Stage-specific GWAS identifies a pleiotropic ankyrin repeat locus near GmSALT3 for salinity tolerance in soybean

Stage-specific GWAS identifies a pleiotropic ankyrin repeat locus near GmSALT3 for salinity tolerance in soybean | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stage-specific GWAS identifies a pleiotropic ankyrin repeat locus near GmSALT3 for salinity tolerance in soybean Preetkumar H. Trivedi, Janhavi Gadhawe, Shreyas M. Salvi, Snehaben M. Dodia, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8173753/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Mar, 2026 Read the published version in Plant Cell Reports → Version 1 posted 4 You are reading this latest preprint version Abstract Soybean is a vital global source of protein and oil, yet its productivity is severely affected by soil salinity, which hampers germination and seedling growth, leading to stunted development and yield reductions. In this study, 198 diverse soybean genotypes were assessed for salinity tolerance at the germination and seedling stages under 200 mM NaCl stress. Seven phenotypic traits were evaluated, and delta percentages (Δ%) across two experiments (E1 and E2) were calculated. Genome-wide association studies (GWASs) were conducted viatwo complementary models (BLINK and FarmCPU), identifying 66 significant SNPs, with 20 consistently detected across both environments and models, confirming their robustness. SNP × SNP interaction analysis revealed 953 significant epistatic interactions, highlighting the complex genetic basis of salinity tolerance. Among these SNPs, rs.Gm03.39710939, linked with the gene Glyma.03G170501 on chromosome 3, hadstrong pleiotropic effects on three major traits: Delta percentage of chlorophyll content (Δ%_SPAD_200mM), Mean of leaf scorch score (LSS_MEAN), and Mean of seedling to flowering survival (SFS_MEAN). This gene encodes an ankyrin repeat (ANK) protein, which plays a crucial role in salt tolerance mechanisms. Structurally, Glyma.03G170501 is locatedapproximately 73 kb upstream of GmCHX1/GmSALT3 ( Glyma.03G171600 ), a well-recognized salt tolerance locus. On the basis of this pleiotropic SNP, a Kompetitive Allele-Specific PCR (KASP) marker was successfully developed and validated. These genomic resources offer valuable tools for improving soybean breeding strategies and developing salt-tolerant varieties. Soybean Salinity tolerance Genome-wide association study (GWAS) Ankyrin repeat protein GmSALT3 Marker-assisted selection (MAS) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Key message GWAS identified 20 salinity-tolerance SNPs in soybean, including a pleiotropic locus near GmSALT3 . A validated KASP marker for rs.Gm03.39710939 enables early screening and marker-assisted breeding. Introduction Soybeans ( Glycine max (L.) Merr.) are a significant legume crop cultivated globally, contributing a major portion of the world's vegetable oil and protein. Approximately 333 million tonnes of soybeans are produced each year worldwide (Feng et al. 2023 ). The leading producers of soybeans worldwide are Brazil, the United States, Argentina, China, and India (Lucić et al. 2025 ). However, soybeans are generally sensitive to salt (Noor et al. 2024 ), and soil salinization affects approximately 19.5% of arable land worldwide, posing a significant threat to their productivity. Salt stress negatively impacts normal plant development, including photosynthesis, leaves gas exchange, flowering time, and yield, making it crucial to improve soybean salinity tolerance for food security (Zhang et al. 2020 ). Salinity has severe impacts on germination and early seedling growth. High NaCl creates intense osmotic stress that reduces water uptake and turgor, while the accumulation of Na⁺ and Cl⁻ causes an ionic imbalance in tissues. Even moderate salt levels markedly delay germination and inhibit shoot and root growth (Zhang et al. 2019 ). Additionally, a high amount of salt produces reactive oxygen species, including hydrogen peroxide (H₂O₂), superoxide radicals (O₂⁻), and hydroxyl radicals (OH⁻). These ROS can damage cells, causing DNA mutations, protein breakdown, and lipid peroxidation (Safar et al. 2019). Salt-stressed soybean seedlings commonly exhibit chlorosis, necrosis, and scorching of leaves, as sodium and chloride disrupt photosynthesis and nutrient homeostasis (Noor et al. 2024 ). All of these mechanisms contribute to the low survival rate of soybean plants under salinity stress. To address these challenges, genetic analyses have begun to identify the loci associated with salt tolerance in soybeans. Several salt tolerance genes have been identified through genome-wide association studies (GWASs), including GmSALT3/GmCHX1, GsERD15B , and GmCDF1 (Guan et al. 2014 ; Qi et al. 2014 ). These genes contribute to ionic homeostasis, the regulation of oxidative stress, and salt stress signaling pathways (Guan et al. 2023 ). Notably, a gene ( GmSALT3) on chromosome 3 was cloned from the salt-tolerant cultivar Tiefeng 8 and has been shown to increase NaCl exclusion and improve yield under saline conditions (Guan et al. 2014 ; Pruthi et al. 2024 ). D et al. (2019) also used GWAS and scanned 305 diverse accessions with traits such as the leaf scorch score (LSS), chlorophyll content ratio (CCR), leaves sodium content (LSC), and leaves chloride content (LCC). They detected significant marker‒trait associations on Chr 1, 3, 8, and 18, notably validating the known GmSALT3 locus on Chr 3. Similarly, Zeng et al. ( 2017 ) evaluated 283 soybean accessions under 120 mM NaCl stress for 12–18 days and identified genetic loci associated with salt tolerance via a GWAS. This study revealed 45 significant single-nucleotide polymorphisms (SNPs) located on chromosomes 2, 3, 7, 8, 10, 13, 14, 16, and 20 that are associated with leaves chloride and chlorophyll concentrations. Among these, 31 SNPs mapped to a primary salt tolerance quantitative trait locus (QTL) on chromosome 3, supporting previous findings on the genetic basis of salt tolerance in soybean. Zhao et al. ( 2020 ) further investigated the role of ankyrin repeat (ANK) proteins in salt stress tolerance. They identified 226 ANK genes in soybean, of which GmANK114 was significantly induced by salt and drought stress. The overexpression of GmANK114 in transgenic Arabidopsis and soybean hairy roots improved germination rates, reduced oxidative damage, and activated key stress-responsive genes, including WRKY13, NAC11, DREB2, MYB84 , and bZIP44 . In addition, classical genetic studies have revealed that salt tolerance in soybean is primarily controlled by a single dominant gene, with subsequent biparental QTL mapping studies consistently identifying a major quantitative trait locus in linkage group N near SSR markers Satt255 and Sat_091, along with additional minor QTLs on other chromosomes (Hamwieh et al. 2011 ). Wild soybean ( Glycine soja ) serves as a valuable genetic resource for improving salt tolerance, with recent studies identifying novel loci, such as a dominant salt tolerance gene mapped to chromosome 3, in populations derived from the salt-tolerant wild accession NY36-87, demonstrating the potential of wild germplasm for enhancing salt tolerance in cultivated soybean (Guo et al. 2021 ). Despite these advances, a comprehensive genetic study of salinity tolerance that spans both the germination and seedling stages is lacking. Previous GWASs have typically targeted a single stage or a single salt level; for example, Wang et al. ( 2024a ) conducted a GWAS on germination index traits under salt stress, whereas other groups have evaluated tolerance at the seedling stage (Pruthi et al. 2024 ). To date, no study has integrated multi-trait data from germination through early seedling development under a range of salinity treatments. We hypothesize that the mechanism for salinity tolerance may differ at the germination and seedling stages. During the germination stage, plants likely focus on maintaining osmotic balance to avoid stress. In contrast, during the seedling stage, plants employ different strategies, such as ion exclusion and chlorophyll retention (Zhang et al. 2022 ). Therefore, GWASs need to be run differently for both stages to identify the distinct specific MTAs/QTLs that contribute to salinity tolerance. In this study, we evaluated 198 diverse soybean genotypes under increasing salinity stress across two different stages (germination and seedling) and two experiments (E1 and E2). The objectives of this study were to evaluate the phenotypic variation in salinity tolerance at both the germination and seedling stages, conduct genome-wide association studies (GWASs) for the identification of stage-specific single-nucleotide polymorphisms (SNPs) and marker–trait associations (MTAs), and validate key SNPs through the development of Kompetitive Allele-Specific PCR (KASP) markers for use in marker-assisted selection (MAS). For this purpose, at the germination stage, we used the mean data from both experiments of Gobade et al. ( 2025 ) to calculate the Δ% change in germination percentage (GP) under 100 mM and 200 mM NaCl. At the seedling stage, under 200 mM NaCl, we evaluated additional traits, including the delta percentage (Δ%) in the number of leaves (NL), seedling length (SL), chlorophyll content (SPAD), mean of leaf scorch score (LSS), and mean of the seedling to flowering survival (SFS), across both experiments. A GWAS was conducted via the FarmCPU and BLINK models, identifying SNPs associated with salinity tolerance across different traits and stages. We aimed to analyze these SNPs to pinpoint known and novel genomic regions or genes associated with salt tolerance and to develop a KASP marker for validation and use in marker-assisted selection (MAS). This research will support the development of salt-tolerant soybean varieties, contributing to global efforts to combat soil salinity. Materials and methods Plant materials A core set of 198 soybean accessions, referred to as the Maharashtra Association for the Cultivation of Sciences (MACS) soybean panel, was selected from the 55-year-old soybean germplasm repository maintained at the Agharkar Research Institute (ARI), Pune (Gobade et al., 2025 ; Patil et al., 2025 ). The details of the 198 soybean genotypes are provided in Supplementary Table S1 . The panel includes indigenous (IC) and exotic collections (EC), promising cultures (PC), farmer collections (FC), advanced breeding lines, and genotypes spanning early, mid-late, and late maturity groups. Experimental design and salinity treatment Seed preparation and sowing The experiment was conducted in a completely randomized design (CRD) under polyhouse conditions at ARI, Pune (18°31′16.6″N, 73°49′53.3″E) for the seedling stage screening. This process was repeated twice, designated E1 (April 2024) and E2 (December 2024), to ensure the reproducibility and consistency of the results. Prior to sowing, the seeds were surface-sterilized with a 0.2% sodium hypochlorite solution (Hi-AR/ACS grade, 4% w/v; HiMedia, Catalog No. AS102-12) for 1 min, followed by thorough rinsing with distilled water. For each accession, a total of eight healthy and uniform seedlings were raised: four for the control and four for the treatment. The control and treatment sets were sown in separate seedling trays, each with 40 wells (5 × 8 layout). Seedlings were maintained under controlled polyhouse conditions at 25°C with optimal watering to ensure uniform growth until the emergence of distinct seedling stages (Gobade et al. 2025 ). Salinity treatment for the seedling stage study Salinity stress treatments of 200 mM NaCl were initiated 15 days after sowing at the seedling stage. The seedling trays were divided into two groups: a control group (15 trays) and a treatment group (15 trays). Each tray was placed in a plastic tray filled with either distilled water (control, zero mM NaCl) or a saline solution (200 mM). To avoid osmotic shock, a stepwise increase in salinity was applied to the treatment group. The salt concentration was increased from an initial concentration of 60 mM NaCl to 120 mM after 2 days, 150 mM after 5 days, and finally to the target concentration of 200 mM after 7 days. All the saline solutions were prepared with 3 L of distilled water per tray. To maintain consistent concentration levels, the solutions in all trays were replenished with fresh water and NaCl every three days (Gobade et al. 2025 ; Javid et al. 2022 ). Phenotypic data collection Germination Stage The mean phenotypic data related to the germination percentage trait for the control, 100 mM NaCl, and 200 mM NaCl treatments were obtained from our previous study by Gobade et al. ( 2025 ). Two traits were used for analysis, and the delta percentage (Δ%) was calculated to evaluate the effect of salinity stress: (1) germination percentage (GP) at 100 mM NaCl (Δ%_GP_100mM) and (2) germination percentage at 200 mM NaCl (Δ%_GP_200mM). Seedling Stage Phenotypic data for the seedling stage were collected twenty-three days after treatment with 200 mM NaCl for SL, NL, and SPAD. Additionally, data for the leaf scorch score (LSS) and seedling survival (SFS) were collected four days later in both experiments (E1 and E2). Traits were recorded under both control and salinity-stressed conditions. Among them, three were quantitative traits: (1) the delta percentage of the number of leaves (Δ%_NL_200mM), measured via manual counting; (2) the delta percentage of seedling length (Δ%_SL_200mM), measured from the base to the tip of the tallest leaves via a ruler; and (3) the delta percentage of chlorophyll content (Δ%_SPAD_200mM), estimated via a chlorophyll meter SPAD-502, Konica Minolta, Inc., Osaka, Japan. The remaining two were qualitative traits: (4) Mean of leaf scorch score (LSS_MEAN), visually assessed on a scale of 0–5, where 0 indicates no damage, one corresponds to up to 10% leaves scorching, 2 represents 11–25% scorching, 3 indicates 26–50% scorching, 4 reflects 51–75% scorching, five indicates very severe (≥ 75%) leaves scorching, as illustrated in Fig. 1 , and (5) Mean of seedling to flowering survival (SFS_MEAN), recorded as a binary score, with 1 representing survival up to flowering and 0 representing posttreatment death. Calculation of the impact of salinity stress on the delta percentage (Δ%) To evaluate the effects of salinity stress, the combined means of the E1 and E2 experimental data were used to determine the mean control and mean treatment data for all salinity-related traits. The delta percentage (Δ%) for each trait was calculated via the following formula: Δ% = [(Mean of control data – Mean of treatment data)/Mean of the control data] × 100. These Δ% values quantified the relative reduction in performance under salinity stress and were used as phenotypic input data for GWASs. Statistical analysis of phenotypic data Phenotypic data were subjected to descriptive statistical analyses and one-way analysis of variance (ANOVA) to assess variation among genotypes, followed by Tukey’s honest significant difference (HSD) post hoc test to compare mean differences. All analyses were performed via JMP software (version 18; SAS Institute Inc., Cary, NC, USA). For each genotype, the combined mean of the data from experiments E1 and E2 was calculated to obtain the overall mean, range, standard deviation (SD), and standard error (SE) of the data. Multivariate analyses, including principal component analysis (PCA), two-way hierarchical clustering, and K-means clustering, were also conducted via JMP 18 (SAS Institute Inc., 2024). Genotyping The SNP marker data were obtained from Patil et al. ( 2025 ), and genome-wide association studies (GWASs) were conducted using 23,574 high-quality SNP markers to identify genetic loci associated with salinity tolerance. GWAS and SNP × SNP interaction analysis In this study, two techniques were employed for association analysis of traits across E1 and E2: the Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK) and the fixed and random model of circulating probability unification (FarmCPU). We only calculated the phenotypic variation explained (PVE%) via the BLINK model, as it evaluates markers directly without assuming an even distribution of causative genes, unlike the FarmCPU model (Wang and Zhang 2021 ). We applied the Bonferroni correction (Bonferroni 1935 ) to maintain an overall Type I error rate of 0.05, which resulted in a stringent -log10 ( P ) threshold of 5.67. For a less restrictive approach, a suggestive threshold of -log10 ( P ) ≥ 3 was also utilized (Javid et al. 2022 ). Additionally, we generated genome-wide Manhattan plots and multitrack quantile‒quantile (Q‒Q) plots to visualize significant marker‒trait associations (MTAs). The analysis of two-dimensional (SNP × SNP) epistatic interactions among the main SNPs associated with salinity tolerance traits was conducted via TBtools software (Chen et al. 2020 ). To identify the best SNP and best chromosome signals, a corrected epistatic P value threshold of ≤ 0.0001 was applied, and interactive graphics (circos plots) were generated (Chen et al. 2022 ). Candidate gene annotation and LD block analysis SoyBase ( http://www.soybase.org ) was utilized to identify known and novel candidate QTLs and genes located within 200 kb upstream and downstream of the significant SNP peaks detected via GWAS for each trait. To identify possible candidate genes, research was conducted on the Williams 82 Glycine max Wm82.a4.v1 (Zhang et al. 2024 ) reference genome ( https://datahub.wildsoydb.org ). LD analysis was performed using the same protocol described by Patil et al. ( 2025 ), with Haploview v4.2 (Barrett et al., 2004 ). Kompetitive allele-specific PCR (KASP) assay The common significant SNPs identified in both E1 and E2 were selected for the traits Δ%_SPAD_200mM, LSS_MEAN, and SFS_MEAN in soybean at the seedling stage. For primer design, the upstream and downstream sequences (± 50 bp) surrounding this SNP position were retrieved from the Wild SoyDB DataHub ( https://datahub.wildsoydb.org ). The KASP primer sequences (assay name: rs_Gm03_39710939) were synthesized by LGC Genomics (United Kingdom). KASP TF V4.0 2× Master Mix (catalog no. KBS-1050-122) and 384-well plates were also purchased from LGC. Each primer pair consisted of two allele-specific forward primers (F1 and F2) and one standard reverse primer (R). The F1 and F2 primers contained 5′ tail sequences with 6-carboxyfluorescein (FAM) and hexachloro-6-methylfluorescein (HEX) fluorescent linkers, respectively (Table S6). Genotypic calls were assigned on the basis of fluorescence signals: samples showing only FAM fluorescence were classified as homozygous for allele 1, those showing only HEX fluorescence were classified as homozygous for allele 2, and samples exhibiting both FAM and HEX fluorescence were designated as heterozygous. The KASP significant genotyping assays were performed in duplicate for each genotype on a 384-well plate. Each reaction had a final volume of 5.0 µL, containing 1.0 µL of genomic DNA (∼10 ng/µL), 2.5 µL of KASP master mixture, 0.07 µL of KASP primer mixture, and 1.43 µL of ddH₂O. At least two no-template controls (NTCs) were included on each plate. The PCR was conducted with the following protocol: an initial hot-start step at 95°C for 15 min; followed by 10 touchdown cycles at 94°C for 20 s and 65–57°C for 60 s (decreasing by 1°C per cycle); and then 30 amplification cycles at 94°C for 20 s and 57°C for 60 s. To improve cluster separation, a final extension of 3 cycles was added, consisting of 20 s at 94°C and 60 s at 57°C. The fluorescence data from the amplified products were detected and analyzed via the QuantStudio 5 Real-Time PCR system (Applied Biosystems) to make genotype calls. Results A total of 198 genotypes of soybean were evaluated for germination and seedling stage traits under control and NaCl stress conditions (100 mM and 200 mM) across two experiments (E1 and E2). The observations and descriptive statistics revealed substantial variation among the genotypes, and NaCl stress consistently reduced trait performance relative to the control conditions (Table 1 ; Table S2; Fig. 2 ). Table 1 Descriptive statistics of phenotypic traits related to the salinity response in soybean. Experiment Traits Treatment Range Mean Std. Error Std. Deviation ANOVA E1 GP_Control Control 0-100 62.77 2.48 34.87 GP_100mM 100 mM 0-100 59.37 2.41 33.95 0.3281434 GP_200mM 200mM 0-100 48.51 2.35 33.04 0.0010053** NL_Control Control 4–10 6.46 0.07 1.01 NL_200mM 200 mM 2–10 6.44 0.09 1.25 0.8784953 SL_Control Control 26–40 33.80 0.18 2.55 SL_200mM 200 mM 25–39 33.27 0.19 2.73 0.0466459* SPAD_Control Control 23–38 32.28 0.16 2.32 SPAD_200mM 200 mM 6–20 11.33 0.17 2.34 0.0010053** E2 GP_Control Control 0-100 82.30 1.59 22.33 GP_100mM 100 mM 0-100 61.95 2.64 37.16 0.0010053** GP_200mM 200 mM 0-100 73.34 1.68 23.66 0.0010053** NL_Control Control 2–17 11.47 0.15 2.18 NL_200mM 200 mM 4–13 7.96 0.11 1.49 0.0010053** SL_Control Control 18–57 39.07 0.53 7.49 SL_200mM 200 mM 4–39 23.62 0.37 5.25 0.0010053** SPAD_Control Control 10–36 24.29 0.33 4.59 SPAD_200mM 200 mM 2–39 20.51 0.51 7.16 0.0010053** Mean GP_Control Control 0-100 72.53 1.67 23.43 GP_100mM 100 mM 0-100 60.66 1.99 27.93 0.0010053** GP_200mM 200 mM 0-100 60.93 1.58 22.18 0.0010053** NL_Control Control 4–12 8.96 0.09 1.25 NL_200mM 200 mM 5–10 7.20 0.07 1.00 0.0010053** SL_Control Control 23–47 36.43 0.31 4.32 SL_200mM 200 mM 15–35 28.44 0.22 3.07 0.0010053** SPAD_Control Control 22–36 28.29 0.19 2.61 SPAD_200mM 200 mM 6–26 15.92 0.27 3.75 0.0010053** Δ% (E1&E2) Δ%_GP_100mM 100 mM (-77.8)-100 17.45 2.21 30.44 - Δ%_GP_200mM 200 mM (-400)-100 13.32 2.58 35.64 - Δ%_NL_200mM 200 mM (-43.48)-49.12 18.25 1.10 15.39 - Δ%_SL_200mM 200 mM (-26.83)-57.80 20.80 0.94 13.15 - Δ%_SPAD_200mM 200 mM 5.24–74.90 43.21 1.04 14.55 - The asterisks indicate statistically significant differences (* P < 0.05 and ** P < 0.01) between the means (control versus treatment). GP_100mM: Germination percentage at 100 mM, GP_200mM: Germination percentage at 200 mM, NL_200mM: Number of leaves at 200 mM, SL_200mM: Seedling length at 200 mM, SPAD_200mM: Chlorophyll content measured by SPAD meter at 200 mM, Δ%_GP_100mM: Delta percentage of germination percentage at 100 mM; Δ%_GP_200 mM: Delta percentage of germination percentage at 200 mM; Δ%_NL_200mM: Delta percentage of number of leaves at 200 mM; Δ%_SL_200mM: Delta percentage of seedling length at 200 mM; Δ%_SPAD_200 mM: Delta percentage of chlorophyll content measured by SPAD meter at 200 mM. Descriptive statistics of salinity-related traits At the germination stage, salinity stress significantly reduced the GP in both environments. In E1, the GP decreased from 62.77% in the control to 