Unveiling the genetic Landscape of agronomic traits in bread wheat through Genome wide association Studies

Unveiling the genetic Landscape of agronomic traits in bread wheat through Genome wide association Studies | 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 Unveiling the genetic Landscape of agronomic traits in bread wheat through Genome wide association Studies Muhammad Jamil, Waseem Ahmad, Areeba Fatima, Sana Rasheed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6466875/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 Critical understanding of the genetic basis of yield-related traits through the identification of quantitative trait loci (QTLs) is essential for accelerating wheat improvement. The aim of this study was to evaluate diverse genotypes for yield-related traits and conducting GWAS to pinpoint genomic regions responsible for these traits. A field trial with 150 diverse bread wheat genotypes was conducted to evaluate eleven yield-related traits. in RCBD design with three replicates. Statistical analysis included Pearson’s correlation, step-wise multiple regression, structural equation modeling, and principal component analysis (PCA) were performed to assess trait relationships. The genome-wide association studies (GWAS) panel was genotyped using a 37K SNP array to identify trait associations. A total of 37,401 single nucleotide polymorphisms (SNPs) were analyzed to recognize 39 marker-trait associations (MTAs) across the panel. Nine MTAs for PH, 18 for PL, one for tillers, one for FLL, three for SL, one for GPS, three for biomass, and three for GYPP were identified. The most important traits contributing to yield were biomass, spike weight, plant height, peduncle length and tillers. Genotypic analysis for the clustering of wheat germplasm in this study revealed significant clusters among the wheat lines. This study provides perceptive of the genetic framework underlying key agronomic traits in bread wheat. The identification of genomic regions related with those traits offers critical insights for breeding programs aimed at improving wheat yield. Future research should validate markers across environments and integrate genomic selection and Marker assisted breeding for resilient wheat. Bread wheat GWAS Agronomic traits MTAs SNP markers Yield Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Bread wheat (Triticum aestivum L.) is one of the largest cultivated food crops holding a strategic role in providing essential nutrients to global human population. It is cultivated in an area of approximately 200 million hectares and fulfils one fifth of the calorie needs of the global population (Tesfaye, 2021 ). To meet the food demand of projected more than 9 billion population of the globe in 2050, the annual increase in wheat production needs to be increased by its current level of 1–1.6% (H. Khan et al., 2022 ).Challenges like climate change, population increase, desertification, evolving pest and disease pressure have negatively correlated to the grain yield of many regions in the past few decades (Ahmad et al., 2024 ). Conventional breeding techniques have contributed to wheat improvement, but progress has been slow due to the complex genetic nature of these traits, which are controlled by multiple genes and influenced by environmental conditions. Breeders and Geneticist are mainly concerned with the parameters like the grain yield and grain quality in wheat. The low heritability and complex genetic landscape of wheat has made yield improvement a challenging objective of wheat breeding program (Li et al., 2019 ). Increasing wheat yield and improving key agronomic traits, such as plant height, grain yield, spike weight, and flag leaf morphology, remain essential for sustainable wheat production. Given the global challenges of food security and climate change, optimizing wheat yield and adaptability is more urgent than ever. It is also important to understand the relationship between agronomic traits which ultimately affects grain yield. Grain yield is a complex polygenic trait result of the combination of many physiological and morphological traits and highly influenced by environmental changes (Chidzanga et al., 2022 ). Grain yield is highly influenced by spike number per unit area (SN), kernel number per spike (KNS) and thousand-kernel weight (TKW) (Li et al., 2019 ). However, grain yield depends upon many other factors like grain shape, spike architecture, plant height (PH), and flag leaf related traits. But these traits impact the grain yield by inducing changes in the grain filling, photosynthetic efficiency, days to heading and translocation of dry matter (Jamil et al., 2019 ; Li et al., 2020 ). Many of the yield component parameters compared to GY are more easily selectable and have significant heritability(h 2 ), especially in the early phases of breeding cycles (Li et al., 2019 ). The selection of these yield components shuns the yield plateaus and provide new opportunities to enhance the genetic gains. The complex and quantitative nature of grain yield and component traits have multiple gene inheritance and affected by genotype x environment interaction. Researchers believes that the potential strategy to improve wheat yield can be achieved by unveiling the genetic landscape of wheat and effective application of molecular breeding technique like marker assisted selection. With the