Genome-Wide Association Study Reveals Key SNP Markers for Agro-Physiological Traits in Bread Wheat (Triticum aestivum L.)

Genome-Wide Association Study Reveals Key SNP Markers for Agro-Physiological Traits in Bread Wheat (Triticum aestivum L.) | 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 Reveals Key SNP Markers for Agro-Physiological Traits in Bread Wheat (Triticum aestivum L.) Zahra Alizadeh-Bidarani, Ghasem Mohammadi-Nejad, Somayeh Sardouei-Nasab, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7424201/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : A genome-wide association scan (GWAS) is a powerful tool for identifying genetic variants and specific loci underlying complex traits. Bread Wheat ( Triticum aestivum L.) is one of the primary food resources in the world, and understanding its physiological parameters will help improve agronomic and yield traits. This study investigated single-nucleotide polymorphism (SNP) markers associated with physiological and agronomic traits in bread wheat to inform breeding programs. A diverse panel of 272 bread wheat genotypes was evaluated across two growing seasons (2019–2021) using a 16 × 17 rectangular lattice design with two replications. Key physiological traits, including carbon dioxide exchange and chlorophyll content, and agronomic traits, such as days to heading, days to maturity, flag leaf length, plant height, grain number per spike, grain weight per plant, thousand-grain weight, biological yield, and harvest index, were measured. Genotyping was conducted using a 90K SNP array at Trait-Genetics, Germany, yielding 17,093 high-quality SNPs after filtering for minor allele frequency and missing data (>10%). Results: Population structure analysis grouped the genotypes into five subgroups based on their genetic variation. GWAS was performed using General Linear Model (GLM), Fixed and random model Circulating Probability Unification (FarmCPU), and Mixed Linear Model (MLM), identifying 320, 302, and 27 significant marker-trait associations (MTAs), respectively. Sixteen MTAs were consistently significant across models, including four stable MTAs detected in two cropping seasons. These MTAs harbored 139 high-confidence genes associated with nine traits. Conclusions: These findings provide valuable insights into the genetic architecture of key wheat traits, facilitating targeted breeding strategies to enhance yield. Bread wheat GWAS SNPs marker-assisted selection agronomic traits Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Wheat is the third most important food crop globally, following maize and rice [ 1 ]. Currently, wheat is cultivated on approximately 220 million hectares worldwide, with the largest harvested areas found in Asia (43.7%), followed by Europe (33%) and the Americas (16.5%) [ 2 ]. As the global population is projected to reach 9.800 billion by 2050, the demand for wheat is expected to rise by approximately 1%–1.6% annually [ 3 ]. Recent climate change, resource scarcity, and increased threats from pests and diseases have presented new challenges to wheat production, quality, and growth cycles [ 4 ]. In recent years, the advancement of sequencing technologies, along with reduced sequencing costs and the availability of reference genomes, has accelerated the development and use of SNP microarrays in crops such as wheat. These technologies have enabled the discovery of numerous genetic loci associated with diverse agronomic and physiological traits [ 5 ]. Several studies have reported marker-trait associations (MTAs) in wheat through GWAS. For example, Wei et al [ 6 ] detected several stable SNPs on chromosomes 2A, 2B, 2D, 3B, 4B, 5A, 5D, 7B, and 7D associated with plant height. Similarly, Sheoran et al. [ 7 ] reported six MTAs associated with plant height, located on chromosomes 1B, 2A, 2B, 7A, and two on 5D. Wang et al. [ 8 ] reported 90 MTAs associated with various agronomic traits. Jamil et al. [ 9 ] identified 139 MTAs associated with seven traits, including days to heading, grain filling duration, plant height, spikes per plant, grain number per spike, thousand kernel weight, and grain yield. Malik et al. [ 10 ] evaluated GWAS studies using single-trait, multi-locus, and multi-trait models in bread wheat, identifying 34 marker-trait associations for yield-related traits. Devate et al. [ 11 ] detected 57 MTAs associated with various agronomic traits under heat and drought stress conditions in bread wheat. In the present study, we aimed to find genomic regions and molecular markers associated with critical agronomic and physiological traits in wheat to support MAS and improve wheat breeding efficiency. Thus, we conducted a GWAS study on 251 wheat accessions using the iSelect 90K SNP array. The objectives were to: (1) identify MTAs for various agro-physiological traits, (2) evaluate correlations among these traits and identify SNPs linked to multiple traits, and (3) discover candidate genes influencing these traits. Materials and Methods Plant Material and Experimental Conditions A diverse panel comprising 272 bread wheat genotypes (Table S1 ), sourced from the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), was used in the field experiments. The study was laid out in a rectangular lattice design (16 × 17) with two replications. Each plot measured 1.2 m × 5 m, with row spacing of 0.25 m. Field trials were conducted across two consecutive cropping seasons (2019–2021) at the Agricultural Research Station of the Department of Agronomy and Plant Breeding, Shahid Bahonar University of Kerman, Iran (30.25°N, 57.10°E; 1760 m above sea level). The experimental soil was classified as sandy loam, with a pH of 7.9 and an electrical conductivity (EC) of 2.11 dS/m. Phenotypic Data Collection and Analysis The following traits were evaluated: plant height (PH; cm), measured as the average height of three plants from soil to spike tip (excluding awns); flag leaf length (FLL; cm), measured as the average length of three flag leaves from collar to tip; days to heading (DH), recorded when 100% of the spikes had fully emerged; and days to maturity (DM), recorded when 100% of the spikes had yellowed; total chlorophyll content (TCC; mg g⁻¹), measured as the average chlorophyll content of three plants using a SPAD-502 meter (Konica Minolta, Japan), carbon dioxide exchange rate (CER; µmol m⁻² s⁻¹), measured using an ADC BioScientific Limited (UK) device; thousand grain weight (TGW; g), measured as the weight of 1.000 grains averaged across replicates; biological yield (BY; t ha⁻¹), measured as total dry biomass per plot expressed per hectare; harvest index (HI; %), measured as the grain weight to total biomass ratio; Grain number per spike (GNPS) calculated as the average of grain number in the main spikes of three randomly plants for each genotype, and grain weight per plant (GWP; gr/m 2 ). Best Linear Unbiased Estimates (BLUEs) across years were calculated for all traits using the REML (Restricted Maximum Likelihood) procedure in GenStat, 14th Edition [ 12 ], with genotype considered as a fixed effect. The BLUEs values were used in the subsequent analyses. Broad-sense heritability for each trait was calculated using the following equation [ 13 ]: Genotyping, Population Structure, and GWAS Analysis Genotyping was performed using the Illumina iSelect 90K SNP array at Trait Genetics, Gatersleben (Germany), using the Illumina Inc., San Diego, USA. The resulting SNP dataset was filtered by removing markers with a minor allele frequency (MAF) less than 5% and those with more than 10% missing data. After filtering, a total of 17,093 high-quality SNP markers were retained for use in the GWAS. Due to inconsistencies in genotypic data, 21 genotypes were excluded from the GWAS; however, their phenotypic data were included in the analysis to maximize the use of field trial data. Population structure was analyzed using the LEA package in R [ 14 ]. The number of subpopulations (K) was tested across a range from 1 to 10, and the optimal number of subpopulations was determined to be K = 5 based on the cross-entropy criterion. An admixture analysis [ 15 ] was performed using sparse nonnegative matrix factorization (sNMF) to estimate the population genetic structure. GWAS was performed using the rMVP package in R [ 16 ], applying GLM, MLM, and FarmCPU models. The FarmCPU model integrates multiple markers as covariates in a stepwise MLM to mitigate confounding effects. Population structure was adjusted using the first three principal components (PCs) and kinship matrix (K) calculated by the VanRaden method [ 17 ]. SNP markers were considered significantly associated with the trait when P ≤ 0.001. Manhattan and quantile-quantile (Q-Q) plots generated using the rMVP package. Candidate gene identification Candidate genes were identified based on the physical positions of significant SNPs. Genes located within the flanking regions of these SNPs were extracted using the IWGSC RefSeq v2.1 wheat genome assembly. Gene annotation was performed using publicly available wheat genome databases, including EnsemblPlants ( Triticum aestivum ) ( http://plants.ensembl.org/Triticum_aestivum ) and Persephone ( https://web.persephonesoft.com ). Functional annotations, including Gene Ontology (GO) terms and putative functions, were retrieved from the Persephone and Ensembl platforms to characterize potential candidate genes associated with the identified SNPs. The potential links to phenotypes were determined using the Knetminer tool. Results Phenotypic evaluations A set of 272 diverse genotypes was evaluated for agro-physiological traits. The descriptive statistics, including mean, range, coefficient of variance, and heritability, are presented in Table 1. An extensive range of variation was observed for all the traits. PH, FLL, BY, HI, TGW, TCC, CER, DH, and DM ranged from 56.75 to 130.25 cm, 14.0–29.5 cm, 26.25–66.75 t ha⁻¹, 6.82–34.17%, 17.22–41.78 g, 40.80–61.32 mg g⁻¹, 257–463 µmol m⁻² s⁻¹, 127–163 days, and 163–190 days, respectively. The coefficient of variation (CV%) ranged from 3.72% for DM to 30.42% for GWP. Broad-sense heritability was estimated for the studied traits, with values ranging from 0.44 for CER to 0.98 for DH. Table 1 Descriptive statistics for traits of 272 bread wheat genotypes based on best linear unbiased estimator (BLUE) values, averaged across two seasons Trait Mean Range Std. Dev. CV % H 2 PH 89.92 56.75-130.25 14.93 16.57 0.96 FLL 20.78 14-29.50 2.90 13.99 0.75 BY 41.91 26.25-66.75 5.87 14.01 0.91 GWP 10.65 3.48-20.42 3.24 30.42 0.93 GNPS 40 17-73 10.61 26.30 0.93 HI 20.35 6.82-34.17 6.04 29.68 0.96 TGW 27.53 17.22-41.78 4.21 15.26 0.91 TCC 52.25 40.80-61.32 3.79 7.26 0.70 CER 354.81 257-463 30.29 8.53 0.44 DH 144 127-163 9.16 6.32 0.98 DM 175 163-190 6.32 3.72 0.96 Abbreviations: Std standard deviation, CV coefficient of variation, H 2 : broad-sense heritability, PH: Plant Height, FLL: Flag Leaf Length, BY: Biological Yield, GWP: grain weight per plant, GNPS: Grain number per spike, HI: Harvest Index, TGW: Thousand Grain Weight, TCC: Total Chlorophyll Content, CER: Carbon Dioxide Exchange Rate, DH: Days to Heading, DM: Days to Maturity. Pearson’s correlation coefficients among physiological and agronomic traits in bread wheat are presented in Fig. 1. TGW showed significant positive correlations with HI, BY, and PH, but negative correlations with DH and DM. GWP was positively correlated with GNPS, TGW, BY, and HI, but negatively correlated with DM, DH, FLL, and PH. GNPS also showed negative correlations with DM, DH, and PH. PH was positively correlated with BY and TGW, but negatively associated with TCC and HI. DM and DH exhibited similar patterns, showing positive correlations with FLL and TCC, but negative associations with TGW, HI, and BY. Additionally, DM was negatively correlated with CER. CER was negatively correlated with TCC and FLL, but showed no significant correlation with TGW. HI had substantial positive correlations with TCC and BY, and significant negative correlations with PH and FLL. Population Structure Analysis Population structure analysis was performed on 251 bread wheat genotypes using 17,093 high-quality SNP markers. The optimal number of subpopulations (K) was determined by evaluating cross-entropy values across a range of K values (K = 1 to 10). Based on cross-entropy analysis (Fig. 2a), five subpopulations were identified as the most appropriate to explain the underlying genetic structure among the genotypes. The admixture plot generated for K = 5 (Fig. 2b) revealed that genotypes were assigned to five genetically distinct groups, although varying levels of admixture were observed among some individuals. Genome-wide association studies GWAS analysis using 17,093 SNPs identified significant MTAs for 11 agronomic and physiological traits. Three models —GLM, MLM (single-locus), and FarmCPU (multi-locus) — were employed to ensure reliability. The results from each year are presented in Tables S2 and S3. In addition, a GWAS was performed using the combined multi-year dataset to increase the power and stability of marker-trait associations (Table S4). In total, 320, 302, and 27 significant MTAs were identified using the GLM, FarmCPU, and MLM models, respectively (Table S4). Among the three models, GLM consistently detected the highest number of significant MTAs, while MLM detected the fewest SNPs. In the GLM model, 117, 167, and 36 MTAs were detected on genomes A, B, and D, respectively. In the FarmCPU model, 106, 164, and 32 MTAs were associated with genomes A, B, and D, respectively. In the MLM model, the corresponding numbers were 16, 8, and 3 MTAs on genomes A, B, and D (Fig. 3a). The number of SNPs associated with each trait was as follows: PH (40), FLL (71), BY (16), GWP (54), GNPS (51), HI (119), TGW (103), TCC (13), CER (32), DH (77), and DM (73) (Fig.3b). Out of the 40 SNPs associated with PH, 16, 20, and 4 were detected by GLM, FarmCPU, and MLM, respectively. For FLL, 21 SNPs were identified by GLM, 49 by FarmCPU, and one by MLM. Similarly, 7, 8, and 1 SNPs were associated with BY by GLM, FarmCPU, and MLM, respectively. For GWP, the highest number of SNPs (28) was detected using the FarmCPU model, followed by 25 SNPs with the GLM model and only 1 SNP with the MLM model. For GNPS, 30 SNPs were identified by FarmCPU and 21 by GLM; no significant SNPs were identified using MLM. In the case of HI, a total of 119 significant SNPs were identified, with 31, 86, and 2 SNPs detected by GLM, FarmCPU, and MLM, respectively. For TGW, 61 SNPs were identified by GLM, 39 by FarmCPU, and three by MLM. For the physiological traits, 13 SNPs were associated with TCC, of which GLM, FarmCPU, and MLM, respectively, detected 4, 7, and 2. For CER, 16 SNPs were detected by GLM, 14 by FarmCPU, and two by MLM. Among phenological traits, DH and DM were associated with 77 and 73 SNPs, respectively. Of these, DH had 60, 10, and 7 SNPs detected by GLM, FarmCPU, and MLM, respectively, while DM had 58, 11, and 4 SNPs detected by the same models (Tables 2–4; Fig. 3b). The Manhattan and quantile–quantile (Q–Q) plots for the GLM, MLM, and FarmCPU models are presented in Supplementary Fig. S1, illustrating the distribution and