Genome-wide Association Study of Common Wheat’s Alkali Tolerance at the Germination Stage

Genome-wide Association Study of Common Wheat’s Alkali Tolerance at the Germination Stage | 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 Genome-wide Association Study of Common Wheat’s Alkali Tolerance at the Germination Stage Haoran Gu, Zixu Wang, Xiaotong Liu, Haotian Cao, Zixin Zhou, Meishan Zhou, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7441024/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Soil alkalization poses a severe threat to global wheat production, and deciphering the genetic basis of alkali tolerance during germination is critical for breeding resistant varieties. Here, a genome-wide association study (GWAS), with a comprehensive analysis, of 314 wheat accessions was executed under 0.15% Na 2 CO 3 stress and control conditions. The phenotypic screening showed seedling biomass and root growth were suppressed under stress, while germination rate remained stable. The 314 accessions were classified by a principal component analysis, as follows: 6 highly tolerant, 57 tolerant, 92 moderate, 110 sensitive, and 35 highly sensitive. The GWAS indicated that 206 significant marker-trait associations (MTAs) were identified for nine germination-related traits. Notably, five loci (MTA25, MTA29, MTA80, MTA129, and MTA166) demonstrated stability across both tested conditions. The alleles effect analysis and candidate gene analysis for three stable loci (MTA25, MTA29, and MTA80) were executed. A principal component analysis-integrated GWAS identified 198 significant MTAs, of which 51 were co-localized with phenotype location-based MTAs, and they were verified as core stress-responsive loci. In addition, Kompetitive Allele Specific PCR markers for sheath length and germination percentage were developed. These findings provide a theoretical foundation for the selection of alkali-resistant wheat and provide important resources for wheat molecular breeding. Alkali stress Genome-wide association study (GWAS) Germination traits Marker-trait associations (MTAs) Candidate genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Message Six highly alkali-tolerant wheat germplasms were identified, 206 MTAs related to germination traits and 198 significant PC-MTAs were detected, and 2 KASP markers for resistance breeding were developed. Introduction With the continuous deterioration of the global climate, the degree of salinization of surface land is increasing, and the proportion of low-salinity land available for crop cultivation is gradually decreasing (Song et al. 2024 ; Wang et al. 2017 ). Compared with physiological metabolic disorders, ion toxicity, and osmotic stress under salt-stress conditions, the strong alkaline environment causes greater damage to plants (Lu et al. 2022 ). In China, the area of saline-alkali land exceeds 100 million mu; therefore, improving the breeding of crops that thrive in saline-alkali land is of great significance (Zhao et al. 2024 ). Wheat ( Triticum aestivum L.) is an important crop because it is a major food crop worldwide (Chen et al. 2021 ). High yield is currently the main goal of wheat breeding (Zhang et al. 2018 ). Alkali stress, as an abiotic stress that affects the normal growth and development of wheat, reduces wheat grain yield (Torbaghan et al. 2017 ; Xiao et al. 2020 ). Therefore, exploring the genetic mechanisms of wheat alkali tolerance is an effective way to improve the utilization rate of alkali land and increase wheat yield. Wheat’s alkali tolerance involves complex physical and chemical pathways from microscopic cells to macroscopic individuals. Therefore, to better understand the potential genetic mechanisms of alkali tolerance, conducting a genetic analysis at a smaller scale within cells is an effective approach (Ayalew et al. 2018 ; Oyiga et al. 2018 ). Molecular markers linked to the target genes are important tools for improving breeding efficiency. Currently, single-nucleotide polymorphism (SNP) is the most abundant type of molecular marker. Due to its high genomic density, low mutation rate, and suitability for high-throughput detection, SNPs are considered the best markers for studying the genetics of complex traits in polyploid wheat and its wild relatives. At present, several SNP arrays, such as 55K, 90K, 820K, and 660K, have been designed (Liu et al. 2024 ; Lou et al. 2021 ; Rasheed et al. 2017 ). Compared with the long time required for constructing biparental populations for linkage analyses, genome-wide association studies (GWASs) based on linkage disequilibrium (LD) represent an effective approach for dissecting the potential associations between genotypes and phenotypes, which can encompass more allelic diversity (Lou et al. 2021 ; Ma et al. 2022 ; Safdar et al. 2020 ; Sukumaran et al. 2018 ). Recently, the GWAS approach has been applied widely in analyzing the genetic bases of complex agronomic traits and it shortened the crop breeding process significantly (Maulana et al. 2020 ; Pang et al. 2021 ; Safdar et al. 2020 ). Multiple marker-trait associations (MTAs), quantitative trait loci (QTLs), and genes associated with seedling traits distributed on 21 wheat chromosomes under saline-alkali stress have been identified. Among them, the QTLs or MTAs for germination percentage (GP) are mainly located on Chromosomes 2A/2D/7A (Akram et al. 2022 ; Guo et al. 2022 ; Hasseb et al. 2022 ; Li et al. 2021 ). Similarly, QTLs or MTAs for seedling length (SL) are detected on Chromosomes 2A/3B/5A (Akram et al. 2022 ; Ghaedrahmati et al. 2018 ; Guo et al. 2022 ; Javid et al. 2022 ; Li et al. 2021 ; Quan et al. 2021 ). For root-related traits, the major QTLs or MTAs for maximum root length (MRL) are detected on Chromosomes 4A/7A (Akram et al. 2022 ; Guo et al. 2022 ; Li et al. 2021 ; Luo et al. 2021 ; Quan et al. 2021 ). The major MTAs for root number (RN) are distributed on Chromosomes 2D, 4A, and 5A, and the genes TaRN1 -3A and TaRN2 -4A have been verified as stable regulators of roots (Akram et al. 2022 ; Li et al. 2021 ). Regarding biomass-related traits, the major QTLs for seedling fresh weight (SFW) are concentrated on Chromosome 2A (Ghaedrahmati et al. 2018 ; Guo et al. 2022 ; Oyiga et al. 2018 ), and the QTLs or MTAs for seedling dry weight (SDW) are distributed on Chromosomes 3B, 6A, and 7A (Ghaedrahmati et al. 2018 ; Guo et al. 2022 ; Li et al. 2021 ; Oyiga et al. 2018 ; Quan et al. 2021 ). The QTLs or MTAs for root fresh weight (RFW) and root dry weight (RDW) are located on Chromosomes 7A and 5B, respectively (Khan et al. 2022 ; Mohamed et al. 2022 ; Oyiga et al. 2018 ; Quan et al. 2021 ), but the related genes have not been cloned. There are limited reports on the mapping of sheath length (SHL) and its stress-specific responses. In this study, a GWAS for nine germination-related traits was performed. Our main objectives were to: (1) grade the alkali tolerance of 314 wheat accessions; (2) identify major stable MTAs, analyze their allelic effects, and verify some stress-responsive loci; and (3) develop functional molecular markers for marker-assisted selection breeding. Materials and Methods Plant materials and hydroponic experiments The natural population used in this study consisted of 314 wheat accessions that had previously undergone genetic diversity and population structure analyses (Table S1 )(Xu et al. 2024 ). Optimized hydroponic culturing was carried out based on similar studies by Ayalew et al. ( 2018 ). In total, nine agronomic traits, including SL, SHL, MRL, RN, RFW, SFW, RDW, SDW, and GP at the germination stage, were investigated. For hydroponic germination, 20 seeds per cultivar were selected and spaced 1 cm apart to ensure uniform growth conditions. Plastic boxes, with a volume of 1,300 mL, were used, and holes with a diameter of 8 mm were drilled in the lids. The top of the lid was lined with filter paper to keep the plants in place and the surface moist. For the group growing under treatment (T) conditions, a Na 2 CO 3 solution (0.15%) was added during seed germination to impose osmotic stress, whereas the group growing under control (CK; non-stressed) conditions was only supplied deionized water. The experiments were conducted in a greenhouse with a relative humidity of 50% and a day/night temperature regime of 22–25 ℃. Hydroponic culturing of wheat at the germination stage was maintained for 7 d under a 16:8-h light–dark cycle (light period: 6:00 a.m. to 10:00 p.m.; dark period: 10:00 p.m. to 6:00 a.m.). Samples of cultured seedlings were taken after7 d, and the trait indicators of each sample were measured using a ruler (cm). To evaluate the agronomic traits, 10 representative plants were randomly selected from each replicate, and each variety exhibited a uniform growth state. The average phenotypic value of each trait for 10 plants in each environment was considered as the phenotypic value of the target trait in that environment. The experiments were conducted in a randomized complete block design, with three replications. SNP genotyping Genomic DNA was isolated from fresh young leaf samples obtained from five individual plants per line following a modified cetyltrimethylammonium bromide-based protocol adapted from previously established methodologies (Xu et al. 2024 ). The genotyping of 314 wheat materials was performed using DNA and the 55K SNP chip from Beijing Compass Biotechnology Co., Ltd. In the analysis results, PLINK1.9 was used to screen and remove SNP markers having minor allele frequencies of less than 5% or missing values of more than 10%. Finally, 24,889 SNP markers were used for the association analysis. Statistical analysis For the phenotypic statistical analysis, IBM SPSS Statistics 25 was used to estimate the descriptive statistics of all the traits, estimate the correlations between traits, and conduct an analysis of variance. In R 4.4.3, the lme4 and corrplot packages were used to calculate the best linear unbiased estimates (BLUE) of each trait, with three replicates, in all the environments and to draw a heatmap of the correlations between traits. Origin 2021 was used to construct histograms and normal curves. Broad-sense heritability ( H ²) was computed using OGAStation 1.0 with the formula: H ² = (genetic variance/(genetic variance + environmental variance) × 100%. To systematically evaluate alkali tolerance in wheat genotypes, a multi-dimensional evaluation system was established using phenotypic data from the following nine traits: SL, SHL, MRL, RN, SFW, SDW, RFW, RDW, and GP. The Stress Tolerance Index (STI) was calculated to quantify the alkali stress response of individual traits using the following formula: $$\:\text{STI}\text{=}\frac{\text{Trait\:value\:under\:alkaline\:stress\:conditions}}{\text{Trait\:value\:under\:control\:conditions}}\text{×100\%}\text{.}$$ Subsequently, Membership Function Values U \(\:\left({\text{Z}}_{\text{Sj}}\right)\text{}\) were derived through range normalization, as follows: $$\:\text{U}\text{(}{\text{Z}}_{\text{Sj}}\text{)}\text{=}\frac{{\text{Z}}_{\text{Sj}}\text{-}{\text{Z}}_{\text{Smin}}}{{\text{Z}}_{\text{Smax}}\text{-}{\text{Z}}_{\text{Smin}}},$$ where \(\:\text{}{\text{Z}}_{\text{Sj}}\) represents the STI value of the composite indicator, and \(\:{\text{Z}}_{\text{Smax}}\text{}\) and \(\:{\text{Z}}_{\text{Smin}}\text{}\) are the maximum and minimum STI values, respectively, of that trait. Weight coefficients \(\:{\text{W}}_{\text{j}\text{}}\) were further assigned based on the variance contribution rate \(\:{\text{P}}_{\text{j}}\) using: \(\:{\text{W}}_{\text{j}}\text{=}\frac{{\text{P}}_{\text{j}}}{{\sum\:}_{\text{j}\text{=1}}^{\text{∞}}{\text{P}}_{\text{j}}}.\:\) Finally, a Comprehensive Alkali Tolerance Index (D S ) was calculated by integrating weighted membership function values, as follows: $$\:{\text{D}}_{\text{s}}\text{=}{\sum\:}_{\text{j}\text{=1}}^{\text{∞}}\left[\text{U}\text{(}{\text{Z}}_{\text{Sj}}\text{)}{\text{W}}_{\text{j}}\right].$$ A higher D S value indicates a stronger alkali tolerance. Standardized data were subjected to a hierarchical cluster analysis in SPSS 25.0. Highly stress-tolerant lines were defined as having D S values of 0.547–0.634, stress-tolerant lines as 0.427–0.519, moderately stress-tolerant lines as 0.359–0.423, stress-sensitive lines as 0.293–0.356, and highly stress-sensitive lines as 0.141–0.292. Population structure analysis, kinship analysis, and LD A population structure analysis was performed using Admixture software, and Blink in the GAPIT statistical package of R software was used to calculate the PCs and kinship matrix. In addition, PopLDdecay software was used to calculate the genome-wide LD. After calculating the squared correlation coefficient (R²), the distance at which R² decreased to half its maximum value was used to evaluate the LD between each pair of SNPs on a chromosome. LDBlockShow 1.40 was used to construct the LD heatmap (Dong et al. 2020 ). GWAS A total of 12 phenotypic datasets were used, including the mean values of each trial (CK1, CK2, CK3, T1, T2, and T3), the alkali tolerance indices of three trials (STI1, STI2, and STI3), and the BLUE (CKBlue, TBlue, and STIBlue) of each trait. The BLINK model provided by R/GAPIT 3.4 was used for the GWAS analysis (Lipka et al. 2012 ). This model iteratively conducts two fixed-effect models, eliminating the requirement that the underlying genes of a trait are evenly distributed in the genome. It takes the results of population stratification and kinship as covariates to minimize false positives. BIC is used to replace REML in FarmCPU to further improve statistical power and computational speed (Huang et al. 2019 ). The R/ggplot2 and CMplot packages were used to generate Manhattan plots and quantile–quantile plots for multiple environments (Sallam et al. 2024 ; Valero Mora 2010 ). Significant MTAs were determined using a threshold P-value of 0.001 (-log 10 P = 3). Stable MTAs were defined as those detected in at least two of the eight datasets (i.e., CK1, CK2, CK3, CKBlue, T1, T2, T3, and TBlue). Principal component analysis-integrated GWAS based on membership function values A PCA was performed on standardized membership function values to reduce dimensionality and mitigate multicollinearity among traits. Each PC with a cumulative contribution rate of 85% was extracted and used as phenotypic traits for the GWAS. Significant loci identified by the PC-GWAS were compared with significant phenotype-derived MTAs. Only genetic loci verified by cross-analysis and stability testing across multiple environments were designated as core alkali stress loci. Prediction of candidate genes Candidate gene identification was performed by integrating genetic association signals and functional annotations. First, genomic regions spanning ± 5 Mb around GWAS loci were defined using the Triticum aestivum Chinese Spring v2.1 genome assembly ( http://202.194.139.32/jbrowse.html ). Gene annotations from the Triticeae-GeneTribe database ( http://wheat.cau.edu.cn/TGT/ ) were then analyzed to screen for genes with potential biological functions. Functional annotations were retrieved from UniProt ( https://www.uniprot.org/ ). To prioritize candidate genes, functional evidence specifically reported for T. aestivum , Oryza sativa , or Arabidopsis thaliana in UniProt entries was given primary consideration, supplemented by a conserved domain analysis. Conversion of SNPs to Kompetitive AlleleSpecific PCR (KASP) markers SNP genotyping was conducted using KASP technology (KASP™ platform, LGC Genomics, Hoddesdon, UK) targeting the MTA29 and MTA80 loci. The PCR reaction (5 µL) contained 2.5 µL sample DNA (30 ng/µL), 2.5 µL KASP Mix (LGC Genomics), and 0.07 µL KASP Assay Mix. Thermal cycling was performed as follows: 94°C for 15 min; 10 touchdown cycles of 94°C for 20 s, 61 to 55°C at 0.6°C per cycle for 60 s, and 26 amplification cycles of 94°C for 20 s and 55°C for 60 s. Automated genotype calling was performed using a SNPline/Array Tape systems with dual-channel fluorescence detection (FAM: 465–510 nm; HEX: 528–560 nm) and validated by KlusterCaller v2.4.0 (Wu et al. 2018 ). Results Phenotypic variation and a correlation analysis The phenotypic images from day 4 to 8 clearly revealed the morphological differences between CK and T conditions (Fig. 1a). Under T conditions, the maximum, minimum, and mean values of all the seedling traits, except for GP and RN, were significantly lower than those under CK conditions (Table 1). Traits showed relatively high coefficient of variation values, except for RN (< 4%), under both CK and T conditions. When plants were subjected to T conditions, the H 2 of most traits decreased significantly (except for those of RFW, SFW, and SDW), and the kurtosis and skewness coefficients were both less than 1.0. As shown in Fig. S1 , all the traits conformed to the normal distribution. Table 1 The descriptive statistics and an analysis of variance of alkalinity responsive traits C: control, T: alkaline-stressed, Min: minimum, Max: maximum, SD : standard deviation, CV : coefficient of variation, Skew: skewness, Kurt: kurtosis, H 2 : broad-sense heritability, GP: germination percentage, SL: seedling length, SHL: sheath length, MRL: maximum root length, RN: root number, RFW: root fresh weight, RDW: root dry weight, SFW: seedling fresh weight, SDW: seedling dry weight. A correlation analysis was performed for different traits (Fig. 1b, c). In the CK group, there were highly significant positive correlations between the SL and SHL, MRL, SFW, and SDW. The MRL was highly significantly positively correlated with RFW, RDW, SFW, and GP. The RFW was highly significantly positively correlated with RDW, SFW, and GP. In the T group, there were highly significant positive correlations between SL and SHL, as well as SFW. The SDW was highly significantly positively correlated with both SFW and RDW. Notably, under both conditions, SFW showed highly significant positive correlations with RFW, SL, and SDW, while RFW maintained a highly significant positive correlation with MRL. Comprehensive evaluation of alkali resistance for the 314 wheat accessions The PCA of nine germination-stage stress tolerance indices identified three components (PC1–3, eigenvalue > 1), collectively explaining 51.49% of phenotypic variance (Table S2 ). PC1 (25.80%) was predominantly characterized by RFW (0.52), RDW (0.47), and SL (0.52), suggesting a focus on biomass allocation traits. PC2 (17.53%) correlated with RN (0.43), SFW (0.59), and SDW (0.53), representing seedling growth. PC3 (11.27%) was mainly contributed to by GP (0.94), indicating germination sensitivity (Table S3). The comprehensive evaluation index (Ds) was calculated using the weighted summation of the membership function values of the nine traits according to their respective weights, in which the weights were determined based on rotated variance contributions (PC1:44.0%, PC2:34.01%, PC3:21.99%). The Ds value, constrained within [0,1], quantitatively ranked stress tolerance across materials. A clustering analysis based on the Ds values categorized the accessions into five distinct groups: 6 highly stress-tolerant, 57 stress-tolerant, 92 moderately stress-tolerant, 110 stress-sensitive, and 35 highly stress-sensitive (phenotypic data of 14 accessions were missing) (Table S4). GWAS of germination-related traits As established previously (Xu et al. 2024 ), a GWAS of 314 wheat accessions using a 55K SNP array identified 24,889 high-quality SNPs having a uniform distribution and weak population structure (mean LD decay: 4.1 Mb, fastest in the D subgenome at 2.3 Mb). As shown in Fig. 2 and Fig. S2 , Manhattan and quantile–quantile plots of the nine traits in the BLUE datasets were analyzed. Using the BLINK model, we identified 206 significant MTAs with replication support (≥ 2 replicates) across CK and T environments. This integrated set combined 112 trait-specific MTAs and 94 multi-trait MTAs. In total, 18 of the MTAs showed significant associations in the STI dataset (Table S5). Additionally, 79 and 66 MTAs were specifically detected under CK and T conditions, respectively. A total of 61 MTAs were detected in both the CK and T environments (including the STI dataset). Among them, the trait with the most