergence rates and early growth characteristics[\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, these studies utilized a few materials and did not incorporate a comprehensive assessment of trait contributions to salt-alkali tolerance for a robust evaluation of salinity tolerance. While quantitative trait locus (QTL) analysis and genome-wide association studies (GWAS) are commonly employed to identify genes associated with stress tolerance traits such as cold and drought resistance in faba bean[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], there is currently no report on QTLs or GWAS related to salt-alkali tolerance.\u003c/p\u003e \u003cp\u003eHigh concentrations of Na\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e-\u003c/sup\u003e ions, coupled with elevated soil pH, pose significant challenges to plant ion homeostasis and metabolic balance, ultimately leading to impaired growth and reduced productivity[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Plants, particularly during the germination stage, exhibit heightened sensitivity to abiotic stress conditions. Even a modest increase in salinity can significantly impair seedling biomass accumulation, highlighting the critical importance of understanding salt-alkali tolerance mechanisms during early development[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Comprehensive studies have been conducted to evaluate salt-alkali tolerance and associated genetic determinants in crops such as wheat[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], rice[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and maize[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Given the polygenic and multifactorial nature of salt-alkali tolerance, researchers have employed a suite of phenotypic indices, including relative germination percentage (RGP), relative germination index (RGI), relative shoot length (RSL), relative shoot dry weight per plant (RSDW), relative root dry weight per plant (RRDW), relative dry weight per plant (RDW), and relative root-to-shoot ratio (RRSR), to evaluate salt-alkali tolerance and identify key quantitative trait loci (QTLs). Meanwhile, the assessment of crop tolerance based on comprehensive traits requires the application of multivariate statistical methods. These methods include tolerance coefficients, multiple linear regression analysis[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], principal component analysis (PCA)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], membership function values, comprehensive evaluation (D) values, and cluster analysis, which were widely used to evaluate crop tolerance under stress conditions[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the context of faba bean, while the large genome size (~\u0026thinsp;13 Gb) and limited molecular marker resources have historically hindered the application of GWAS, recent advancements in high-throughput sequencing technologies and the release of the faba bean reference genome have made it possible to conduct a comprehensive analysis of the genetic basis of stress response in faba bean[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this study, we applied saline-alkali stress and control conditions(CK) to a faba bean panel containing 240 accessions and 12 morphological and physiological traits were measured to evaluate the saline-alkali tolerance based on principal component analysis (PCA) and membership function values. A 130K gene chip was selected, and combined with GWAS, the target loci and genes were mapped, and the candidate genes for alkaline tolerance were predicted. The results provided genetic resources and potential targets for molecular design breeding of saline-alkali tolerance in faba bean, which are significant for promoting the process of faba bean saline-alkali resistance breeding.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis of 12 phenotype traits under saline-alkali stress\u003c/h2\u003e \u003cp\u003ePhenotypic responses to salt-alkali stress exhibited significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) compared to the control condition across all measured traits (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Under salt-alkali stress, most growth and biomass traits showed notable reductions in mean values relative to the control. For instance, the average fresh weight of shoots (FWS) decreased from 4.28 g in the control group to 2.30 g under salt-alkali stress, representing a 46% reduction. Shoot dry weight (DWS) decreased by 60% (from 0.40 g to 0.16 g), root fresh weight (FWR) decreased by 33% (from 5.70 g to 3.83 g), and leaf area (LA) decreased by 35% (from 8.67 cm\u0026sup2; to 5.63 cm\u0026sup2;). Additionally, leaf number (LN) and stem diameter (SD) also decreased significantly under salt-alkali stress. A few traits showed little change or even increased under stress. There was no significant difference in dry weight of shoot(DWS) between the stress and control conditions. The relative chlorophyll content (RCC) of leaves slightly increased under stress (from an average of 38.1 SPAD units to 41.1 SPAD units). The germination rate (GR) under stress conditions only decreased slightly and seedling time (ST) increased slightly compared to the control group.\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\u003eStatistical analysis of 12 traits in controlled and saline-alkali stress conditions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCV(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFWS(g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFWR(g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWS(g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDWR(g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\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\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRCC(SPAD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e54.