statistical significance of the detected associations across the genome. To ensure reliability, only SNPs consistently detected by all three models were considered stable. A total of 16 significant SNPs were identified across all methods (Table 5). Among these, 3 SNPs were associated with PH and TGW each; 2 SNPs were linked to HI, CER, and DH; and 1 SNP was associated with each of the following traits: TCC, FLL, BY, and DM. Co-localized and Stable MTAs The identification of common markers controlling multiple traits is particularly valuable in plant breeding programs, as it facilitates the simultaneous selection for several agronomic characteristics [18, 19]. In this study, two SNPs (tplb0057n10_689 and IAAV9150) were detected as being associated with more than one trait across multiple methods. Among these, one SNP (tplb0057n10_689) located at 22.45 Mbp on chromosome 2D is associated with HI and DH. Similarly, IAAV9150 on chromosome 6A was significantly associated with DH and DM (Table 5). The pleiotropic nature of these SNPs on DH, DM, and HI was further supported by correlation analysis (Fig. 1). We further checked co-detected common SNPs across two years using multiple methods and identified four highly stable SNPs: tplb0057n10_689, IAAV9150, Tdurum_contig48695_527, and wsnp_Ku_c19037_28455905. The first two SNPs are the previously mentioned pleiotropic SNPs, while the latter two are associated with PH and located on chromosome 7B at 457.38 Mbp (Table 5). Table2 The results of the genome-wide association study based on the GLM method Traits No. of significant marker-trait Chr. Pos. ( Mbp ) -log P - value PH 16 3A-3B-4A-4B-4D-5B- 5D-7B 12.73-724.79 3.58-4.77 FL 21 2B-3B-4A-5A-5B-7B 47-696.67 3.59-5.38 BY 7 2B-7A 140.77-657.51 3.63-3.96 GWP 25 2B-3A-3D-5B-7B 41.27-703.24 3.59-4.97 GNPS 21 2B-3B-5B-6B-7B 123.74-741.80 3.58-4.44 HI 31 1B-2D-3B-4A-5B-6A-6D-7B 8.69-740.03 3.58-5.27 TGW 61 1B-2B-3D-6A-6B-7A 71.44-788.52 3.60-4.61 TCC 4 2B-4B-6A 19.74-486.82 3.64-3.92 CER 16 1B-2D-3B-5A-5B-7B 493.12-750.60 3.63-4.79 DH 60 1A-1B-2A-2B-2D-3B-3D - 5A-5B-6A-7A 4.50-783.47 3.58-7.10 DM 58 1A-1B-2A-2B-2D-3B-4A-5A-5B-6A-7A 6.72-826.08 3.59-6.59 Table 3 The results of the genome-wide association study based on the FarmCPU method Traits No. of significant marker-trait Chr. Pos. ( Mbp ) -log P - value PH 20 1B-2B-3B-4A-4D-5D-7B 12.77-497.88 3.61-4.97 FL 49 2B-3B-4A-5A-5B-6A-6B-7A-7B 12.83-752.83 3.60-5.54 BY 8 5A-7A-7D 158.29-668.52 3.61-4.37 GWP 28 2B-3A-5A-5B-7B 25.39-698.50 3.63-4.71 GNPS 30 2B-3B-5B-6B-7B 54.78-741.80 3.58-4.24 HI 86 1A-1B- 2A-2B-2D-3A 3B-4A-5A-5B-5D-6A-6B-7A-7B 3.11-796.80 3.58-5.56 TGW 39 1B-6A-6B-7A 26.18-674.27 3.59-4.69 TCC 7 2B-6A 68.59-440.25 3.46-4.67 CER 14 1B-2B-2D-3B-5A-5B-7B 28.33-741.57 3.59-4.63 DH 10 1A-1B-2A-2B-2D-3B-5B-6A-6D 6.73-776.55 3.62-4.92 DM 11 1A-1B-2A-2B-2D-3A-3B-6A 6.73-776.55 3.64-5.53 Table 4 The results of the genome-wide association study based on the MLM method Traits No. of significant marker-trait Chr. Pos. ( Mbp ) -log P - value PH 4 4D-7B 12.77-457.38 3.68-4.25 FL 1 4A 595.55 3.64 BY 1 7A 657.51 3.78 GWP 1 3A 511.07 3.63 GNPS - - - - HI 2 2D 22.44-22.45 3.62-3.76 TGW 3 7A 674.27 3.61-4.23 TCC 2 2B 68.59-69.04 3.72-3.96 CER 2 1B-2D 561.70-654.82 3.58-3.78 DH 7 2D-6A 6.72-22.45 3.75-4.12 DM 4 6A 6.73 3.62 Table 5 Common Marker-Trait Associations Identified Across FarmCPU, GLM, and MLM Methods Trait SNPs Chr. Pos. (Mbp) REF ALT Effect -log P - value TCC Tdurum_contig42636_1245 2B 68.59 C T -1.781395477 3.68 PH Kukri_rep_c68594_530 4D 12.77 A G -4.094561307 4.07 Tdurum_contig48695_527 7B 457.38 C T 5.383632354 4.97 wsnp_Ku_c19037_28455905 7B 457.40 G A 5.383632354 4.97 FLL wsnp_Ex_c21165_30292808 4A 595.55 G A -1.353385681 5.55 BY RAC875_c20121_561 7A 657.51 G A 2.787908718 4.38 HI tplb0053n05_793 2D 22.44 C T 2.248129661 5.43 tplb0057n10_689* 2D 22.45 C T 2.236985176 5.45 TGW BS00026622_51 7A 674.27 G T 1.351655156 4.69 RAC875_c19111_628 7A 674.27 C T 1.208374003 3.99 wsnp_Ku_rep_c104159_90704469 7A 674.27 A G 1.208374003 3.99 CER wsnp_Ex_rep_c66389_64588992 1B 561.70 A G -12.39567732 4.63 Jagger_c7527_143 2D 645.82 T C 8.218462186 3.85 DH tplb0057n10_689* 2D 22.45 C T -2.045626942 6.09 IAAV9150* 6A 6.73 G A -1.659482056 4.93 DM IAAV9150* 6A 6.73 G A -1.356264894 5.53 MTAs marked with a star indicate co-localized loci that are associated with multiple traits, while bold SNPs indicate consistent detection across two cropping seasons (2019 and 2020). Candidate gene identification To further understand the genetic basis of the studied traits, SNPs consistently identified by all three methods, associated with multiple traits, and detected across both years, were selected for candidate gene prediction. We predicted a total of 139 high-confidence genes around the 16 MTAs associated with nine traits (TCC, PH, FLL, BY, HI, TGW, CER, DH, and DM). The candidate genes encompass a variety of functions (Fig. 4; Table S5), including transcriptional regulation (e.g., zinc finger proteins, MYB domains), enzyme activity (e.g., cytochrome P450, protein kinases), structural/transport roles (e.g., H+-transporting ATPases, nucleolar proteins, 11 genes), photosynthetic efficiency (e.g., plastocyanin-like domains), stress/defense responses (e.g., leucine-rich repeat proteins, disease resistance proteins), and phenological regulation (e.g., response regulators, flavonoid 3'-monooxygenase). Several putative candidate genes for CER are annotated to contain plastocyanin-like domains, which are involved in electron transfer and photosynthesis —key processes for enhancing photosynthetic efficiency [20]. Similarly, four putative cytochrome P450 genes associated with DH, DM, and HI were identified. These genes are known to regulate hormonal pathways that influence developmental timing and resource allocation, thereby affecting phenology and grain yield partitioning under diverse environmental conditions [21, 22]. Additionally, 10 putative candidate genes surrounding family significant SNPs associated with FLL, TGW, CER, and PH were annotated as members of the zinc finger protein family, which are known to regulate grain production and plant growth [23, 24]. Glycosylation is a crucial post-translational modification of proteins in plants, playing a pivotal role in regulating numerous biological processes. Glycosyltransferases (GTs) are among the most important enzymes mediating this modification [5]. Nine candidate genes annotated as glucosyl transferases were identified in association with DH and HI. Four genes annotated as peroxidase were associated with PH. A previous study in bread wheat and its F1 hybrids reported a significant negative correlation between peroxidase activity and plant height [25]. Further examples are presented in Fig. 4 and Supplementary Table S5. While these findings are promising, more research is needed to confirm the role of these candidate genes in related traits. Discussion Phenotypic variation The comprehensive phenotypic evaluation of the 272 bread wheat genotypes revealed substantial variability across all agro-physiological traits, underlining the genetic diversity within the GWAS panel. Descriptive statistics demonstrated a wide range of trait values. Medium to high heritability estimates were obtained for each trait, ranging from 0.44 to 0.98, indicating differing levels of genetic control and trait stability. Highly significant variation among genotypes and the high heritability for most traits confirmed the suitability of GWAS analysis. GWP was positively correlated with GNPS, TGW, BY, and HI, in agreement with previous reports [26, 27, 28]. The negative correlation between phenological traits (DH, DM) and yield-related traits (GWP, GNSP, TGW, BY, HI) suggests that early-maturing wheat genotypes enhance resource allocation to grain production, boosting yield. This aligns with studies such as [29, 30], which emphasize the role of optimized phenology in maximizing wheat yield potential. Population Structure Population structure analysis of 251 wheat genotypes using 17,093 high-quality SNPs revealed five distinct subpopulations. This genetic stratification, supported by cross-entropy analysis and admixture patterns, highlights the underlying genetic diversity within the panel. The observed admixture in some genotypes suggests gene flow and historical hybridization among these groups, consistent with findings from previous wheat diversity studies [31, 32]. Understanding such population structure is improve the accuracy of marker-trait associations. GWAS and candidate genes Recent advancements in GWAS-based breeding strategies have played a crucial role in the development of improved cultivars with desirable agronomic traits [33]. Multiple GWAS models have been successfully applied in association analyses across various crops, including wheat, demonstrating their effectiveness in diverse genetic backgrounds [34, 35, 36, 37, 38]. In this study, a total of 320, 302, and 27 significant MTAs were identified using the GLM, FarmCPU, and MLM models, respectively, across 11 key agronomic and physiological traits. The GLM model detected the highest number of MTAs, while the MLM model identified the fewest. This pattern aligns with previous findings, where the GLM’s higher sensitivity often results in a greater number of false positives. In contrast, the MLM’s correction for population structure and kinship makes it more conservative and less sensitive. The FarmCPU model, which balances statistical power with control of false discovery, identified an intermediate number of MTAs and has been increasingly favored for the dissection of complex traits [39, 40, 41]. To further understand the genetic basis of the studied traits, we focused on the most reliable MTAs, those consistently identified by all three GWAS methods, as well as SNPs showing pleiotropic effects across multiple traits and stability across two growing seasons. This analysis resulted in 16 MTAs associated with multiple characteristics, including PH, TGW, HI, and phenological traits such as DH and DM. Notably, two pleiotropic SNPs—tplb0057n10_689 on chromosome 2D (linked to HI and DH) and IAAV9150 on chromosome 6A (linked to DH and DM)-were consistently detected across both seasons, underscoring their stability. These SNPs, along with two additional SNPs associated with PH identified in both years, represent valuable targets for MAS in wheat breeding programs aiming for multi-trait improvement. Several of the MTAs identified in this study were consistent with those reported in previous research, highlighting their potential value for MAS in wheat improvement. For instance, MTAs associated with DH have been reported on chromosome 6A [42, 43], and those for DM were also located on chromosome 6A [44, 45]. Plant height-related MTAs have been previously identified on chromosomes 4D and 7B [38, 44, 46]. Similarly, MTAs for thousand-grain weight (TGW) have been reported on chromosome 7A [46], and those related to leaf chlorophyll content were identified on chromosome 2B [47]. To identify potential candidate genes underlying these stable MTAs, we used the physical positions of the 16 SNPs mapped to the wheat reference genome. This led to the identification of 139 high-confidence genes associated with nine traits: TCC, PH, FLL, BY, HI, TGW, CER, DH, and DM. These genes spanned a wide range of functional categories. The most represented groups included the zinc finger protein family (10 genes), glucuronosyltransferases (9 genes), followed by genes with plastocyanin-like domains (8), and protein kinase domains (6). For the stable and pleiotropic SNP tplb0057n10_689 located on chromosome 2D, we identified 10 candidate genes, which encode glucuronosyltransferases, ribosome-binding factors, protein kinases, cytochrome P450s, and F-box-associated domain (FBD) proteins. These genes are potentially involved in signaling pathways, secondary metabolism, and protein degradation. Similarly, for SNP IAAV9150 on chromosome 6A, we identified 10 genes with functions related to seed maturation, grain number determination, flowering time regulation, and photosynthetic efficiency, based on annotations from NetMinerWheat. These findings underscore the presence of potential regulatory genes underlying pleiotropic SNP effects and provide strong candidates for future functional validation and molecular breeding efforts in wheat. Conclusion In most crops, including wheat, quantitative traits exhibiting continuous variation are inherently complex and governed by numerous loci with both main effects and interactions. In this study, we employed three GWAS models: GLM, MLM, and FarmCPU to dissect the genetic architecture of 11 key agro-physiological traits in a diverse panel of wheat accessions. We identified 16 MTAs, including four highly stable SNPs that were consistently detected across all three models and in all growing seasons. Candidate gene prediction around these MTAs revealed several genes involved in transcriptional regulation, enzyme activity, structural/transport roles, photosynthetic efficiency, stress/defense responses, and phenological regulation. These findings provide valuable genomic resources and candidate loci for MAS and the genetic improvement of complex traits in wheat breeding programs aimed at enhancing both productivity and adaptability. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All phenotypic data generated and analysed during this study are included in this published article and its supplementary information files. The genotypic datasets used and/or analysed during the current study are available in the Zenodo repository (https://doi.org/10.5281/zenodo.17141663). Competing interests The authors declare no competing interests. Funding Not applicable. Authors' contributions Designed study: S.S.N., G.M.N. Coordinated study: G.M.N., S.S.N. Provided data or materials: A.B; A.M.A. Performed experiments: Z.A.B. Analyzed data: S.S.N., Z.A.B. The initial manuscript was written by S.S.N. Z.A.B. All authors contributed to and approved the final version of the manuscript. Acknowledgements We gratefully acknowledge the IPK genebank for providing the wheat germplasm used in this study and Afzalipour Research Institute, Shahid Bahonar University of Kerman for providing field materials. References Green AJ, Berger G, Griffey C, Pitman R, Thomason W, Balota M, et al. Genetic yield improvement in soft red winter wheat in the Eastern United States from 1919 to 2009. Crop Sci . 2012;52(5):2097-2108. FAOSTAT. Food and Agriculture Organization of the United Nations. Production domain. In: Crops [Internet]. Rome, Italy: FAO; 2023 [cited 2025 Apr 25]. Available from: https://www.fao.org/faostat. Hall AJ, Richards RA. Prognosis for genetic improvement of yield potential and water-limited yield of major grain crops. Field Crops Res . 2013; (143):18-33. Morales F., Ancín M., Fakhet D., González-Torralba J., Gámez A.L., Seminario A., et al. Photosynthetic metabolism under stressful growth conditions as a basis for crop breeding and yield improvement. Plants. 2020;9(1):88. Ding Y, Fang H, Gao Y, Fan G, Shi X, Yu S, et al. Genome-wide association analysis of time to heading and maturity in bread wheat using 55K microarrays. Front Plant Sci . 