MTAs was SHL (48), followed by SL (43), SDW (40), MRL (38), GP (35), RDW (32), SFW (32), RFW (24), and the least was RN (18). The most MTAs were detected in the A genome (75), followed by the B genome (66) and the D genome (65). The most MTAs (19) were detected on Chromosome 5B, and the least (2) were detected on Chromosome 3B (Table S5). A total of 35 significant MTAs for GP were identified, distributed across 16 chromosomes: 1A, 1D, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 5A, 5B, 6B, 6D, 7A, 7B, and 7D. An environmental specificity analysis revealed 17 MTAs unique to CK conditions, 13 MTAs specific to T conditions, and 1 stable MTA locus (MTA80) consistently detected in both environments. A pleiotropy analysis revealed that 25 MTAs (71.4%) were significantly associated with 8 other germination-related traits (Table S5). A total of 43 significant MTAs for SL were identified, distributed across 16 chromosomes: 1A, 1B, 2D, 3A, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6D, 7A, 7B, and 7D. An environmental specificity analysis revealed 21 MTAs unique to CK conditions and 15 specific to T conditions. A pleiotropy analysis revealed that 28 MTAs (62.2%) were significantly associated with 7 other germination-related traits (Table S5). A total of 48 significant MTAs for SHL were identified, distributed across 16 chromosomes: 1A, 1B, 2A, 2B, 2D, 3A, 4A, 4B, 4D, 5B, 5D, 6A, 6B, 6D, 7A, and 7D. An environmental specificity analysis revealed 27 MTAs unique to CK conditions, 16 specific to T conditions and 2 stable MTA loci (MTA29, MTA166) consistently detected in both environments. A pleiotropy analysis revealed that 27 MTAs (56.3%) were significantly associated with 6 other germination-related traits (Table S5). A total of 38 significant MTAs for MRL were identified, distributed across 16 chromosomes: 1A, 1D, 2A, 2B, 2D, 3A, 4A, 4D, 5B, 5D, 6A, 6B, 6D, 7A, 7B, and 7D. An environmental specificity analysis revealed 25 MTAs unique to CK conditions and 12 specific to T conditions. A pleiotropy analysis revealed that 24 MTAs (63.2%) were significantly associated with 8 other germination-related traits (Table S5). A total of 18 significant MTAs for RN were identified, distributed across 12 chromosomes: 2A, 2B, 2D, 3A, 3D, 4A, 4D, 5A, 5B, 5D, 6A, and 6B. An environmental specificity analysis revealed 13 MTAs unique to CK conditions and 5 specific to alkali stress. A pleiotropy analysis revealed that 13 MTAs (72.2%) were significantly associated with 5 other germination-related traits (Table S5). A total of 32 significant MTAs for SFW were identified, distributed across 15 chromosomes: 1A, 2A, 2D, 3A, 3B, 3D, 4B, 4D, 5A, 5B, 5D, 6A, 7A, 7B, and 7D. An environmental specificity analysis revealed 11 MTAs unique to CK conditions, 19 specific to stress conditions, and 1 stable MTA locus (MTA25) consistently detected in both environments. A pleiotropy analysis revealed that 25 MTAs (78.1%) were significantly associated with 7 other germination-related traits (Table S5). A total of 40 significant MTAs for SDW were identified, distributed across 18 chromosomes: 1A, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, and 7B. An environmental specificity analysis revealed 15 MTAs unique to CK conditions and 24 specific to stress conditions. A pleiotropy analysis revealed that 21 MTAs (52.5%) were significantly associated with 6 other germination-related traits (Table S5). A total of 24 significant MTAs for RFW were identified, distributed across 12 chromosomes: 1A, 1B, 2A, 2D, 3A, 3B, 3D, 4A, 6A, 6B, 7A, and 7B. An environmental specificity analysis revealed 14 MTAs unique to CK conditions and 7 specific to stress conditions. A pleiotropy analysis revealed that 19 MTAs (79.2%) were significantly associated with 7 other germination-related traits (Table S5). A total of 32 significant MTAs for RDW were identified, distributed across 12 chromosomes: 1A, 1D, 2B, 2D, 3A, 4B, 4D, 5A, 5B, 5D, 6B, and 7D. An environmental specificity analysis revealed 14 MTAs unique to CK conditions, 16 specific to stress conditions, and 1 stable MTA locus (MTA129) consistently detected in both environments. A pleiotropy analysis revealed that 18 MTAs (58.06%) were significantly associated with 6 other germination-related traits (Table S5). Validation of alkali stress-responsive loci via PCA-integrated GWAS A PCA of nine stress tolerance indices resolved seven components (PC1–7), capturing 88.2% cumulative variance (Table S6). The BLINK-based GWAS on membership functions identified 198 significant PC_MTAs ( P < 10 − 3 ) with the following component-wise distribution: PC1 (28 PC_MTAs), PC2 (25 PC_MTAs), PC3 (12 PC_MTAs), PC4 (54 PC_MTAs), PC5 (22 PC_MTAs), PC6 (31 PC_MTAs), and PC7 (26 PC_MTAs). These PC_MTAs were distributed across all chromosomes except 1D, with the highest numbers observed on Chromosomes 4A (38 PC_MTAs), 3B (22 PC_MTAs), and 6A (18 PC_MTAs). A physical proximity analysis (5 Mb windows) revealed that 51 PC_MTAs co-localized with phenotype location-based MTAs, including the 8 directly overlapping loci (MTA1, MTA31, MTA63, MTA75, MTA80, MTA167, and MTA183) and 43 proximity loci, e.g., MTA166 (425226363 on 6D), were located near PC2-44_MTAs (426803871 on 6D) (Table S7). Allele analysis and candidate genes predicted for stable MTAs Based on the above data, three stable loci, MTA25 (AX-110072425), MTA29 (AX-108780339), and MTA80 (AX-110536071), were selected from 206 significant MTAs. The genetic effect and CG analyses were then conducted for these loci within a genomic region of ± 5 Mb. The MTA25 locus on Chromosome 2A showed a significant association with SFW (Fig. 3a). Among the 314 wheat accessions, materials carrying Allele A (255) significantly outnumbered those with Allele T (26). An allele-specific analysis demonstrated that, compared to Allele T, Allele A decreased SFW by 11.6% ( P < 0.001) under CK conditions, 19.4% ( P < 0.001) under T conditions, and decreased STI by 7.5% ( P < 0.05) (Fig. 3c). Notably, Allele A still significantly increased PH (+ 10.8%) but showed no association with reproductive traits, including PL, PN, SN, TGW, and YPP (Fig. 3b). Allele A underwent significant positive selection during the transition from landraces to modern varieties (Fig. 3d), and its geographical distribution analysis showed significantly higher frequencies than Allele T across six continents (Fig. S3a). This dual evidence of selection and distribution supports that Allele A confers broader environmental adaptability by optimizing vegetative growth strategies. Within the LD block encompassing MTA25, 75 candidate genes were identified (Table S8). Four core genes ( TraesCS2A03G0338900 , TraesCS2A03G0340600 , TraesCS2A03G0342900 , and TraesCS2A03G0346700 ) (Fig. 3a) were predicted as candidate genes based on their functional annotations, which were associated with stress-response mechanisms relevant to alkali tolerance or with seedling growth. The MTA29 locus on Chromosome 2A showed a significant association with SHL (Fig. 4a). Among the 314 wheat materials genotyped using this marker, 303 were classified into two groups: those carrying Allele T (259) and those carrying Allele C (44). The haplotype analysis based on BLUE values revealed that Allele T reduced SL by 15.2% ( P < 0.001) under CK conditions and 15.4% ( P < 0.001) under T conditions, but showed no association with STI, compared to Allele C (Fig. 4c). A yield-related trait analysis showed that Allele T significantly decreased PH (-14.7%) and PL (-8%), while increasing TGW (+ 7.3%) and YPP (+ 18.1%), with no association to PN or SN (Fig. 4b). Allele T underwent significant positive selection during wheat improvement, showing an increased frequency from landraces to modern cultivars (Fig. 4d). A geographical distribution analysis confirmed that Allele T exhibited significantly higher frequencies than Allele C across six continents (Fig. S3b). This dual evolutionary and geographical evidence suggests that Allele T may optimize environmental adaptability by balancing stress response and yield formation. Within the LD block encompassing MTA29, 101 candidate genes were identified (Table S8). Five core genes ( TraesCS2A03G1053800 , TraesCS2A03G1062000 , TraesCS2A03G1065600 , TraesCS2A03G1065700 , and TraesCS2A03G1066400 ) (Fig. 4a) were predicted as candidate genes owing to their explicit functional associations with alkali tolerance or coleoptile development. The MTA80 locus on Chromosome 4A showed a significant association with GP (Fig. 5a). Among the 314 wheat materials, 312 were classified into two groups: those carrying Allele A (286) and those carrying Allele C (26). A haplotype analysis based on BLUE values revealed that Allele A increased germination potential by 17.6% ( P < 0.001) under CK conditions and 10% ( P < 0.001) under T conditions, but it decreased STI by 5.3% ( P < 0.05) compared to Allele C (Fig. 5c). A yield-related trait analysis showed that Allele A significantly decreased PH (-23.5%) and PL (-10.1%), while increasing TGW (+ 7.1%) and YPP (+ 22.4%), with no association to PN or SN (Fig. 5b). Allele A underwent significant positive selection during wheat improvement, showing an increased frequency from landraces to modern cultivars (Fig. 5d). A geographical distribution analysis confirmed that Allele A exhibited significantly higher frequencies than Allele C across six continents (Fig. S4a). This evolutionary and geographical pattern implies that Allele A may balance adaptive trade-offs by enhancing specific stress responses and optimizing plant architecture. Within the LD block encompassing MTA80, 63 candidate genes were identified (Table S8). Four core genes ( TraesCS4A03G0161700 , TraesCS4A03G0165200 , TraesCS4A03G0165300 , and TraesCS4A03G0165400 ) (Fig. 5a) were predicted as candidate genes based on their explicit functional associations with alkali tolerance and seed germination rate. Development of molecular markers for major stable MTAs used in marker-assisted selection In this study, we successfully converted the SNP marker AX-108780339 of MTA29 associated with SHL, along with the SNP marker AX-110915963 of MTA80 associated with GP, into robust KASP markers (Tables S1, S9; Fig. S4b, c), enabling efficient genotyping of germplasm resources. These markers will provide important resources for the molecular breeding of wheat. Discussion Because wheat is an important food crop worldwide, research on its response to alkali stress is of great significance to ensure food security in saline-alkali areas. However, compared with salt stress, research on what response mechanisms to alkali stress is relatively scarce, and saline-alkali stress often exacerbates plant physiological damage through synergistic effects (Fang et al. 2021 ). The germination stage a stress-sensitive developmental stage in plants, directly influencing seedling establishment and subsequent yield potential (Bayuelo Jiménez et al. 2002 ; Jamil et al. 2011 ; Song et al. 2008 ). This study systematically investigated the genetic basis of alkali stress responses during the wheat germination stage by analyzing related traits. Phenotypic responses under different stresses There have been many studies on responses to salt stress and saline-alkaline stress in wheat (Akram et al. 2022 ; Chaurasia et al. 2020 ; Gu et al. 2024 ; Guo et al. 2022 ; Khan et al. 2022 ; Li et al. 2021 ; Luo et al. 2021 ; Mohamed et al. 2022 ; Zhang et al. 2020 ). However, research focusing specifically on alkali stress remains limited. Zhang et al. ( 2020 ) treated wheat cultivars with a mixed saline-alkaline solution (75 mmol·L − 1 NaCl + 75 mmol·L − 1 NaHCO 3 ) and found that SL is significantly inhibited at the seedling stage, whereas RDW increases significantly in different cultivars. Gu et al. ( 2024 ) found that GP decreases by 39.4% after a 100 mmol·L − 1 alkali (NaHCO 3 :Na 2 CO 3 = 9:1) treatment compared with the control at the germination stage. Guo et al. ( 2022 ) evaluated the effects of NaCl stress at varying concentrations (0 mM control, 50 mM, 100 mM, and 200 mM) on early seedling traits. Compared with the control, RDW shows no significant changes under low salt (50 mM), whereas RDW increases significantly, by approximately 33%, under 100 mM NaCl stress. Li et al. ( 2021 ) found GP is significantly inhibited at the germination stage under 205.3 mM NaCl treatment, whereas SL is significantly inhibited at the seedling stage under 136.9 mM NaCl treatment. Notably, RN increases by 31.2% at the seedling stage after a 136.9 mM NaCl treatment. In this study, SL declined by 31.2%, aligning with the reported inhibition of SL by Zhang et al. ( 2020 ), Guo et al. ( 2022 ), and Li et al. ( 2021 ). GP exhibited a marginal increase (+ 1.9%) under alkali stress, contrasting with the findings of Guo et al. ( 2022 ), Li et al. ( 2021 ), and Gu et al. ( 2024 ). RDW decreased by 37.5%, which contrasted with the effects of 100 mM NaCl stress found by Guo et al. ( 2022 ) and Zhang et al. ( 2020 ). The RN in our study showed no significant change, which was inconsistent with the findings of Li et al. ( 2021 ). The different results may be due to different mechanisms responsible for plant responses to alkali stress and salt stress. Guo et al. ( 2022 ) reported that under control conditions, significant positive correlations are observed between SFW and both SL and MRL, and the significant positive correlations are maintained across all the tested concentrations under salt-stress conditions. Additionally, SL is significantly positively correlated with MRL. Luo et al. ( 2021 ) reported that under salt stress, SFW is significantly positively correlated with both SL and MRL. However, the significant positive correlations are not observed under control conditions. In our study, both control and alkali stress conditions showed highly significant positive correlations between SFW and SL. This aligns with the findings of salt stress in Guo et al. ( 2022 ). Different MTAs detected in our study To systematically dissect the genetic basis of alkali-stress tolerance during wheat germination, this study employed a GWAS approach, leveraging its high resolution, ability to detect pleiotropy, and suitability for analyzing natural populations. Compared with a traditional linkage analysis, GWAS has become an ideal tool for dissecting complex stress traits in wheat due to its high resolution, pleiotropy detection, and adaptability to natural populations. In addition, it has more advantages in mining rare allelic variations. Our multi-tiered screening strategy, PCA-integrated GWAS, provides a robust framework for alkali stress evaluation. Unlike single-trait selection (Choudhary et al. 2021 ), this aligned with the PCA-integrated comprehensive salt tolerance index system that successfully identified elite salt-tolerant cultivars, like Lude 1 (Li et al. 2021 ), confirming that a multivariate analysis enhances selection accuracy for complex traits. A membership function analysis identified a cluster of 5 SNPs near PC3_MTA (2D: 650–651 Mb). Based on this, further analyses identified several prominent SNP-rich regions that may harbor potential loci contributing to alkali tolerance, including a cluster of 7 SNPs on Chromosome 7B (138–140 Mb) near PC1_MTA, a cluster of 20 SNPs on Chromosome 3B (95–127 Mb) near PC4_MTA, and a cluster of 25 SNPs on Chromosome 4A (114–135 Mb) near PC4_MTA (Table S6). These genomic regions represent promising candidates for future functional validation of their role in alkali-stress response. Among the 206 significant MTAs, 66 exhibited alkali-specificity. Representative alkali-specific MTAs, including MTA4 (1A), MTA75 (3D), MTA103 (4D), and MTA126 (5B). MTA75 was validated by membership function-integrated GWAS and contained a cluster of 7 significant SNPs near PC1_MTA at 618–619 Mb. MTA126 was detected consistently across all four alkali environments and exhibited a cluster of 7 significant SNPs on Chromosome 5B (554–559 Mb) near PC6_MTA. Five out of 206 MTAs exhibited stability across both CK and T stress conditions, with MTA25, MTA29, MTA80, MTA129, and MTA166 demonstrating particular consistency across multiple environments. In the membership function analysis, MTA80 was directly verified within PC5_MTA, and MTA166 was verified in the adjacent segment at the same location as PC2_MTA. The above results directly demonstrated the association of MTA75, MTA126, MTA80, and MTA166 with alkali stress (Tables S5, S7). The following three densely concentrated regions were identified: 6 MTAs in Chr3A 717–724 Mb (MTA61–66) associated with SDW, SFW, and SHL; 7 MTAs in Chr5D 565–567 Mb (MTA139–145) regulating RDW, SFW, and SL; and 8 MTAs in Chr5B 79–89 Mb (MTA114–121) formed a control-specific hotspot for SHL. The regions of Chr5B and Chr5D were not reported before, whereas Chr3A cluster’s involvement in alkali co-regulation was newly demonstrated despite proximity to the plant height-associated QTL (Li et al. 2021 ; Tables S5). Co-localized with previously reported loci To decipher the biological significances of the loci, we systematically compared their genetic positions with previously reported loci associated with stress response and yield-related traits. Among the MTAs, 36.4% (75 loci) co-localized with previously reported loci. For example, MTA14 (1B) overlapped with the salinity tolerance locus IWB57398T (Quamruzzaman et al. 2022 ); MTA86 (4B) co-localized with reported SL QTLs under salt stress (Li et al. 2021 ; Luo et al. 2021 ); and MTA194 (7B) co-localized with reported QTLs for MRL and RDW under saline conditions (Li et al. 2021 ; Chaurasia et al. 2021). The germination-stage salt stress QTL identified by Akram et al. ( 2022 ) is located at 125.3 Mb (marker M4431 ) and is significantly associated with SL under control conditions, as well as the regulation of root/shoot ratio and relative SL under salt stress conditions. Li et al. ( 2019a ) mapped a marker (IWB45503 ) significantly associated with kernel number per spike at 117.2–117.3 Mb. In our study, MTA25, located at 122.8 Mb (2A), was co-localized with both the above loci (Table S5). MTA29 is located at 700.8 Mb (2A) and co-localizes with multiple seedling-stage ion-related QTLs identified by Hussain et al. ( 2017 ), including a root iron concentration QTL ( qRFeC.2A.1 ), a root manganese concentration QTL (qRMnC.2A.2), a root Na⁺ concentration/exclusion ( qRNAX.2A.1 ), a root zinc concentration QTL ( qRZnC.2A.1 ), and a shoot K⁺ concentration QTL ( qSKC.2A.1 ). The TaFLZ2A gene within this genomic region is positioned at 702 Mb, and it exhibits no expression response to salt stress at the seedling stage (Qin et al. 2023 ). Furthermore, Li et al. ( 2019a ) mapped a marker (AX-89674107) associated with heading date within the 704.7–710 Mb interval of this region, and Liu et al. ( 2018 ) independently identified multiple markers linked to kernels per spike near 693 Mb. These findings collectively suggest that the genomic region harboring MTA29 is broadly involved in the regulation of ion homeostasis and the coordination of yield traits. Thus, these findings demonstrate developmental stage specificity and indicate that this locus aids in regulating reproductive development (Table S5). MTA166 (6D:425.2 Mb) co-localizes with the genetic locus governing spike exertion length and grain number per spike identified in our previous GWAS using the same population (Xu et al. 2024 ), in which pleiotropic and candidate gene analyses were conducted. There were no related reported related candidate genes near the MTA80 locus. Candidate gene analyses of the three core MTAs As an initial step towards understanding the molecular mechanisms underlying alkali-stress tolerance, we performed candidate gene analyses of the three core MTAs (MTA25, MTA29, and MTA80). There were 75 candidate genes of MTA25, including TraesCS2A03G0340600 , which encodes a serine/threonine-protein kinase that is a key regulator of the hyperosmotic stress response in rice, It phosphorylates the transcription factor OsbZIP46 to mediate abscisic acid signaling and drought/osmotic stress tolerance (Tang et al. 2012a ). This mechanism is highly similar to the genes function of enhancing salt tolerance in rice by coordinately regulating Na + /K + homeostasis and