Genome-wide association study identifies candidate genes for salt tolerance in traditional rice landraces

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Thus, identifying salt-tolerant rice varieties is of great importance for ensuring stable rice production. In this study, we systematically evaluated the salt tolerance of 372 rice landraces at the tillering stage through dynamic phenotypic monitoring, using the average salt injury score (ASIS) as an indicator at two (T2W) and four weeks (T4W) after salt treatment. A genome-wide association study (GWAS) identified 39 loci significantly associated with salt tolerance. Among these, two high-confidence candidate genes, OsST8.1 and OsST8.2 , both members of the BTB-MATH protein family, were implicated in salt tolerance during the tillering stage. Haplotype analysis revealed significant differences ( p < 0.05) in salt tolerance among germplasm carrying different haplotypes, with accessions harboring the superior haplotype exhibiting enhanced tolerance. Consistently, qRT-PCR analysis showed significantly lower or higher expression levels ( p < 0.05) of OsST8.1 or OsST8.2 in accessions with the superior haplotype following salt treatment, suggesting that they may regulate rice responses to salinity stress. Collectively, this study provides valuable genetic resources and a theoretical foundation for elucidating the genetic basis of salt tolerance and for breeding new salt-tolerant rice varieties. rice tillering stage salt stress GWAS candidate gene Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Salt stress is one of the major abiotic factors severely affecting rice growth, grain quality, and yield (Chen et al, 2022 , Yang and Guo, 2018 , Kamran et al, 2020 ). Globally, approximately 30% of rice-growing areas are threatened by salinity, particularly in Asia, Africa, and the Middle East (Qadir et al, 2014 ) .In China, saline–alkali soils are widely distributed in eastern coastal regions, posing a serious challenge to rice production. In recent decades, soil salinization has intensified worldwide as a result of climate change, industrial pollution, and unsustainable irrigation practices (Shahid et al, 2018 , Kumar and Sharma, 2020 , Atta et al, 2023 ). The tillering stage represents a critical growth phase that largely determines rice yield. High concentrations of salt ions induce osmotic stress and ion toxicity, impairing water and nutrient uptake. This disruption leads to reduced tillering efficiency, premature root senescence, and nutrient deficiencies, ultimately constraining yield potential (Li et al, 2020 ). Therefore, mining salt tolerance–related genes and identifying superior haplotypes at the tillering stage, along with screening germplasm resources carrying these haplotypes, is of great importance for breeding salt-tolerant rice varieties. Traditional rice landraces have accumulated abundant genetic variation through long-term adaptation to diverse environments and often display enhanced resistance to abiotic stresses (Dwivedi et al, 2016 , Behera et al, 2023 ). These landraces harbor numerous favorable alleles that confer stable growth and yield performance under adverse conditions such as salinity, drought, and nutrient limitation. Their superior stress tolerance compared with modern high-yielding cultivars makes them valuable reservoirs of genetic resources for salt tolerance improvement (Adak et al, 2020 , Das et al, 2013 ). Thus, in-depth exploration of the genetic basis of salt tolerance in landraces is essential for the identification of novel candidate genes and favorable haplotypes, and for advancing the breeding of stress-resilient rice varieties. Salt tolerance is a complex quantitative trait controlled by multiple genes and strongly influenced by environmental factors. Its physiological basis primarily involves several pathways, including ion homeostasis (e.g., Na⁺/K⁺ transport), reactive oxygen species (ROS) scavenging, osmotic adjustment, and signal transduction (Chinnusamy et al, 2005 , Reddy et al, 2017 ). In recent years, both quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) have contributed to the identification of key loci for salt tolerance (Prakash et al, 2022 ). Bonilla et al. ( 2002 ) used a recombinant inbred line (RIL, F₈) population derived from ‘IR29’ and ‘Pokkali’ to map the major QTL Saltol , located between RM23 and RM140 on chromosome 1, which controls Na⁺ content, K⁺ content, and the Na⁺/K⁺ ratio. Zang et al. ( 2008 a) employed a BC₂F₈ introgression line population derived from the indica variety ‘IR64’ and the japonica variety ‘Binam’ and identified 13 and 22 salt tolerance–related QTLs at the seedling and tillering stages, respectively. Yuan et al. ( 2020 ) used 664 cultivated accessions from the 3,000 Rice Genomes Project (3KRGP) and identified 21 QTLs, validating two candidate genes, OsSTL1 and OsSTL2 . Le et al. ( 2021 ) conducted GWAS on 179 Vietnamese rice landraces at the seedling stage using 21,623 SNP markers, identifying 26 QTLs, 10 of which exhibited pleiotropy and regulated multiple salt tolerance–related traits. Although numerous QTLs associated with salt tolerance in rice have been reported, only a limited number have been finely mapped or cloned. Lin et al. ( 2004 ) identified 11 QTLs related to salt tolerance using an F₂/F₃ population derived from the salt-tolerant variety ‘Nona Bokra’ and the salt-sensitive variety ‘Koshihikari’. Among these, the major locus qSKC-1 on chromosome 1 explained 40.1% of the total phenotypic variation. Subsequently, Ren et al. ( 2005 ) cloned SKC1 ( OsHKT1;5 ), a gene encoding a high-affinity potassium transporter, through map-based cloning. Huang et al. ( 2009 ) screened an EMS-mutagenized mutant library of ‘Zhonghua 11’ under drought and salt stress conditions and identified DST , a zinc finger protein transcription factor that negatively regulates stomatal closure and is associated with drought and salt tolerance. Takagi et al. ( 2015 ) employed the MutMap method to map the salt tolerance gene Hst1 ( OsRR22 ) on chromosome 6. He et al. ( 2019 ) used chromosome segment substitution lines (CSSLs) derived from ‘Jiucaiqing’ and ‘IR26’ to map the major QTL qSE3 , which promotes seed germination and seedling development under salt stress; this locus encodes the potassium transporter OsHAK21 . However, most current studies rely on linkage analysis or mutant libraries, which do not adequately capture the superior alleles present in natural variation. In particular, salt tolerance genes directly applicable to molecular breeding remain scarce. In the present study, we evaluated a natural population of 372 diverse rice landraces. By combining systematic salt tolerance phenotyping with genome-wide association studies (GWAS), we identified QTLs and candidate genes associated with salt tolerance, along with their superior haplotypes. Our aim was to uncover the genetic architecture of salt tolerance in rice, enrich breeding resources for salt tolerance, and provide a theoretical foundation for the development of rice varieties adapted to saline–alkali soils. Materials and Methods Materials This study utilized a natural population of 372 rice landraces described in a previous study. The population consisted of a mixture of indica and japonica landraces with broad genetic diversity. It included 104 accessions from Qiandongnan Prefecture, Guizhou Province (Kam Sweet Rice, KSR), 104 accessions from other regions of Guizhou Province (excluding Qiandongnan) (Guizhou, GZ), and landraces collected from regions south of the Yangtze River, including 23 from Central China (CC), 74 from East China (EC), 36 from South China (SC), and 31 from Southwest China (SW). Whole-genome resequencing yielded a curated dataset containing 3,566,872 high-quality single-nucleotide polymorphisms (SNPs), with an average sequencing depth of ~ 12.43 from South Chi (Liu et al, 2023 )(Table S1 ). Identification of salt tolerance at the tillering stage The salt stress experiment was conducted at the experimental base of the Binhai Agricultural Research Institute, Hebei Academy of Agriculture and Forestry Sciences. Seedlings were raised using the film-moistening method and transplanted into a salt tolerance identification pool at the 3-leaf–1-heart stage. Plants were spaced 25 cm × 13 cm apart, with each accession planted in two rows of 10 hills per row, and one seedling per hill. After a recovery period of 7 days, plants were irrigated with saline water at a concentration of 0.5% NaCl. The saline water was collected from coastal underground sources at a depth of ~ 20 m (original salt concentration ~ 2%) and diluted with fresh water to the target concentration (electrical conductivity ≥ 10 ms/cm, 25 ℃). Water depth in the pool was maintained at 3–5 cm. Salt concentration was monitored daily with a portable conductivity meter, and electrical conductivity was adjusted to remain stable at the target 0.5% salt level by adding fresh or saline water as needed. Salt injury was assessed according to the agricultural industry standard NY/T 