2023; (14):1296197. Wei TM, Chang XP, Min DH, Jing RL. Analysis of genetic diversity and tapping elite alleles for plant height in drought tolerant wheat varieties. Acta Agron. Sin. 2010;36(6):895-904. Sheoran S., Jaiswal S., Kumar D., Raghav N., Sharma R., Pawar S., Paul S., Iquebal M.A., Jaiswar A., Sharma P., Singh R., Singh C.P., Gupta A., Kumar N., Angadi U.B., Rai A., Singh G.P., Kumar D., Tiwari R. Uncovering genomic regions associated with 36 agro-morphological traits in Indian spring wheat using GWAS. Front. Plant Sci. 2019; (10):527 . Wang S, Xu S, Chao S, Sun Q, Liu S, Xia G. A genome-wide association study of highly heritable agronomic traits in durum wheat. Front Plant Sci. 2019; (10):919 . Jamil M, Ali A, Gul A, Rasheed A, Ahmed I, Imtiaz M, et al. Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat. BMC Plant Biol. 2019; (19):149 . Malik P, Kumar J, Singh S, Sharma S, Meher PK, Sharma MK, et al. Single-trait, multi-locus, and multi-trait GWAS using four different models for yield traits in bread wheat. Mol Breed . 2021;41(7):46–21 . Devate NB, Krishna H, Parmeshwarappa SKV, Manjunath KK, Chauhan D, Singh S, et al. Genome-wide association mapping for component traits of drought and heat tolerance in wheat. Front Plant Sci . 2022; (13):943033. VSN International. GenStat for Windows, 14th ed. Hemel Hempstead, UK: VSN International Ltd.; 2011 . Allard RW. Principles of plant breeding: John Wiley & Sons; 1999. Frichot E, François O. LEA: An R package for landscape and ecological association studies. Methods Ecol Evol . 2015;6(8):925-9. Patterson N, Price AL, Reich D. Population structure and eigenanalysis. PLoS Genet . 2006;2(12):e190. Yin L, Zhang H, Tang Z, Xu J, Yin D, Zhang Z, et al. rMVP: a memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study. Genomics, proteomics & bioinformatics . 2021;19(4):619-28. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91:4414-23. Tuberosa R, Salvi S, Sanguineti MC, Landi P, Maccaferri M, Conti S. Mapping QTLs regulating morpho-physiological traits and yield in drought-stressed maize: case studies, shortcomings and perspectives. Ann Bot. 2002; (89):941-963 . Hittalmani S, Huang N, Courtois B, Venuprasad R, Shashidhar HE, Zhuang JY, et al. Identification of QTL for growth and grain yield-related traits in rice across nine locations of Asia. Theor Appl Genet . 2003; (107):679-690 . Schmidt L, Christensen HE, Harris P. Structure of plastocyanin from the cyanobacterium Anabaena variabilis. Biol Crystallogr . 2006; 62(9):1022-1029 . Li Y, Wei K. Comparative functional genomics analysis of cytochrome P450 gene superfamily in wheat and maize. BMC Plant Biol . 2020;20(1):93. Gunupuru LR, Arunachalam C, Malla KB, Kahla A, Perochon A, Jia J, et al. A wheat cytochrome P450 enhances both resistance to deoxynivalenol and grain yield. PLoS One. 2018;13(10):e0204992. Li S, Zhao B, Yuan D, Duan M, Qian Q, Tang L, et al. Rice zinc finger protein DST enhances grain production through controlling Gn1a/OsCKX2 expression. Proc Natl Acad Sci U S A. 2013;110(8):3167-3172. Xu T, Lee HJ, Sy ND, Kang H. Wheat ( Triticum aestivum ) zinc finger-containing glycine-rich RNA-binding protein TaRZ1 affects plant growth and defense response in Arabidopsis thaliana. Plant Growth Regul . 2015;76(3):243-50. Singhal N, Mehta S, Singh M. Peroxidase activities in relation to plant height and grain weight in bread wheat (Triticum aestivum L.). Theor Appl Genet . 1979;55(2):87-92. Kumar K, Tarkeshwar, Singh M, Gupta A, Singh S, Gaur SC. Character association and path coefficient studies in bread wheat ( Triticum aestivum L. em. Thell ). Int J Curr Microbiol Appl Sci. 2020;Special Issue-11:1323-30. Nageshwar S, Singh L, Kumar S, Kumar N, Singh A. Correlation studies among yield and its components in bread wheat ( Triticum aestivum L.). Pharma Innov J. 2021;10(12):978-82. Gaur AK, Singh PN, Gaur SC, Tarkeshwar, Kumar K, Gautam U, et al. Analysis of correlation and path coefficient in F1 and F2 generations of bread wheat (Triticum aestivum L.). Int J Res Agron . 2025;8(2):302-8. Mondal S, Singh R, Crossa J, Huerta-Espino J, Sharma I, Chatrath R, et al. Earliness in wheat: a key to adaptation under terminal and continual high temperature stress in South Asia. Field Crops Res . 2013; ( 151 ):19-26. Slafer GA, Savin R, Sadras VO. Coarse and fine regulation of wheat yield components in response to genotype and environment. Field Crops Res. 2014; ( 157 ):71-83 . He F, Pasam R, Shi F, Kant S, Keeble-Gagnere G, Kay P, et al. Exome sequencing highlights the role of wild-relative introgression in shaping the adaptive landscape of the wheat genome. Nat Genet. 2019;51(5):896-904. Chou C-H, Lin H-S, Wen C-H, Tung C-W. Patterns of genetic variation and QTLs controlling grain traits in a collection of global wheat germplasm revealed by high-quality SNP markers. BMC Plant Biol . 2022;22(1):455. Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, et al. Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. Mol Breeding . 2022;42(1):1. Aleliūnas A, Gorash A, Armonienė R, Tamm I, Ingver A, Bleidere M, et al. Genome-wide association study reveals 18 QTL for major agronomic traits in a Nordic–Baltic spring wheat germplasm. Front Plant Sci . 2024;15(1):1393170. Tsonev S, Dragov R, Taneva K, Christov NK, Bozhanova V, Todorovska EG. Genome-wide association studies of agronomic and quality traits in durum wheat. Agriculture . 2024;14(10):1743. Tyrka M, Krajewski P, Bednarek PT, Rączka K, Drzazga T, Matysik P, et al. Genome-wide association mapping in elite winter wheat breeding for yield improvement. J Appl Genet . 2023;64(3):377-91. Soumya PR, Burridge AJ, Singh N, et al. Population structure and genome-wide association studies in bread wheat for phosphorus efficiency traits using 35 K Wheat Breeder’s Affymetrix array. Sci Rep . 2021;11(1):7601 . Muhammad A, Li J, Hu W, Yu J, Khan SU, Khan MHU, et al. Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using different GWAS models. Sci Rep . 2021;11(1):6767. Liu X, Huang M, Fan B, Buckler ES, Zhang Z. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet . 2016;12(2):e1005767. Kaler AS, Gillman JD, Beissinger T, Purcell LC. Comparing different statistical models and multiple testing corrections for association mapping in soybean and maize. Front Plant Sci. 2020;10:1794. Yoosefzadeh-Najafabadi M, Eskandari M, Torabi S, Torkamaneh D, Tulpan D, Rajcan I. Machine-learning-based genome-wide association studies for uncovering QTL underlying soybean yield and its components. IJMS. 2022;23(10):5538. Sandhu KS, Burke AB, Merrick LF, Pumphrey MO, Carter AH. Comparing performances of different statistical models and multiple threshold methods in a nested association mapping population of wheat. Front Plant Sci . 2024;15:1460353. Wang Z, Lai X, Wang C, Yang H, Liu Z, Fan Z, Li J, Zhang H, Liu M, Zhang Y. Exploring the drought tolerant quantitative trait loci in spring wheat. Plants (Basel). 2024;13(6):898. Alemu A, Suliman S, Hagras A, Thabet S, Al-Abdallat A, Abdelmula AA, et al. Multi-model genome-wide association and genomic prediction analysis of 16 agronomic, physiological and quality related traits in ICARDA spring wheat. Euphytica. 2021;217(11):205. Sukumaran S, Dreisigacker S, Lopes M, Chavez P, Reynolds MP. Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theor Appl Genet . 2015;128(2):353-63. Guan P, Lu L, Jia L, Kabir MR, Zhang J, Lan T, et al. Global QTL analysis identifies genomic regions on chromosomes 4A and 4B harboring stable loci for yield-related traits across different environments in wheat ( Triticum aestivum L .). Front Plant Sci) . 2018;9:529. Maulana F, Ayalew H, Anderson JD, Kumssa TT, Huang W and Ma X-F . Genome-Wide Association Mapping of Seedling Heat Tolerance in Winter Wheat. Front. Plant Sci . 2018;9:1272. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFig.S1.docx snpdata.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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-7424201","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524503784,"identity":"14b1790e-feff-4010-9d97-89bd116b5444","order_by":0,"name":"Zahra Alizadeh-Bidarani","email":"","orcid":"","institution":"Shahid Bahonar University of Kerman","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Alizadeh-Bidarani","suffix":""},{"id":524503785,"identity":"da89a7fc-c206-4332-bfdf-4a7fc3c6b8ce","order_by":1,"name":"Ghasem Mohammadi-Nejad","email":"","orcid":"","institution":"Shahid Bahonar University of Kerman","correspondingAuthor":false,"prefix":"","firstName":"Ghasem","middleName":"","lastName":"Mohammadi-Nejad","suffix":""},{"id":524503786,"identity":"76d197eb-bedf-4e2c-9df4-6446d77cff18","order_by":2,"name":"Somayeh Sardouei-Nasab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYJACCSDmATGYGRhswDwGBgPitaQRr4UBquUwgocLyLeffXjjY5uNjDn78YefC/6cT5zZwPzwA0PBPZxaDM6kG1vObEvjsezJMZae2XY7cTYDm7EEg0Exbi0MaWzSvG2HeQwO5DBI8zbcTpzHwGAGFE/A7bD+ZyAt/3kMzj9//JvnzzmgFvZveLUw3ADbcoDH4EaCmTQP2wGgw3jw22Jw4xmz5YxzyUAtb8ysZ7YlG89s5imWSMDrsDTGGx/K7OwNzqc/vl3wx052xvH2jR8+/MHjMEwAjB0GkjSMglEwCkbBKMAAAPtwTOW+Bi+nAAAAAElFTkSuQmCC","orcid":"","institution":"Shahid Bahonar University of Kerman","correspondingAuthor":true,"prefix":"","firstName":"Somayeh","middleName":"","lastName":"Sardouei-Nasab","suffix":""},{"id":524503787,"identity":"6d8c40cd-2c0b-418b-bcc9-07fc2ed74719","order_by":3,"name":"Azadeh Lohrasbi-Nejad","email":"","orcid":"","institution":"Shahid Bahonar University of Kerman","correspondingAuthor":false,"prefix":"","firstName":"Azadeh","middleName":"","lastName":"Lohrasbi-Nejad","suffix":""},{"id":524503788,"identity":"7470a437-c91a-493a-9f64-d44ec037a83e","order_by":4,"name":"Andreas Börner","email":"","orcid":"","institution":"Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Börner","suffix":""},{"id":524503789,"identity":"4a559488-420c-4584-84b1-7c0ab4c6d549","order_by":5,"name":"Ahmad M. Alqudah","email":"","orcid":"","institution":"Qatar University","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"M.","lastName":"Alqudah","suffix":""}],"badges":[],"createdAt":"2025-08-21 08:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7424201/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7424201/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92847070,"identity":"ee718a91-5b55-49a5-9c8e-85aefe7b1a13","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":313135,"visible":true,"origin":"","legend":"","description":"","filename":"Alizadehetal.BMCGenomic7.docx","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/558f71772455adea2d8ffc94.docx"},{"id":92847326,"identity":"1b4fe810-6e14-4f23-a881-8f2fd55dc26d","added_by":"auto","created_at":"2025-10-06 09:51:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":256636,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.docx","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/5d931ab4dede8700048187f7.docx"},{"id":92847068,"identity":"bb44027a-34b7-44b4-b189-3c40fdb82d17","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27315,"visible":true,"origin":"","legend":"","description":"","filename":"tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/2dfbfd7338c4d7ab1cfaab23.docx"},{"id":92847327,"identity":"45d355e6-711f-44b7-b16f-a5eab04d5faa","added_by":"auto","created_at":"2025-10-06 09:51:00","extension":"json","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8186,"visible":true,"origin":"","legend":"","description":"","filename":"c09129f4eb1f4e7690cdaad0c85b026f.json","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/d1f73797389bdb7150659f9f.json"},{"id":92847330,"identity":"505fea46-4085-4415-a7dc-67541abe0daf","added_by":"auto","created_at":"2025-10-06 09:51:00","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":749995,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.S1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/efdd654a7b3f92519aa415e3.docx"},{"id":92847329,"identity":"ab31ed3a-288c-42e0-85a8-75f149ec5fcb","added_by":"auto","created_at":"2025-10-06 09:51:00","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":200118,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1S5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/ac15a850bfa1a35e090e3e80.xlsx"},{"id":92847083,"identity":"39c6647b-92f3-4be5-b927-ab34fc99e096","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15576112,"visible":true,"origin":"","legend":"","description":"","filename":"snpdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/c5e4e71fca074fc69bb7d718.xlsx"},{"id":92847332,"identity":"58c37202-61bd-4006-b4c8-c4896cf7f685","added_by":"auto","created_at":"2025-10-06 09:51:00","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":182945,"visible":true,"origin":"","legend":"","description":"","filename":"c09129f4eb1f4e7690cdaad0c85b026f1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/d2fb4215ffb9ec321a5267b5.xml"},{"id":92847072,"identity":"fcc81d1e-2bf0-43f9-aa7e-e3d6caafc42e","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120864,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/fb367d3d26c70e069ac61897.jpeg"},{"id":92847079,"identity":"b2af8326-5104-4e85-9135-f7cbff2d93ac","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124473,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/79d9168346d8be2e69d2c525.jpeg"},{"id":92847075,"identity":"8c009f46-967c-4ade-983b-356628c4053b","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":364783,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/5b130de7bbfcfbaedeb25a23.jpeg"},{"id":92847077,"identity":"d73e16d0-3909-426b-9a97-03c12a2bce5d","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":289445,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/0ddd8648f3cfe64b60251c34.jpeg"},{"id":92847334,"identity":"4806762c-8e45-4c34-b7a4-b84d0c93195f","added_by":"auto","created_at":"2025-10-06 09:51:00","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120864,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/b08b8226f5d1aed3246db5e7.jpeg"},{"id":92847094,"identity":"d50b160b-1664-4e27-8d02-0e3acd9a3600","added_by":"auto","created_at":"2025-10-06 09:43:01","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":487213,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/cc6cce3bfea79b92219bdd59.jpeg"},{"id":92847089,"identity":"a7a2b90e-66bd-4701-b70d-6bf515589af7","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":289445,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/167206b15836993bf8462be7.jpeg"},{"id":92848279,"identity":"7906d40e-0af2-440b-bef9-6908b84b1a68","added_by":"auto","created_at":"2025-10-06 10:07:00","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48138,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/a78809c55cea8b179eea1044.png"},{"id":92847084,"identity":"a8c0f8b4-23c4-40ad-ac95-24aba2c9b6a6","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24970,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/3317212168b052465a6a84aa.png"},{"id":92848122,"identity":"8cf281c9-a0d2-4e9b-a6e6-2d8404fdc3dd","added_by":"auto","created_at":"2025-10-06 09:59:00","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53085,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/4f74e18d3ab6b752674164f5.png"},{"id":92847080,"identity":"66ba7312-a693-433e-ad01-b1f6bc7f03f1","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53314,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/778378748705f50e8e252554.png"},{"id":92848124,"identity":"cb727c6c-c881-4629-85ec-f65faae28768","added_by":"auto","created_at":"2025-10-06 09:59:00","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48138,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/99383b074531e47aae77eb96.png"},{"id":92847092,"identity":"d7282893-b2de-4761-93ee-6d5f47369006","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83048,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/2b942556e42b3cf3bb579d31.png"},{"id":92847328,"identity":"724fb957-2423-4b4a-b3de-e43101669d64","added_by":"auto","created_at":"2025-10-06 09:51:00","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53314,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/e1f38030c50380edfa718f55.png"},{"id":92847093,"identity":"ecf0b5b5-297d-4ef2-baa8-b5c03961a536","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"xml","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181586,"visible":true,"origin":"","legend":"","description":"","filename":"c09129f4eb1f4e7690cdaad0c85b026f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/cf747e2df1d2e3a9a6c1a01d.xml"},{"id":92847091,"identity":"6fb102f6-711d-4260-a989-7c5ab8c0ab7c","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191747,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/9e8503bf53d08a6c32a77f65.html"},{"id":92847065,"identity":"f061d435-4e55-4cd9-8374-c1656e8b4bca","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111699,"visible":true,"origin":"","legend":"\u003cp\u003ePearson’s correlation coefficients among physiological and agronomic traits in bread wheat. Abbreviations: PH: Plant Height, FLL: Flag Leaf Length, BY: Biological Yield, GWP: grain weight per plant, GNPS: Grain number per spike, HI: Harvest Index, TGW: Thousand Grain Weight, TCC: Total Chlorophyll Content, CER: Carbon Dioxide Exchange Rate, DH: Days to Heading, DM: Days to Maturity.