advancement of genomic tools, many techniques and approaches have been adopted to fully promote sustainable production to cope up with the issue of food insecurity. Among the approaches, genome-wide association studies (GWAS) have emerged as a powerful approach to dissect the genetic basis of complex agronomic traits (Jamil et al., 2025 ). Identification of genetic loci associated with key agronomic traits is critical for developing high-yielding and stress-resilient wheat varieties. GWAS provides a high-resolution approach to uncover genetic variants linked to desirable traits, allowing breeders to integrate favorable alleles into breeding programs efficiently (Kumar et al., 2024 ). By mapping trait-associated loci, researchers can develop molecular markers for early selection, reducing breeding cycles and increasing genetic gain. In a last decade, researchers have speedup their efforts to decipher the genetic basis of agronomic traits in bread wheat. Quantitative trait loci (QTL) mapping based on bi parental population has been extensively applied to identify QTLs related to yield components related traits due to low-cost (Gao et al., 2015 ; Isham et al., 2021 ; Jia et al., 2024 ; Jin et al., 2020 ; Kang et al., 2020 ; Li et al., 2022 ; Ma et al., 2023 ; Zhang et al., 2016 ).The drawbacks of using QTL mapping are low markers density and low resolutions of one or more cross overs. The reduction in the cost of High throughput genotyping has led to emerge genome wide association studies (GWAS) as a powerful and robust genotyping approach to elucidate the genetic basis of grain yield and components traits in wheat (Jamil et al., 2025 ). A huge shift in studies have been reported over the last few years in which QTLs controlling grain yield related traits has been identified (Chidzanga et al., 2022 ; Eltaher et al., 2021 ; H. Khan et al., 2022 ; Li et al., 2019 ; Mehvish et al., 2023 ; Turuspekov et al., 2017 ; Tyrka et al., 2023 ; Wang et al., 2017 ; Zhao et al., 2023 ). Th main reason behind the success of GWAS is the use of high-density markers SNP genotyping platform like Affymetrix and Illumina which are offering rich resource high-throughput genotyping data for studying the diverse panels of wheat (Saini & Kumar, 2024 ). While previous studies have identified several genomic regions associated with key agronomic traits in bread wheat, gaps remain in understanding their stability across diverse genetic backgrounds and environments. Despite various GWAS efforts in bread wheat, many underlying loci associated with important agronomic traits remain unidentified or poorly characterized. Many identified loci have not been validated in multiple populations, and their functional roles are still unknown. Moreover, the complex interaction between these QTLs, pleotropic effect on other agronomic traits and epistatic effect makes it difficult to unveil the genetic landscape of grain yield in bread wheat. Consequently, there is a need of continued research to advance our understanding of the genetic factors affecting grain yield and to apply these findings in developing novel breeding strategies. A deeper understanding of the genetic architecture of these traits can enhance marker-assisted breeding and accelerate wheat improvement programs. This research provides a comprehensive understanding of the genomic landscape of agronomic traits in bread wheat by conducting a large-scale GWAS using diverse genetic material. By analyzing a broad panel of wheat genotypes, this study will identify stable and novel SNPs associated with plant height, grain yield, spike morphology, and flag leaf characteristics. Pakistan has developed a number of new wheat varieties, but more high-yielding varieties are still needed. Wheat production has increased by 35 to 50 percent after the introduction of newly engineered genotypes (Ullah et al., 2021 ). This study is designed to use GWAS to describe genomic regions associated with wheat yield and its contributing traits. Moreover, clustering of wheat germplasm by genotypic analysis was also performed. The identified SNP markers will be evaluated for their potential use in marker-assisted selection, facilitating their application in wheat breeding programs. By addressing these knowledge gaps, this research will contribute to the development of improved wheat varieties with enhanced yield potential and optimized agronomic traits. The objectives of our study are: 1) to study phenotypic evaluation of bread wheat varieties: 2) to determine the yield contributor of wheat: and 3) To identify the genomic regions contributing to the yield and major agronomic traits. 2. Material and Methodology 2.1 Plant material and field trails The experiment used diverse panel of 150 wheat genotypes taken from the department of plant sciences, Quaid-e-Azam University, Islamabad, Pakistan. These wheat lines with broad genetic background were grown in the field area of the Islamia University of Bahawalpur during the cropping season Dec to April. The field trails were conducted for two years, 2022-23 and 2023-24 in randomized complete block design with three replicates. To account for environmental variation across years, we calculated Best Linear Unbiased Estimates (BLUEs) for each genotype using data from both years. In a set of 150 genotypes, each was planted one foot apart from other in a single block and there were three replicates representing three blocks. Standard agronomic practice was adopted according to the local practices of the region. The soil consisted of clay, loam, and sand. Genotypes were planted in one-meter beds, with irrigation, weed control, and crop protection measures applied as needed. Urea granules were applied during tillering, heading, and grain filling, while nets were used at the booting stage to prevent lodging. 