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e59.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSL(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRL(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSD(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eST(d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGR(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLA(cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e FWS, fresh weight of shoot (g); FWR, fresh weight of root (g); DWS, dry weight of shoot (g); DWR, dry weight of root (g); RCC, relative chlorophyll content (SPAD); SL, shoot length (cm); RL, root length (cm); SD, stem diameter (cm); LN, leaf number; ST, seedling time (days); GR, germination rate (%); LA, leaf area (cm\u0026sup2;).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe boxplot in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed that the coefficient of variation (CV) of all traits under stress was higher than that in the control group, indicating that various genotypes had significant differences in responses to salt-alkali stress. The CV of the fresh weight of shoots was 40.4%. The CV of the germination rate and the dry weight of shoot under stress reached 75.0% and 56.3% respectively. These traits demonstrated substantial genetic variability, which offers a wide range for selecting salt-alkali tolerance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComprehensive evaluation (D) of salt-alkali tolerance\u003c/h3\u003e\n\u003cp\u003eSalt-alkali resistance is a complex polygenic trait influenced by multiple genetic and environmental factors, poorly reflected by a single trait. Tavakoli et al.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and Benabderrahim et al.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] used a comprehensive analysis of multi-index results to better reflect the salt tolerance of alfalfa seedlings. Therefore, we performed correlation and PCA on 12 phenotypic traits to develop a salt-alkali comprehensive evaluation index (D).\u003c/p\u003e\n\u003ch3\u003eCorrelation analysis of 12 traits\u003c/h3\u003e\n\u003cp\u003eWe constructed a correlation matrix for 12 traits under salt-alkali stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and identified strong positive correlations(\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.70, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among biomass-related indices, particularly between fresh weight of shoots (FWS) and fresh weight of roots (FWR). This indicated that genotypes capable of maintaining higher shoot biomass under stress typically exhibited higher root biomass as well. Additionally, plant height (SL), leaf area (LA), and leaf number (LN) displayed highly significant correlations (\u003cem\u003er\u003c/em\u003e ranged from 0.50 to 0.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, relative chlorophyll content (RCC) exhibited weak correlations with most other traits (|\u003cem\u003er\u003c/em\u003e|\u0026lt; 0.33).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePrincipal component analysis\u003c/h3\u003e\n\u003cp\u003ePCA was used to reduce the dimensionality of the traits. We performed PCA on the standardized values. This analysis grouped the 12 estimated variables into 6 principal comprehensive indices, namely, PC1, PC2, PC3, PC4, PC5, and PC6, which contributed 44.267%, 14.415%, 9.832%, 6.217%, 5.703% and 4.623%, respectively. The cumulative contribution reached 85.057 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PC1 was related to shoot biomass, PC2 represented germination-related indicators, PC3 was related to RCC and FWR, PC4 was related to root-related indicators, PC5 was related to RL, and PC6 was related to SD. The phenotypic interpretation rates of PC1 and PC2 were relatively high, and the cumulative contribution reached 58.682%. Under stress, PC1 and PC2 played a major role in the overall phenotypic variation.\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\u003eEigen vectors and percentage of accumulated contribution of principal components.\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\u003eComponent\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 \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePC5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePC6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExplained Variance (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative Variance (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e85.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFWR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eComprehensive evaluation of salt-alkali tolerance and hierarchical cluster\u003c/h3\u003e\n\u003cp\u003eSubordinate function \u0026micro; values of each accession were calculated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For the same comprehensive index, the salt-alkali tolerance of materials could be evaluated according to the subordinate function \u0026micro; value. For PC1, the \u0026micro;(\u003cem\u003ex\u003c/em\u003e1) values of 8 accessions (T298, T307, T351, T590, T780, T781, T790, and T1060) exceeded 0.9, indicating the highest level of salt-alkali tolerance. Conversely, 3 materials including T1020, T1099, and T1130 had the lowest \u0026micro;(\u003cem\u003ex\u003c/em\u003e1) value of 0.000, suggesting the poorest salt-alkali tolerance.