GWAS-Driven Identification of Genomic Regions Conferring Yield and Related Traits Under Drought Stress in Wheat

GWAS-Driven Identification of Genomic Regions Conferring Yield and Related Traits Under Drought Stress in Wheat | 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 GWAS-Driven Identification of Genomic Regions Conferring Yield and Related Traits Under Drought Stress in Wheat Muhammad Jamil, Komal Saeed, Hanasul Hanan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6479357/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 Drought stress highlights the urgent need to create drought-tolerant wheat varieties. The genome-wide association analysis was performed to identify genetic markers linked to wheat yield traits under drought stress. A diverse panel of 190 bread wheat genotypes was evaluated under both well-watered and water-limited conditions throughout three growing seasons (2021–2024) in Bahawalpur, Punjab, Pakistan using an alpha lattice design with three replications. Genotyping was performed using a 37 K SNP array. Genomic prediction models were developed using genomic best linear unbiased predictions (g-BLUPs). Heritability was the highest (0.88) for yield and the lowest (0.53) for spikes per plant. Overall stress tolerance indices ranged from 1.21 for thousand kernel weight to 0.42 for grain yield. The additive main effects and multiplicative interaction (AMMI) model and principal component analysis (PCA) identified 10 high-yielding genotypes under water-limited conditions. The 59 genomic regions were significantly associated with yield and related traits on 21 chromosomes and clustered as ten QTLs. The significant QTL for yield under drought stress on chromosomes 3A and 5B explained a phenotypic variance of 7.1% and 5.08%. The genomic best linear unbiased predictions (g-BLUP) indicated notable accuracies, for traits related to large-effect QTLs, ranging from 0.1 for flag leaf length to 0.53 for biomass. This study concluded significant variation in wheat genotypes under water-limited conditions, with moderate to high heritability. It identifies ten elite genotypes and 59 key genomic regions for drought tolerance, and the genomic prediction model, g-BLUP, aids breeding programs in developing climate-resilient wheat varieties. Wheat GWAS Drought Stress Genomic Prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Bread wheat ( Triticum aestivum L.) grows worldwide, and it is a crucial crop of more than 40 percent world's population relies on it as stable food that provides 20% of calories (Wang et al. 2021 ). Wheat is cultivated worldwide in more than 85 countries, including Pakistan, India, Kazakhstan, Egypt, and China, to meet food demand. More than 12 million hectares of land are used to raise wheat. According to FAO's most recent report from 2020, 219 million acres were harvested, and the total production position as the second-largest crop in the world is strengthened by the production of 761 million tons. The annual wheat cultivation has increased by 1.18 percent (Akram et al. 2022 ). Changes in environmental conditions cause problems in crop production, with up to a 2 to 5 percent decrease due to various factors present in stored grains (Mastrorilli et al. 2020 ). Abiotic stresses include light intensity, ultraviolet radiation, temperature, moisture status, and drought (Bacher et al. 2022 ). Drought stress disturbs various physiological and morphological processes, molecular levels, and productivity of plants. Severe drought conditions in Pakistan have resulted in a significant reduction of 30–40% in wheat yield, impacting approximately 22–25 million hectares of agricultural land. Global drought has reduced wheat yields by 10–25% across 1.5 billion hectares of farmland. Regions like North America (-20-40%), India (-15-30%), China (-10-25%), and Australia (-20-40%) have been severely impacted (Ahmed et al. 2021 ). Water shortages are alarming worldwide, including Pakistan, due to several notable problems in agriculture and crop production (Ahmed et al. 2021 ). Our knowledge of the genetic underpinnings and molecular basis of complex traits, such as drought tolerance, has increased as a result of recent developments in high-throughput genotyping (Saini et al. 2022 ), phenotyping techniques (Xiao et al. 2022 ) and methods designed to identify genotype-phenotype associations by evaluating the genetic variants across the genomes of many individuals (Pu et al. 2022 ). Genome-wide association study (GWAS) is a highly effective tool that uses linkage disequilibrium (LD) to uncover the relationship between SNPs markers and specific traits. Revolutionizes the link between QTL analysis and MAS, enabling the efficient selection of complex traits in the face of rapid LD decay and multiple genetic factors. (Devate et al. 2022 ). Compared with bi-parental mapping, GWAS offer several advantages. It captures all recombination events in an individual’s evolutionary history, unlike pedigrees with limited recombination opportunities. This approach allows the screening of a larger and more diverse gene pool, facilitating the analysis of complex genetic traits (Liu et al.2022). The diverse and unstructured natural populations used in GWAS have allowed the accumulation of a large amount of information on historical recombination, thus delivering a relatively high mapping resolution (Alqudah et al. 2020 ). In recent years, association studies have been extensively used for the genetic dissection of drought tolerance in several crops such as wheat (Jamil et al. 2019 ; Qaseem et al. 2019 ). Genomic prediction (GP) is a productive approach to enhance breeding schedule efficiency and expedite the process by reducing periods and improving selection accuracy. This method facilitates the identification of candidate genes by genotyping before phenotypic determination. Genomic prediction utilizes all the genetic markers within a model to train a prediction model, which is subsequently employed as a validation set to evaluate its accuracy. However, due to the substantial influence of population structure and trait architecture on marker effects, uncertainty remains regarding the optimal algorithm for genomic selection and GWAS. In the field trial, this study involved conducting a Genome-Wide Association Study (GWAS) to investigate the drought response of multiple bread wheat genotypes in well-water and water-limited environments in field trials. This study also helps in the discovery of novel SNPs linked to drought tolerance and the introduction of new possibilities for genetic improvement. Developing drought-tolerant wheat varieties is crucial for food security in water-scarce areas, enhancing yield stability, and sustainable food production amid climate change. However, genome-wide association studies have been conducted on wheat panels to dissect drought-tolerance mechanisms genetically. The objectives of this study were: (i) to identify drought-resistant genetic resources among 190 bread wheat genotypes for potential use in breeding programs; (ii) to estimate genomic best linear unbiased predictions (gBLUPs) for agronomic traits across multiple environments and years; (iii) to evaluate genotype-by-environment (G×E) interactions through multi-environment and multi-year phenotypic trials; and (iv) to perform genome-wide association studies (GWAS) to identify significant marker-trait associations. Materials and Methods Material and Field Experiment A panel of 190 bread wheat genotypes was collected from the Department of Plant Sciences, Quaid-i-Azam University, Islamabad, Pakistan; germplasm detail is given in (Supplementary Table S1). A field experiment was conducted using alpha lattice designs in Bahawalpur (29°15'23.0’ N, 71°42'44.4' E) with three replications during November over three years (2021-22, 2022-23, and 2023-24). The experiment included two types of field conditions: well-watered and water-limited conditions. All genotypes were planted in triplicate, with each row measuring 1 m and maintaining a plant-to-plant distance of 5–10 cm. Customized watering was applied to each plant based on water usage and flag leaf characteristics to regulate water treatments and observe variations in growth and phenological traits. Two water regimes were used. Well-watered treatment : Irrigation was maintained at 80% field capacity during three critical growth stages: tillering (35 days after sowing, DAS), booting (85 DAS), and milking (112 DAS). Water-limited treatment This regime followed a well-watered protocol until flag leaf emergence, after which intermittent drought stress was imposed by withholding irrigation when drooping symptoms appeared on the last three leaves. Irrigation was provided at 40% field capacity for the well-watered treatment and 24% field capacity (60% of the well-watered treatment) for the water-limited treatment, as described by (Abo-Elwafa et al. 2021). Phenotyping The following traits were recorded for each experimental unit after germination. Plant height (cm), flag leaf area (cm), peduncle length (cm), tillers per plant, number of spikes, spike length (cm), spikelet per spike, spike weight (g), thousand kernel weight (g), plant dry weight including spike and straw (biomass), and kernel yield per plot (YM 2 g/plot). Muller's (1991) formula was used to calculate the flag leaf area. Flag leaf area = maximum length × maximum width x 0.74. The following formula was used to calculate the drought tolerance indices for the studied traits: Stress tolerance index (STI) = \(\:\text{S}\text{T}\text{I}\::\frac{\text{Y}\text{p}\:+\:\text{Y}\text{s}\:\:\:\:}{{\:\text{y}}^{-2}\text{p}}\) (Fernandaz 1992). where y p is the average yield of a genotype under well-water conditions, y s is the average yield of the same genotype under water-limited conditions, and y s and y¯ p are the average yields of all genotypes in water-limited and well-water environments, respectively. Statistical Analysis of Phenotypic Traits The years of the study and the water regimes were considered as the environment, and ANOVA table type III with Satterwaites across the water regime package in R-studio using "lmer Test" was created. The best linear unbiased estimation (BLUE) for each line was determined. Yield traits were analyzed statistically using a split-split plot (R. v 4.1.0 (Yu et al. 2019). Heritability (H 2 ) was determined from the observed variance. $$\:\left(a\right)individual\::\frac{\sigma\:²g}{\sigma\:²g+\:\frac{\sigma\:²g}{r}}*100$$ where \(\:\sigma\:²g\) , \(\:\:\sigma\:²e\) and \(\:\sigma\:²gy\) are the genetic variance, residual variance, and variation, respectively, resulting from the interplay of genotype and environmental factors. Replications and environments overall are indicated by the letters "y" and "r," respectively (Gaur et al. 2022). Principal component analysis was used to biplot the variables and factors to determine the differences between the coefficients of correlation. A scree plot was used to analyze the total variation in the data. The data, including the effects of G × E, were examined using a linear mixed model with the effects of years and genotypes. Principal component analysis (PCA) was used to determine distinguishing characteristics (Bilgrami et al. 2020 ). The additive main effect and multiplicative interaction model is a statistical technique that analyzes two-way data structures, such as genotypes and environments, by breaking down the variance into additive and multiplicative components. The AMMI model can be expressed as follows: Yij = µ + gi + ej + Σ ( λn * in * jn) + εij, Where: Yij is the yield of genotype i in environment j, µ is the overall mean, gi and ej are the genotypes and environmental effects, respectively, λn is the eigenvalue of the n principal component axis, in and jn are the genotypes and environment principal component scores for the n axis, and εij is the random error term. The number of principal components retained in the model was determined using the F-test proposed by Gollob ( 1968 ). Genotyping Genotyping of a germplasm panel consisting of 190 lines involved the extraction of genomic DNA from the leaves of young, two-week-old bread wheat seedlings using the CTAB method. The DNA concentration in each sample was adjusted to 30ng/ µL. Genotyping was performed using a 37 K Illumina iSelected SNP array to determine the genotypes of all the 190 lines. SNP markers were selected based on a minor allele frequency (MAF) threshold of 0.25%. Samples with missing data exceeding 2% were excluded from further analysis as reported by Khan et al. ( 2022 ). Kinship, Population Structure, and Linkage Disequilibrium Analysis The structure of the complete collection was examined using the model-based clustering method admixture models with associated allele frequency, and principal component analysis was performed using TASSEL 5.2.94, using genotypic data with 7677 SNP markers, and the genotypes were scattered along five principle components PCs. A cladogram showing the genetic diversity among 190 genotypes was generated using the Archeopteryx function. The linkage disequilibrium in the study was independently assessed for each genome A, B, and D, and the average LD for the three genomes. The genetic distance LD decay plot and pairwise R 2 were plotted using the R statistical platform (RStudio Team, 2015). An identity-based scaled population kinship matrix (Kin) was calculated based on the state IBS technique was calculated (Weiss et al. 2020 ). The study measured pair-wise linkage disequilibrium (LD) from cleaned filtered sites on 379444 single nucleotide polymorphism (SNPs) markers using a 50 kb window size and 1042297 pair comparisons with R 2 = 0.2 and distance < 2,00,000 base pairs (bp). Heat maps of LD measurements were generated using R. Studio version 3.4.4, and scatter plots were used to determine the putative disequilibrium between markers along and covered base pair distance. Marker Trait Association (MTA) Marker-trait associations were analyzed using a mixed linear model (MLM) in TASSEL. PCA and kinship matrices (K) were incorporated, and QQ plots were used to validate the associations. Significant SNP markers for drought tolerance were identified, and chromosomal maps were created using MapChart v3.5.4, providing a spatial understanding of the genomic regions linked to adaptive traits in bread wheat under varying water availability conditions. Genomic prediction The genomic prediction was performed on a panel of 190 lines using the genomic best linear unbiased prediction (gBLUP) method to identify regions associated with drought stress tolerance within the panel. prediction accuracy was assessed using five cross-validations with 20 iterations, where the dataset was randomly split into 10% for testing and 90% for training. The Prediction accuracy was evaluated by measuring the correlation between true and expected values in the 10% testing subset (Ed-Daoudy et al. 2024 ). Determination of the mean and standard deviation of the prediction accuracy across multiple iterations ensured a comprehensive assessment of the genomic prediction model performance. Results Phenotyping assessments This study analyzed 190 bread wheat varieties across three seasons and two conditions: well-water (T1) and water-limited (T2). The results showed a positive correlation between harvest periods and traits. The heritability analysis revealed substantial genetic variation across the studied wheat traits under drought stress. The thousand kernel