48.51% at 200 mM NaCl, whereas in E2, the reduction ranged from 82.30% to 73.34%. The mean values across environments indicated an overall decrease of 11.6% at 200 mM, with ANOVA confirming significant differences ( P < 0.01) (Table 1 , Fig. 2 A–B). At the seedling stage, leaves formation was also affected, with the number of leaves (NL) decreasing from 8.96 (control) to 7.20 at 200 mM (Fig. 2 C), corresponding to a 19.64% reduction. The mean seedling length (SL) of the control showed a wide range (23–47) of high variability, with a more substantial decline of 21.93%, with pooled means decreasing from 36.43 cm to 28.44 cm under 200 mM NaCl (Fig. 2 D). The mean SPAD values exhibited the most drastic decrease, from 28.29 (control) to 15.92 (200 mM), corresponding to a 43.7% reduction (Fig. 2 E). These reductions were highly significant ( P < 0.01), confirming that the chlorophyll content is the most sensitive trait under salinity stress. The stress-specific qualitative indicators further supported the negative impact of salinity. The analysis of leaf scorch scores (LSSs) at 200 mM salinity revealed a bimodal distribution. Environment E2 had extreme results, with both the highest number of highly tolerant plants (LSS = 0; 66 genotypes) and the highest number of severely susceptible plants (LSS = 5; 86 genotypes). This clustering at the extremes confirmed high genotypic variation in salinity tolerance, suggesting strong potential for selection in both environments (Fig. 2 F). Furthermore, E2 resulted in notably better seedling to flowering survival (SFS) under 200 mM salinity, with 65 genotypes surviving up to the flowering stage, whereas only 18 genotypes survived in E1. This considerable difference highlights the significant environmental influence on the genotype's ability to overcome prolonged salinity stress beyond the seedling stage (Fig. 2 G). The Δ% change highlights the magnitude and direction of the change in traits under the highest stress level (200 mM) compared with the control, averaged across E1 and E2. All the traits presented a negative Δ% percentage change, indicating that salinity stress significantly impacts soybean growth and development, with the SPAD chlorophyll content (43.21%) being the most affected trait in terms of overall percentage reduction due to 200 mM salinity stress (Table 1 ). Two-way hierarchical clustering grouped the 198 soybean genotypes into distinct clusters based on NL, SL, and SPAD values. The heatmap clearly indicates a separation between the tolerant and sensitive groups, with tolerant genotypes exhibiting higher trait values (red) than sensitive genotypes (blue). This clustering pattern highlights substantial genotypic diversity in response to salinity (Fig. 3 A). PCA revealed that the first two components explained 73.3% of the total phenotypic variation, with PC1 contributing 39.8% and PC2 contributing 33.5% (Table S3, Fig. 3 B). NL contributed most strongly to PC1 (eigenvector = 0.7117), and SL contributed most strongly to PC2 (eigenvector = 0.8812). The SPAD content contributed moderately to PC1 and PC2. In the biplot, the genotypes on the right side were the best performers, with the highest values for all three traits under salinity, whereas those clustered on the left were the poorest performers, with the lowest values for these traits. The SL_MEAN_200mM and NL_MEAN_200mM vectors were located primarily in the positive direction of Component 1 and were closely related, suggesting that these two traits were highly positively correlated. SPAD_MEAN_200mM was located in the positive direction of Component 1 but with a slight upward tilt toward Component 2, indicating that it was also positively correlated with the other two traits, but to a lesser extent than the correlation between SL and NL. The biplot clearly separated genotypes along the principal components, with tolerant and sensitive genotypes distributed in opposite quadrants, reflecting contrasting trait performance under stress. K-means clustering further partitioned the genotypes into four distinct clusters (Fig. 3 C) based on the three quantitative traits at the seedling stage. The clusters differed in their mean trait values, with one cluster comprising relatively tolerant lines (high NL, SL, and SPAD). In contrast, the others represented moderately tolerant or highly sensitive genotypes. The overlap between clusters in the PCA space confirmed the continuous variation in the salinity response but also supported the presence of distinct phenotypic groups. Genome-wide association study of salinity-related traits A GWAS was performed for seven phenotypic traits under salinity stress in 198 soybean cultivars via the FarmCPU and BLINK models. A total of 66 significant main-effect SNPs associated with two germination-stage and five seedling-stage traits across the E1 and E2 experiments were identified via a significance threshold of -log10 ( P ) ≥ 3. Among these, eight SNPs exceeded the Bonferroni-corrected threshold of -log10 ( P ) ≥ 5.67 across both GWAS models. These results are visualized in Manhattan and quantile‒quantile (Q‒Q) plots (Table S4, Fig. 5 ). During the germination stage, 18 significant SNPs were detected for the Δ%_GP trait under both 100 mM NaCl stress and 200 mM NaCl stress. Importantly, SNPs identified at 100 mM generally presented negative effects, suggesting that the associated allele increases susceptibility. In contrast, the SNPs identified at 200 mM consistently presented positive effects, indicating an increase in tolerance. Specifically, for Δ%_GP_100mM​, a total of 10 significant SNP associations were identified across the models, with four common SNPs found by both FarmCPU and BLINK: rs.Gm07.39079550, rs.Gm07.39090791, rs.Gm11.33334736, and rs.Gm16.6978282 (located on chromosomes 7, 11, and 16, respectively). The most significant SNP, rs.Gm16.6978282, displayed the strongest association -log10 ( P ) 5.54 from FarmCPU and 5.84 from BLINK) and explained the highest proportion of phenotypic variance (PVE = 35.21%), indicating that it is a primary locus controlling the germination response at 100 mM Similarly, for Δ%_GP_200mM​, a total of eight significant SNP associations were detected across the models, with four common SNPs found by both FarmCPU and BLINK: rs.Gm05.9457555, rs.Gm10.35460454, rs.Gm18.1916285, and rs.Gm20.45859448 (located on chromosomes 5, 10, 18, and 20, respectively). The most significant SNP, rs.Gm10.35460454, displayed the strongest association (-log10 ​( P ) 4.50 from FarmCPU and 4.79 from BLINK). Notably, these Δ%_GP_200mM​ SNPs all presented positive SNP effects, supporting the role of the minor allele in increased tolerance at relatively high stress levels (Table S4, Fig. 5 A, B). The GWAS for seedling-stage traits under 200 mM salinity stress revealed 48 significant associations, highlighting a divergence in genetic effects on the basis of the measured trait. For Δ%_NL_200mM, a total of 11 significant SNPs were identified across the models, primarily on chromosomes 1, 6, 13, and 18. Four SNPs were found to be common to both FarmCPU and BLINK, including rs.Gm01.3888173, rs.Gm06.12647562, rs.Gm06.12769083, and rs.Gm18.47911124. The majority of these common SNPs exhibited negative SNP effects, indicating that the minor allele at these loci contributes to a greater reduction in leaves count, thus suggesting increased susceptibility. The strongest association was detected on chromosome 6 with the SNP rs.Gm06.12769083, which had -log10 ( P ) values of 3.97 (FarmCPU) and 4.18 (BLINK) (Table S4, Fig. 5 C). Furthermore, for Δ%_SL_200mM, 15 significant SNPs were identified on chromosomes 1, 11, 14, 15, and 20. Four SNPs were found to be common to both FarmCPU and BLINK: rs.Gm01.6204693, rs.Gm11.37469933, rs.Gm14.41787022, and rs.Gm20.30040900. A key SNP on chromosome 1, rs.Gm01.6204693 had -log10 ( P ) values of 4.17 (FarmCPU) and 4.34 (BLINK). (Table S4, Fig. 5 D). For Δ%_SPAD_200mM, six significant SNPs were identified on chromosomes 1 and 3. Three SNPs were found to be common to both FarmCPU and BLINK: rs.Gm01.43332855, rs.Gm03.38716419, and rs.Gm03.39710939. The strongest association was detected for SNP rs.Gm03.38716419 on chromosome 3, which had -log10 ( P ) values of 3.51 (FarmCPU) and 3.69 (BLINK). The majority of the SNPs associated with Δ%_SL_200mM and Δ%_SPAD_200mM presented positive SNP effects, suggesting that the minor allele contributes to tolerance by mitigating the loss of seedling length and chlorophyll content (Table S4, Fig. 5 E). In addition, for the LSS_MEAN and SFS_MEAN traits, eight significant SNPs were detected for each. For LSS_MEAN, significant SNPs were located on chromosomes 3, 9, 11, 12, 13, and 18, with two common SNPs—rs.Gm03.39710939 and rs.Gm09.843177—identified across both models. The SNP rs.Gm03.39710939 on chromosome 3 exhibited the strongest association, with exceptionally high -log10 ​( P ) values of 6.92 (FarmCPU) and 10.51 (BLINK), explaining a significant proportion of the phenotypic variance (PVE of 35.07%) (Table S4, Fig. 5 F). Similarly, for SFS_MEAN, the most significant SNP was also rs.Gm03.39710939 on chromosome 3, which presented high -log10 ​( P ) values of 6.93 (FarmCPU) and 8.13 (BLINK) and explained the highest PVE of 43.75%, confirming its strong pleiotropic effect on survival (Table S4, Fig. 5 G). Overall, the SNP rs.Gm03.39710939 displayed a pleiotropic effect, influencing the Δ%_SPAD_200mM, LSS_MEAN, and SFS_MEAN traits. Its strong and consistent association across multiple traits makes it a promising candidate for gene annotation and functional validation, as well as a key target for improving salinity tolerance at the vegetative stage (Table S4). Epistatic (SNP × SNP) interactions among significant loci Epistatic (SNP × SNP) interactions were analyzed among 20 significant SNPs associated with salinity tolerance in soybean (Tables 3 , S5; Fig. 6 ). These loci interacted with 953 additional SNPs, collectively influencing seven traits across the germination and seedling stages in E1 and E2. Table 3 Genome-wide SNP × SNP (epistatic) interactions of significant SNPs Stage Trait Main effect SNP Total SNP × SNP interactions of Main effect SNP BEST_Chr. BEST_SNP (Epistatic) Gene Location Function Germination Δ%_GP_100mM rs.Gm07.39079550 2 GM09 rs.Gm09.21628729 Glyma.09G113000 Gm09:21705841..21711265 myb family transcription factor APL-like isoform X1 [ Glycine max ];IPR025756 (MYB-CC type transcription factor rs.Gm07.39090791 3 GM09 rs.Gm09.21628729 Glyma.09G113000 Gm09:21705841..21711265 myb family transcription factor APL-like isoform X1 [ Glycine max ];IPR025756 (MYB-CC type transcription factor rs.Gm11.33334736 4 GM18 rs.Gm18.56023158 Glyma.18G275200 Gm18:56005084..56024960 Auxin transport protein (BIG) rs.Gm16.6978282 25 GM02 rs.Gm02.14916620 Glyma.02G139700 Gm02:14922905..14927137 Transcription factor bHLH30-like [ Glycine max ]; IPR011598 (Myc-type, basic helix-loop-helix (bHLH) domain) Δ%_GP_200mM rs.Gm05.9457555 6 GM10 rs.Gm10.21857428 Glyma.10G104100 Gm10:21780444..21793043 L-galactono-1,4-lactone dehydrogenase; GO:0016633 (galactonolactone dehydrogenase activity) rs.Gm10.35460454 61 GM16 rs.Gm16.33616912 Glyma.16G173900 Gm16:33610721..33620422 Receptor-like protein kinase 2; IPR001611 (Leucine-rich repeat) rs.Gm18.1916285 323 GM15 rs.Gm15.35061311 Glyma.15G213700 Gm15:35024771..35028314 Receptor-like serine/threonine kinase 2; IPR024171 (S-receptor-like serine/threonine-protein kinase); GO:0004672 (protein kinase activity) rs.Gm20.45859448 232 GM15 rs.Gm15.44600021 Glyma.15G233100 Gm15:44547552..44552077 Disease resistance protein (TIR-NBS-LRR class) family; IPR001611 (Leucine-rich repeat),GO:0006952 (defence response) Seedling (200 mM) Δ%_NL_200mM rs.Gm01.3888173 2 GM06 rs.Gm06.39548568 Glyma.06G242000 Gm06:39475973..39479036 Ankyrin repeat family protein; IPR020683 (Ankyrin repeat-containing domain) rs.Gm06.12647562 1 GM06 rs.Gm06.28907845 Glyma.06G222800 Gm06:28839029..28842106 Uncharacterized protein rs.Gm06.12769083 1 GM06 rs.Gm06.28907845 Glyma.06G222800 Gm06:28839029..28842106 Uncharacterized protein rs.Gm18.47911124 1 GM16 rs.Gm16.36043781 Glyma.16G196400 Gm16:35998156..36006453 Ankyrin repeat-containing protein; IPR020683 (Ankyrin repeat-containing, GO:0008270 (zinc ion binding) Δ%_SL_200mM rs.Gm01.6204693 5 GM07 rs.Gm07.35731062 Glyma.07G186100 Gm07:35722638..35727539 Branched-chain amino acid transaminase 2; GO:0009081 (branched-chain amino acid metabolic process) rs.Gm11.37469933 3 GM13 rs.Gm13.30031982 Glyma.13G193200 Gm13:30065083..30075075 Argonaute family protein; IPR003100 (Argonaute/Dicer protein rs.Gm14.41787022 10 GM20 rs.Gm20.25517507 Glyma.20G071666 Gm20:25424912..25438559 Phosphoinositide phosphatase family protein rs.Gm20.30040900 17 GM09 rs.Gm09.39499095 Glyma.09G161600 Gm09:39483962..39488683 Alcohol dehydrogenase 1; IPR002085 (Alcohol dehydrogenase superfamily, zinc-type) Δ%_SPAD_200mM rs.Gm01.43332855 1 GM07 rs.Gm07.2345032 Glyma.07G029100 Gm07:2340211..2346295 HAT family dimerization domain-containing protein rs.Gm03.38716419 1 GM10 rs.Gm10.36634108 Glyma.10G135500 Gm10:36601101..36604423 F-box/RNI-like superfamily protein; IPR001810 (F-box domain); GO:0005515 (protein binding) rs.Gm03.39710939 2 GM04 rs.Gm04.48363183 Glyma.04G225300 Gm04:48368443..48377451 Vacuolar iron transporter-like protein LSS_MEAN rs.Gm03.39710939 11 GM14 rs.Gm14.21949373 Glyma.04G225300 Gm04:48368443..48377451 Vacuolar iron transporter-like protein rs.Gm09.843177 3 GM12 rs.Gm12.3161544 Glyma.12G043800 Gm12:3162105..3167056 Glucan endo-1,3-beta-D-glucosidase-like [ Glycine max ]; IPR000490 (Glycoside hydrolase, family 17) SFS_MEAN rs.Gm03.39710939 239 GM05 rs.Gm05.13429009 Glyma.04G225300 Gm04:48368443..48377451 Vacuolar iron transporter-like protein Δ%_GP_100mM: Delta percentage of germination percentage at 100 mM; Δ%_GP_200mM: Delta percentage of germination percentage at 200 mM; Δ%_NL_200 mM: Delta percentage of number of leaves at 200 mM; Δ%_SL_200mM: Delta percentage of seedling length at 200 mM; Δ%_SPAD_200mM: Delta percentage of chlorophyll content by SPAD meter at 200 mM; LSS_MEAN: Mean of leaf scorch score at 200 mM; SFS_200mM: Mean of seedling to flowering survival at 200 mM. Best_Chr. : chromosome of best SNP, Best_SNP: SNP identifier of best SNP. Bold SNPs are common in the FarmCPU/BLINK methods and E1 and E2. For the Δ%_GP_100mM trait, four main-effect SNP × SNP epistatic interactions were detected. These four main-effect interactions involved 34 other SNPs and were distributed across multiple chromosomes (Gm02, Gm06, Gm07, Gm10, Gm11, Gm16, Gm17, Gm18, and Gm20). Specifically, the four main-effect interactions were rs.Gm16.6978282 × rs.Gm02.14916620, rs.Gm07.39090791 × rs.Gm09.21628729, rs.Gm07.39079550 × rs.Gm09.21628729, and rs.Gm11.33334736 × rs.Gm18.56023158 (Tables 3 , S5; Fig. 6 A). Similarly, for the high-salinity germination trait, the Δ%_GP_200mM trait, four main-effect SNPs exhibited epistatic interactions, with 622 other SNPs distributed across all 20 chromosomes. The four main-effect SNP × SNP interactions identified were rs.Gm10.35460454 × rs.Gm16.33616912, rs.Gm05.9457555 × rs.Gm10.21857428, rs.Gm20.45859448 (involved in 232 interactions) × rs.Gm15.44600021, and rs.Gm18.1916285 (involved in 323 interactions) × rs.Gm15.35061311 (Tables 3 , S5; Fig. 6 B). For the Δ%_NL_200mM trait, four main-effect SNPs exhibited epistatic interactions. The SNPs rs.Gm06.12769083 and rs.Gm06.12647562 each showed a single interaction with rs.Gm06.28907845 on chromosome Gm06. The remaining two main-effect SNP × SNP interactions were rs.Gm01.3888173 × rs.Gm06.39548568 and rs.Gm18.47911124 × rs.Gm16.36043781 (Tables 3 , S5; Fig. 6 C). With respect to seedling length, the Δ%_SL_200mM trait involved four main-effect SNPs in epistasis but had a markedly greater number of interactions. The SNPs were as follows: rs.Gm01.6204693 × rs.Gm07.35731062, rs.Gm20.30040900 × rs.Gm09.39499095. The other two SNPs included rs.Gm11.37469933 (3 interactions) × rs.Gm13.30031982 and rs.Gm14.41787022 × rs.Gm20.25517507 (10 interactions) (Tables 3 , S5; Fig. 6 D). For the Δ%_SPAD_200mM trait, three main-effect SNPs were involved in epistatic interactions. The SNPs rs.Gm03.38716419 × rs.Gm10.36634108, rs.Gm03.39710939 × rs.Gm04.48363183 and rs.Gm01.43332855 × rs.Gm07.2345032. (Tables 3 , S5; Fig. 6 E). Finally, for the LSS_MEAN trait, two main-effect SNPs demonstrated epistatic interactions. The SNP rs.Gm09.843177 exhibited three interactions, with the most significant interaction occurring with rs.Gm12.3161544. In contrast, rs.Gm03.39710939 had 11 interactions, the strongest of which was with rs.Gm14.21949373. Notably, the same SNP, rs.Gm03.39710939, also had a pleiotropic effect on the SFS_MEAN trait, engaging in an extensive epistatic network of 239 interactions. Its strongest interaction partner was rs.Gm05.13429009. These SNPs have emerged as key determinants of the genetic architecture governing salinity tolerance at the germination and seedling survival stages (Tables 3 , S5; Fig. 6 E–G). GWAS-based identification of QTLs, candidate genes, allelic effects for salinity-related traits, and LD analysis of significant SNPs on chromosome Gm03 In the GWAS analysis, several putative candidate genes associated with salinity-related traits at both the germination and seedling stages were identified within QTL regions (Table 2 ). Table 2 Gene annotation of commonly significant SNPs found in both FarmCPU and BLINK from GWAS results Stage Traits Peak SNP ID Ref/Alt Allele Chr. Position Gene Location Function Germination Δ%_GP_100mM rs.Gm07.39079550 G/T GM07 39079550 Glyma.07G215700 Gm07:39132935..39134698 LRR-RLKs detect stress signals (Leucine-rich repeat receptor-like kinases) rs.Gm07.39090791 T/C GM07 39090791 Glyma.07G215700 Gm07:39132935..39134698 LRR-RLKs detect stress signals(Leucine-rich repeat receptor-like kinases) rs.Gm11.33334736 T/C GM11 33334736 Glyma.11G201600 Gm11:33407132..33409547 HSP70(Heat Shock Protein) stabilizes proteins. Protects cells from salinity stress rs.Gm16.6978282 T/C GM16 6978282 Glyma.16G070200 Gm16:6960313..6961147 Enhance stress tolerance and defence Δ%_GP_200mM rs.Gm05.9457555 G/A GM05 9457555 Glyma.05G075400 Gm05:9450431..9464707 CSN (COP9 signalosome) regulates germination under salt stress. Balances GA/ABA hormones rs.Gm10.35460454 A/G GM10 35460454 Glyma.10G130600 Gm10:35478799..35481145 Late embryogenesis abundant (LEA) proteins support growth rs.Gm18.1916285 C/T GM18 1916285 Glyma.18G025700 Gm18:1909325..1910772 RING-H2 finger protein 2B regulates stress genes rs.Gm20.45859448 G/C GM20 45859448 Glyma.20G225000 Gm20:45883078..45886009 IAA14 controls germination via auxin Seedling (200 mM) Δ%_NL_200mM rs.Gm01.3888173 T/G GM01 3888173 Glyma.01G036500 Gm01:3812454..3816990 DNAJ proteins help in abiotic and biotic stress rs.Gm06.12647562 C/T GM06 12647562 Glyma.06G155200 Gm06:12636441..12639109 Cation calcium exchanger 4 balances Na⁺/Ca²⁺. Supports ion transport under stress. rs.Gm06.12769083 C/A GM06 12769083 Glyma.06G156100 Gm06:12765825..12768593 Photosystem II oxygen-evolving complex protein PsbP rs.Gm18.47911124 G/T GM18 47911124 Glyma.18G198300 Gm18:47890828..47893406 Plasma Membrane Intrinsic Protein 1 transports water across membranes Δ%_SL_200mM rs.Gm01.6204693 T/C GM01 6204693 Glyma.01G051700 Gm01:6242003..6243662 MYB64 regulates stress genes. Modulates ABA signaling and ions rs.Gm11.37469933 T/A GM11 37469933 Glyma.11G230500 Gm11:37461012..37462619 The protein kinase superfamily phosphorylates proteins. Binds ATP and signals stress. rs.Gm14.41787022 C/T GM14 41787022 Glyma.14G165300 Gm14:41725688..41726147 HSP70(Heat Shock Protein) stabilizes proteins. Protects cells from salinity stress rs.Gm20.30040900 T/C GM20 30040900 Glyma.20G080700 Gm20:30159414..30163042 RLK2 detects stress signals. Regulates salt stress response Δ%_SPAD_200mM rs.Gm01.43332855 A/T GM01 43332855 Glyma.01G122800 Gm01:43359677..43364314 vacuolar cation/proton exchanger 3 rs.Gm03.38716419 A/G GM03 38716419 Glyma.03G160300 Gm03:38711991..38713760 Cytochrome P450 regulates stress. Modulates hormones for salt tolerance. rs.Gm03.39710939 T/A GM03 39710939 Glyma.03G170501 Gm03:39711565..39712080 ANK(Ankyrin repeat) proteins support growth. Regulate hormones and stress response. LSS_MEAN rs.Gm03.39710939 T/A GM03 39710939 Glyma.03G170501 Gm03:39711565..39712080 ANK(Ankyrin repeat) proteins support growth. Regulate hormones and stress response. rs.Gm09.843177 G/T GM09 843177 Glyma.09G010800 Gm09:834070..834660 The tonoplast dicarboxylate transporter moves sodium. Maintains ion balance under stress. SFS_MEAN rs.Gm03.39710939 T/A GM03 39710939 Glyma.03G170501 Gm03:39711565..39712080 ANK(Ankyrin repeat) proteins support growth. Regulate hormones and stress response. Δ%_GP_100mM: Delta percentage of germination percentage at 100 mM; Δ%_GP_200mM: Delta percentage of germination percentage at 200 mM; Δ%_NL_200mM: Delta percentage of number of leaves at 200 mM; Δ%_SL_200mM: Delta percentage of seedling length at 200 mM; Δ%_SPAD_200mM: Delta percentage of chlorophyll content by SPAD meter at 200 mM; LSS_MEAN: Mean of leaf scorch score at 200 mM; SFS_MEAN: Mean of seedling to flowering survival at 200 mM. Bold SNPs are common in the FarmCPU/BLINK methods and E1 and E2. In the case of the Δ%_GP_100mM trait, a significant SNP, rs.Gm16.6978282, was mapped to Glyma.16G070200 , a gene implicated in enhancing general stress tolerance and defence responses. Furthermore, the SNP rs.Gm11.33334736 was located within Glyma.11G201600 , which encodes a heat shock protein 70 (HSP70). This molecular chaperone is known to stabilize proteins and protect cells from salinity stress. Additionally, two SNPs, rs.Gm07.39090791 and rs.Gm07.39079550, were identified within Glyma.07G215700 . This gene encodes leucine-rich repeat receptor-like kinases (LRR-RLKs), which play pivotal roles in detecting external stress signals and initiating early defence responses. Analysis of allelic effects revealed that two of these SNPs, rs.Gm16.6978282 and rs.Gm07.39090791, had highly significant effects on the trait ( P < 0.01) (Fig. 8 A). In addition, for the Δ%_GP_200mM trait, four significant SNPs were located within genes vital for the salinity stress response. Specifically, the SNP rs.Gm10.35460454 was linked to Glyma.10G130600 , which encodes a late embryogenesis abundant (LEA) protein known to promote seedling growth under stress conditions. Other associated SNPs include rs.Gm05.9457555, located within Glyma.05G075400 (encoding a COP9 signalosome subunit involved in hormone signaling during germination), rs.Gm18.1916285 within Glyma.18G025700 (RING-H2 finger protein that regulates stress-responsive genes), and rs.Gm20.45859448 within Glyma.20G225000 (which encodes an IAA14 protein involved in auxin-mediated germination control). However, the analysis of allelic effects for these SNPs did not reveal any statistically significant differences ( P > 0.05) (Fig. 8 B). At the seedling stage, for Δ%_NL_200mM, two SNPs were mapped on chromosome 06: rs.Gm06.12647562 and rs.Gm06.12769083. These SNPs were associated with Glyma.06G155200 (encoding a cation calcium exchanger involved in Na⁺/Ca²⁺ homeostasis and ion transport under stress) and Glyma.06G156100 (linked to the maintenance of photosynthesis under salinity), respectively. Both genes were located in close genomic proximity and exhibited significant allelic effects ( P < 0.01). In addition, rs.Gm01.3888173, located in Glyma.01G036500 (DNAJ protein associated with abiotic and biotic stress tolerance), and rs.Gm18.47911124, associated with Glyma.18G198300 (plasma membrane intrinsic protein 1, involved in water transport across membranes), also presented statistically significant allelic effects ( P < 0.05) (Fig. 8 C). The Δ%_SL_200mM, a key parameter for salinity stress, presented four SNPs with significant allelic effects ( P < 0.01). The SNP rs.Gm01.6204693 on chromosome 1 was mapped to Glyma.01G051700 , an MYB transcription factor that regulates stress responses through ABA signaling. On chromosome 11, rs.Gm11.37469933 corresponded to Glyma.11G230500 , a protein kinase involved in signaling pathways. Furthermore, rs.Gm14.41787022 on chromosome 14 was linked to Glyma.14G165300 , a heat shock protein that provides cellular protection from salt-induced damage. Finally, rs.Gm20.30040900 on chromosome 20 was associated with Glyma.20G080700 , which encodes a receptor-like protein kinase (RLK2) that regulates signal transduction under stress (Fig. 8 D). For the trait Δ%_SPAD_200mM, the SNP rs.Gm01.43332855 was identified within the gene Glyma.01G122800 , which encodes a vacuolar exchanger essential for maintaining ion balance under stress. Additionally, two significant SNPs were located on chromosome 3: rs.Gm03.38716419 in Glyma.03G160300 (a cytochrome P450 that regulates stress hormones for salt tolerance) and rs.Gm03.39710939 in Glyma.03G170501 (an ankyrin (ANK) repeat protein that supports growth and regulates stress responses). All three SNPs demonstrated significant allelic effects ( P < 0.01) in the analysis (Fig. 8 E). For the LSS_MEAN trait, the SNP rs.Gm09.843177 is located within the Glyma.09G010800 gene, which is related to the tonoplast dicarboxylate transporter responsible for maintaining ion balance under stress. Additionally, the SNP rs.Gm03.39710939 has a significant pleiotropic effect, influencing both the LSS_MEAN and SFS_MEAN traits. It is associated with the Glyma.03G170501 gene, which encodes an ankyrin (ANK) repeat protein that supports growth and regulates stress responses. All the SNPs had significant allelic effects ( P < 0.01) (Fig. 8 F-G). Furthermore, linkage disequilibrium (LD) analysis revealed that while the SNP rs.Gm03.39710939 is located on the boundary of the 6 kb haplotype block; it exhibits high pairwise LD with its immediately adjacent marker rs.Gm03.39710913, confirming that it is part of a larger region of strong genetic linkage (Fig. 7 ). Development and validation of KASP markers for salinity tolerance in soybean From the screening of the GWAS results, a KASP marker was developed for the pleiotropically significant SNP rs.Gm03.39710939 from the seedling stage. This marker has also been validated with 198 genotypes of soybean, among which ⁓21% have Homozygous Allele 2/Allele 2 (AA alleles), which represent salt-tolerant genotypes; ⁓67% have Homozygous Allele 1/Allele 1 (TT alleles), which represent salt-susceptible genotypes; and the rest have Heterozygous Allele 1/Allele 2 (TA alleles). A representative subset of 14 highly tolerant and susceptible genotypes showed a 100% correlation between the KASP marker call and the SFS_MEAN phenotype (Table S7, Fig. 9 ). Discussion Salt tolerance is a significant abiotic stress that involves various mechanisms during the germination and seedling stages. By studying 198 genotypes of soybean, this study successfully identified 20 unique, highly significant SNPs via a multi-model GWAS approach, offering the most comprehensive genetic insights into soybean salt tolerance across the germination and seedling stages to date. Phenotypic variation among soybean genotypes under salinity stress Salinity stress had a significant effect on soybean growth across the 198-geneotype panel. Key phenotypic traits, including SL, NL, SPAD, and SFS, were markedly lower in the 200 mM NaCl treatment group than in the control group. During the initial salt treatment, the leaves of the control plants remained light green, whereas the treated plants developed noticeably darker green leaves. This observation suggests increased chlorophyll retention despite the stress caused by salt. However, after the final treatment with 200 mM salt, the leaves of the treated plants lost their green color and turned yellow due to the effects of salinity. In response to the final treatment, various traits significantly differed from those of the control group. The control plants presented broader leaves areas and taller stature, along with faster-growing shoots. In contrast, the treated plants had narrower leaves, were shorter overall, and presented slower shoot development. Additionally, the root systems of the control group plants grew rapidly and extensively, whereas the roots of the salt-treated plants were stunted and developed more slowly. Although a subset of genotypes maintained robust growth and completed the seedling-to-flowering transition under salinity, indicating inherent tolerance, many others were severely inhibited, underscoring the diverse phenotypic strategies employed by soybean in response to salt stress (Fig. 10 ). Similar results have been shown previously for crops such as soybean (Kokebie et al. 2024 ), rapeseed (Wang et al. 2024b ), and cucumber (Amerian et al. 2024 ). Differential salinity response across development stages Our findings, along with those of a recent report by Gobade et al. ( 2025 ) on the seed germination stage, reveal apparent differences in salt tolerance that depend on the developmental stage. Gobade et al. ( 2025 ) identified seven genotypes as salt tolerant at 200 mM NaCl during germination. In contrast, at the seedling stage, we identified 14 salt-tolerant genotypes, none of which overlapped with those that were tolerant at germination. This distinct lack of overlap strongly supports our hypothesis that the mechanisms underlying salt tolerance differ between the germination and seedling stages. Similarly, Ghosh et al. ( 2025 ) reported that the mechanisms involved in the germination stage are crucial, as tolerance mainly depends on initial water uptake and the activation of enzymes to respond to osmotic stress and ion toxicity from the seed's immediate environment. During the seedling stage, tolerance develops, including longer-term physiological and molecular adjustments that facilitate growth, such as nutrient uptake, photosynthesis, and active ion homeostasis. These processes involve more complex genetic and biochemical responses. Collectively, these findings support the hypothesis that salinity tolerance in soybean varies across different developmental stages. According to a previous study, during germination, osmotic adjustment occurs through the accumulation of proline, glycine betaine, and sugars, which help retain water. Additionally, these compounds maintain ionic balance by compartmentalizing or exporting sodium ions (Na⁺) via the NHX and HKT transport systems (Mansour and Ali 2017 ). Hormone levels also shift to favor germination, with an increase in gibberellin (GA) and a decrease in abscisic acid (ABA) (Shu et al. 2017 ). Antioxidants such as superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT), and peroxidase (POD) are elevated to detoxify reactive oxygen species (ROS) (Hernandez-Leon and Valenzuela-Soto 2023 ). Furthermore, a protective seed coat limits the entry of salt into the seed. At the molecular level, various transporters and stress-responsive transcription factors (TFs), such as DREB, HSP, and WRKY, are activated to coordinate these responses (Feng et al. 2023 ). Together, these mechanisms protect the embryo and promote radicle emergence even under saline conditions. In contrast, seedling-stage tolerance involves more complex physiological strategies, including active sodium exclusion via SOS1 (salt overly sensitive 1) and NHX (Na + /H + exchangers) transporters, compartmentalization of toxic ions into vacuoles, and improved K + /Na + selectivity to maintain cytoplasmic homeostasis (Dinler et al. 2022 ; Khan et al. 2025 ). Additionally, plants secrete phytohormones such as abscisic acid (ABA), salicylic acid (SA), and brassinosteroids (BRs), which contribute to salinity tolerance by regulating reactive oxygen species (ROS) accumulation in roots and maintaining stomatal development and photosynthetic activity in leaves (Ghosh et al. 2025 ). Genomic loci specific to germination and seedling traits To date, many genes and SNPs linked to salinity tolerance during the germination and seedling stages have been reported through association studies and GWASs in various crops and plants. However, our GWAS identified 20 significant SNPs that were consistently found across both models and were distributed over 12 chromosomes. These SNPs are associated with salt tolerance at both the germination stage (100 and 200 mM NaCl) and the seedling stage (200 mM NaCl) in soybean across seven different traits. During the germination stage, the Δ%_GP_100mM SNPs, such as rs.Gm07.39079550 and rs.Gm07.39090791, which are associated with the same gene as Glyma.07G215700 , encoding leucine-rich repeat receptor-like kinases in the plasma membrane, are upregulated by ABA, which is important in seed maturation, seed dormancy, stomatal closure, and stress response (Li et al. 2022 ; Osakabe et al. 2005 ). Other SNPs are novel, such as rs.Gm11.33334736 and rs.Gm16.6978282, which were not mentioned earlier; these SNPs are associated with the genes Glyma.11G201600 and Glyma.16G070200 , respectively. Glyma.11G201600 encodes a heat shock protein that plays a significant role as a molecular chaperone in protecting cells from stress (Do et al. 2023 ), and Glyma.16G070200 is linked to stress tolerance and defence. In the context of Δ%_GP_200mM, the SNP rs.Gm05.9457555 is novel, as it has not been previously reported in relation to the gene Glyma.05G075400 . This gene encodes the COP9 signalosome (CSN), which plays a crucial role in plant growth, development, and stress responses by modulating the ubiquitination pathway (Lu et al. 2025 ). In contrast, known SNPs such as rs.Gm10.35460454, rs.Gm18.1916285, and rs.Gm20.45859448 are associated with genes that serve specific functions. For example, Glyma.10G130600 encodes late embryogenesis abundant (LEA) proteins, which are vital phytomolecules that primarily accumulate in the later stages of seed development, as well as in vegetative tissues in response to external stressors (Banerjee and Roychoudhury 2015 ; B. Guo et al. 2023 ). Glyma.18G025700 encodes a RING-H2 finger protein that participates in abiotic stress responses by modifying and degrading stress-related proteins (Han et al. 2022 ; Yang et al. 2025 ). Additionally, Glyma.20G225000 encodes IAA14, a protein that regulates germination through auxin mediation (Neres et al. 2023 ). During the seedling stage, the Δ%_NL_200mM trait is associated with four SNPs, rs.Gm01.3888173, rs.Gm06.12647562, rs.Gm06.12769083, and rs.Gm18.47911124, which are associated with the genes Glyma.01G036500, Glyma.06G155200, Glyma.06G156100 , and Glyma.18G198300 , respectively. The gene Glyma.01G036500 encodes DNAJ proteins that contribute to tolerance to both abiotic and biotic stresses, as noted by Song et al. ( 2017 ) in relation to alkalinity stress. Glyma.06G155200 (encoding a cation calcium exchanger involved in Na⁺/Ca²⁺ homeostasis and ion transport under stress) supports ion transport as a cation calcium exchanger 4 (Zeng et al. 2020 ). Glyma.06G156100 encodes a protein involved in the photosystem II oxygen-evolving complex (PsbP). Finally, Glyma.18G198300 functions as Plasma Membrane Intrinsic Protein 1, which facilitates water transport across membranes, as highlighted in the context of alkalinity stress by Waters et al. ( 2018 ). The Δ%_SL_200mM trait is associated with SNPs located on chromosomes GM01, GM11, GM14, and GM20, which are linked to several genes. Glyma.01G051700 encodes the MYB64 transcription factor, which regulates ABA signaling and ion homeostasis (Wang et al. 2020 ). Glyma.11G230500 is related to the protein kinase superfamily and is responsible for phosphorylating proteins that bind ATP and signal stress responses. Glyma.14G165300 encodes HSP70, a protein that protects against salinity stress (Do et al. 2023 ). Finally, Glyma.20G080700 encodes a receptor-like protein kinase (RLK2) that detects signals related to salinity stress (Almeida-Silva and Venancio 2021 ; Zhu et al. 2023 ). For the Δ%_SPAD_200mM trait, the SNP rs.Gm01.43332855 has been functionally annotated to the gene Glyma.01G122800 , which encodes vacuolar cation/proton exchanger 3, a known gene previously described by Waters et al. ( 2018 ). Additionally, two other SNPs have not been previously mentioned: rs.Gm03.38716419, which is associated with Glyma.03G160300 and encodes a cytochrome P450 involved in regulating stress (Pandian et al. 2020 );, and rs.Gm03.39710939, which is linked to Glyma.03G170501 , encoding ANK (Ankyrin repeat) proteins that support growth (Zhang et al. 2016 ). However, for the LSS_MEAN trait, two SNPs associated with leaves scorch stress were identified. One of these was a novel SNP, rs.Gm03.39710939, which is linked to Glyma.03G170501 . This gene encodes ankyrin repeat (ANK) proteins that support plant growth (Zhang et al. 2016 ). The other SNP, known as rs.Gm09.843177, is related to Glyma.09G010800 , which encodes a tonoplast dicarboxylate transporter that facilitates the movement of sodium and helps maintain ion balance under stress, as previously studied by Pruthi et al. ( 2025 ). With respect to the SFS_MEAN trait, which indicates a plant's ability to survive salinity stress and complete flowering, a significant novel pleiotropic SNP, rs.Gm03.39710939, was identified. This SNP is linked to the candidate gene Glyma.03G170501 , which encodes an ankyrin repeat protein that aids in salinity tolerance (Zhang et al. 2016 ). The GmSALT3 locus and the pleiotropic ANK gene on chromosome 3 Ankyrin repeat (ANK) proteins are widely recognized for their involvement in plant growth, development, and response to hormonal and environmental signals. Earlier work by Zhao et al. ( 2020 ) revealed that ANK proteins are strongly induced under stress conditions, where they help reduce the accumulation of damaging reactive oxygen species. Their study also demonstrated that ANKs influence the expression of several well-known stress-responsive genes, including WRKY13, NAC11, DREB2, MYB84, and bZIP44 , highlighting the important regulatory role these proteins play during salinity and drought stress. In our study, the pleiotropic SNP rs.Gm03.39710939 was linked to Glyma.03G170501 , an ANK gene that reflects many of these reported functions. This gene is located approximately 73 kb upstream of GmCHX1/GmSALT3 ( Glyma.03G171600 ) (Guan et al. 2014 ; Qi et al. 2014 ; Ren et al. 2016 ), a well-known salt tolerance locus in soybean. Since both genes are within the typical soybean LD range (100–200 kb), the association at this SNP may represent either a functional role of Glyma.03G170501 itself or physical linkage to the GmSALT3 region. Overall, these findings suggest that the identified ANK gene may contribute to salinity tolerance and could act alongside, or in cooperation with, the major GmSALT3 locus on chromosome 3. Epistatic (SNP × SNP) interactions for salinity-related traits in soybean The SNP × SNP interactions were analyzed in relation to germination-related traits, specifically Δ%_GP_100mM. The identified epistatic SNPs include rs.Gm09.21628729, rs.Gm18.56023158, and rs.Gm02.14916620, which are associated with the following genes: Glyma.09G113000 , encoding the MYB family transcription factor APL-like isoform X1, which regulates stress signals (Shao et al. 2021 ); Glyma.18G275200 , which encodes an auxin transport protein (BIG) that regulates auxin levels and distribution during salt stress (Singh and Jain 2015 ); and Glyma.02G139700 , encoding the transcription factor bHLH30, which is involved in iron homeostasis (Filiz et al. 2017 ). For the Δ%_GP_200mM trait, the main SNPs included rs.Gm10.21857428, which is associated with the gene Glyma.10G104100 and related to L-galactono-1,4-lactone dehydrogenase; however, its function concerning salinity remains unknown. Additionally, rs.Gm16.33616912 and rs.Gm15.35061311 are linked to the same gene functions as Glyma.16G173900 and Glyma.15G213700 , which encode receptor-like protein kinase 2, respectively (Zhu et al. 2023 ). Furthermore, rs.Gm15.44600021 is connected to the Glyma.15G233100 gene encoding a disease resistance protein, which plays a role in plant defence (Zhou et al. 2022 ). Epistatic interactions affecting seedling stage traits, such as the Δ%_NL_200mM trait, were identified for three SNPs: rs.Gm06.39548568, rs.Gm06.28907845, and rs.Gm16.36043781. Among these, rs.Gm06.39548568 and rs.Gm16.36043781 are linked to the genes Glyma.06G242000 and Glyma.16G196400 , respectively, both of which encode proteins with similar functions, specifically an ankyrin repeat-containing protein that supports plant growth (Zhang et al. 2016 ). The other SNP, rs.Gm06.28907845, is associated with the uncharacterized protein encoded by the Glyma.06G222800 gene. The analysis identified four main effect SNPs associated with Δ%_SL_200mM. The first SNP, rs.Gm07.35731062, is linked to the gene Glyma.07G186100 , which encodes branched-chain amino acid transaminase 2, as previously noted by Shim et al. ( 2022 ). The second SNP, rs.Gm13.30031982, is associated with the gene Glyma.13G193200 , which codes for an argonaute family protein that plays a role in the rhizobium interaction process (Valdés-López et al. 2019 ). The third SNP, rs.Gm20.25517507, interacts with the gene Glyma.20G071666 , which encodes a phosphoinositide phosphatase family protein that contributes to increased salinity tolerance (Jia et al. 2025 ). Finally, the SNP rs.Gm09.39499095 is linked to the gene Glyma.09G161600 , which functions as an alcohol dehydrogenase 1 enzyme, aiding in the control of salinity stress (Komatsu et al. 2011 ). In terms of Δ%_SPAD_200mM, the main effect SNP included rs.Gm07.2345032, which is associated with the Glyma.07G029100 gene . This gene encodes a protein containing a HAT family dimerization domain, although its function under salinity stress is not yet understood. Another SNP, rs.Gm10.36634108, maps to the gene Glyma.10G135500 , which encodes a protein from the F-box/RNI-like superfamily that plays a role in leaves development (Iantcheva et al. 2021 ). Finally, the SNP rs.Gm04.48363183 is associated with the gene Glyma.04G225300 , which encodes a vacuolar iron transporter-like protein that helps maintain ion balance under stress conditions (Mansour 2022 ). LSS_MEAN exhibits two SNP × SNP interactions. The first SNP, rs.Gm14.21949373, is linked to the gene Glyma.04G225300 , which encodes a vacuolar iron transporter-like protein that helps maintain ion balance during stress (Mansour 2022 ). The second SNP, rs.Gm12.3161544, is associated with the Glyma.12G043800 gene, which encodes glucan endo-1,3-beta-D-glucosidase and is involved in responses to various biotic and abiotic stresses (Kebede and Kebede 2021 ). In the context of SFS_MEAN, the main effect SNP identified is rs.Gm05.13429009, which is associated with the gene Glyma.04G225300 . This gene encodes a vacuolar iron transporter-like protein that plays a crucial role in maintaining ion balance under stress conditions (Mansour 2022 ). Application of the KASP marker in soybean breeding KASP genotyping offers a practical advantage in soybean improvement, as it enables breeders to screen large populations quickly, accurately, and at a relatively low cost. By identifying plants that carry the desired alleles for salinity tolerance without extensive field or greenhouse evaluations, KASP markers help shorten the breeding cycle and improve the efficiency of marker-assisted selection (MAS). This makes it easier to track useful alleles, combine them in breeding lines, and increase the confidence of tolerant genotypes under stress-prone conditions (Zhao et al. 2020 ). In our study, we developed and validated a KASP marker for the pleiotropic SNP rs.Gm03.39710939, which is linked to the ankyrin repeat protein-encoding gene Glyma.03G170501 . Because this SNP is strongly associated with several key salt-response traits, the marker provides a practical and breeder-friendly tool that can be readily incorporated into soybean breeding programs aimed at improving salinity tolerance. Conclusion The current study presents a thorough multi-experimental GWAS focused on the germination and seedling stages of a diverse panel of 198 genotypes. By employing two GWAS models, we identified 20 SNPs associated with salinity tolerance spread across 12 chromosomes. The functional annotation revealed seven novel and 12 previously reported genes, underscoring both the discovery of new loci and the validation of known stress-related genes. Notably, the main effect of SNP × SNP interactions was observed in 953 interactions across 14 chromosomes. Among these, the pleiotropic SNP rs.Gm03.39710939, which is linked to the ankyrin repeat protein-encoding gene Glyma.03G170501 , exhibited strong multi-trait associations and lies in proximity to GmCHX1/GmSALT3 , a well-established salt tolerance gene. We successfully developed a breeder-friendly KASP marker for this SNP, which has been validated across all genotypes. This novel KASP marker, along with our findings, will aid in the early stages of breeding. Genomic data regarding SNPs from this research can be incorporated into genomic prediction models to evaluate their potential for selecting future soybean varieties with increased resistance to salinity. Abbreviations GWAS: genome-wide association study, MTAs: marker‒trait associations, SNPs: single-nucleotide polymorphisms, FarmCPU: fixed and random model circulating probability unification, BLINK: Bayesian information and linkage disequilibrium iteratively nested keyway, LD: linkage disequilibrium, KASP: Kompetitive allele-specific PCR, MAS: marker-assisted selection, PCA: principal component analysis, PVE: percentage of variance explained, QTL: quantitative trait loci Declarations Supplementary Information: The online version contains supplementary material available at Acknowledgments The authors also extend their sincere thanks to Dr. Prashant Dhakephalkar (Director, Agharkar Research Institute), Dr. Manoj D. Oak (Head of the Department, Department of Genetics and Plant Breeding), Dr. Ravindra Patil, Mr. Santosh Jaybhay and Mrs. Anuja Deshpande from the Department of Genetics and Plant Breeding, Agharkar Research Institute, for their generous provision of resources and facilities essential to the successful completion of this study. Author contributions Preetkumar H. Trivedi: Grew E2 seedlings, collected phenotypic data, analyzed the results, and wrote the original draft. Janhavi Gadhawe: Grew E1 seedlings and collected the phenotypic data. Shreyas M. Salvi assisted in collecting the phenotypic data. Snehaben M. Dodia: Manuscript review. Deepak Pawar: Handled seed threshing and packet preparation. Abhinandan S. Patil: Secured funding, conceived the concept, designed the analysis, and provided critical revisions to the manuscript. All the authors read and approved the manuscript. Funding: This work was supported by a Ramalingaswami Re-entry Fellowship (BT/RLF/Re-entry/01/2021) provided by the Department of Biotechnology (DBT), Government of India. Data availability The datasets created and/or analyzed during the current investigation are accessible from the corresponding author upon reasonable request. Conflict of interest: The authors declare that they have no conflicts of interest. References Almeida-Silva F, Venancio TM (2021) Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens. Sci Rep 11:24453. https://doi.org/10.1038/s41598-021-03864-x Amerian M, Palangi A, Gohari G, Ntatsi G (2024) Humic acid and grafting as sustainable agronomic practices for increased growth and secondary metabolism in cucumber subjected to salt stress. 