3692 − 2020: Technical Specification for Salt Tolerance Identification in Rice . Injury scores were recorded two weeks (T2W) and four weeks (T4W) after salt treatment. For each accession, the two outermost plants in each row were excluded, and the remaining 10 consecutive plants in the center were scored individually. The average salt injury score (ASIS) was calculated as: Average Salt Injury Score (ASIS) = Σ(Number of plants at each salt injury grade × Salt injury grade) / Total number of plants investigated The correspondence between salt injury grade and phenotypic symptoms was defined as follows: Grade 1 Tillering growth essentially normal; no visible leaf damage. Grade 3 Tillering growth nearly normal; leaf tips or upper half whitened or curled; or tillering slightly inhibited with some curled leaves. Grade 5 Tillering growth severely inhibited; most leaves curled, only a few elongating. Grade 7 Tillering growth ceased; most leaves withered. Grade 9 Plant dead or nearly dead. Statistical analysis of phenotypic data Descriptive statistics, including mean, standard deviation, range, coefficient of variation (CV), and generalized heritability (H²), were calculated using IBM SPSS Statistics v26. Pearson correlation coefficients between salt tolerance scores at different time points were computed in R v4.2.1 using the ggcorrplot package to assess phenotypic correlations. Multiple comparison tests with Bonferroni correction (e.g., Tukey’s HSD) were performed using the ggstatsplot and ggplot2 packages, and significant differences were annotated with distinct letters (e.g., a, b, ab). This approach was applied to compare salt tolerance score distributions across subpopulations as well as to assess phenotypic differences among haplotypes of candidate genes. Violin plots, scatter plots, and boxplots were generated using ggpubr , ggplot2 , and GraphPad Prism v10. Genome-wide association study (GWAS) GWAS was performed using the TASSEL platform (v5.2.40) under a mixed linear model (MLM) (Bradbury et al, 2007 ). A total of 3,566,872 SNP markers were used for genotyping after filtering for a minor allele frequency (MAF) > 0.05 and missing rate ≤ 20%. The population structure Q matrix was included as a covariate to reduce false positives. The genome-wide significance threshold was determined using the Bonferroni correction at p < 1 × 10⁻⁵ (Li et al, 2012 ). Significant SNPs were functionally annotated using SnpEff, and candidate genes were identified within a 100-kb window upstream and downstream of significant loci, following established workflows (Cui et al, 2022 ). Manhattan plots were generated with the CMplot package in R, while linkage disequilibrium (LD) heatmaps were constructed using LDBlockShow v1.40. qRT-PCR Total RNA was extracted from rice tissues using the Plant RNA Extraction Kit (Genstone Biotech, Cat. #TR154-D-200). One microgram of RNA was reverse-transcribed to cDNA using the All-In-One 5X RT MasterMix (ABM, Cat. #G592). Quantitative real-time PCR (qRT-PCR) was performed with StarLighter Color HP SYBR Green qPCR Mix (Qixing Biotech, Cat. #fs-q1008-01) on a real-time PCR system. The 10 µL reaction mixture consisted of 5.0 µL of SYBR qPCR Mix, 3.8 µL of double-distilled water, 0.5 µL of cDNA template, 0.25 µL of each forward and reverse primer, and 0.2 µL of 50X ROX reference dye (Low/High). Gene expression levels were calculated using the 2 ⁻ΔΔCt method, with UBI as the internal reference gene. Each assay included three biological replicates. Primer sequences used in this study are listed in Supplementary Table (Table S2 ). Results Phenotypic variation in the study population Salt tolerance was systematically evaluated in the natural rice population through dynamic phenotypic monitoring during the tillering stage. Average salt injury scores (ASIS) were recorded at two weeks (T2W) and four weeks (T4W) after salt treatment. Under saline conditions, ASIS values at both time points displayed continuous distributions (Fig. 1 A–B), consistent with the inheritance of a typical quantitative trait. Descriptive statistics indicated a progressive decline in salt tolerance with increasing treatment duration. At T2W, the mean ASIS was 4.70 (range: 2–9), with a coefficient of variation (CV) of 23.98%. By T4W, the mean ASIS had increased to 7.59 (range: 3–9), while the CV decreased to 14.05%, reflecting both intensified stress symptoms and reduced phenotypic variation. Generalized heritability (H²) was high at both stages, estimated at 92.1% for T2W and 90.7% for T4W, confirming that salt tolerance is a highly heritable trait in this population. Correlation analysis revealed a significant positive correlation between ASIS at T2W and T4W ( r = 0.505, p < 0.01), suggesting that early-stage responses to salt stress are predictive of subsequent tolerance performance. Multiple comparison tests further identified significant differences in salt tolerance among subpopulations (Fig. 1 C–D). At T2W, GZ (4.39) and KSR (4.56) exhibited the highest tolerance, EC (4.75) and CC (4.83) showed intermediate levels, while SC (5.53) and SW (5.08) were relatively more sensitive. By T4W, EC (7.24) and CC (7.31) retained stronger tolerance, followed by GZ (7.63) and SW (7.69), whereas KSR (7.72) and SC (7.90) were the most sensitive. Importantly, the EC subpopulation—originating from eastern coastal regions—consistently displayed superior and stable salt tolerance across both time points, underscoring its adaptability and potential as a valuable genetic resource for salt-tolerance improvement in rice. Identification of candidate genes within the stable QTL region on chromosome 8 Based on GWAS, a total of 39 loci significantly associated with ASIS ( p < 1 × 10⁻⁵) were identified across both time points. At T2W, 16 loci were detected, distributed across chromosomes 1, 2, 3, 4, 6, 7, 8, 9, and 10, with phenotypic variation explained (PVE) ranging from 6.53% to 10.14%. At T4W, 23 loci were identified on chromosomes 1, 3, 6, 7, 8, 9, 10, 11, and 12, with PVE values ranging from 5.99% to 10.99% (Fig. 2 , Table S3). Among these loci, 10 (26%) overlapped with previously reported salt tolerance QTLs or known genes, including OsHAK3 (Ju et al, 2022 ), OsHAK12 (Zhang et al, 2021 ), OsCUL1-3 (Kim et al, 2018 ), OsClpD1 (Mishra et al, 2016), OsDPK1 (Gu et al, 2005 ), and OsNHAD (Song Liu et al, 2020 ), confirming the robustness of our results (Table S3). Notably, two adjacent loci on chromosome 8— qT2W8.3 (top SNP: Chr08_Pos 7,707,975, p = 2.52 × 10⁻⁶) and qT2W8.4 (top SNP: Chr08_Pos 7,906,989, p = 6.95 × 10⁻⁶)—were consistently detected at both T2W and T4W. This overlap strongly suggests that this region harbors a stable QTL with an important role in salt tolerance across developmental stages. Under salt stress, different allelic combinations of salt tolerance genes/QTLs had significant effects on ASIS (Fig. 3 , Table S4). At the T2W stage, four major salt tolerance genes/QTLs showed significant differences among allelic combinations ( p < 0.05) (Fig. 3 A). Accessions carrying a greater number of favorable alleles exhibited significantly reduced ASIS values, reflecting stronger salt tolerance. In contrast, accessions carrying disadvantageous alleles had higher ASIS values and weaker tolerance. At the T4W stage, although overall ASIS values increased due to prolonged stress exposure, the relative trends in tolerance among different gene combinations remained consistent with those observed at T2W (Fig. 3 B). Further analysis of allele pyramiding revealed a clear additive effect of favorable alleles on salt tolerance. Specifically, as the number of favorable alleles increased, mean ASIS values showed a significant downward trend. At both stages, ASIS was significantly and negatively correlated with the number of favorable alleles carried (T2W: R = − 0.2194, p < 0.05; T4W: R = − 0.1898, p < 0.05) (Fig. 3 C–D). These results indicate that pyramiding favorable alleles can effectively enhance salt tolerance, providing a practical strategy for molecular breeding of salt-tolerant rice varieties. Discovery of candidate salt tolerance genes We identified a stable association signal for salt tolerance within the 7.60–7.80 Mb region of chromosome 8, consistently detected at both T2W and T4W. This interval lies within a large linkage disequilibrium (LD) block (> 200 kb) containing 26 annotated genes with nonsynonymous mutations (Fig. 4 A, Fig. 5 A). Among these, two BTB-MATH family genes — OsST8.1 (LOC_Os08g13020) and OsST8.2 (LOC_Os08g13030) — were prioritized as high-confidence candidate genes. Members of the BTB-MATH protein family are known to regulate substrate protein stability and have been implicated in plant responses to abiotic stresses such as salinity and drought. OsST8.1 spans 1,604 bp, contains three exons, and encodes a BTB and MATH domain protein. Ten nonsynonymous variants were identified within its coding region, allowing classification of accessions into three haplotypes (Fig. 4 B). Among these, Hap3 was identified as the superior haplotype, conferring significantly