weight (TKW, H² = 0.92) and yield (YLD, H² = 0.88) showed the highest broad-sense heritability, indicating strong genetic control. Moderate heritability was observed for plant height, biomass weight, and other traits. Spike length showed lower heritability (H² = 0.65), suggesting a greater environmental influence. The high coefficient of determination (R²) for most traits indicated the effectiveness of the model in explaining genetic variation, as shown in (Table 1 ). The stress tolerance index (S.T.I.) varied significantly among the 190 wheat varieties. Thousand kernel weight (TKW) showed high drought tolerance with an STI of 1.21, whereas yield (YLD) displayed lower drought resilience with an STI of 0.42. These findings suggest differential responses to drought stress across wheat traits (Table 2 ). Table 1: Mean squares of traits obtained through split-split plot design of experiment along with model R 2 , grand mean (GM), and broad sense heritability (H 2 ) Table 2: Stress tolerance indices of all traits under water-limited conditions for three years. Under well-watered conditions, peduncle length was positively correlated with thousand kernel weight (r = + 0.18), but negatively correlated with spike weight and yield (r = -0.18). Yield showed a positive correlation with spikelets per spike, which correlated with total productive tillers and stress tolerance index, whereas flag leaf width was positively associated with peduncle length. Spike length, biomass per plant, and flag leaf length-to-width ratio were negatively correlated with most of the traits. Flag leaf area showed strong positive correlations with flag leaf width (r = + 0.589) and flag leaf length (r = 0.85) (Fig. 1 ). Under water-limited conditions, the stress tolerance index was positively correlated with yield, spike weight, biological mass, spike length, and thousand kernel weight but negatively correlated with plant height, flag leaf traits, and peduncle length. Yield was positively correlated with spike weight, biological mass, and spike length, whereas spike weight was positively correlated with biological mass. Total productive tillers showed strong positive correlations with spikes per plant (r = 0.81). Flag leaf area and length were positively correlated, whereas plant height was inversely associated with stress tolerance traits. The study categorized the 15 traits into three classes based on their clustering patterns and genotype performance. Under both well-water- and water-limited conditions, the traits were grouped into three categories. Principal Component Analysis (PCA) Principal Component Analysis (PCA) was conducted on a dataset of 15 variables and 3420 observations, resulting in 15 principal components. The first component, represented by an eigenvalue of 2.57, accounted for 70.46% of the total variation. The second component, orthogonal to PC1, explained 16.2% of the variation. The total of Six principal components with eigenvalues greater than one collectively accounted for 70.46% of the overall variance. Genotypes 96, 106, 103, 90, and 129 showed significant variability with longer vectors for parameters, such as STI and YLD, whereas SW, FLW, BM, PL, SPP, TPP, SPS, PH, FLA, and FLLWR showed maximum variability. The PC biplot analysis visualized relationships and variability among parameters and genotypes, revealing that genotype 53 exhibited greater variability with longer vector lengths than the other genotypes. This biplot visualization allowed a clear comparison of differences among 190 bread wheat genotypes for key agro-morphological traits under well-water and water-limited conditions, as shown in (Fig. 2 ). Yield Stability Assessment Using Additive Main Effect and Multiplicative Interaction (AMMI) Analysis The GGE biplot analysis revealed that genotypes responded to environmental variations effectively, with genotypes furthest from the midpoint showing the greatest sensitivity. The first principal component (PC1) accounts for 71.47% of the total variance, while the second component (PC2) accounts for 16.37%. The study found that Genotypes 96, 109, 138, and 129 performed better than the other genotypes, and genotype 153 showed consistent yield stability under different conditions. The GGE biplot analysis also showed that environments represented by vectors could differentiate between genotypes, with 2022 and 2023 having the longest vectors. The AMMI framework suggests that genotypes on the right have higher average grain yields than the overall mean, as shown in (Fig. 3 ). Statistical analysis of genotypes All 190 accessions were genotyped using the wheat iSelect 37 K array. After filtering the missing rate > 20% and minor allele frequency < 0.05), 7677 high-quality SNPs were retained and used for genetic analysis. These were distributed on all 21 chromosomes, with 3475, 3512, and 690 SNPs in the A, B, and D subgenomes, respectively. Chromosome 2A had the most SNPs (1020) but the shortest average distance between SNPs (575Mbp). In contrast, 4D had the least 50 SNPs. Principal Component Analysis, Population Structure A total of 190 bread wheat genotypes with 7,677 single nucleotide frequency (SNPs) minor allele frequency < 0.25 (MAF) were included in the genome-wide association analysis (GWAS). The study focused on the population structure, with the first 20 principal component analyses explaining 20% variation, as shown in (Fig. 4 ). The Population structure analysis divided these genotypes into four clusters, with the first five principal components representing 33% variation in the overall genomic data. This study revealed intricate patterns of genetic architecture across five distinct groups, providing insights into the evolution of wheat breeding programs. Group-1, comprising historical cultivars from the 1970s-2000s, exhibited substantial genetic admixture, suggesting that they likely served as foundational germplasms in breeding programs. Group-2, featuring more recent breeding lines, displayed more distinctive genetic structuring, possibly indicating more focused breeding objectives in modern varieties. Group-3, though smaller, showed a balanced distribution among PCs, suggesting their role as transitional germplasms between traditional and breeding lines. Group-4 exhibited the most distinct genetic stratification, indicating possible distinct breeding pools or ecological adaptations. Group-5, containing varieties such as Pirsabak-21 and Khirman-15, displayed remarkably uniform genetic compositions, suggesting shared ancestry or similar selection pressures during breeding, as shown in (Fig. 5 ). The significant genetic admixture observed across most groups, particularly in historical varieties, contrasts with the more defined genetic structuring observed in modern cultivars, highlighting the evolution of breeding strategies. The neighbor-joining tree divided the panel into four main groups, as shown in (Fig S1). Linkage disequilibrium A total of 7677 sites were subjected to linkage disequilibrium analysis. A total of 1042297 pairs were selected using a window size of 137 for pair comparison (R 2 ). The second selection was performed on these 1042297 pairs by keeping the threads held (R 2 = 0.2). So 379444 pairs were obtained with an LD above (R 2 = 0.2). Out of 379444, 379214 pairs showed intrachromosomal LD. They were divided into four classes based on distance in megabase pairs (Mbp). In this study, the overall LD analysis involved 379444 pairs with an overall mean of (R 2 of 0.372). The Significant linkage disequilibrium (LD) was observed, as shown in Table 3 , in a total of 34922 pairs which contained 9.22% of all pairs examined. Table 3 Overall Summary of Linkage Disequilibrium (LD) Analysis Mbp Total pairs Mean R2 Pairs LD 1 > .4 % pairs LD > .4 Pairs LD1 < 150 350667 0.67 26037 7.42 60067 150 < 300 20194 0.39 7938 39.31 92 300 450 1161 0.27 58 5.00 Zero Inter-chr 230 0.23 Zero -- Zero Overall 379444 0.372 34992 9.22 60159 Marker Trait Association and Quantitative Trait Loci (QTLs) The 59 significant (MTAs were identified as highly significant in R 2 (Table 4 ). QTLs for flag leaf traits clustered on chromosomes, such as FLA found on 7A, FLLWR found on 1D, 2 B, and 7D, and FLW found on 2A,5B, and 2D. QTLs for morphological traits such as PL clustered on 2A, 4 B, SL 6A, 7 B, and SW on 3 B. QTLs for STI clustered on chromosome 2D and TKW on chromosomes 1A, 5A, and 6 B. TPP QTLs on chromosomes 1A and 4B, and YLD QTLs were found on chromosomes 3A, 6A, and 5 B. Table 4 Marker-trait association (MTAs) using MLM (PC + K model) Distance Allele p-value Association Trait Marker (Mbp) Chr Major/Minor MAF F -log10 R2 (%) FLA 7A_610217135 610.217 7A C/A 0.49 6.020 2.531 6.490 FLA 7A_15677256 15.677 7A C/T 0.42 5.965 2.509 6.440 FLLWR 2B_759732710 759.733 2B G/A 0.34 8.198 3.410 8.710 FLLWR 7D_489015092 489.015 7D A/T 0.48 6.497 2.724 7.030 FLLWR 1D_415144671 415.145 1D G/T 0.39 6.169 2.591 6.640 FLLWR 1D_415144661 415.145 1D G/T 0.39 6.156 2.586 6.620 FLW 5D_116251109 116.251 5D T/G 0.42 8.690 3.605 9.290 FLW 2B_759732710 759.733 2B G/A 0.34 8.115 3.377 8.670 FLW 7D_55067055 55.067 7D C/T 0.45 7.129 2.980 7.840 FLW 7D_489015092 489.015 7D A/T 0.48 6.932 2.900 7.570 FLW 1D_415144661 415.145 1D G/T 0.39 6.756 2.829 7.380 FLW 1D_415144671 415.145 1D G/T 0.39 6.747 2.826 7.370 FLW 7B_648671942 648.672 7B C/T 0.37 6.284 2.640 6.720 FLW 2D_64723966 64.724 2D A/C 0.29 5.957 2.505 6.370 FLW 6D_243306317 243.306 6D T/C 0.45 6.617 2.774 7.080 PL 4B_3830663 3.831 4B G/T 0.27 8.097 3.369 8.620 PL 2A_91136366 91.136 2A C/T 0.42 7.279 3.042 7.750 PL 2A_97659173 97.659 2A G/T 0.42 7.279 3.042 7.750 PL 2A_90933999 90.934 2A T/C 0.42 7.245 3.028 7.710 PL 2A_98845116 98.845 2A T/C 0.42 7.245 3.028 7.710 PL 2A_94273517 94.274 2A T/C 0.42 7.241 3.026 7.720 PL 2A_94273538 94.274 2A G/A 0.42 7.241 3.026 7.720 PL 2A_94273611 94.274 2A T/C 0.42 7.241 3.026 7.720 PL 2A_95320750 95.321 2A T/C 0.42 7.241 3.026 7.720 PL 2A_95320777 95.321 2A G/C 0.42 7.241 3.026 7.720 PL 2A_97659286 97.659 2A T/C 0.42 7.241 3.026 7.720 PL 2A_101107999 101.108 2A T/C 0.42 7.241 3.026 7.720 PL 2A_90934006 90.934 2A A/G 0.42 7.241 3.026 7.720 PL 2A_90934066 90.934 2A A/G 0.42 7.241 3.026 7.720 PL 2A_91136228 91.136 2A A/G 0.42 7.241 3.026 7.720 PL 2A_94124763 94.125 2A C/T 0.42 7.241 3.026 7.720 PL 2A_95608283 95.608 2A A/G 0.42 7.241 3.026 7.720 PL 2A_96605553 96.606 2A A/G 0.42 7.241 3.026 7.720 PL 2A_96605619 96.606 2A G/T 0.42 7.241 3.026 7.720 PL 2A_97659330 97.659 2A C/T 0.42 7.241 3.026 7.720 SL 6A_22201090 22.201 6A A/G 0.49 6.488 2.722 6.980 SL 7B_597591187 597.591 7B A/G 0.43 6.234 2.619 6.700 SPP 2D_601107080 601.107 2D C/T 0.32 6.724 2.816 7.180 SPS 5B_681028944 681.029 5B A/G 0.32 7.614 3.176 8.190 SPS 5B_681028907 681.029 5B T/C 0.32 7.562 3.155 8.140 SPS 5B_672547138 672.547 5B G/A 0.45 9.313 2.583 5.010 SPS 2A_198593852 198.594 2A G/T 0.37 6.099 2.564 6.560 SPS 2A_198593801 198.594 2A C/T 0.36 5.945 2.502 6.390 SPS 2A_198593802 198.594 2A A/G 0.36 5.945 2.502 6.390 SW 3B_749982772 749.983 3B T/A 0.43 8.075 3.360 8.730 SW 3B_749982788 749.983 3B A/G 0.42 7.611 3.174 8.220 SW 3B_750131682 750.132 3B G/T 0.41 6.426 2.697 6.870 STI 2D_18237499 18.237 2D A/C 0.3 7.384 3.083 8.050 TKW 6B_712323218 712.323 6B C/T 0.42 7.262 3.034 7.890 TKW 5A_378790530 378.791 5A G/T 0.46 6.785 2.842 7.280 TKW 5A_364608895 364.609 5A G/A 0.47 6.669 2.796 7.160 TKW 1A_548717003 548.717 1A T/G 0.29 6.431 2.699 6.900 TKW 1A_548750891 548.751 1A C/T 0.29 6.250 2.625 6.720 TKW 5A_344530310 344.53 5A G/A 0.46 6.158 2.588 6.610 TPP 4B_644355516 644.356 4B A/G 0.34 6.150 2.584 6.570 TPP 1A_565376578 565.377 1A C/T 0.3 6.096 2.563 6.510 YLD 6A_53181158 53.181 6A G/A 0.28 6.585 2.761 7.130 YLD 3A_720224638 720.225 3A C/T 0.37 6.568 2.754 7.110 YLD 5B_559783496 559.783 5B T/C 0.27 9.386 2.599 5.080 Manhattan Plots and Q-Q Plots This study evaluated the association between phenotypic traits and genomic loci by using a linear mixed model. A quantile–quantile plot showed deviations from the expected distribution for the studied traits, with minor differences and increased slopes as the plot approached higher values. Manhattan plots revealed significant markers associated with drought stress, with several exceeding the -log10 threshold. These markers were distributed across three wheat genomes (A, B, and D). Favored allele estimates were calculated to identify the alleles that were most strongly associated with drought tolerance. This study highlights the importance of understanding the relationship between traits and genomic loci (Fig. 6 ). Physical Map of Genome-wide Association Study (GWAS) Markers Associated with Drought Stress A previous study mapped 81 significant GWAS markers associated with drought stress across 21 chromosomes of the wheat genome, spanning 0-650 megabase pairs. The distribution of these markers varied across the three genomes: genome A had the most markers (51), followed by genomes B (21) and D (9). Potentially associated markers were identified on chromosomes 1A, 1 B, and 1D. Three regions on 1A were associated with yield-contributing traits, whereas two regions on 1 B were associated with spike traits and peduncle length. On chromosome 2A, 34 potentially associated markers were found, with peduncle length under drought stress spanning from 0 to 650 megabase pairs. The study identified three markers on chromosome 2 B and two markers on chromosome 2D that were associated with yield-contributing traits, flag leaf traits, and spike traits. The two regions spanning distances of 0–550 Mbps contained seven markers associated with spike traits. Two markers on chromosome 3 B were associated with spike traits, whereas two markers on chromosome 4 B were associated with peduncle length. Three regions on chromosomes 5A and 5 B were found to contribute to yield, whereas two regions were associated with peduncle length markers, spanning 0 to 150 Mbps. The remaining two regions were associated with spike traits and yield and ranged from 0 to 50 Mbps. These markers were found on chromosomes 6A and 6 B, respectively. Chromosome 6D had three regions associated with flag leaf traits, whereas the three regions on chromosome 7A were associated with flag leaf traits. The two regions on chromosome 7 B are associated with spike and flag leaf traits, respectively, spanning 0 to 50 Mbps. The two regions on chromosome 7D are associated with spike traits spanning 0–450 Mbps, as shown in (Fig S2 (a) and (b)). Genomic prediction The genomic best linear unbiased prediction (gBLUP) study on a panel of 190 bread wheat varieties revealed intricate insights into genetic prediction across 15 drought-related traits using cross-validation with TASSEL 5.2.9.4, researchers analyzed traits including biomass, spike characteristics, leaf parameters, and stress tolerance index, through 5-fold cross-validation with 20 iterations. The analysis demonstrated varying prediction accuracies, with biomass showing the highest mean genomic prediction accuracy of 0.53, whereas traits such as spike length and flag leaf length exhibited lower accuracies of approximately 0.1. The mean prediction accuracy was 0.3, with 53.3% of observations exceeding this threshold. This study highlighted gBLUP's effectiveness of gBLUP in capturing genetic variation, particularly for traits with strong genetic control, such as biomass and spike weight, while showing limitations for complex traits influenced by multiple genetic and environmental factors. These findings suggest a significant