the osmotic stress response pathway (Lou et al. 2017 ; Lou et al. 2018 ). TraesCS2A03G0338900 encodes calmodulin-binding transcription activator 1, which is a Ca 2+ /calmodulin-mediated transcription factor, that contains stress-responsive cis-elements in its promoter (Yang et al. 2020 ) and may directly activate antioxidant enzymes and osmoprotective genes (Noman et al. 2019 ; Pandey et al. 2013a ). In Arabidopsis, this gene enhances stress tolerance through hormone signaling (Pandey et al. 2013b ). TraesCS2A03G0342900 encodes Wall-associated receptor kinase 2 ( WAK2 ). In rice, OsWAK112 suppresses ethylene biosynthesis to negatively regulate salt-stress tolerance (Lin et al. 2021 ), implicating WAK-mediated ethylene signaling in osmotic stress responses. TraesCS2A03G0346700 encodes Trehalose-phosphate phosphatase B ( TPPB ). This enzyme catalyzes trehalose synthesis for osmoprotection, with functional conservation demonstrated by OsTPP3 , which enhances stress tolerance in rice (Jiang et al. 2019 )d thaliana TPPs (Lin et al. 2020 ). In T. aestivum , a genome-wide analysis identified 31 TPP genes having stress-inducible expression patterns during salinity/drought and leaf senescence (Islam et al. 2021 ), confirming the TPP family’s conserved role in cereal stress adaptation. Consequently, we predicted that the above four genes were candidate genes of MAT25. There were 101 candidate genes of MTA29, including TraesCS2A03G1053800 , which encodes vacuolar cation/proton exchanger 1b ( CAX1b ). CAX1b, a cation/H + exchanger, regulates cellular ion homeostasis through vacuolar compartmentalization of Ca 2+ (Waight et al. 2013 ). Notably, constitutive overexpression of CAX1 results in salt sensitivity in plants (Cheng et al. 2004 ). Modareszadeh et al. ( 2021 ) found that CAX proteins mitigate stress-induced oxidative damage by reducing reactive oxygen species accumulation through the activation of antioxidant enzymes. Additionally, cax1 / cax2 double-knockout mutants exhibit delayed seed germination, demonstrating the functional importance of these transporters during normal development in response to environmental cues (Connorton et al. 2012 ). TraesCS2A03G1062000 encodes transcription factor MYC2 . It acts as a central transcription factor integrating abscisic acid and jasmonic acid signaling pathways, with documented roles in drought response regulation (Zeng et al. 2025 ), although its effects on salt tolerance vary across species (Verma et al. 2020 ). TraesCS2A03G1066400 encodes E3 ubiquitin-protein ligase PUB23. It negatively regulates drought responses by targeting ABA receptors for degradation (Zhao et al. 2017 ), consistent with soybean ortholog PUB8 (Wang et al. 2016 ). TraesCS2A03G1065600 / TraesCS2A03G1065700 encodes late embryogenesis abundant protein 18 ( LEA18 ). LEA18 belongs to a protein family conferring drought protection through cellular homeostasis maintenance, with wheat LEA isoforms showing genotype-specific stress induction (Li et al. 2018 ). Thus, the above four genes were predicted to be candidate genes of MAT29. There were 63 candidate genes of MTA80, including TraesCS4A03G0165200 , which encodes V-type proton ATPase subunit a3 ( VHA-a3 ). VHA-a3 is an ATPase that constitutes the vacuolar proton pump. As the pump’s core catalytic subunit, it establishes a transmembrane proton gradient by catalyzing ATP hydrolysis, and it plays dual physiological roles in nutrient storage and ion balance in plant cells (Krebs et al. 2010 ; Liang et al. 2020 ; Zhang et al. 2019 ). There have been reports in Arabidopsis , pea, and rapeseed that the VHA-a3 protease is involved in the accumulation and assimilation of vacuolar phosphate. Additionally, it plays a significant role in hormone signal transduction (Bak et al. 2013 ; Han et al. 2014 ; Jiang et al. 2023 ; Liang et al. 2020 ; Tang et al. 2012b ). TraesCS4A03G0161700 encodes zinc finger protein 8. It acts as a zinc finger transcription factor that negatively regulates ABA signaling during germination and early seedling development (Jin et al. 2018 ). TraesCS4A03G0165300 encodes calcium-dependent lipid-binding protein. It functions as a transcriptional repressor that suppresses stress-responsive genes, including AtTHAS1 , through ceramide binding, with its knockout enhancing stress resistance (de Silva et al. 2011 ). TraesCS4A03G0165400 encodes, and it shares functional homology with A. thaliana berberine bridge enzyme-like 28, which, when mutated reduces biomass and salt tolerance (Daniel et al. 2016 ). Thus, we speculated that TraesCS4A03G0165200 , TraesCS4A03G0161700 , TraesCS4A03G0165300 , and TraesCS4A03G0165400 are candidate genes of MAT80. Conclusion We identified six highly alkali-tolerant wheat germplasms in this study. In total, 206 MTAs for the 9 germination-related traits were detected by GWAS, and 5 MTAs (MAT25, MAT29, MTA80, MTA129, and MTA166) were environmentally stable loci. Using a PCA-GWAS analysis, 198 significant association loci (PC_MTAs) were identified, with 51 being co-localized with phenotype location-based MTAs. They were verified as core stress-responsive loci. In addition, single KASP markers for SHL and GP were developed. This study describes critical genetic mechanisms for alkali tolerance during wheat germination that may assist in the crop’s molecular breeding. Declarations Author contribution statement CZ, HR, and FC analyzed the data and drafted the manuscript. HR, ZW, XL, HT, XY, MZ and ZZ performed phenotype evaluation. RQ, YW, and HS helped design the study and revised the manuscript. All of the authors have read and approved the final version of the manuscript. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 32472134), the Foundation of Shandong Province, China (Grant No. ZR2022MC119), the Shandong Provincial Key Research and Development Program of 2024 (Grant No. 2024LZGCQY012), Taishan scholar young expert (grant no. 20230119), the Shandong Provincial Fund for Excellent Young Scholars (Grant No. ZR2022YQ19). Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Conflict of interest The authors declare the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Akram S, Ghaffar M, Wadood A, Shokat S, Hameed A, Waheed MQ, Arif MAR (2022) A GBS-based genome-wide association study reveals the genetic basis of salinity tolerance at the seedling stage in bread wheat ( Triticum aestivum L). Front Genet 13:997901. http://dx.doi.org/10.3389/fgene.2022.997901 Ayalew H, Liu H, Börner A, Kobiljski B, Liu C, Yan G (2018) Genome-wide association mapping of major root length QTLs under PEG induced water stress in wheat. Front Plant Sci 9:1759. http://dx.doi.org/10.3389/fpls.2018.01759 Bak G, Lee EJ, Lee Y, Kato M, Segami S, Sze H, Maeshima M, Hwang JU, Lee Y (2013) Rapid structural changes and acidification of guard cell vacuoles during stomatal closure require phosphatidylinositol 3,5-bisphosphate. Plant Cell 25:2202–2216. http://dx.doi.org/10.1105/tpc.113.110411 Bayuelo Jiménez JS, Craig R, Lynch JP (2002) Salinity tolerance of phaseolus species during germination and early seedling growth. Crop Sci 42:1584–1594. https://doi.org/10.2135/cropsci2002.1584 Chaurasia S, Singh AK, Songachan LS, Sharma AD, Bhardwaj R, Singh K (2020) Multi-locus genome-wide association studies reveal novel genomic regions associated with vegetative stage salt tolerance in bread wheat ( Triticum aestivum L). Genomics 112:4608–4621. http://dx.doi.org/10.1016/j.ygeno.2020.08.006 Chen H, Zheng C, Chen F, Qiao Y, Du S, Cao C, Zhang W (2021) Less N 2 O emission from newly high-yielding cultivars of winter wheat. Agric Ecosyst Environ 320:107557. http://dx.doi.org/https://doi.org/10.1016/j.agee.2021.107557 Cheng N, Pittman JK, Zhu J, Hirschi KD (2004) The Protein Kinase SOS 2 Activates the Arabidopsis H + /Ca 2+ Antiporter CAX 1 to Integrate Calcium Transport and Salt Tolerance. J Biol Chem 279:2922–2926. http://dx.doi.org/10.1074/jbc.M309084200 Choudhary A, Kaur N, Sharma A, Kumar A (2021) Evaluation and screening of elite wheat germplasm for salinity stress at the seedling phase. Physiol Plant 173:2207–2215. http://dx.doi.org/https://doi.org/10.1111/ppl.13571 Connorton JM, Webster RE, Cheng N, Pittman JK (2012) Knockout of Multiple Arabidopsis Cation/H + Exchangers Suggests Isoform-Specific Roles in Metal Stress Response, Germination and Seed Mineral Nutrition. PLoS ONE 7:e47455. http://dx.doi.org/10.1371/journal.pone.0047455 Daniel B, Wallner S, Steiner B, Oberdorfer G, Kumar P, van der Graaff E, Roitsch T, Sensen CW, Gruber K, Macheroux P (2016) Structure of a Berberine Bridge Enzyme-Like Enzyme with an Active Site Specific to the Plant Family Brassicaceae. PLoS ONE 11:e0156892. http://dx.doi.org/10.1371/journal.pone.0156892 de Silva K, Laska B, Brown C, Sederoff HW, Khodakovskaya M (2011) Arabidopsis thaliana calcium-dependent lipid-binding protein (AtCLB): a novel repressor of abiotic stress response. J Exp Bot 62:2679–2689. http://dx.doi.org/10.1093/jxb/erq468 Dong S, He W, Ji J, Zhang C, Guo Y, Yang T (2020) LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files. Brief Bioinform 22:bbaa227. http://dx.doi.org/10.1093/bib/bbaa227 Fang S, Hou X, Liang X (2021) Response mechanisms of plants under saline-alkali stress. Front Plant Sci 12:667458. http://dx.doi.org/10.3389/fpls.2021.667458 Ghaedrahmati M, Mardi M, Naghavi MR, Majidi Heravan E, Nakhoda B, Azadi A, Kazemi M (2018) Mapping QTLs associated with salt tolerance related traits in wheat ( Triticum aestivum L). Agric Sci Technol 16:1413–1428. http://dx.doi.org/http://hdl.handle.net/123456789/4330 Gu Y, Feng G, Hou P, Zhou Y, Zhang H, Wang X, Luo B, Chen L (2024) Nondestructive detection of saline-alkali stress in wheat ( Triticum aestivum L .) seedlings via fusion technology. Plant Methods 20:136. http://dx.doi.org/10.1186/s13007-024-01248-6 Guo X, Wu C, Wang D, Wang G, Jin K, Zhao Y, Tian J, Deng Z (2022) Conditional QTL mapping for seed germination and seedling traits under salt stress and candidate gene prediction in wheat. Sci Rep 12:21010. http://dx.doi.org/10.1038/s41598-022-25703-3 Han Y, Liu Q, Gu J, Gong J, Guan C, Lepo JE, Rong X, Song H, Zhang Z (2014) V-ATPase and V-PPase at the Tonoplast Affect NO 3 – content in Brassica napus by Controlling Distribution of NO 3 – Between the Cytoplasm and Vacuole. J Plant Growth Regul 34:22–34. http://dx.doi.org/10.1007/s00344-014-9439-8 Hasseb NM, Sallam A, Karam MA, Gao L, Wang RRC, Moursi YS (2022) High-LD SNP markers exhibiting pleiotropic effects on salt tolerance at germination and seedlings stages in spring wheat. Plant Mol Biol 108:585–603. http://dx.doi.org/10.1007/s11103-022-01248-x Huang M, Liu X, Zhou Y, Summers RM, Zhang Z (2019) BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions. GigaScience 8:giy154. http://dx.doi.org/10.1093/gigascience/giy154 Hussain B, Lucas SJ, Ozturk L, Budak H (2017) Mapping QTLs conferring salt tolerance and micronutrient concentrations at seedling stage in wheat. Sci Rep 7:15662. http://dx.doi.org/10.1038/s41598-017-15726-6 Islam MA, Rahman MM, Rahman MM, Jin X, Sun L, Zhao K, Wang S, Sikdar A, Noor H, Jeon J-S, Zhang W, Sun D (2021) In Silico and Transcription Analysis of Trehalose-6-phosphate Phosphatase Gene Family of Wheat: Trehalose Synthesis Genes Contribute to Salinity, Drought Stress and Leaf Senescence. Genes 12:1652. http://dx.doi.org/https://doi.org/10.3390/genes12111652 Jamil A, Riaz S, Ashraf M, Foolad MR (2011) Gene expression profiling of plants under salt stress. Crit Rev Plant Sci 30:435–458. http://dx.doi.org/10.1080/07352689.2011.605739 Javid S, Bihamta MR, Omidi M, Abbasi AR, Alipour H, Ingvarsson PK (2022) Genome-wide association study (GWAS) and genome prediction of seedling salt tolerance in bread wheat ( Triticum aestivum L). BMC Plant Biol 22:581. http://dx.doi.org/10.1186/s12870-022-03936-8 Jiang D, Chen W, Gao J, Yang F, Zhuang C (2019) Overexpression of the trehalose-6-phosphate phosphatase OsTPP3 increases drought tolerance in rice. Plant Biotechnol Rep 13:285–292. http://dx.doi.org/10.1007/s11816-019-00541-4 Jiang Y, Yang L, Zheng J, Geng X, Bai Y, Wang Y, Xue H, Lin W (2023) Vacuolar H + -ATPase and BZR1 form a feedback loop to regulate the homeostasis of BR signaling in Arabidopsis . Mol Plant 16:1976–1989. http://dx.doi.org/10.1016/j.molp.2023.10.007 Jin Y, Piao R, Yan Y, Chen M, Wang L, He H, Liu X, Gao X, Jiang W, Lin X (2018) Overexpression of a New Zinc Finger Protein Transcription Factor OsCTZFP8 Improves Cold Tolerance in Rice. Int J Genomics 2018:5480617. http://dx.doi.org/https://doi.org/10.1155/2018/5480617 Khan RWA, Khan RSA, Awan FS, Akrem A, Iftikhar A, Anwar FN, Alzahrani HAS, Alsamadany H, Iqbal RK (2022) Genome-wide association studies of seedling quantitative trait loci against salt tolerance in wheat. Front Genet 13:946869. http://dx.doi.org/10.3389/fgene.2022.946869 Krebs M, Beyhl D, Görlich E, Al Rasheid KAS, Marten I, Stierhof YD, Hedrich R, Schumacher K (2010) Arabidopsis V-ATPase activity at the tonoplast is required for efficient nutrient storage but not for sodium accumulation. Proc Natl Acad Sci USA 107:3251–3256. http://dx.doi.org/10.1073/pnas.0913035107 Li F, Wen W, Liu J, Zhang Y, Cao S, He Z, Rasheed A, Jin H, Zhang C, Yan J, Zhang P, Wan Y, Xia X (2019a) Genetic architecture of grain yield in bread wheat based on genome-wide association studies. BMC Plant Biol 19:168. http://dx.doi.org/10.1186/s12870-019-1781-3 Li L, Peng Z, Mao X, Wang J, Li C, Chang X, Jing R, Rebetzke G (2021) Genetic insights into natural variation underlying salt tolerance in wheat. J Exp Bot 72:1135–1150. http://dx.doi.org/10.1093/jxb/eraa500 Li N, Zhang S, Liang Y, Qi Y, Chen J, Zhu W, Zhang L (2018) Label-free quantitative proteomic analysis of drought stress-responsive late embryogenesis abundant proteins in the seedling leaves of two wheat ( Triticum aestivum L.) genotypes. J Proteom 172:122–142. https://doi.org/10.1016/j.jprot.2017.09.016 Liang G, Song H, Xiao Y, Zhang Z (2020) Ammonium accumulation caused by reduced tonoplast V-ATPase activity in Arabidopsis thaliana . Int J Mol Sci 22:2. http://dx.doi.org/10.3390/ijms22010002 Lin Q, Wang S, Dao Y, Wang J, Wang K (2020) Arabidopsis thaliana trehalose-6-phosphate phosphatase gene TPPI enhances drought tolerance by regulating stomatal apertures. J Exp Bot 71:4285–4297. http://dx.doi.org/10.1093/jxb/eraa173 Lin W, Wang Y, Liu X, Shang J, Zhao L (2021) OsWAK112 , A Wall-Associated Kinase, Negatively Regulates Salt Stress Responses by Inhibiting Ethylene Production. Front Plant Sci 12:751965. http://dx.doi.org/10.3389/fpls.2021.751965 Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397–2399. http://dx.doi.org/10.1093/bioinformatics/bts444 Liu J, Xu Z, Fan X, Zhou Q, Cao J, Wang F, Ji G, Yang L, Feng B, Wang T (2018) A Genome-Wide Association Study of Wheat Spike Related Traits in China. Front Plant Sci 9:1584. https://doi.org/10.3389/fpls.2018.01584 Liu X, Yang Z, Hu W, Liu S, Sun R, Jin S, Nergui K, Zhao G, Gao L, Liu Y, Deng X (2024) A genome-wide association study identifies novel QTL for wheat yield stability under drought stress. Curr Plant Biol 37:100326. https://doi.org/10.1016/j.cpb.2024.100326 Lou D, Wang H, Liang G, Yu D (2017) OsSAPK2 confers abscisic acid sensitivity and tolerance to drought stress in rice. Front Plant Sci 8:993. http://dx.doi.org/10.3389/fpls.2017.00993 Lou D, Wang H, Yu D (2018) The sucrose non-fermenting-1-related protein kinases SAPK1 and SAPK2 function collaboratively as positive regulators of salt stress tolerance in rice. BMC Plant Biol 18:17. http://dx.doi.org/10.1186/s12870-018-1408-0 Lou H, Zhang R, Liu Y, Guo D, Zhai S, Chen A, Zhang Y, Xie C, You M, Peng H, Liang R, Ni Z, Sun Q, Li B (2021) Genome-wide association study of six quality-related traits in common wheat (Triticum aestivum L.) under two sowing conditions. Theor Appl Genet 134:399–418. http://dx.doi.org/10.1007/s00122-020-03704-y Lu X, Ma L, Zhang C, Yan H, Bao J, Gong M, Wang W, Li S, Ma S, Chen B (2022) Grapevine (Vitis vinifera) responses to salt stress and alkali stress: transcriptional and metabolic profiling. BMC Plant Biol 22:528. http://dx.doi.org/10.1186/s12870-022-03907-z Luo Q, Hu P, Yang G, Li H, Liu L, Wang Z, Li B, Li Z, Zheng Q (2021) Mapping QTL for seedling morphological and physiological traits under normal and salt treatments in a RIL wheat population. Theor Appl Genet 134:2991–3011. http://dx.doi.org/10.1007/s00122-021-03872-5 Ma Y, Qiu C, Fan Y, Huang X, Khan W, Wu F, Zhou M, Wang Y, Cao F (2022) Genome-wide association and transcriptome analysis reveals candidate genes for potassium transport under salinity stress in wheat. Environ Exp Bot 202:105034. http://dx.doi.org/10.1016/j.envexpbot.2022.105034 Maulana F, Huang W, Anderson JD, Ma X (2020) Genome-wide association mapping of seedling drought tolerance in winter wheat. Front Plant Sci 11:573786. http://dx.doi.org/10.3389/fpls.2020.573786 Modareszadeh M, Bahmani R, Kim D, Hwang S (2021) CAX3 (cation/proton exchanger) mediates a Cd tolerance by decreasing ROS through Ca elevation in Arabidopsis . Plant Mol Biol 105:115–132. http://dx.doi.org/10.1007/s11103-020-01072-1 Mohamed M, Siddiqui MN, Oyiga BC, Léon J, Ballvora A (2022) Validation of a QTL on chromosome 1DS showing a major effect on salt tolerance in winter wheat. Int J Mol Sci 23:13745. http://dx.doi.org/10.3390/ijms232213745 Noman M, Jameel A, Qiang W, Ahmad N, Liu W, Wang F, Li H (2019) Overexpression of GmCAMTA12 enhanced drought tolerance in Arabidopsis and Soybean. Int J Mol Sci 20:4849. http://dx.doi.org/10.3390/ijms20194849 Oyiga BC, Sharma RC, Baum M, Ogbonnaya FC, Léon J, Ballvora A (2018) Allelic variations and differential expressions detected at quantitative trait loci for salt stress tolerance in wheat. Plant Cell Environ 41:919–935. http://dx.doi.org/10.1111/pce.12898 Pandey N, Ranjan A, Pant P, Tripathi RK, Ateek F, Pandey HP, Patre UV, Sawant SV (2013a) CAMTA1 regulates drought responses in Arabidopsis thaliana . BMC Genomics 14:216. http://dx.doi.org/10.1186/1471-2164-14-216 Pandey N, Ranjan A, Pant P, Tripathi RK, Ateek F, Pandey HP, Patre UV, Sawant SV (2013b) CAMTA 1 regulates drought responses in Arabidopsis thalian a. BMC Genomics 14:216. http://dx.doi.org/10.1186/1471-2164-14-216 Pang Y, Wu Y, Liu C, Li W, Amand PS, Bernardo A, Wang D, Dong L, Yuan X, Zhang H, Zhao M, Li L, Wang L, He F, Liang Y, Yan Q, Lu Y, Su Y, Jiang H, Wu J, Li A, Kong L, Bai G, Liu S (2021) High-resolution genome-wide association study and genomic prediction for disease resistance and cold tolerance in wheat. Theor Appl Genet 134:2857–2873. http://dx.doi.org/10.1007/s00122-021-03863-6 Qin Y, Zhang B, Cui S, Qin X, Li G (2023) TaFLZ54D enhanced salt stress tolerance of wheat by interacting with TaSGT1a and TaPP2Ca . J Integr Agric 24:1017–1029. http://dx.doi.org/https://doi.org/10.1016/j.jia.2023.09.018 Quamruzzaman M, Manik SMN, Shabala S, Cao F, Zhou M (2022) Genome-wide association study reveals a genomic region on 5AL for salinity tolerance in wheat. Theor Appl Genet 135:709–721. http://dx.doi.org/10.1007/s00122-021-03996-8 Quan X, Liu J, Zhang N, Xie C, Li H, Xia X, He W, Qin Y (2021) Genome-wide association study uncover the genetic architecture of salt tolerance-related traits in common wheat ( Triticum aestivum L). Front Genet 12:663941. http://dx.doi.org/10.3389/fgene.2021.663941 Rasheed A, Hao Y, Xia X, Khan A, Xu Y, Varshney RK, He Z (2017) Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives. Mol Plant 10:1047–1064. https://doi.org/10.1016/j.molp.2017.06.008 Safdar LB, Andleeb T, Latif S, Umer MJ, Tang M, Li X, Liu S, Quraishi UM (2020) Genome-wide association study and QTL meta-analysis identified novel genomic loci controlling potassium use efficiency and agronomic traits in bread wheat. Front Plant Sci 11:70. http://dx.doi.org/10.3389/fpls.2020.00070 Sallam A, Dawood MFA, Jarquín D, Mohamed EA, Hussein MY, Börner A, Ahmed AAM (2024) Genome-wide scanning to identify and validate single nucleotide polymorphism markers associated with drought tolerance in spring wheat seedlings. Plant Genome 17:e20444. http://dx.doi.org/10.1002/tpg2.20444 Song J, Fan H, Zhao Y, Jia Y, Du X, Wang B (2008) Effect of salinity on germination, seedling emergence, seedling growth and ion accumulation of a euhalophyte Suaeda salsa in an intertidal