\u003c/p\u003e \u003cp\u003eThe comprehensive weights for the comprehensive indices were calculated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The comprehensive weights of the 5 indices (\u003cem\u003eW1\u003c/em\u003e, \u003cem\u003eW2\u003c/em\u003e, \u003cem\u003eW3\u003c/em\u003e, \u003cem\u003eW4\u003c/em\u003e, \u003cem\u003eW5\u003c/em\u003e) were 52.04%, 16.95%, 11.56%, 7.31%, 6.71%, and 5.44%, respectively. The comprehensive evaluation D value for salt-alkali tolerance at the seedling stage was calculated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There were a total of 33 accessions with a D value greater than 0.7.\u003c/p\u003e \u003cp\u003eHierarchical clustering based on the D values classified the examined 240 materials into five groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Cluster Ⅴ, the highly salt-alkali-tolerant group, included 38 materials, such as T255. These materials exhibited significantly higher biomass and chlorophyll content compared to the other groups. Cluster Ⅳ, the salt-alkali-tolerant group, included 76 materials such as T123. Cluster Ⅲ, the moderately salt-alkali-tolerant group, included 59 materials such as T119. Cluster Ⅱ, the low salt-alkali-tolerant group, included 55 materials such as T109. The Cluster Ⅰ, the salt-alkali-sensitive group, included 12 materials such as T163.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe analyzed the correlation between 12 indicators and the D value. With the exception of seeding time, the other 11 indices exhibited extremely significant positive correlations with the D value. Notably, the correlation coefficient between stem diameter and D reached 0.8, indicating a strong positive relationship.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePopulation genetic structure\u003c/h2\u003e \u003cp\u003eWe genotyped the association population using SNPs derived from the faba bean 130K SNP array. A total of 30,338 filtered polymorphic SNP markers were retained for further analyses. The population structure of 240 faba bean resources was analyzed using the Admixture software, and the CV error was employed to determine the optimal number of subgroups. The results indicated that the CV error reached its minimum value at \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), suggesting that the materials could be partitioned into four distinct subgroups(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea): Subpop I, Subpop II, Subpop III, and Subpop IV. Accessions with membership probabilities\u0026thinsp;\u0026ge;\u0026thinsp;0.50 were considered to belong to the same group. Among the 240 accessions, 51 were assigned to Subpop I, 81 to Subpop II, 42 to Subpop III, and 31 to Subpop IV, while the remaining materials were classified as a mixed group. Notably, materials from diverse geographical origins or ecological types were grouped within the same subgroups, which implies the occurrence of gene flow or introgression among the faba bean resources.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo better elucidate the genetic relationships among the materials, PCA was performed using PLINK software on the 240 accessions, and the results were visualized as a three-dimensional scatter plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The first three principal components, PC1 (35.22%), PC2 (14.52%), and PC3 (10.96%), collectively captured 60.70% of the total genetic variation. The results revealed that the 240 materials were clearly divided into four distinct clusters, with some overlap observed among certain accessions. These findings aligned with the results of the population structure analysis, thereby demonstrating the reliability of the groups classification.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGWAS for salt-alkali tolerance indexes\u003c/h3\u003e\n\u003cp\u003eTo elucidate the genetic basis of salt-alkali tolerance in faba bean seedlings, we conducted a GWAS that combined phenotypic data with genotypic data through GLM and MLM. After referring to Bonferroni correction, a stringent significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.0\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e was determined. A total of 242 significant SNP loci were detected across the genome. These included 107 loci consistently identified by both GLM and MLM, as well as 18 loci that exhibited significant associations with four or more traits simultaneously. These multi-trait-associated loci likely play critical roles in the salt-alkali stress response in faba bean and warrant further investigation as key candidate loci. Among these, the greatest number of loci (145) were associated with germination rate (GR), whereas FWR and RL were associated with fewer than 10 loci. The Manhattan plot and QQ plot were provided in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eCandidate Gene Analysis\u003c/h3\u003e\n\u003cp\u003eBased on the \u003cem\u003eHedin/2\u003c/em\u003e reference genome in faba bean, a total of 103 genes were predicted, which are distributed across multiple chromosomes. Among them, 10 candidate genes closely related to salt-alkali tolerance traits were identified. The gene \u003cem\u003eVfaba.Hedin2.R1.3g023640\u003c/em\u003e, located on chromosome 3, was annotated as \u003cem\u003eL-GalLDH\u003c/em\u003e. This gene encodes L-galactono-1,4-lactone dehydrogenase, an enzyme that is of critical importance in the synthesis pathway related to maintaining redox balance[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. \u003cem\u003eVfaba.Hedin2.R1.2g026040\u003c/em\u003e was annotated as \u003cem\u003eZAT4\u003c/em\u003e, a member of the \u003cem\u003eC2H2\u003c/em\u003e zinc finger TF family. Previous studies have reported that \u003cem\u003eZAT4\u003c/em\u003e can enhance plant tolerance to osmotic stress and salt stress via the abscisic acid(ABA) signaling pathway[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. \u003cem\u003eVfaba.Hedin2.R1.2g148560\u003c/em\u003e was annotated as \u003cem\u003eNAC domain-containing protein 82\u003c/em\u003e. NAC transcription factors are one of the largest families of plant transcription factors and have been widely proven to be involved in abiotic stress adaptation[\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe remaining candidate genes were predominantly involved in ion transport, signal protein synthesis, and the synthesis of protective enzymes associated with the antioxidant pathway. These functions are all integral to the plant\u0026rsquo;s ability to cope with salt-alkali stress, as they help maintain cellular ion balance, transmit stress signals, and detoxify ROS. In summary, these 10 candidate genes(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) provide valuable insights into the genetic basis of salt-alkali tolerance in faba bean and serve as potential targets for further genetic improvement and functional studies.