potential for genomic selection to accelerate wheat breeding programs, especially in developing drought-tolerant varieties, by providing a robust method for predicting phenotypic traits from genetic information, as shown in (Fig. 7 ). Discussion This study evaluated 14 phenotypic traits of 190 bread wheat under two conditions: well-water and water-limited. The results showed significant variation in drought tolerance-related traits, suggesting that the genotypes used in this study were good genetic sources for drought tolerance. Drought affects up to 50% of wheat agriculture, necessitates urgent genetic solutions. Drought tolerance indicators included flag leaf characteristics, tillers per plant, spike parameters, biomass, and yield. The urgent need for climate-resilient wheat varieties emphasizes the importance of integrating genetic diversity and advanced breeding techniques to ensure food security in drought-sensitive regions. This study examined the effects of drought stress on wheat yield and related traits. The results indicated a reduced yield, biomass, spike weight, and other traits under water-limited conditions. However, the significant heritability estimates for these traits suggest the potential for genetic improvement. A panel of 190 wheat genotypes showed phenotypic variation, indicating their potential to identify genes linked to drought tolerance. Genetic diversity within the wheat germplasm is a valuable resource for enhancing drought tolerance (Gahlaut et al. 2021 ). This study also revealed significant phenotypic differences among genotypes under drought stress, highlighting the importance of genotype selection. The heritability (H²) values for all drought-related traits ranged from 0.49 0.86. These values are consistent with those reported for winter wheat by (Zhao et al. 2023 ), underscoring the heritable nature of these traits. This suggests that breeders can rely on these characteristics to select for drought-resistant wheat. The significant heritability of all parameters suggests that selecting genotypes for drought resistance during the germination stage could be effective. Selection under controlled conditions is often more successful than field experiments in improving drought tolerance (Dolferus et al. 2019 ). Achieving a balance between drought resistance and crop productivity is crucial for adapting to variable environments while maintaining consistent yields. Heritability is essential for trait selection in genome-wide association studies (GWAS), marker-assisted selection, and plant breeding. Traits with a higher heritability across environments are more valuable for breeding purposes. The present study found high heritability values (~ 0.84) for all phenotypic traits, which were higher than those observed in Arabidopsis (Gamba et al. 2024 ), suggesting reduced genotype-by-environment interactions and consistent trial conditions. Stress tolerance indices (STI) were categorized into five groups (HT, T, MT, MS, and HS), and correlations with yield-related traits showed that higher STI values were associated with better drought tolerance. Traits such as thousand kernel weight and plant height showed positive correlations with STI, whereas traits such as biomass and yield were negatively correlated, indicating decreased drought tolerance. These findings are consistent with those of previous studies on drought stress (Li et al. 2020; Wu et al. 2022 ). This study also highlighted the impact of environmental factors, such as sowing dates, on plant height. By employing advanced analysis techniques, this research revealed key traits for drought tolerance, emphasizing the importance of multi-environmental studies for breeding more resilient wheat varieties. This study applied the Additive Main Effect and Multiplicative Interaction (AMMI) model to assess yield stability and drought tolerance in wheat genotypes using multiple research sources (Zhang et al. 2023 ; Singh et al. 2023 ; Khan et al. 2023 ; Ahmed et al. 2022 ; Yan et al. 2024 ; Bhandari et al. 2024 ). It evaluated ten bread wheat genotypes over three years in both well-watered and water-limited environments, identifying several stable and drought-tolerant lines, including varieties 106, 153, 103, and others. The AMMI model revealed significant genotype-by-environment interactions, with 153 showing the highest stability and drought tolerance. This study highlights the importance of selecting genotypes that maintain consistent performance across environmental conditions, offering valuable insights for breeding resilient wheat varieties that are adaptable to climate challenges. Genome-wide association studies have been instrumental in understanding the genetic basis of drought tolerance in wheat (Itam et al. 2022 ). It has revealed complex genetic architectures linked to stress responses, revealing numerous marker-trait associations (MTAs) for grain weight, stress indices, and other traits under different stress conditions. A GWAS of 190 wheat varieties using 7,677 SNPs provided genetic insights, highlighting the genetic complexity of wheat. Marker-trait associations and quantitative trait loci (QTLs) are essential for understanding drought resistance, as observed in studies of Ethiopian durum wheat (Negisho et al. 2022 ) and winter wheat (Maulana et al. 2020 ). These findings will advance breeding strategies for drought-tolerant varieties. This study also identified significant QTLs linked to drought resistance and improved breeding program efficiency. Important QTLs for traits such as flag leaf area, spike length, and yield further support marker-assisted selection for improving drought resistance in wheat. This study identified several marker-trait associations (MTAs) for agronomic traits in wheat, including flag leaf area, flag leaf length-width ratio, flag leaf width, peduncle length, spike length, spikelets per spike, spike weight, stress tolerance index, thousand kernel weight, tillers per plant, and yield. Significant markers were identified across chromosomes 1A, 2A, 2 B, 3A, 3 B, 5A, 5 B, 6A, 6 B, 7 B, and 7D. Chromosomes 2A and 1D had the highest numbers of MTAs. This study aligned with (Qaseem et al. 2019 ), where chromosomes 5 B and 2 B had the most MTAs. A genome-wide association study (GWAS) of 9,236 SNPs showed the B genome had the highest frequency of significant SNPs (51%), followed by the A (37%) and D (12%) genomes. Quantitative trait loci (QTLs) were identified for traits such as plant height, spike length, spike weight, and yield, with notable markers on chromosomes 2A, 4 B, 6A, and 7 B. These findings support those of (Ali et al. 2021) for traits such as plant height. The study also found QTLs linked to traits such as peduncle length, thousand kernel weight, and yield, which is consistent with previous research by (Abou-Elwafa et al. 2019 ). The identified MTAs and QTLs can be applied in marker-assisted selection (MAS) to improve wheat traits, particularly drought tolerance and yield. This study emphasizes the advantages of GWAS over traditional QTL mapping and provides insights for future breeding programs aimed at improving wheat productivity through the genetic selection of traits. A chromosomal mapping study identified 81 significant markers associated with drought tolerance in wheat, distributed across multiple genomic regions (Devate et al. 2022 ; Jamil et al. 2019 ). These markers were found on chromosomes 1A, 2A, 3A, 5A, 6A, 7A, 1 B, 2 B, 3 B, 4 B, 5 B, 7 B, 1D, 2D, 6D, and 7D, and unique SNPs were identified on chromosomes 1A, 3D, and 6D. These markers were linked to important traits, such as peduncle length, yield, spike traits, tillers per plant, and flag leaf attributes, demonstrating the complex genetic architecture of drought tolerance. These findings highlight the potential of advanced genomic mapping techniques to develop more drought-resistant wheat varieties. The genomic best linear unbiased prediction (gBLUP) model has shown varying effectiveness in predicting wheat trait performance, depending on the genetic complexity of the traits (El Gataa et al. 2024 ). High prediction accuracies were observed for the biomass (0.53) and spike weight (0.45), reflecting strong genetic components (Shabannejad et al. 2021 ). Traits such as spikelet per plant and flag leaf length width ratio showed moderate prediction accuracies of approximately 0.4, whereas more complex traits such as yield (0.20), thousand kernel weight (0.125), and stress tolerance index (0.125) had significantly lower predictive power. The effectiveness of the gBLUP model is influenced by genetic variation, trait architecture, and linkage disequilibrium, with certain traits such as spike length and flag leaf length showing the lowest prediction accuracies (MGP = 0.10). The gBLUP model is particularly valuable for detecting genetic impacts in wheat populations, with significant improvements in predictive accuracy when genotype × environment (GE) interactions are incorporated, increasing accuracy by 17–34% compared to traditional models (Tadesse et al. 2023 ). This approach is particularly beneficial for breeding programs focused on drought tolerance, enabling fine-mapping of quantitative trait loci (QTLs) and marker-assisted selection (MajidiMehr et al. 2023; Montesinos et al. 2023). The reliability of the model is bolstered by the extensive availability of single-nucleotide polymorphism (SNP) markers, which contribute to robust genome-wide association studies (GWAS) and provide consistent prediction accuracy across different agricultural environments (Ed-Daoudy et al. 2024 ). Conclusion The study concluded by evaluating genotypes tolerant to drought stress using GWAS over three years and identifying 7677 markers for the entire bread wheat genome along their loci. The study also concluded that four subpopulations were found within the germplasm and their phylogenetic connections were identified based on genetic diversity influenced by population structure. The study also determined a pair of loci among 21 chromosomes in linkage disequilibrium spanning 575 Mbps. This study explored genomic regions linked to drought stress and identified SNP markers potentially linked to marker-trait associations (MTAs). This study validated a genomic selection method based on (BLUPS) is, its prediction accuracy, which confirms its ability to reliably predict drought stress tolerance in wheat varieties, in addition to future breeding efforts. The present study successfully identified numerous breeding lines that significantly outperformed the cultivars in terms of agronomic performance and resistance to drought. GWAS is a valuable tool for understanding the genetic structure of drought tolerance in bread wheat. It helps to identify significant genetic markers and their associations with yield-related traits, enabling the development of resilient bread wheat varieties through targeted breeding strategies. These insights are crucial for sustainable wheat production and food security; as global agricultural challenges are increasing due to climate change. Breeders aim to broaden the gene pool by incorporating diversity among wheat relatives, thereby enhancing recombination breeding efforts against drought stress. Genomic selection tools facilitate phenotypic screening, drought tolerance, and yield stability breeding strategies. Declarations Credit authorship contribution statement Komal Saeed: Investigation, Methodology, curation, writing – original draft, review, and editing. Muhammad Jamil: Conceptualization, Project administration, Supervision, Methodology, Data analysis, and editing. Hanasul Hanan: Writing, review, and editing. Declaration of competing interest The authors declare that they have no competing financial interests or personal relationships that could have influenced the work reported in this paper. Funding source No funding was released for this study Data availability Data will be available on demand Acknowledgement: This work is part of the PhD dissertation of the manuscript's first author. Authors acknowledge Awais Rasheed from department of plant sciences, Quaid-i-azam University, Islamabad, Pakistan for providing germplasm and genotyping facility. References Abou-Elwafa SF, Shehzad T (2021) Genetic diversity, GWAS and prediction for drought and terminal heat stress tolerance in bread wheat ( Triticum aestivum L). Genet Resour Crop Evol 68(2):711–728 Abou-Elwafa SF, Amin AEEAZ, Shehzad T (2019) Genetic mapping and transcriptional profiling of phytoremediation and heavy metals responsive genes in sorghum. Ecotoxicol Environ Saf 173:366–372 Ahmed AAM, Mohamed EA, Hussein MY, Sallam A (2021) Genomic regions associated with leaf wilting traits under drought stress in spring wheat at the seedling stage revealed by GWAS. Environ Exp Bot 184:104393 Ahmed HGMD, Zeng Y, Iqbal M, Rashid MAR, Raza H, Ullah A, Shah AN (2022) Genome-wide association mapping of bread wheat genotypes for sustainable food security and yield potential under limited water conditions. PLoS ONE, 17(3), e0263263 Akram Z, Ahmad Q, Ullah A, Shabbir G, Mahmood T, Ahmed Z, Shah ZH (2022) Physiological alterations in wheat during drought stress tolerance at different growth stages. Pure Appl Biology 11(1):331–339 Ali ML, Baenziger PS, Ajlouni ZA, Campbell BT, Gill KS, Eskridge KM, Dweikat I (2011) Mapping QTL for agronomic traits on wheat chromosome 3A and a comparison of recombinant inbred chromosome line populations. Crop Sci 51(2):553–566 Alqudah AM, Haile JK, Alomari DZ, Pozniak CJ, Kobiljski B, Börner A (2020) Genome-wide and SNP network analyses reveal genetic control of spikelet sterility and yield-related traits in wheat. Scientific Reports, 10(1), 2098 Bacher H, Sharaby Y, Walia H, Peleg Z (2022) Modifying root-to-shoot ratio improves root water influxes in wheat under drought stress. J Exp Bot 73(5):1643–1654. https://doi.org/10.1093/JXB/ERAB500 Bhandari R, Paudel H, Nyaupane S, Poudel MR (2024) Climate resilient breeding for high yields and stable wheat ( Triticum aestivum L.) lines under irrigated and abiotic stress environments. Plant Stress 11:100352 Bilgrami SS, Ramandi HD, Shariati V, Razavi K, Tavakol E, Fakheri BA, Mahdi Nezhad N, Ghaderian M (2020) Detection of genomic regions associated with tiller number in Iranian bread wheat under different water regimes using genome-wide association study. Sci Rep 10(1):1–17 Devate NB, Krishna H, Parmeshwarappa SKV, Manjunath KK, Chauhan D, Singh S, Singh JB, Kumar M, Patil R, Khan H, Jain N, Singh GP, Singh PK (2022) Genome-wide association mapping for component traits of drought and heat tolerance in wheat. Front Plant Sci 13:943033 Dolferus R, Thavamanikumar S, Sangma H, Kleven S, Wallace X, Forrest K, Cullis B (2019) Determining the genetic architecture of reproductive stage drought tolerance in wheat using a correlated trait and correlated marker effect model. G3: Genes Genomes Genet 9(2):473–489 Ed-Daoudy L, El Gataa Z, Sbabou L, Tadesse W (2024) Genome-wide association and genomic prediction study of elite spring bread wheat ( Triticum aestivum L.) genotypes under drought conditions across different locations. Plant Gene 39:100461 El Gataa Z, Abebe A, El Hanafi S, Kehel Z, Rachdad FE, Tadesse W (2024) Genetic dissection and genomic prediction of drought indices in bread wheat ( Triticum aestivum L.) genotypes. Crop Design, p 100084 Fernandez GCJ (1992) Effective selection criteria for assessing plant stress tolerance. In: Kuo CG (ed) Adaptation of food crops to temperature and