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Table S7 Duplicate KASP genotyping results with associated phenotypes and genotypic calls. Cite Share Download PDF Status: Published Journal Publication published 16 Mar, 2026 Read the published version in Plant Cell Reports → Version 1 posted Reviewers agreed at journal 02 Dec, 2025 Reviewers invited by journal 25 Nov, 2025 Editor assigned by journal 22 Nov, 2025 First submitted to journal 21 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8173753","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550481430,"identity":"42337cb2-9378-4112-a077-35112bde1d66","order_by":0,"name":"Preetkumar H. 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10:03:28","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":249427,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/7427c5eb7edcd7d3349d2f93.html"},{"id":97009714,"identity":"5a1ef48d-a457-497f-b0cf-40ce4b877dee","added_by":"auto","created_at":"2025-11-28 15:24:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":910108,"visible":true,"origin":"","legend":"\u003cp\u003eLeaf scorching score scale used for salinity stress assessment in soybean. The scoring ranges from 0 to 5, where a score of 0 indicates no visible leaves damage (healthy leaves). A score of 1 corresponds to up to 10% leaves scorching, 2 represents 11–25% scorching, 3 indicates 26–50% scorching, 4 reflects 51–75% scorching, and a score of 5 denotes that more than 75% of the leaves area is affected by scorching. The top right corner number represents “Genotype line no.”\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/a5d67f63961e47906eb90266.png"},{"id":97009715,"identity":"9d74e34b-59ea-4794-a4c9-7f61c9ae0898","added_by":"auto","created_at":"2025-11-28 15:24:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100932,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of salinity treatment on soybean traits across two experiments. The data are presented as the means ± SEs from two experiments for various traits at the germination and seedling stages. The asterisks indicate statistically significant differences (* \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 and ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) between the means (control versus treatment) as measured by one-way ANOVA with a post hoc Tukey HSD test. \u003cstrong\u003eA \u003c/strong\u003eGP_100mM: Germination percentage at 100 mM; \u003cstrong\u003eB \u003c/strong\u003eGP_200mM: Germination percentage at 200 mM;\u003cstrong\u003e C \u003c/strong\u003eNL_200mM: Number of leaves at 200 mM;\u003cstrong\u003e D \u003c/strong\u003eSL_200mM: Seedling length at 200 mM;\u003cstrong\u003e E \u003c/strong\u003eSPAD_200mM: Chlorophyll content measured by SPAD meter at 200 mM;\u003cstrong\u003e F \u003c/strong\u003eLSS_200mM: Leaf scorch score at 200 mM, where a score of 0 indicates no visible leaves damage (healthy leaves). A score of 1 corresponds to up to 10% leaves scorching, 2 represents 11–25% scorching, 3 represents 26–50% scorching, 4 reflects 51–75% scorching, and a score of 5 denotes more than 75% leaves area affected by scorching;\u003cstrong\u003e G \u003c/strong\u003eSFS_200 mM: Seedling to flowering survival at 200 mM, where a score of 0 represents posttreatment death\u003cstrong\u003e \u003c/strong\u003eand 1 corresponds to survival up to flowering.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/2dfeaa0e752e610ab50c82ef.png"},{"id":97139122,"identity":"42a8c61d-803b-4406-aad0-b2a9bcc04512","added_by":"auto","created_at":"2025-12-01 09:59:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122363,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate analysis of the three quantitative traits of soybean genotypes at the seedling stage. A two-way hierarchical clustering dendrogram grouping genotypes with similar trait profiles, with a color scale representing trait values from low (blue) to high (red).\u003cstrong\u003e B\u003c/strong\u003e Principal component analysis (PCA) plot visualizes the genotypes in a 2D space, with Component 1 accounting for 39.8% of the total variance and Component 2 accounting for 33.5%, effectively summarizing the data's variability. \u003cstrong\u003eC\u003c/strong\u003e K-means cluster analysis partitions the genotypes into four distinct groups on the basis of the three quantitative traits.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/bd6aa1d2310a222338a9dac1.png"},{"id":97009719,"identity":"043179ea-7da9-403c-929e-02a76d4c0705","added_by":"auto","created_at":"2025-11-28 15:24:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1080468,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic response of soybean genotypes to salinity stress at the seedling stage. Representative tolerant genotypes (left) and susceptible genotypes (right) are shown under 200 mM NaCl treatment. The central panels (E1 and E2) represent overall plant performance under control and salinity stress conditions across the two experiments.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/933c9f83bf35a7495d47a9dd.png"},{"id":97009720,"identity":"ed2e1460-b6ce-499d-bf0d-959e1f74ce6e","added_by":"auto","created_at":"2025-11-28 15:24:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":389803,"visible":true,"origin":"","legend":"\u003cp\u003eGenome-wide association study (GWAS) results for germination and seedling stage traits. Circular Manhattan plots and quantile‒quantile (QQ) plots are shown for seven traits: \u003cstrong\u003eA \u003c/strong\u003eΔ%_GP_100mM: Delta percentage of germination percentage at 100 mM; \u003cstrong\u003eB \u003c/strong\u003eΔ%_GP_200mM: Delta percentage of germination percentage at 200 mM;\u003cstrong\u003e C \u003c/strong\u003eΔ%_NL_200mM: Delta percentage of number of leaves at 200 mM;\u003cstrong\u003e D \u003c/strong\u003eΔ%_SL_200 mM: Delta percentage of seedling length at 200 mM;\u003cstrong\u003e E \u003c/strong\u003eΔ%_SPAD_200mM: Delta percentage of chlorophyll content by SPAD meter at 200 mM;\u003cstrong\u003e F \u003c/strong\u003eLSS_MEAN: Mean of leaf scorch score at 200 mM;\u003cstrong\u003e G \u003c/strong\u003eSFS_MEAN: Mean of seedling–to-flowering survival at 200 mM. The red dotted line in the plot represents a significant SNP on a specific chromosome.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/5523fc643a64209072d62b4f.png"},{"id":97139179,"identity":"96781f2d-319f-449e-a82a-ea14e0dd7e9f","added_by":"auto","created_at":"2025-12-01 09:59:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":435486,"visible":true,"origin":"","legend":"\u003cp\u003eCircos plot illustrating the genome-wide epistatic interaction of a significant SNP associated with salinity. \u003cstrong\u003eA \u003c/strong\u003eΔ%_GP_100mM: Delta percentage of germination percentage at 100 mM; \u003cstrong\u003eB \u003c/strong\u003eΔ%_GP_200mM: Delta percentage of germination percentage at 200 mM;\u003cstrong\u003e C \u003c/strong\u003eΔ%_NL_200mM: Delta percentage of number of leaves at 200 mM;\u003cstrong\u003e D \u003c/strong\u003eΔ%_SL_200mM: Delta percentage of seedling length at 200 mM;\u003cstrong\u003e E \u003c/strong\u003eΔ%_SPAD_200mM: Delta percentage of chlorophyll content by SPAD meter at 200 mM;\u003cstrong\u003e F \u003c/strong\u003eLSS_MEAN: Mean of leaf scorch score at 200 mM;\u003cstrong\u003e G \u003c/strong\u003eSFS_MEAN: Mean of seedling to flowering survival at 200 mM. The color of the arcs in Circos represents the interaction between SNPs.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/048a66a076356bdeaf90d119.png"},{"id":97138052,"identity":"d38c7ccb-cb24-4e18-883c-cba75820a0a2","added_by":"auto","created_at":"2025-12-01 09:58:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":178054,"visible":true,"origin":"","legend":"\u003cp\u003eLD structure surrounding SNP rs.Gm03.39710939. The color intensity of each diamond reflects the strength of the LD, ranging from bright red (D ′ = high) to white (D ′ = low). A single haplotype block, indicated by the solid black triangle, shows strong coinheritance of alleles within this genomic region.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/aa216224dd0bb5bc0d5a7a0e.png"},{"id":97009723,"identity":"c7ff3230-46d5-43ba-8da1-f581c958a673","added_by":"auto","created_at":"2025-11-28 15:24:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":189806,"visible":true,"origin":"","legend":"\u003cp\u003eAllelic effects of closely associated SNPs on various phenotypic traits during the germination and seedling stages. The distribution of SNP effects on relevant chromosomes during these stages is shown by box plots: \u003cstrong\u003eA \u003c/strong\u003eΔ%_GP_100mM: delta percentage of germination percentage at 100 mM; \u003cstrong\u003eB \u003c/strong\u003eΔ%_GP_200mM: delta percentage of germination percentage at 200 mM;\u003cstrong\u003e C \u003c/strong\u003eΔ%_NL_200mM: delta percentage of the number of leaves at 200 mM;\u003cstrong\u003e D \u003c/strong\u003eΔ%_SL_200mM: delta percentage of seedling length at 200 mM;\u003cstrong\u003e E \u003c/strong\u003eΔ%_SPAD_200mM: delta percentage of chlorophyll content by SPAD meter at 200 mM;\u003cstrong\u003e F \u003c/strong\u003eLSS_MEAN: Mean of leaf scorch score at 200 mM;\u003cstrong\u003e G \u003c/strong\u003eSFS_MEAN: Mean of seedling to flowering survival at 200 mM. The sample mean is indicated by the plus (+) symbol, and the number of SNP alleles linked to each trait is shown on the X-axis. The ref and altra alleles are represented by red and green box plots, respectively.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/4b6f674cddb7e178d2594a9e.png"},{"id":97009725,"identity":"7c3f3ea2-b863-4a24-807a-b08e58e209b5","added_by":"auto","created_at":"2025-11-28 15:24:04","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":219717,"visible":true,"origin":"","legend":"\u003cp\u003eAllelic discrimination plot for the SNP rs.Gm03.39710939. KASP genotyping results are shown for the following soybean lines phenotyped for Δ%_SPAD_200mM: delta percentage of chlorophyll content by SPAD meter at 200 mM, LSS_MEAN: leaf scorch score at 200mM, and SFS_MEAN traits: seedling to flowering survival at 200mM. \u003cstrong\u003eA\u003c/strong\u003e Selection of 20 genotypes (lines: 25, 35, 43, 45, 54, 62, 68, 79, 94, 114, 118, 125, 134, 150, 159, 161, 171, 176, 186, 207). \u003cstrong\u003eB\u003c/strong\u003e The complete set of 198 genotypes (lines no 1-208, excluding the subset shown in A). Each data point represents a technical replicate. Homozygous allele 1 (TT) is shown in red, homozygous allele 2 (AA) is shown in blue, and heterozygous (TA) samples are shown in green. The '×' symbol represents the no-template control (NTC) and denotes an undetermined allele.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/0d58bf2de801e0d5bdf3ae0e.png"},{"id":97009729,"identity":"8b1adc3b-f5d3-4115-a694-a8679fe48173","added_by":"auto","created_at":"2025-11-28 15:24:04","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":500837,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of multiple phenotypic traits in control and 200 mM NaCl-treated soybean plants. \u003cstrong\u003eA\u003c/strong\u003e Plant height, \u003cstrong\u003eB\u003c/strong\u003e Root length, \u003cstrong\u003eC\u003c/strong\u003e Leaves size, \u003cstrong\u003eD\u003c/strong\u003e SPAD (Chlorophyll) value, and \u003cstrong\u003eE\u003c/strong\u003e Plant health status (live vs. dead)\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/bfc16437d98ed9b210b75686.png"},{"id":105223756,"identity":"e30907fe-4b50-49ee-b96e-04c64e02fea9","added_by":"auto","created_at":"2026-03-23 16:10:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6010536,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/01a579d9-a40c-41e7-954f-a562066524ff.pdf"},{"id":97009726,"identity":"1b345e6a-23e8-45bf-8da2-7238703db590","added_by":"auto","created_at":"2025-11-28 15:24:04","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5923775,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Tables \u003c/strong\u003e(Attached in the supplemental file)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e Details of 198 soybean accessions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2\u003c/strong\u003e Phenotypic data for germination and seedling stage traits calculated via the Δ% formula.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3\u003c/strong\u003e Eigenvalues and eigenvectors of the quantitative traits at the seedling stage at a NaCl concentration of 200 mM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4\u003c/strong\u003e List of significant marker‒trait associations with -log10 (\u003cem\u003eP\u003c/em\u003e) ≥ 3.0 for germination and seedling stage traits of 198 soybean accessions grown in E1 and E2 analyzed via the FarmCPU and BLINK GWASs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S5\u003c/strong\u003e Epistatic SNP × SNP interactions for germination and seedling stage traits of 198 soybean accessions grown in E1 and E2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S6 \u003c/strong\u003eKASP assay primer information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S7 \u003c/strong\u003eDuplicate KASP genotyping results with associated phenotypes and genotypic calls.\u003c/p\u003e","description":"","filename":"Supplimentarydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8173753/v1/d4ded3b6bb70f5a53e0ae2e0.xlsx"}],"financialInterests":"","formattedTitle":"Stage-specific GWAS identifies a pleiotropic ankyrin repeat locus near GmSALT3 for salinity tolerance in soybean","fulltext":[{"header":"Key message","content":"\u003cp\u003eGWAS identified 20 salinity-tolerance SNPs in soybean, including a pleiotropic locus near \u003cem\u003eGmSALT3\u003c/em\u003e. A validated KASP marker for rs.Gm03.39710939 enables early screening and marker-assisted breeding.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eSoybeans (\u003cem\u003eGlycine max\u003c/em\u003e (L.) Merr.) are a significant legume crop cultivated globally, contributing a major portion of the world's vegetable oil and protein. Approximately 333\u0026nbsp;million tonnes of soybeans are produced each year worldwide (Feng et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The leading producers of soybeans worldwide are Brazil, the United States, Argentina, China, and India (Lucić et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, soybeans are generally sensitive to salt (Noor et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and soil salinization affects approximately 19.5% of arable land worldwide, posing a significant threat to their productivity. Salt stress negatively impacts normal plant development, including photosynthesis, leaves gas exchange, flowering time, and yield, making it crucial to improve soybean salinity tolerance for food security (Zhang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSalinity has severe impacts on germination and early seedling growth. High NaCl creates intense osmotic stress that reduces water uptake and turgor, while the accumulation of Na⁺ and Cl⁻ causes an ionic imbalance in tissues. Even moderate salt levels markedly delay germination and inhibit shoot and root growth (Zhang et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, a high amount of salt produces reactive oxygen species, including hydrogen peroxide (H₂O₂), superoxide radicals (O₂⁻), and hydroxyl radicals (OH⁻). These ROS can damage cells, causing DNA mutations, protein breakdown, and lipid peroxidation (Safar et al. 2019). Salt-stressed soybean seedlings commonly exhibit chlorosis, necrosis, and scorching of leaves, as sodium and chloride disrupt photosynthesis and nutrient homeostasis (Noor et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). All of these mechanisms contribute to the low survival rate of soybean plants under salinity stress.\u003c/p\u003e\u003cp\u003eTo address these challenges, genetic analyses have begun to identify the loci associated with salt tolerance in soybeans. Several salt tolerance genes have been identified through genome-wide association studies (GWASs), including \u003cem\u003eGmSALT3/GmCHX1, GsERD15B\u003c/em\u003e, and \u003cem\u003eGmCDF1\u003c/em\u003e (Guan et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Qi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These genes contribute to ionic homeostasis, the regulation of oxidative stress, and salt stress signaling pathways (Guan et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, a gene (\u003cem\u003eGmSALT3)\u003c/em\u003e on chromosome 3 was cloned from the salt-tolerant cultivar Tiefeng 8 and has been shown to increase NaCl exclusion and improve yield under saline conditions (Guan et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pruthi et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). D et al. (2019) also used GWAS and scanned 305 diverse accessions with traits such as the leaf scorch score (LSS), chlorophyll content ratio (CCR), leaves sodium content (LSC), and leaves chloride content (LCC). They detected significant marker‒trait associations on Chr 1, 3, 8, and 18, notably validating the known \u003cem\u003eGmSALT3\u003c/em\u003e locus on Chr 3.\u003c/p\u003e\u003cp\u003eSimilarly, Zeng et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) evaluated 283 soybean accessions under 120 mM NaCl stress for 12\u0026ndash;18 days and identified genetic loci associated with salt tolerance via a GWAS. This study revealed 45 significant single-nucleotide polymorphisms (SNPs) located on chromosomes 2, 3, 7, 8, 10, 13, 14, 16, and 20 that are associated with leaves chloride and chlorophyll concentrations. Among these, 31 SNPs mapped to a primary salt tolerance quantitative trait locus (QTL) on chromosome 3, supporting previous findings on the genetic basis of salt tolerance in soybean. Zhao et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) further investigated the role of ankyrin repeat (ANK) proteins in salt stress tolerance. They identified 226 ANK genes in soybean, of which \u003cem\u003eGmANK114\u003c/em\u003e was significantly induced by salt and drought stress. The overexpression of \u003cem\u003eGmANK114\u003c/em\u003e in transgenic Arabidopsis and soybean hairy roots improved germination rates, reduced oxidative damage, and activated key stress-responsive genes, including \u003cem\u003eWRKY13, NAC11, DREB2, MYB84\u003c/em\u003e, and \u003cem\u003ebZIP44\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eIn addition, classical genetic studies have revealed that salt tolerance in soybean is primarily controlled by a single dominant gene, with subsequent biparental QTL mapping studies consistently identifying a major quantitative trait locus in linkage group N near SSR markers Satt255 and Sat_091, along with additional minor QTLs on other chromosomes (Hamwieh et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Wild soybean (\u003cem\u003eGlycine soja\u003c/em\u003e) serves as a valuable genetic resource for improving salt tolerance, with recent studies identifying novel loci, such as a dominant salt tolerance gene mapped to chromosome 3, in populations derived from the salt-tolerant wild accession NY36-87, demonstrating the potential of wild germplasm for enhancing salt tolerance in cultivated soybean (Guo et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite these advances, a comprehensive genetic study of salinity tolerance that spans both the germination and seedling stages is lacking. Previous GWASs have typically targeted a single stage or a single salt level; for example, Wang et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) conducted a GWAS on germination index traits under salt stress, whereas other groups have evaluated tolerance at the seedling stage (Pruthi et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To date, no study has integrated multi-trait data from germination through early seedling development under a range of salinity treatments.\u003c/p\u003e\u003cp\u003eWe hypothesize that the mechanism for salinity tolerance may differ at the germination and seedling stages. During the germination stage, plants likely focus on maintaining osmotic balance to avoid stress. In contrast, during the seedling stage, plants employ different strategies, such as ion exclusion and chlorophyll retention (Zhang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, GWASs need to be run differently for both stages to identify the distinct specific MTAs/QTLs that contribute to salinity tolerance.