2.2 Phenotyping Eleven phenotypic traits, grain yield per plant (GYPP), spike weight (SW), spikelet per spike (SLPS), grains per spike (GPS), spike length (SL), plant height (PH), tillers, peduncle length (PL), flag leaf length (FLL), flag leaf width (FLW) and biomass were assessed in this diverse panel. All plants were harvested in each plot at the physiological maturity. Grain yield per plant (GYPP) was recorded at harvest by weighing the total grain yield per plant after drying. Spike weight (SW) was determined by drying 10 spikes per plot at 75°C for 48 hours and then taking their average weight. The number of spikelets per spike (SLPS) was counted from 10 randomly selected spikes per plot, while grains per spike (GPS) were counted after threshing the same sample. Spike length (SL) was measured from the base to the tip, excluding awns, in 10 spikes per plot. Plant height (PH) was recorded as the distance from the soil surface to the tip of the spike (excluding awns). Tillers per plant were counted considering only productive tillers. Peduncle length (PL), representing the uppermost internode length, was also measured. Flag leaf length (FLL) was measured from the base to the tip in 5 flag leaves per plant at the mid-grain filling stage, while flag leaf width (FLW) was measured at its widest point using a caliper. 2.3 Statistical analysis Statistical analysis was conducted to examine the relationships between grain yield and other agronomic traits in bread wheat. Descriptive analysis, ANOVA, correlation analysis and heritability estimates were conducted in the R statistical package (Team, 2020 ). Pearson’s correlation coefficients (𝑟) were calculated to determine the associations among traits, with traits grouped using agglomerative hierarchical clustering (AHC). Heatmap of the whole standardized data was used in unraveling the k-mean based clustering of traits (PH, PL, tillers, biomass, SW, GYPP, GPS, SL, SLPS, FLW and FLL) and genotypes. Principal Component Analysis (PCA) was performed to evaluate trait contributions to variation across 150 wheat genotypes, including eigenvalue analysis, variance percentage, score plots, and loading plots. Stepwise multiple regression analysis was used to identify the optimal combination of traits contributing to grain yield. Structural Equation Modeling (SEM) was used to assess direct and indirect effects of traits on yield, biomass, and spike weight. These comprehensive statistical approaches provided insights into the genetic architecture and key determinants of yield in bread wheat. 2.4 Genotyping and SNP filtering The extraction of DNA from the leaves of young seedlings was taken by DNA extraction kit from Prima Scientific (Bangkok, Thailand). The genotyping of wheat panel was performed using Illumina Infinium Wheat 37K SNP Array as done by (Khan et al., 2022 ). A total no of 37,401 Single Nucleotide Polymorphism (SNP) markers were initially analyzed, covering all 21 chromosomes of the wheat genome. A pipeline of TASSEL 5 software was used for SNP calling against the whole wheat (CS) genome assembly (WGA 0.4, International Wheat Genome Sequencing Consortium). The monomorphic SNPs with minor allele frequency (MAF) of 10% and heterozygote frequency > 20% were removed from analysis. 2.5 Genome-Wide Association Study (GWAS) Genome-wide association study (GWAS) was conducted by means of a mixed linear model (MLM) to identify genomic regions related with eleven phenotypic traits in 150 bread wheat genotypes. The GWAS was performed using the average BLUEs derived from two years of field data to minimize the influence of year-specific environmental effects. 37,401 single nucleotide polymorphisms (SNPs) were used for analysis with a minor allele frequency (MAF) greater than 0.05. The phenotypic values of eleven agronomic traits of bread wheat of 150 diverse genotypes along with corresponding genotyping data were used in GWAS analysis. Significant no of MTAs were identified using the BLINK (Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway) model implemented in Genome Association and Prediction Integrated Tool (GAPIT) version 3.0 in the R software package (Wang & Zhang, 2021 ). Determining the correct p-value threshold for statistical significance is critical to differentiate the true positives from false positives. To determine the statistical significance threshold in GWAS, Bonferroni correction has been employed. To estimate Bonferroni correction, α was set to 0.05 which is divided by the total number of SNPs. The Bonferroni-corrected SNPs were considered for significant association and R2 was used to describe the percentage variation explained (PVE) by significant MTAs. 3. Results 3.1. Descriptive statistics of eleven agronomic traits The descriptive statistical analysis of eleven agronomic traits in 150 wheat genotypes revealed substantial variation, indicating significant genetic diversity within the population (Table 1 ). Plant height ranged from 55 cm to 112 cm, with a mean value of 