water stress. AVRDC, Shanhua, pp 257–270 Gahlaut V, Jaiswal V, Balyan HS, Joshi AK, Gupta PK (2021) Multi-locus GWAS for grain weight-related traits under rain-fed conditions in common wheat ( Triticum aestivum L). Front Plant Sci 12:758631 Gamba D, Lorts CM, Haile A, Sahay S, Lopez L, Xia T, Lasky JR (2024) The genomics and physiology of abiotic stressors associated with global elevational gradients in Arabidopsis thaliana. New Phytol 244(5):2026–2077 Gollob HF (1968) A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33(1):73–115 Itam MO, Mega R, Gorafi YS, Yamasaki Y, Tahir IS, Akashi K, Tsujimoto H (2022) Genomic analysis for heat and combined heat–drought resilience in bread wheat under field conditions. Theor Appl Genet, 1–14 Jamil M, Ali A, Gul A, Ghafoor A, Napar AA, Ibrahim AMH, Naveed NH, Yasin NA, Mujeeb-Kazi A (2019) Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat. BMC Plant Biol 19(1):1–18 Khan MI, Kainat Z, Maqbool S, Mehwish A, Ahmad S, Suleman HM, Li H (2022) Genome-wide association for heat tolerance at seedling stage in historical spring wheat cultivars. Front Plant Sci 13:972481 Khan I, Gul S, Khan NU, Fawibe OO, Akhtar N, Rehman M, Rauf A (2023) Stability analysis of wheat through genotype by environment interaction in three regions of Khyber Pakhtunkhwa, Pakistan. SABRAO J Breed Genet 55(1):50–60 Li C, Guo J, Wang D, Chen X, Guan H, Li Y, Li Y (2023) Genomic insight into changes of root architecture under drought stress in maize. Plant Cell Environ 46(6):1860–1872 Liu H, Mullan D, Zhao S, Zhang Y, Ye J, Wang Y, Zhang A, Zhao X, Liu G, Zhang C, Chan K, Lu Z, Yan G (2022) Genomic regions controlling yield-related traits in spring wheat: a mini review and a case study for rainfed environments in Australia and China. Genomics 114(2):1–23 MajidiMehr A, Pahlavani MH, El Gataa Z, Amiri-Fahliani R (2024) Genome-wide association mapping and genomic prediction of water deficit stress tolerance indices in spring bread wheat. Gene Rep 37:102054 Mastrorilli M, Gorjanc G, Pignone D, Lüttringhaus S, Gornott C, Wittkop B, Noleppa S, Lotze-Campen H (2020) The economic impact of exchanging breeding material: assessing winter wheat production in Germany. Front Plant Sci 11:601013 Maulana F, Huang W, Anderson JD, Ma XF (2020) Genome-wide association mapping of seedling drought tolerance in winter wheat. Front Plant Sci 11:573786 Montesinos-López OA, Crossa J, Saint Pierre C, Gerard G, Valenzo-Jiménez MA, Vitale P, Crespo-Herrera L (2023) Multivariate genomic hybrid prediction with kernels and parental information. Int J Mol Sci 24(18):13799 Negisho K, Shibru S, Matros A, Pillen K, Ordon F, Wehner G (2022) Genomic dissection reveals QTLs for grain biomass and correlated traits under drought stress in Ethiopian durum wheat ( Triticum turgidum ssp. durum ). Plant Breeding 141(3):338–354 Pu ZE, Ye XL, Yang LI, Shi BX, Zhu GUO, Dai SF, Zheng YL (2022) Identification and validation of novel loci associated with wheat quality through a genome-wide association study. J Integr Agric 21(11):3131–3147 Qaseem MF, Qureshi R, Shaheen H, Shafqat N (2019) Genome-wide association analyses for yield and yield-related traits in bread wheat ( Triticum aestivum L.) under pre-anthesis combined heat and drought stress in field conditions. PLoS ONE, 14(3) Rabieyan E, Bihamta MR, Moghaddam ME, Mohammadi V, Alipour H (2022) Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions. BMC Genomics 23(1):831 RStudio Team 2015 RStudio: integrated development for R. MA: RStudio, Inc. Boston. Available at http://www.rstudio.com Rustgi S, Shafqat MN, Kumar N, Baenziger PS, Ali ML, Dweikat I, Gill KS (2013) Genetic dissection of yield and its component traits using high-density composite map of wheat chromosome 3A: bridging gaps between QTLs and underlying genes. PLoS ONE, 8(7), e70526 Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P (2022) Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. Mol Breeding 42:1–52 Shabannejad M, Bihamta MR, Majidi-Hervan E, Alipour H, Ebrahimi A (2021) A classic approach for determining genomic prediction accuracy under terminal drought stress and well-watered conditions in wheat landraces and cultivars. PLoS ONE, 16(3), e0247824 Singh SK, Khaire A, Korada M, Majhi PK, Singh DK (2023) An analytical approach integrating GGE-Biplot and AMMI techniques for assessing genotype-environment interactions and yield stability in rice ( Oryza sativa L.) genotypes. Emergent Life Sci Res 9:168–176 Tadesse W, Gataa ZE, Rachda FE, Baouchi AE, Kehel Z, Alemu A (2023) Single-and multi-trait genomic prediction and genome-wide association analysis of grain yield and micronutrient-related traits in ICARDA wheat under drought environment. Mol Genet Genomics 298(6):1515–1526 Wang X, Guan P, Xin M, Wang Y, Chen X, Zhao A, Sun Q (2021) Genome-wide association study identifies QTL for thousand grain weight in winter wheat under normal-and late-sown stressed environments. Theor Appl Genet 134:143–157 Weiss M, Sniezko RA, Puiu D, Crepeau MW, Stevens K, Salzberg SL, De La Torre AR (2020) Genomic basis of white pine blister rust quantitative disease resistance and its relationship with qualitative resistance. Plant J 104(2):365–376 Wu X, Wang J, Wu D, Jiang W, Gao Z, Li D, Zhang Y (2022) Identification of new resistance loci against wheat sharp eyespot through genome-wide association study. Front Plant Sci 13:1056935 Xiao Q, Bai X, Zhang C, He Y (2022) Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review. J Adv Res 35:215–230 Yan X, Zhang X, Kou M, Gebrewahid TW, Xi J, Li Z, Yao Z (2024) Quantitative Trait Loci Mapping for Adult-Plant Stripe Rust Resistance in Chinese Wheat Cultivar Weimai 8. Agronomy 14(2):264 Zhang D, Liu J, Li D, Batchelor WD, Wu D, Zhen X, Ju H (2023) Future climate change impacts on wheat grain yield and protein in the North China Region. Sci Total Environ 902:166147 Zhao J, Sun L, Gao H, Hu M, Mu L, Cheng X, Zhang Y (2023) Genome-wide association study of yield-related traits in common wheat ( Triticum aestivum L.) under normal and drought treatment conditions. Front Plant Sci 13:1098560 Supplementary Files SupplementaryMaterials.docx 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-6479357","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453062250,"identity":"491a5d3a-e39f-4f81-a9c9-65d9f3eee752","order_by":0,"name":"Muhammad Jamil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYFACHjDJ2NjAwHDgQYUEgwGQJwHEBsRpSThDipYGEJnYxkBYi+6M3IOfbubYyTa3n048kDjPInE7A/PB2zwMd4xxaTG7kZcsnbst2bixJ3fDgcRtEok7G9iSrXkYnpnh1pJjANTCnNjYANWy4QCPmTQPw2EbPFqMf+duq09s7H8L1DIHpIX/GyEtZkBbDic2zgDZ0gC2hQ2kBbfDzrwxs87ddty4cQbQloRjEsY7m9mMLecYPMPt/eM5xrdzt1XLbuzP3fzhQ02d7Hb25oc33lTcMWzApQcGECqYQYTBAUIaGBjk0fhEaBkFo2AUjIKRAgD0M2KPHln3HQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5416-5471","institution":"The Islamia University of Bahawalpur Pakistan","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Jamil","suffix":""},{"id":453062251,"identity":"88df2e3c-7f0f-4230-93f8-ec0dc43ef32b","order_by":1,"name":"Komal Saeed","email":"","orcid":"","institution":"The Islamia University of Bahawalpur Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Komal","middleName":"","lastName":"Saeed","suffix":""},{"id":453062252,"identity":"9e644b53-8e96-4824-8097-2894fe1d2b2f","order_by":2,"name":"Hanasul Hanan","email":"","orcid":"","institution":"The Islamia University of Bahawalpur Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Hanasul","middleName":"","lastName":"Hanan","suffix":""}],"badges":[],"createdAt":"2025-04-18 13:31:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6479357/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6479357/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82505328,"identity":"304d51c0-7360-4993-84f5-fe33f680a459","added_by":"auto","created_at":"2025-05-12 09:25:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":346523,"visible":true,"origin":"","legend":"\u003cp\u003ePearson’s correlation analysis of 15 traits of 190 genotypes under well-watered (WW) and water-limited (WL) conditions. Traits have been ordered as sorted by the first principal component. The correlation r-value less than -.18 and more than .18 is significant at a p-value of 0.01 with 188 degrees of freedom.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6479357/v1/221e2ee77ad2a60abce1937b.png"},{"id":82505676,"identity":"ed28c44f-ffb1-4e76-8494-d71fd4b5a515","added_by":"auto","created_at":"2025-05-12 09:33:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":386512,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) of 190 genotypes studied with 15 traits, an eigenvalue analysis (a), and contribution percentage of traits to the first five principal components (PCs) disclose the set of traits at PC1 and PC2 in the form of colored squares (b) and a biplot (c) of genotype and traits; genotypes are clustered into six groups and their quality of representation is shown as squared cosine (cos2)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6479357/v1/8c990c2d0e662e535a3790ad.png"},{"id":82505659,"identity":"54c9abdd-0923-49af-a04a-adc7006e4d47","added_by":"auto","created_at":"2025-05-12 09:33:14","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76564,"visible":true,"origin":"","legend":"\u003cp\u003eYield stability analysis of top-identified elite genotypes using Additive Main Effects and Multiplicative Interaction (AMMI) model is shown by a tester-centered G+GE-Biplot; without scaling (SVP: HJ-(Dual Metric Preserving).\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6479357/v1/a41984dd1342e3f5caaf08e7.jpeg"},{"id":82505330,"identity":"f91463f3-f550-4b18-b2f5-298536676aca","added_by":"auto","created_at":"2025-05-12 09:25:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":381503,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) using the Genomic Information of 190 Genotypes Provided by 7677 SNPs. Out of 190 Principal Components (PCs), eigenvalues with variation proportion explained by the first 20 PCs have been shown at the top. Twenty percent variation has been described by the first two PCs (PC1 and PC2) utilized to generate a score plot of 7677 SNPs and a Vector plot of 190 genotypes; this SNP-based PCA classified these genotypes into four clusters.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6479357/v1/18c14c62172a301d1fb95c1f.png"},{"id":82505335,"identity":"92ac1e40-fac4-4b21-9f43-c9ef8fbefbe6","added_by":"auto","created_at":"2025-05-12 09:25:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":670826,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation Structure analysis membership is assigned by the first five Principal Components, representing 33 % of the variation in the overall genomic data.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6479357/v1/9053988446bea04700e45430.png"},{"id":82505685,"identity":"afd87e46-ba8c-4809-bbc6-b445286b46c5","added_by":"auto","created_at":"2025-05-12 09:33:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":765720,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan Plots and Q-Q Plots\u003cstrong\u003e \u003c/strong\u003eof various studied traits\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6479357/v1/a87f9f115a04ca20a0e4b4ce.png"},{"id":82505336,"identity":"a144781b-975d-4682-b100-532e5fd264c4","added_by":"auto","created_at":"2025-05-12 09:25:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":70241,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic prediction of 15 traits using genomic best linear unbiased prediction (gBLUP) model.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6479357/v1/cdc22cbe1f235a303c331f14.png"},{"id":89609226,"identity":"17a94af4-42f4-4e14-ac13-87f597db4b6e","added_by":"auto","created_at":"2025-08-21 22:20:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4102540,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6479357/v1/aae4f6e7-fac2-4f06-b0d3-df4f4f43d788.pdf"},{"id":82505332,"identity":"2b96a586-a5d5-4775-88d1-56df52f7ba4a","added_by":"auto","created_at":"2025-05-12 09:25:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1411058,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6479357/v1/3ae0e7016ce534fd1599767e.docx"}],"financialInterests":"","formattedTitle":"GWAS-Driven Identification of Genomic Regions Conferring Yield and Related Traits Under Drought Stress in Wheat","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) grows worldwide, and it is a crucial crop of more than 40 percent world's population relies on it as stable food that provides 20% of calories (Wang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Wheat is cultivated worldwide in more than 85 countries, including Pakistan, India, Kazakhstan, Egypt, and China, to meet food demand. More than 12\u0026nbsp;million hectares of land are used to raise wheat. According to FAO's most recent report from 2020, 219\u0026nbsp;million acres were harvested, and the total production position as the second-largest crop in the world is strengthened by the production of 761\u0026nbsp;million tons. The annual wheat cultivation has increased by 1.18 percent (Akram et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Changes in environmental conditions cause problems in crop production, with up to a 2 to 5 percent decrease due to various factors present in stored grains (Mastrorilli et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Abiotic stresses include light intensity, ultraviolet radiation, temperature, moisture status, and drought (Bacher et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Drought stress disturbs various physiological and morphological processes, molecular levels, and productivity of plants. Severe drought conditions in Pakistan have resulted in a significant reduction of 30\u0026ndash;40% in wheat yield, impacting approximately 22\u0026ndash;25\u0026nbsp;million hectares of agricultural land. Global drought has reduced wheat yields by 10\u0026ndash;25% across 1.5\u0026nbsp;billion hectares of farmland. Regions like North America (-20-40%), India (-15-30%), China (-10-25%), and Australia (-20-40%) have been severely impacted (Ahmed et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Water shortages are alarming worldwide, including Pakistan, due to several notable problems in agriculture and crop production (Ahmed et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our knowledge of the genetic underpinnings and molecular basis of complex traits, such as drought tolerance, has increased as a result of recent developments in high-throughput genotyping (Saini et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), phenotyping techniques (Xiao et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and methods designed to identify genotype-phenotype associations by evaluating the genetic variants across the genomes of many individuals (Pu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Genome-wide association study (GWAS) is a highly effective tool that uses linkage disequilibrium (LD) to uncover the relationship between SNPs markers and specific traits. Revolutionizes the link between QTL analysis and MAS, enabling the efficient selection of complex traits in the face of rapid LD decay and multiple genetic factors. (Devate et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Compared with bi-parental mapping, GWAS offer several advantages. It captures all recombination events in an individual\u0026rsquo;s evolutionary history, unlike pedigrees with limited recombination opportunities. This approach allows the screening of a larger and more diverse gene pool, facilitating the analysis of complex genetic traits (Liu et al.2022). The diverse and unstructured natural populations used in GWAS have allowed the accumulation of a large amount of information on historical recombination, thus delivering a relatively high mapping resolution (Alqudah et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In recent years, association studies have been extensively used for the genetic dissection of drought tolerance in several crops such as wheat (Jamil et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Qaseem et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Genomic prediction (GP) is a productive approach to enhance breeding schedule efficiency and expedite the process by reducing periods and improving selection accuracy. This method facilitates the identification of candidate genes by genotyping before phenotypic determination. Genomic prediction utilizes all the genetic markers within a model to train a prediction model, which is subsequently employed as a validation set to evaluate its accuracy. However, due to the substantial influence of population structure and trait architecture on marker effects, uncertainty remains regarding the optimal algorithm for genomic selection and GWAS.