zone and on saline inland. Aquat Bot 88:331–337. https://doi.org/10.1016/j.aquabot.2007.11.004 Song Y, Pan Y, Xiang M, Yang W, Zhan D, Wang X, Lu M (2024) A WebGIS-based system for supporting saline-alkali soil ecological monitoring: a case study in Yellow River Delta, China. Remote Sens 16:1948. http://dx.doi.org/10.3390/rs16111948 Sukumaran S, Reynolds MP, Sansaloni C (2018) Genome-wide association analyses identify QTL hotspots for yield and component traits in durum wheat grown under yield potential, drought, and heat stress environments. Front Plant Sci 9:81. http://dx.doi.org/10.3389/fpls.2018.00081 Tang N, Zhang H, Li X, Xiao J, Xiong L (2012a) Constitutive activation of transcription factor OsbZIP46 improves drought tolerance in rice. Plant Physiol 158:1755–1768. http://dx.doi.org/10.1104/pp.111.190389 Tang R, Liu H, Yang Y, Yang L, Gao X, Garcia VJ, Luan S, Zhang H (2012b) Tonoplast calcium sensors CBL2 and CBL3 control plant growth and ion homeostasis through regulating V-ATPase activity in Arabidopsis . Cell Res 22:1650–1665. http://dx.doi.org/10.1038/cr.2012.161 Torbaghan ME, Lakzian A, Astaraei AR, Fotovat A, Besharati H (2017) Salt and alkali stresses reduction in wheat by plant growth promoting haloalkaliphilic bacteria. J Soil Sci Plant Nutr 17:1058–1087. http://dx.doi.org/10.4067/S0718-95162017000400016 Valero Mora PM (2010) ggplot2: elegant graphics for data analysis. J Stat Softw Book Rev 35:1–3. http://dx.doi.org/10.18637/jss.v035.b01 Verma D, Jalmi SK, Bhagat PK, Verma N, Sinha AK (2020) A bHLH transcription factor, MYC2, imparts salt intolerance by regulating proline biosynthesis in Arabidopsis. FEBS J 287:2560–2576. https://doi.org/10.1111/febs.15157 Waight AB, Pedersen BP, Schlessinger A, Bonomi M, Chau BH, Roe-Zurz Z, Risenmay AJ, Sali A, Stroud RM (2013) Structural basis for alternating access of a eukaryotic calcium/proton exchanger. Nature 499:107–110. http://dx.doi.org/10.1038/nature12233 Wang N, Liu Y, Cong Y, Wang T, Zhong X, Yang S, Li Y, Gai J (2016) Genome-Wide Identification of Soybean U-Box E3 Ubiquitin Ligases and Roles of GmPUB8 in Negative Regulation of Drought Stress Response in Arabidopsis. Plant Cell Physiol 57:1189–1209. http://dx.doi.org/10.1093/pcp/pcw068 Wang S, Chen Q, Li Y, Zhuo YQ, Xu LZ (2017) Research on saline-alkali soil amelioration with FGD gypsum. Resour Conserv Recycl 121:82–92. https://doi.org/10.1016/j.resconrec.2016.04.005 Wu J, Liu S, Wang Q, Zeng Q, Mu J, Huang S, Yu S, Han D, Kang Z (2018) Rapid identification of an adult plant stripe rust resistance gene in hexaploid wheat by high-throughput SNP array genotyping of pooled extremes. Theor Appl Genet 131:43–58. http://dx.doi.org/10.1007/s00122-017-2984-3 Xiao B, Lu H, Li C, Bhanbhro N, Cui X, Yang C (2020) Carbohydrate and plant hormone regulate the alkali stress response of hexaploid wheat ( Triticum aestivum L). Environ Exp Bot 175:104053. https://doi.org/10.1016/j.envexpbot.2020.104053 Xu H, Wang Z, Wang F, Hu X, Ma C, Jiang H, Xie C, Gao Y, Ding G, Zhao C, Qin R, Cui D, Sun H, Cui F, Wu Y (2024) Genome-wide association study and genomic selection of spike-related traits in bread wheat. Theor Appl Genet 137:131. http://dx.doi.org/10.1007/s00122-024-04640-x Yang F, Dong F, Hu F, Liu Y, Chai J, Zhao H, Lv M, Zhou S (2020) Genome-wide identification and expression analysis of the calmodulin-binding transcription activator ( CAMTA ) gene family in wheat ( Triticum aestivum L). BMC Genet 21:105. http://dx.doi.org/10.1186/s12863-020-00916-5 Zeng T, Su H, Wang M, He J, Gu L, Wang H, Du X, Wang C, Zhu B (2025) The Role of MYC2 Transcription Factors in Plant Secondary Metabolism and Stress Response Mechanisms. Plants 14:1255. https://doi.org/10.3390/plants14081255 Zhang A, Philipp N, Weichert H, Bohra U, Weschke W, Schulthess AW, Weber H (2018) Grain number and grain yield distribution along the spike remain stable despite breeding for high yield in winter wheat. PLoS ONE 13:e0205452. http://dx.doi.org/10.1371/journal.pone.0205452 Zhang F, Yan X, Han X, Tang R, Chu M, Yang Y, Yang Y, Zhao F, Fu A, Luan S, Lan W (2019) A defective vacuolar proton pump enhances aluminum tolerance by reducing vacuole sequestration of organic acids. Plant Physiol 181:743–761. http://dx.doi.org/10.1104/pp.19.00626 Zhang K, Tang J, Wang Y, Kang H, Zeng J (2020) The tolerance to saline–alkaline stress was dependent on the roots in wheat. Physiol Mol Biol Plants 26:947–954. http://dx.doi.org/10.1007/s12298-020-00799-x Zhao J, Zhao L, Zhang M, Zafar SA, Fang J, Li M, Zhang W, Li X (2017) Arabidopsis E3 Ubiquitin Ligases PUB22 and PUB23 Negatively Regulate Drought Tolerance by Targeting ABA Receptor PYL9 for Degradation. Int J Mol Sci 18:1841. https://doi.org/10.3390/ijms18091841 Zhao X, Yu X, Gao J, Qu J, Borjigin Q, Meng T, Li D (2024) Improvement of saline-alkali soil and straw degradation efficiency in cold and arid areas using klebsiella sp. and Pseudomonas sp. Agronomy 14:2499. http://dx.doi.org/10.3390/agronomy14112499 Supplementary Files Supplementarymaterial.xlsx supplymentfig.pdf Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Minor revisions 12 Nov, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers invited by journal 09 Sep, 2025 Editor assigned by journal 26 Aug, 2025 First submitted to journal 24 Aug, 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-7441024","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512681759,"identity":"452615f1-ea3c-44c9-b135-c4e8000e05da","order_by":0,"name":"Haoran Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACNv7mww8SKiTk+PmbDxCnhU/iWJrBgzMWxpIzjiUQp0WOIcdA8mFbRaLBgRwDIh3GcMbAIIFNIkGy4czHG28Y7OR0GwhpYW4reJDAI5HHz9y72XIOQ7Kx2QGCthzeYJAgIVEs2XB2mzQPw4HEbYS1JBhIAFHihgM5z4jVkgLUkgDWwkakFlAgJxyQAAWyseUcAyL8It/ffPjhz391oKh8eONNhZ0cQS0oQIKHyKhB1kKqjlEwCkbBKBgRAADr6UPJRxrGCgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0009-3470-8457","institution":"Ludong University","correspondingAuthor":true,"prefix":"","firstName":"Haoran","middleName":"","lastName":"Gu","suffix":""},{"id":512681760,"identity":"1b33fd0b-bade-453b-b906-f3e054f8977b","order_by":1,"name":"Zixu Wang","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Zixu","middleName":"","lastName":"Wang","suffix":""},{"id":512681761,"identity":"50dcfe88-b82b-4a8b-ab46-1a80a59a156d","order_by":2,"name":"Xiaotong Liu","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Liu","suffix":""},{"id":512681762,"identity":"7d673fcc-a132-45fb-acef-8cca328bb400","order_by":3,"name":"Haotian Cao","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Haotian","middleName":"","lastName":"Cao","suffix":""},{"id":512681763,"identity":"4b60d29a-4885-4086-af8b-c6e1ed23e830","order_by":4,"name":"Zixin Zhou","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Zixin","middleName":"","lastName":"Zhou","suffix":""},{"id":512681764,"identity":"66c445b7-21ab-4c8b-840c-e8e02f8ccfb9","order_by":5,"name":"Meishan Zhou","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Meishan","middleName":"","lastName":"Zhou","suffix":""},{"id":512681765,"identity":"25fee7b4-3e81-4da5-af36-9947ef4f11ba","order_by":6,"name":"Xinzhi Yang","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Xinzhi","middleName":"","lastName":"Yang","suffix":""},{"id":512681766,"identity":"062d51f4-bc0f-4877-9bcd-16e17f70211d","order_by":7,"name":"Han Sun","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Sun","suffix":""},{"id":512681767,"identity":"913da55c-6723-49f7-add4-2cdbda9982fc","order_by":8,"name":"Ran Qin","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Qin","suffix":""},{"id":512681768,"identity":"103bf0f9-6ce0-4ef2-b972-35cacdbd8514","order_by":9,"name":"Chunhua Zhao","email":"","orcid":"https://orcid.org/0009-0005-1822-2263","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Chunhua","middleName":"","lastName":"Zhao","suffix":""},{"id":512681769,"identity":"7c76cc01-16e3-469e-8006-216d64e7ff4b","order_by":10,"name":"Yongzhen Wu","email":"","orcid":"https://orcid.org/0000-0001-9028-8949","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Yongzhen","middleName":"","lastName":"Wu","suffix":""},{"id":512681770,"identity":"f8c138b5-638a-4a48-9c8c-bde6a4f47823","order_by":11,"name":"Fa Cui","email":"","orcid":"https://orcid.org/0000-0001-7493-7005","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Fa","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2025-08-23 11:39:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7441024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7441024/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-026-05154-4","type":"published","date":"2026-02-04T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91562786,"identity":"f8daba0b-b76b-430c-b1c7-cd3191a9c98b","added_by":"auto","created_at":"2025-09-17 18:59:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":551859,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441024/v1/668bf1e294af1867ae535e13.jpg"},{"id":91562787,"identity":"0b83200f-876e-4143-a4e8-30182736ce4a","added_by":"auto","created_at":"2025-09-17 18:59:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":706665,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441024/v1/7326fe7ad803af94ce9b1cf1.jpg"},{"id":91562792,"identity":"772c6a03-1911-48f4-aadd-bef541aad4ee","added_by":"auto","created_at":"2025-09-17 18:59:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":675418,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441024/v1/ca384bb748b852f0ccb2e89b.jpg"},{"id":91563534,"identity":"9b5faa05-8183-44af-a966-65f84ce33ec9","added_by":"auto","created_at":"2025-09-17 19:07:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":673823,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441024/v1/e676ebc4fcc585eca9d41aea.jpg"},{"id":91564819,"identity":"dadb7868-35cf-4a27-bbd2-7931dc51b87a","added_by":"auto","created_at":"2025-09-17 19:15:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":689527,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7441024/v1/be1dbb25ab15bdb02fe92a50.jpg"},{"id":102233965,"identity":"41bcfaf3-bfe0-42b3-b16c-3ec057726b63","added_by":"auto","created_at":"2026-02-09 16:00:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4358651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7441024/v1/8ecd541c-e77b-4001-98e2-47070e51d100.pdf"},{"id":91562796,"identity":"48b9982a-3ea2-4052-950c-bec8ef13cdc8","added_by":"auto","created_at":"2025-09-17 18:59:30","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":130966,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7441024/v1/c9cc5b3baa339ecfd185b6ed.xlsx"},{"id":91562803,"identity":"243477c1-56ef-4613-8fb8-16a7ebcc8aaf","added_by":"auto","created_at":"2025-09-17 18:59:30","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":50061007,"visible":true,"origin":"","legend":"","description":"","filename":"supplymentfig.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7441024/v1/49c63424cabaad2474d69342.pdf"}],"financialInterests":"","formattedTitle":"Genome-wide Association Study of Common Wheat’s Alkali Tolerance at the Germination Stage","fulltext":[{"header":"Key Message","content":"\u003cp\u003eSix highly alkali-tolerant wheat germplasms were identified, 206 MTAs related to germination traits and 198 significant PC-MTAs were detected, and 2 KASP markers for resistance breeding were developed.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eWith the continuous deterioration of the global climate, the degree of salinization of surface land is increasing, and the proportion of low-salinity land available for crop cultivation is gradually decreasing (Song et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Compared with physiological metabolic disorders, ion toxicity, and osmotic stress under salt-stress conditions, the strong alkaline environment causes greater damage to plants (Lu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In China, the area of saline-alkali land exceeds 100\u0026nbsp;million mu; therefore, improving the breeding of crops that thrive in saline-alkali land is of great significance (Zhao et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is an important crop because it is a major food crop worldwide (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). High yield is currently the main goal of wheat breeding (Zhang et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Alkali stress, as an abiotic stress that affects the normal growth and development of wheat, reduces wheat grain yield (Torbaghan et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xiao et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, exploring the genetic mechanisms of wheat alkali tolerance is an effective way to improve the utilization rate of alkali land and increase wheat yield.\u003c/p\u003e\u003cp\u003eWheat\u0026rsquo;s alkali tolerance involves complex physical and chemical pathways from microscopic cells to macroscopic individuals. Therefore, to better understand the potential genetic mechanisms of alkali tolerance, conducting a genetic analysis at a smaller scale within cells is an effective approach (Ayalew et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Oyiga et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Molecular markers linked to the target genes are important tools for improving breeding efficiency. Currently, single-nucleotide polymorphism (SNP) is the most abundant type of molecular marker. Due to its high genomic density, low mutation rate, and suitability for high-throughput detection, SNPs are considered the best markers for studying the genetics of complex traits in polyploid wheat and its wild relatives. At present, several SNP arrays, such as 55K, 90K, 820K, and 660K, have been designed (Liu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lou et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rasheed et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCompared with the long time required for constructing biparental populations for linkage analyses, genome-wide association studies (GWASs) based on linkage disequilibrium (LD) represent an effective approach for dissecting the potential associations between genotypes and phenotypes, which can encompass more allelic diversity (Lou et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Safdar et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sukumaran et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recently, the GWAS approach has been applied widely in analyzing the genetic bases of complex agronomic traits and it shortened the crop breeding process significantly (Maulana et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Safdar et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMultiple marker-trait associations (MTAs), quantitative trait loci (QTLs), and genes associated with seedling traits distributed on 21 wheat chromosomes under saline-alkali stress have been identified. Among them, the QTLs or MTAs for germination percentage (GP) are mainly located on Chromosomes 2A/2D/7A (Akram et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hasseb et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, QTLs or MTAs for seedling length (SL) are detected on Chromosomes 2A/3B/5A (Akram et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ghaedrahmati et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Javid et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Quan et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For root-related traits, the major QTLs or MTAs for maximum root length (MRL) are detected on Chromosomes 4A/7A (Akram et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Quan et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The major MTAs for root number (RN) are distributed on Chromosomes 2D, 4A, and 5A, and the genes \u003cem\u003eTaRN1\u003c/em\u003e-3A and \u003cem\u003eTaRN2\u003c/em\u003e-4A have been verified as stable regulators of roots (Akram et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Regarding biomass-related traits, the major QTLs for seedling fresh weight (SFW) are concentrated on Chromosome 2A (Ghaedrahmati et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Oyiga et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the QTLs or MTAs for seedling dry weight (SDW) are distributed on Chromosomes 3B, 6A, and 7A (Ghaedrahmati et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oyiga et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Quan et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The QTLs or MTAs for root fresh weight (RFW) and root dry weight (RDW) are located on Chromosomes 7A and 5B, respectively (Khan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohamed et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Oyiga et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Quan et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but the related genes have not been cloned. There are limited reports on the mapping of sheath length (SHL) and its stress-specific responses.\u003c/p\u003e\u003cp\u003eIn this study, a GWAS for nine germination-related traits was performed. Our main objectives were to: (1) grade the alkali tolerance of 314 wheat accessions; (2) identify major stable MTAs, analyze their allelic effects, and verify some stress-responsive loci; and (3) develop functional molecular markers for marker-assisted selection breeding.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant materials and hydroponic experiments\u003c/h2\u003e\u003cp\u003eThe natural population used in this study consisted of 314 wheat accessions that had previously undergone genetic diversity and population structure analyses (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)(Xu et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Optimized hydroponic culturing was carried out based on similar studies by Ayalew et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In total, nine agronomic traits, including SL, SHL, MRL, RN, RFW, SFW, RDW, SDW, and GP at the germination stage, were investigated. For hydroponic germination, 20 seeds per cultivar were selected and spaced 1 cm apart to ensure uniform growth conditions. Plastic boxes, with a volume of 1,300 mL, were used, and holes with a diameter of 8 mm were drilled in the lids. The top of the lid was lined with filter paper to keep the plants in place and the surface moist. For the group growing under treatment (T) conditions, a Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e solution (0.15%) was added during seed germination to impose osmotic stress, whereas the group growing under control (CK; non-stressed) conditions was only supplied deionized water.\u003c/p\u003e\u003cp\u003eThe experiments were conducted in a greenhouse with a relative humidity of 50% and a day/night temperature regime of 22\u0026ndash;25 ℃. Hydroponic culturing of wheat at the germination stage was maintained for 7 d under a 16:8-h light\u0026ndash;dark cycle (light period: 6:00 a.m. to 10:00 p.m.; dark period: 10:00 p.m. to 6:00 a.m.). Samples of cultured seedlings were taken after7 d, and the trait indicators of each sample were measured using a ruler (cm). To evaluate the agronomic traits, 10 representative plants were randomly selected from each replicate, and each variety exhibited a uniform growth state. The average phenotypic value of each trait for 10 plants in each environment was considered as the phenotypic value of the target trait in that environment. The experiments were conducted in a randomized complete block design, with three replications.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSNP genotyping\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was isolated from fresh young leaf samples obtained from five individual plants per line following a modified cetyltrimethylammonium bromide-based protocol adapted from previously established methodologies (Xu et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The genotyping of 314 wheat materials was performed using DNA and the 55K SNP chip from Beijing Compass Biotechnology Co., Ltd. In the analysis results, PLINK1.9 was used to screen and remove SNP markers having minor allele frequencies of less than 5% or missing values of more than 10%. Finally, 24,889 SNP markers were used for the association analysis.