enhanced salt tolerance compared to Hap1 and Hap2 across both treatment stages ( p < 0.05) (Fig. 4 C–D). qRT-PCR analysis further revealed that OsST8.1 expression in Hap3 accessions was significantly downregulated under salt treatment compared to control conditions, whereas no significant changes were detected in Hap1 or Hap2 (Fig. 4 E). The frequency of Hap3 varied among subpopulations: KSR (18.63%), GZ (41.67%), CC (38.10%), EC (40.85%), SC (48.57%), and SW (70.37%). Notably, Hap3 was relatively enriched in accessions from saline-alkali regions, including EC (40.85%) and SW (70.37%), supporting its adaptive role in enhancing salt tolerance (Fig. 4 F). OsST8.2 spans 1,091 bp, consists of a single exon, and encodes an MBTB21 protein. Haplotype analysis revealed a nonsynonymous substitution Alaiontionalysis revealed mutation at Chr08:7,740,763 in its coding region, defining two haplotypes (Fig. 5 B). Accessions carrying Hap2 exhibited significantly lower ASIS values ( p < 0.05) and therefore stronger salt tolerance compared to Hap1 across all treatment durations (Fig. 5 C–D). qRT-PCR analysis confirmed that OsST8.2 expression was significantly upregulated under salt stress in Hap2 accessions, whereas Hap1 accessions showed no significant transcriptional changes between control and treatment conditions (Fig. 5 E). Hap2 frequencies across subpopulations were: KSR (14.14%), GZ (37.08%), CC (30.00%), EC (29.03%), SC (37.93%), and SW (58.33%) (Fig. 5 F). In addition to OsST8.1 and OsST8.2 , integrated evidence from association mapping, functional annotation, and haplotype analysis highlighted two additional novel high-confidence candidate genes within the same LD block: OsSST1 ( LOC_Os08g12800 ) (Figure S1 ) and OsPPR1 ( LOC_Os08g12850 ) (Figure S2 ), both of which may contribute to salt tolerance at the tillering stage. Discussion Salt stress is one of the major abiotic factors restricting crop productivity worldwide (Gupta and Huang, 2014 ). Although extensive research has focused on salt tolerance in rice, most studies have concentrated on early developmental stages such as germination and seedling growth (Kojonna et al, 2022 , Yin et al, 2023 ). By contrast, the tillering stage—an essential determinant of rice yield—has received comparatively less attention. At this stage, salt stress inhibits growth, reduces tiller number, induces leaf chlorosis, and in severe cases, leads to plant death (Zhang et al, 2023 ). Thus, salt tolerance at the tillering stage is a critical indicator for evaluating the adaptive potential of rice under saline conditions. In this study, we systematically evaluated salt tolerance during the tillering stage using a natural population of 372 diverse rice landraces. Our results demonstrated that salt tolerance declined progressively with prolonged exposure, yet the East China (EC) subpopulation consistently exhibited stronger tolerance compared to other groups. This enhanced performance likely reflects long-term adaptation to saline coastal environments, shaped by both natural selection and traditional human-mediated selection. Through genome-wide association analysis (GWAS), we identified 39 loci significantly associated with salt tolerance, distributed across multiple chromosomes, with phenotypic variation explained (PVE) ranging from 5.99% to 10.99%. The polygenic distribution of these loci highlights the quantitative and complex nature of salt tolerance in rice. Importantly, 10 loci (26%) overlapped with previously reported QTLs or known salt tolerance genes, providing strong validation for our findings. For example, Ju et al. ( 2022 ) reported 52 loci associated with salt tolerance at the germination stage in japonica rice, including OsHAK3 and OsITPK5 . The locus qT2W1 identified here overlapped with the OsHAK3 region. Similarly, Zang et al. ( 2008 b) mapped multiple QTLs using a backcross introgression line population, including qSKC11 for shoot K⁺ concentration at the seedling stage, which overlaps with qT4W11.1 in our study. Wang et al. ( 2012 ) identified a major seedling-stage QTL, qDSW6.1, for shoot dry weight, overlapping with qT2W6.2 and qT4W6 reported here. Moreover, the seedling-stage QTL qST3 (detected in MG RILs derived from Milyang 23 × Gihobyeo) corresponds to qT4W3.1 in our study (Lee et al, 2006 ). Several known salt stress-related genes, including OsDPK1 and OsNHAD , were also located within or adjacent to QTL regions detected in this study, further supporting the robustness of our results. Analysis of favorable allele combinations confirmed a significant additive effect on salt tolerance. Accessions carrying multiple favorable alleles consistently exhibited lower ASIS values and greater tolerance, while those with fewer favorable alleles were more sensitive. This pattern was evident at both T2W and T4W, despite increasing stress severity. These findings underscore the polygenic nature of salt tolerance and highlight the potential of pyramiding superior alleles to enhance resistance (Thomson et al, 2010 ). Consistent with this conclusion, Singh et al. ( 2021 ) reported that although Saltol significantly contributes to seedling-stage tolerance, the presence of this single locus alone is insufficient; pyramiding multiple salt tolerance loci is necessary for robust field-level performance and yield stability. Protein ubiquitination is a critical regulatory mechanism for plant stress responses (Du et al, 2021 , Lechner et al, 2011 , Adams and Spoel, 2018 ). BTB-MATH (Broad-complex, Tramtrack and Bric-à-brac–Meprin and TRAF Homology) proteins function as substrate recognition factors for Cullin3-based E3 ubiquitin ligase (CRL3) complexes, targeting stress-related proteins for degradatio n(Shu and Yang, 2017 , Figueroa et al, 2005 , Wang et al, 2022 ). Previous studies have shown that OsCUL1 proteins (e.g., OsCUL1-1 and OsCUL1-3 ) interact with OsMBTB32 and may suppress its activity during salt stress (Ullah et al, 2023 ). In this study, we identified two BTB-MATH family genes on chromosome 8, OsST8.1 ( LOC_Os08g13020 ) and OsST8.2 ( LOC_Os08g13030 ), as high-confidence candidates for salt tolerance. Haplotype analysis revealed that superior haplotypes of both genes were significantly associated with enhanced tolerance. Expression analysis demonstrated contrasting regulatory responses: OsST8.1 was significantly downregulated under salt stress, whereas OsST8.2 was significantly upregulated. Together, these results suggest that OsST8.1 and OsST8.2 may function cooperatively to mediate protein degradation, thereby influencing ion homeostasis and stress signaling pathways, ultimately conferring improved tolerance. Furthermore, we identified 13 elite salt-tolerant accessions—including Liushizi , Changshennuo , Hannuo, and Banmaozhan—each carrying more than six favorable alleles across multiple loci (Table S5). These accessions represent valuable germplasm resources for breeding and can serve as key parental lines in the development of salt-tolerant rice varieties. Looking ahead, functional validation of OsST8.1 and OsST8.2 through CRISPR/Cas9-mediated knockout and overexpression studies will provide critical mechanistic insights into their roles in salt stress response. Combined with the incorporation of superior haplotypes into molecular design breeding, these efforts will accelerate the development of high-yielding, salt-tolerant rice varieties, offering both theoretical advances and practical solutions for sustainable agriculture in saline-alkali environments. Conclusion This study systematically dissected the genetic basis of salt tolerance in rice at the tillering stage through dynamic phenotypic monitoring and GWAS. A total of 39 loci were identified, of which 29 (74.4%) represent novel associations. Within a stable QTL region on chromosome 8, we identified two high-confidence candidate genes, OsST8.1 and OsST8.2 , both members of the BTB-MATH protein family. Haplotype analysis revealed that superior alleles—such as OsST8.1 -Hap3 and OsST8.2 -Hap2—were significantly associated with enhanced salt tolerance and represent valuable targets for marker-assisted selection. These findings not only provide new insights into the genetic architecture of salt tolerance at a critical developmental stage but also deliver practical genetic resources and molecular targets to accelerate the breeding of salt-tolerant rice varieties adapted to saline-alkali environments. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and material The datasets supporting the conclusions of this article are provided within the article and its additional files. Competing interests On behalf of all authors, the corresponding author states that there is no conflict of interest. Funding This work was supported by Hebei Natural Science Foundation (grant No. C2023301121) , the National Key Research and Development Program of China (2021YFD1200500), the Key R&D Program of Ningxia Hui Autonomous Region (2023BCF01010)，and the CAAS Science and Technology Innovation Program. Authors' contributions H.L., C.L. and L.G. performed the experiments and analyzed the data. L.H. and D.C. conceived and designed the study. L.H., D.C., Z.Z., X.M. and B.H. contributed reagents, materials, analysis tools or data. H.L. wrote the paper. All authors reviewed and approved the final manuscript. Acknowledgements Not applicable. References Adak S, Datta S, Bhattacharya S, Ghose TK, Lahiri Majumder A (2020). Diversity analysis of selected rice landraces from West Bengal and their linked molecular markers for salinity tolerance. 