\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\u003eThe information of significant association loci related to 12 candidate genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGene Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGene Description\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\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edou_TRINITY_DN47983_c0_g1_503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.92E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.3g023640\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL-galactono-1,4-lactone dehydrogenase, mitochondrial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edou_TRINITY_DN50263_c0_g1_698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.35E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.2g026040\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZinc finger protein ZAT4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.27E-05\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\u003eFWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehua_TRINITY_DN149364_c1_g1_436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.15E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.1g188000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1-aminocyclopropane-1-carboxylate oxidase homolog 1\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\u003eGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eye_TRINITY_DN135934_c6_g2_1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.85E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.3g108200\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNAC domain containing protein 50\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\u003eGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eye_TRINITY_DN125437_c2_g1_365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.63E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.3g108200\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNAC domain containing protein 50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eye_TRINITY_DN128763_c0_g1_87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.23E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.4g007000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSucrose transport protein SUC3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.91E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eye_TRINITY_DN142350_c3_g1_1444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.74E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.4g007000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSucrose transport protein SUC3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.00E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eye_TRINITY_DN129792_c1_g2_262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.3g084000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGibberellin 20 oxidase 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.82E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eye_TRINITY_DN129792_c1_g2_293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.00E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.3g084000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGibberellin 20 oxidase 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.94E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eye_TRINITY_DN130469_c4_g4_600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.3g214960\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eK(+) efflux antiporter 6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.39E-06\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\u003eGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eye_TRINITY_DN136702_c1_g3_568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.96E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.2g082800\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMitogen-activated protein kinase kinase kinase 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eye_TRINITY_DN146501_c1_g2_1391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.3g036920\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eE3 ubiquitin-protein ligase PUB22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.82E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDWR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.00E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.88E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.69E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.70E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.34E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.76E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.03E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eye_TRINITY_DN150763_c2_g1_517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.99E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVfaba.Hedin2.R1.1g093640\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChloride channel protein CLC-c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.22E-05\u003c/p\u003e \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\u003eIn China, saline-alkali land accounts for 25% of agricultural cultivated areas, particularly in the northwest, north, and parts of the northeast regions [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These lands remain underutilized due to soil constraints. With continuous changes in land use patterns, the area of saline-alkali land has been increasing annually [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Under such circumstances, investigating crop responses to saline-alkali stress becomes critically important. As a vital temperate legume crop [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], the salt-alkali tolerance of faba bean directly determines its cultivation feasibility in saline-alkali regions. The seedling stage represents the most vulnerable phase in the crop lifecycle, during which environmental stresses exert the most pronounced impacts [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Therefore, analyzing phenotypic changes at this stage under abiotic stress can effectively reflect crop stress tolerance. Previous studies have demonstrated that biomass-related traits of faba bean seedlings, including fresh seedling weight, plant height, and leaf area, are significantly reduced under saline-alkali stress [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Investigating the phenotypic responses of faba bean seedlings to saline-alkali stress will contribute to elucidating the underlying tolerance mechanisms and provide theoretical support for breeding salt-alkali tolerant faba bean cultivars.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eResponse of faba bean seedling phenotypes to saline-alkali stress\u003c/h2\u003e \u003cp\u003eIn this study, saline-alkali stress was applied to faba bean materials during the seedling stage, and significant phenotypic alterations were observed. Comparative analysis between the control and stress groups further revealed the negative effects of saline-alkali stress on seedling growth, manifested by reductions in biomass-related traits, including fresh seedling weight, root fresh weight, and seedling length. These results indicate that saline-alkali stress impairs plant water uptake and developmental processes, which aligns with findings reported by Ziche et al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and Rajhi et al. [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. However, a slight increase in the relative chlorophyll content of leaf was detected, suggesting a potential adaptive mechanism by which faba beans enhance photosynthetic efficiency to mitigate stress impacts. This hypothesis has been previously proposed in studies by Zhuang J et al. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and Xu L et al. [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Furthermore, saline-alkali stress exhibited inhibitory effects on seed germination, characterized by reduced germination rates and prolonged germination periods, consistent with observations by Diab et al. [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Notably, significant differences in the coefficient of variation (CV) among genotypes under stress conditions highlight phenotypic variability, providing a basis for screening salt-alkali tolerant cultivars.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive evaluation for salt-alkali tolerance of faba bean accession at the seedling stage\u003c/h2\u003e \u003cp\u003eSalt-alkali tolerance in crops is a complex polygenic trait, and single phenotypic traits often fail to comprehensively evaluate this capability [\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In this study, a multi-indicator integrated evaluation approach was implemented, combining principal component analysis (PCA) and membership function values to systematically assess the salt-alkali tolerance of 240 faba bean accessions. PCA revealed that the first two principal components accounted for 58.68% of the total variation, primarily associated with biomass traits (fresh seedling weight, root fresh weight, seedling length) and germination characteristics (emergence time), indicating their pivotal roles in salt-alkali tolerance. Based on the comprehensive evaluation D-value calculated for each trait, the 240 accessions were classified into five clusters. Notably, Cluster Ⅴ exhibited the strongest salt-alkali tolerance, containing 38 accessions (e.g., T255, T289), However, evaluating only the seedling stage of faba bean does not necessarily represent the overall performance of these germplasms throughout the entire growth period. Therefore, further comprehensive assessment across the full growth period is required to screen and validate these genetic resources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of salt-alkali tolerance-related loci and candidate genes in faba bean\u003c/h2\u003e \u003cp\u003eThis study identified 242 significant SNP loci associated with salt-alkali tolerance in faba bean through genome-wide association study, among which 57 SNPs were consistently detected by both the Generalized Linear Model (GLM) and Mixed Linear Model (MLM). Functional annotation identified 10 candidate genes strongly linked to salt-alkali tolerance traits. Notably, the \u003cem\u003eL-GalLDH\u003c/em\u003e gene (\u003cem\u003eVfaba.Hedin2.R1.3g023640\u003c/em\u003e) is involved in antioxidant responses. Zhang G-Y et al. [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] demonstrated that overexpression of \u003cem\u003eL-GalLDH\u003c/em\u003e in rice (\u003cem\u003eOryza sativa\u003c/em\u003e) via transgenic methods significantly increased ascorbate content and enhanced salt stress tolerance. The \u003cem\u003eZAT4\u003c/em\u003e gene (\u003cem\u003eVfaba.Hedin2.R1.2g026040\u003c/em\u003e) plays a critical role in osmotic stress regulation in faba bean. Sun L et al. [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] reported that the overexpression of soybean \u003cem\u003eGmZAT4\u003c/em\u003e in Arabidopsis thaliana markedly improved salt tolerance, which was accompanied by elevated activities of ascorbate peroxidase (APX) and superoxide dismutase (SOD). Additionally, the \u003cem\u003eNAC domain-containing protein 82\u003c/em\u003e gene (\u003cem\u003eVfaba.Hedin2.R1.2g148560\u003c/em\u003e) enhances faba bean tolerance to saline-alkali stress by maintaining cellular osmotic homeostasis and strengthening antioxidant capacity. Bokolia et al. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] conducted a genome-wide identification of NAC transcription factors in oat (\u003cem\u003eAvena sativa\u003c/em\u003e), revealing their expression patterns under salt stress. In addition to the aforementioned genes, other candidate genes also play certain roles in the mechanisms underlying crop tolerance to saline-alkali stress. These candidate genes not only deepen the understanding of salt-alkali tolerance mechanisms in faba bean. The application of these genes in faba bean requires further experimental validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study evaluated the salt-alkali tolerance of 240 faba bean germplasm accessions at the seedling stage, employing a multidimensional analytical framework that integrated principal component analysis and the comprehensive evaluation D-value. The comprehensive assessment identified 38 accessions (e.g., T255, T289) exhibiting strong tolerance under saline-alkali stress, which represent critical genetic resources for breeding salt-alkali tolerant cultivars. Genome-wide association study revealed 242 significant single nucleotide polymorphism (SNP) loci associated with salt-alkali tolerance, including 57 loci consistently detected by both the Generalized Linear Model and Mixed Linear Model, thereby elucidating the genetic architecture underlying this trait. Functional annotation further prioritized 10 candidate genes (e.g., \u003cem\u003eL-GalLDH\u003c/em\u003e, \u003cem\u003eZAT4\u003c/em\u003e, \u003cem\u003eNAC82\u003c/em\u003e) involved in faba bean salt-alkali tolerance. These genes are functionally enriched in antioxidant responses, osmotic regulation, and signal transduction pathways, providing actionable targets for molecular design breeding.\u003c/p\u003e \u003cp\u003eIn summary, this study advances the understanding of the genetic mechanisms governing salt-alkali tolerance in faba bean and provides valuable gene resources for breeding programs. Future efforts focusing on functional validation of these candidate genes and the application of gene editing technologies are expected to enhance faba bean performance in saline-alkali regions, ultimately facilitating its widespread cultivation in marginal lands.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePlant Materials\u003c/h2\u003e \u003cp\u003eA panel of 240 faba bean accessions from different countries was phenotyped for seedling-stage saline-alkali tolerance. Of these, 183 were from China, and 57 were from other countries. These 240 accessions of faba been germplasm resources used in this research come from Qinghai Academy of Agricultural and Forestry Sciences, and all germplasm resources were cultivated at the Academy of Agriculture and Forestry Sciences of Qinghai University.\u003c/p\u003e \u003cp\u003eThe panel had been genotyped with the faba bean 130 K targeted next-generation sequencing SNP genotyping platform[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. From this SNP dataset, 30,338 SNPs were selected as high-quality SNPs with minor-allele frequency (MAF) greater than 0.05 and maximum missing rate less than 20%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eField trial and phenotyping\u003c/h2\u003e \u003cp\u003eFive seeds were used for each accession to assess seedling saline-alkali tolerance. Seeds were sown in pots and grown in an artificial climate chamber with an 18 h/6 h light/dark photoperiod, day/night temperatures of 22\u0026deg;C/17\u0026deg;C, and approximately 50% relative humidity. A saline-alkaline solution composed of NaCl, sodium carbonate (Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e), and sodium bicarbonate (NaHCO\u003csub\u003e3\u003c/sub\u003e) with a 9:1:1 ratio was used to irrigate the stress group. The final concentration of this solution was 100 mmol/L and the pH was adjusted to 9.2. Control plants were irrigated with ddH\u003csub\u003e2\u003c/sub\u003eO.\u003c/p\u003e \u003cp\u003eA total of 12 morphological and physiological traits of faba been plants were measured after being irrigated with a saline-alkaline solution for 21 days. These traits include shoot fresh weight (FWS), root fresh weight (FWR), shoot dry weight (DWS), root dry weight (DWR), relative chlorophyll content (RCC) in leaves, root length (RL), shoot length (SL), stem diameter (SD), leaf number (LN), germination rate (GR), germination time (ST), and leaf area (LA). Standard protocols were used for all measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eRefer to the comprehensive evaluation (D) value of crop stress tolerance evaluation to conduct statistical analysis on the data[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The saline-alkaline tolerance coefficient (SATC) of each of the above indices was calculated.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{SATC}_{ij}=\\frac{{\\text{X}}_{\\text{i}\\text{j}}\\left(\\text{s}\\text{t}\\text{r}\\text{e}\\text{s}\\text{s}\\right)}{{\\text{X}}_{\\text{i}\\text{j}}\\left(\\text{c}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSATC\u003csub\u003eij\u003c/sub\u003e represents the saline-alkaline tolerance coefficient of index (j) for accessions (i); X\u003csub\u003eij\u003c/sub\u003e(control) and X\u003csub\u003eij\u003c/sub\u003e(stress) denote the values of the index for the accessions evaluated under ddH\u003csub\u003e2\u003c/sub\u003eO and saline-alkaline treatments, respectively.