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eFor the phenotypic statistical analysis, IBM SPSS Statistics 25 was used to estimate the descriptive statistics of all the traits, estimate the correlations between traits, and conduct an analysis of variance. In R 4.4.3, the lme4 and corrplot packages were used to calculate the best linear unbiased estimates (BLUE) of each trait, with three replicates, in all the environments and to draw a heatmap of the correlations between traits. Origin 2021 was used to construct histograms and normal curves. Broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u0026sup2;) was computed using OGAStation 1.0 with the formula: \u003cem\u003eH\u003c/em\u003e\u0026sup2; = (genetic variance/(genetic variance\u0026thinsp;+\u0026thinsp;environmental variance) \u0026times; 100%. To systematically evaluate alkali tolerance in wheat genotypes, a multi-dimensional evaluation system was established using phenotypic data from the following nine traits: SL, SHL, MRL, RN, SFW, SDW, RFW, RDW, and GP. The Stress Tolerance Index (STI) was calculated to quantify the alkali stress response of individual traits using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{STI}\\text{=}\\frac{\\text{Trait\\:value\\:under\\:alkaline\\:stress\\:conditions}}{\\text{Trait\\:value\\:under\\:control\\:conditions}}\\text{\u0026times;100\\%}\\text{.}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSubsequently, Membership Function Values \u003cem\u003eU\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({\\text{Z}}_{\\text{Sj}}\\right)\\text{}\\)\u003c/span\u003e\u003c/span\u003ewere derived through range normalization, as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{U}\\text{(}{\\text{Z}}_{\\text{Sj}}\\text{)}\\text{=}\\frac{{\\text{Z}}_{\\text{Sj}}\\text{-}{\\text{Z}}_{\\text{Smin}}}{{\\text{Z}}_{\\text{Smax}}\\text{-}{\\text{Z}}_{\\text{Smin}}},$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{}{\\text{Z}}_{\\text{Sj}}\\)\u003c/span\u003e\u003c/span\u003e represents the STI value of the composite indicator, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{\\text{Smax}}\\text{}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{\\text{Smin}}\\text{}\\)\u003c/span\u003e\u003c/span\u003eare the maximum and minimum STI values, respectively, of that trait. Weight coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{W}}_{\\text{j}\\text{}}\\)\u003c/span\u003e\u003c/span\u003e were further assigned based on the variance contribution rate \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{j}}\\)\u003c/span\u003e\u003c/span\u003eusing:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{W}}_{\\text{j}}\\text{=}\\frac{{\\text{P}}_{\\text{j}}}{{\\sum\\:}_{\\text{j}\\text{=1}}^{\\text{\u0026infin;}}{\\text{P}}_{\\text{j}}}.\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eFinally, a Comprehensive Alkali Tolerance Index (D\u003csub\u003eS\u003c/sub\u003e) was calculated by integrating weighted membership function values, as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{\\text{D}}_{\\text{s}}\\text{=}{\\sum\\:}_{\\text{j}\\text{=1}}^{\\text{\u0026infin;}}\\left[\\text{U}\\text{(}{\\text{Z}}_{\\text{Sj}}\\text{)}{\\text{W}}_{\\text{j}}\\right].$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA higher D\u003csub\u003eS\u003c/sub\u003e value indicates a stronger alkali tolerance. Standardized data were subjected to a hierarchical cluster analysis in SPSS 25.0. Highly stress-tolerant lines were defined as having D\u003csub\u003eS\u003c/sub\u003e values of 0.547\u0026ndash;0.634, stress-tolerant lines as 0.427\u0026ndash;0.519, moderately stress-tolerant lines as 0.359\u0026ndash;0.423, stress-sensitive lines as 0.293\u0026ndash;0.356, and highly stress-sensitive lines as 0.141\u0026ndash;0.292.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePopulation structure analysis, kinship analysis, and LD\u003c/h3\u003e\n\u003cp\u003eA population structure analysis was performed using Admixture software, and Blink in the GAPIT statistical package of R software was used to calculate the PCs and kinship matrix. In addition, PopLDdecay software was used to calculate the genome-wide LD. After calculating the squared correlation coefficient (R\u0026sup2;), the distance at which R\u0026sup2; decreased to half its maximum value was used to evaluate the LD between each pair of SNPs on a chromosome. LDBlockShow 1.40 was used to construct the LD heatmap (Dong et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGWAS\u003c/h3\u003e\n\u003cp\u003eA total of 12 phenotypic datasets were used, including the mean values of each trial (CK1, CK2, CK3, T1, T2, and T3), the alkali tolerance indices of three trials (STI1, STI2, and STI3), and the BLUE (CKBlue, TBlue, and STIBlue) of each trait. The BLINK model provided by R/GAPIT 3.4 was used for the GWAS analysis (Lipka et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This model iteratively conducts two fixed-effect models, eliminating the requirement that the underlying genes of a trait are evenly distributed in the genome. It takes the results of population stratification and kinship as covariates to minimize false positives. BIC is used to replace REML in FarmCPU to further improve statistical power and computational speed (Huang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The R/ggplot2 and CMplot packages were used to generate Manhattan plots and quantile\u0026ndash;quantile plots for multiple environments (Sallam et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Valero Mora \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Significant MTAs were determined using a threshold P-value of 0.001 (-log\u003csub\u003e10\u003c/sub\u003eP\u0026thinsp;=\u0026thinsp;3). Stable MTAs were defined as those detected in at least two of the eight datasets (i.e., CK1, CK2, CK3, CKBlue, T1, T2, T3, and TBlue).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePrincipal component analysis-integrated GWAS based on membership function values\u003c/h2\u003e\u003cp\u003eA PCA was performed on standardized membership function values to reduce dimensionality and mitigate multicollinearity among traits. Each PC with a cumulative contribution rate of 85% was extracted and used as phenotypic traits for the GWAS. Significant loci identified by the PC-GWAS were compared with significant phenotype-derived MTAs. Only genetic loci verified by cross-analysis and stability testing across multiple environments were designated as core alkali stress loci.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePrediction of candidate genes\u003c/h3\u003e\n\u003cp\u003eCandidate gene identification was performed by integrating genetic association signals and functional annotations. First, genomic regions spanning\u0026thinsp;\u0026plusmn;\u0026thinsp;5 Mb around GWAS loci were defined using the \u003cem\u003eTriticum aestivum\u003c/em\u003e Chinese Spring v2.1 genome assembly (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://202.194.139.32/jbrowse.html\u003c/span\u003e\u003cspan address=\"http://202.194.139.32/jbrowse.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gene annotations from the Triticeae-GeneTribe database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wheat.cau.edu.cn/TGT/\u003c/span\u003e\u003cspan address=\"http://wheat.cau.edu.cn/TGT/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were then analyzed to screen for genes with potential biological functions. Functional annotations were retrieved from UniProt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To prioritize candidate genes, functional evidence specifically reported for \u003cem\u003eT. aestivum\u003c/em\u003e, \u003cem\u003eOryza sativa\u003c/em\u003e, or \u003cem\u003eArabidopsis thaliana\u003c/em\u003e in UniProt entries was given primary consideration, supplemented by a conserved domain analysis.\u003c/p\u003e\n\u003ch3\u003eConversion of SNPs to Kompetitive AlleleSpecific PCR (KASP) markers\u003c/h3\u003e\n\u003cp\u003eSNP genotyping was conducted using KASP technology (KASP\u0026trade; platform, LGC Genomics, Hoddesdon, UK) targeting the MTA29 and MTA80 loci. The PCR reaction (5 \u0026micro;L) contained 2.5 \u0026micro;L sample DNA (30 ng/\u0026micro;L), 2.5 \u0026micro;L KASP Mix (LGC Genomics), and 0.07 \u0026micro;L KASP Assay Mix. Thermal cycling was performed as follows: 94\u0026deg;C for 15 min; 10 touchdown cycles of 94\u0026deg;C for 20 s, 61 to 55\u0026deg;C at 0.6\u0026deg;C per cycle for 60 s, and 26 amplification cycles of 94\u0026deg;C for 20 s and 55\u0026deg;C for 60 s. Automated genotype calling was performed using a SNPline/Array Tape systems with dual-channel fluorescence detection (FAM: 465\u0026ndash;510 nm; HEX: 528\u0026ndash;560 nm) and validated by KlusterCaller v2.4.0 (Wu et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePhenotypic variation and a correlation analysis\u003c/h2\u003e\u003cp\u003eThe phenotypic images from day 4 to 8 clearly revealed the morphological differences between CK and T conditions (Fig.\u0026nbsp;1a). Under T conditions, the maximum, minimum, and mean values of all the seedling traits, except for GP and RN, were significantly lower than those under CK conditions (Table\u0026nbsp;1). Traits showed relatively high coefficient of variation values, except for RN (\u0026lt;\u0026thinsp;4%), under both CK and T conditions. When plants were subjected to T conditions, the \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e of most traits decreased significantly (except for those of RFW, SFW, and SDW), and the kurtosis and skewness coefficients were both less than 1.0. As shown in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, all the traits conformed to the normal distribution.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;1 The descriptive statistics and an analysis of variance of alkalinity responsive traits\u003c/p\u003e\u003cp\u003eC: control, T: alkaline-stressed, Min: minimum, Max: maximum, \u003cem\u003eSD\u003c/em\u003e: standard deviation, \u003cem\u003eCV\u003c/em\u003e: coefficient of variation, Skew: skewness, Kurt: kurtosis, \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e: broad-sense heritability, GP: germination percentage, SL: seedling length, SHL: sheath length, MRL: maximum root length, RN: root number, RFW: root fresh weight, RDW: root dry weight, SFW: seedling fresh weight, SDW: seedling dry weight.\u003c/p\u003e\u003cp\u003eA correlation analysis was performed for different traits (Fig.\u0026nbsp;1b, c). In the CK group, there were highly significant positive correlations between the SL and SHL, MRL, SFW, and SDW. The MRL was highly significantly positively correlated with RFW, RDW, SFW, and GP. The RFW was highly significantly positively correlated with RDW, SFW, and GP. In the T group, there were highly significant positive correlations between SL and SHL, as well as SFW. The SDW was highly significantly positively correlated with both SFW and RDW. Notably, under both conditions, SFW showed highly significant positive correlations with RFW, SL, and SDW, while RFW maintained a highly significant positive correlation with MRL.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eComprehensive evaluation of alkali resistance for the 314 wheat accessions\u003c/h2\u003e\u003cp\u003eThe PCA of nine germination-stage stress tolerance indices identified three components (PC1\u0026ndash;3, eigenvalue\u0026thinsp;\u0026gt;\u0026thinsp;1), collectively explaining 51.49% of phenotypic variance (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). PC1 (25.80%) was predominantly characterized by RFW (0.52), RDW (0.47), and SL (0.52), suggesting a focus on biomass allocation traits. PC2 (17.53%) correlated with RN (0.43), SFW (0.59), and SDW (0.53), representing seedling growth. PC3 (11.27%) was mainly contributed to by GP (0.94), indicating germination sensitivity (Table S3). The comprehensive evaluation index (Ds) was calculated using the weighted summation of the membership function values of the nine traits according to their respective weights, in which the weights were determined based on rotated variance contributions (PC1:44.0%, PC2:34.01%, PC3:21.99%). The Ds value, constrained within [0,1], quantitatively ranked stress tolerance across materials. A clustering analysis based on the Ds values categorized the accessions into five distinct groups: 6 highly stress-tolerant, 57 stress-tolerant, 92 moderately stress-tolerant, 110 stress-sensitive, and 35 highly stress-sensitive (phenotypic data of 14 accessions were missing) (Table S4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eGWAS of germination-related traits\u003c/h2\u003e\u003cp\u003eAs established previously (Xu et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), a GWAS of 314 wheat accessions using a 55K SNP array identified 24,889 high-quality SNPs having a uniform distribution and weak population structure (mean LD decay: 4.1 Mb, fastest in the D subgenome at 2.3 Mb).\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;2 and Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Manhattan and quantile\u0026ndash;quantile plots of the nine traits in the BLUE datasets were analyzed. Using the BLINK model, we identified 206 significant MTAs with replication support (\u0026ge;\u0026thinsp;2 replicates) across CK and T environments. This integrated set combined 112 trait-specific MTAs and 94 multi-trait MTAs. In total, 18 of the MTAs showed significant associations in the STI dataset (Table S5). Additionally, 79 and 66 MTAs were specifically detected under CK and T conditions, respectively. A total of 61 MTAs were detected in both the CK and T environments (including the STI dataset). Among them, the trait with the most MTAs was SHL (48), followed by SL (43), SDW (40), MRL (38), GP (35), RDW (32), SFW (32), RFW (24), and the least was RN (18). The most MTAs were detected in the A genome (75), followed by the B genome (66) and the D genome (65). The most MTAs (19) were detected on Chromosome 5B, and the least (2) were detected on Chromosome 3B (Table S5).\u003c/p\u003e\u003cp\u003eA total of 35 significant MTAs for GP were identified, distributed across 16 chromosomes: 1A, 1D, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 5A, 5B, 6B, 6D, 7A, 7B, and 7D. An environmental specificity analysis revealed 17 MTAs unique to CK conditions, 13 MTAs specific to T conditions, and 1 stable MTA locus (MTA80) consistently detected in both environments. A pleiotropy analysis revealed that 25 MTAs (71.4%) were significantly associated with 8 other germination-related traits (Table S5).\u003c/p\u003e\u003cp\u003eA total of 43 significant MTAs for SL were identified, distributed across 16 chromosomes: 1A, 1B, 2D, 3A, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6D, 7A, 7B, and 7D. An environmental specificity analysis revealed 21 MTAs unique to CK conditions and 15 specific to T conditions. A pleiotropy analysis revealed that 28 MTAs (62.2%) were significantly associated with 7 other germination-related traits (Table S5).\u003c/p\u003e\u003cp\u003eA total of 48 significant MTAs for SHL were identified, distributed across 16 chromosomes: 1A, 1B, 2A, 2B, 2D, 3A, 4A, 4B, 4D, 5B, 5D, 6A, 6B, 6D, 7A, and 7D. An environmental specificity analysis revealed 27 MTAs unique to CK conditions, 16 specific to T conditions and 2 stable MTA loci (MTA29, MTA166) consistently detected in both environments. A pleiotropy analysis revealed that 27 MTAs (56.3%) were significantly associated with 6 other germination-related traits (Table S5).\u003c/p\u003e\u003cp\u003eA total of 38 significant MTAs for MRL were identified, distributed across 16 chromosomes: 1A, 1D, 2A, 2B, 2D, 3A, 4A, 4D, 5B, 5D, 6A, 6B, 6D, 7A, 7B, and 7D. An environmental specificity analysis revealed 25 MTAs unique to CK conditions and 12 specific to T conditions. A pleiotropy analysis revealed that 24 MTAs (63.2%) were significantly associated with 8 other germination-related traits (Table S5).\u003c/p\u003e\u003cp\u003eA total of 18 significant MTAs for RN were identified, distributed across 12 chromosomes: 2A, 2B, 2D, 3A, 3D, 4A, 4D, 5A, 5B, 5D, 6A, and 6B. An environmental specificity analysis revealed 13 MTAs unique to CK conditions and 5 specific to alkali stress. A pleiotropy analysis revealed that 13 MTAs (72.2%) were significantly associated with 5 other germination-related traits (Table S5).\u003c/p\u003e\u003cp\u003eA total of 32 significant MTAs for SFW were identified, distributed across 15 chromosomes: 1A, 2A, 2D, 3A, 3B, 3D, 4B, 4D, 5A, 5B, 5D, 6A, 7A, 7B, and 7D. An environmental specificity analysis revealed 11 MTAs unique to CK conditions, 19 specific to stress conditions, and 1 stable MTA locus (MTA25) consistently detected in both environments. A pleiotropy analysis revealed that 25 MTAs (78.1%) were significantly associated with 7 other germination-related traits (Table S5).\u003c/p\u003e\u003cp\u003eA total of 40 significant MTAs for SDW were identified, distributed across 18 chromosomes: 1A, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, and 7B. An environmental specificity analysis revealed 15 MTAs unique to CK conditions and 24 specific to stress conditions. A pleiotropy analysis revealed that 21 MTAs (52.5%) were significantly associated with 6 other germination-related traits (Table S5).\u003c/p\u003e\u003cp\u003eA total of 24 significant MTAs for RFW were identified, distributed across 12 chromosomes: 1A, 1B, 2A, 2D, 3A, 3B, 3D, 4A, 6A, 6B, 7A, and 7B. An environmental specificity analysis revealed 14 MTAs unique to CK conditions and 7 specific to stress conditions. A pleiotropy analysis revealed that 19 MTAs (79.2%) were significantly associated with 7 other germination-related traits (Table S5).\u003c/p\u003e\u003cp\u003eA total of 32 significant MTAs for RDW were identified, distributed across 12 chromosomes: 1A, 1D, 2B, 2D, 3A, 4B, 4D, 5A, 5B, 5D, 6B, and 7D. An environmental specificity analysis revealed 14 MTAs unique to CK conditions, 16 specific to stress conditions, and 1 stable MTA locus (MTA129) consistently detected in both environments. A pleiotropy analysis revealed that 18 MTAs (58.06%) were significantly associated with 6 other germination-related traits (Table S5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eValidation of alkali stress-responsive loci via PCA-integrated GWAS\u003c/h2\u003e\u003cp\u003eA PCA of nine stress tolerance indices resolved seven components (PC1\u0026ndash;7), capturing 88.2% cumulative variance (Table S6). The BLINK-based GWAS on membership functions identified 198 significant PC_MTAs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) with the following component-wise distribution: PC1 (28 PC_MTAs), PC2 (25 PC_MTAs), PC3 (12 PC_MTAs), PC4 (54 PC_MTAs), PC5 (22 PC_MTAs), PC6 (31 PC_MTAs), and PC7 (26 PC_MTAs). These PC_MTAs were distributed across all chromosomes except 1D, with the highest numbers observed on Chromosomes 4A (38 PC_MTAs), 3B (22 PC_MTAs), and 6A (18 PC_MTAs). A physical proximity analysis (5 Mb windows) revealed that 51 PC_MTAs co-localized with phenotype location-based MTAs, including the 8 directly overlapping loci (MTA1, MTA31, MTA63, MTA75, MTA80, MTA167, and MTA183) and 43 proximity loci, e.g., MTA166 (425226363 on 6D), were located near PC2-44_MTAs (426803871 on 6D) (Table S7).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eAllele analysis and candidate genes predicted for stable MTAs\u003c/h2\u003e\u003cp\u003eBased on the above data, three stable loci, MTA25 (AX-110072425), MTA29 (AX-108780339), and MTA80 (AX-110536071), were selected from 206 significant MTAs. The genetic effect and CG analyses were then conducted for these loci within a genomic region of \u0026plusmn;\u0026thinsp;5 Mb.\u003c/p\u003e\u003cp\u003eThe MTA25 locus on Chromosome 2A showed a significant association with SFW (Fig.\u0026nbsp;3a). Among the 314 wheat accessions, materials carrying Allele A (255) significantly outnumbered those with Allele T (26). An allele-specific analysis demonstrated that, compared to Allele T, Allele A decreased SFW by 11.6% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) under CK conditions, 19.4% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) under T conditions, and decreased STI by 7.5% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;3c). Notably, Allele A still significantly increased PH (+\u0026thinsp;10.8%) but showed no association with reproductive traits, including PL, PN, SN, TGW, and YPP (Fig.