81.46 cm. More than 50% of the genotypes exhibited heights between 75 and 90 cm, suggesting a balanced distribution of medium-statured plants. Peduncle length varied from 0 to 19 cm, with an average of 8.92 cm, and the majority of genotypes fell within the 6 to 11 cm range. The number of tillers per plant exhibited wide variation, ranging from 2 to 25, with a mean of 15.05, indicating genotypic differences in tillering ability, a key yield component. Flag leaf length and width, crucial for photosynthetic efficiency, showed ranges of 13–33 cm and 1–2.3 cm, with mean values of 24 cm and 1.58 cm, respectively. Spike-related traits, such as spike length and spikelets per spike, varied from 7 to 15 cm and 15 to 25, with means of 11.13 cm and 19.61, respectively. Significant variation in spike weight (4–100, mean 37.89) and grains per spike (12.50–65.45, mean 38.37) suggests diverse genetic potential for grain production. Biomass ranged from 9 to 157, averaging 63.51, reflecting the capacity for dry matter accumulation. Grain yield per plant, the most crucial economic trait, exhibited a broad range (4–61) with an average of 24.24, underscoring the potential for selecting high-yielding genotypes. These findings highlight the genetic variability among the studied wheat genotypes, providing insights for breeding programs focused on improving yield and agronomic performance. Table 1 Descriptive statistics of the studied traits of 150 wheat genotypes Trait Unit Code Min-Max Mean ± SD Q1(25%) Median Q3(75%) Plant Height cm PH 55–112 81.46 ± 12.16 75 80 90 Peduncle Length cm PL 0–19 8.92 ± 3.90 6 8.50 11 Tillers 2–25 15.05 ± 5.73 11 14.50 20 Flag Leaf Length cm FLL 13–33 24 ± 4.51 20 23.50 28 Flag Leaf width cm FLW 1-2.3 1.58 ± 0.30 1.40 1.50 1.80 Spike Length cm SL 7–15 11.13 ± 1.63 10 11 12 Spikelet per Spike SLPS 15–25 19.61 ± 2.16 18 20 21 Spike Weight SW 4-100 37.89 ± 14.78 27 36 47.250 Grains per spike GPS 12.50-65.45 38.37 ± 11.22 30 37.81 46.96 Biomass 9-157 63.507 ± 25.432 48 60.5 78 Grain yield per plant GYPP 4–61 24.24 ± 10.28 17 24 30 3.2 Pearson’s correlation coefficient analysis Pearson correlation analysis among the 11 agronomic traits in bread wheat revealed varying degrees of association, with correlation coefficients ranging from − 0.03 to 0.89. Many trait associations exhibited moderate to strong correlations, while a few showed weak or negligible relationships. Eleven variables have been classified to Agglomerative Hierarchical Clustering (AHC) in to four clusters ( Fig. 1 ) . First cluster have two variables that are plant height and peduncle length. Second cluster have four variables that are tillers, grain yield per plant, spike weight and biomass. Third cluster have two variables that are flag leaf length and flag leaf width. Fourth cluster have three variables that are “grains per spike, spike length and spikelets per spike”. There are 55 pair-wise correlations among 11 traits. In first cluster, Plant height is in significantly (p < 0.05) positive correlation with peduncle length (0.61), GYPP (0.18), SW (0.19) and biomass (0.38). Peduncle length is in significantly (p < .05) positive correlation with tillers (0.22) and biomass (0.24). Higher the peduncle length, higher will be the tillers and biomass. Peduncle length is negatively correlated with GPS, FLW and SLPS. Tillers are in highly significant (p < .05) positive correlation with GYPP (0.35), SW (0.36) and Biomass (0.4). Its means that if we increase tillers than yield and biomass should be increase. Tillers are negatively correlated with FLL and FLW. In the second cluster, grain yield per plant is in significantly (p < .05) positive correlation with SW (0.85), biomass (0.86), GPS (0.36) and SLPS (0.2). Grain yield per plant is negatively correlated with FLW. Spike weight is in significantly (p < .05) positive correlation with biomass (0.89), GPS (0.21) and SLPS (0.24). Spike weight is not negatively correlated with any variable or trait. Biomass is in significantly (p < .05) positive correlation with SLPS (0.2). Biomass is negatively correlated with FLW. Flag leaf length is in significantly (p < .05) positive correlation with FLW (0.2). Flag leaf length is negatively correlated with GPS. It means by increasing GPS, flag leaf length will decrease. Flag leaf width is in not significantly (p < .05) positive correlation with any other trait. It is negatively correlated with GPS. GPS, SL and SLPS have not significantly (p < .05) positive and negative correlation with any other trait as shown in (Fig. 1 ). 3.3 Principal Component Analysis of Eleven Traits Across 150 Genotypes Principal Component Analysis (PCA) was performed to explore the patterns of variation among all the studied agronomic traits in bread wheat. PCA of 11 traits across 150 wheat genotypes identified four significant principal components (PCs) explaining 72% of the total variance (Fig. 2 a). PC1 had the highest eigenvalue (3.27) and explained 30% of the variance, followed by PC2 (1.77, 16%), PC3 (1.44, 14%), and PC4 (1.23, 12%). These components collectively summarized the variation in key traits, with PC1 associated with grain yield per plant, biomass, tillers, and spike weight, while PC2 captured plant height, peduncle length, and grains per spike. PC3 explained spikelets per spike, flag leaf length, and flag leaf width, whereas PC4 mainly represented spike length. Trait contributions (Fig. 2 