\u003c/p\u003e\n\u003cp\u003eStatistical significance is indicated as follows: * p \u0026lt; 0.05; ** p \u0026lt; 0.01; *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/a8988beb931b49ffd1578e46.png"},{"id":92847066,"identity":"01c11f0e-2768-4262-89e6-b2697b73ae41","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70317,"visible":true,"origin":"","legend":"\u003cp\u003eCross-entropy plot for determining the optimal number of clusters (K = 1–10). The lowest cross-entropy value was observed at K = 5, indicating the most appropriate number of subpopulations (a). Population structure of 251 bread wheat genotypes based on 17,093 SNP markers. Each color represents a distinct subpopulation (K = 5) (b). The horizontal axis shows the individual genotypes, while the vertical axis indicates the proportional membership of each genotype to the inferred subpopulations.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/2d75268cbe9e590535aa8751.png"},{"id":92847325,"identity":"d3659a11-5500-4589-b83d-8d0f2ceae7fd","added_by":"auto","created_at":"2025-10-06 09:51:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40428,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of significant SNPs distributed across different genomes (a), Significant SNPs identified using three GWAS models: GLM, FarmCPU, and MLM (b).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/1770787288d285567d4c66a3.png"},{"id":92847073,"identity":"e275c7ea-dba0-46d6-a151-fb514495475d","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136927,"visible":true,"origin":"","legend":"\u003cp\u003ePutative candidate genes responsible for important functions associated with various Agro-Physiological traits in wheat.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/baaa390f461c378be31b26b8.png"},{"id":93775605,"identity":"73ef8883-b258-4e31-a4f3-708c0cd0c48e","added_by":"auto","created_at":"2025-10-17 12:32:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1397939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/633f47a6-500e-44b5-abb4-4d845f004ab9.pdf"},{"id":92847069,"identity":"4d99b536-c7c1-464b-bc7d-52dbe987f71a","added_by":"auto","created_at":"2025-10-06 09:43:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":749995,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.S1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/c1104ba84abf1ada84196fe6.docx"},{"id":92847095,"identity":"08d44a5d-42b4-46c1-a4d4-f048d4a66969","added_by":"auto","created_at":"2025-10-06 09:43:01","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15576112,"visible":true,"origin":"","legend":"","description":"","filename":"snpdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7424201/v1/6a7564231662fc627c661fab.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-Wide Association Study Reveals Key SNP Markers for Agro-Physiological Traits in Bread Wheat (Triticum aestivum L.)","fulltext":[{"header":"Background","content":"\u003cp\u003eWheat is the third most important food crop globally, following maize and rice [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Currently, wheat is cultivated on approximately 220\u0026nbsp;million hectares worldwide, with the largest harvested areas found in Asia (43.7%), followed by Europe (33%) and the Americas (16.5%) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As the global population is projected to reach 9.800\u0026nbsp;billion by 2050, the demand for wheat is expected to rise by approximately 1%\u0026ndash;1.6% annually [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent climate change, resource scarcity, and increased threats from pests and diseases have presented new challenges to wheat production, quality, and growth cycles [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In recent years, the advancement of sequencing technologies, along with reduced sequencing costs and the availability of reference genomes, has accelerated the development and use of SNP microarrays in crops such as wheat. These technologies have enabled the discovery of numerous genetic loci associated with diverse agronomic and physiological traits [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral studies have reported marker-trait associations (MTAs) in wheat through GWAS. For example, Wei et al [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] detected several stable SNPs on chromosomes 2A, 2B, 2D, 3B, 4B, 5A, 5D, 7B, and 7D associated with plant height. Similarly, Sheoran et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] reported six MTAs associated with plant height, located on chromosomes 1B, 2A, 2B, 7A, and two on 5D. Wang et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported 90 MTAs associated with various agronomic traits. Jamil et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] identified 139 MTAs associated with seven traits, including days to heading, grain filling duration, plant height, spikes per plant, grain number per spike, thousand kernel weight, and grain yield. Malik et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] evaluated GWAS studies using single-trait, multi-locus, and multi-trait models in bread wheat, identifying 34 marker-trait associations for yield-related traits. Devate et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] detected 57 MTAs associated with various agronomic traits under heat and drought stress conditions in bread wheat. In the present study, we aimed to find genomic regions and molecular markers associated with critical agronomic and physiological traits in wheat to support MAS and improve wheat breeding efficiency. Thus, we conducted a GWAS study on 251 wheat accessions using the iSelect 90K SNP array. The objectives were to: (1) identify MTAs for various agro-physiological traits, (2) evaluate correlations among these traits and identify SNPs linked to multiple traits, and (3) discover candidate genes influencing these traits.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003ePlant Material and Experimental Conditions\u003c/h2\u003e\n \u003cp\u003eA diverse panel comprising 272 bread wheat genotypes (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e), sourced from the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), was used in the field experiments. The study was laid out in a rectangular lattice design (16 \u0026times; 17) with two replications. Each plot measured 1.2 m \u0026times; 5 m, with row spacing of 0.25 m.\u003c/p\u003e\n \u003cp\u003eField trials were conducted across two consecutive cropping seasons (2019\u0026ndash;2021) at the Agricultural Research Station of the Department of Agronomy and Plant Breeding, Shahid Bahonar University of Kerman, Iran (30.25\u0026deg;N, 57.10\u0026deg;E; 1760 m above sea level). The experimental soil was classified as sandy loam, with a pH of 7.9 and an electrical conductivity (EC) of 2.11 dS/m.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePhenotypic Data Collection and Analysis\u003c/h3\u003e\n\u003cp\u003eThe following traits were evaluated: plant height (PH; cm), measured as the average height of three plants from soil to spike tip (excluding awns); flag leaf length (FLL; cm), measured as the average length of three flag leaves from collar to tip; days to heading (DH), recorded when 100% of the spikes had fully emerged; and days to maturity (DM), recorded when 100% of the spikes had yellowed; total chlorophyll content (TCC; mg g⁻\u0026sup1;), measured as the average chlorophyll content of three plants using a SPAD-502 meter (Konica Minolta, Japan), carbon dioxide exchange rate (CER; \u0026micro;mol m⁻\u0026sup2; s⁻\u0026sup1;), measured using an ADC BioScientific Limited (UK) device; thousand grain weight (TGW; g), measured as the weight of 1.000 grains averaged across replicates; biological yield (BY; t ha⁻\u0026sup1;), measured as total dry biomass per plot expressed per hectare; harvest index (HI; %), measured as the grain weight to total biomass ratio; Grain number per spike (GNPS) calculated as the average of grain number in the main spikes of three randomly plants for each genotype, and grain weight per plant (GWP; gr/m\u003csup\u003e2\u003c/sup\u003e). Best Linear Unbiased Estimates (BLUEs) across years were calculated for all traits using the REML (Restricted Maximum Likelihood) procedure in GenStat, 14th Edition [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], with genotype considered as a fixed effect. The BLUEs values were used in the subsequent analyses.\u003c/p\u003e\n\u003cp\u003eBroad-sense heritability for each trait was calculated using the following equation [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAm4AAAEyCAYAAABDDFwCAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAC/pSURBVHhe7d3faxxXgvbxp9774LTkq5CEQdWBNZtFIUjOkCi5MEQlFDAJCVEryYVhFjutDIGdyUqJJBNM4pDW5AeYRLJMAr7YsTprB4YBayUFbNZqzNqRg8SOycKomqAdvDfd8nryB5z3YvrUVh91Sy25Jbms7wcau0+drjpVdbrrUdWpbs8YYwQAAIB73v9zCwAAAHBvIrgBAAAkBMENAAAgIQhuAAAACUFwAwAASAiCGwAAQEIQ3AAAABKC4AYAAJAQBDcAAICEILgBAAAkBMENAAAgIQhuAAAACUFwAwAASAiCGwAAQEIQ3AAAABKC4AYAAJAQBDcAAICEILgBAAAkBMENAAAgIQhuAAAACUFwAwAASAiCGwAAQEIQ3AAAABKC4AYAAJAQBDcAAICEILgBAAAkBMFti0ZGRtTS0iLP8zQ2NuZOBgAAaDqC2xbduHFDly9f1uLioi5duuROBgAAaDrPGGPcQjSmXC7rs88+06uvvqr29nZ3MgAAQFNxxm2LyuWyvv76a/3mN7/R5OSkOxkAAKDpOOO2RQMDA5qYmIiesxkBAMB2I7itw55VO3/+vBYWFqJyNhkAANgNXCpdR09Pj3766Sd98803Gh4e1sWLFwltAABg12xrcMvn80qn0/I8Tz09Pe7ke1qhUJAkjY+Pq62tTZL0pz/9KZq+tLQUrVs6nY7KAQAAtsu2BrfR0VFdu3ZNYRjq+vXrURhKmkKhoImJCT3++ONR2csvv6yzZ8+qVCoplUpV1QcAANgO2xrclpeX1draqra2Nvm+rwMHDrhVJEnFYlEDAwPK5/PupIZNT08rk8k0LRweOHBAra2t8jxPhw8f1rvvvqve3l6pEuS6u7vV1dWla9euqbOz0335lhSLxXW/0Lenp0ee58nzPHeS1MB0AACQbJsObmNjY1E4iAcMW+Z53prwNDk5qRMnTqi1tbWqXJVLjt3d3Tp27JgymUxUZi9DjoyMuC9RoVBQOp1WuVyOynp7e/XRRx/pn/7pn+4qAFqtra2amZmRMUarq6saHByMpt28eVOpVErlclnvv/9+U77DbWxsTL7vu8WRnp4eHTp0SMYYzc/PrwlnG00HAAD3AbMFHR0dJgiCqrKLFy8aSeb06dNV5VNTU2ZqaqqqzCqVSiaVSpn5+fmqct/3zcWLF02pVDK+7695ve/7a15j2XkuLi66k5omDEOTSqVMEASmr6+vblu2QpLJ5XJVZfPz88bdVUEQRPU2mg4AAO4Pmz7jJkkLCws6cuRIVdl///d/S5KOHTsWldkzX5lMRtPT02vOxH399dfRJUerWCwqDEP19vaqtbVVR48e1crKSjR9bGxMHR0dVa+Ja21tVTab1dDQkDupKcrlsm7duqXV1VWNjo5qYWGhblusWme/enp61myPeq5evaogCKrKDh06FP3U1kbTAQDA/WHTwc2Gjeeff76q/KuvvqoKD8ViUf39/erv75fneXrhhReq6kvSmTNn9OKLL1aV3bp1a00IsZaWlvTxxx/ryy+/dCdVefXVVzU7O6tisShJ0WXXeo+BgQF3FnX9+OOPOnz4sDzP05EjR3Tq1Cm3yhrGGHmeV9We0dHRDQOfdenSpejO1rjl5eWGpgMAgPvDpoPbv/3bv0mS9u/fXxV+FhYW9NJLL0X12traZIypetQ6s/bwww9HZZL00EMPaXZ2tqrMGhoa0vj4uL777ju1tLTI87yaPzdlx5z913/9l1QJMG5b4o/x8XFnDvV1dXVpdXVVxhgtLy9HNyxsxBij7u5ueZ6nubm5hkMbAACAtengNjc3p+Hh4argMz8/L0n65S9/6Vav69atW1IlCMW1tbUplUppenpa5XJZZ86c0dNPPx0FtOeff16jo6O6fPmyFhcX9d5770Vnslzx713bLPesXKOP9YRhKMXWHQAAYDM2FdzK5bIWFhb0zDPPVJVfvXpVvu835e5KSfqXf/kXvfHGG9q/f7/6+vp04MABvffeexofH9ePP/6odDqt9vZ2tbe36+DBg7p+/bo7iypbuVTqnpVr9FGP53lRnQ8//LDh8W2qjFdzw+lPP/0UffHvRtMBAMD9YVPB7bvvvpMkPfXUU1Xlly5dUnd3d1VZo+Jf6WH19vZGlyNPnjypt956S++++27NcVySqm5eqKWZl0o3y343WzzUzczM6MMPP2z4a0uefvrpNZePi8VidIPIRtMBAMD9YVPB7ezZs1Llzk1raWlJs7Ozm/71gIceekiqDPZfz/T0tMIwrPoeNVf8Fw3i6pXvJDvWzzUzMxN9b91Gurq6FARB9L15+Xxey8vL0es3mg4AAO4T7veD1JPL5YwkI8lks9mo3JZJ2vT3mdX6jrY4+z1u8fmGYWgkmcXFxej71MIwrHqd/V4zt7yZhoeHTSqVqvm9a1sxNTVVtS1933erGN/3o+m1bDQdAAAkm2dqnQ7aIWNjY7p06ZJmZmbcSZIU/WrCyZMnq8rz+Xw0Lm18fHzNmaWRkRHduHGj7nyboaenR7lcTqrc7bqdywIAAJCkXQ1u5XJZjz32mP74xz+uubt0q4rFojo6OnT58uWm3SxRT7lc1meffaZXX31125cFAACwqTFuzdba2qrLly/ryJEjWlpacidvWrlcVl9fn8bHx7c9SJXLZX399df6zW9+U/O75AAAAJptV8+4WcViUZ988omee+65NZc9G1UoFPTFF1/o17/+ddPO3tlwdv78eS0sLETlxhgNDAxoYmKiqgwAAGA73RPB7V7V2dmpzs5OvfPOO/r666/1zDPPNPxLCQAAAM22q5dK72X2C3LHx8ej74+7m19iAAAAuFsEtwYUCgVNTExE3ws3MDCglpYW5fN5tbS0qKenx30JAABA0xHc6jhw4IBaW1vleZ4OHz6sd999N7pMeuzYMR08eFCS9Oc//3nNrxYAAABsB8a4bVFnZ6e+//57lctlPfXUU1peXnarAAAANBVn3LagUChEP/t1/PhxHT161K0CAADQdAS3Lbh586aWl5fleZ5WV1f1q1/9yq0CAADQdAS3LVhaWtLZs2dljFE+n4/OvgEAAGwnxrhtQTqdVjqd5vdJAQDAjiK4AQAAJASXSgEAABKC4AYAAJAQBDcAAICEILgBAAAkBMENAAAgIQhuAAAACUFwAwAASAiCGwAAQEIQ3AAAABKC4AYAAJAQBDcAAICEILgBAAAkBMENAAAgIQhuAAAACUFwAwAASAiCGwAAQEIQ3AAAABKC4AYAAJAQBDcAAICEILgBAAAkBMENAAAgIQhuAAAACUFwAwAASAiCGwAAQEIQ3AAAABKC4AYAAJAQBDcAAICEILgBAAAkBMENAAAgIQhuAAAACUFwAwAASAiCGwDEFItFDQwMyPM8tbS0aHp62q2y4/L5vNLptDzPU09PjzsZwB5CcAOAmAsXLqi9vV3GGGWzWZ06dcqtsuNGR0d17do1hWGo69evq1AouFUA7BGeMca4hQCAv53pWllZ0eDgoDtp13R2dmpmZkatra3uJAB7AMENAGool8t66623lM/n3Um7ZnJyUo888oh6e3vdSQD2CC6VAoCjXC7r+PHj+vLLL91Juyafz2vfvn2ENmCPI7gBQIwNbR988IFaW1s1MjLiVtlx9qxfJpPR9PQ0Y9yAPYzgBmBPyefz6unpked5VQ8bjo4fP66JiQnt379fnufpxo0b7iyayt7Fau8atY90Oh1N7+/vV39/vzzP0wsvvODOAsAewhg3AHtCuVyOvkrjxIkT6u3tVTqd1tzcnNra2tzqOyKfz6u/v1+5XE6vvPKKvvvuO62srOjkyZNuVQCQOOMGYK/4+uuvJUkzMzPROLF0Oq3vvvvOqdm4sbGxNWfuaj1qKZfLGhgY0NTUlAYHB9XW1qa///u/18TEhFsVACIENwB7wscff6zf/va3VV+jcf36dd25c6eq3mYMDg7KGLPho5YLFy6opaVFmUwmKrt586Zu375dVQ8A4ghuAPaE27dv6+GHH46eT09P6/bt23r88cejssnJSY2NjUXPt9OdO3eicWzWV199pY6OjqoyAIgjuAHYE3zf182bNyVJhUJBb7zxhoIgiC6bjoyM6M0339TQ0JA8z1OxWHTm0FyPPvqolpeXVS6Xo8umCwsL+vzzz6M6ttyr/PwWd5MCILgB2BU7/fub3377rX73u9/J8zwdPnxY2WxWv//976PpJ0+eVCqVUqlUkjFm229YyGQy6uvr0/79+7V//359//33mp+fV1dXV1Tn9ddfV3t7u8Iw1MGDB6PgCWDv4q5SALsinU7r2rVrunPnjjo6OvTHP/6xKrTstEKhoC+++OKe+aWEpaUlPfHEE1LlbOHRo0fvqZ/eArA7OOMGYFcsLy+rtbVVbW1t8n1fBw4ccKvsqKtXr8r3fS0tLWl6etqdvON+/vlndXR0yBijubk5/fDDD24VAHsQwQ3ArpqcnNSJEyd2/UfTH330UX300UcaGhrSU0895U7ecQcOHFBra6s8z9PAwIA++ugjtwqAPYhLpQB2TfynnAAAG+OMG4Bdwe9vAsDmEdwANN12/P6m+2sEtR479R1sALBbuFQKoKn4/U0A2D4ENwBNUy6X9dhjj2l8fDwat1YoFHT48GGtrq661XeMV+f3QpOOj29g7+FSKYCm2c7f33Qvi9Z61LtU6v526P3yALD3ENwANM12/v6mG1pqPfiCWgD3O4IbgKZp5Pc3l5aW1NPTI69yswJfBQIAjSO4AWiaRn5/8+WXX9ZLL70kY4z6+vr05JNPVs0DAFAfNycA2DHT09N6//339f3330uSOjs79fnnn+/qb5QCQJJwxg3AjvnTn/6kzs5OSdLY2JgWFhYIbQCwCQQ3ADvm6aefVj6fVzqd1qOPPtqUmxYAYC8huAHYEZOTk7p69apWV1c1NzenTz/9VP/4j//oVgMArIPgBmBHPPLIIzpz5ow8z1NHR4e6u7t17Ngxt1oijYyMyPM8jYyMqFwuK5PJrPu9cgCwVdycAAB3IZ/PS5JWVlZ0/vx5dXZ26oMPPtDrr7+ut99+W729ve5LAGDLCG4A0AQDAwOam5vTtWvX1Nra6k4GgKbgUikANMHc3Jz++Z//mdAGYFsR3ADgLhWLRYVhqF/+8pfuJABoKoIbANyl69evK5VKqb293Z0EAE1FcAOAu3TlyhV1d3e7xQDQdNycAAAAkBCccQMAAEgIghsAAEBCENwAAAASguAGAACQEAQ3AACAhCC4AQAAJATBDQAAICEIbgAAAAlBcAMAAEgIghsAAEBCENwAAAASguAGAACQEAQ3AACAhCC4AQAAJATBDQAAICEIbgAAAAlBcAMAAEgIghsAAEBCENwAAAASguAGAACQEAQ3AACAhCC4AQAAJATBDQAAICEIbgAAAAlBcAMAAEgIghsAAEBCENwAAAASguAGAACQEAQ3AACAhCC4AQAAJATBDUAi5PN5pdNpeZ6nnp4ed/KuKRaLGhgYkOd5amlp0fT0tFtlx92r2wrA3SO4AUiE0dFRXbt2TWEY6vr16yoUCm6VXXHhwgW1t7fLGKNsNqtTp065VXbcvbqtANw9zxhj3EIAuJd1dnZqZmZGra2t7qRdlc/ntbKyosHBQXfSrrlXtxWAreGMG4BEmZyc1IkTJ+65IFIul/WHP/xhU6GtUChs66XMe3VbAdg6ghuAxMjn89q3b596e3vdSbuqXC7r+PHj+vLLL91Ju+Ze3VYA7g7BDUAi5PN5SVImk9H09PQ9M27LhrYPPvhAra2tGhkZcavsuHt1WwG4ewQ3ALvO3plp74S0j3Q6HU3v7+9Xf3+/PM/TCy+84M5ijfh86j3Gxsbcl62Rz+fV09Oz5rU2HB0/flwTExPav3+/PM/TjRs33Fk01XZsKwDJwc0JAHZVPp9Xf3+/crmcXnnlFX333XdaWVnRyZMn3ao7qlwuR+PPTpw4od7eXqXTac3Nzamtrc2tviWFQkEffvihZmZm3Ek13avbCsDOIbgB2DXlclmPPfaYxsfHlclkpEqYOXz4sFZXV93qO2psbEznz5+vuiOzp6dHL730ko4dO+ZWb8jY2JiGhobc4jVqfSzfy9sKwM7hUimAXXPhwgW1tLREQUSSbt68qdu3b1fV2wr30matx3qXSj/++GP99re/rboj8/r167pz505Vvc0YHByUMSZ6zM/PKwiCqrJaoU3bvK0AJAfBDcCuuXPnTjQ2y/rqq6/U0dFRVbYVbhiq9Vjvqztu376thx9+OHo+PT2t27dv6/HHH6+qt1O2c1sBSA6CG4Bd8+ijj2p5eVnlclnlclkDAwNaWFjQ559/LklaWlpSOp1WJpNRPp9XS0uLOjs73dlsC9/3dfPmTalySfKNN95QEARVX68xNjamlpYWeZ6nzs7Obb17c6Ntpcrl1PjPb21newDsDoIbgF2TyWTU19en/fv3a//+/fr+++81Pz+vrq4uSdKPP/6oU6dOaW5uTnfu3NHq6qpu3769I4Hk22+/1e9+9zt5nqfDhw8rm83q97//fTQ9n8/r/PnzWlhYUKlU0sLCQtTu7bDRtpKk119/Xe3t7QrDUAcPHoyCJ4D7BzcnALin5fN5/ed//md052RLS4sWFhaadmfnVnV2dkZ3m05PT+vUqVMN3x1qbfau0vUsLS3piSeekCpnC48ePbrupWAAycQZNwD3tCtXruiZZ56JLgN2d3fvemiTpIWFBf3d3/2dlpaW9P777+vQoUNulQ11dXU1JbRJ0s8//6yOjg4ZYzQ3N6cffvjBrQLgPkBwA3BPm5ub0xtvvKH9+/dL0j3xs1LFYlHDw8PyfV//+q//Kt/3d+2mBevAgQNqbW2V53kaGBjQRx995FYBcB/gUimAe9bS0pKGhoaadlaqWdLptK5du6bW1lZNTk7qvffe47vUAOwIzrgBuGf9x3/8h2ZnZ3fkZoTN6OjoiH7i6quvvtLly5fdKgCwLTjjBgAAkBCccQMAAEgIghsAAEBCENwAAAASguAGAACQEAQ3AACAhCC4AQAAJATBDQAAICEIbgAAAAlBcAMAAEgIghsAAEBCENwAAAASguAGAACQEAQ3AACAhCC4AQAAJATBDQAAICEIbgAAAAmxI8HN8zzl83m3GAAAAJvQcHDr6emR53nyPE/pdFrFYjF67nmeenp6orrpdLpqGgAAAO5ew8FtZmZGuVxOkrS8vKy2tjYZY+T7voIg0MzMTFR3eXlZQRAoDEMZY2JzAQAAwFY1HNwkaXBwUJJULBajsg8//FCzs7NVZcViUW1tbWpra4vKAAAAcHc2FdwkKQgCXbhwIXr+8MMPS1JV2YULF/Taa69Fzy17uTWdTleVxy+t2rFwhUJBnudpYGAg+leS8vl81SVba2xsTJ7nVQVIAACA+8mmg9uRI0d05syZ6Pm5c+cUBIEuXboUlV26dEldXV3Rc0nq7+/X6OiojDEKw1CFQkGqhLa5uTkZYzQ1NaX+/n79+7//u5599llJ0i9+8QsZYzQ+Pq58Pq8rV67IGBNdgrWBbnBwUMYYzvIBAID71qaD28GDB6uClyphzl4utZdJXVNTU1GY831ff/nLX1QoFBSGoXzfl+d56u/vlyT9z//8j+bn5yVJr7zySjSPs2fPamJiIjrjFoah5ubmoukAAAD3s00Ht7a2NgVBoHPnzqlQKOi5557TwYMHJUnXr1/X9evXa14mXY89g2YfmUzGrRLJ5XJVdZeXl90qAAAA96VNBzdJOnTokObm5nT16lUdPHgwCnNnz57VlStX1lwm3Uh8XFqhUFj3O99++umnquf2UikAAMD9bkvB7ZVXXlEYhjpz5kx0WfTQoUOanZ3VL37xC7d6XV1dXfJ9X93d3VHZuXPnojN4riNHjmhiYiIKdtyIAAAA9pItBbe2tjb5vq+jR49GZXYs2tNPPx2r+bdfTVDl5oR8Pq90Oq0wDKPny8vLCsMwGrf23HPP6datW9HNCb7vRwEtk8kol8upv79fnuepu7tb4+PjEneVAgCAPcAzfEMuAABAImzpjBsAAAB2HsENAAAgIQhuAAAACUFwAwAASAiCGwAAQEIQ3AAAABKC4AYAAJAQBDcAAICEILgBAAAkBMENAAAgIQhuAAAACUFwAwAASAiCGwAAQEIQ3AAAABKC4AYAAJAQBDcAAICEILgBAAAkBMENAAAgIQhuAAAACUFwAwAASAiCGwAAQEIQ3AAAABKC4AYAAJAQBDcAAICEILgBAAAkBMENAAAgIQhuAAAACUFwAwAASAiCGwAAQEIQ3AAAABKC4AYAAJAQBDcAe14+n1c6nZbneerp6XEnA8A9o+nBrVgsamBgQJ7nqaWlRdPT024VoCH0JeyU0dFRXbt2TWEY6vr16yoUCm4VAHXwh8/Oanpwu3Dhgtrb22WMUTab1alTp9wqe4bnecrn827xjroX2rBVO9WX8vm8PM9zixNjYGBg1z8s74U23I3l5WW1traqra1Nvu/rwIEDbhUAdfCHz87yjDHGLWyWfD6vlZUVDQ4OupOATdmuvlQoFPTss89KkrbxrYCEmJyc1COPPKLe3l53EhKuXC7r+PHjmpiYkCTNz8+rq6vLrYa71NnZqZmZGbW2trqT0CRNP+Nmlctl/eEPf2j6gXa7DQwMqFgsusXYRdvZl7q6ujQ1NeUWb7tCoaCxsTG3GLson89r3759hLb71PHjx7W6uipjjPr6+tzJ8jxv288U7cQy1jM2NratZ8YnJyd14sQJQts225bgZv+y+fLLL91J97RisRj9NYZ7Q1L70kY+/PBDtwi7yA4nyGQymp6e3tWDK7ZHsVjUk08+KVX2d/xs2078sb4Ty9jITz/95BY1DX/47CCzSWEYmmw2a3zfN5Kix/DwsDHGmFKpZLLZrCmVSsYYE5Xncrmq+u5jamqqajmWW0+SmZ+fN8aZZxAE0Wt83ze5XK5qelw2m43Kfd83xhgzPz9ftYz4+tk6YRhWzS8IApPNZqvmF4ZhVV3bVuO0184zLt6GesuMLyu+vKmpKSMpmp7L5da0IT4vW9faaJsFQVDzde48bXsasZN9ab19ZbddfF1yuVz02nrrmMvljO/7VdPjy7bzdV/nrm/8ud1XdntPTU1Fy4nPL74cu26W25dr7ZONlunOI749FOtntp+6baj1/jGV/hsEQdX84+8Rd5vFxedZax+vZ35+3vT19ZlUKlU1/4sXL67Zv26btsPi4mLUlsXFRdPR0WFU6fulUinaF0EQRP3fVLaP3Q5BEJjFxcWa02y/NLHPDPv+TqVSJpVKVb02Lt4eSaajoyOadvHixaplDA8PR33DltttF1+u1Ugbh4eHo/VzX+Ouc1ypVDJ9fX1Ru/v6+qK6dp72Ed+/i4uLVX3LtsOWXbx4Mdofdl2Gh4ej/Rf/jDJ11rGRZdi2231u2+z7ftU6x/eP7/vm4sWLxjSwn91toE0c/td7/5jKOtv35MWLF7f9/bPXNb7nYh82uVwu6lh2x1nxA6PthFsVBEH0RrEf9LZD2M5p2Q4bDxjxDxT7f1vH8n3f+E54sx8m9gM9fuCzB6f4etoOGwRBNC87Ld7e+HLjr4urtUxbz06zfN832Wy26iAYn6fbhmw2G20He4AMw3DDbVZrP4RhaMIwjNbXzj/+fD072Zc22ld2W8Sf2//XW0f7mvjrcpUPYhPbTvHX2e1mnO1rnG1sKq+3H4Z2Obbv5SrBNawE3/g0d7mBE6ji3GWaWF8LgiD6v12eiR2c4/vCbcPU1FQ03fbZqUoAtetip2crQc6+Lr6t49vIjx3k4323EVNTUyaVSlWtW6OvrcWuw3qPRth9ZcPa6dOno+1YKpXM4uKiUezg6H4G9vX1Rdvu9OnTpq+vz5RKpajPuv3L930zPz8fBcO+vr5oelwQBNEfSfZ9amLLt9vRPnc/G2z7TOxz2WyijYuLi1EfcucZX2dXEAQmqISeUqlkOjo6qvpTrf5uucuxFAtS9vPTvs/DMDSlUqlqPdZbx42WEVYCnn2+uLgYzT/+x6z93LTLS6VSUXDcaD/bbbQZG71/7Hs8/nDXMc6tW+uB9W1qC/m+b06fPh09zzlhpNn8ygGy1nPf+etFsYNBrQ8E+9ztVPE3k/1/vFPGDyz2uRXUOdMRVkJNfFn2zdSIIHbQNLGDqStbOYNkYgezOLcNtabZda23zWy9WnJ1znw1clDc6b603r5yt52dZjZYR/sBbsWDR7zPWaoTjE2NPxribXWXY5zgH183e3BphD04xp/XEt8eps4fHe72jYu/b933U3y/u/3esst3H7Xqumz/jZ+xqLVvdoO7XU2Nz6d4n8lms1UH4Vqvt9x1dJ+v936z4ch9H7v7zjjti3+W1pructvkPjebWOdan3W2rt33teZv1Wq7qbE/zCY/u+LLbHQZbjuDWNiyISou/nr3tW7b4vNqxL38/tnLGh7jtrS0pDAMdezYsajszp07VXXWMzY2Js/z6j5qfWXF0aNHdfbsWakyPiAMQx08eDCaPjU1pUr4lDFGMzMzsVevVWuMgR3n8Je//MWdJEl65513NDs7q2KxqHw+r9dee82tEnnooYfcokgYhm5RXUeOHNHo6KhUafPDDz9cNb1YLMrzvC2PxxsYGJDv+25xTbdu3XKLqgRBULUPjDFqa2tzq1XZjb7kWm9fubayjsvLy26RgiCoO8akq6tLQRDowoUL7qSa6u2/Wn28nkwmo9nZ2XXHc/X09ER33W6W/ZqVRvt+rW0W5+6DTCbjVlnju+++UxAEam9vj8rK5XJVnaQoFov65ptvon5u90uhUFC5XI4Gnnuep9nZWfflDfvyyy+jr0Xp6enR0tKStMm+VctW2rjeOset9zn1888/u0V3JQxDvfnmm1GbhoaGonXZyjpuxsrKim7fvl31eSdJV69edas2xf30/rmfNBzcfv75ZwVBUFX2zTffbHgAswYHB9d88G70ITw4OKjZ2Vl5niff9zU1NVW1vJWVlar6AwMDVc9d9rW1Orkbjqy2tjYFQaBPPvlEV65caej28VrbxPf9uuHQlclkFIahCoWCLly4ULVMz/PU3d0tU/lus82wgee5555r+GBqA069D233YDs2Nla3rrUbfameRpa5lXVMp9O6dOmSW6xf/OIXblHkyJEjGhoa2vAPBKtWn21ra6sbDmvJZrM6d+6cCoVC1fzsFx+Pjo5qfn6+6jUbKRQK8jxPV65ckTGmbsh0pdPpNe/puPg2LxQKDQX0O3fu6NChQ9HzYrGohYUFPfroo1FZJpNZEwTWEz9o1ntshwcffFDZbHZNf+/q6tLx48d1/vx5vf322yqVSmveX5vR2tqq8fFxlUoltbW16eWXX3ar1PTAAw+4RVW20sb11jnOLvuvf/1rVbka/CNto7bHpVKpNScNjDHSBuu4mWXUs2/fPvm+v2bZjd5x/+CDD7pF62rk/bNZ7nul1gPrazi4PfDAA7p+/bqKxaLK5bIGBga0urqqd955J6ozPT0dfXtyZ2fnXX/dQU9Pj8IwjDpn/IB89OhRDQ0NRR+4hUJh3YOilc1mNTQ0FB0E8vm8giBQV1dX1Rs83vbR0VFNTEzUnP/c3Fz0/08++aRumDp69Kj6+/urytYLmvaAGpfP5+X7/pog0cgBTJLOnDmjqampNcFmvdfb4Bpvqz1ovvLKKwrDsGrapUuXNgxDG/WlkZEReZXb5u1frs341YRG91XcVtfxyJEjmp2djbZtsVjU7Oxs9AGbTqelyra3fTGTycj3fY2Ojq45MIVhWNVnFTtbHPfaa69pYmKiKois189s/atXr1bNb2JiYs33XBWLxYYCzrlz55TNZjU+Pl5Vvl4/Uyy4xgPawMCAurq65Pu+uru7o/Jz585VnX2vZ9++fbp06ZLK5bKKxaL6+vrU0dERvQc6Ozv1zTff6Nlnn432yUbcg2atRyNsyLBnMDY6k/Hiiy8qn89H+6BYLEbrUSwW1d3drd7eXv34448ql8tVZ7Hd53fu3Km7vJaWFhWLRbW2tqq9vV2rq6uSpJdeekmzs7PRGTh3f9qzMjdv3oymx0P7ZtuoDdY5rr29Xb7v6/3331e5XFa5XNYXX3yhIAii92qt+Vvxti8tLSmfz0fbxw2DmUxGn376adRPC4WCRkZGpA3WsdFl1NsvkvT8888rDENNTk5Klbrxr7By19Hdz08++aSWl5dVLpejNq9no/ePKsfKlpYWeZ4XtWs97nul1gMbcK+drid+J03g3N0ThqFJpVLRtfaOjo411/I3K+sMTleNO9Rsub1uHx/7Zq/vx5/Xe51lX++23Y8NjrbseAF3XnZcgH3Yebnrsx47HsJdZvz1tq3ueD9Tpw05567WWv+vt83ideLbzLbTPmx77TzqjUOq15dKpVI09iqojLNxx2JtRb19Zce32Ye7PqbOOrrbMj4f20fdecfZ17vjwnLOTTe2zK/cROPOK75O7nhH+3D7sst3xtsZp6/W62fxMXbxNrjbq9brg8o4mfhzU2NMYbz/11p2ve1olZw7DbPOHYD2Dr3dYPu/HcNl22n7j90H8TsDc5W7Be02s+V27JMdRG63y9TUVHTTgypjlewAeElVY7Ws+P60fduybfIrNxHIGcNml2unB0EQtb/RNrqfGfXW2WWXZ+djbxIwlUH89eZvxe9mjfeb+PY3sbvd7fzifWq9ddxoGSbWzvg2c9vt3tlrb15pZD8vVu5ute1zue+njd4/uVzOZLNZE4ahGR4ernvDC5pr/fSwCcPDw1V3vmiDYNII92BiKgfRjQ5E26HWgSFYZ0A2/qbWPmyE7/tVH7p3u52Tsq9ylRtC3DJ7MEdtW+1nuVyuZnhBY7TOzQdIrkb3qQ3UqVRqTajD9mn4UulGbty4oX/4h39QuVzWW2+91dD4hfXk83mdOXPGLda5c+dqXiLaTvl8Xs8995xbjHXYS5yNjr2IKxaLSqVSam1tVaFQ0HvvvVd1I8P9rJHLsPg/dtzm008/7U5qyKVLl/TII49oeno6ugQI7FWbfT/dvn1bpVJJf/7zn6VN3mSGu+Amua2wl7cUuwRiz77djfhpb/vYybNt9nR0rTM17uUhNI+93CDnUsBW3ev7yl6eqPV2jE/jrFvz2Utezfi82mtqXcrF3hL/kuCdPDbvdU35kflMJqNf//rX6urqUqFQ0OHDh3X58uWqW4iBRsX7EwAA+D9NuVT65JNP6vDhw/I8T0eOHNH4+DihDVs2NzfHb3kCAFBDU864AQAAYPs15YwbAAAAth/BDQAAICEIbgAAAAlBcAMAAEgIghsAAEBCENwAAAASguAGAACQEAQ3AACAhCC4AQAAJATBDQAAICEIbgAAAAlBcAMAAEgIghsAAEBCENwAAAASguAGAACQEAQ3AACAhCC4AQAAJATBDQAAICEIbgAAAAlBcAMAAEgIghsAAEBCENwAAAASguAGAACQEAQ37HkjIyPyPE9jY2PupB01PT2tdDotz/PU09PjTm6qfD6/ZjljY2Pbttyenp5d374AcD8guGHPO3nypIIgcIt3VLFY1AsvvKBvv/1Wi4uL7uSmy2QyGh4edoubgpAGANuH4AbcA27duiVJam9vV3t7u2ZmZtwqTbdv376q54ODg01Z7vLyslukmZkZDQ4OusUAgE3aVHArl8vKZDLyPE8tLS3RX9VLS0tqaWmR53laWlqKLvecPXu2ZnmhUKial+d5ymQyWlpakiQNDAxEl67sZSz3Eo67zM7OTnmep5GREZXLZfX09ESvK5fL0etGRkai1w0MDFRNy+fzURvT6bSKxaLktGdsbEwtLS1qaWmJ2uuKz8e2yYpfDkun0xoZGYm2Y3z7yFlurXnXa6O7zeKv6enpqdvu9dht2tLSomKxqHw+r5aWljX7pRlsW6enp6P16uzsjPbVRtvJ3V92Wy0tLUXtbmlpUT6fr1quKpcLa02v1/fjlxztvD3Pi83x/7j7Lv7+OXLkiCRV7TdrvX0b7/vpdFrT09N1X+OuU1ytS6flcjmaT3wb2vq1+mFnZ6fCMNTQ0FA0P3f/yGl3S0tL1Xtxo/0PAHua2YQgCExfX58plUpmcXHRSDLz8/PGGGPm5+eNJDM8PGyMMcb3fTM/P1+3PAgCEwSBKZVKplQqmY6ODuP7ftWyfN83i4uLZmpqygRBEE2z4vMulUrm9OnTRpLJZrNVbbx48aIxxphcLmd83zdhGJpSqWR83ze5XM4YY8zp06ejdQvDsGqaibVnfn7elEqlaFvUEl9mvO22vVNTU1XP7XLsc7tN7bw228b4NnPn2dfXV3NbbmR4eNiEYWg6OjpMNps1U1NT5uLFiyabzbpVjalsa0nrPuJtd0kyQRCYMAxNGIYmlUqZ06dPG9PAdjI19pfv+6ajoyPa9n19faajo6NmfRNrfxiG0fR6fT+Xy5lUKmWmpqZMGIZGNd5Wts3xfqEafaGeWvu2VCqZVCpV1T9SqZQplUrGGBO9pxYXF40xxmSzWSMpmp7L5ar6gvs8CALT0dER9bdUKmXm5+c37IfuvrDzsmW23fYzYXFx0aRSqaq+tN7+B4C9rP6RwmEPSPYgYJwP43oHnlrldl7xA6+tZ+cfn3c9tebtzjd+EPF9v+rD3z1QxbnLd5+v91p7ULIHSCubza55Tbx9jQSSOLdN7nNTWWY8YNbaZpuxXnuayd0OtfraetvJ3Rbu/spVgpkVBEEUJCxVgtVGfd+ddy219n0QC/8b7Rd3fUwl/KVSqaqy+HZxX1Mqlaqmu+2OP6/1Hq3HXY67L9w6NrTG5Srh13KX7S4DAPaqhi+V2jE4TzzxRHTpZHZ2VpcuXXKrbsjOq5aff/7ZLWqaMAz15ptvRu0fGhrS7OysVLksNFa5q86u21ZdvnxZN27c0P79+6suAdnLSVu1lTYWi0V988030To/++yzkhRdZtwM+5pf/epX7qT7gjvmS5JWVlaa0vfr7fv//d//dYsatrKyotu3b0dt8iqXaK9evepWlSS1tra6RXWt9x7dSj+MW1lZcYskSbdv33aLAACOhoPbAw88IFXCT+VMnYwxWxrMbOf117/+1Z2khx56yC1qmlQqpampqar2/+2Pe+n48eM6f/683n77bZVKpbu6y9AOLg/DUN9//72OHz/uVqnJbpd6ttLGBx98UNlsds06d3V1uVU3dPPmTQVB0FAAsGPL1nvExzxtxkbbqRls2H788ceb0vcffPDBmmO02tra3KKG7du3T77vr9m39W4CqBcea1lvG2/UD1OpVNVzlw3I7vbwfb/qOQBgrYaDW3t7u3zf1yeffBJ94E5OTkaDnW0Icz+Ma5Xbeb3//vsql8sql8v64osvFARBdCArl8u6c+dO9Jpa3Hm7y3ZlMhl9+umn0QGsUChENw4Ui0V1d3ert7dXP/7445rlu8/v3LlTc3mFQiG6IaKtrU2+72t1dVWS9NJLL2l2drZqgHdce3u7VAlIdnr8YLbZNkrSiy++qHw+H50tKxaLymQyVXUadfnyZR06dMgtrmlwcHBNoHAf9QKG3a7xYB9ft422k1tflf21kTNnzkR947PPPlMqldJTTz21Yd+v1xfiXnzxRS0sLESvKRQKmp2d1WuvvSbV6Msud30k6fnnn1cYhpqcnIzqDAwMVAW08+fPR/P85JNPlEqlotDuzi++Hnadv/jiC5Ur71E774364cGDB/XDDz+oXDkzJ6f9zz//vFQJgKr0yTNnzujo0aNRXa2z/wFgT3Ovna5ncXHRBEFgJFUNijaVcV2Sqm4wWK88Pi9J0WBnUxlkbcvt4O1a7LztOKG+vr6qZdnB2KlUyiwuLppSqRSVKXYTg4mNF7KDzO0YqKmpqar2LC4uRoPTJa0ZML24uGg6Ojqi6R0dHVVjo+zy7aBxOeOB7HLt9CAIovY32kZ3m9nxQ6oM+I63J87uD/f1ViqVigbXbye7H+2YJ3f7mw22k1s/vr+mpqbMYmUwvCp9wFTW/fTp08b3faMa+80uw7bL7rP4vOvdqGHF94Pv+1Xbud77xGzwfrh48WLUZt/3q/ZPULm5wM47vk52nJkqfTi+HrZPL8b6cnze6/XD+HS7b9z94bY7FbtRwTS4/wFgr/KMqVwrxK7wPE+5XK7u2afdMDY2dk+1B1vT09OjQ4cOsS8B4D7S8KVS3P/sYHMO9PeHepddAQDJxRm3XTQwMKCJiQmlUildvnw5GrsF3C3btyRpcXGRvgUA9wmCGwAAQEJwqRQAACAhCG4AAAAJQXADAABICIIbAABAQhDcAAAAEoLgBgAAkBAENwAAgIQguAEAACQEwQ0AACAhCG4AAAAJQXADAABICIIbAABAQhDcAAAAEuL/A/zifrQcAnVGAAAAAElFTkSuQmCC\"\u003e\u003c/p\u003e\n\u003ch3\u003eGenotyping, Population Structure, and GWAS Analysis\u003c/h3\u003e\n\u003cp\u003eGenotyping was performed using the Illumina iSelect 90K SNP array at Trait Genetics, Gatersleben (Germany), using the Illumina Inc., San Diego, USA. The resulting SNP dataset was filtered by removing markers with a minor allele frequency (MAF) less than 5% and those with more than 10% missing data. After filtering, a total of 17,093 high-quality SNP markers were retained for use in the GWAS. Due to inconsistencies in genotypic data, 21 genotypes were excluded from the GWAS; however, their phenotypic data were included in the analysis to maximize the use of field trial data.\u003c/p\u003e\n\u003cp\u003ePopulation structure was analyzed using the LEA package in R [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. The number of subpopulations (K) was tested across a range from 1 to 10, and the optimal number of subpopulations was determined to be K\u0026thinsp;=\u0026thinsp;5 based on the cross-entropy criterion. An admixture analysis [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] was performed using sparse nonnegative matrix factorization (sNMF) to estimate the population genetic structure.\u003c/p\u003e\n\u003cp\u003eGWAS was performed using the rMVP package in R [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], applying GLM, MLM, and FarmCPU models. The FarmCPU model integrates multiple markers as covariates in a stepwise MLM to mitigate confounding effects. Population structure was adjusted using the first three principal components (PCs) and kinship matrix (K) calculated by the VanRaden method [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. SNP markers were considered significantly associated with the trait when P\u0026thinsp;\u0026le;\u0026thinsp;0.001. Manhattan and quantile-quantile (Q-Q) plots generated using the rMVP package.\u003c/p\u003e\n\u003ch3\u003eCandidate gene identification\u003c/h3\u003e\n\u003cp\u003eCandidate genes were identified based on the physical positions of significant SNPs. Genes located within the flanking regions of these SNPs were extracted using the IWGSC RefSeq v2.1 wheat genome assembly. Gene annotation was performed using publicly available wheat genome databases, including EnsemblPlants (\u003cem\u003eTriticum aestivum\u003c/em\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://plants.ensembl.org/Triticum_aestivum\u003c/span\u003e\u003c/span\u003e) and Persephone (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.persephonesoft.com\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFunctional annotations, including Gene Ontology (GO) terms and putative functions, were retrieved from the Persephone and Ensembl platforms to characterize potential candidate genes associated with the identified SNPs. The potential links to phenotypes were determined using the Knetminer tool.