\u003c/p\u003e\u003cp\u003eIn this study, we evaluated 198 diverse soybean genotypes under increasing salinity stress across two different stages (germination and seedling) and two experiments (E1 and E2). The objectives of this study were to evaluate the phenotypic variation in salinity tolerance at both the germination and seedling stages, conduct genome-wide association studies (GWASs) for the identification of stage-specific single-nucleotide polymorphisms (SNPs) and marker\u0026ndash;trait associations (MTAs), and validate key SNPs through the development of Kompetitive Allele-Specific PCR (KASP) markers for use in marker-assisted selection (MAS). For this purpose, at the germination stage, we used the mean data from both experiments of Gobade et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) to calculate the Δ% change in germination percentage (GP) under 100 mM and 200 mM NaCl. At the seedling stage, under 200 mM NaCl, we evaluated additional traits, including the delta percentage (Δ%) in the number of leaves (NL), seedling length (SL), chlorophyll content (SPAD), mean of leaf scorch score (LSS), and mean of the seedling to flowering survival (SFS), across both experiments. A GWAS was conducted via the FarmCPU and BLINK models, identifying SNPs associated with salinity tolerance across different traits and stages. We aimed to analyze these SNPs to pinpoint known and novel genomic regions or genes associated with salt tolerance and to develop a KASP marker for validation and use in marker-assisted selection (MAS). This research will support the development of salt-tolerant soybean varieties, contributing to global efforts to combat soil salinity.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant materials\u003c/h2\u003e\u003cp\u003eA core set of 198 soybean accessions, referred to as the Maharashtra Association for the Cultivation of Sciences (MACS) soybean panel, was selected from the 55-year-old soybean germplasm repository maintained at the Agharkar Research Institute (ARI), Pune (Gobade et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Patil et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The details of the 198 soybean genotypes are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The panel includes indigenous (IC) and exotic collections (EC), promising cultures (PC), farmer collections (FC), advanced breeding lines, and genotypes spanning early, mid-late, and late maturity groups.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExperimental design and salinity treatment\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eSeed preparation and sowing\u003c/h2\u003e\u003cp\u003eThe experiment was conducted in a completely randomized design (CRD) under polyhouse conditions at ARI, Pune (18\u0026deg;31\u0026prime;16.6\u0026Prime;N, 73\u0026deg;49\u0026prime;53.3\u0026Prime;E) for the seedling stage screening. This process was repeated twice, designated E1 (April 2024) and E2 (December 2024), to ensure the reproducibility and consistency of the results. Prior to sowing, the seeds were surface-sterilized with a 0.2% sodium hypochlorite solution (Hi-AR/ACS grade, 4% w/v; HiMedia, Catalog No. AS102-12) for 1 min, followed by thorough rinsing with distilled water. For each accession, a total of eight healthy and uniform seedlings were raised: four for the control and four for the treatment. The control and treatment sets were sown in separate seedling trays, each with 40 wells (5 \u0026times; 8 layout). Seedlings were maintained under controlled polyhouse conditions at 25\u0026deg;C with optimal watering to ensure uniform growth until the emergence of distinct seedling stages (Gobade et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSalinity treatment for the seedling stage study\u003c/h3\u003e\n\u003cp\u003eSalinity stress treatments of 200 mM NaCl were initiated 15 days after sowing at the seedling stage. The seedling trays were divided into two groups: a control group (15 trays) and a treatment group (15 trays). Each tray was placed in a plastic tray filled with either distilled water (control, zero mM NaCl) or a saline solution (200 mM). To avoid osmotic shock, a stepwise increase in salinity was applied to the treatment group. The salt concentration was increased from an initial concentration of 60 mM NaCl to 120 mM after 2 days, 150 mM after 5 days, and finally to the target concentration of 200 mM after 7 days. All the saline solutions were prepared with 3 L of distilled water per tray. To maintain consistent concentration levels, the solutions in all trays were replenished with fresh water and NaCl every three days (Gobade et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Javid et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePhenotypic data collection\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGermination Stage\u003c/h2\u003e\u003cp\u003eThe mean phenotypic data related to the germination percentage trait for the control, 100 mM NaCl, and 200 mM NaCl treatments were obtained from our previous study by Gobade et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Two traits were used for analysis, and the delta percentage (Δ%) was calculated to evaluate the effect of salinity stress: (1) germination percentage (GP) at 100 mM NaCl (Δ%_GP_100mM) and (2) germination percentage at 200 mM NaCl (Δ%_GP_200mM).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSeedling Stage\u003c/h3\u003e\n\u003cp\u003ePhenotypic data for the seedling stage were collected twenty-three days after treatment with 200 mM NaCl for SL, NL, and SPAD. Additionally, data for the leaf scorch score (LSS) and seedling survival (SFS) were collected four days later in both experiments (E1 and E2). Traits were recorded under both control and salinity-stressed conditions. Among them, three were quantitative traits: (1) the delta percentage of the number of leaves (Δ%_NL_200mM), measured via manual counting; (2) the delta percentage of seedling length (Δ%_SL_200mM), measured from the base to the tip of the tallest leaves via a ruler; and (3) the delta percentage of chlorophyll content (Δ%_SPAD_200mM), estimated via a chlorophyll meter SPAD-502, Konica Minolta, Inc., Osaka, Japan. The remaining two were qualitative traits: (4) Mean of leaf scorch score (LSS_MEAN), visually assessed on a scale of 0\u0026ndash;5, where 0 indicates no damage, one corresponds to up to 10% leaves scorching, 2 represents 11\u0026ndash;25% scorching, 3 indicates 26\u0026ndash;50% scorching, 4 reflects 51\u0026ndash;75% scorching, five indicates very severe (\u0026ge;\u0026thinsp;75%) leaves scorching, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and (5) Mean of seedling to flowering survival (SFS_MEAN), recorded as a binary score, with 1 representing survival up to flowering and 0 representing posttreatment death.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCalculation of the impact of salinity stress on the delta percentage (Δ%)\u003c/h3\u003e\n\u003cp\u003eTo evaluate the effects of salinity stress, the combined means of the E1 and E2 experimental data were used to determine the mean control and mean treatment data for all salinity-related traits. The delta percentage (Δ%) for each trait was calculated via the following formula: Δ% = [(Mean of control data \u0026ndash; Mean of treatment data)/Mean of the control data] \u0026times; 100. These Δ% values quantified the relative reduction in performance under salinity stress and were used as phenotypic input data for GWASs.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis of phenotypic data\u003c/h2\u003e\u003cp\u003ePhenotypic data were subjected to descriptive statistical analyses and one-way analysis of variance (ANOVA) to assess variation among genotypes, followed by Tukey\u0026rsquo;s honest significant difference (HSD) post hoc test to compare mean differences. All analyses were performed via JMP software (version 18; SAS Institute Inc., Cary, NC, USA). For each genotype, the combined mean of the data from experiments E1 and E2 was calculated to obtain the overall mean, range, standard deviation (SD), and standard error (SE) of the data. Multivariate analyses, including principal component analysis (PCA), two-way hierarchical clustering, and K-means clustering, were also conducted via JMP 18 (SAS Institute Inc., 2024).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eGenotyping\u003c/h2\u003e\u003cp\u003eThe SNP marker data were obtained from Patil et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and genome-wide association studies (GWASs) were conducted using 23,574 high-quality SNP markers to identify genetic loci associated with salinity tolerance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGWAS and SNP\u003c/b\u003e \u0026times; \u003cb\u003eSNP interaction analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, two techniques were employed for association analysis of traits across E1 and E2: the Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK) and the fixed and random model of circulating probability unification (FarmCPU). We only calculated the phenotypic variation explained (PVE%) via the BLINK model, as it evaluates markers directly without assuming an even distribution of causative genes, unlike the FarmCPU model (Wang and Zhang \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We applied the Bonferroni correction (Bonferroni \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1935\u003c/span\u003e) to maintain an overall Type I error rate of 0.05, which resulted in a stringent -log10 (\u003cem\u003eP\u003c/em\u003e) threshold of 5.67. For a less restrictive approach, a suggestive threshold of -log10 (\u003cem\u003eP\u003c/em\u003e)\u0026thinsp;\u0026ge;\u0026thinsp;3 was also utilized (Javid et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, we generated genome-wide Manhattan plots and multitrack quantile‒quantile (Q‒Q) plots to visualize significant marker‒trait associations (MTAs). The analysis of two-dimensional (SNP \u0026times; SNP) epistatic interactions among the main SNPs associated with salinity tolerance traits was conducted via TBtools software (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To identify the best SNP and best chromosome signals, a corrected epistatic \u003cem\u003eP\u003c/em\u003e value threshold of \u0026le;\u0026thinsp;0.0001 was applied, and interactive graphics (circos plots) were generated (Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCandidate gene annotation and LD block analysis\u003c/h2\u003e\u003cp\u003eSoyBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.soybase.org\u003c/span\u003e\u003cspan address=\"http://www.soybase.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to identify known and novel candidate QTLs and genes located within 200 kb upstream and downstream of the significant SNP peaks detected via GWAS for each trait. To identify possible candidate genes, research was conducted on the Williams 82 Glycine max Wm82.a4.v1 (Zhang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reference genome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://datahub.wildsoydb.org\u003c/span\u003e\u003cspan address=\"https://datahub.wildsoydb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). LD analysis was performed using the same protocol described by Patil et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), with Haploview v4.2 (Barrett et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eKompetitive allele-specific PCR (KASP) assay\u003c/h2\u003e\u003cp\u003eThe common significant SNPs identified in both E1 and E2 were selected for the traits Δ%_SPAD_200mM, LSS_MEAN, and SFS_MEAN in soybean at the seedling stage. For primer design, the upstream and downstream sequences (\u0026plusmn;\u0026thinsp;50 bp) surrounding this SNP position were retrieved from the Wild SoyDB DataHub (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://datahub.wildsoydb.org\u003c/span\u003e\u003cspan address=\"https://datahub.wildsoydb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The KASP primer sequences (assay name: rs_Gm03_39710939) were synthesized by LGC Genomics (United Kingdom). KASP TF V4.0 2\u0026times; Master Mix (catalog no. KBS-1050-122) and 384-well plates were also purchased from LGC. Each primer pair consisted of two allele-specific forward primers (F1 and F2) and one standard reverse primer (R). The F1 and F2 primers contained 5\u0026prime; tail sequences with 6-carboxyfluorescein (FAM) and hexachloro-6-methylfluorescein (HEX) fluorescent linkers, respectively (Table S6). Genotypic calls were assigned on the basis of fluorescence signals: samples showing only FAM fluorescence were classified as homozygous for allele 1, those showing only HEX fluorescence were classified as homozygous for allele 2, and samples exhibiting both FAM and HEX fluorescence were designated as heterozygous. The KASP significant genotyping assays were performed in duplicate for each genotype on a 384-well plate. Each reaction had a final volume of 5.0 \u0026micro;L, containing 1.0 \u0026micro;L of genomic DNA (\u0026sim;10 ng/\u0026micro;L), 2.5 \u0026micro;L of KASP master mixture, 0.07 \u0026micro;L of KASP primer mixture, and 1.43 \u0026micro;L of ddH₂O. At least two no-template controls (NTCs) were included on each plate.\u003c/p\u003e\u003cp\u003eThe PCR was conducted with the following protocol: an initial hot-start step at 95\u0026deg;C for 15 min; followed by 10 touchdown cycles at 94\u0026deg;C for 20 s and 65\u0026ndash;57\u0026deg;C for 60 s (decreasing by 1\u0026deg;C per cycle); and then 30 amplification cycles at 94\u0026deg;C for 20 s and 57\u0026deg;C for 60 s. To improve cluster separation, a final extension of 3 cycles was added, consisting of 20 s at 94\u0026deg;C and 60 s at 57\u0026deg;C. The fluorescence data from the amplified products were detected and analyzed via the QuantStudio 5 Real-Time PCR system (Applied Biosystems) to make genotype calls.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 198 genotypes of soybean were evaluated for germination and seedling stage traits under control and NaCl stress conditions (100 mM and 200 mM) across two experiments (E1 and E2). The observations and descriptive statistics revealed substantial variation among the genotypes, and NaCl stress consistently reduced trait performance relative to the control conditions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S2; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eDescriptive statistics of phenotypic traits related to the salinity response in soybean.\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=\"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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperiment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eANOVA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003e\u003cb\u003eE1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGP_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGP_100mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e33.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.3281434\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGP_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e33.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNL_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.8784953\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSL_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0466459*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPAD_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23\u0026ndash;38\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\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPAD_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003e\u003cb\u003eE2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGP_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGP_100mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e61.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e37.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGP_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e73.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNL_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u0026ndash;17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u0026ndash;13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSL_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u0026ndash;57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPAD_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u0026ndash;36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPAD_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20.51\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\u003e7.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGP_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGP_100mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e27.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGP_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0-100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNL_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSL_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23\u0026ndash;47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.44\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\u003e3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPAD_Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22\u0026ndash;36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPAD_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u0026ndash;26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0010053**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eΔ%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(E1\u0026amp;E2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΔ%_GP_100mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-77.8)-100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e30.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΔ%_GP_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-400)-100\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\u003e2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΔ%_NL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-43.48)-49.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΔ%_SL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-26.83)-57.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΔ%_SPAD_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.24\u0026ndash;74.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eThe asterisks indicate statistically significant differences (* \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and ** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between the means (control versus treatment). GP_100mM: Germination percentage at 100 mM, GP_200mM: Germination percentage at 200 mM, NL_200mM: Number of leaves at 200 mM, SL_200mM: Seedling length at 200 mM, SPAD_200mM: Chlorophyll content measured by SPAD meter at 200 mM, Δ%_GP_100mM: Delta percentage of germination percentage at 100 mM; Δ%_GP_200 mM: Delta percentage of germination percentage at 200 mM; Δ%_NL_200mM: Delta percentage of number of leaves at 200 mM; Δ%_SL_200mM: Delta percentage of seedling length at 200 mM; Δ%_SPAD_200 mM: Delta percentage of chlorophyll content measured by SPAD meter at 200 mM.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive statistics of salinity-related traits\u003c/h2\u003e\u003cp\u003eAt the germination stage, salinity stress significantly reduced the GP in both environments. In E1, the GP decreased from 62.77% in the control to 48.51% at 200 mM NaCl, whereas in E2, the reduction ranged from 82.30% to 73.34%. The mean values across environments indicated an overall decrease of 11.6% at 200 mM, with ANOVA confirming significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;B).\u003c/p\u003e\u003cp\u003eAt the seedling stage, leaves formation was also affected, with the number of leaves (NL) decreasing from 8.96 (control) to 7.20 at 200 mM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), corresponding to a 19.64% reduction. The mean seedling length (SL) of the control showed a wide range (23\u0026ndash;47) of high variability, with a more substantial decline of 21.93%, with pooled means decreasing from 36.43 cm to 28.44 cm under 200 mM NaCl (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The mean SPAD values exhibited the most drastic decrease, from 28.29 (control) to 15.92 (200 mM), corresponding to a 43.7% reduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These reductions were highly significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming that the chlorophyll content is the most sensitive trait under salinity stress.\u003c/p\u003e\u003cp\u003eThe stress-specific qualitative indicators further supported the negative impact of salinity. The analysis of leaf scorch scores (LSSs) at 200 mM salinity revealed a bimodal distribution. Environment E2 had extreme results, with both the highest number of highly tolerant plants (LSS\u0026thinsp;=\u0026thinsp;0; 66 genotypes) and the highest number of severely susceptible plants (LSS\u0026thinsp;=\u0026thinsp;5; 86 genotypes). This clustering at the extremes confirmed high genotypic variation in salinity tolerance, suggesting strong potential for selection in both environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Furthermore, E2 resulted in notably better seedling to flowering survival (SFS) under 200 mM salinity, with 65 genotypes surviving up to the flowering stage, whereas only 18 genotypes survived in E1. This considerable difference highlights the significant environmental influence on the genotype's ability to overcome prolonged salinity stress beyond the seedling stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eThe Δ% change highlights the magnitude and direction of the change in traits under the highest stress level (200 mM) compared with the control, averaged across E1 and E2. All the traits presented a negative Δ% percentage change, indicating that salinity stress significantly impacts soybean growth and development, with the SPAD chlorophyll content (43.21%) being the most affected trait in terms of overall percentage reduction due to 200 mM salinity stress (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTwo-way hierarchical clustering grouped the 198 soybean genotypes into distinct clusters based on NL, SL, and SPAD values. The heatmap clearly indicates a separation between the tolerant and sensitive groups, with tolerant genotypes exhibiting higher trait values (red) than sensitive genotypes (blue). This clustering pattern highlights substantial genotypic diversity in response to salinity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePCA revealed that the first two components explained 73.3% of the total phenotypic variation, with PC1 contributing 39.8% and PC2 contributing 33.5% (Table S3, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). NL contributed most strongly to PC1 (eigenvector\u0026thinsp;=\u0026thinsp;0.7117), and SL contributed most strongly to PC2 (eigenvector\u0026thinsp;=\u0026thinsp;0.8812). The SPAD content contributed moderately to PC1 and PC2. In the biplot, the genotypes on the right side were the best performers, with the highest values for all three traits under salinity, whereas those clustered on the left were the poorest performers, with the lowest values for these traits. The SL_MEAN_200mM and NL_MEAN_200mM vectors were located primarily in the positive direction of Component 1 and were closely related, suggesting that these two traits were highly positively correlated. SPAD_MEAN_200mM was located in the positive direction of Component 1 but with a slight upward tilt toward Component 2, indicating that it was also positively correlated with the other two traits, but to a lesser extent than the correlation between SL and NL. The biplot clearly separated genotypes along the principal components, with tolerant and sensitive genotypes distributed in opposite quadrants, reflecting contrasting trait performance under stress.