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18:23:43","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":542024,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7832023/v1/9476fcc82960d304512e3750.png"},{"id":95051369,"identity":"f310842b-bc34-4373-b9a3-63878f26e971","added_by":"auto","created_at":"2025-11-03 18:23:43","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140646,"visible":true,"origin":"","legend":"","description":"","filename":"31a80e573a444faf9fd0bc9fa35feab11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7832023/v1/927c871945710e9376af313d.xml"},{"id":95051370,"identity":"8b164497-99ea-48af-9273-9338321ea5ec","added_by":"auto","created_at":"2025-11-03 18:23:43","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150721,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7832023/v1/81e99f6c4ea02d97b0e51da7.html"},{"id":95051341,"identity":"90490fbe-6d59-4ce2-a154-e74556e0c8c2","added_by":"auto","created_at":"2025-11-03 18:23:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4227214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSalt tolerance performance of rice landraces after salt treatment.\u003c/strong\u003e(A–B) Distribution of average salt injury scores (ASIS) measured at two weeks (T2W) and four weeks (T4W) after salt treatment. (C–D) Comparison of ASIS among subpopulations at T2W and T4W, showing significant variation in salt tolerance. Different lowercase letters indicate significant differences at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, determined by one-way ANOVA with Tukey’s post-hoc test.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7832023/v1/c328e884b66c1c8360155991.png"},{"id":95222228,"identity":"806b163a-0d25-4a3a-85ef-e149e1f743fd","added_by":"auto","created_at":"2025-11-05 16:20:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8391512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan and quantile–quantile (Q–Q) plots for the GWAS under salt stress.\u003c/strong\u003e (A–B) Results at two weeks after salt treatment (T2W). (C–D) Results at four weeks after salt treatment (T4W).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7832023/v1/f277839420e1d13a71be5932.png"},{"id":95222096,"identity":"6664654c-2bd5-4547-8efe-bdecc996032a","added_by":"auto","created_at":"2025-11-05 16:20:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2818508,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of gene combinations and favorable allele number on salt tolerance.\u003c/strong\u003e(A) Analysis of allelic effects at T2W. (B) Analysis of allelic effects at T4W. (C) Correlation between the number of favorable alleles and ASIS at T2W. (D) Correlation between the number of favorable alleles and ASIS at T4W. Different lowercase letters indicate significant differences at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (one-way ANOVA, Tukey’s post-hoc test). Orange dots represent favorable alleles, blue dots represent unfavorable alleles. The optimal allele combination for each trait is highlighted in the black box.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7832023/v1/8eeb47219aee6eec6aeb31c5.png"},{"id":95222336,"identity":"edda4552-c551-41f8-88c6-7d281113adb5","added_by":"auto","created_at":"2025-11-05 16:20:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10391133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHaplotype analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eOsST8.1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e associated with salt tolerance.\u003c/strong\u003e (A) Partial Manhattan plot (top) and LD heatmap (bottom) surrounding the peak region on chromosome 8. The red dot indicates the position of a missense variant in \u003cem\u003eOsST8.1\u003c/em\u003e. (B) Gene structure and polymorphism sites in \u003cem\u003eOsST8.1\u003c/em\u003e. (C–D) Violin plots showing ASIS values at T2W and T4W for different \u003cem\u003eOsST8.1\u003c/em\u003e haplotypes (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). (E) qRT-PCR expression analysis of \u003cem\u003eOsST8.1\u003c/em\u003e under control (CK) and salt treatment (6 h). S33: Maonuo; S36: Banbiannuodao; S12: Dongbannuo; S141: Zaobenghonghe. Error bars represent ± SD (\u003cem\u003en\u003c/em\u003e = 3 biological replicates). (F) Frequency distribution of haplotypes across different subpopulations.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7832023/v1/cafcbdfb216931de879aa3d9.png"},{"id":95222426,"identity":"a6094c18-3a5f-4071-b5e0-18320a0337a5","added_by":"auto","created_at":"2025-11-05 16:20:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9573019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHaplotype analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eOsST8.2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e associated with salt tolerance.\u003c/strong\u003e (A) Partial Manhattan plot (top) and LD heatmap (bottom) surrounding the peak region on chromosome 8. The red dot indicates the position of a missense variant in \u003cem\u003eOsST8.2\u003c/em\u003e. (B) Gene structure and polymorphism sites in \u003cem\u003eOsST8.2\u003c/em\u003e. (C–D) Violin plots showing ASIS values at T2W and T4W for different \u003cem\u003eOsST8.2\u003c/em\u003e haplotypes (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). (E) qRT-PCR expression analysis of \u003cem\u003eOsST8.2\u003c/em\u003e under control (CK) and salt treatment (6 h). S33: Maonuo; S36: Banbiannuodao; S140: Tonghuahe; S164: Datonghe. Error bars represent ± SD (\u003cem\u003en\u003c/em\u003e = 3 biological replicates). (F) Frequency distribution of haplotypes across different subpopulations.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7832023/v1/0668063b5d573b48781a631d.png"},{"id":106343778,"identity":"4224203f-ee91-42cd-bc60-866be559ac4b","added_by":"auto","created_at":"2026-04-07 16:09:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":28353654,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7832023/v1/e909d7f5-720f-4100-8bd9-6f344e8a60fb.pdf"},{"id":95223125,"identity":"194ec956-cff5-4fa2-9df7-358b6802e999","added_by":"auto","created_at":"2025-11-05 16:21:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":957262,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7832023/v1/6cacc24942aba17d673d63a3.docx"},{"id":95051344,"identity":"f9242c34-d592-47dd-a1c4-500bd284f6ba","added_by":"auto","created_at":"2025-11-03 18:23:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40326,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7832023/v1/4816965c1d5f86e76a5465f0.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide association study identifies candidate genes for salt tolerance in traditional rice landraces","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSalt stress is one of the major abiotic factors severely affecting rice growth, grain quality, and yield (Chen et al, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Yang and Guo, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Kamran et al, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Globally, approximately 30% of rice-growing areas are threatened by salinity, particularly in Asia, Africa, and the Middle East (Qadir et al, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) .In China, saline\u0026ndash;alkali soils are widely distributed in eastern coastal regions, posing a serious challenge to rice production. In recent decades, soil salinization has intensified worldwide as a result of climate change, industrial pollution, and unsustainable irrigation practices (Shahid et al, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Kumar and Sharma, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Atta et al, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The tillering stage represents a critical growth phase that largely determines rice yield. High concentrations of salt ions induce osmotic stress and ion toxicity, impairing water and nutrient uptake. This disruption leads to reduced tillering efficiency, premature root senescence, and nutrient deficiencies, ultimately constraining yield potential (Li et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, mining salt tolerance\u0026ndash;related genes and identifying superior haplotypes at the tillering stage, along with screening germplasm resources carrying these haplotypes, is of great importance for breeding salt-tolerant rice varieties.