\u0026nbsp;3b). Allele A underwent significant positive selection during the transition from landraces to modern varieties (Fig.\u0026nbsp;3d), and its geographical distribution analysis showed significantly higher frequencies than Allele T across six continents (Fig. S3a). This dual evidence of selection and distribution supports that Allele A confers broader environmental adaptability by optimizing vegetative growth strategies. Within the LD block encompassing MTA25, 75 candidate genes were identified (Table S8). Four core genes (\u003cem\u003eTraesCS2A03G0338900\u003c/em\u003e, \u003cem\u003eTraesCS2A03G0340600\u003c/em\u003e, \u003cem\u003eTraesCS2A03G0342900\u003c/em\u003e, and \u003cem\u003eTraesCS2A03G0346700\u003c/em\u003e) (Fig.\u0026nbsp;3a) were predicted as candidate genes based on their functional annotations, which were associated with stress-response mechanisms relevant to alkali tolerance or with seedling growth.\u003c/p\u003e\u003cp\u003eThe MTA29 locus on Chromosome 2A showed a significant association with SHL (Fig.\u0026nbsp;4a). Among the 314 wheat materials genotyped using this marker, 303 were classified into two groups: those carrying Allele T (259) and those carrying Allele C (44). The haplotype analysis based on BLUE values revealed that Allele T reduced SL by 15.2% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) under CK conditions and 15.4% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) under T conditions, but showed no association with STI, compared to Allele C (Fig.\u0026nbsp;4c). A yield-related trait analysis showed that Allele T significantly decreased PH (-14.7%) and PL (-8%), while increasing TGW (+\u0026thinsp;7.3%) and YPP (+\u0026thinsp;18.1%), with no association to PN or SN (Fig.\u0026nbsp;4b). Allele T underwent significant positive selection during wheat improvement, showing an increased frequency from landraces to modern cultivars (Fig.\u0026nbsp;4d). A geographical distribution analysis confirmed that Allele T exhibited significantly higher frequencies than Allele C across six continents (Fig. S3b). This dual evolutionary and geographical evidence suggests that Allele T may optimize environmental adaptability by balancing stress response and yield formation. Within the LD block encompassing MTA29, 101 candidate genes were identified (Table S8). Five core genes (\u003cem\u003eTraesCS2A03G1053800\u003c/em\u003e, \u003cem\u003eTraesCS2A03G1062000\u003c/em\u003e, \u003cem\u003eTraesCS2A03G1065600\u003c/em\u003e, \u003cem\u003eTraesCS2A03G1065700\u003c/em\u003e, and \u003cem\u003eTraesCS2A03G1066400\u003c/em\u003e) (Fig.\u0026nbsp;4a) were predicted as candidate genes owing to their explicit functional associations with alkali tolerance or coleoptile development.\u003c/p\u003e\u003cp\u003eThe MTA80 locus on Chromosome 4A showed a significant association with GP (Fig.\u0026nbsp;5a). Among the 314 wheat materials, 312 were classified into two groups: those carrying Allele A (286) and those carrying Allele C (26). A haplotype analysis based on BLUE values revealed that Allele A increased germination potential by 17.6% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) under CK conditions and 10% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) under T conditions, but it decreased STI by 5.3% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to Allele C (Fig.\u0026nbsp;5c). A yield-related trait analysis showed that Allele A significantly decreased PH (-23.5%) and PL (-10.1%), while increasing TGW (+\u0026thinsp;7.1%) and YPP (+\u0026thinsp;22.4%), with no association to PN or SN (Fig.\u0026nbsp;5b). Allele A underwent significant positive selection during wheat improvement, showing an increased frequency from landraces to modern cultivars (Fig.\u0026nbsp;5d). A geographical distribution analysis confirmed that Allele A exhibited significantly higher frequencies than Allele C across six continents (Fig. S4a). This evolutionary and geographical pattern implies that Allele A may balance adaptive trade-offs by enhancing specific stress responses and optimizing plant architecture. Within the LD block encompassing MTA80, 63 candidate genes were identified (Table S8). Four core genes (\u003cem\u003eTraesCS4A03G0161700\u003c/em\u003e, \u003cem\u003eTraesCS4A03G0165200\u003c/em\u003e, \u003cem\u003eTraesCS4A03G0165300\u003c/em\u003e, and \u003cem\u003eTraesCS4A03G0165400\u003c/em\u003e) (Fig.\u0026nbsp;5a) were predicted as candidate genes based on their explicit functional associations with alkali tolerance and seed germination rate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eDevelopment of molecular markers for major stable MTAs used in marker-assisted selection\u003c/h2\u003e\u003cp\u003eIn this study, we successfully converted the SNP marker AX-108780339 of MTA29 associated with SHL, along with the SNP marker AX-110915963 of MTA80 associated with GP, into robust KASP markers (Tables S1, S9; Fig. S4b, c), enabling efficient genotyping of germplasm resources. These markers will provide important resources for the molecular breeding of wheat.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBecause wheat is an important food crop worldwide, research on its response to alkali stress is of great significance to ensure food security in saline-alkali areas. However, compared with salt stress, research on what response mechanisms to alkali stress is relatively scarce, and saline-alkali stress often exacerbates plant physiological damage through synergistic effects (Fang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The germination stage a stress-sensitive developmental stage in plants, directly influencing seedling establishment and subsequent yield potential (Bayuelo Jim\u0026eacute;nez et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Jamil et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Song et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This study systematically investigated the genetic basis of alkali stress responses during the wheat germination stage by analyzing related traits.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePhenotypic responses under different stresses\u003c/h2\u003e\u003cp\u003eThere have been many studies on responses to salt stress and saline-alkaline stress in wheat (Akram et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chaurasia et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mohamed et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, research focusing specifically on alkali stress remains limited. Zhang et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) treated wheat cultivars with a mixed saline-alkaline solution (75 mmol\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NaCl\u0026thinsp;+\u0026thinsp;75 mmol\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NaHCO\u003csub\u003e3\u003c/sub\u003e) and found that SL is significantly inhibited at the seedling stage, whereas RDW increases significantly in different cultivars. Gu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that GP decreases by 39.4% after a 100 mmol\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e alkali (NaHCO\u003csub\u003e3\u003c/sub\u003e:Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;9:1) treatment compared with the control at the germination stage. Guo et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) evaluated the effects of NaCl stress at varying concentrations (0 mM control, 50 mM, 100 mM, and 200 mM) on early seedling traits. Compared with the control, RDW shows no significant changes under low salt (50 mM), whereas RDW increases significantly, by approximately 33%, under 100 mM NaCl stress. Li et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found GP is significantly inhibited at the germination stage under 205.3 mM NaCl treatment, whereas SL is significantly inhibited at the seedling stage under 136.9 mM NaCl treatment. Notably, RN increases by 31.2% at the seedling stage after a 136.9 mM NaCl treatment. In this study, SL declined by 31.2%, aligning with the reported inhibition of SL by Zhang et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Guo et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and Li et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). GP exhibited a marginal increase (+\u0026thinsp;1.9%) under alkali stress, contrasting with the findings of Guo et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Li et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Gu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). RDW decreased by 37.5%, which contrasted with the effects of 100 mM NaCl stress found by Guo et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Zhang et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The RN in our study showed no significant change, which was inconsistent with the findings of Li et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The different results may be due to different mechanisms responsible for plant responses to alkali stress and salt stress.\u003c/p\u003e\u003cp\u003eGuo et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that under control conditions, significant positive correlations are observed between SFW and both SL and MRL, and the significant positive correlations are maintained across all the tested concentrations under salt-stress conditions. Additionally, SL is significantly positively correlated with MRL. Luo et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that under salt stress, SFW is significantly positively correlated with both SL and MRL. However, the significant positive correlations are not observed under control conditions. In our study, both control and alkali stress conditions showed highly significant positive correlations between SFW and SL. This aligns with the findings of salt stress in Guo et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eDifferent MTAs detected in our study\u003c/h2\u003e\u003cp\u003eTo systematically dissect the genetic basis of alkali-stress tolerance during wheat germination, this study employed a GWAS approach, leveraging its high resolution, ability to detect pleiotropy, and suitability for analyzing natural populations. Compared with a traditional linkage analysis, GWAS has become an ideal tool for dissecting complex stress traits in wheat due to its high resolution, pleiotropy detection, and adaptability to natural populations. In addition, it has more advantages in mining rare allelic variations.\u003c/p\u003e\u003cp\u003eOur multi-tiered screening strategy, PCA-integrated GWAS, provides a robust framework for alkali stress evaluation. Unlike single-trait selection (Choudhary et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), this aligned with the PCA-integrated comprehensive salt tolerance index system that successfully identified elite salt-tolerant cultivars, like Lude 1 (Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), confirming that a multivariate analysis enhances selection accuracy for complex traits. A membership function analysis identified a cluster of 5 SNPs near PC3_MTA (2D: 650\u0026ndash;651 Mb). Based on this, further analyses identified several prominent SNP-rich regions that may harbor potential loci contributing to alkali tolerance, including a cluster of 7 SNPs on Chromosome 7B (138\u0026ndash;140 Mb) near PC1_MTA, a cluster of 20 SNPs on Chromosome 3B (95\u0026ndash;127 Mb) near PC4_MTA, and a cluster of 25 SNPs on Chromosome 4A (114\u0026ndash;135 Mb) near PC4_MTA (Table S6). These genomic regions represent promising candidates for future functional validation of their role in alkali-stress response.\u003c/p\u003e\u003cp\u003eAmong the 206 significant MTAs, 66 exhibited alkali-specificity. Representative alkali-specific MTAs, including MTA4 (1A), MTA75 (3D), MTA103 (4D), and MTA126 (5B). MTA75 was validated by membership function-integrated GWAS and contained a cluster of 7 significant SNPs near PC1_MTA at 618\u0026ndash;619 Mb. MTA126 was detected consistently across all four alkali environments and exhibited a cluster of 7 significant SNPs on Chromosome 5B (554\u0026ndash;559 Mb) near PC6_MTA. Five out of 206 MTAs exhibited stability across both CK and T stress conditions, with MTA25, MTA29, MTA80, MTA129, and MTA166 demonstrating particular consistency across multiple environments. In the membership function analysis, MTA80 was directly verified within PC5_MTA, and MTA166 was verified in the adjacent segment at the same location as PC2_MTA. The above results directly demonstrated the association of MTA75, MTA126, MTA80, and MTA166 with alkali stress (Tables S5, S7).\u003c/p\u003e\u003cp\u003eThe following three densely concentrated regions were identified: 6 MTAs in Chr3A 717\u0026ndash;724 Mb (MTA61\u0026ndash;66) associated with SDW, SFW, and SHL; 7 MTAs in Chr5D 565\u0026ndash;567 Mb (MTA139\u0026ndash;145) regulating RDW, SFW, and SL; and 8 MTAs in Chr5B 79\u0026ndash;89 Mb (MTA114\u0026ndash;121) formed a control-specific hotspot for SHL. The regions of Chr5B and Chr5D were not reported before, whereas Chr3A cluster\u0026rsquo;s involvement in alkali co-regulation was newly demonstrated despite proximity to the plant height-associated QTL (Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tables S5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eCo-localized with previously reported loci\u003c/h2\u003e\u003cp\u003eTo decipher the biological significances of the loci, we systematically compared their genetic positions with previously reported loci associated with stress response and yield-related traits. Among the MTAs, 36.4% (75 loci) co-localized with previously reported loci. For example, MTA14 (1B) overlapped with the salinity tolerance locus \u003cem\u003eIWB57398T\u003c/em\u003e (Quamruzzaman et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); MTA86 (4B) co-localized with reported SL QTLs under salt stress (Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); and MTA194 (7B) co-localized with reported QTLs for MRL and RDW under saline conditions (Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chaurasia et al. 2021). The germination-stage salt stress QTL identified by Akram et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) is located at 125.3 Mb (marker \u003cem\u003eM4431\u003c/em\u003e) and is significantly associated with SL under control conditions, as well as the regulation of root/shoot ratio and relative SL under salt stress conditions. Li et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e) mapped a marker \u003cem\u003e(IWB45503\u003c/em\u003e) significantly associated with kernel number per spike at 117.2\u0026ndash;117.3 Mb. In our study, MTA25, located at 122.8 Mb (2A), was co-localized with both the above loci (Table S5).\u003c/p\u003e\u003cp\u003eMTA29 is located at 700.8 Mb (2A) and co-localizes with multiple seedling-stage ion-related QTLs identified by Hussain et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), including a root iron concentration QTL (\u003cem\u003eqRFeC.2A.1\u003c/em\u003e), a root manganese concentration QTL (qRMnC.2A.2), a root Na⁺ concentration/exclusion (\u003cem\u003eqRNAX.2A.1\u003c/em\u003e), a root zinc concentration QTL (\u003cem\u003eqRZnC.2A.1\u003c/em\u003e), and a shoot K⁺ concentration QTL (\u003cem\u003eqSKC.2A.1\u003c/em\u003e). The \u003cem\u003eTaFLZ2A\u003c/em\u003e gene within this genomic region is positioned at 702 Mb, and it exhibits no expression response to salt stress at the seedling stage (Qin et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, Li et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e) mapped a marker (AX-89674107) associated with heading date within the 704.7\u0026ndash;710 Mb interval of this region, and Liu et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) independently identified multiple markers linked to kernels per spike near 693 Mb. These findings collectively suggest that the genomic region harboring MTA29 is broadly involved in the regulation of ion homeostasis and the coordination of yield traits. Thus, these findings demonstrate developmental stage specificity and indicate that this locus aids in regulating reproductive development (Table S5).\u003c/p\u003e\u003cp\u003eMTA166 (6D:425.2 Mb) co-localizes with the genetic locus governing spike exertion length and grain number per spike identified in our previous GWAS using the same population (Xu et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), in which pleiotropic and candidate gene analyses were conducted. There were no related reported related candidate genes near the MTA80 locus.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eCandidate gene analyses of the three core MTAs\u003c/h2\u003e\u003cp\u003eAs an initial step towards understanding the molecular mechanisms underlying alkali-stress tolerance, we performed candidate gene analyses of the three core MTAs (MTA25, MTA29, and MTA80). There were 75 candidate genes of MTA25, including \u003cem\u003eTraesCS2A03G0340600\u003c/em\u003e, which encodes a serine/threonine-protein kinase that is a key regulator of the hyperosmotic stress response in rice, It phosphorylates the transcription factor \u003cem\u003eOsbZIP46\u003c/em\u003e to mediate abscisic acid signaling and drought/osmotic stress tolerance (Tang et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e). This mechanism is highly similar to the genes function of enhancing salt tolerance in rice by coordinately regulating Na\u003csup\u003e+\u003c/sup\u003e/K\u003csup\u003e+\u003c/sup\u003e homeostasis and the osmotic stress response pathway (Lou et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lou et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eTraesCS2A03G0338900\u003c/em\u003e encodes calmodulin-binding transcription activator 1, which is a Ca\u003csup\u003e2+\u003c/sup\u003e/calmodulin-mediated transcription factor, that contains stress-responsive cis-elements in its promoter (Yang et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and may directly activate antioxidant enzymes and osmoprotective genes (Noman et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pandey et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013a\u003c/span\u003e). In Arabidopsis, this gene enhances stress tolerance through hormone signaling (Pandey et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e). \u003cem\u003eTraesCS2A03G0342900\u003c/em\u003e encodes Wall-associated receptor kinase 2 (\u003cem\u003eWAK2\u003c/em\u003e). In rice, \u003cem\u003eOsWAK112\u003c/em\u003e suppresses ethylene biosynthesis to negatively regulate salt-stress tolerance (Lin et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), implicating WAK-mediated ethylene signaling in osmotic stress responses. \u003cem\u003eTraesCS2A03G0346700\u003c/em\u003e encodes Trehalose-phosphate phosphatase B (\u003cem\u003eTPPB\u003c/em\u003e). This enzyme catalyzes trehalose synthesis for osmoprotection, with functional conservation demonstrated by \u003cem\u003eOsTPP3\u003c/em\u003e, which enhances stress tolerance in rice (Jiang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)d \u003cem\u003ethaliana TPPs\u003c/em\u003e (Lin et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In \u003cem\u003eT. aestivum\u003c/em\u003e, a genome-wide analysis identified 31 \u003cem\u003eTPP\u003c/em\u003e genes having stress-inducible expression patterns during salinity/drought and leaf senescence (Islam et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), confirming the TPP family\u0026rsquo;s conserved role in cereal stress adaptation. Consequently, we predicted that the above four genes were candidate genes of MAT25.