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhenotypic evaluations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA set of 272 diverse genotypes was evaluated for\u0026nbsp;agro-physiological traits. The descriptive statistics, including mean, range, coefficient of variance, and heritability, are presented in Table 1. An extensive range of variation was observed for all the traits. PH, FLL, BY, HI, TGW, TCC, CER, DH, and DM ranged from 56.75 to 130.25 cm, 14.0\u0026ndash;29.5 cm, 26.25\u0026ndash;66.75 t ha⁻\u0026sup1;, 6.82\u0026ndash;34.17%, 17.22\u0026ndash;41.78 g, 40.80\u0026ndash;61.32 mg g⁻\u0026sup1;, 257\u0026ndash;463 \u0026micro;mol m⁻\u0026sup2; s⁻\u0026sup1;, 127\u0026ndash;163 days, and 163\u0026ndash;190 days, respectively. The coefficient of variation (CV%) ranged from 3.72% for DM to 30.42% for GWP. Broad-sense heritability was estimated for the studied traits, with values ranging from 0.44 for CER to 0.98 for DH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eDescriptive statistics for traits of 272 bread wheat genotypes based on best linear unbiased estimator (BLUE) values, averaged across two seasons\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e89.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e56.75-130.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e14.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e16.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eFLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e20.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e14-29.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e13.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eBY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e41.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e26.25-66.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e14.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eGWP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.48-20.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e30.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eGNPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e17-73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e10.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e26.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e20.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6.82-34.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e6.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e29.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e27.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e17.22-41.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e15.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e52.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e40.80-61.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e354.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e257-463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e30.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e8.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e127-163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e6.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e163-190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e6.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: \u0026nbsp;Std standard deviation, CV coefficient of variation, H\u003csup\u003e2\u003c/sup\u003e: broad-sense heritability, PH: Plant Height, FLL: Flag Leaf Length, BY: Biological Yield, GWP: grain weight per plant, GNPS: Grain number per spike, HI: Harvest Index, TGW: Thousand Grain Weight, TCC: Total Chlorophyll Content, CER: Carbon Dioxide Exchange Rate, DH: Days to Heading, DM: Days to Maturity.\u003c/p\u003e\n\u003cp\u003ePearson\u0026rsquo;s correlation coefficients among physiological and agronomic traits in bread wheat are presented in Fig. 1. TGW showed significant positive correlations with HI, BY, and PH, but negative correlations with DH and DM. GWP was positively correlated with GNPS, TGW, BY, and HI, but negatively correlated with DM, DH, FLL, and PH. GNPS also showed negative correlations with DM, DH, and PH. PH was positively correlated with BY and TGW, but negatively associated with TCC and HI. DM and DH exhibited similar patterns, showing positive correlations with FLL and TCC, but negative associations with TGW, HI, and BY. Additionally, DM was negatively correlated with CER. CER was negatively correlated with TCC and FLL, but showed no significant correlation with TGW. HI had substantial positive correlations with TCC and BY, and significant negative correlations with PH and FLL.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation Structure Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation structure analysis was performed on 251 bread wheat genotypes using 17,093 high-quality SNP markers. The optimal number of subpopulations (K) was determined by evaluating cross-entropy values across a range of K values (K = 1 to 10). Based on cross-entropy analysis (Fig. 2a), five subpopulations were identified as the most appropriate to explain the underlying genetic structure among the genotypes. The admixture plot generated for K = 5 (Fig. 2b) revealed that genotypes were assigned to five genetically distinct groups, although varying levels of admixture were observed among some individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide association studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWAS analysis using 17,093 SNPs identified significant MTAs for 11 agronomic and physiological traits. Three models \u0026mdash;GLM, MLM (single-locus), and FarmCPU (multi-locus) \u0026mdash; were employed to ensure reliability. The results from each year are presented in Tables S2 and S3. In addition, a GWAS was performed using the combined multi-year dataset to increase the power and stability of marker-trait associations (Table S4). In total, 320, 302, and 27 significant MTAs were identified using the GLM, FarmCPU, and MLM models, respectively (Table S4). Among the three models, GLM consistently detected the highest number of significant MTAs, while MLM detected the fewest SNPs. \u0026nbsp;In the GLM model, 117, 167, and 36 MTAs were detected on genomes A, B, and D, respectively. In the FarmCPU model, 106, 164, and 32 MTAs were associated with genomes A, B, and D, respectively. \u0026nbsp;In the MLM model, the corresponding numbers were 16, 8, and 3 MTAs on genomes A, B, and D (Fig. 3a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe number of SNPs associated with each trait was as follows: PH (40), FLL (71), BY (16), GWP (54), GNPS (51), HI (119), TGW (103), TCC (13), CER (32), DH (77), and DM (73) (Fig.3b). Out of the 40 SNPs associated with PH, 16, 20, and 4 were detected by GLM, FarmCPU, and MLM, respectively. For FLL, 21 SNPs were identified by GLM, 49 by FarmCPU, and one by MLM. Similarly, 7, 8, and 1 SNPs were associated with BY by GLM, FarmCPU, and MLM, respectively. For GWP, the highest number of SNPs (28) was detected using the FarmCPU model, followed by 25 SNPs with the GLM model and only 1 SNP with the MLM model. For GNPS, 30 SNPs were identified by FarmCPU and 21 by GLM; no significant SNPs were identified using MLM. In the case of HI, a total of 119 significant SNPs were identified, with 31, 86, and 2 SNPs detected by GLM, FarmCPU, and MLM, respectively. For TGW, 61 SNPs were identified by GLM, 39 by FarmCPU, and three by MLM. For the physiological traits, 13 SNPs were associated with TCC, of which GLM, FarmCPU, and MLM, respectively, detected 4, 7, and 2. For CER, 16 SNPs were detected by GLM, 14 by FarmCPU, and two by MLM. Among phenological traits, DH and DM were associated with 77 and 73 SNPs, respectively. Of these, DH had 60, 10, and 7 SNPs detected by GLM, FarmCPU, and MLM, respectively, while DM had 58, 11, and 4 SNPs detected by the same models (Tables 2\u0026ndash;4; Fig. 3b). The Manhattan and quantile\u0026ndash;quantile (Q\u0026ndash;Q) plots for the GLM, MLM, and FarmCPU models are presented in Supplementary Fig. S1, illustrating the distribution and statistical significance of the detected associations across the genome.\u003c/p\u003e\n\u003cp\u003eTo ensure reliability, only SNPs consistently detected by all three models were considered stable. A total of 16 significant SNPs were identified across all methods (Table 5). Among these, 3 SNPs were associated with PH and TGW each; 2 SNPs were linked to HI, CER, and DH; and 1 SNP was associated with each of the following traits: TCC, FLL, BY, and DM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-localized and Stable MTAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identification of common markers controlling multiple traits is particularly valuable in plant breeding programs, as it facilitates the simultaneous selection for several agronomic characteristics [18, 19]. In this study, two SNPs (tplb0057n10_689 and IAAV9150) were detected as being associated with more than one trait across multiple methods. Among these, one SNP (tplb0057n10_689) located at 22.45 Mbp on chromosome 2D is associated with HI and DH. Similarly, IAAV9150 on chromosome 6A was significantly associated with DH and DM (Table 5). The pleiotropic nature of these SNPs on DH, DM, and HI was further supported by correlation analysis (Fig. 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe further checked co-detected common SNPs across two years using multiple methods and identified four highly stable SNPs: tplb0057n10_689, IAAV9150, Tdurum_contig48695_527, and wsnp_Ku_c19037_28455905. The first two SNPs are the previously mentioned pleiotropic SNPs, while the latter two are associated with PH and located on chromosome 7B at 457.38 Mbp (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable2\u003c/strong\u003e The results of the genome-wide association study based on the GLM method\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"92%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of significant marker-trait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePos. (\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Mbp\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-log\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP - value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3A-3B-4A-4B-4D-5B- 5D-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e12.73-724.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.58-4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e2B-3B-4A-5A-5B-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e47-696.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.59-5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eBY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e2B-7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e140.77-657.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.63-3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eGWP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e2B-3A-3D-5B-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e41.27-703.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.59-4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eGNPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e2B-3B-5B-6B-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e123.74-741.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.58-4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e1B-2D-3B-4A-5B-6A-6D-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e8.69-740.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.58-5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e1B-2B-3D-6A-6B-7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e71.44-788.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.60-4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e2B-4B-6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e19.74-486.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.64-3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e1B-2D-3B-5A-5B-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e493.12-750.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.63-4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e1A-1B-2A-2B-2D-3B-3D\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e5A-5B-6A-7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e4.50-783.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.58-7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e1A-1B-2A-2B-2D-3B-4A-5A-5B-6A-7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e6.72-826.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.59-6.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e The results of the genome-wide association study based on the FarmCPU method\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of significant marker-trait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePos. (\u003c/strong\u003e\u003cstrong\u003eMbp\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-log\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP - value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e1B-2B-3B-4A-4D-5D-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e12.77-497.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.61-4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e2B-3B-4A-5A-5B-6A-6B-7A-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e12.83-752.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.60-5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eBY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e5A-7A-7D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e158.29-668.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.61-4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eGWP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e2B-3A-5A-5B-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e25.39-698.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.63-4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eGNPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e2B-3B-5B-6B-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e54.78-741.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.58-4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e1A-1B- 2A-2B-2D-3A 3B-4A-5A-5B-5D-6A-6B-7A-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3.11-796.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.58-5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e1B-6A-6B-7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e26.18-674.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.59-4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e2B-6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e68.59-440.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.46-4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e1B-2B-2D-3B-5A-5B-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e28.33-741.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.59-4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e1A-1B-2A-2B-2D-3B-5B-6A-6D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.73-776.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.62-4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e1A-1B-2A-2B-2D-3A-3B-6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.73-776.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.64-5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e The results of the genome-wide association study based on the MLM method\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of significant marker-trait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePos. (\u003c/strong\u003e\u003cstrong\u003eMbp\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-log\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP - value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e4D-7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e12.77-457.