b) varied across PCs, with plant height mainly explained by PC2 (27%) and PC4 (6%), while peduncle length was largely associated with PC2 (35%). Grain yield per plant showed the highest contribution in PC1 (25%), with additional minor influences from PC2, PC3, and PC4. The clustering analysis grouped genotypes into four clusters (Fig. 2 c) based on PCA scores. Cluster 1 (blue) contained 28 genotypes with high grain yield, biomass, tillers, and spike weight, while Cluster 2 (red) included 65 genotypes with dominant plant height and peduncle length. Cluster 3 (green) comprised 18 genotypes with high spikelets per spike, flag leaf length, and flag leaf width, whereas Cluster 4 (orange) contained 39 genotypes characterized by spike length. The loading plot (Fig. 2 d) further illustrated trait associations, confirming strong correlations among yield-related traits along PC1 and plant structural traits along PC2, highlighting the significance of these traits in explaining genetic variability among wheat genotypes. Principal Component Analysis (PCA) Of 11 Traits Studied in 150 Wheat Genotypes Shows Eigenvalue Analysis ( a ), Percentage Contribution ( b ) of each Trait to the First Four Principal Components (PCs), Score Plot ( c ) Displaying Two-Dimensional Spread of 150 Wheat Genotypes, and the Loading Plot ( d ) of Traits as Vector Lengths Classified into Four Colors Representing their Respective PCs. Eigenvalue and Variation Percentage Explained by PC1 and PC2 are also Given Along the x and y-Axis. 3.4 Agglomerative Hierarchical Clustering (AHC) The heatmap was created by clustering of traits and genotypes (Fig. 3 ). Each line on the right side of map (y-axis) represents one genotype. Eleven traits are on the x-axis. A color key is on the right side. Higher the expression value of any particular trait the color would be blue as the expression would be low, so the color is orange. Blue lines in the map shows higher values and orange lines shows the lower values. These lines in the map actually shows the genotype behavior with respect to the traits. Cluster in the bottom shows that these genotypes have low expressions of the studied traits. The clustering dendrograms along both axes reflect the similarity patterns: Tillers, biomass, spike weight and grain yield grouped in single cluster is the indication of their close association. Peduncle length was higher in top cluster of genotypes and lower in the bottom. For tillers, genotypes that are located at upper side have high expression, then the genotypes in the center. Biomass have low expression genotypes at the bottom and highly expressed genotypes are present in the upper end and in center. Spike weight has highly expressed genotypes at the top. Grain yield per plant was lower in the bottom cluster than the others. Notably, traits like GYPP, SW, and GPS, which are central to yield, cluster together and may guide selection criteria in breeding programs. FLL, FLW, SLPS, and SL, suggest potential correlation or shared genetic regulation, while genotypes forming distinct clusters indicate phenotypically similar groups. The application of k-means clustering, superimposed with hierarchical clustering, provides a refined stratification of data, allowing breeders and researchers to identify high-performing or genetically diverse genotypes based on multi-trait performance. 3.5 Step-wise Multiple Regression Modeling Using the data from 150 wheat genotypes the eleven traits in this study were subjected to a stepwise regression analysis to determine the significant variables contributing to the variation in GYPP (dependent variable) (Table 2 ). The selection terms in stepwise regression used 0.15 alpha to enter and to remove. $$\:\text{G}\text{Y}\text{P}\text{P}=\:-2.03-0.067\text{*}\text{P}\text{H}+0.16\text{*}\text{S}\text{W}+0.22\text{*}\text{G}\text{P}\text{S}+0.27\text{*}\text{b}\text{i}\text{o}\text{m}\text{a}\text{s}\text{s}$$ Where GYPP is grain yield (g) per plant, PH: plant height (cm), SW: spike weight (g), GPS: grains per spike. With this regression equation, grain yield per plant (GYPP) is the dependent variable predicted through step-wise selected potential independent variables PH, SW, GPS and biomass. It suggests that for each unit increase in plant height (PH), the grain yield per plant decreases by 0.067 units, indicating a negative association. Conversely, spike weight (SW) positively influences grain yield per plant, with each unit increase in spike weight resulting in a 0.16 unit increase in GYPP. Similarly, an increase in grains per spike (GPS) enhances the grain yield per plant by 0.22 units for each additional grain per spike. Biomass also plays a significant role, contributing to an increase of 0.27 units in grain yield per plant for each unit increase in biomass. The constant term (-2.03) represents the baseline grain yield per plant when all predictor variables are zero, setting a foundational reference point for the model. The Table 2 summarizes the results of a multiple regression analysis, indicating that the model describes 83.91% of the change in the dependent variable, with an adjusted R-squared of 83.47%. The standard error of the regression is 4.18, and the model has a predicted R- squared of 82.14%, demonstrating strong predictive power. The Corrected Akaike Information Criterion (AICc) and Bayesian Information Criterion (BIC)” are 862.53 and 880, respectively, suggesting