\u003c/p\u003e \u003cp\u003eIn the field trial, this study involved conducting a Genome-Wide Association Study (GWAS) to investigate the drought response of multiple bread wheat genotypes in well-water and water-limited environments in field trials. This study also helps in the discovery of novel SNPs linked to drought tolerance and the introduction of new possibilities for genetic improvement. Developing drought-tolerant wheat varieties is crucial for food security in water-scarce areas, enhancing yield stability, and sustainable food production amid climate change. However, genome-wide association studies have been conducted on wheat panels to dissect drought-tolerance mechanisms genetically.\u003c/p\u003e \u003cp\u003eThe objectives of this study were: (i) to identify drought-resistant genetic resources among 190 bread wheat genotypes for potential use in breeding programs; (ii) to estimate genomic best linear unbiased predictions (gBLUPs) for agronomic traits across multiple environments and years; (iii) to evaluate genotype-by-environment (G\u0026times;E) interactions through multi-environment and multi-year phenotypic trials; and (iv) to perform genome-wide association studies (GWAS) to identify significant marker-trait associations.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMaterial and Field Experiment\u003c/h2\u003e \u003cp\u003eA panel of 190 bread wheat genotypes was collected from the Department of Plant Sciences, Quaid-i-Azam University, Islamabad, Pakistan; germplasm detail is given in (Supplementary Table S1).\u003c/p\u003e \u003cp\u003eA field experiment was conducted using alpha lattice designs in Bahawalpur (29\u0026deg;15'23.0\u0026rsquo; N, 71\u0026deg;42'44.4' E) with three replications during November over three years (2021-22, 2022-23, and 2023-24). The experiment included two types of field conditions: well-watered and water-limited conditions. All genotypes were planted in triplicate, with each row measuring 1 m and maintaining a plant-to-plant distance of 5\u0026ndash;10 cm.\u003c/p\u003e \u003cp\u003eCustomized watering was applied to each plant based on water usage and flag leaf characteristics to regulate water treatments and observe variations in growth and phenological traits. Two water regimes were used.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eWell-watered treatment\u003c/b\u003e: Irrigation was maintained at 80% field capacity during three critical growth stages: tillering (35 days after sowing, DAS), booting (85 DAS), and milking (112 DAS).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eWater-limited treatment\u003c/strong\u003e \u003cp\u003eThis regime followed a well-watered protocol until flag leaf emergence, after which intermittent drought stress was imposed by withholding irrigation when drooping symptoms appeared on the last three leaves. Irrigation was provided at 40% field capacity for the well-watered treatment and 24% field capacity (60% of the well-watered treatment) for the water-limited treatment, as described by (Abo-Elwafa et al. 2021).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhenotyping\u003c/h3\u003e\n\u003cp\u003eThe following traits were recorded for each experimental unit after germination. Plant height (cm), flag leaf area (cm), peduncle length (cm), tillers per plant, number of spikes, spike length (cm), spikelet per spike, spike weight (g), thousand kernel weight (g), plant dry weight including spike and straw (biomass), and kernel yield per plot (YM\u003csup\u003e2\u003c/sup\u003eg/plot). Muller's (1991) formula was used to calculate the flag leaf area. Flag leaf area\u0026thinsp;=\u0026thinsp;maximum length \u0026times; maximum width x 0.74. The following formula was used to calculate the drought tolerance indices for the studied traits:\u003c/p\u003e \u003cp\u003eStress tolerance index (STI) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}\\text{T}\\text{I}\\::\\frac{\\text{Y}\\text{p}\\:+\\:\\text{Y}\\text{s}\\:\\:\\:\\:}{{\\:\\text{y}}^{-2}\\text{p}}\\)\u003c/span\u003e\u003c/span\u003e (Fernandaz 1992).\u003c/p\u003e \u003cp\u003ewhere y\u003csub\u003ep\u003c/sub\u003e is the average yield of a genotype under well-water conditions, y\u003csub\u003es\u003c/sub\u003e is the average yield of the same genotype under water-limited conditions, and y\u003csub\u003es\u003c/sub\u003e and y\u0026macr;\u003csub\u003ep\u003c/sub\u003e are the average yields of all genotypes in water-limited and well-water environments, respectively.\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis of Phenotypic Traits\u003c/h3\u003e\n\u003cp\u003eThe years of the study and the water regimes were considered as the environment, and ANOVA table type III with Satterwaites across the water regime package in R-studio using \"lmer Test\" was created. The best linear unbiased estimation (BLUE) for each line was determined. Yield traits were analyzed statistically using a split-split plot (R. v 4.1.0 (Yu et al. 2019). Heritability (H\u003csup\u003e2\u003c/sup\u003e) was determined from the observed variance.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\left(a\\right)individual\\::\\frac{\\sigma\\:\u0026sup2;g}{\\sigma\\:\u0026sup2;g+\\:\\frac{\\sigma\\:\u0026sup2;g}{r}}*100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\u0026sup2;g\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\sigma\\:\u0026sup2;e\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\u0026sup2;gy\\)\u003c/span\u003e\u003c/span\u003e are the genetic variance, residual variance, and variation, respectively, resulting from the interplay of genotype and environmental factors. Replications and environments overall are indicated by the letters \"y\" and \"r,\" respectively (Gaur et al. 2022).\u003c/p\u003e \u003cp\u003ePrincipal component analysis was used to biplot the variables and factors to determine the differences between the coefficients of correlation. A scree plot was used to analyze the total variation in the data. The data, including the effects of G \u0026times; E, were examined using a linear mixed model with the effects of years and genotypes. Principal component analysis (PCA) was used to determine distinguishing characteristics (Bilgrami et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The additive main effect and multiplicative interaction model is a statistical technique that analyzes two-way data structures, such as genotypes and environments, by breaking down the variance into additive and multiplicative components. The AMMI model can be expressed as follows: Yij\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;gi\u0026thinsp;+\u0026thinsp;ej\u0026thinsp;+\u0026thinsp;Σ ( λn * in * jn) + εij, Where: Yij is the yield of genotype i in environment j, \u0026micro; is the overall mean, gi and ej are the genotypes and environmental effects, respectively, λn is the eigenvalue of the n principal component axis, in and jn are the genotypes and environment principal component scores for the n axis, and εij is the random error term. The number of principal components retained in the model was determined using the F-test proposed by Gollob (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1968\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGenotyping\u003c/h3\u003e\n\u003cp\u003eGenotyping of a germplasm panel consisting of 190 lines involved the extraction of genomic DNA from the leaves of young, two-week-old bread wheat seedlings using the CTAB method. The DNA concentration in each sample was adjusted to 30ng/ \u0026micro;L. Genotyping was performed using a 37 K Illumina iSelected SNP array to determine the genotypes of all the 190 lines. SNP markers were selected based on a minor allele frequency (MAF) threshold of 0.25%. Samples with missing data exceeding 2% were excluded from further analysis as reported by Khan et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eKinship, Population Structure, and Linkage Disequilibrium Analysis\u003c/h3\u003e\n\u003cp\u003eThe structure of the complete collection was examined using the model-based clustering method admixture models with associated allele frequency, and principal component analysis was performed using TASSEL 5.2.94, using genotypic data with 7677 SNP markers, and the genotypes were scattered along five principle components PCs. A cladogram showing the genetic diversity among 190 genotypes was generated using the Archeopteryx function. The linkage disequilibrium in the study was independently assessed for each genome A, B, and D, and the average LD for the three genomes. The genetic distance LD decay plot and pairwise R\u003csup\u003e2\u003c/sup\u003e were plotted using the R statistical platform (RStudio Team, 2015). An identity-based scaled population kinship matrix (Kin) was calculated based on the state IBS technique was calculated (Weiss et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The study measured pair-wise linkage disequilibrium (LD) from cleaned filtered sites on 379444 single nucleotide polymorphism (SNPs) markers using a 50 kb window size and 1042297 pair comparisons with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2 and distance\u0026thinsp;\u0026lt;\u0026thinsp;2,00,000 base pairs (bp). Heat maps of LD measurements were generated using R. Studio version 3.4.4, and scatter plots were used to determine the putative disequilibrium between markers along and covered base pair distance.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMarker Trait Association (MTA)\u003c/h2\u003e \u003cp\u003eMarker-trait associations were analyzed using a mixed linear model (MLM) in TASSEL. PCA and kinship matrices (K) were incorporated, and QQ plots were used to validate the associations. Significant SNP markers for drought tolerance were identified, and chromosomal maps were created using MapChart v3.5.4, providing a spatial understanding of the genomic regions linked to adaptive traits in bread wheat under varying water availability conditions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenomic prediction\u003c/h3\u003e\n\u003cp\u003eThe genomic prediction was performed on a panel of 190 lines using the genomic best linear unbiased prediction (gBLUP) method to identify regions associated with drought stress tolerance within the panel. prediction accuracy was assessed using five cross-validations with 20 iterations, where the dataset was randomly split into 10% for testing and 90% for training. The Prediction accuracy was evaluated by measuring the correlation between true and expected values in the 10% testing subset (Ed-Daoudy et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Determination of the mean and standard deviation of the prediction accuracy across multiple iterations ensured a comprehensive assessment of the genomic prediction model performance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003ePhenotyping assessments\u003c/h2\u003e\n \u003cp\u003eThis study analyzed 190 bread wheat varieties across three seasons and two conditions: well-water (T1) and water-limited (T2). The results showed a positive correlation between harvest periods and traits. The heritability analysis revealed substantial genetic variation across the studied wheat traits under drought stress. The thousand kernel weight (TKW, H\u0026sup2; = 0.92) and yield (YLD, H\u0026sup2; = 0.88) showed the highest broad-sense heritability, indicating strong genetic control. Moderate heritability was observed for plant height, biomass weight, and other traits. Spike length showed lower heritability (H\u0026sup2; = 0.65), suggesting a greater environmental influence. The high coefficient of determination (R\u0026sup2;) for most traits indicated the effectiveness of the model in explaining genetic variation, as shown in (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The stress tolerance index (S.T.I.) varied significantly among the 190 wheat varieties. Thousand kernel weight (TKW) showed high drought tolerance with an STI of 1.21, whereas yield (YLD) displayed lower drought resilience with an STI of 0.42. These findings suggest differential responses to drought stress across wheat traits (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eMean squares of traits obtained through split-split plot design of experiment along with model R\u003csup\u003e2\u003c/sup\u003e, grand mean (GM), and broad sense heritability (H\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003eStress tolerance indices of all traits under water-limited conditions for three years.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003eUnder well-watered conditions, peduncle length was positively correlated with thousand kernel weight (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.18), but negatively correlated with spike weight and yield (r = -0.18). Yield showed a positive correlation with spikelets per spike, which correlated with total productive tillers and stress tolerance index, whereas flag leaf width was positively associated with peduncle length. Spike length, biomass per plant, and flag leaf length-to-width ratio were negatively correlated with most of the traits. Flag leaf area showed strong positive correlations with flag leaf width (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.589) and flag leaf length (r\u0026thinsp;=\u0026thinsp;0.85) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eUnder water-limited conditions, the stress tolerance index was positively correlated with yield, spike weight, biological mass, spike length, and thousand kernel weight but negatively correlated with plant height, flag leaf traits, and peduncle length. Yield was positively correlated with spike weight, biological mass, and spike length, whereas spike weight was positively correlated with biological mass. Total productive tillers showed strong positive correlations with spikes per plant (r\u0026thinsp;=\u0026thinsp;0.81). Flag leaf area and length were positively correlated, whereas plant height was inversely associated with stress tolerance traits. The study categorized the 15 traits into three classes based on their clustering patterns and genotype performance. Under both well-water- and water-limited conditions, the traits were grouped into three categories.