\u003c/p\u003e\u003cp\u003eK-means clustering further partitioned the genotypes into four distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) based on the three quantitative traits at the seedling stage. The clusters differed in their mean trait values, with one cluster comprising relatively tolerant lines (high NL, SL, and SPAD). In contrast, the others represented moderately tolerant or highly sensitive genotypes. The overlap between clusters in the PCA space confirmed the continuous variation in the salinity response but also supported the presence of distinct phenotypic groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eGenome-wide association study of salinity-related traits\u003c/h2\u003e\u003cp\u003eA GWAS was performed for seven phenotypic traits under salinity stress in 198 soybean cultivars via the FarmCPU and BLINK models. A total of 66 significant main-effect SNPs associated with two germination-stage and five seedling-stage traits across the E1 and E2 experiments were identified via a significance threshold of -log10 (\u003cem\u003eP\u003c/em\u003e)\u0026thinsp;\u0026ge;\u0026thinsp;3. Among these, eight SNPs exceeded the Bonferroni-corrected threshold of -log10 (\u003cem\u003eP\u003c/em\u003e)\u0026thinsp;\u0026ge;\u0026thinsp;5.67 across both GWAS models. These results are visualized in Manhattan and quantile‒quantile (Q‒Q) plots (Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDuring the germination stage, 18 significant SNPs were detected for the Δ%_GP trait under both 100 mM NaCl stress and 200 mM NaCl stress. Importantly, SNPs identified at 100 mM generally presented negative effects, suggesting that the associated allele increases susceptibility. In contrast, the SNPs identified at 200 mM consistently presented positive effects, indicating an increase in tolerance.\u003c/p\u003e\u003cp\u003eSpecifically, for Δ%_GP_100mM​, a total of 10 significant SNP associations were identified across the models, with four common SNPs found by both FarmCPU and BLINK: rs.Gm07.39079550, rs.Gm07.39090791, rs.Gm11.33334736, and rs.Gm16.6978282 (located on chromosomes 7, 11, and 16, respectively). The most significant SNP, rs.Gm16.6978282, displayed the strongest association -log10 (\u003cem\u003eP\u003c/em\u003e) 5.54 from FarmCPU and 5.84 from BLINK) and explained the highest proportion of phenotypic variance (PVE\u0026thinsp;=\u0026thinsp;35.21%), indicating that it is a primary locus controlling the germination response at 100 mM\u003c/p\u003e\u003cp\u003eSimilarly, for Δ%_GP_200mM​, a total of eight significant SNP associations were detected across the models, with four common SNPs found by both FarmCPU and BLINK: rs.Gm05.9457555, rs.Gm10.35460454, rs.Gm18.1916285, and rs.Gm20.45859448 (located on chromosomes 5, 10, 18, and 20, respectively). The most significant SNP, rs.Gm10.35460454, displayed the strongest association (-log10 ​(\u003cem\u003eP\u003c/em\u003e) 4.50 from FarmCPU and 4.79 from BLINK). Notably, these Δ%_GP_200mM​ SNPs all presented positive SNP effects, supporting the role of the minor allele in increased tolerance at relatively high stress levels (Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B).\u003c/p\u003e\u003cp\u003eThe GWAS for seedling-stage traits under 200 mM salinity stress revealed 48 significant associations, highlighting a divergence in genetic effects on the basis of the measured trait. For Δ%_NL_200mM, a total of 11 significant SNPs were identified across the models, primarily on chromosomes 1, 6, 13, and 18. Four SNPs were found to be common to both FarmCPU and BLINK, including rs.Gm01.3888173, rs.Gm06.12647562, rs.Gm06.12769083, and rs.Gm18.47911124. The majority of these common SNPs exhibited negative SNP effects, indicating that the minor allele at these loci contributes to a greater reduction in leaves count, thus suggesting increased susceptibility. The strongest association was detected on chromosome 6 with the SNP rs.Gm06.12769083, which had -log10 (\u003cem\u003eP\u003c/em\u003e) values of 3.97 (FarmCPU) and 4.18 (BLINK) (Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eFurthermore, for Δ%_SL_200mM, 15 significant SNPs were identified on chromosomes 1, 11, 14, 15, and 20. Four SNPs were found to be common to both FarmCPU and BLINK: rs.Gm01.6204693, rs.Gm11.37469933, rs.Gm14.41787022, and rs.Gm20.30040900. A key SNP on chromosome 1, rs.Gm01.6204693 had -log10 (\u003cem\u003eP\u003c/em\u003e) values of 4.17 (FarmCPU) and 4.34 (BLINK). (Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). For Δ%_SPAD_200mM, six significant SNPs were identified on chromosomes 1 and 3. Three SNPs were found to be common to both FarmCPU and BLINK: rs.Gm01.43332855, rs.Gm03.38716419, and rs.Gm03.39710939. The strongest association was detected for SNP rs.Gm03.38716419 on chromosome 3, which had -log10 (\u003cem\u003eP\u003c/em\u003e) values of 3.51 (FarmCPU) and 3.69 (BLINK). The majority of the SNPs associated with Δ%_SL_200mM and Δ%_SPAD_200mM presented positive SNP effects, suggesting that the minor allele contributes to tolerance by mitigating the loss of seedling length and chlorophyll content (Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eIn addition, for the LSS_MEAN and SFS_MEAN traits, eight significant SNPs were detected for each. For LSS_MEAN, significant SNPs were located on chromosomes 3, 9, 11, 12, 13, and 18, with two common SNPs\u0026mdash;rs.Gm03.39710939 and rs.Gm09.843177\u0026mdash;identified across both models. The SNP rs.Gm03.39710939 on chromosome 3 exhibited the strongest association, with exceptionally high -log10 ​(\u003cem\u003eP\u003c/em\u003e) values of 6.92 (FarmCPU) and 10.51 (BLINK), explaining a significant proportion of the phenotypic variance (PVE of 35.07%) (Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Similarly, for SFS_MEAN, the most significant SNP was also rs.Gm03.39710939 on chromosome 3, which presented high -log10 ​(\u003cem\u003eP\u003c/em\u003e) values of 6.93 (FarmCPU) and 8.13 (BLINK) and explained the highest PVE of 43.75%, confirming its strong pleiotropic effect on survival (Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eOverall, the SNP rs.Gm03.39710939 displayed a pleiotropic effect, influencing the Δ%_SPAD_200mM, LSS_MEAN, and SFS_MEAN traits. Its strong and consistent association across multiple traits makes it a promising candidate for gene annotation and functional validation, as well as a key target for improving salinity tolerance at the vegetative stage (Table S4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eEpistatic (SNP \u0026times; SNP) interactions among significant loci\u003c/h2\u003e\u003cp\u003eEpistatic (SNP \u0026times; SNP) interactions were analyzed among 20 significant SNPs associated with salinity tolerance in soybean (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S5; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These loci interacted with 953 additional SNPs, collectively influencing seven traits across the germination and seedling stages in E1 and E2.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenome-wide SNP \u0026times; SNP (epistatic) interactions of significant SNPs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage\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\u003eMain effect SNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal SNP \u0026times; SNP interactions of Main effect SNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBEST_Chr.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBEST_SNP\u003c/p\u003e\u003cp\u003e(Epistatic)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFunction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGermination\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΔ%_GP_100mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm07.39079550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm09.21628729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.09G113000\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm09:21705841..21711265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003emyb family transcription factor APL-like isoform X1 [\u003cem\u003eGlycine max\u003c/em\u003e];IPR025756 (MYB-CC type transcription factor\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm07.39090791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm09.21628729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.09G113000\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm09:21705841..21711265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003emyb family transcription factor APL-like isoform X1 [\u003cem\u003eGlycine max\u003c/em\u003e];IPR025756 (MYB-CC type transcription factor\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm11.33334736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm18.56023158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.18G275200\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm18:56005084..56024960\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAuxin transport protein (BIG)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm16.6978282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm02.14916620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.02G139700\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm02:14922905..14927137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTranscription factor bHLH30-like [\u003cem\u003eGlycine max\u003c/em\u003e]; IPR011598 (Myc-type, basic helix-loop-helix (bHLH) domain)\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\u003eΔ%_GP_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm05.9457555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm10.21857428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.10G104100\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm10:21780444..21793043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eL-galactono-1,4-lactone dehydrogenase; GO:0016633 (galactonolactone dehydrogenase activity)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm10.35460454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm16.33616912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.16G173900\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm16:33610721..33620422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReceptor-like protein kinase 2; IPR001611 (Leucine-rich repeat)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm18.1916285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm15.35061311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.15G213700\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm15:35024771..35028314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReceptor-like serine/threonine kinase 2; IPR024171 (S-receptor-like serine/threonine-protein kinase); GO:0004672 (protein kinase activity)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm20.45859448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm15.44600021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.15G233100\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm15:44547552..44552077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family; IPR001611 (Leucine-rich repeat),GO:0006952 (defence response)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSeedling (200 mM)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΔ%_NL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm01.3888173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm06.39548568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.06G242000\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm06:39475973..39479036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAnkyrin repeat family protein; IPR020683 (Ankyrin repeat-containing domain)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm06.12647562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm06.28907845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.06G222800\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm06:28839029..28842106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eUncharacterized protein\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm06.12769083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm06.28907845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.06G222800\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm06:28839029..28842106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eUncharacterized protein\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm18.47911124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm16.36043781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.16G196400\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm16:35998156..36006453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAnkyrin repeat-containing protein; IPR020683 (Ankyrin repeat-containing, GO:0008270 (zinc ion binding)\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\u003eΔ%_SL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm01.6204693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm07.35731062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.07G186100\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm07:35722638..35727539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eBranched-chain amino acid transaminase 2; GO:0009081 (branched-chain amino acid metabolic process)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm11.37469933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm13.30031982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.13G193200\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm13:30065083..30075075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eArgonaute family protein; IPR003100 (Argonaute/Dicer protein\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm14.41787022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm20.25517507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.20G071666\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm20:25424912..25438559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePhosphoinositide phosphatase family protein\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm20.30040900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm09.39499095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.09G161600\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm09:39483962..39488683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAlcohol dehydrogenase 1; IPR002085 (Alcohol dehydrogenase superfamily, zinc-type)\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\u003eΔ%_SPAD_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm01.43332855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm07.2345032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.07G029100\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm07:2340211..2346295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHAT family dimerization domain-containing protein\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm03.38716419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm10.36634108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.10G135500\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm10:36601101..36604423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eF-box/RNI-like superfamily protein; IPR001810 (F-box domain); GO:0005515 (protein binding)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ers.Gm03.39710939\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm04.48363183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.04G225300\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm04:48368443..48377451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eVacuolar iron transporter-like protein\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\u003eLSS_MEAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ers.Gm03.39710939\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm14.21949373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.04G225300\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm04:48368443..48377451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eVacuolar iron transporter-like protein\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm09.843177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm12.3161544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.12G043800\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm12:3162105..3167056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGlucan endo-1,3-beta-D-glucosidase-like [\u003cem\u003eGlycine max\u003c/em\u003e]; IPR000490 (Glycoside hydrolase, family 17)\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\u003eSFS_MEAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ers.Gm03.39710939\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ers.Gm05.13429009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.04G225300\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm04:48368443..48377451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eVacuolar iron transporter-like protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eΔ%_GP_100mM: Delta percentage of germination percentage at 100 mM; Δ%_GP_200mM: Delta percentage of germination percentage at 200 mM; Δ%_NL_200 mM: Delta percentage of number of leaves at 200 mM; Δ%_SL_200mM: Delta percentage of seedling length at 200 mM; Δ%_SPAD_200mM: Delta percentage of chlorophyll content by SPAD meter at 200 mM; LSS_MEAN: Mean of leaf scorch score at 200 mM; SFS_200mM: Mean of seedling to flowering survival at 200 mM. Best_Chr. : chromosome of best SNP, Best_SNP: SNP identifier of best SNP. Bold SNPs are common in the FarmCPU/BLINK methods and E1 and E2.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor the Δ%_GP_100mM trait, four main-effect SNP \u0026times; SNP epistatic interactions were detected. These four main-effect interactions involved 34 other SNPs and were distributed across multiple chromosomes (Gm02, Gm06, Gm07, Gm10, Gm11, Gm16, Gm17, Gm18, and Gm20). Specifically, the four main-effect interactions were rs.Gm16.6978282 \u0026times; rs.Gm02.14916620, rs.Gm07.39090791 \u0026times; rs.Gm09.21628729, rs.Gm07.39079550 \u0026times; rs.Gm09.21628729, and rs.Gm11.33334736 \u0026times; rs.Gm18.56023158 (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S5; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eSimilarly, for the high-salinity germination trait, the Δ%_GP_200mM trait, four main-effect SNPs exhibited epistatic interactions, with 622 other SNPs distributed across all 20 chromosomes. The four main-effect SNP \u0026times; SNP interactions identified were rs.Gm10.35460454 \u0026times; rs.Gm16.33616912, rs.Gm05.9457555 \u0026times; rs.Gm10.21857428, rs.Gm20.45859448 (involved in 232 interactions) \u0026times; rs.Gm15.44600021, and rs.Gm18.1916285 (involved in 323 interactions) \u0026times; rs.Gm15.35061311 (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S5; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eFor the Δ%_NL_200mM trait, four main-effect SNPs exhibited epistatic interactions. The SNPs rs.Gm06.12769083 and rs.Gm06.12647562 each showed a single interaction with rs.Gm06.28907845 on chromosome Gm06. The remaining two main-effect SNP \u0026times; SNP interactions were rs.Gm01.3888173 \u0026times; rs.Gm06.39548568 and rs.Gm18.47911124 \u0026times; rs.Gm16.36043781 (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S5; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eWith respect to seedling length, the Δ%_SL_200mM trait involved four main-effect SNPs in epistasis but had a markedly greater number of interactions. The SNPs were as follows: rs.Gm01.6204693 \u0026times; rs.Gm07.35731062, rs.Gm20.30040900 \u0026times; rs.Gm09.39499095. The other two SNPs included rs.Gm11.37469933 (3 interactions) \u0026times; rs.Gm13.30031982 and rs.Gm14.41787022 \u0026times; rs.Gm20.25517507 (10 interactions) (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S5; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eFor the Δ%_SPAD_200mM trait, three main-effect SNPs were involved in epistatic interactions. The SNPs rs.Gm03.38716419 \u0026times; rs.Gm10.36634108, rs.Gm03.39710939 \u0026times; rs.Gm04.48363183 and rs.Gm01.43332855 \u0026times; rs.Gm07.2345032. (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S5; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eFinally, for the LSS_MEAN trait, two main-effect SNPs demonstrated epistatic interactions. The SNP rs.Gm09.843177 exhibited three interactions, with the most significant interaction occurring with rs.Gm12.3161544. In contrast, rs.Gm03.39710939 had 11 interactions, the strongest of which was with rs.Gm14.21949373. Notably, the same SNP, rs.Gm03.39710939, also had a pleiotropic effect on the SFS_MEAN trait, engaging in an extensive epistatic network of 239 interactions. Its strongest interaction partner was rs.Gm05.13429009. These SNPs have emerged as key determinants of the genetic architecture governing salinity tolerance at the germination and seedling survival stages (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S5; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE\u0026ndash;G).