a good model fit. The regression coefficients for PH (-0.0664), SW (0.1564), GPS (0.2229), and biomass (0.2709) are all statistically significant, with PH, GPS, and biomass having p-values < 0.05. This indicates that an increase in PH significantly (p-value < .05) decreases GYPP, while increase in SW, GPS, and biomass increase GYPP. The Analysis of Variance (ANOVA) table demonstrated that the overall model is highly significant (p-value < .0001), and PH, SW, GPS, and biomass significantly contributing to the model. SW and biomass have the highest contributions, explaining 69.68% and 7.41% of the variation, respectively. The model's fit is further confirmed by the high F-value (189.06) and low error contribution (16.09%), indicating that the predictors effectively explain the variation in the dependent variable. The Durbin-Watson statistic of 1.92 suggests no autocorrelation in the residuals, further validating the model's reliability. The Pareto chart of potential predictors identified through the stepwise regression model (Fig. 4 ) provides a clear visualization of the most influential factors affecting grain yield per plant (GYPP). The chart ranks predictor variables by their standardized effects, highlighting their relative importance. The y-axis lists predictors labeled from A to K, including PH (A), SW (H), GPS (J), and biomass (K). The x-axis represents the standardized effect size, with larger values indicating stronger influences on GYPP. A red dashed line at 1.447 marks the statistical significance threshold at the α = 0.15, with bars extending beyond this line indicating significant predictors. Biomass (K) has the largest standardized effect at 8.17, making it the most influential predictor on GYPP. Grains per spike (GPS - J) follows, with a substantial effect of 6.9. Spike weight (SW - H) also significantly impacts GYPP with a standardized effect of 2.85. Lastly, the plant height (PH - A) shows a significant effect on GYPP with an effect size of 2.04. This Pareto chart underscores the importance of biomass, GPS, SW, and PH in predicting GYPP, with biomass and GPS being the most critical factors. The chart provides a clear guide for prioritizing these variables in further agricultural analysis and practice to optimize grain yield per plant. Table 2 Model Summary, Coefficients and Analysis of Variance of Stepwise Regression S R-sq R-sq(adj) PRESS R-sq(pred) AICc BIC 4.18348 83.91% 83.47% 2816.46 82.14% 862.53 880 Term Coef SE Coef 95% CI T-Value P-Value VIF Constant -2.03 2.88 (-7.72, 3.67) -0.7 0.483 PH -0.0664 0.0325 (-0.1306, -0.0022) -2.04 0.043 1.33 SW 0.1564 0.0549 (0.0479, 0.2648) 2.85 0.005 5.6 GPS 0.2229 0.0323 (0.1590, 0.2868) 6.9 < .001 1.12 biomass 0.2709 0.0332 (0.2053, 0.3364) 8.17 < .001 6.05 Source DF Adj SS Contribution Adj MS F-Value P-Value Regression 4 13235.6 83.91% 3308.91 189.06 0 PH 1 73.1 3.24% 73.08 4.18 0.043 SW 1 142.1 69.68% 142.05 8.12 0.005 GPS 1 832.4 3.59% 832.36 47.56 < .001 biomass 1 1168.4 7.41% 1168.41 66.76 < .001 Error 145 2537.7 16.09% 17.5 Durbin-Watson Statistic=1.92; 3.6 Structure Equation Modeling (SEM) Out of eleven variables, biomass is variable that has been predicted by peduncle length, tillers and plant height (Fig. 5 ). So, peduncle length, tillers and plant height have direct impact on biomass and at the same time plant height and tillers have indirect impact on grain yield per plant. Biomass has through impact on grain yield per plant. Biomass was predicted by three variables (plant height, tillers and peduncle length). The effect of peduncle length on biomass was 0.38; it means, one-unit change in peduncle length, biomass would be changes 0.38 times. Biomass was significantly (p-value < .001) predicted by tillers with effect of 1.61. It means, one-unit change in tillers, 1.61 times change in biomass would be observed. Tillers are correlated positively with biomass. It indicates that by increasing tillers, biomass would be increased. Biomass was also significantly (p-value < .001) predicted by plant height with the effect of 0.75. It means, one-unit change in plant height, biomass would be changes 0.75 times. Arrow indicates the dependence of biomass on peduncle length, tillers and plant height. Plant height and tillers have indirect impact on grain yield per plant. Grain yield per plant was significantly (p-value .001) predicted by plant height and tillers (0.13, 0.28) respectively. So, they were positively associated with grain yield per plant. Grains per spike, spike length and spikelets per spike” have straight impact on Spike weight. Arrow indicates the dependence of spike weight on spike length, spikelets per spike and grains per spike. Spike weight was significantly (p-value < .001) predicted by grains per spike with the effect of 0.24. It means, one-unit change in grains per spike, spike weight would be changes up to 0.24 times. Spike weight is positively correlated with grains per spike. If we increase grains per spike, spike weight would be increased. Spike weight was predicted by spike length and was 0.74. It means, one-unit change in spike length, spike weight would be changes 0.74 times. Spike weight was predicted by spikelets per spike and was 0.71. It means, one unit change in spikelets per spike, spike weight would be changes 0.71 times. Spikelets per spike have direct impact on spike length. Arrow indicates the dependence of spikelets per