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003ePrincipal Component Analysis (PCA)\u003c/h2\u003e\n \u003cp\u003ePrincipal Component Analysis (PCA) was conducted on a dataset of 15 variables and 3420 observations, resulting in 15 principal components. The first component, represented by an eigenvalue of 2.57, accounted for 70.46% of the total variation. The second component, orthogonal to PC1, explained 16.2% of the variation. The total of Six principal components with eigenvalues greater than one collectively accounted for 70.46% of the overall variance. Genotypes 96, 106, 103, 90, and 129 showed significant variability with longer vectors for parameters, such as STI and YLD, whereas SW, FLW, BM, PL, SPP, TPP, SPS, PH, FLA, and FLLWR showed maximum variability. The PC biplot analysis visualized relationships and variability among parameters and genotypes, revealing that genotype 53 exhibited greater variability with longer vector lengths than the other genotypes. This biplot visualization allowed a clear comparison of differences among 190 bread wheat genotypes for key agro-morphological traits under well-water and water-limited conditions, as shown in (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eYield Stability Assessment Using Additive Main Effect and Multiplicative Interaction (AMMI) Analysis\u003c/h2\u003e\n \u003cp\u003eThe GGE biplot analysis revealed that genotypes responded to environmental variations effectively, with genotypes furthest from the midpoint showing the greatest sensitivity. The first principal component (PC1) accounts for 71.47% of the total variance, while the second component (PC2) accounts for 16.37%. The study found that Genotypes 96, 109, 138, and 129 performed better than the other genotypes, and genotype 153 showed consistent yield stability under different conditions. The GGE biplot analysis also showed that environments represented by vectors could differentiate between genotypes, with 2022 and 2023 having the longest vectors. The AMMI framework suggests that genotypes on the right have higher average grain yields than the overall mean, as shown in (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis of genotypes\u003c/h2\u003e\n \u003cp\u003eAll 190 accessions were genotyped using the wheat iSelect 37 K array. After filtering the missing rate\u0026thinsp;\u0026gt;\u0026thinsp;20% and minor allele frequency\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 7677 high-quality SNPs were retained and used for genetic analysis. These were distributed on all 21 chromosomes, with 3475, 3512, and 690 SNPs in the A, B, and D subgenomes, respectively. Chromosome 2A had the most SNPs (1020) but the shortest average distance between SNPs (575Mbp). In contrast, 4D had the least 50 SNPs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePrincipal Component Analysis, Population Structure\u003c/h2\u003e\n \u003cp\u003eA total of 190 bread wheat genotypes with 7,677 single nucleotide frequency (SNPs) minor allele frequency\u0026thinsp;\u0026lt;\u0026thinsp;0.25 (MAF) were included in the genome-wide association analysis (GWAS). The study focused on the population structure, with the first 20 principal component analyses explaining 20% variation, as shown in (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The Population structure analysis divided these genotypes into four clusters, with the first five principal components representing 33% variation in the overall genomic data. This study revealed intricate patterns of genetic architecture across five distinct groups, providing insights into the evolution of wheat breeding programs. Group-1, comprising historical cultivars from the 1970s-2000s, exhibited substantial genetic admixture, suggesting that they likely served as foundational germplasms in breeding programs. Group-2, featuring more recent breeding lines, displayed more distinctive genetic structuring, possibly indicating more focused breeding objectives in modern varieties. Group-3, though smaller, showed a balanced distribution among PCs, suggesting their role as transitional germplasms between traditional and breeding lines. Group-4 exhibited the most distinct genetic stratification, indicating possible distinct breeding pools or ecological adaptations. Group-5, containing varieties such as Pirsabak-21 and Khirman-15, displayed remarkably uniform genetic compositions, suggesting shared ancestry or similar selection pressures during breeding, as shown in (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The significant genetic admixture observed across most groups, particularly in historical varieties, contrasts with the more defined genetic structuring observed in modern cultivars, highlighting the evolution of breeding strategies. The neighbor-joining tree divided the panel into four main groups, as shown in (Fig S1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eLinkage disequilibrium\u003c/h2\u003e\n \u003cp\u003eA total of 7677 sites were subjected to linkage disequilibrium analysis. A total of 1042297 pairs were selected using a window size of 137 for pair comparison (R\u003csup\u003e2\u003c/sup\u003e). The second selection was performed on these 1042297 pairs by keeping the threads held (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2). So 379444 pairs were obtained with an LD above (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2). Out of 379444, 379214 pairs showed intrachromosomal LD. They were divided into four classes based on distance in megabase pairs (Mbp). In this study, the overall LD analysis involved 379444 pairs with an overall mean of (R\u003csup\u003e2\u003c/sup\u003e of 0.372). The Significant linkage disequilibrium (LD) was observed, as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, in a total of 34922 pairs which contained 9.22% of all pairs examined.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOverall Summary of Linkage Disequilibrium (LD) Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMbp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal pairs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean R2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePairs LD 1\u0026thinsp;\u0026gt;\u0026thinsp;.4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% pairs LD\u0026thinsp;\u0026gt;\u0026thinsp;.4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePairs LD1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e350667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u0026thinsp;\u0026lt;\u0026thinsp;300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u0026thinsp;\u0026lt;\u0026thinsp;450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZero\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZero\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInter-chr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZero\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZero\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e379444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eMarker Trait Association and Quantitative Trait Loci (QTLs)\u003c/h2\u003e\n \u003cp\u003eThe 59 significant (MTAs were identified as highly significant in R\u003csup\u003e2\u003c/sup\u003e (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). QTLs for flag leaf traits clustered on chromosomes, such as FLA found on 7A, FLLWR found on 1D, 2 B, and 7D, and FLW found on 2A,5B, and 2D. QTLs for morphological traits such as PL clustered on 2A, 4 B, SL 6A, 7 B, and SW on 3 B. QTLs for STI clustered on chromosome 2D and TKW on chromosomes 1A, 5A, and 6 B. TPP QTLs on chromosomes 1A and 4B, and YLD QTLs were found on chromosomes 3A, 6A, and 5 B.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMarker-trait association (MTAs) using MLM (PC\u0026thinsp;+\u0026thinsp;K model)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAssociation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarker\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(Mbp)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChr\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMajor/Minor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e-log10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR2 (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7A_610217135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e610.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7A_15677256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLLWR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2B_759732710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e759.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLLWR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7D_489015092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e489.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLLWR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1D_415144671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e415.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLLWR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1D_415144661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e415.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5D_116251109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2B_759732710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e759.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7D_55067055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7D_489015092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e489.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1D_415144661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e415.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1D_415144671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e415.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7B_648671942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e648.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2D_64723966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6D_243306317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e243.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4B_3830663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_91136366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_97659173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_90933999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_98845116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_94273517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_94273538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_94273611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_95320750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_95320777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_97659286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_101107999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_90934006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_90934066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_91136228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_94124763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_95608283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_96605553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_96605619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_97659330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6A_22201090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7B_597591187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e597.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2D_601107080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e601.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5B_681028944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e681.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5B_681028907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e681.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5B_672547138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e672.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_198593852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.560\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_198593801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A_198593802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B_749982772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e749.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B_749982788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e749.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B_750131682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e750.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2D_18237499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTKW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6B_712323218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e712.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTKW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5A_378790530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e378.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTKW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5A_364608895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e364.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTKW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1A_548717003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e548.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTKW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1A_548750891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e548.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTKW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5A_344530310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e344.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4B_644355516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e644.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1A_565376578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e565.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6A_53181158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3A_720224638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e720.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5B_559783496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e559.