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGWAS-based identification of QTLs, candidate genes, allelic effects for salinity-related traits, and LD analysis of significant SNPs on chromosome Gm03\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the GWAS analysis, several putative candidate genes associated with salinity-related traits at both the germination and seedling stages were identified within QTL regions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGene annotation of commonly significant SNPs found in both FarmCPU and BLINK from GWAS results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeak SNP ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef/Alt Allele\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\u003ePosition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFunction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003eGermination\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eΔ%_GP_100mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm07.39079550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39079550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.07G215700\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm07:39132935..39134698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLRR-RLKs detect stress signals (Leucine-rich repeat receptor-like kinases)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm07.39090791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39090791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.07G215700\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm07:39132935..39134698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLRR-RLKs detect stress signals(Leucine-rich repeat receptor-like kinases)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm11.33334736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33334736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.11G201600\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm11:33407132..33409547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHSP70(Heat Shock Protein) stabilizes proteins. Protects cells from salinity stress\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm16.6978282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6978282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.16G070200\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm16:6960313..6961147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eEnhance stress tolerance and defence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eΔ%_GP_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm05.9457555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9457555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.05G075400\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm05:9450431..9464707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCSN (COP9 signalosome) regulates germination under salt stress. Balances GA/ABA hormones\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm10.35460454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35460454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.10G130600\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm10:35478799..35481145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLate embryogenesis abundant (LEA) proteins support growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm18.1916285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1916285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.18G025700\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm18:1909325..1910772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRING-H2 finger protein 2B regulates stress genes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm20.45859448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45859448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.20G225000\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm20:45883078..45886009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eIAA14 controls germination via auxin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e\u003cp\u003e\u003cb\u003eSeedling (200 mM)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eΔ%_NL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm01.3888173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3888173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.01G036500\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm01:3812454..3816990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDNAJ proteins help in abiotic and biotic stress\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm06.12647562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12647562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.06G155200\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm06:12636441..12639109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCation calcium exchanger 4 balances Na⁺/Ca\u0026sup2;⁺. Supports ion transport under stress.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm06.12769083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12769083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.06G156100\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm06:12765825..12768593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePhotosystem II oxygen-evolving complex protein PsbP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm18.47911124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e47911124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.18G198300\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm18:47890828..47893406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePlasma Membrane Intrinsic Protein 1 transports water across membranes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eΔ%_SL_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm01.6204693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6204693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.01G051700\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm01:6242003..6243662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMYB64 regulates stress genes. Modulates ABA signaling and ions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm11.37469933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e37469933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.11G230500\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm11:37461012..37462619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eThe protein kinase superfamily phosphorylates proteins. Binds ATP and signals stress.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm14.41787022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41787022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.14G165300\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm14:41725688..41726147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHSP70(Heat Shock Protein) stabilizes proteins. Protects cells from salinity stress\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm20.30040900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30040900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.20G080700\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm20:30159414..30163042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRLK2 detects stress signals. Regulates salt stress response\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eΔ%_SPAD_200mM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm01.43332855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43332855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.01G122800\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm01:43359677..43364314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003evacuolar cation/proton exchanger 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm03.38716419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e38716419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.03G160300\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm03:38711991..38713760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCytochrome P450 regulates stress. Modulates hormones for salt tolerance.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ers.Gm03.39710939\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39710939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.03G170501\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm03:39711565..39712080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eANK(Ankyrin repeat) proteins support growth. Regulate hormones and stress response.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLSS_MEAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ers.Gm03.39710939\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39710939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.03G170501\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm03:39711565..39712080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eANK(Ankyrin repeat) proteins support growth. Regulate hormones and stress response.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers.Gm09.843177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e843177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.09G010800\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm09:834070..834660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eThe tonoplast dicarboxylate transporter moves sodium. Maintains ion balance under stress.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSFS_MEAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ers.Gm03.39710939\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGM03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39710939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eGlyma.03G170501\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGm03:39711565..39712080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eANK(Ankyrin repeat) proteins support growth. Regulate hormones and stress response.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eΔ%_GP_100mM: Delta percentage of germination percentage at 100 mM; Δ%_GP_200mM: Delta percentage of germination percentage at 200 mM; Δ%_NL_200mM: Delta percentage of number of leaves at 200 mM; Δ%_SL_200mM: Delta percentage of seedling length at 200 mM; Δ%_SPAD_200mM: Delta percentage of chlorophyll content by SPAD meter at 200 mM; LSS_MEAN: Mean of leaf scorch score at 200 mM; SFS_MEAN: Mean of seedling to flowering survival at 200 mM. Bold SNPs are common in the FarmCPU/BLINK methods and E1 and E2.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the case of the Δ%_GP_100mM trait, a significant SNP, rs.Gm16.6978282, was mapped to \u003cem\u003eGlyma.16G070200\u003c/em\u003e, a gene implicated in enhancing general stress tolerance and defence responses. Furthermore, the SNP rs.Gm11.33334736 was located within \u003cem\u003eGlyma.11G201600\u003c/em\u003e, which encodes a heat shock protein 70 (HSP70). This molecular chaperone is known to stabilize proteins and protect cells from salinity stress. Additionally, two SNPs, rs.Gm07.39090791 and rs.Gm07.39079550, were identified within \u003cem\u003eGlyma.07G215700\u003c/em\u003e. This gene encodes leucine-rich repeat receptor-like kinases (LRR-RLKs), which play pivotal roles in detecting external stress signals and initiating early defence responses. Analysis of allelic effects revealed that two of these SNPs, rs.Gm16.6978282 and rs.Gm07.39090791, had highly significant effects on the trait (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn addition, for the Δ%_GP_200mM trait, four significant SNPs were located within genes vital for the salinity stress response. Specifically, the SNP rs.Gm10.35460454 was linked to \u003cem\u003eGlyma.10G130600\u003c/em\u003e, which encodes a late embryogenesis abundant (LEA) protein known to promote seedling growth under stress conditions. Other associated SNPs include rs.Gm05.9457555, located within \u003cem\u003eGlyma.05G075400\u003c/em\u003e (encoding a COP9 signalosome subunit involved in hormone signaling during germination), rs.Gm18.1916285 within \u003cem\u003eGlyma.18G025700\u003c/em\u003e (RING-H2 finger protein that regulates stress-responsive genes), and rs.Gm20.45859448 within \u003cem\u003eGlyma.20G225000\u003c/em\u003e (which encodes an IAA14 protein involved in auxin-mediated germination control). However, the analysis of allelic effects for these SNPs did not reveal any statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eAt the seedling stage, for Δ%_NL_200mM, two SNPs were mapped on chromosome 06: rs.Gm06.12647562 and rs.Gm06.12769083. These SNPs were associated with \u003cem\u003eGlyma.06G155200\u003c/em\u003e (encoding a cation calcium exchanger involved in Na⁺/Ca\u0026sup2;⁺ homeostasis and ion transport under stress) and \u003cem\u003eGlyma.06G156100\u003c/em\u003e (linked to the maintenance of photosynthesis under salinity), respectively. Both genes were located in close genomic proximity and exhibited significant allelic effects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In addition, rs.Gm01.3888173, located in \u003cem\u003eGlyma.01G036500\u003c/em\u003e (DNAJ protein associated with abiotic and biotic stress tolerance), and rs.Gm18.47911124, associated with \u003cem\u003eGlyma.18G198300\u003c/em\u003e (plasma membrane intrinsic protein 1, involved in water transport across membranes), also presented statistically significant allelic effects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eThe Δ%_SL_200mM, a key parameter for salinity stress, presented four SNPs with significant allelic effects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The SNP rs.Gm01.6204693 on chromosome 1 was mapped to \u003cem\u003eGlyma.01G051700\u003c/em\u003e, an MYB transcription factor that regulates stress responses through ABA signaling. On chromosome 11, rs.Gm11.37469933 corresponded to \u003cem\u003eGlyma.11G230500\u003c/em\u003e, a protein kinase involved in signaling pathways. Furthermore, rs.Gm14.41787022 on chromosome 14 was linked to \u003cem\u003eGlyma.14G165300\u003c/em\u003e, a heat shock protein that provides cellular protection from salt-induced damage. Finally, rs.Gm20.30040900 on chromosome 20 was associated with \u003cem\u003eGlyma.20G080700\u003c/em\u003e, which encodes a receptor-like protein kinase (RLK2) that regulates signal transduction under stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eFor the trait Δ%_SPAD_200mM, the SNP rs.Gm01.43332855 was identified within the gene \u003cem\u003eGlyma.01G122800\u003c/em\u003e, which encodes a vacuolar exchanger essential for maintaining ion balance under stress. Additionally, two significant SNPs were located on chromosome 3: rs.Gm03.38716419 in \u003cem\u003eGlyma.03G160300\u003c/em\u003e (a cytochrome P450 that regulates stress hormones for salt tolerance) and rs.Gm03.39710939 in \u003cem\u003eGlyma.03G170501\u003c/em\u003e (an ankyrin (ANK) repeat protein that supports growth and regulates stress responses). All three SNPs demonstrated significant allelic effects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eFor the LSS_MEAN trait, the SNP rs.Gm09.843177 is located within the \u003cem\u003eGlyma.09G010800\u003c/em\u003e gene, which is related to the tonoplast dicarboxylate transporter responsible for maintaining ion balance under stress. Additionally, the SNP rs.Gm03.39710939 has a significant pleiotropic effect, influencing both the LSS_MEAN and SFS_MEAN traits. It is associated with the \u003cem\u003eGlyma.03G170501\u003c/em\u003e gene, which encodes an ankyrin (ANK) repeat protein that supports growth and regulates stress responses. All the SNPs had significant allelic effects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eF-G).\u003c/p\u003e\u003cp\u003eFurthermore, linkage disequilibrium (LD) analysis revealed that while the SNP rs.Gm03.39710939 is located on the boundary of the 6 kb haplotype block; it exhibits high pairwise LD with its immediately adjacent marker rs.Gm03.39710913, confirming that it is part of a larger region of strong genetic linkage (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eDevelopment and validation of KASP markers for salinity tolerance in soybean\u003c/h2\u003e\u003cp\u003eFrom the screening of the GWAS results, a KASP marker was developed for the pleiotropically significant SNP rs.Gm03.39710939 from the seedling stage. This marker has also been validated with 198 genotypes of soybean, among which ⁓21% have Homozygous Allele 2/Allele 2 (AA alleles), which represent salt-tolerant genotypes; ⁓67% have Homozygous Allele 1/Allele 1 (TT alleles), which represent salt-susceptible genotypes; and the rest have Heterozygous Allele 1/Allele 2 (TA alleles). A representative subset of 14 highly tolerant and susceptible genotypes showed a 100% correlation between the KASP marker call and the SFS_MEAN phenotype (Table S7, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSalt tolerance is a significant abiotic stress that involves various mechanisms during the germination and seedling stages. By studying 198 genotypes of soybean, this study successfully identified 20 unique, highly significant SNPs via a multi-model GWAS approach, offering the most comprehensive genetic insights into soybean salt tolerance across the germination and seedling stages to date.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003ePhenotypic variation among soybean genotypes under salinity stress\u003c/h2\u003e\u003cp\u003eSalinity stress had a significant effect on soybean growth across the 198-geneotype panel. Key phenotypic traits, including SL, NL, SPAD, and SFS, were markedly lower in the 200 mM NaCl treatment group than in the control group. During the initial salt treatment, the leaves of the control plants remained light green, whereas the treated plants developed noticeably darker green leaves. This observation suggests increased chlorophyll retention despite the stress caused by salt. However, after the final treatment with 200 mM salt, the leaves of the treated plants lost their green color and turned yellow due to the effects of salinity. In response to the final treatment, various traits significantly differed from those of the control group. The control plants presented broader leaves areas and taller stature, along with faster-growing shoots. In contrast, the treated plants had narrower leaves, were shorter overall, and presented slower shoot development. Additionally, the root systems of the control group plants grew rapidly and extensively, whereas the roots of the salt-treated plants were stunted and developed more slowly. Although a subset of genotypes maintained robust growth and completed the seedling-to-flowering transition under salinity, indicating inherent tolerance, many others were severely inhibited, underscoring the diverse phenotypic strategies employed by soybean in response to salt stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Similar results have been shown previously for crops such as soybean (Kokebie et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), rapeseed (Wang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e), and cucumber (Amerian et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eDifferential salinity response across development stages\u003c/h2\u003e\u003cp\u003eOur findings, along with those of a recent report by Gobade et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) on the seed germination stage, reveal apparent differences in salt tolerance that depend on the developmental stage. Gobade et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) identified seven genotypes as salt tolerant at 200 mM NaCl during germination. In contrast, at the seedling stage, we identified 14 salt-tolerant genotypes, none of which overlapped with those that were tolerant at germination. This distinct lack of overlap strongly supports our hypothesis that the mechanisms underlying salt tolerance differ between the germination and seedling stages. Similarly, Ghosh et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) reported that the mechanisms involved in the germination stage are crucial, as tolerance mainly depends on initial water uptake and the activation of enzymes to respond to osmotic stress and ion toxicity from the seed's immediate environment. During the seedling stage, tolerance develops, including longer-term physiological and molecular adjustments that facilitate growth, such as nutrient uptake, photosynthesis, and active ion homeostasis. These processes involve more complex genetic and biochemical responses. Collectively, these findings support the hypothesis that salinity tolerance in soybean varies across different developmental stages.\u003c/p\u003e\u003cp\u003eAccording to a previous study, during germination, osmotic adjustment occurs through the accumulation of proline, glycine betaine, and sugars, which help retain water. Additionally, these compounds maintain ionic balance by compartmentalizing or exporting sodium ions (Na⁺) via the NHX and HKT transport systems (Mansour and Ali \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Hormone levels also shift to favor germination, with an increase in gibberellin (GA) and a decrease in abscisic acid (ABA) (Shu et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Antioxidants such as superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT), and peroxidase (POD) are elevated to detoxify reactive oxygen species (ROS) (Hernandez-Leon and Valenzuela-Soto \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, a protective seed coat limits the entry of salt into the seed. At the molecular level, various transporters and stress-responsive transcription factors (TFs), such as DREB, HSP, and WRKY, are activated to coordinate these responses (Feng et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Together, these mechanisms protect the embryo and promote radicle emergence even under saline conditions.\u003c/p\u003e\u003cp\u003eIn contrast, seedling-stage tolerance involves more complex physiological strategies, including active sodium exclusion via SOS1 (salt overly sensitive 1) and NHX (Na\u003csup\u003e+\u003c/sup\u003e/H\u003csup\u003e+\u003c/sup\u003e exchangers) transporters, compartmentalization of toxic ions into vacuoles, and improved K\u003csup\u003e+\u003c/sup\u003e/Na\u003csup\u003e+\u003c/sup\u003e selectivity to maintain cytoplasmic homeostasis (Dinler et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, plants secrete phytohormones such as abscisic acid (ABA), salicylic acid (SA), and brassinosteroids (BRs), which contribute to salinity tolerance by regulating reactive oxygen species (ROS) accumulation in roots and maintaining stomatal development and photosynthetic activity in leaves (Ghosh et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eGenomic loci specific to germination and seedling traits\u003c/h2\u003e\u003cp\u003eTo date, many genes and SNPs linked to salinity tolerance during the germination and seedling stages have been reported through association studies and GWASs in various crops and plants. However, our GWAS identified 20 significant SNPs that were consistently found across both models and were distributed over 12 chromosomes. These SNPs are associated with salt tolerance at both the germination stage (100 and 200 mM NaCl) and the seedling stage (200 mM NaCl) in soybean across seven different traits.