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.68-4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e595.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eBY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e657.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eGWP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e511.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eGNPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22.44-22.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.62-3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e674.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.61-4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e68.59-69.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.72-3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e1B-2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e561.70-654.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.58-3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e2D-6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.72-22.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.75-4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e\u0026nbsp; Common Marker-Trait Associations Identified Across FarmCPU, GLM, and MLM Methods\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChr.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePos. (Mbp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eALT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e-log\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP - value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTdurum_contig42636_1245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.781395477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKukri_rep_c68594_530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.094561307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTdurum_contig48695_527\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e457.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.383632354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ewsnp_Ku_c19037_28455905\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e457.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.383632354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ewsnp_Ex_c21165_30292808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e595.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.353385681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRAC875_c20121_561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e657.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.787908718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003etplb0053n05_793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.248129661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003etplb0057n10_689*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.236985176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBS00026622_51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e674.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.351655156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRAC875_c19111_628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e674.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.208374003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ewsnp_Ku_rep_c104159_90704469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e674.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.208374003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ewsnp_Ex_rep_c66389_64588992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e561.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-12.39567732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJagger_c7527_143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e645.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.218462186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003etplb0057n10_689*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.045626942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIAAV9150*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.659482056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIAAV9150*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.356264894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMTAs marked with a star indicate co-localized loci that are associated with multiple traits, while bold SNPs indicate consistent detection across two cropping seasons (2019 and 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate gene identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further understand the genetic basis of the studied traits, SNPs consistently identified by all three methods, associated with multiple traits, and detected across both years, were selected for candidate gene prediction. \u0026nbsp;We predicted a total of 139\u003cstrong\u003e\u0026nbsp;high-confidence genes around the 16 MTAs associated with nine traits (TCC, PH, FLL, BY, HI, TGW, CER, DH, and DM).\u003c/strong\u003e The candidate genes encompass a variety of functions (Fig. 4; Table S5), including transcriptional regulation (e.g., zinc finger proteins, MYB domains), enzyme activity (e.g., cytochrome P450, protein kinases), structural/transport roles (e.g., H+-transporting ATPases, nucleolar proteins, 11 genes), photosynthetic efficiency (e.g., plastocyanin-like domains), stress/defense responses (e.g., leucine-rich repeat proteins, disease resistance proteins), and phenological regulation (e.g., response regulators, flavonoid 3\u0026apos;-monooxygenase). Several putative candidate genes for CER are annotated to contain plastocyanin-like domains, which are involved in electron transfer and photosynthesis \u0026mdash;key processes for enhancing photosynthetic efficiency [20]. Similarly, four putative cytochrome P450 genes associated with DH, DM, and HI were identified. These genes are known to regulate hormonal pathways that influence developmental timing and resource allocation, thereby affecting phenology and grain yield partitioning under diverse environmental conditions [21, 22]. Additionally, 10 putative candidate genes surrounding family significant SNPs associated with FLL, TGW, CER, and PH were annotated as members of the zinc finger protein family, which are known to regulate grain production and plant growth [23, 24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGlycosylation is a crucial post-translational modification of proteins in plants, playing a pivotal role in regulating numerous biological processes. Glycosyltransferases (GTs) are among the most important enzymes mediating this modification [5]. Nine candidate genes annotated as glucosyl transferases were identified in association with DH and HI. Four genes annotated as peroxidase were associated with PH. A previous study in bread wheat and its F1 hybrids reported a significant negative correlation between peroxidase activity and plant height [25]. Further examples are presented in Fig. 4 and Supplementary Table S5. While these findings are promising, more research is needed to confirm the role of these candidate genes in related traits.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003ePhenotypic variation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comprehensive phenotypic evaluation of the 272 bread wheat genotypes revealed substantial variability across all agro-physiological traits, underlining the genetic diversity within the GWAS panel. Descriptive statistics demonstrated a wide range of trait values. Medium to high heritability estimates were obtained for each trait, ranging from 0.44 to 0.98, indicating differing levels of genetic control and trait stability. Highly significant variation among genotypes and the high heritability for most traits confirmed the suitability of GWAS analysis. GWP was positively correlated with GNPS, TGW, BY, and HI, in agreement with previous reports [26, 27, 28]. The negative correlation between phenological traits (DH, DM) and yield-related traits (GWP, GNSP, TGW, BY, HI) suggests that early-maturing wheat genotypes enhance resource allocation to grain production, boosting yield. This aligns with studies such as [29, 30], which emphasize the role of optimized phenology in maximizing wheat yield potential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation Structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation structure analysis of 251 wheat genotypes using 17,093 high-quality SNPs revealed five distinct subpopulations. This genetic stratification, supported by cross-entropy analysis and admixture patterns, highlights the underlying genetic diversity within the panel. The observed admixture in some genotypes suggests gene flow and historical hybridization among these groups, consistent with findings from previous wheat diversity studies [31, 32]. Understanding such population structure is improve the accuracy of marker-trait associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS and candidate genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecent advancements in GWAS-based breeding strategies have played a crucial role in the development of improved cultivars with desirable agronomic traits [33]. Multiple GWAS models have been successfully applied in association analyses across various crops, including wheat, demonstrating their effectiveness in diverse genetic backgrounds [34,\u0026nbsp;35,\u0026nbsp;36,\u0026nbsp;37,\u0026nbsp;38]. In this study, a total of 320, 302, and 27 significant MTAs were identified using the GLM, FarmCPU, and MLM models, respectively, across 11 key agronomic and physiological traits. The GLM model detected the highest number of MTAs, while the MLM model identified the fewest. This pattern aligns with previous findings, where the GLM\u0026rsquo;s higher sensitivity often results in a greater number of false positives. In contrast, the MLM\u0026rsquo;s correction for population structure and kinship makes it more conservative and less sensitive. The FarmCPU model, which balances statistical power with control of false discovery, identified an intermediate number of MTAs and has been increasingly favored for the dissection of complex traits [39, 40, 41].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further understand the genetic basis of the studied traits, we focused on the most reliable MTAs, those consistently identified by all three GWAS methods, as well as SNPs showing pleiotropic effects across multiple traits and stability across two growing seasons. This analysis resulted in 16 MTAs associated with multiple characteristics, including PH, TGW, HI, and phenological traits such as DH and DM. Notably, two pleiotropic SNPs\u0026mdash;tplb0057n10_689 on chromosome 2D (linked to HI and DH) and IAAV9150 on chromosome 6A (linked to DH and DM)-were consistently detected across both seasons, underscoring their stability. These SNPs, along with two additional SNPs associated with PH identified in both years, represent valuable targets for MAS in wheat breeding programs aiming for multi-trait improvement.\u003c/p\u003e\n\u003cp\u003eSeveral of the MTAs identified in this study were consistent with those reported in previous research, highlighting their potential value for MAS in wheat improvement. For instance, MTAs associated with DH have been reported on chromosome 6A [42, 43], and those for DM were also located on chromosome 6A [44, 45]. Plant height-related MTAs have been previously identified on chromosomes 4D and 7B [38, 44, 46]. Similarly, MTAs for thousand-grain weight (TGW) have been reported on chromosome 7A [46], and those related to leaf chlorophyll content were identified on chromosome 2B [47].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo identify potential candidate genes underlying these stable MTAs, we used the physical positions of the 16 SNPs mapped to the wheat reference genome. This led to the identification of 139 high-confidence genes associated with nine traits: TCC, PH, FLL, BY, HI, TGW, CER, DH, and DM. These genes spanned a wide range of functional categories. The most represented groups included the zinc finger protein family (10 genes), glucuronosyltransferases (9 genes), followed by genes with plastocyanin-like domains (8), and protein kinase domains (6). \u0026nbsp;For the stable and pleiotropic SNP tplb0057n10_689 located on chromosome 2D, we identified 10 candidate genes, which encode glucuronosyltransferases, ribosome-binding factors, protein kinases, cytochrome P450s, and F-box-associated domain (FBD) proteins. These genes are potentially involved in signaling pathways, secondary metabolism, and protein degradation. Similarly, for SNP IAAV9150 on chromosome 6A, we identified 10 genes with functions related to seed maturation, grain number determination, flowering time regulation, and photosynthetic efficiency, based on annotations from NetMinerWheat. These findings underscore the presence of potential regulatory genes underlying pleiotropic SNP effects and provide strong candidates for future functional validation and molecular breeding efforts in wheat.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn most crops, including wheat, quantitative traits exhibiting continuous variation are inherently complex and governed by numerous loci with both main effects and interactions. In this study, we employed three GWAS models: GLM, MLM, and FarmCPU to dissect the genetic architecture of 11 key agro-physiological traits in a diverse panel of wheat accessions. We identified 16 MTAs, including four highly stable SNPs that were consistently detected across all three models and in all growing seasons. Candidate gene prediction around these MTAs revealed several genes involved in transcriptional regulation, enzyme activity, structural/transport roles, photosynthetic efficiency, stress/defense responses, and phenological regulation. These findings provide valuable genomic resources and candidate loci for MAS and the genetic improvement of complex traits in wheat breeding programs aimed at enhancing both productivity and adaptability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll phenotypic data generated and analysed during this study are included in this published article and its supplementary information files. The genotypic datasets used and/or analysed during the current study are available in the Zenodo repository (https://doi.org/10.5281/zenodo.17141663).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDesigned study: S.S.N., G.M.N. \u0026nbsp;Coordinated study: G.M.N., S.S.N. Provided data or materials: A.B; A.M.A. Performed experiments: Z.A.B. Analyzed data: S.S.N., Z.A.B. The initial manuscript was written by S.S.N. Z.A.B. All authors contributed to and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the IPK genebank for providing the wheat germplasm used in this study and Afzalipour Research Institute, Shahid Bahonar University of Kerman for providing field materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGreen AJ, Berger G, Griffey C, Pitman R, Thomason W, Balota M, et al. Genetic yield improvement in soft red winter wheat in the Eastern United States from 1919 to 2009. \u003cem\u003eCrop Sci\u003cspan dir=\"RTL\"\u003e.