\u003c/p\u003e\u003cp\u003eThere were 101 candidate genes of MTA29, including \u003cem\u003eTraesCS2A03G1053800\u003c/em\u003e, which encodes vacuolar cation/proton exchanger 1b (\u003cem\u003eCAX1b\u003c/em\u003e). CAX1b, a cation/H\u003csup\u003e+\u003c/sup\u003e exchanger, regulates cellular ion homeostasis through vacuolar compartmentalization of Ca\u003csup\u003e2+\u003c/sup\u003e (Waight et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Notably, constitutive overexpression of \u003cem\u003eCAX1\u003c/em\u003e results in salt sensitivity in plants (Cheng et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Modareszadeh et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that CAX proteins mitigate stress-induced oxidative damage by reducing reactive oxygen species accumulation through the activation of antioxidant enzymes. Additionally, \u003cem\u003ecax1\u003c/em\u003e/\u003cem\u003ecax2\u003c/em\u003e double-knockout mutants exhibit delayed seed germination, demonstrating the functional importance of these transporters during normal development in response to environmental cues (Connorton et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). \u003cem\u003eTraesCS2A03G1062000\u003c/em\u003e encodes transcription factor \u003cem\u003eMYC2\u003c/em\u003e. It acts as a central transcription factor integrating abscisic acid and jasmonic acid signaling pathways, with documented roles in drought response regulation (Zeng et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), although its effects on salt tolerance vary across species (Verma et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eTraesCS2A03G1066400\u003c/em\u003e encodes E3 ubiquitin-protein ligase \u003cem\u003ePUB23.\u003c/em\u003e It negatively regulates drought responses by targeting ABA receptors for degradation (Zhao et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), consistent with soybean ortholog \u003cem\u003ePUB8\u003c/em\u003e (Wang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). \u003cem\u003eTraesCS2A03G1065600\u003c/em\u003e/\u003cem\u003eTraesCS2A03G1065700\u003c/em\u003e encodes late embryogenesis abundant protein 18 (\u003cem\u003eLEA18\u003c/em\u003e). LEA18 belongs to a protein family conferring drought protection through cellular homeostasis maintenance, with wheat LEA isoforms showing genotype-specific stress induction (Li et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, the above four genes were predicted to be candidate genes of MAT29.\u003c/p\u003e\u003cp\u003eThere were 63 candidate genes of MTA80, including \u003cem\u003eTraesCS4A03G0165200\u003c/em\u003e, which encodes V-type proton ATPase subunit a3 (\u003cem\u003eVHA-a3\u003c/em\u003e). VHA-a3 is an ATPase that constitutes the vacuolar proton pump. As the pump\u0026rsquo;s core catalytic subunit, it establishes a transmembrane proton gradient by catalyzing ATP hydrolysis, and it plays dual physiological roles in nutrient storage and ion balance in plant cells (Krebs et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There have been reports in \u003cem\u003eArabidopsis\u003c/em\u003e, pea, and rapeseed that the VHA-a3 protease is involved in the accumulation and assimilation of vacuolar phosphate. Additionally, it plays a significant role in hormone signal transduction (Bak et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Han et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e). \u003cem\u003eTraesCS4A03G0161700\u003c/em\u003e encodes zinc finger protein 8. It acts as a zinc finger transcription factor that negatively regulates ABA signaling during germination and early seedling development (Jin et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eTraesCS4A03G0165300\u003c/em\u003e encodes calcium-dependent lipid-binding protein. It functions as a transcriptional repressor that suppresses stress-responsive genes, including \u003cem\u003eAtTHAS1\u003c/em\u003e, through ceramide binding, with its knockout enhancing stress resistance (de Silva et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). \u003cem\u003eTraesCS4A03G0165400\u003c/em\u003e encodes, and it shares functional homology with \u003cem\u003eA. thaliana\u003c/em\u003e berberine bridge enzyme-like 28, which, when mutated reduces biomass and salt tolerance (Daniel et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Thus, we speculated that \u003cem\u003eTraesCS4A03G0165200\u003c/em\u003e, \u003cem\u003eTraesCS4A03G0161700\u003c/em\u003e, \u003cem\u003eTraesCS4A03G0165300\u003c/em\u003e, and \u003cem\u003eTraesCS4A03G0165400\u003c/em\u003e are candidate genes of MAT80.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe identified six highly alkali-tolerant wheat germplasms in this study. In total, 206 MTAs for the 9 germination-related traits were detected by GWAS, and 5 MTAs (MAT25, MAT29, MTA80, MTA129, and MTA166) were environmentally stable loci. Using a PCA-GWAS analysis, 198 significant association loci (PC_MTAs) were identified, with 51 being co-localized with phenotype location-based MTAs. They were verified as core stress-responsive loci. In addition, single KASP markers for SHL and GP were developed. This study describes critical genetic mechanisms for alkali tolerance during wheat germination that may assist in the crop\u0026rsquo;s molecular breeding.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u0026nbsp;\u003c/strong\u003eCZ, HR, and FC analyzed the data and drafted the manuscript. HR, ZW, XL, HT, XY, MZ and ZZ performed phenotype evaluation. RQ, YW, and HS helped design the study and revised the manuscript. All of the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 32472134), the Foundation of Shandong Province, China (Grant No. ZR2022MC119), the Shandong Provincial Key Research and Development Program of 2024 (Grant No. 2024LZGCQY012), Taishan scholar young expert (grant no. 20230119), the Shandong Provincial Fund for Excellent Young Scholars (Grant No. ZR2022YQ19).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkram S, Ghaffar M, Wadood A, Shokat S, Hameed A, Waheed MQ, Arif MAR (2022) A GBS-based genome-wide association study reveals the genetic basis of salinity tolerance at the seedling stage in bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L). Front Genet 13:997901. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3389/fgene.2022.997901\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2022.997901\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyalew H, Liu H, B\u0026ouml;rner A, Kobiljski B, Liu C, Yan G (2018) Genome-wide association mapping of major root length QTLs under PEG induced water stress in wheat. Front Plant Sci 9:1759. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3389/fpls.2018.01759\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2018.01759\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBak G, Lee EJ, Lee Y, Kato M, Segami S, Sze H, Maeshima M, Hwang JU, Lee Y (2013) Rapid structural changes and acidification of guard cell vacuoles during stomatal closure require phosphatidylinositol 3,5-bisphosphate. Plant Cell 25:2202\u0026ndash;2216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1105/tpc.113.110411\u003c/span\u003e\u003cspan address=\"10.1105/tpc.113.110411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBayuelo Jim\u0026eacute;nez JS, Craig R, Lynch JP (2002) Salinity tolerance of phaseolus species during germination and early seedling growth. Crop Sci 42:1584\u0026ndash;1594. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2002.1584\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2002.1584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChaurasia S, Singh AK, Songachan LS, Sharma AD, Bhardwaj R, Singh K (2020) Multi-locus genome-wide association studies reveal novel genomic regions associated with vegetative stage salt tolerance in bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L). Genomics 112:4608\u0026ndash;4621. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.ygeno.2020.08.006\u003c/span\u003e\u003cspan address=\"10.1016/j.ygeno.2020.08.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen H, Zheng C, Chen F, Qiao Y, Du S, Cao C, Zhang W (2021) Less N\u003csub\u003e2\u003c/sub\u003eO emission from newly high-yielding cultivars of winter wheat. Agric Ecosyst Environ 320:107557. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/https://doi.org/10.1016/j.agee.2021.107557\u003c/span\u003e\u003cspan address=\"10.1016/j.agee.2021.107557\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng N, Pittman JK, Zhu J, Hirschi KD (2004) The Protein Kinase SOS\u003csub\u003e2\u003c/sub\u003e Activates the \u003cem\u003eArabidopsis\u003c/em\u003e H\u003csup\u003e+\u003c/sup\u003e/Ca\u003csup\u003e2+\u003c/sup\u003e Antiporter CAX\u003csub\u003e1\u003c/sub\u003e to Integrate Calcium Transport and Salt Tolerance. J Biol Chem 279:2922\u0026ndash;2926. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1074/jbc.M309084200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M309084200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoudhary A, Kaur N, Sharma A, Kumar A (2021) Evaluation and screening of elite wheat germplasm for salinity stress at the seedling phase. Physiol Plant 173:2207\u0026ndash;2215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/https://doi.org/10.1111/ppl.13571\u003c/span\u003e\u003cspan address=\"10.1111/ppl.13571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConnorton JM, Webster RE, Cheng N, Pittman JK (2012) Knockout of Multiple \u003cem\u003eArabidopsis\u003c/em\u003e Cation/H\u0026thinsp;+\u0026thinsp;Exchangers Suggests Isoform-Specific Roles in Metal Stress Response, Germination and Seed Mineral Nutrition. PLoS ONE 7:e47455. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1371/journal.pone.0047455\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0047455\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDaniel B, Wallner S, Steiner B, Oberdorfer G, Kumar P, van der Graaff E, Roitsch T, Sensen CW, Gruber K, Macheroux P (2016) Structure of a Berberine Bridge Enzyme-Like Enzyme with an Active Site Specific to the Plant Family Brassicaceae. PLoS ONE 11:e0156892. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1371/journal.pone.0156892\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0156892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Silva K, Laska B, Brown C, Sederoff HW, Khodakovskaya M (2011) \u003cem\u003eArabidopsis thaliana\u003c/em\u003e calcium-dependent lipid-binding protein (AtCLB): a novel repressor of abiotic stress response. J Exp Bot 62:2679\u0026ndash;2689. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1093/jxb/erq468\u003c/span\u003e\u003cspan address=\"10.1093/jxb/erq468\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong S, He W, Ji J, Zhang C, Guo Y, Yang T (2020) LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files. Brief Bioinform 22:bbaa227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1093/bib/bbaa227\u003c/span\u003e\u003cspan address=\"10.1093/bib/bbaa227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang S, Hou X, Liang X (2021) Response mechanisms of plants under saline-alkali stress. Front Plant Sci 12:667458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3389/fpls.2021.667458\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2021.667458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhaedrahmati M, Mardi M, Naghavi MR, Majidi Heravan E, Nakhoda B, Azadi A, Kazemi M (2018) Mapping QTLs associated with salt tolerance related traits in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L). Agric Sci Technol 16:1413\u0026ndash;1428. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/http://hdl.handle.net/123456789/4330\u003c/span\u003e\u003cspan address=\"http://dx.doi.org/http://hdl.handle.net/123456789/4330\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGu Y, Feng G, Hou P, Zhou Y, Zhang H, Wang X, Luo B, Chen L (2024) Nondestructive detection of saline-alkali stress in wheat (\u003cem\u003eTriticum aestivum L\u003c/em\u003e.) seedlings via fusion technology. Plant Methods 20:136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/s13007-024-01248-6\u003c/span\u003e\u003cspan address=\"10.1186/s13007-024-01248-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo X, Wu C, Wang D, Wang G, Jin K, Zhao Y, Tian J, Deng Z (2022) Conditional QTL mapping for seed germination and seedling traits under salt stress and candidate gene prediction in wheat. Sci Rep 12:21010. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1038/s41598-022-25703-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-25703-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan Y, Liu Q, Gu J, Gong J, Guan C, Lepo JE, Rong X, Song H, Zhang Z (2014) V-ATPase and V-PPase at the Tonoplast Affect NO\u003csub\u003e3\u003c/sub\u003e \u003csup\u003e\u0026ndash;\u003c/sup\u003e content in \u003cem\u003eBrassica\u003c/em\u003e napus by Controlling Distribution of NO\u003csub\u003e3\u003c/sub\u003e \u003csup\u003e\u0026ndash;\u003c/sup\u003e Between the Cytoplasm and Vacuole. J Plant Growth Regul 34:22\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s00344-014-9439-8\u003c/span\u003e\u003cspan address=\"10.1007/s00344-014-9439-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHasseb NM, Sallam A, Karam MA, Gao L, Wang RRC, Moursi YS (2022) High-LD SNP markers exhibiting pleiotropic effects on salt tolerance at germination and seedlings stages in spring wheat. Plant Mol Biol 108:585\u0026ndash;603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s11103-022-01248-x\u003c/span\u003e\u003cspan address=\"10.1007/s11103-022-01248-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang M, Liu X, Zhou Y, Summers RM, Zhang Z (2019) BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions. GigaScience 8:giy154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1093/gigascience/giy154\u003c/span\u003e\u003cspan address=\"10.1093/gigascience/giy154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHussain B, Lucas SJ, Ozturk L, Budak H (2017) Mapping QTLs conferring salt tolerance and micronutrient concentrations at seedling stage in wheat. Sci Rep 7:15662. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1038/s41598-017-15726-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-017-15726-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIslam MA, Rahman MM, Rahman MM, Jin X, Sun L, Zhao K, Wang S, Sikdar A, Noor H, Jeon J-S, Zhang W, Sun D (2021) In Silico and Transcription Analysis of Trehalose-6-phosphate Phosphatase Gene Family of Wheat: Trehalose Synthesis Genes Contribute to Salinity, Drought Stress and Leaf Senescence. Genes 12:1652. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/https://doi.org/10.3390/genes12111652\u003c/span\u003e\u003cspan address=\"10.3390/genes12111652\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJamil A, Riaz S, Ashraf M, Foolad MR (2011) Gene expression profiling of plants under salt stress. Crit Rev Plant Sci 30:435\u0026ndash;458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1080/07352689.2011.605739\u003c/span\u003e\u003cspan address=\"10.1080/07352689.2011.605739\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJavid S, Bihamta MR, Omidi M, Abbasi AR, Alipour H, Ingvarsson PK (2022) Genome-wide association study (GWAS) and genome prediction of seedling salt tolerance in bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L). BMC Plant Biol 22:581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/s12870-022-03936-8\u003c/span\u003e\u003cspan address=\"10.1186/s12870-022-03936-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang D, Chen W, Gao J, Yang F, Zhuang C (2019) Overexpression of the trehalose-6-phosphate phosphatase \u003cem\u003eOsTPP3\u003c/em\u003e increases drought tolerance in rice. Plant Biotechnol Rep 13:285\u0026ndash;292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s11816-019-00541-4\u003c/span\u003e\u003cspan address=\"10.1007/s11816-019-00541-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang Y, Yang L, Zheng J, Geng X, Bai Y, Wang Y, Xue H, Lin W (2023) Vacuolar H\u003csup\u003e+\u003c/sup\u003e-ATPase and BZR1 form a feedback loop to regulate the homeostasis of BR signaling in \u003cem\u003eArabidopsis\u003c/em\u003e. Mol Plant 16:1976\u0026ndash;1989. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.molp.2023.10.007\u003c/span\u003e\u003cspan address=\"10.1016/j.molp.2023.10.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin Y, Piao R, Yan Y, Chen M, Wang L, He H, Liu X, Gao X, Jiang W, Lin X (2018) Overexpression of a New Zinc Finger Protein Transcription Factor \u003cem\u003eOsCTZFP8\u003c/em\u003e Improves Cold Tolerance in Rice. Int J Genomics 2018:5480617. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/https://doi.org/10.1155/2018/5480617\u003c/span\u003e\u003cspan address=\"10.1155/2018/5480617\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan RWA, Khan RSA, Awan FS, Akrem A, Iftikhar A, Anwar FN, Alzahrani HAS, Alsamadany H, Iqbal RK (2022) Genome-wide association studies of seedling quantitative trait loci against salt tolerance in wheat. Front Genet 13:946869. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3389/fgene.2022.946869\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2022.946869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrebs M, Beyhl D, G\u0026ouml;rlich E, Al Rasheid KAS, Marten I, Stierhof YD, Hedrich R, Schumacher K (2010) Arabidopsis V-ATPase activity at the tonoplast is required for efficient nutrient storage but not for sodium accumulation. Proc Natl Acad Sci USA 107:3251\u0026ndash;3256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1073/pnas.0913035107\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0913035107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi F, Wen W, Liu J, Zhang Y, Cao S, He Z, Rasheed A, Jin H, Zhang C, Yan J, Zhang P, Wan Y, Xia X (2019a) Genetic architecture of grain yield in bread wheat based on genome-wide association studies. BMC Plant Biol 19:168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/s12870-019-1781-3\u003c/span\u003e\u003cspan address=\"10.1186/s12870-019-1781-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi L, Peng Z, Mao X, Wang J, Li C, Chang X, Jing R, Rebetzke G (2021) Genetic insights into natural variation underlying salt tolerance in wheat. J Exp Bot 72:1135\u0026ndash;1150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1093/jxb/eraa500\u003c/span\u003e\u003cspan address=\"10.1093/jxb/eraa500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi N, Zhang S, Liang Y, Qi Y, Chen J, Zhu W, Zhang L (2018) Label-free quantitative proteomic analysis of drought stress-responsive late embryogenesis abundant proteins in the seedling leaves of two wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) genotypes. J Proteom 172:122\u0026ndash;142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jprot.2017.09.016\u003c/span\u003e\u003cspan address=\"10.1016/j.jprot.2017.09.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang G, Song H, Xiao Y, Zhang Z (2020) Ammonium accumulation caused by reduced tonoplast V-ATPase activity in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. Int J Mol Sci 22:2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3390/ijms22010002\u003c/span\u003e\u003cspan address=\"10.3390/ijms22010002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin Q, Wang S, Dao Y, Wang J, Wang K (2020) \u003cem\u003eArabidopsis thaliana\u003c/em\u003e trehalose-6-phosphate phosphatase gene \u003cem\u003eTPPI\u003c/em\u003e enhances drought tolerance by regulating stomatal apertures. J Exp Bot 71:4285\u0026ndash;4297. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1093/jxb/eraa173\u003c/span\u003e\u003cspan address=\"10.1093/jxb/eraa173\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin W, Wang Y, Liu X, Shang J, Zhao L (2021) \u003cem\u003eOsWAK112\u003c/em\u003e, A Wall-Associated Kinase, Negatively Regulates Salt Stress Responses by Inhibiting Ethylene Production. Front Plant Sci 12:751965. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3389/fpls.2021.751965\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2021.751965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397\u0026ndash;2399. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1093/bioinformatics/bts444\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/bts444\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Xu Z, Fan X, Zhou Q, Cao J, Wang F, Ji G, Yang L, Feng B, Wang T (2018) A Genome-Wide Association Study of Wheat Spike Related Traits in China. Front Plant Sci 9:1584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2018.01584\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2018.01584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu X, Yang Z, Hu W, Liu S, Sun R, Jin S, Nergui K, Zhao G, Gao L, Liu Y, Deng X (2024) A genome-wide association study identifies novel QTL for wheat yield stability under drought stress. Curr Plant Biol 37:100326. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cpb.2024.100326\u003c/span\u003e\u003cspan address=\"10.1016/j.cpb.2024.100326\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLou D, Wang H, Liang G, Yu D (2017) \u003cem\u003eOsSAPK2\u003c/em\u003e confers abscisic acid sensitivity and tolerance to drought stress in rice. Front Plant Sci 8:993. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3389/fpls.2017.00993\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2017.00993\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLou D, Wang H, Yu D (2018) The sucrose non-fermenting-1-related protein kinases SAPK1 and SAPK2 function collaboratively as positive regulators of salt stress tolerance in rice. BMC Plant Biol 18:17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/s12870-018-1408-0\u003c/span\u003e\u003cspan address=\"10.1186/s12870-018-1408-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLou H, Zhang R, Liu Y, Guo D, Zhai S, Chen A, Zhang Y, Xie C, You M, Peng H, Liang R, Ni Z, Sun Q, Li B (2021) Genome-wide association study of six quality-related traits in common wheat \u003cem\u003e(Triticum aestivum\u003c/em\u003e L.) under two sowing conditions. Theor Appl Genet 134:399\u0026ndash;418. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s00122-020-03704-y\u003c/span\u003e\u003cspan address=\"10.1007/s00122-020-03704-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu X, Ma L, Zhang C, Yan H, Bao J, Gong M, Wang W, Li S, Ma S, Chen B (2022) Grapevine (Vitis vinifera) responses to salt stress and alkali stress: transcriptional and metabolic profiling. BMC Plant Biol 22:528. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/s12870-022-03907-z\u003c/span\u003e\u003cspan address=\"10.1186/s12870-022-03907-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo Q, Hu P, Yang G, Li H, Liu L, Wang Z, Li B, Li Z, Zheng Q (2021) Mapping QTL for seedling morphological and physiological traits under normal and salt treatments in a RIL wheat population. Theor Appl Genet 134:2991\u0026ndash;3011. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s00122-021-03872-5\u003c/span\u003e\u003cspan address=\"10.1007/s00122-021-03872-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa Y, Qiu C, Fan Y, Huang X, Khan W, Wu F, Zhou M, Wang Y, Cao F (2022) Genome-wide association and transcriptome analysis reveals candidate genes for potassium transport under salinity stress in wheat. Environ Exp Bot 202:105034. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.envexpbot.2022.105034\u003c/span\u003e\u003cspan address=\"10.1016/j.envexpbot.2022.105034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaulana F, Huang W, Anderson JD, Ma X (2020) Genome-wide association mapping of seedling drought tolerance in winter wheat. Front Plant Sci 11:573786. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3389/fpls.2020.573786\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2020.573786\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eModareszadeh M, Bahmani R, Kim D, Hwang S (2021) CAX3 (cation/proton exchanger) mediates a Cd tolerance by decreasing ROS through Ca elevation in \u003cem\u003eArabidopsis\u003c/em\u003e. Plant Mol Biol 105:115\u0026ndash;132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s11103-020-01072-1\u003c/span\u003e\u003cspan address=\"10.1007/s11103-020-01072-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohamed M, Siddiqui MN, Oyiga BC, L\u0026eacute;on J, Ballvora A (2022) Validation of a QTL on chromosome 1DS showing a major effect on salt tolerance in winter wheat. Int J Mol Sci 23:13745. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3390/ijms232213745\u003c/span\u003e\u003cspan address=\"10.3390/ijms232213745\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNoman M, Jameel A, Qiang W, Ahmad N, Liu W, Wang F, Li H (2019) Overexpression of \u003cem\u003eGmCAMTA12\u003c/em\u003e enhanced drought tolerance in Arabidopsis and Soybean. Int J Mol Sci 20:4849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3390/ijms20194849\u003c/span\u003e\u003cspan address=\"10.3390/ijms20194849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOyiga BC, Sharma RC, Baum M, Ogbonnaya FC, L\u0026eacute;on J, Ballvora A (2018) Allelic variations and differential expressions detected at quantitative trait loci for salt stress tolerance in wheat. Plant Cell Environ 41:919\u0026ndash;935. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1111/pce.12898\u003c/span\u003e\u003cspan address=\"10.1111/pce.12898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePandey N, Ranjan A, Pant P, Tripathi RK, Ateek F, Pandey HP, Patre UV, Sawant SV (2013a) CAMTA1 regulates drought responses in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. BMC Genomics 14:216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/1471-2164-14-216\u003c/span\u003e\u003cspan address=\"10.1186/1471-2164-14-216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePandey N, Ranjan A, Pant P, Tripathi RK, Ateek F, Pandey HP, Patre UV, Sawant SV (2013b) CAMTA 1 regulates drought responses in \u003cem\u003eArabidopsis thalian\u003c/em\u003ea. BMC Genomics 14:216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/1471-2164-14-216\u003c/span\u003e\u003cspan address=\"10.1186/1471-2164-14-216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePang Y, Wu Y, Liu C, Li W, Amand PS, Bernardo A, Wang D, Dong L, Yuan X, Zhang H, Zhao M, Li L, Wang L, He F, Liang Y, Yan Q, Lu Y, Su Y, Jiang H, Wu J, Li A, Kong L, Bai G, Liu S (2021) High-resolution genome-wide association study and genomic prediction for disease resistance and cold tolerance in wheat. Theor Appl Genet 134:2857\u0026ndash;2873. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s00122-021-03863-6\u003c/span\u003e\u003cspan address=\"10.1007/s00122-021-03863-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQin Y, Zhang B, Cui S, Qin X, Li G (2023) \u003cem\u003eTaFLZ54D\u003c/em\u003e enhanced salt stress tolerance of wheat by interacting with \u003cem\u003eTaSGT1a\u003c/em\u003e and \u003cem\u003eTaPP2Ca\u003c/em\u003e. J Integr Agric 24:1017\u0026ndash;1029. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/https://doi.org/10.1016/j.jia.2023.09.018\u003c/span\u003e\u003cspan address=\"10.1016/j.jia.2023.09.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuamruzzaman M, Manik SMN, Shabala S, Cao F, Zhou M (2022) Genome-wide association study reveals a genomic region on 5AL for salinity tolerance in wheat. Theor Appl Genet 135:709\u0026ndash;721. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s00122-021-03996-8\u003c/span\u003e\u003cspan address=\"10.1007/s00122-021-03996-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuan X, Liu J, Zhang N, Xie C, Li H, Xia X, He W, Qin Y (2021) Genome-wide association study uncover the genetic architecture of salt tolerance-related traits in common wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L). Front Genet 12:663941. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3389/fgene.2021.663941\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2021.663941\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRasheed A, Hao Y, Xia X, Khan A, Xu Y, Varshney RK, He Z (2017) Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives. Mol Plant 10:1047\u0026ndash;1064. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.molp.2017.06.008\u003c/span\u003e\u003cspan address=\"10.1016/j.molp.2017.06.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSafdar LB, Andleeb T, Latif S, Umer MJ, Tang M, Li X, Liu S, Quraishi UM (2020) Genome-wide association study and QTL meta-analysis identified novel genomic loci controlling potassium use efficiency and agronomic traits in bread wheat. Front Plant Sci 11:70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3389/fpls.2020.00070\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2020.00070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSallam A, Dawood MFA, Jarqu\u0026iacute;n D, Mohamed EA, Hussein MY, B\u0026ouml;rner A, Ahmed AAM (2024) Genome-wide scanning to identify and validate single nucleotide polymorphism markers associated with drought tolerance in spring wheat seedlings. Plant Genome 17:e20444. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1002/tpg2.20444\u003c/span\u003e\u003cspan address=\"10.1002/tpg2.20444\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong J, Fan H, Zhao Y, Jia Y, Du X, Wang B (2008) Effect of salinity on germination, seedling emergence, seedling growth and ion accumulation of a euhalophyte \u003cem\u003eSuaeda\u003c/em\u003e salsa in an intertidal zone and on saline inland. Aquat Bot 88:331\u0026ndash;337. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aquabot.2007.11.004\u003c/span\u003e\u003cspan address=\"10.1016/j.aquabot.2007.11.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong Y, Pan Y, Xiang M, Yang W, Zhan D, Wang X, Lu M (2024) A WebGIS-based system for supporting saline-alkali soil ecological monitoring: a case study in Yellow River Delta, China. Remote Sens 16:1948. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3390/rs16111948\u003c/span\u003e\u003cspan address=\"10.3390/rs16111948\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSukumaran S, Reynolds MP, Sansaloni C (2018) Genome-wide association analyses identify QTL hotspots for yield and component traits in durum wheat grown under yield potential, drought, and heat stress environments. Front Plant Sci 9:81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3389/fpls.2018.00081\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2018.00081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang N, Zhang H, Li X, Xiao J, Xiong L (2012a) Constitutive activation of transcription factor \u003cem\u003eOsbZIP46\u003c/em\u003e improves drought tolerance in rice. Plant Physiol 158:1755\u0026ndash;1768. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1104/pp.111.190389\u003c/span\u003e\u003cspan address=\"10.1104/pp.111.190389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang R, Liu H, Yang Y, Yang L, Gao X, Garcia VJ, Luan S, Zhang H (2012b) Tonoplast calcium sensors CBL2 and CBL3 control plant growth and ion homeostasis through regulating V-ATPase activity in \u003cem\u003eArabidopsis\u003c/em\u003e. Cell Res 22:1650\u0026ndash;1665. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1038/cr.2012.161\u003c/span\u003e\u003cspan address=\"10.1038/cr.2012.161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTorbaghan ME, Lakzian A, Astaraei AR, Fotovat A, Besharati H (2017) Salt and alkali stresses reduction in wheat by plant growth promoting haloalkaliphilic bacteria. J Soil Sci Plant Nutr 17:1058\u0026ndash;1087. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.4067/S0718-95162017000400016\u003c/span\u003e\u003cspan address=\"10.4067/S0718-95162017000400016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValero Mora PM (2010) ggplot2: elegant graphics for data analysis. J Stat Softw Book Rev 35:1\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.18637/jss.v035.b01\u003c/span\u003e\u003cspan address=\"10.18637/jss.v035.b01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerma D, Jalmi SK, Bhagat PK, Verma N, Sinha AK (2020) A bHLH transcription factor, MYC2, imparts salt intolerance by regulating proline biosynthesis in Arabidopsis. FEBS J 287:2560\u0026ndash;2576. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/febs.15157\u003c/span\u003e\u003cspan address=\"10.1111/febs.15157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWaight AB, Pedersen BP, Schlessinger A, Bonomi M, Chau BH, Roe-Zurz Z, Risenmay AJ, Sali A, Stroud RM (2013) Structural basis for alternating access of a eukaryotic calcium/proton exchanger. Nature 499:107\u0026ndash;110. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1038/nature12233\u003c/span\u003e\u003cspan address=\"10.1038/nature12233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang N, Liu Y, Cong Y, Wang T, Zhong X, Yang S, Li Y, Gai J (2016) Genome-Wide Identification of Soybean U-Box E3 Ubiquitin Ligases and Roles of GmPUB8 in Negative Regulation of Drought Stress Response in Arabidopsis. Plant Cell Physiol 57:1189\u0026ndash;1209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1093/pcp/pcw068\u003c/span\u003e\u003cspan address=\"10.1093/pcp/pcw068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang S, Chen Q, Li Y, Zhuo YQ, Xu LZ (2017) Research on saline-alkali soil amelioration with FGD gypsum. Resour Conserv Recycl 121:82\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.resconrec.2016.04.005\u003c/span\u003e\u003cspan address=\"10.1016/j.resconrec.2016.04.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu J, Liu S, Wang Q, Zeng Q, Mu J, Huang S, Yu S, Han D, Kang Z (2018) Rapid identification of an adult plant stripe rust resistance gene in hexaploid wheat by high-throughput SNP array genotyping of pooled extremes. Theor Appl Genet 131:43\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s00122-017-2984-3\u003c/span\u003e\u003cspan address=\"10.1007/s00122-017-2984-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao B, Lu H, Li C, Bhanbhro N, Cui X, Yang C (2020) Carbohydrate and plant hormone regulate the alkali stress response of hexaploid wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L). Environ Exp Bot 175:104053. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envexpbot.2020.104053\u003c/span\u003e\u003cspan address=\"10.1016/j.envexpbot.2020.104053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu H, Wang Z, Wang F, Hu X, Ma C, Jiang H, Xie C, Gao Y, Ding G, Zhao C, Qin R, Cui D, Sun H, Cui F, Wu Y (2024) Genome-wide association study and genomic selection of spike-related traits in bread wheat. Theor Appl Genet 137:131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s00122-024-04640-x\u003c/span\u003e\u003cspan address=\"10.1007/s00122-024-04640-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang F, Dong F, Hu F, Liu Y, Chai J, Zhao H, Lv M, Zhou S (2020) Genome-wide identification and expression analysis of the calmodulin-binding transcription activator (\u003cem\u003eCAMTA\u003c/em\u003e) gene family in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L). BMC Genet 21:105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/s12863-020-00916-5\u003c/span\u003e\u003cspan address=\"10.1186/s12863-020-00916-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng T, Su H, Wang M, He J, Gu L, Wang H, Du X, Wang C, Zhu B (2025) The Role of MYC2 Transcription Factors in Plant Secondary Metabolism and Stress Response Mechanisms. Plants 14:1255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants14081255\u003c/span\u003e\u003cspan address=\"10.3390/plants14081255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang A, Philipp N, Weichert H, Bohra U, Weschke W, Schulthess AW, Weber H (2018) Grain number and grain yield distribution along the spike remain stable despite breeding for high yield in winter wheat. PLoS ONE 13:e0205452. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1371/journal.pone.0205452\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0205452\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang F, Yan X, Han X, Tang R, Chu M, Yang Y, Yang Y, Zhao F, Fu A, Luan S, Lan W (2019) A defective vacuolar proton pump enhances aluminum tolerance by reducing vacuole sequestration of organic acids. Plant Physiol 181:743\u0026ndash;761. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1104/pp.19.00626\u003c/span\u003e\u003cspan address=\"10.1104/pp.19.00626\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang K, Tang J, Wang Y, Kang H, Zeng J (2020) The tolerance to saline\u0026ndash;alkaline stress was dependent on the roots in wheat. Physiol Mol Biol Plants 26:947\u0026ndash;954. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s12298-020-00799-x\u003c/span\u003e\u003cspan address=\"10.1007/s12298-020-00799-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao J, Zhao L, Zhang M, Zafar SA, Fang J, Li M, Zhang W, Li X (2017) \u003cem\u003eArabidopsis\u003c/em\u003e E3 Ubiquitin Ligases PUB22 and PUB23 Negatively Regulate Drought Tolerance by Targeting ABA Receptor PYL9 for Degradation. Int J Mol Sci 18:1841. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms18091841\u003c/span\u003e\u003cspan address=\"10.3390/ijms18091841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao X, Yu X, Gao J, Qu J, Borjigin Q, Meng T, Li D (2024) Improvement of saline-alkali soil and straw degradation efficiency in cold and arid areas using \u003cem\u003eklebsiella\u003c/em\u003e sp. and \u003cem\u003ePseudomonas\u003c/em\u003e sp. Agronomy 14:2499. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3390/agronomy14112499\u003c/span\u003e\u003cspan address=\"10.3390/agronomy14112499\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Alkali stress, Genome-wide association study (GWAS), Germination traits, Marker-trait associations (MTAs), Candidate genes","lastPublishedDoi":"10.21203/rs.3.rs-7441024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7441024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil alkalization poses a severe threat to global wheat production, and deciphering the genetic basis of alkali tolerance during germination is critical for breeding resistant varieties. Here, a genome-wide association study (GWAS), with a comprehensive analysis, of 314 wheat accessions was executed under 0.15% Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e stress and control conditions. The phenotypic screening showed seedling biomass and root growth were suppressed under stress, while germination rate remained stable. The 314 accessions were classified by a principal component analysis, as follows: 6 highly tolerant, 57 tolerant, 92 moderate, 110 sensitive, and 35 highly sensitive. The GWAS indicated that 206 significant marker-trait associations (MTAs) were identified for nine germination-related traits. Notably, five loci (MTA25, MTA29, MTA80, MTA129, and MTA166) demonstrated stability across both tested conditions. The alleles effect analysis and candidate gene analysis for three stable loci (MTA25, MTA29, and MTA80) were executed. A principal component analysis-integrated GWAS identified 198 significant MTAs, of which 51 were co-localized with phenotype location-based MTAs, and they were verified as core stress-responsive loci. In addition, Kompetitive Allele Specific PCR markers for sheath length and germination percentage were developed. These findings provide a theoretical foundation for the selection of alkali-resistant wheat and provide important resources for wheat molecular breeding.\u003c/p\u003e","manuscriptTitle":"Genome-wide Association Study of Common Wheat’s Alkali Tolerance at the Germination Stage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 18:59:25","doi":"10.21203/rs.3.rs-7441024/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2025-11-12T16:06:32+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-10T04:07:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-09T18:06:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-26T17:20:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2025-08-24T06:52:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2326e17b-8e38-40b8-b07e-b587eff0b982","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:00:17+00:00","versionOfRecord":{"articleIdentity":"rs-7441024","link":"https://doi.org/10.1007/s00122-026-05154-4","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2026-02-04 15:57:00","publishedOnDateReadable":"February 4th, 2026"},"versionCreatedAt":"2025-09-17 18:59:25","video":"","vorDoi":"10.1007/s00122-026-05154-4","vorDoiUrl":"https://doi.org/10.1007/s00122-026-05154-4","workflowStages":[]},"version":"v1","identity":"rs-7441024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7441024","identity":"rs-7441024","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}