spike, on spike length. Spikelets per spike was significantly (p-value < .001) predicted by spike length and the effect was 0.33. It means, one unit change in spike length, spikelets per spike would be changes 0.33** times. Spikelets per spike and spike length are positively correlated. Grain yield per plant and spike weight are correlated with one another (77.81). It means if we want to maximize the yield, spike weight must be increased, which depends on spike length, spikelets per spike and grains per spike. For higher yield, biomass should be higher which depends on plant height, peduncle length and tillers”. With this path analysis the role of individual character is vital in defining the grain yield as well as spike weight was observed. Path Diagram Generated After Structure Equation Model Showing Direct and Indirect Effects of Traits Contributing to Biomass, Yield and Spike Weight. PH: Plant Height; PL: Peduncle Length; GPS: Grains Per Spike; SL: Spike Length; GYPP: Grain Yield Per Plant; SW: Spike Weight; SLPS: Spikelets Per Spike 3.7 Genome-wide Association Study (GWAS) A genome-wide association study (GWAS) was conducted using a mixed linear model (MLM) to identify genomic regions associated with eleven phenotypic traits in 150 bread wheat genotypes. The analysis used 37,401 single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) greater than 0.05. A total of 39 genomic regions also known as marker trait associations (MTAs) were identified (Table 3 ). The first column specifies the trait being analyzed, such as “Plant Height (PH), Peduncle Length (PL), Tillers, Flag Leaf Length (FLL), Spike Length (SL), Grains per spike (GPS), Biomass, and Grain Yield per Plant (GYPP)”. Second column lists the genetic marker associated with each trait, typically identified by its position on a specific chromosome. Third one is (Chr) Chromosome where the marker is located. Fourth one indicates p-value obtained from the statistical model. A lower p-value (e.g., 2.77E-04) indicates stronger evidence against the null hypothesis (no association). R2 is proportion of variance explained by the marker-trait association. Allele indicates the allele associated with the trait. For example, 'T' or 'C' are alleles, and 'Minor' or 'Major' denotes the effect allele. Effects positive values show an increase in the trait value associated with that allele, while negative values show a decrease. MAF is the frequency of the less common allele in the population being studied. For Plant Height (PH), marker 3B_513710969 on chromosome 3B has a significant association (p = 2.77E-04) with a minor allele T having an effect size of 9.16, whereas the major allele C has an effect size of 18.52. A minor allele T might increase Plant Height by 9.16 units, while the major allele C increases it by 18.52 units, both alleles having a minor allele frequency (MAF) of 0.46. The Table 3 reveals significant associations between PH and several genetic markers located on chromosome 3B (markers 3B_513710969, 3B_513710985, 3B_513711040, 3B_513712090, 3B_513712139) and chromosome 4A (marker 4A_691119517, 4A_691119528, 4A_691403255). These markers demonstrate low p-values (2.77E-04 to 2.86E-04) and a consistent R2 value of 0.12. Allele effects vary across markers, with alleles like 'T', 'C', 'G', and 'A' influencing PH positively. These findings are crucial for breeding programs aiming to optimize crop architecture for improved yield and adaptability, ensuring sustainable agricultural yield. The MLM analysis for peduncle length identifies several genetic markers on chromosomes 1A and 1B significantly associated with peduncle length variations. These markers exhibit low p-values (ranging from 1.91E-04 to 4.15E-04) and moderate R2 values (0.11 to 0.12). Allele effects vary; for instance, alleles like 'G', 'T', 'C', and 'A' influence peduncle length positively, highlighting genetic diversity in controlling this trait. Tillering, represented in the analysis by genetic marker 3B_717368919, plays a major role in determination of yield. The marker shows a significant association with tillers (p = 3.33E-04, R2 = 0.12), with the minor allele 'T' contributing positively by increasing tiller numbers (effect size of 6.79), while the major allele 'C' has a lesser effect (1.23). Flag leaf length has genetic marker 3A_589180579 shows a strong association (p = 3.44E-05, R2 = 0.15), with alleles 'C' and 'G' exerting contrasting effects of -1.33 units and − 6.62 units, respectively. The low MAF of 0.11 indicates the less frequent occurrence of these alleles. Spike length influences grain yield and is influenced by genetic markers on chromosomes 1A and 6B. The markers (1A_502117641, 1A_502117650, 6B_477210508) show significant associations with SL (p-values ranging from 3.87E-04 to 5.53E-04) and varying R2 values (0.11 to 0.25). Allelic effects vary across these markers ('C', 'T') and influence SL positively. Understanding these genetic determinants helps in breeding crops with optimal spike length for improved grain production. Grain per spike show genetic marker 2B_75613744 exhibits a significant association with (p = 3.20E-04, R2 = 0.11), where alleles 'A' and 'G' influence GPS positively by 0.99 and 8.33 respectively. The MAF of 0.40 indicates a relatively common occurrence of these alleles. Biomass production is a key determinant of overall crop yield. Genetic markers on chromosomes 1B, 