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.080\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\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eManhattan Plots and Q-Q Plots\u003c/h2\u003e\n \u003cp\u003eThis study evaluated the association between phenotypic traits and genomic loci by using a linear mixed model. A quantile\u0026ndash;quantile plot showed deviations from the expected distribution for the studied traits, with minor differences and increased slopes as the plot approached higher values. Manhattan plots revealed significant markers associated with drought stress, with several exceeding the -log10 threshold. These markers were distributed across three wheat genomes (A, B, and D). Favored allele estimates were calculated to identify the alleles that were most strongly associated with drought tolerance. This study highlights the importance of understanding the relationship between traits and genomic loci (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003ePhysical Map of Genome-wide Association Study (GWAS) Markers Associated with Drought Stress\u003c/h2\u003e\n \u003cp\u003eA previous study mapped 81 significant GWAS markers associated with drought stress across 21 chromosomes of the wheat genome, spanning 0-650 megabase pairs. The distribution of these markers varied across the three genomes: genome A had the most markers (51), followed by genomes B (21) and D (9). Potentially associated markers were identified on chromosomes 1A, 1 B, and 1D. Three regions on 1A were associated with yield-contributing traits, whereas two regions on 1 B were associated with spike traits and peduncle length. On chromosome 2A, 34 potentially associated markers were found, with peduncle length under drought stress spanning from 0 to 650 megabase pairs. The study identified three markers on chromosome 2 B and two markers on chromosome 2D that were associated with yield-contributing traits, flag leaf traits, and spike traits. The two regions spanning distances of 0\u0026ndash;550 Mbps contained seven markers associated with spike traits. Two markers on chromosome 3 B were associated with spike traits, whereas two markers on chromosome 4 B were associated with peduncle length. Three regions on chromosomes 5A and 5 B were found to contribute to yield, whereas two regions were associated with peduncle length markers, spanning 0 to 150 Mbps. The remaining two regions were associated with spike traits and yield and ranged from 0 to 50 Mbps. These markers were found on chromosomes 6A and 6 B, respectively. Chromosome 6D had three regions associated with flag leaf traits, whereas the three regions on chromosome 7A were associated with flag leaf traits. The two regions on chromosome 7 B are associated with spike and flag leaf traits, respectively, spanning 0 to 50 Mbps. The two regions on chromosome 7D are associated with spike traits spanning 0\u0026ndash;450 Mbps, as shown in (Fig S2 (a) and (b)).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eGenomic prediction\u003c/h2\u003e\n \u003cp\u003eThe genomic best linear unbiased prediction (gBLUP) study on a panel of 190 bread wheat varieties revealed intricate insights into genetic prediction across 15 drought-related traits using cross-validation with TASSEL 5.2.9.4, researchers analyzed traits including biomass, spike characteristics, leaf parameters, and stress tolerance index, through 5-fold cross-validation with 20 iterations. The analysis demonstrated varying prediction accuracies, with biomass showing the highest mean genomic prediction accuracy of 0.53, whereas traits such as spike length and flag leaf length exhibited lower accuracies of approximately 0.1. The mean prediction accuracy was 0.3, with 53.3% of observations exceeding this threshold. This study highlighted gBLUP\u0026apos;s effectiveness of gBLUP in capturing genetic variation, particularly for traits with strong genetic control, such as biomass and spike weight, while showing limitations for complex traits influenced by multiple genetic and environmental factors. These findings suggest a significant potential for genomic selection to accelerate wheat breeding programs, especially in developing drought-tolerant varieties, by providing a robust method for predicting phenotypic traits from genetic information, as shown in (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated 14 phenotypic traits of 190 bread wheat under two conditions: well-water and water-limited. The results showed significant variation in drought tolerance-related traits, suggesting that the genotypes used in this study were good genetic sources for drought tolerance. Drought affects up to 50% of wheat agriculture, necessitates urgent genetic solutions. Drought tolerance indicators included flag leaf characteristics, tillers per plant, spike parameters, biomass, and yield. The urgent need for climate-resilient wheat varieties emphasizes the importance of integrating genetic diversity and advanced breeding techniques to ensure food security in drought-sensitive regions. This study examined the effects of drought stress on wheat yield and related traits. The results indicated a reduced yield, biomass, spike weight, and other traits under water-limited conditions. However, the significant heritability estimates for these traits suggest the potential for genetic improvement. A panel of 190 wheat genotypes showed phenotypic variation, indicating their potential to identify genes linked to drought tolerance. Genetic diversity within the wheat germplasm is a valuable resource for enhancing drought tolerance (Gahlaut et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study also revealed significant phenotypic differences among genotypes under drought stress, highlighting the importance of genotype selection. The heritability (H\u0026sup2;) values for all drought-related traits ranged from 0.49 0.86. These values are consistent with those reported for winter wheat by (Zhao et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), underscoring the heritable nature of these traits. This suggests that breeders can rely on these characteristics to select for drought-resistant wheat. The significant heritability of all parameters suggests that selecting genotypes for drought resistance during the germination stage could be effective. Selection under controlled conditions is often more successful than field experiments in improving drought tolerance (Dolferus et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Achieving a balance between drought resistance and crop productivity is crucial for adapting to variable environments while maintaining consistent yields. Heritability is essential for trait selection in genome-wide association studies (GWAS), marker-assisted selection, and plant breeding. Traits with a higher heritability across environments are more valuable for breeding purposes. The present study found high heritability values (~\u0026thinsp;0.84) for all phenotypic traits, which were higher than those observed in Arabidopsis (Gamba et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), suggesting reduced genotype-by-environment interactions and consistent trial conditions. Stress tolerance indices (STI) were categorized into five groups (HT, T, MT, MS, and HS), and correlations with yield-related traits showed that higher STI values were associated with better drought tolerance. Traits such as thousand kernel weight and plant height showed positive correlations with STI, whereas traits such as biomass and yield were negatively correlated, indicating decreased drought tolerance. These findings are consistent with those of previous studies on drought stress (Li et al. 2020; Wu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study also highlighted the impact of environmental factors, such as sowing dates, on plant height. By employing advanced analysis techniques, this research revealed key traits for drought tolerance, emphasizing the importance of multi-environmental studies for breeding more resilient wheat varieties.\u003c/p\u003e \u003cp\u003eThis study applied the Additive Main Effect and Multiplicative Interaction (AMMI) model to assess yield stability and drought tolerance in wheat genotypes using multiple research sources (Zhang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ahmed et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yan et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bhandari et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It evaluated ten bread wheat genotypes over three years in both well-watered and water-limited environments, identifying several stable and drought-tolerant lines, including varieties 106, 153, 103, and others. The AMMI model revealed significant genotype-by-environment interactions, with 153 showing the highest stability and drought tolerance. This study highlights the importance of selecting genotypes that maintain consistent performance across environmental conditions, offering valuable insights for breeding resilient wheat varieties that are adaptable to climate challenges.\u003c/p\u003e \u003cp\u003eGenome-wide association studies have been instrumental in understanding the genetic basis of drought tolerance in wheat (Itam et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has revealed complex genetic architectures linked to stress responses, revealing numerous marker-trait associations (MTAs) for grain weight, stress indices, and other traits under different stress conditions. A GWAS of 190 wheat varieties using 7,677 SNPs provided genetic insights, highlighting the genetic complexity of wheat. Marker-trait associations and quantitative trait loci (QTLs) are essential for understanding drought resistance, as observed in studies of Ethiopian durum wheat (Negisho et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and winter wheat (Maulana et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These findings will advance breeding strategies for drought-tolerant varieties. This study also identified significant QTLs linked to drought resistance and improved breeding program efficiency. Important QTLs for traits such as flag leaf area, spike length, and yield further support marker-assisted selection for improving drought resistance in wheat. This study identified several marker-trait associations (MTAs) for agronomic traits in wheat, including flag leaf area, flag leaf length-width ratio, flag leaf width, peduncle length, spike length, spikelets per spike, spike weight, stress tolerance index, thousand kernel weight, tillers per plant, and yield. Significant markers were identified across chromosomes 1A, 2A, 2 B, 3A, 3 B, 5A, 5 B, 6A, 6 B, 7 B, and 7D. Chromosomes 2A and 1D had the highest numbers of MTAs. This study aligned with (Qaseem et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), where chromosomes 5 B and 2 B had the most MTAs. A genome-wide association study (GWAS) of 9,236 SNPs showed the B genome had the highest frequency of significant SNPs (51%), followed by the A (37%) and D (12%) genomes. Quantitative trait loci (QTLs) were identified for traits such as plant height, spike length, spike weight, and yield, with notable markers on chromosomes 2A, 4 B, 6A, and 7 B. These findings support those of (Ali et al. 2021) for traits such as plant height. The study also found QTLs linked to traits such as peduncle length, thousand kernel weight, and yield, which is consistent with previous research by (Abou-Elwafa et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The identified MTAs and QTLs can be applied in marker-assisted selection (MAS) to improve wheat traits, particularly drought tolerance and yield. This study emphasizes the advantages of GWAS over traditional QTL mapping and provides insights for future breeding programs aimed at improving wheat productivity through the genetic selection of traits.\u003c/p\u003e \u003cp\u003eA chromosomal mapping study identified 81 significant markers associated with drought tolerance in wheat, distributed across multiple genomic regions (Devate et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jamil et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These markers were found on chromosomes 1A, 2A, 3A, 5A, 6A, 7A, 1 B, 2 B, 3 B, 4 B, 5 B, 7 B, 1D, 2D, 6D, and 7D, and unique SNPs were identified on chromosomes 1A, 3D, and 6D. These markers were linked to important traits, such as peduncle length, yield, spike traits, tillers per plant, and flag leaf attributes, demonstrating the complex genetic architecture of drought tolerance. These findings highlight the potential of advanced genomic mapping techniques to develop more drought-resistant wheat varieties. The genomic best linear unbiased prediction (gBLUP) model has shown varying effectiveness in predicting wheat trait performance, depending on the genetic complexity of the traits (El Gataa et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). High prediction accuracies were observed for the biomass (0.53) and spike weight (0.45), reflecting strong genetic components (Shabannejad et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Traits such as spikelet per plant and flag leaf length width ratio showed moderate prediction accuracies of approximately 0.4, whereas more complex traits such as yield (0.20), thousand kernel weight (0.125), and stress tolerance index (0.125) had significantly lower predictive power. The effectiveness of the gBLUP model is influenced by genetic variation, trait architecture, and linkage disequilibrium, with certain traits such as spike length and flag leaf length showing the lowest prediction accuracies (MGP\u0026thinsp;=\u0026thinsp;0.10). The gBLUP model is particularly valuable for detecting genetic impacts in wheat populations, with significant improvements in predictive accuracy when genotype \u0026times; environment (GE) interactions are incorporated, increasing accuracy by 17\u0026ndash;34% compared to traditional models (Tadesse et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This approach is particularly beneficial for breeding programs focused on drought tolerance, enabling fine-mapping of quantitative trait loci (QTLs) and marker-assisted selection (MajidiMehr et al. 2023; Montesinos et al. 2023). The reliability of the model is bolstered by the extensive availability of single-nucleotide polymorphism (SNP) markers, which contribute to robust genome-wide association studies (GWAS) and provide consistent prediction accuracy across different agricultural environments (Ed-Daoudy et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study concluded by evaluating genotypes tolerant to drought stress using GWAS over three years and identifying 7677 markers for the entire bread wheat genome along their loci. The study also concluded that four subpopulations were found within the germplasm and their phylogenetic connections were identified based on genetic diversity influenced by population structure. The study also determined a pair of loci among 21 chromosomes in linkage disequilibrium spanning 575 Mbps. This study explored genomic regions linked to drought stress and identified SNP markers potentially linked to marker-trait associations (MTAs). This study validated a genomic selection method based on (BLUPS) is, its prediction accuracy, which confirms its ability to reliably predict drought stress tolerance in wheat varieties, in addition to future breeding efforts. The present study successfully identified numerous breeding lines that significantly outperformed the cultivars in terms of agronomic performance and resistance to drought. GWAS is a valuable tool for understanding the genetic structure of drought tolerance in bread wheat. It helps to identify significant genetic markers and their associations with yield-related traits, enabling the development of resilient bread wheat varieties through targeted breeding strategies. These insights are crucial for sustainable wheat production and food security; as global agricultural challenges are increasing due to climate change. Breeders aim to broaden the gene pool by incorporating diversity among wheat relatives, thereby enhancing recombination breeding efforts against drought stress. Genomic selection tools facilitate phenotypic screening, drought tolerance, and yield stability breeding strategies.