\u0026nbsp;\u003c/span\u003e\u003c/em\u003e2012;52(5):2097-2108.\u003c/li\u003e\n \u003cli\u003eFAOSTAT. Food and Agriculture Organization of the United Nations. Production domain. In: Crops [Internet]. Rome, Italy: FAO; 2023 [cited 2025 Apr 25]. Available from: https://www.fao.org/faostat.\u003c/li\u003e\n \u003cli\u003eHall AJ, Richards RA. Prognosis for genetic improvement of yield potential and water-limited yield of major grain crops. \u003cem\u003eField Crops Res\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/em\u003e2013; (143):18-33.\u003c/li\u003e\n \u003cli\u003eMorales F., Anc\u0026iacute;n M., Fakhet D., Gonz\u0026aacute;lez-Torralba J., G\u0026aacute;mez A.L., Seminario A., et al. Photosynthetic metabolism under stressful growth conditions as a basis for crop breeding and yield improvement. \u003cem\u003ePlants.\u003c/em\u003e 2020;9(1):88.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDing Y, Fang H, Gao Y, Fan G, Shi X, Yu S, et al. Genome-wide association analysis of time to heading and maturity in bread wheat using 55K microarrays. \u003cem\u003eFront Plant Sci\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/em\u003e 2023; (14):1296197.\u003c/li\u003e\n \u003cli\u003eWei TM, Chang XP, Min DH, Jing RL. Analysis of genetic diversity and tapping elite alleles for plant height in drought tolerant wheat varieties. \u003cem\u003eActa Agron. Sin.\u003c/em\u003e 2010;36(6):895-904.\u003c/li\u003e\n \u003cli\u003eSheoran S., Jaiswal S., Kumar D., Raghav N., Sharma R., Pawar S., Paul S., Iquebal M.A., Jaiswar A., Sharma P., Singh R., Singh C.P., Gupta A., Kumar N., Angadi U.B., Rai A., Singh G.P., Kumar D., Tiwari R. Uncovering genomic regions associated with 36 agro-morphological traits in Indian spring wheat using GWAS. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e 2019; (10):527\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eWang S, Xu S, Chao S, Sun Q, Liu S, Xia G. A genome-wide association study of highly heritable agronomic traits in durum wheat. \u003cem\u003eFront Plant Sci.\u003c/em\u003e 2019; (10):919\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eJamil M, Ali A, Gul A, Rasheed A, Ahmed I, Imtiaz M, et al. Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat. \u003cem\u003eBMC Plant Biol.\u003c/em\u003e 2019; (19):149\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eMalik P, Kumar J, Singh S, Sharma S, Meher PK, Sharma MK, et al. Single-trait, multi-locus, and multi-trait GWAS using four different models for yield traits in bread wheat. \u003cem\u003eMol Breed\u003c/em\u003e. 2021;41(7):46\u0026ndash;21\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eDevate NB, Krishna H, Parmeshwarappa SKV, Manjunath KK, Chauhan D, Singh S, et al. Genome-wide association mapping for component traits of drought and heat tolerance in wheat. \u003cem\u003eFront Plant Sci\u003c/em\u003e. 2022; (13):943033.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVSN International. \u003cem\u003eGenStat\u003c/em\u003e for Windows, 14th ed. Hemel Hempstead, UK: VSN International Ltd.; 2011\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eAllard RW. \u003cem\u003ePrinciples of plant breeding:\u003c/em\u003e John Wiley \u0026amp; Sons; 1999.\u003c/li\u003e\n \u003cli\u003eFrichot E, Fran\u0026ccedil;ois O. LEA: An R package for landscape and ecological association studies. \u003cem\u003eMethods Ecol Evol\u003c/em\u003e. 2015;6(8):925-9.\u003c/li\u003e\n \u003cli\u003ePatterson N, Price AL, Reich D. Population structure and eigenanalysis. \u003cem\u003ePLoS Genet\u003c/em\u003e. 2006;2(12):e190.\u003c/li\u003e\n \u003cli\u003eYin L, Zhang H, Tang Z, Xu J, Yin D, Zhang Z, et al. rMVP: a memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study. \u003cem\u003eGenomics, proteomics \u0026amp; bioinformatics\u003c/em\u003e. 2021;19(4):619-28.\u003c/li\u003e\n \u003cli\u003eVanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91:4414-23.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTuberosa R, Salvi S, Sanguineti MC, Landi P, Maccaferri M, Conti S. Mapping QTLs regulating morpho-physiological traits and yield in drought-stressed maize: case studies, shortcomings and perspectives. \u003cem\u003eAnn Bot.\u003c/em\u003e 2002; (89):941-963\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eHittalmani S, Huang N, Courtois B, Venuprasad R, Shashidhar HE, Zhuang JY, et al. Identification of QTL for growth and grain yield-related traits in rice across nine locations of Asia. \u003cem\u003eTheor Appl Genet\u003c/em\u003e. 2003; (107):679-690\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eSchmidt L, Christensen HE, Harris P. Structure of plastocyanin from the cyanobacterium Anabaena variabilis. \u003cem\u003eBiol Crystallogr\u003c/em\u003e. 2006; 62(9):1022-1029\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eLi Y, Wei K. Comparative functional genomics analysis of cytochrome P450 gene superfamily in wheat and maize. \u003cem\u003eBMC Plant Biol\u003c/em\u003e. 2020;20(1):93.\u003c/li\u003e\n \u003cli\u003eGunupuru LR, Arunachalam C, Malla KB, Kahla A, Perochon A, Jia J, et al. A wheat cytochrome P450 enhances both resistance to deoxynivalenol and grain yield. \u003cem\u003ePLoS One.\u003c/em\u003e 2018;13(10):e0204992.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLi S, Zhao B, Yuan D, Duan M, Qian Q, Tang L, et al. Rice zinc finger protein DST enhances grain production through controlling Gn1a/OsCKX2 expression. \u003cem\u003eProc Natl Acad Sci U S A.\u003c/em\u003e 2013;110(8):3167-3172.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eXu T, Lee HJ, Sy ND, Kang H. Wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e) zinc finger-containing glycine-rich RNA-binding protein TaRZ1 affects plant growth and defense response in Arabidopsis thaliana. \u003cem\u003ePlant Growth Regul\u003c/em\u003e. 2015;76(3):243-50.\u003c/li\u003e\n \u003cli\u003eSinghal N, Mehta S, Singh M. Peroxidase activities in relation to plant height and grain weight in bread wheat (Triticum aestivum L.). \u003cem\u003eTheor Appl Genet\u003c/em\u003e. 1979;55(2):87-92.\u003c/li\u003e\n \u003cli\u003eKumar K, Tarkeshwar, Singh M, Gupta A, Singh S, Gaur SC. Character association and path coefficient studies in bread wheat (\u003cem\u003eTriticum aestivum L. em. Thell\u003c/em\u003e). \u003cem\u003eInt J Curr Microbiol Appl Sci.\u003c/em\u003e 2020;Special Issue-11:1323-30.\u003c/li\u003e\n \u003cli\u003eNageshwar S, Singh L, Kumar S, Kumar N, Singh A. Correlation studies among yield and its components in bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.). Pharma Innov J. 2021;10(12):978-82.\u003c/li\u003e\n \u003cli\u003eGaur AK, Singh PN, Gaur SC, Tarkeshwar, Kumar K, Gautam U, et al. Analysis of correlation and path coefficient in F1 and F2 generations of bread wheat (Triticum aestivum L.). \u003cem\u003eInt J Res Agron\u003c/em\u003e. 2025;8(2):302-8.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMondal S, Singh R, Crossa J, Huerta-Espino J, Sharma I, Chatrath R, et al. Earliness in wheat: a key to adaptation under terminal and continual high temperature stress in South Asia. Field Crops Res\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e2013; (\u003cem\u003e151\u003c/em\u003e):19-26.\u003c/li\u003e\n \u003cli\u003eSlafer GA, Savin R, Sadras VO. Coarse and fine regulation of wheat yield components in response to genotype and environment. Field Crops Res. 2014; (\u003cem\u003e157\u003c/em\u003e):71-83\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eHe F, Pasam R, Shi F, Kant S, Keeble-Gagnere G, Kay P, et al. Exome sequencing highlights the role of wild-relative introgression in shaping the adaptive landscape of the wheat genome. Nat Genet. 2019;51(5):896-904.\u003c/li\u003e\n \u003cli\u003eChou C-H, Lin H-S, Wen C-H, Tung C-W. Patterns of genetic variation and QTLs controlling grain traits in a collection of global wheat germplasm revealed by high-quality SNP markers. \u003cem\u003eBMC Plant Biol\u003c/em\u003e. 2022;22(1):455.\u003c/li\u003e\n \u003cli\u003eSaini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, et al. Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. \u003cem\u003eMol Breeding\u003c/em\u003e. 2022;42(1):1.\u003c/li\u003e\n \u003cli\u003eAleliūnas A, Gorash A, Armonienė R, Tamm I, Ingver A, Bleidere M, et al. Genome-wide association study reveals 18 QTL for major agronomic traits in a Nordic\u0026ndash;Baltic spring wheat germplasm. \u003cem\u003eFront Plant Sci\u003c/em\u003e. 2024;15(1):1393170.\u003c/li\u003e\n \u003cli\u003eTsonev S, Dragov R, Taneva K, Christov NK, Bozhanova V, Todorovska EG. Genome-wide association studies of agronomic and quality traits in durum wheat. \u003cem\u003eAgriculture\u003c/em\u003e. 2024;14(10):1743.\u003c/li\u003e\n \u003cli\u003eTyrka M, Krajewski P, Bednarek PT, Rączka K, Drzazga T, Matysik P, et al. Genome-wide association mapping in elite winter wheat breeding for yield improvement. \u003cem\u003eJ Appl Genet\u003c/em\u003e. 2023;64(3):377-91.\u003c/li\u003e\n \u003cli\u003eSoumya PR, Burridge AJ, Singh N, et al. Population structure and genome-wide association studies in bread wheat for phosphorus efficiency traits using 35 K Wheat Breeder\u0026rsquo;s Affymetrix array. \u003cem\u003eSci Rep\u003c/em\u003e. 2021;11(1):7601\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eMuhammad A, Li J, Hu W, Yu J, Khan SU, Khan MHU, et al. Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using different GWAS models. \u003cem\u003eSci Rep\u003c/em\u003e. 2021;11(1):6767.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu X, Huang M, Fan B, Buckler ES, Zhang Z. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. \u003cem\u003ePLoS Genet\u003c/em\u003e. 2016;12(2):e1005767.\u003c/li\u003e\n \u003cli\u003eKaler AS, Gillman JD, Beissinger T, Purcell LC. Comparing different statistical models and multiple testing corrections for association mapping in soybean and maize. Front Plant Sci. 2020;10:1794.\u003c/li\u003e\n \u003cli\u003eYoosefzadeh-Najafabadi M, Eskandari M, Torabi S, Torkamaneh D, Tulpan D, Rajcan I. Machine-learning-based genome-wide association studies for uncovering QTL underlying soybean yield and its components. IJMS. 2022;23(10):5538.\u003c/li\u003e\n \u003cli\u003eSandhu KS, Burke AB, Merrick LF, Pumphrey MO, Carter AH. Comparing performances of different statistical models and multiple threshold methods in a nested association mapping population of wheat. \u003cem\u003eFront Plant Sci\u003c/em\u003e. 2024;15:1460353.\u003c/li\u003e\n \u003cli\u003eWang Z, Lai X, Wang C, Yang H, Liu Z, Fan Z, Li J, Zhang H, Liu M, Zhang Y. Exploring the drought tolerant quantitative trait loci in spring wheat. \u003cem\u003ePlants\u003c/em\u003e (Basel). 2024;13(6):898.\u003c/li\u003e\n \u003cli\u003eAlemu A, Suliman S, Hagras A, Thabet S, Al-Abdallat A, Abdelmula AA, et al. Multi-model genome-wide association and genomic prediction analysis of 16 agronomic, physiological and quality related traits in ICARDA spring wheat. Euphytica. 2021;217(11):205.\u003c/li\u003e\n \u003cli\u003eSukumaran S, Dreisigacker S, Lopes M, Chavez P, Reynolds MP. Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. \u003cem\u003eTheor Appl Genet\u003c/em\u003e. 2015;128(2):353-63.\u003c/li\u003e\n \u003cli\u003eGuan P, Lu L, Jia L, Kabir MR, Zhang J, Lan T, et al. Global QTL analysis identifies genomic regions on chromosomes 4A and 4B harboring stable loci for yield-related traits across different environments in wheat (\u003cem\u003eTriticum aestivum L\u003c/em\u003e.). \u003cem\u003eFront Plant Sci)\u003c/em\u003e. 2018;9:529.\u003c/li\u003e\n \u003cli\u003eMaulana F, Ayalew H, Anderson JD,\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eKumssa TT, Huang W and Ma X-F\u003cspan dir=\"RTL\"\u003e.\u0026nbsp;\u003c/span\u003e Genome-Wide Association\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eMapping of Seedling Heat Tolerance\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ein Winter Wheat.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003cem\u003eFront. Plant Sci\u003c/em\u003e. 2018;9:1272.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bread wheat, GWAS, SNPs, marker-assisted selection, agronomic traits","lastPublishedDoi":"10.21203/rs.3.rs-7424201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7424201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: A genome-wide association scan (GWAS) is a powerful tool for identifying genetic variants and specific loci underlying complex traits. Bread Wheat (\u003cem\u003eTriticum aestivum \u003c/em\u003eL.) is one of the primary food resources in the world, and understanding its physiological parameters will help improve agronomic and yield traits. This study investigated single-nucleotide polymorphism (SNP) markers associated with physiological and agronomic traits in bread wheat to inform breeding programs. A diverse panel of 272 bread wheat genotypes was evaluated across two growing seasons (2019–2021) using a 16 × 17 rectangular lattice design with two replications. Key physiological traits, including carbon dioxide exchange and chlorophyll content, and agronomic traits, such as days to heading, days to maturity, flag leaf length, plant height, grain number per spike, grain weight per plant, thousand-grain weight, biological yield, and harvest index, were measured. Genotyping was conducted using a 90K SNP array at Trait-Genetics, Germany, yielding 17,093 high-quality SNPs after filtering for minor allele frequency and missing data (\u0026gt;10%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Population structure analysis grouped the genotypes into five subgroups based on their genetic variation. GWAS was performed using General Linear Model (GLM), Fixed and random model Circulating Probability Unification (FarmCPU), and Mixed Linear Model (MLM), identifying 320, 302, and 27 significant marker-trait associations (MTAs), respectively. Sixteen MTAs were consistently significant across models, including four stable MTAs detected in two cropping seasons.\u003cstrong\u003e \u003c/strong\u003eThese MTAs harbored 139 high-confidence genes associated with nine traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e These findings provide valuable insights into the genetic architecture of key wheat traits, facilitating targeted breeding strategies to enhance yield.\u003c/p\u003e","manuscriptTitle":"Genome-Wide Association Study Reveals Key SNP Markers for Agro-Physiological Traits in Bread Wheat (Triticum aestivum L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 09:42:55","doi":"10.21203/rs.3.rs-7424201/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80d14093-666e-412b-bc2b-47ad81e2947f","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T12:23:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 09:42:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7424201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7424201","identity":"rs-7424201","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}