\u003c/p\u003e \u003cp\u003eThe membership function value \u003cem\u003e\u0026micro;\u003c/em\u003e was calculated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\mu\\:\\left(\\text{X}i\\right)=\\frac{(\\text{X}i-\\text{X}i\\text{m}\\text{i}\\text{n})}{(\\text{X}imax-\\text{X}imin)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe X\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the \u003cem\u003eith\u003c/em\u003e comprehensive index; X\u003csub\u003e\u003cem\u003eimax\u003c/em\u003e\u003c/sub\u003e and X\u003csub\u003e\u003cem\u003eimin\u003c/em\u003e\u003c/sub\u003e represent the maximum and minimum values for the \u003cem\u003eith\u003c/em\u003e comprehensive index of each material, respectively.\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) the weight function W\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e was calculated and represents the relative importance of the \u003cem\u003eith\u003c/em\u003e comprehensive index for a accession.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{W}_{i}=\\frac{{P}_{\\text{i}}}{\\sum\\:_{i-1}^{n}{P}_{i}}\\:i=\\text{1,2},3\\cdots\\:,n$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u003c/em\u003e \u003csub\u003e \u003cem\u003ei\u003c/em\u003e \u003c/sub\u003e represents the contribution to the \u003cem\u003eith\u003c/em\u003e comprehensive index.\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) the comprehensive evaluation parameter for saline-alkaline tolerance resilience (D) for each cultivar was calculated to identify the saline-alkaline tolerance capacity of the different accessions.\u003c/p\u003e \u003cp\u003eThen, hierarchical cluster analysis was also used to evaluate salt tolerance.\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:D={\\sum\\:}_{i-1}^{n}\\left[\\text{\u0026mu;(}{X}_{i}\\text{)\u0026times;}{W}_{i}\\right]\\:i=\\text{1,2},3\\cdots\\:,n\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e\u0026micro;(X\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) is the membership function value of \u003cem\u003eith\u003c/em\u003e comprehensive index; W\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the relative importance of the \u003cem\u003eith\u003c/em\u003e comprehensive index.\u003c/p\u003e \u003cp\u003eSPSS26.0 and R4.3.2 software were used to conduct correlation analysis, principal component analysis, membership function analysis, and cluster analysis. R4.1.3, Origin 2022, and GraphPad Prism 9 were used to draw the images required in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Structure Analysis\u003c/h2\u003e \u003cp\u003eTo estimate the population structure and phylogenetic relationships within the panel of faba bean, the Admixture software was run from 2 to 10 to analyze 30,338 markers. A tenfold cross-validation (CV) scheme was repeated 10 times for each value of K. The optimal grouping was determined by identifying the K-value associated with the minimum CV error, which was visualized through a curve plotting the CV error against the K-value. Accessions with membership probabilities\u0026thinsp;\u0026ge;\u0026thinsp;0.50 were considered to belong to the same group. We then utilized R4.1.3 to visually represent the admixture proportions. For PCA, the PLINK v.1.9 software was applied, setting the number of principal components (PCs) equal to the number of samples[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The resulting eigenvector plot was generated using the ggplot2 package in R, following the methodology described by Wickham[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide association analysis\u003c/h2\u003e \u003cp\u003eThe Generalized Linear Model (GLM) and Mixed Linear Model (MLM) in Tassel 5 software were used to conduct GWAS for traits related to salt-alkali tolerance. The significance of associated markers was evaluated using P-values, with a stringent threshold of -log10(\u003cem\u003eP\u003c/em\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;4.0 applied to identify significant associations. This threshold was determined based on Bonferroni correction to account for multiple testing. Finally, the CMplot package in R software was used to visualize the results of the GWAS through a Manhattan plot and a quantile-quantile (Q-Q) plot. Functional annotations of candidate genes within the identified regions were retrieved using the \u003cem\u003eHedin/2\u003c/em\u003e reference genome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://projects.au.dk/fabagenome/genomics-data\u003c/span\u003e\u003cspan address=\"https://projects.au.dk/fabagenome/genomics-data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n \u003cli\u003eFWS: shoot fresh weight\u003c/li\u003e\n \u003cli\u003eFWR: root fresh weight\u003c/li\u003e\n \u003cli\u003eDWS: shoot dry weight\u003c/li\u003e\n \u003cli\u003eDWR: root dry weight\u003c/li\u003e\n \u003cli\u003eRCC: relative chlorophyll content\u003c/li\u003e\n \u003cli\u003eRL: root length\u003c/li\u003e\n \u003cli\u003eSL: shoot length\u003c/li\u003e\n \u003cli\u003eSD: stem diameter\u003c/li\u003e\n \u003cli\u003eLN: leaf number\u003c/li\u003e\n \u003cli\u003eGR: germination rate\u003c/li\u003e\n \u003cli\u003eST: germination time\u003c/li\u003e\n \u003cli\u003eLA: leaf area\u003c/li\u003e\n \u003cli\u003eGWAS: Genome - Wide Association Study\u003c/li\u003e\n \u003cli\u003eSNP: Single Nucleotide Polymorphism\u003c/li\u003e\n \u003cli\u003eGLM: Generalized Linear Model\u003c/li\u003e\n \u003cli\u003eMLM: Mixed Linear Model\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were conducted following the guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the materials were provided by the Legume Research Group of the Qinghai Academy of Agricultural and Forestry Sciences. All the data generated were listed in the supplementary tables and figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Key Research and Development and Transformation Plan of Qinghai Province (2022-NK-109); National Natural Science Foundation of China (NSFC,42267008) and the China Agriculture Research System of MOF and MARA (CARS-08-G06).