\u003c/p\u003e\u003cp\u003eTraditional rice landraces have accumulated abundant genetic variation through long-term adaptation to diverse environments and often display enhanced resistance to abiotic stresses (Dwivedi et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Behera et al, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These landraces harbor numerous favorable alleles that confer stable growth and yield performance under adverse conditions such as salinity, drought, and nutrient limitation. Their superior stress tolerance compared with modern high-yielding cultivars makes them valuable reservoirs of genetic resources for salt tolerance improvement (Adak et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Das et al, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Thus, in-depth exploration of the genetic basis of salt tolerance in landraces is essential for the identification of novel candidate genes and favorable haplotypes, and for advancing the breeding of stress-resilient rice varieties.\u003c/p\u003e\u003cp\u003eSalt tolerance is a complex quantitative trait controlled by multiple genes and strongly influenced by environmental factors. Its physiological basis primarily involves several pathways, including ion homeostasis (e.g., Na⁺/K⁺ transport), reactive oxygen species (ROS) scavenging, osmotic adjustment, and signal transduction (Chinnusamy et al, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Reddy et al, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In recent years, both quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) have contributed to the identification of key loci for salt tolerance (Prakash et al, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Bonilla et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) used a recombinant inbred line (RIL, F₈) population derived from \u0026lsquo;IR29\u0026rsquo; and \u0026lsquo;Pokkali\u0026rsquo; to map the major QTL \u003cem\u003eSaltol\u003c/em\u003e, located between RM23 and RM140 on chromosome 1, which controls Na⁺ content, K⁺ content, and the Na⁺/K⁺ ratio. Zang et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003ea) employed a BC₂F₈ introgression line population derived from the indica variety \u0026lsquo;IR64\u0026rsquo; and the japonica variety \u0026lsquo;Binam\u0026rsquo; and identified 13 and 22 salt tolerance\u0026ndash;related QTLs at the seedling and tillering stages, respectively. Yuan et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) used 664 cultivated accessions from the 3,000 Rice Genomes Project (3KRGP) and identified 21 QTLs, validating two candidate genes, \u003cem\u003eOsSTL1\u003c/em\u003e and \u003cem\u003eOsSTL2\u003c/em\u003e. Le et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted GWAS on 179 Vietnamese rice landraces at the seedling stage using 21,623 SNP markers, identifying 26 QTLs, 10 of which exhibited pleiotropy and regulated multiple salt tolerance\u0026ndash;related traits.\u003c/p\u003e\u003cp\u003eAlthough numerous QTLs associated with salt tolerance in rice have been reported, only a limited number have been finely mapped or cloned. Lin et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) identified 11 QTLs related to salt tolerance using an F₂/F₃ population derived from the salt-tolerant variety \u0026lsquo;Nona Bokra\u0026rsquo; and the salt-sensitive variety \u0026lsquo;Koshihikari\u0026rsquo;. Among these, the major locus \u003cem\u003eqSKC-1\u003c/em\u003e on chromosome 1 explained 40.1% of the total phenotypic variation. Subsequently, Ren et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) cloned \u003cem\u003eSKC1\u003c/em\u003e (\u003cem\u003eOsHKT1;5\u003c/em\u003e), a gene encoding a high-affinity potassium transporter, through map-based cloning. Huang et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) screened an EMS-mutagenized mutant library of \u0026lsquo;Zhonghua 11\u0026rsquo; under drought and salt stress conditions and identified \u003cem\u003eDST\u003c/em\u003e, a zinc finger protein transcription factor that negatively regulates stomatal closure and is associated with drought and salt tolerance. Takagi et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) employed the MutMap method to map the salt tolerance gene \u003cem\u003eHst1\u003c/em\u003e(\u003cem\u003eOsRR22\u003c/em\u003e) on chromosome 6. He et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) used chromosome segment substitution lines (CSSLs) derived from \u0026lsquo;Jiucaiqing\u0026rsquo; and \u0026lsquo;IR26\u0026rsquo; to map the major QTL \u003cem\u003eqSE3\u003c/em\u003e, which promotes seed germination and seedling development under salt stress; this locus encodes the potassium transporter \u003cem\u003eOsHAK21\u003c/em\u003e. However, most current studies rely on linkage analysis or mutant libraries, which do not adequately capture the superior alleles present in natural variation. In particular, salt tolerance genes directly applicable to molecular breeding remain scarce.\u003c/p\u003e\u003cp\u003eIn the present study, we evaluated a natural population of 372 diverse rice landraces. By combining systematic salt tolerance phenotyping with genome-wide association studies (GWAS), we identified QTLs and candidate genes associated with salt tolerance, along with their superior haplotypes. Our aim was to uncover the genetic architecture of salt tolerance in rice, enrich breeding resources for salt tolerance, and provide a theoretical foundation for the development of rice varieties adapted to saline\u0026ndash;alkali soils.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMaterials\u003c/h2\u003e\u003cp\u003eThis study utilized a natural population of 372 rice landraces described in a previous study. The population consisted of a mixture of indica and japonica landraces with broad genetic diversity. It included 104 accessions from Qiandongnan Prefecture, Guizhou Province (Kam Sweet Rice, KSR), 104 accessions from other regions of Guizhou Province (excluding Qiandongnan) (Guizhou, GZ), and landraces collected from regions south of the Yangtze River, including 23 from Central China (CC), 74 from East China (EC), 36 from South China (SC), and 31 from Southwest China (SW). Whole-genome resequencing yielded a curated dataset containing 3,566,872 high-quality single-nucleotide polymorphisms (SNPs), with an average sequencing depth of ~\u0026thinsp;12.43 from South Chi (Liu et al, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIdentification of salt tolerance at the tillering stage\u003c/h3\u003e\n\u003cp\u003eThe salt stress experiment was conducted at the experimental base of the Binhai Agricultural Research Institute, Hebei Academy of Agriculture and Forestry Sciences. Seedlings were raised using the film-moistening method and transplanted into a salt tolerance identification pool at the 3-leaf\u0026ndash;1-heart stage. Plants were spaced 25 cm \u0026times; 13 cm apart, with each accession planted in two rows of 10 hills per row, and one seedling per hill.\u003c/p\u003e\u003cp\u003eAfter a recovery period of 7 days, plants were irrigated with saline water at a concentration of 0.5% NaCl. The saline water was collected from coastal underground sources at a depth of ~\u0026thinsp;20 m (original salt concentration\u0026thinsp;~\u0026thinsp;2%) and diluted with fresh water to the target concentration (electrical conductivity\u0026thinsp;\u0026ge;\u0026thinsp;10 ms/cm, 25 ℃). Water depth in the pool was maintained at 3\u0026ndash;5 cm. Salt concentration was monitored daily with a portable conductivity meter, and electrical conductivity was adjusted to remain stable at the target 0.5% salt level by adding fresh or saline water as needed.\u003c/p\u003e\u003cp\u003eSalt injury was assessed according to the agricultural industry standard NY/T 3692\u0026thinsp;\u0026minus;\u0026thinsp;2020: \u003cem\u003eTechnical Specification for Salt Tolerance Identification in Rice\u003c/em\u003e. Injury scores were recorded two weeks (T2W) and four weeks (T4W) after salt treatment. For each accession, the two outermost plants in each row were excluded, and the remaining 10 consecutive plants in the center were scored individually. The average salt injury score (ASIS) was calculated as:\u003c/p\u003e\u003cp\u003eAverage Salt Injury Score (ASIS) = Σ(Number of plants at each salt injury grade \u0026times; Salt injury grade) / Total number of plants investigated\u003c/p\u003e\u003cp\u003eThe correspondence between salt injury grade and phenotypic symptoms was defined as follows:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGrade 1\u003c/strong\u003e\u003cp\u003eTillering growth essentially normal; no visible leaf damage.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGrade 3\u003c/strong\u003e\u003cp\u003eTillering growth nearly normal; leaf tips or upper half whitened or curled; or tillering slightly inhibited with some curled leaves.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGrade 5\u003c/strong\u003e\u003cp\u003eTillering growth severely inhibited; most leaves curled, only a few elongating.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGrade 7\u003c/strong\u003e\u003cp\u003eTillering growth ceased; most leaves withered.