\u003c/p\u003e\u003cp\u003eDuring the germination stage, the Δ%_GP_100mM SNPs, such as rs.Gm07.39079550 and rs.Gm07.39090791, which are associated with the same gene as \u003cem\u003eGlyma.07G215700\u003c/em\u003e, encoding leucine-rich repeat receptor-like kinases in the plasma membrane, are upregulated by ABA, which is important in seed maturation, seed dormancy, stomatal closure, and stress response (Li et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Osakabe et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Other SNPs are novel, such as rs.Gm11.33334736 and rs.Gm16.6978282, which were not mentioned earlier; these SNPs are associated with the genes \u003cem\u003eGlyma.11G201600\u003c/em\u003e and \u003cem\u003eGlyma.16G070200\u003c/em\u003e, respectively. \u003cem\u003eGlyma.11G201600\u003c/em\u003e encodes a heat shock protein that plays a significant role as a molecular chaperone in protecting cells from stress (Do et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and \u003cem\u003eGlyma.16G070200\u003c/em\u003e is linked to stress tolerance and defence.\u003c/p\u003e\u003cp\u003eIn the context of Δ%_GP_200mM, the SNP rs.Gm05.9457555 is novel, as it has not been previously reported in relation to the gene \u003cem\u003eGlyma.05G075400\u003c/em\u003e. This gene encodes the COP9 signalosome (CSN), which plays a crucial role in plant growth, development, and stress responses by modulating the ubiquitination pathway (Lu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In contrast, known SNPs such as rs.Gm10.35460454, rs.Gm18.1916285, and rs.Gm20.45859448 are associated with genes that serve specific functions. For example, \u003cem\u003eGlyma.10G130600\u003c/em\u003e encodes late embryogenesis abundant (LEA) proteins, which are vital phytomolecules that primarily accumulate in the later stages of seed development, as well as in vegetative tissues in response to external stressors (Banerjee and Roychoudhury \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; B. Guo et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eGlyma.18G025700\u003c/em\u003e encodes a RING-H2 finger protein that participates in abiotic stress responses by modifying and degrading stress-related proteins (Han et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, \u003cem\u003eGlyma.20G225000\u003c/em\u003e encodes IAA14, a protein that regulates germination through auxin mediation (Neres et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDuring the seedling stage, the Δ%_NL_200mM trait is associated with four SNPs, rs.Gm01.3888173, rs.Gm06.12647562, rs.Gm06.12769083, and rs.Gm18.47911124, which are associated with the genes \u003cem\u003eGlyma.01G036500, Glyma.06G155200, Glyma.06G156100\u003c/em\u003e, and \u003cem\u003eGlyma.18G198300\u003c/em\u003e, respectively. The gene \u003cem\u003eGlyma.01G036500\u003c/em\u003e encodes DNAJ proteins that contribute to tolerance to both abiotic and biotic stresses, as noted by Song et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in relation to alkalinity stress. \u003cem\u003eGlyma.06G155200\u003c/em\u003e (encoding a cation calcium exchanger involved in Na⁺/Ca\u0026sup2;⁺ homeostasis and ion transport under stress) supports ion transport as a cation calcium exchanger 4 (Zeng et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eGlyma.06G156100\u003c/em\u003e encodes a protein involved in the photosystem II oxygen-evolving complex (PsbP). Finally, \u003cem\u003eGlyma.18G198300\u003c/em\u003e functions as Plasma Membrane Intrinsic Protein 1, which facilitates water transport across membranes, as highlighted in the context of alkalinity stress by Waters et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Δ%_SL_200mM trait is associated with SNPs located on chromosomes GM01, GM11, GM14, and GM20, which are linked to several genes. \u003cem\u003eGlyma.01G051700\u003c/em\u003e encodes the MYB64 transcription factor, which regulates ABA signaling and ion homeostasis (Wang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eGlyma.11G230500\u003c/em\u003e is related to the protein kinase superfamily and is responsible for phosphorylating proteins that bind ATP and signal stress responses. \u003cem\u003eGlyma.14G165300\u003c/em\u003e encodes HSP70, a protein that protects against salinity stress (Do et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Finally, \u003cem\u003eGlyma.20G080700\u003c/em\u003e encodes a receptor-like protein kinase (RLK2) that detects signals related to salinity stress (Almeida-Silva and Venancio \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor the Δ%_SPAD_200mM trait, the SNP rs.Gm01.43332855 has been functionally annotated to the gene \u003cem\u003eGlyma.01G122800\u003c/em\u003e, which encodes vacuolar cation/proton exchanger 3, a known gene previously described by Waters et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, two other SNPs have not been previously mentioned: rs.Gm03.38716419, which is associated with \u003cem\u003eGlyma.03G160300\u003c/em\u003e and encodes a cytochrome P450 involved in regulating stress (Pandian et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e);, and rs.Gm03.39710939, which is linked to \u003cem\u003eGlyma.03G170501\u003c/em\u003e, encoding ANK (Ankyrin repeat) proteins that support growth (Zhang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, for the LSS_MEAN trait, two SNPs associated with leaves scorch stress were identified. One of these was a novel SNP, rs.Gm03.39710939, which is linked to \u003cem\u003eGlyma.03G170501\u003c/em\u003e. This gene encodes ankyrin repeat (ANK) proteins that support plant growth (Zhang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The other SNP, known as rs.Gm09.843177, is related to \u003cem\u003eGlyma.09G010800\u003c/em\u003e, which encodes a tonoplast dicarboxylate transporter that facilitates the movement of sodium and helps maintain ion balance under stress, as previously studied by Pruthi et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith respect to the SFS_MEAN trait, which indicates a plant's ability to survive salinity stress and complete flowering, a significant novel pleiotropic SNP, rs.Gm03.39710939, was identified. This SNP is linked to the candidate gene \u003cem\u003eGlyma.03G170501\u003c/em\u003e, which encodes an ankyrin repeat protein that aids in salinity tolerance (Zhang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe\u003c/b\u003e \u003cb\u003eGmSALT3\u003c/b\u003e \u003cb\u003elocus and the pleiotropic ANK gene on chromosome 3\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAnkyrin repeat (ANK) proteins are widely recognized for their involvement in plant growth, development, and response to hormonal and environmental signals. Earlier work by Zhao et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) revealed that ANK proteins are strongly induced under stress conditions, where they help reduce the accumulation of damaging reactive oxygen species. Their study also demonstrated that ANKs influence the expression of several well-known stress-responsive genes, including \u003cem\u003eWRKY13, NAC11, DREB2, MYB84, and bZIP44\u003c/em\u003e, highlighting the important regulatory role these proteins play during salinity and drought stress.\u003c/p\u003e\u003cp\u003eIn our study, the pleiotropic SNP rs.Gm03.39710939 was linked to \u003cem\u003eGlyma.03G170501\u003c/em\u003e, an ANK gene that reflects many of these reported functions. This gene is located approximately 73 kb upstream of \u003cem\u003eGmCHX1/GmSALT3\u003c/em\u003e (\u003cem\u003eGlyma.03G171600\u003c/em\u003e) (Guan et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Qi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ren et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), a well-known salt tolerance locus in soybean. Since both genes are within the typical soybean LD range (100\u0026ndash;200 kb), the association at this SNP may represent either a functional role of \u003cem\u003eGlyma.03G170501\u003c/em\u003e itself or physical linkage to the \u003cem\u003eGmSALT3\u003c/em\u003e region. Overall, these findings suggest that the identified ANK gene may contribute to salinity tolerance and could act alongside, or in cooperation with, the major \u003cem\u003eGmSALT3\u003c/em\u003e locus on chromosome 3.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eEpistatic (SNP \u0026times; SNP) interactions for salinity-related traits in soybean\u003c/h2\u003e\u003cp\u003eThe SNP \u0026times; SNP interactions were analyzed in relation to germination-related traits, specifically Δ%_GP_100mM. The identified epistatic SNPs include rs.Gm09.21628729, rs.Gm18.56023158, and rs.Gm02.14916620, which are associated with the following genes: \u003cem\u003eGlyma.09G113000\u003c/em\u003e, encoding the MYB family transcription factor APL-like isoform X1, which regulates stress signals (Shao et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); \u003cem\u003eGlyma.18G275200\u003c/em\u003e, which encodes an auxin transport protein (BIG) that regulates auxin levels and distribution during salt stress (Singh and Jain \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); and \u003cem\u003eGlyma.02G139700\u003c/em\u003e, encoding the transcription factor bHLH30, which is involved in iron homeostasis (Filiz et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor the Δ%_GP_200mM trait, the main SNPs included rs.Gm10.21857428, which is associated with the gene \u003cem\u003eGlyma.10G104100\u003c/em\u003e and related to L-galactono-1,4-lactone dehydrogenase; however, its function concerning salinity remains unknown. Additionally, rs.Gm16.33616912 and rs.Gm15.35061311 are linked to the same gene functions as \u003cem\u003eGlyma.16G173900\u003c/em\u003e and \u003cem\u003eGlyma.15G213700\u003c/em\u003e, which encode receptor-like protein kinase 2, respectively (Zhu et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, rs.Gm15.44600021 is connected to the \u003cem\u003eGlyma.15G233100\u003c/em\u003e gene encoding a disease resistance protein, which plays a role in plant defence (Zhou et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEpistatic interactions affecting seedling stage traits, such as the Δ%_NL_200mM trait, were identified for three SNPs: rs.Gm06.39548568, rs.Gm06.28907845, and rs.Gm16.36043781. Among these, rs.Gm06.39548568 and rs.Gm16.36043781 are linked to the genes \u003cem\u003eGlyma.06G242000\u003c/em\u003e and \u003cem\u003eGlyma.16G196400\u003c/em\u003e, respectively, both of which encode proteins with similar functions, specifically an ankyrin repeat-containing protein that supports plant growth (Zhang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The other SNP, rs.Gm06.28907845, is associated with the uncharacterized protein encoded by the \u003cem\u003eGlyma.06G222800\u003c/em\u003e gene.\u003c/p\u003e\u003cp\u003eThe analysis identified four main effect SNPs associated with Δ%_SL_200mM. The first SNP, rs.Gm07.35731062, is linked to the gene \u003cem\u003eGlyma.07G186100\u003c/em\u003e, which encodes branched-chain amino acid transaminase 2, as previously noted by Shim et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The second SNP, rs.Gm13.30031982, is associated with the gene \u003cem\u003eGlyma.13G193200\u003c/em\u003e, which codes for an argonaute family protein that plays a role in the rhizobium interaction process (Vald\u0026eacute;s-L\u0026oacute;pez et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The third SNP, rs.Gm20.25517507, interacts with the gene \u003cem\u003eGlyma.20G071666\u003c/em\u003e, which encodes a phosphoinositide phosphatase family protein that contributes to increased salinity tolerance (Jia et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Finally, the SNP rs.Gm09.39499095 is linked to the gene \u003cem\u003eGlyma.09G161600\u003c/em\u003e, which functions as an alcohol dehydrogenase 1 enzyme, aiding in the control of salinity stress (Komatsu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn terms of Δ%_SPAD_200mM, the main effect SNP included rs.Gm07.2345032, which is associated with the \u003cem\u003eGlyma.07G029100 gene\u003c/em\u003e. This gene encodes a protein containing a HAT family dimerization domain, although its function under salinity stress is not yet understood. Another SNP, rs.Gm10.36634108, maps to the gene \u003cem\u003eGlyma.10G135500\u003c/em\u003e, which encodes a protein from the F-box/RNI-like superfamily that plays a role in leaves development (Iantcheva et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, the SNP rs.Gm04.48363183 is associated with the gene \u003cem\u003eGlyma.04G225300\u003c/em\u003e, which encodes a vacuolar iron transporter-like protein that helps maintain ion balance under stress conditions (Mansour \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLSS_MEAN exhibits two SNP \u0026times; SNP interactions. The first SNP, rs.Gm14.21949373, is linked to the gene \u003cem\u003eGlyma.04G225300\u003c/em\u003e, which encodes a vacuolar iron transporter-like protein that helps maintain ion balance during stress (Mansour \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The second SNP, rs.Gm12.3161544, is associated with the \u003cem\u003eGlyma.12G043800\u003c/em\u003e gene, which encodes glucan endo-1,3-beta-D-glucosidase and is involved in responses to various biotic and abiotic stresses (Kebede and Kebede \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the context of SFS_MEAN, the main effect SNP identified is rs.Gm05.13429009, which is associated with the gene \u003cem\u003eGlyma.04G225300\u003c/em\u003e. This gene encodes a vacuolar iron transporter-like protein that plays a crucial role in maintaining ion balance under stress conditions (Mansour \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eApplication of the KASP marker in soybean breeding\u003c/h2\u003e\u003cp\u003eKASP genotyping offers a practical advantage in soybean improvement, as it enables breeders to screen large populations quickly, accurately, and at a relatively low cost. By identifying plants that carry the desired alleles for salinity tolerance without extensive field or greenhouse evaluations, KASP markers help shorten the breeding cycle and improve the efficiency of marker-assisted selection (MAS). This makes it easier to track useful alleles, combine them in breeding lines, and increase the confidence of tolerant genotypes under stress-prone conditions (Zhao et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In our study, we developed and validated a KASP marker for the pleiotropic SNP rs.Gm03.39710939, which is linked to the ankyrin repeat protein-encoding gene \u003cem\u003eGlyma.03G170501\u003c/em\u003e. Because this SNP is strongly associated with several key salt-response traits, the marker provides a practical and breeder-friendly tool that can be readily incorporated into soybean breeding programs aimed at improving salinity tolerance.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study presents a thorough multi-experimental GWAS focused on the germination and seedling stages of a diverse panel of 198 genotypes. By employing two GWAS models, we identified 20 SNPs associated with salinity tolerance spread across 12 chromosomes. The functional annotation revealed seven novel and 12 previously reported genes, underscoring both the discovery of new loci and the validation of known stress-related genes. Notably, the main effect of SNP \u0026times; SNP interactions was observed in 953 interactions across 14 chromosomes. Among these, the pleiotropic SNP rs.Gm03.39710939, which is linked to the ankyrin repeat protein-encoding gene \u003cem\u003eGlyma.03G170501\u003c/em\u003e, exhibited strong multi-trait associations and lies in proximity to \u003cem\u003eGmCHX1/GmSALT3\u003c/em\u003e, a well-established salt tolerance gene. We successfully developed a breeder-friendly KASP marker for this SNP, which has been validated across all genotypes. This novel KASP marker, along with our findings, will aid in the early stages of breeding. Genomic data regarding SNPs from this research can be incorporated into genomic prediction models to evaluate their potential for selecting future soybean varieties with increased resistance to salinity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGWAS: genome-wide association study, MTAs: marker‒trait associations, SNPs: single-nucleotide polymorphisms, FarmCPU: fixed and random model circulating probability unification, BLINK: Bayesian information and linkage disequilibrium iteratively nested keyway, LD: linkage disequilibrium, KASP: Kompetitive allele-specific PCR, MAS: marker-assisted selection, PCA: principal component analysis, PVE: percentage of variance explained, QTL: quantitative trait loci\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information:\u003c/strong\u003e The online version contains supplementary material available at\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors also extend their sincere thanks to Dr. Prashant Dhakephalkar (Director, Agharkar Research Institute), Dr. Manoj D. Oak (Head of the Department, Department of Genetics and Plant Breeding), Dr. Ravindra Patil, Mr. Santosh Jaybhay and Mrs. Anuja Deshpande from the Department of Genetics and Plant Breeding, Agharkar Research Institute, for their generous provision of resources and facilities essential to the successful completion of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreetkumar H. Trivedi: Grew E2 seedlings, collected phenotypic data, analyzed the results, and wrote the original draft. Janhavi Gadhawe: Grew E1 seedlings and collected the phenotypic data. Shreyas M. Salvi assisted in collecting the phenotypic data. Snehaben M. Dodia: Manuscript review. Deepak Pawar: Handled seed threshing and packet preparation. Abhinandan S. Patil: Secured funding, conceived the concept, designed the analysis, and provided critical revisions to the manuscript. All the authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ea\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eRamalingaswami Re-entry Fellowship (BT/RLF/Re-entry/01/2021) provided by the Department of Biotechnology (DBT), Government of India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets created and/or analyzed during the current investigation are accessible from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmeida-Silva F, Venancio TM (2021) Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens. 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Plants 12:427. https://doi.org/10.3390/plants12030427\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":"plant-cell-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pcre","sideBox":"Learn more about [Plant Cell Reports](https://www.springer.com/journal/299)","snPcode":"299","submissionUrl":"https://submission.nature.com/new-submission/299/3","title":"Plant Cell Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Soybean, Salinity tolerance, Genome-wide association study (GWAS), Ankyrin repeat protein, GmSALT3, Marker-assisted selection (MAS)","lastPublishedDoi":"10.21203/rs.3.rs-8173753/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8173753/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoybean is a vital global source of protein and oil, yet its productivity is severely affected by soil salinity, which hampers germination and seedling growth, leading to stunted development and yield reductions. In this study, 198 diverse soybean genotypes were assessed for salinity tolerance at the germination and seedling stages under 200 mM NaCl stress. Seven phenotypic traits were evaluated, and delta percentages (Δ%) across two experiments (E1 and E2) were calculated. Genome-wide association studies (GWASs) were conducted viatwo complementary models (BLINK and FarmCPU), identifying 66 significant SNPs, with 20 consistently detected across both environments and models, confirming their robustness. SNP × SNP interaction analysis revealed 953 significant epistatic interactions, highlighting the complex genetic basis of salinity tolerance. Among these SNPs, rs.Gm03.39710939, linked with the gene \u003cem\u003eGlyma.03G170501\u003c/em\u003e on chromosome 3, hadstrong pleiotropic effects on three major traits: Delta percentage of chlorophyll content (Δ%_SPAD_200mM), Mean of leaf scorch score (LSS_MEAN), and Mean of seedling to flowering survival (SFS_MEAN). This gene encodes an ankyrin repeat (ANK) protein, which plays a crucial role in salt tolerance mechanisms. Structurally, \u003cem\u003eGlyma.03G170501\u003c/em\u003e is locatedapproximately 73 kb upstream of \u003cem\u003eGmCHX1/GmSALT3\u003c/em\u003e (\u003cem\u003eGlyma.03G171600\u003c/em\u003e), a well-recognized salt tolerance locus. On the basis of this pleiotropic SNP, a Kompetitive Allele-Specific PCR (KASP) marker was successfully developed and validated. These genomic resources offer valuable tools for improving soybean breeding strategies and developing salt-tolerant varieties.\u003c/p\u003e","manuscriptTitle":"Stage-specific GWAS identifies a pleiotropic ankyrin repeat locus near GmSALT3 for salinity tolerance in soybean","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 15:23:56","doi":"10.21203/rs.3.rs-8173753/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-02T09:50:56+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T10:10:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-22T15:42:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Cell Reports","date":"2025-11-21T07:45:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-cell-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pcre","sideBox":"Learn more about [Plant Cell Reports](https://www.springer.com/journal/299)","snPcode":"299","submissionUrl":"https://submission.nature.com/new-submission/299/3","title":"Plant Cell Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"032ae8a9-dfa5-4247-a7ea-b827e9ad1e51","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:07:18+00:00","versionOfRecord":{"articleIdentity":"rs-8173753","link":"https://doi.org/10.1007/s00299-026-03779-9","journal":{"identity":"plant-cell-reports","isVorOnly":false,"title":"Plant Cell Reports"},"publishedOn":"2026-03-16 15:58:08","publishedOnDateReadable":"March 16th, 2026"},"versionCreatedAt":"2025-11-28 15:23:56","video":"","vorDoi":"10.1007/s00299-026-03779-9","vorDoiUrl":"https://doi.org/10.1007/s00299-026-03779-9","workflowStages":[]},"version":"v1","identity":"rs-8173753","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8173753","identity":"rs-8173753","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}