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.P. contributed to software, data analysis, writing, and editing. Z.S. contributed to software, data analysis, and editing. X.Z. contributed to software, supervision, data analysis, and writing. X.W. contributed to the investigation, review, and editing. D.Z. contributed to software, investigation, and writing. C.T. contributed to software, data analysis, and writing. H.Z. contributed to experimental design, methodology, software, investigation, data analysis, and writing the original draft. W.H contributed to software, data analysis. L.Y. contributed to supervision, funding acquisition, writing, review, and editing. The author(s) read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1 Qinghai University, Xining 810016, China. 2 Laboratory of Research and Utilization of Crop Germplasm Resources on the Qinghai-Tibet Plateau, Xining 810016, China. 3 National Duplicate Genebank for Crops, Xining 810016, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGuti\u0026eacute;rrez N, P\u0026eacute;gard M, Balko C and Torres AM (2023) Genome-wide association analysis for drought tolerance and associated traits in faba bean(Vicia faba L.). Front. Plant Sci. 14:1091875.\u003c/li\u003e\n \u003cli\u003eSingh AK, Bharati RC, Manibhushan NC, Pedpati A (2013) An assessment of faba bean (Vicia faba L.) current status and future prospect. 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Plant Stress, 10, 100276. https://doi.org/10.1016/j.stress.2023.100276\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Vicia faba L., Compound saline-alkali tolerance, Comprehensive evaluation, GWAS","lastPublishedDoi":"10.21203/rs.3.rs-6519550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6519550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFaba bean (\u003cem\u003eVicia faba\u003c/em\u003e L.) is a crucial cool-season legume crop, which is highly valued for its high protein content and key role in crop rotation systems. Considering the increasing threat of soil salinity and alkalinity globally, it is critical to screen germplasm resources with salt and alkali tolerance in faba bean and to identify the underlying genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, 12 morphological and physiological traits under compound saline-alkali stress were measured to evaluate saline-alkali tolerance of 240 germplasm based on principal component analysis. The results showed that biomass-related traits such as fresh weight of shoot and leaf number had relatively high weights in the evaluation of salt and alkali tolerance at the seedling stage, and 38 highly saline-alkali tolerant materials were identified. A total of 242 SNPs affecting seedling saline-alkali tolerance were identified in a genome-wide association study of 240 faba bean accessions, with 57 SNPs significantly associated with 7 traits and identified by GLM and MLM models. It was found that 10 genes (such as \u003cem\u003eL-GalLDH\u003c/em\u003e, \u003cem\u003eZAT4\u003c/em\u003e, \u003cem\u003eGA20ox2\u003c/em\u003e) overlapped with the reported genes related to salt-alkali tolerance or stress resistance by functional annotation of candidate genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhich enhanced our understanding of the regulatory network of saline-alkali tolerance in faba beans and provided genetic resources and potential targets for molecular design breeding of saline-alkali tolerance in faba bean.\u003c/p\u003e","manuscriptTitle":"Comprehensive evaluation of compound saline-alkali tolerance and gene mining by GWAS in Vicia faba L.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 10:52:40","doi":"10.21203/rs.3.rs-6519550/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-11T19:49:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-30T08:11:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145136655206736912166331338755371280797","date":"2025-05-22T11:02:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117989222932779349094388932146756873663","date":"2025-05-20T10:05:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-20T08:18:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76612110504938485426953580153998716889","date":"2025-05-15T00:28:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-13T00:18:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-12T17:27:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-29T09:38:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-29T08:20:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-04-29T08:19:27+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":"8fb63635-ee58-4b58-991f-bd25bf2ff9e8","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:06:08+00:00","versionOfRecord":{"articleIdentity":"rs-6519550","link":"https://doi.org/10.1186/s12870-025-07249-4","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-09-30 15:57:31","publishedOnDateReadable":"September 30th, 2025"},"versionCreatedAt":"2025-05-15 10:52:40","video":"","vorDoi":"10.1186/s12870-025-07249-4","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07249-4","workflowStages":[]},"version":"v1","identity":"rs-6519550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6519550","identity":"rs-6519550","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}