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGrade 9\u003c/strong\u003e\u003cp\u003ePlant dead or nearly dead.\u003c/p\u003e\u003c/p\u003e\n\u003ch3\u003eStatistical analysis of phenotypic data\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics, including mean, standard deviation, range, coefficient of variation (CV), and generalized heritability (H\u0026sup2;), were calculated using IBM SPSS Statistics v26. Pearson correlation coefficients between salt tolerance scores at different time points were computed in R v4.2.1 using the \u003cem\u003eggcorrplot\u003c/em\u003e package to assess phenotypic correlations. Multiple comparison tests with Bonferroni correction (e.g., Tukey\u0026rsquo;s HSD) were performed using the \u003cem\u003eggstatsplot\u003c/em\u003e and \u003cem\u003eggplot2\u003c/em\u003e packages, and significant differences were annotated with distinct letters (e.g., a, b, ab). This approach was applied to compare salt tolerance score distributions across subpopulations as well as to assess phenotypic differences among haplotypes of candidate genes. Violin plots, scatter plots, and boxplots were generated using \u003cem\u003eggpubr\u003c/em\u003e, \u003cem\u003eggplot2\u003c/em\u003e, and GraphPad Prism v10.\u003c/p\u003e\n\u003ch3\u003eGenome-wide association study (GWAS)\u003c/h3\u003e\n\u003cp\u003eGWAS was performed using the TASSEL platform (v5.2.40) under a mixed linear model (MLM) (Bradbury et al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). A total of 3,566,872 SNP markers were used for genotyping after filtering for a minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and missing rate\u0026thinsp;\u0026le;\u0026thinsp;20%. The population structure Q matrix was included as a covariate to reduce false positives. The genome-wide significance threshold was determined using the Bonferroni correction at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10⁻⁵ (Li et al, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Significant SNPs were functionally annotated using SnpEff, and candidate genes were identified within a 100-kb window upstream and downstream of significant loci, following established workflows (Cui et al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Manhattan plots were generated with the \u003cem\u003eCMplot\u003c/em\u003e package in R, while linkage disequilibrium (LD) heatmaps were constructed using LDBlockShow v1.40.\u003c/p\u003e\n\u003ch3\u003eqRT-PCR\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from rice tissues using the Plant RNA Extraction Kit (Genstone Biotech, Cat. #TR154-D-200). One microgram of RNA was reverse-transcribed to cDNA using the All-In-One 5X RT MasterMix (ABM, Cat. #G592). Quantitative real-time PCR (qRT-PCR) was performed with StarLighter Color HP SYBR Green qPCR Mix (Qixing Biotech, Cat. #fs-q1008-01) on a real-time PCR system. The 10 \u0026micro;L reaction mixture consisted of 5.0 \u0026micro;L of SYBR qPCR Mix, 3.8 \u0026micro;L of double-distilled water, 0.5 \u0026micro;L of cDNA template, 0.25 \u0026micro;L of each forward and reverse primer, and 0.2 \u0026micro;L of 50X ROX reference dye (Low/High). Gene expression levels were calculated using the 2\u003csup\u003e⁻ΔΔCt\u003c/sup\u003e method, with UBI as the internal reference gene. Each assay included three biological replicates. Primer sequences used in this study are listed in Supplementary Table (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003ePhenotypic variation in the study population\u003c/h2\u003e\u003cp\u003eSalt tolerance was systematically evaluated in the natural rice population through dynamic phenotypic monitoring during the tillering stage. Average salt injury scores (ASIS) were recorded at two weeks (T2W) and four weeks (T4W) after salt treatment. Under saline conditions, ASIS values at both time points displayed continuous distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;B), consistent with the inheritance of a typical quantitative trait.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDescriptive statistics indicated a progressive decline in salt tolerance with increasing treatment duration. At T2W, the mean ASIS was 4.70 (range: 2\u0026ndash;9), with a coefficient of variation (CV) of 23.98%. By T4W, the mean ASIS had increased to 7.59 (range: 3\u0026ndash;9), while the CV decreased to 14.05%, reflecting both intensified stress symptoms and reduced phenotypic variation. Generalized heritability (H\u0026sup2;) was high at both stages, estimated at 92.1% for T2W and 90.7% for T4W, confirming that salt tolerance is a highly heritable trait in this population.\u003c/p\u003e\u003cp\u003eCorrelation analysis revealed a significant positive correlation between ASIS at T2W and T4W (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.505, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that early-stage responses to salt stress are predictive of subsequent tolerance performance. Multiple comparison tests further identified significant differences in salt tolerance among subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u0026ndash;D). At T2W, GZ (4.39) and KSR (4.56) exhibited the highest tolerance, EC (4.75) and CC (4.83) showed intermediate levels, while SC (5.53) and SW (5.08) were relatively more sensitive. By T4W, EC (7.24) and CC (7.31) retained stronger tolerance, followed by GZ (7.63) and SW (7.69), whereas KSR (7.72) and SC (7.90) were the most sensitive.\u003c/p\u003e\u003cp\u003eImportantly, the EC subpopulation\u0026mdash;originating from eastern coastal regions\u0026mdash;consistently displayed superior and stable salt tolerance across both time points, underscoring its adaptability and potential as a valuable genetic resource for salt-tolerance improvement in rice.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIdentification of candidate genes within the stable QTL region on chromosome 8\u003c/h3\u003e\n\u003cp\u003eBased on GWAS, a total of 39 loci significantly associated with ASIS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10⁻⁵) were identified across both time points. At T2W, 16 loci were detected, distributed across chromosomes 1, 2, 3, 4, 6, 7, 8, 9, and 10, with phenotypic variation explained (PVE) ranging from 6.53% to 10.14%. At T4W, 23 loci were identified on chromosomes 1, 3, 6, 7, 8, 9, 10, 11, and 12, with PVE values ranging from 5.99% to 10.99% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong these loci, 10 (26%) overlapped with previously reported salt tolerance QTLs or known genes, including \u003cem\u003eOsHAK3\u003c/em\u003e (Ju et al, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), \u003cem\u003eOsHAK12\u003c/em\u003e (Zhang et al, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), \u003cem\u003eOsCUL1-3\u003c/em\u003e (Kim et al, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), \u003cem\u003eOsClpD1\u003c/em\u003e (Mishra et al, 2016), \u003cem\u003eOsDPK1\u003c/em\u003e (Gu et al, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), and \u003cem\u003eOsNHAD\u003c/em\u003e (Song Liu et al, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), confirming the robustness of our results (Table S3). Notably, two adjacent loci on chromosome 8\u0026mdash;\u003cem\u003eqT2W8.3\u003c/em\u003e (top SNP: Chr08_Pos 7,707,975, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.52 \u0026times; 10⁻⁶) and qT2W8.4 (top SNP: Chr08_Pos 7,906,989, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.95 \u0026times; 10⁻⁶)\u0026mdash;were consistently detected at both T2W and T4W. This overlap strongly suggests that this region harbors a stable QTL with an important role in salt tolerance across developmental stages.\u003c/p\u003e\u003cp\u003eUnder salt stress, different allelic combinations of salt tolerance genes/QTLs had significant effects on ASIS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S4). At the T2W stage, four major salt tolerance genes/QTLs showed significant differences among allelic combinations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Accessions carrying a greater number of favorable alleles exhibited significantly reduced ASIS values, reflecting stronger salt tolerance. In contrast, accessions carrying disadvantageous alleles had higher ASIS values and weaker tolerance. At the T4W stage, although overall ASIS values increased due to prolonged stress exposure, the relative trends in tolerance among different gene combinations remained consistent with those observed at T2W (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurther analysis of allele pyramiding revealed a clear additive effect of favorable alleles on salt tolerance. Specifically, as the number of favorable alleles increased, mean ASIS values showed a significant downward trend. At both stages, ASIS was significantly and negatively correlated with the number of favorable alleles carried (T2W: \u003cem\u003eR\u003c/em\u003e = \u0026minus;\u0026thinsp;0.2194, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; T4W: \u003cem\u003eR\u003c/em\u003e = \u0026minus;\u0026thinsp;0.1898, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u0026ndash;D). These results indicate that pyramiding favorable alleles can effectively enhance salt tolerance, providing a practical strategy for molecular breeding of salt-tolerant rice varieties.