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKomal Saeed:\u003c/strong\u003e Investigation, Methodology, curation, writing – original draft, review, and editing. \u003cstrong\u003eMuhammad Jamil:\u003c/strong\u003e Conceptualization, Project administration, Supervision, Methodology, Data analysis, and editing. \u003cstrong\u003eHanasul Hanan:\u003c/strong\u003e Writing, review, and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was released for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be available on demand\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is part of the PhD dissertation of the manuscript's first author. Authors acknowledge Awais Rasheed from department of plant sciences, Quaid-i-azam University, Islamabad, Pakistan for providing germplasm and genotyping facility.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbou-Elwafa SF, Shehzad T (2021) Genetic diversity, GWAS and prediction for drought and terminal heat stress tolerance in bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L). Genet Resour Crop Evol 68(2):711\u0026ndash;728\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbou-Elwafa SF, Amin AEEAZ, Shehzad T (2019) Genetic mapping and transcriptional profiling of phytoremediation and heavy metals responsive genes in sorghum. Ecotoxicol Environ Saf 173:366\u0026ndash;372\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed AAM, Mohamed EA, Hussein MY, Sallam A (2021) Genomic regions associated with leaf wilting traits under drought stress in spring wheat at the seedling stage revealed by GWAS. Environ Exp Bot 184:104393\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed HGMD, Zeng Y, Iqbal M, Rashid MAR, Raza H, Ullah A, Shah AN (2022) Genome-wide association mapping of bread wheat genotypes for sustainable food security and yield potential under limited water conditions. PLoS ONE, 17(3), e0263263\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkram Z, Ahmad Q, Ullah A, Shabbir G, Mahmood T, Ahmed Z, Shah ZH (2022) Physiological alterations in wheat during drought stress tolerance at different growth stages. Pure Appl Biology 11(1):331\u0026ndash;339\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli ML, Baenziger PS, Ajlouni ZA, Campbell BT, Gill KS, Eskridge KM, Dweikat I (2011) Mapping QTL for agronomic traits on wheat chromosome 3A and a comparison of recombinant inbred chromosome line populations. Crop Sci 51(2):553\u0026ndash;566\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlqudah AM, Haile JK, Alomari DZ, Pozniak CJ, Kobiljski B, B\u0026ouml;rner A (2020) Genome-wide and SNP network analyses reveal genetic control of spikelet sterility and yield-related traits in wheat. Scientific Reports, 10(1), 2098\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBacher H, Sharaby Y, Walia H, Peleg Z (2022) Modifying root-to-shoot ratio improves root water influxes in wheat under drought stress. J Exp Bot 73(5):1643\u0026ndash;1654. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/JXB/ERAB500\u003c/span\u003e\u003cspan address=\"10.1093/JXB/ERAB500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhandari R, Paudel H, Nyaupane S, Poudel MR (2024) Climate resilient breeding for high yields and stable wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) lines under irrigated and abiotic stress environments. Plant Stress 11:100352\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilgrami SS, Ramandi HD, Shariati V, Razavi K, Tavakol E, Fakheri BA, Mahdi Nezhad N, Ghaderian M (2020) Detection of genomic regions associated with tiller number in Iranian bread wheat under different water regimes using genome-wide association study. Sci Rep 10(1):1\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevate NB, Krishna H, Parmeshwarappa SKV, Manjunath KK, Chauhan D, Singh S, Singh JB, Kumar M, Patil R, Khan H, Jain N, Singh GP, Singh PK (2022) Genome-wide association mapping for component traits of drought and heat tolerance in wheat. Front Plant Sci 13:943033\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDolferus R, Thavamanikumar S, Sangma H, Kleven S, Wallace X, Forrest K, Cullis B (2019) Determining the genetic architecture of reproductive stage drought tolerance in wheat using a correlated trait and correlated marker effect model. G3: Genes Genomes Genet 9(2):473\u0026ndash;489\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEd-Daoudy L, El Gataa Z, Sbabou L, Tadesse W (2024) Genome-wide association and genomic prediction study of elite spring bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) genotypes under drought conditions across different locations. Plant Gene 39:100461\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Gataa Z, Abebe A, El Hanafi S, Kehel Z, Rachdad FE, Tadesse W (2024) Genetic dissection and genomic prediction of drought indices in bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) genotypes. Crop Design, p 100084\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandez GCJ (1992) Effective selection criteria for assessing plant stress tolerance. In: Kuo CG (ed) Adaptation of food crops to temperature and water stress. AVRDC, Shanhua, pp 257\u0026ndash;270\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGahlaut V, Jaiswal V, Balyan HS, Joshi AK, Gupta PK (2021) Multi-locus GWAS for grain weight-related traits under rain-fed conditions in common wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L). Front Plant Sci 12:758631\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGamba D, Lorts CM, Haile A, Sahay S, Lopez L, Xia T, Lasky JR (2024) The genomics and physiology of abiotic stressors associated with global elevational gradients in Arabidopsis thaliana. New Phytol 244(5):2026\u0026ndash;2077\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGollob HF (1968) A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33(1):73\u0026ndash;115\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eItam MO, Mega R, Gorafi YS, Yamasaki Y, Tahir IS, Akashi K, Tsujimoto H (2022) Genomic analysis for heat and combined heat\u0026ndash;drought resilience in bread wheat under field conditions. Theor Appl Genet, 1\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJamil M, Ali A, Gul A, Ghafoor A, Napar AA, Ibrahim AMH, Naveed NH, Yasin NA, Mujeeb-Kazi A (2019) Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat. BMC Plant Biol 19(1):1\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan MI, Kainat Z, Maqbool S, Mehwish A, Ahmad S, Suleman HM, Li H (2022) Genome-wide association for heat tolerance at seedling stage in historical spring wheat cultivars. Front Plant Sci 13:972481\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan I, Gul S, Khan NU, Fawibe OO, Akhtar N, Rehman M, Rauf A (2023) Stability analysis of wheat through genotype by environment interaction in three regions of Khyber Pakhtunkhwa, Pakistan. SABRAO J Breed Genet 55(1):50\u0026ndash;60\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Guo J, Wang D, Chen X, Guan H, Li Y, Li Y (2023) Genomic insight into changes of root architecture under drought stress in maize. Plant Cell Environ 46(6):1860\u0026ndash;1872\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Mullan D, Zhao S, Zhang Y, Ye J, Wang Y, Zhang A, Zhao X, Liu G, Zhang C, Chan K, Lu Z, Yan G (2022) Genomic regions controlling yield-related traits in spring wheat: a mini review and a case study for rainfed environments in Australia and China. Genomics 114(2):1\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMajidiMehr A, Pahlavani MH, El Gataa Z, Amiri-Fahliani R (2024) Genome-wide association mapping and genomic prediction of water deficit stress tolerance indices in spring bread wheat. Gene Rep 37:102054\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMastrorilli M, Gorjanc G, Pignone D, L\u0026uuml;ttringhaus S, Gornott C, Wittkop B, Noleppa S, Lotze-Campen H (2020) The economic impact of exchanging breeding material: assessing winter wheat production in Germany. Front Plant Sci 11:601013\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaulana F, Huang W, Anderson JD, Ma XF (2020) Genome-wide association mapping of seedling drought tolerance in winter wheat. Front Plant Sci 11:573786\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontesinos-L\u0026oacute;pez OA, Crossa J, Saint Pierre C, Gerard G, Valenzo-Jim\u0026eacute;nez MA, Vitale P, Crespo-Herrera L (2023) Multivariate genomic hybrid prediction with kernels and parental information. Int J Mol Sci 24(18):13799\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNegisho K, Shibru S, Matros A, Pillen K, Ordon F, Wehner G (2022) Genomic dissection reveals QTLs for grain biomass and correlated traits under drought stress in Ethiopian durum wheat (\u003cem\u003eTriticum turgidum\u003c/em\u003e ssp. \u003cem\u003edurum\u003c/em\u003e). Plant Breeding 141(3):338\u0026ndash;354\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePu ZE, Ye XL, Yang LI, Shi BX, Zhu GUO, Dai SF, Zheng YL (2022) Identification and validation of novel loci associated with wheat quality through a genome-wide association study. J Integr Agric 21(11):3131\u0026ndash;3147\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQaseem MF, Qureshi R, Shaheen H, Shafqat N (2019) Genome-wide association analyses for yield and yield-related traits in bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) under pre-anthesis combined heat and drought stress in field conditions. PLoS ONE, 14(3)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabieyan E, Bihamta MR, Moghaddam ME, Mohammadi V, Alipour H (2022) Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions. BMC Genomics 23(1):831\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRStudio Team 2015 RStudio: integrated development for R. MA: RStudio, Inc. Boston. Available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rstudio.com\u003c/span\u003e\u003cspan address=\"http://www.rstudio.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRustgi S, Shafqat MN, Kumar N, Baenziger PS, Ali ML, Dweikat I, Gill KS (2013) Genetic dissection of yield and its component traits using high-density composite map of wheat chromosome 3A: bridging gaps between QTLs and underlying genes. PLoS ONE, 8(7), e70526\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P (2022) Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. Mol Breeding 42:1\u0026ndash;52\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShabannejad M, Bihamta MR, Majidi-Hervan E, Alipour H, Ebrahimi A (2021) A classic approach for determining genomic prediction accuracy under terminal drought stress and well-watered conditions in wheat landraces and cultivars. PLoS ONE, 16(3), e0247824\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh SK, Khaire A, Korada M, Majhi PK, Singh DK (2023) An analytical approach integrating GGE-Biplot and AMMI techniques for assessing genotype-environment interactions and yield stability in rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) genotypes. Emergent Life Sci Res 9:168\u0026ndash;176\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTadesse W, Gataa ZE, Rachda FE, Baouchi AE, Kehel Z, Alemu A (2023) Single-and multi-trait genomic prediction and genome-wide association analysis of grain yield and micronutrient-related traits in ICARDA wheat under drought environment. Mol Genet Genomics 298(6):1515\u0026ndash;1526\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Guan P, Xin M, Wang Y, Chen X, Zhao A, Sun Q (2021) Genome-wide association study identifies QTL for thousand grain weight in winter wheat under normal-and late-sown stressed environments. Theor Appl Genet 134:143\u0026ndash;157\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiss M, Sniezko RA, Puiu D, Crepeau MW, Stevens K, Salzberg SL, De La Torre AR (2020) Genomic basis of white pine blister rust quantitative disease resistance and its relationship with qualitative resistance. Plant J 104(2):365\u0026ndash;376\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu X, Wang J, Wu D, Jiang W, Gao Z, Li D, Zhang Y (2022) Identification of new resistance loci against wheat sharp eyespot through genome-wide association study. Front Plant Sci 13:1056935\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao Q, Bai X, Zhang C, He Y (2022) Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review. J Adv Res 35:215\u0026ndash;230\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan X, Zhang X, Kou M, Gebrewahid TW, Xi J, Li Z, Yao Z (2024) Quantitative Trait Loci Mapping for Adult-Plant Stripe Rust Resistance in Chinese Wheat Cultivar Weimai 8. Agronomy 14(2):264\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang D, Liu J, Li D, Batchelor WD, Wu D, Zhen X, Ju H (2023) Future climate change impacts on wheat grain yield and protein in the North China Region. Sci Total Environ 902:166147\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Sun L, Gao H, Hu M, Mu L, Cheng X, Zhang Y (2023) Genome-wide association study of yield-related traits in common wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) under normal and drought treatment conditions. Front Plant Sci 13:1098560\u003c/span\u003e\u003c/li\u003e\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":"Wheat, GWAS, Drought Stress, Genomic Prediction","lastPublishedDoi":"10.21203/rs.3.rs-6479357/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6479357/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought stress highlights the urgent need to create drought-tolerant wheat varieties. The genome-wide association analysis was performed to identify genetic markers linked to wheat yield traits under drought stress. A diverse panel of 190 bread wheat genotypes was evaluated under both well-watered and water-limited conditions throughout three growing seasons (2021\u0026ndash;2024) in Bahawalpur, Punjab, Pakistan using an alpha lattice design with three replications. Genotyping was performed using a 37 K SNP array. Genomic prediction models were developed using genomic best linear unbiased predictions (g-BLUPs).\u003c/p\u003e \u003cp\u003eHeritability was the highest (0.88) for yield and the lowest (0.53) for spikes per plant. Overall stress tolerance indices ranged from 1.21 for thousand kernel weight to 0.42 for grain yield. The additive main effects and multiplicative interaction (AMMI) model and principal component analysis (PCA) identified 10 high-yielding genotypes under water-limited conditions. The 59 genomic regions were significantly associated with yield and related traits on 21 chromosomes and clustered as ten QTLs. The significant QTL for yield under drought stress on chromosomes 3A and 5B explained a phenotypic variance of 7.1% and 5.08%. The genomic best linear unbiased predictions (g-BLUP) indicated notable accuracies, for traits related to large-effect QTLs, ranging from 0.1 for flag leaf length to 0.53 for biomass. This study concluded significant variation in wheat genotypes under water-limited conditions, with moderate to high heritability. It identifies ten elite genotypes and 59 key genomic regions for drought tolerance, and the genomic prediction model, g-BLUP, aids breeding programs in developing climate-resilient wheat varieties.\u003c/p\u003e","manuscriptTitle":"GWAS-Driven Identification of Genomic Regions Conferring Yield and Related Traits Under Drought Stress in Wheat","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 09:25:09","doi":"10.21203/rs.3.rs-6479357/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":"ca79c727-3536-4af1-b729-1ca4e0ab8b71","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-21T22:12:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-12 09:25:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6479357","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6479357","identity":"rs-6479357","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}