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDiscovery of candidate salt tolerance genes\u003c/h2\u003e\u003cp\u003eWe identified a stable association signal for salt tolerance within the 7.60\u0026ndash;7.80 Mb region of chromosome 8, consistently detected at both T2W and T4W. This interval lies within a large linkage disequilibrium (LD) block (\u0026gt;\u0026thinsp;200 kb) containing 26 annotated genes with nonsynonymous mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Among these, two BTB-MATH family genes \u0026mdash; \u003cem\u003eOsST8.1\u003c/em\u003e(LOC_Os08g13020) and \u003cem\u003eOsST8.2\u003c/em\u003e (LOC_Os08g13030) \u0026mdash; were prioritized as high-confidence candidate genes. Members of the BTB-MATH protein family are known to regulate substrate protein stability and have been implicated in plant responses to abiotic stresses such as salinity and drought.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eOsST8.1\u003c/em\u003e spans 1,604 bp, contains three exons, and encodes a BTB and MATH domain protein. Ten nonsynonymous variants were identified within its coding region, allowing classification of accessions into three haplotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Among these, Hap3 was identified as the superior haplotype, conferring significantly enhanced salt tolerance compared to Hap1 and Hap2 across both treatment stages (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;D). qRT-PCR analysis further revealed that \u003cem\u003eOsST8.1\u003c/em\u003e expression in Hap3 accessions was significantly downregulated under salt treatment compared to control conditions, whereas no significant changes were detected in Hap1 or Hap2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The frequency of Hap3 varied among subpopulations: KSR (18.63%), GZ (41.67%), CC (38.10%), EC (40.85%), SC (48.57%), and SW (70.37%). Notably, Hap3 was relatively enriched in accessions from saline-alkali regions, including EC (40.85%) and SW (70.37%), supporting its adaptive role in enhancing salt tolerance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003cem\u003eOsST8.2\u003c/em\u003e spans 1,091 bp, consists of a single exon, and encodes an MBTB21 protein. Haplotype analysis revealed a nonsynonymous substitution Alaiontionalysis revealed mutation at Chr08:7,740,763 in its coding region, defining two haplotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Accessions carrying Hap2 exhibited significantly lower ASIS values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and therefore stronger salt tolerance compared to Hap1 across all treatment durations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u0026ndash;D). qRT-PCR analysis confirmed that \u003cem\u003eOsST8.2\u003c/em\u003e expression was significantly upregulated under salt stress in Hap2 accessions, whereas Hap1 accessions showed no significant transcriptional changes between control and treatment conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Hap2 frequencies across subpopulations were: KSR (14.14%), GZ (37.08%), CC (30.00%), EC (29.03%), SC (37.93%), and SW (58.33%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eIn addition to \u003cem\u003eOsST8.1\u003c/em\u003e and \u003cem\u003eOsST8.2\u003c/em\u003e, integrated evidence from association mapping, functional annotation, and haplotype analysis highlighted two additional novel high-confidence candidate genes within the same LD block: \u003cem\u003eOsSST1\u003c/em\u003e (\u003cem\u003eLOC_Os08g12800\u003c/em\u003e) (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and \u003cem\u003eOsPPR1\u003c/em\u003e (\u003cem\u003eLOC_Os08g12850\u003c/em\u003e) (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), both of which may contribute to salt tolerance at the tillering stage.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSalt stress is one of the major abiotic factors restricting crop productivity worldwide (Gupta and Huang, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although extensive research has focused on salt tolerance in rice, most studies have concentrated on early developmental stages such as germination and seedling growth (Kojonna et al, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Yin et al, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By contrast, the tillering stage\u0026mdash;an essential determinant of rice yield\u0026mdash;has received comparatively less attention. At this stage, salt stress inhibits growth, reduces tiller number, induces leaf chlorosis, and in severe cases, leads to plant death (Zhang et al, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, salt tolerance at the tillering stage is a critical indicator for evaluating the adaptive potential of rice under saline conditions.\u003c/p\u003e\u003cp\u003eIn this study, we systematically evaluated salt tolerance during the tillering stage using a natural population of 372 diverse rice landraces. Our results demonstrated that salt tolerance declined progressively with prolonged exposure, yet the East China (EC) subpopulation consistently exhibited stronger tolerance compared to other groups. This enhanced performance likely reflects long-term adaptation to saline coastal environments, shaped by both natural selection and traditional human-mediated selection.\u003c/p\u003e\u003cp\u003eThrough genome-wide association analysis (GWAS), we identified 39 loci significantly associated with salt tolerance, distributed across multiple chromosomes, with phenotypic variation explained (PVE) ranging from 5.99% to 10.99%. The polygenic distribution of these loci highlights the quantitative and complex nature of salt tolerance in rice. Importantly, 10 loci (26%) overlapped with previously reported QTLs or known salt tolerance genes, providing strong validation for our findings. For example, Ju et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported 52 loci associated with salt tolerance at the germination stage in japonica rice, including \u003cem\u003eOsHAK3\u003c/em\u003e and \u003cem\u003eOsITPK5\u003c/em\u003e. The locus qT2W1 identified here overlapped with the \u003cem\u003eOsHAK3\u003c/em\u003e region. Similarly, Zang et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003eb) mapped multiple QTLs using a backcross introgression line population, including qSKC11 for shoot K⁺ concentration at the seedling stage, which overlaps with qT4W11.1 in our study. Wang et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) identified a major seedling-stage QTL, qDSW6.1, for shoot dry weight, overlapping with qT2W6.2 and qT4W6 reported here. Moreover, the seedling-stage QTL qST3 (detected in MG RILs derived from Milyang 23 \u0026times; Gihobyeo) corresponds to qT4W3.1 in our study (Lee et al, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Several known salt stress-related genes, including \u003cem\u003eOsDPK1\u003c/em\u003e and \u003cem\u003eOsNHAD\u003c/em\u003e, were also located within or adjacent to QTL regions detected in this study, further supporting the robustness of our results.\u003c/p\u003e\u003cp\u003eAnalysis of favorable allele combinations confirmed a significant additive effect on salt tolerance. Accessions carrying multiple favorable alleles consistently exhibited lower ASIS values and greater tolerance, while those with fewer favorable alleles were more sensitive. This pattern was evident at both T2W and T4W, despite increasing stress severity. These findings underscore the polygenic nature of salt tolerance and highlight the potential of pyramiding superior alleles to enhance resistance (Thomson et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Consistent with this conclusion, Singh et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that although \u003cem\u003eSaltol\u003c/em\u003e significantly contributes to seedling-stage tolerance, the presence of this single locus alone is insufficient; pyramiding multiple salt tolerance loci is necessary for robust field-level performance and yield stability.\u003c/p\u003e\u003cp\u003eProtein ubiquitination is a critical regulatory mechanism for plant stress responses (Du et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Lechner et al, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Adams and Spoel, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). BTB-MATH (Broad-complex, Tramtrack and Bric-\u0026agrave;-brac\u0026ndash;Meprin and TRAF Homology) proteins function as substrate recognition factors for Cullin3-based E3 ubiquitin ligase (CRL3) complexes, targeting stress-related proteins for degradatio n(Shu and Yang, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Figueroa et al, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Wang et al, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous studies have shown that \u003cem\u003eOsCUL1\u003c/em\u003e proteins (e.g., \u003cem\u003eOsCUL1-1\u003c/em\u003e and \u003cem\u003eOsCUL1-3\u003c/em\u003e) interact with \u003cem\u003eOsMBTB32\u003c/em\u003e and may suppress its activity during salt stress (Ullah et al, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, we identified two BTB-MATH family genes on chromosome 8, \u003cem\u003eOsST8.1\u003c/em\u003e (\u003cem\u003eLOC_Os08g13020\u003c/em\u003e) and \u003cem\u003eOsST8.2\u003c/em\u003e (\u003cem\u003eLOC_Os08g13030\u003c/em\u003e), as high-confidence candidates for salt tolerance. Haplotype analysis revealed that superior haplotypes of both genes were significantly associated with enhanced tolerance. Expression analysis demonstrated contrasting regulatory responses: \u003cem\u003eOsST8.1\u003c/em\u003e was significantly downregulated under salt stress, whereas \u003cem\u003eOsST8.2\u003c/em\u003e was significantly upregulated. Together, these results suggest that \u003cem\u003eOsST8.1\u003c/em\u003e and \u003cem\u003eOsST8.2\u003c/em\u003e may function cooperatively to mediate protein degradation, thereby influencing ion homeostasis and stress signaling pathways, ultimately conferring improved tolerance.\u003c/p\u003e\u003cp\u003eFurthermore, we identified 13 elite salt-tolerant accessions\u0026mdash;including \u003cem\u003eLiushizi\u003c/em\u003e, \u003cem\u003eChangshennuo\u003c/em\u003e, Hannuo, and Banmaozhan\u0026mdash;each carrying more than six favorable alleles across multiple loci (Table S5). These accessions represent valuable germplasm resources for breeding and can serve as key parental lines in the development of salt-tolerant rice varieties.\u003c/p\u003e\u003cp\u003eLooking ahead, functional validation of \u003cem\u003eOsST8.1\u003c/em\u003e and \u003cem\u003eOsST8.2\u003c/em\u003e through CRISPR/Cas9-mediated knockout and overexpression studies will provide critical mechanistic insights into their roles in salt stress response. Combined with the incorporation of superior haplotypes into molecular design breeding, these efforts will accelerate the development of high-yielding, salt-tolerant rice varieties, offering both theoretical advances and practical solutions for sustainable agriculture in saline-alkali environments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study systematically dissected the genetic basis of salt tolerance in rice at the tillering stage through dynamic phenotypic monitoring and GWAS. A total of 39 loci were identified, of which 29 (74.4%) represent novel associations. Within a stable QTL region on chromosome 8, we identified two high-confidence candidate genes, \u003cem\u003eOsST8.1\u003c/em\u003e and \u003cem\u003eOsST8.2\u003c/em\u003e, both members of the BTB-MATH protein family. Haplotype analysis revealed that superior alleles\u0026mdash;such as \u003cem\u003eOsST8.1\u003c/em\u003e-Hap3 and \u003cem\u003eOsST8.2\u003c/em\u003e-Hap2\u0026mdash;were significantly associated with enhanced salt tolerance and represent valuable targets for marker-assisted selection.\u003c/p\u003e\u003cp\u003eThese findings not only provide new insights into the genetic architecture of salt tolerance at a critical developmental stage but also deliver practical genetic resources and molecular targets to accelerate the breeding of salt-tolerant rice varieties adapted to saline-alkali environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are provided within the article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Hebei Natural Science Foundation (grant No. C2023301121) , the National Key Research and Development Program of China (2021YFD1200500), the Key R\u0026amp;D Program of Ningxia Hui Autonomous Region (2023BCF01010)，and\u0026nbsp;the CAAS Science and Technology Innovation Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.L., C.L. and L.G. performed the experiments and analyzed the data. L.H. and D.C. conceived and designed the study. L.H., D.C., Z.Z., X.M. and B.H. contributed reagents, materials, analysis tools or data. H.L. wrote the paper. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdak S, Datta S, Bhattacharya S, Ghose TK, Lahiri Majumder A (2020). Diversity analysis of selected rice landraces from West Bengal and their linked molecular markers for salinity tolerance. Physiol Mol Biol Plants 26:669\u0026ndash;682. https://doi.org/10.1007/s12298-020-00772-8\u003c/li\u003e\n\u003cli\u003eAdams EHG, Spoel SH (2018). The ubiquitin\u0026ndash;proteasome system as a transcriptional regulator of plant immunity. 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PLOS ONE 18: e0279192. https://doi.org/10.1371/journal.pone.0279192\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":"rice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rice","sideBox":"Learn more about [Rice](http://thericejournal.springeropen.com)","snPcode":"12284","submissionUrl":"https://submission.nature.com/new-submission/12284/3","title":"Rice","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"rice, tillering stage, salt stress, GWAS, candidate gene","lastPublishedDoi":"10.21203/rs.3.rs-7832023/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7832023/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSalt stress is one of the major abiotic factors limiting rice yield, with the tillering stage\u0026mdash;an essential growth phase that strongly influences rice productivity\u0026mdash;being particularly sensitive to salinity. Thus, identifying salt-tolerant rice varieties is of great importance for ensuring stable rice production. In this study, we systematically evaluated the salt tolerance of 372 rice landraces at the tillering stage through dynamic phenotypic monitoring, using the average salt injury score (ASIS) as an indicator at two (T2W) and four weeks (T4W) after salt treatment. A genome-wide association study (GWAS) identified 39 loci significantly associated with salt tolerance. Among these, two high-confidence candidate genes, \u003cem\u003eOsST8.1\u003c/em\u003e and \u003cem\u003eOsST8.2\u003c/em\u003e, both members of the BTB-MATH protein family, were implicated in salt tolerance during the tillering stage. Haplotype analysis revealed significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in salt tolerance among germplasm carrying different haplotypes, with accessions harboring the superior haplotype exhibiting enhanced tolerance. Consistently, qRT-PCR analysis showed significantly lower or higher expression levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of \u003cem\u003eOsST8.1\u003c/em\u003e or \u003cem\u003eOsST8.2\u003c/em\u003e in accessions with the superior haplotype following salt treatment, suggesting that they may regulate rice responses to salinity stress. Collectively, this study provides valuable genetic resources and a theoretical foundation for elucidating the genetic basis of salt tolerance and for breeding new salt-tolerant rice varieties.\u003c/p\u003e","manuscriptTitle":"Genome-wide association study identifies candidate genes for salt tolerance in traditional rice landraces","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 18:23:38","doi":"10.21203/rs.3.rs-7832023/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-03T06:41:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T02:21:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T06:43:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-25T07:54:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311736138693027358583477793726037595775","date":"2025-10-22T12:00:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107595909522417058066232278271566117071","date":"2025-10-22T09:59:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227759710240334720096345941777250278595","date":"2025-10-22T09:04:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-22T07:49:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-13T10:34:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-13T10:32:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Rice","date":"2025-10-11T05:39:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"rice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rice","sideBox":"Learn more about [Rice](http://thericejournal.springeropen.com)","snPcode":"12284","submissionUrl":"https://submission.nature.com/new-submission/12284/3","title":"Rice","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"334d4519-77a6-4647-88a3-9015cf774294","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:04:37+00:00","versionOfRecord":{"articleIdentity":"rs-7832023","link":"https://doi.org/10.1186/s12284-026-00907-7","journal":{"identity":"rice","isVorOnly":false,"title":"Rice"},"publishedOn":"2026-04-03 15:58:41","publishedOnDateReadable":"April 3rd, 2026"},"versionCreatedAt":"2025-11-03 18:23:38","video":"","vorDoi":"10.1186/s12284-026-00907-7","vorDoiUrl":"https://doi.org/10.1186/s12284-026-00907-7","workflowStages":[]},"version":"v1","identity":"rs-7832023","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7832023","identity":"rs-7832023","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}