4B, and 5A show significant associations with (p-values ranging from 1.27E-04 to 4.04E-04), with moderate R2 values (0.11 to 0.13). Allelic effects ('C', 'T', 'G' and 'A') vary, influencing biomass positively or negatively. Grain yield per plant is a fundamental trait determining crop productivity. Genetic markers on chromosomes 4A and 6B exhibit significant associations with GYPP (p-values ranging from 2.46E-04 to 5.28E- 05) and varying R2 values (0.12 to 0.14). Allelic effects are diverse, impacting GYPP positively or negatively. This analysis provides crucial insights into the genetic markers influencing important agronomic characteristics, aiding in future breeding efforts to enhance crop yield. Table 3 Marker-trait Association Obtained after Mixed Linear Model (MLM) Minor Major Trait Marker Chr p R 2 Allele Effect Allele Effect MAF PH 3B_513710969 3B 2.77E-04 0.12 T 9.16 C 18.52 0.46 PH 3B_513710985 3B 2.77E-04 0.12 T 9.16 G 18.52 0.46 PH 3B_513711040 3B 2.77E-04 0.12 C 9.16 T 18.52 0.46 PH 3B_513712090 3B 2.77E-04 0.12 G 9.16 A 18.52 0.46 PH 3B_513712139 3B 2.77E-04 0.12 C 9.16 T 18.52 0.46 PH 3B_625593951 3B 3.24E-04 0.11 A 14.83 C 26.79 0.14 PH 4A_691119517 4A 2.86E-04 0.12 G 16.84 C 26.24 0.21 PH 4A_691119528 4A 2.86E-04 0.12 T 16.84 C 26.24 0.21 PH 4A_691403255 4A 2.80E-04 0.12 A 17.17 C 26.26 0.23 PL 1A_115371695 1A 2.74E-04 0.12 G 11.96 A 8.87 0.19 PL 1A_142330282 1A 2.74E-04 0.12 G 11.96 A 8.87 0.19 PL 1A_166070521 1A 3.78E-04 0.11 T 12.15 C 8.99 0.16 PL 1A_195192114 1A 2.74E-04 0.12 C 11.96 T 8.87 0.19 PL 1A_225085343 1A 2.74E-04 0.12 G 11.96 A 8.87 0.19 PL 1A_242118598 1A 2.74E-04 0.12 C 11.96 A 8.87 0.19 PL 1A_249081921 1A 3.78E-04 0.11 G 12.15 A 8.99 0.16 PL 1A_249082698 1A 3.78E-04 0.11 A 12.15 C 8.99 0.16 PL 1A_249082795 1A 3.78E-04 0.11 G 12.15 A 8.99 0.16 PL 1A_249144497 1A 3.78E-04 0.11 C 12.15 T 8.99 0.16 PL 1A_249144590 1A 3.78E-04 0.11 A 12.15 G 8.99 0.16 PL 1A_249776740 1A 3.78E-04 0.11 A 12.15 G 8.99 0.16 PL 1A_252356259 1A 3.79E-04 0.11 T 12.26 C 8.97 0.17 PL 1A_252356260 1A 3.79E-04 0.11 G 12.26 T 8.97 0.17 PL 1A_252356373 1A 3.78E-04 0.11 G 12.15 T 8.99 0.16 PL 1B_289096605 1B 3.99E-04 0.11 C 9.90 T 7.39 0.22 PL 1B_311150885 1B 1.91E-04 0.12 T 12.58 C 8.94 0.14 PL 1B_503816495 1B 4.15E-04 0.11 A 8.68 C 4.83 0.14 tillers 3B_717368919 3B 3.33E-04 0.12 T 6.79 C 1.23 0.13 FLL 3A_589180579 3A 3.44E-05 0.15 C -1.33 G -6.62 0.11 SL 1A_502117641 1A 5.53E-04 0.25 C 1.92 T 5.67 0.45 SL 1A_502117650 1A 5.53E-04 0.25 C 1.92 T 5.67 0.45 SL 6B_477210508 6B 3.87E-04 0.11 T 2.45 C 0.70 0.35 GPS 2B_75613744 2B 3.20E-04 0.11 A 0.99 G 8.33 0.40 biomass 1B_526272352 1B 4.04E-04 0.11 C 51.43 T -76.09 0.10 biomass 4B_326084122 4B 3.57E-04 0.11 A 52.10 G -75.65 0.10 biomass 5A_647969803 5A 1.27E-04 0.13 T -51.93 C 50.00 0.17 GYPP 4A_597902329 4A 5.28E-05 0.14 A -38.37 G 28.98 0.12 GYPP 6B_442751575 6B 2.64E-04 0.12 G -9.26 A 0.68 0.26 GYPP 6B_442751636 6B 2.46E-04 0.12 T -10.99 C 22.50 0.26 Chr: Chromosome; p: p-value of model; MAF: Minor allele frequency 3. Discussion The present study tested the genotypic diversity of germplasm to determine the association of yield related traits in bread wheat and dissected the genetic basis of variation in agronomic traits. Our work highlights the importance of biomass, GPS, SW, and PH in predicting GYPP, with biomass and GPS being the most critical factors. It informs us to prioritize these variables in further agricultural analysis and practice to optimize grain yield per plant. The comprehensive phenotypic evaluation coupled with high-density genotyping, enabled identified 39 marker-trait associations (MTAs) linked to yield and related traits in a diverse panel of bread wheat genotypes using genome-wide association studies (GWAS). Our findings not only validate previously reported QTLs but also uncover potentially novel loci, reiterating the effectiveness of using diverse germplasm for dissecting complex traits in wheat. The phenotypic data shows a significant variation among all the 150 genotypes of all the studied agronomic traits, showcasing the broad genetic diversity of the bread wheat panel. Grain yield per plant (GYPP) being the significant indicator of yield directly be predicted by biomass, spike weight (SW), spikelets per spike (SLPS) and grains per spike (GPS). It has been well documented that higher biomass correlates with increased grain yield due to the allocation of photosynthates to grains (An et al., 2022 ; Kumar et al., 2025 ; Russell et al., 2020 ).On the other hand, spike weight (SW) is directly impacted by Grains per spike, spike length and spikelets per spike (Knežević et al., 2015 ; Liu et al., 2022 ). Structural Equation Modeling further confirmed the direct and indirect effects of these traits on GYPP, emphasizing their potential use as selection indices in breeding programs. These results align with previous reports highlighting the importance of spike-related traits in determining wheat yield (Liu et al., 2018 ; Luo et al., 2023 ; Sheoran et al., 2022 ; Würschum et al., 2018 ; Xu et al., 2024 ). Additionally, traits such as flag leaf area (FLL) and flag leaf width (FLW), which influence photosynthetic capacity, were moderately correlated with biomass and yield, further supporting their role in enhancing source capacity during grain filling. A total of 39 Bonferroni-corrected MTAs was identified for PH (9), PL (18), tillers (1), FLL (1), SL (3), GPS (1), biomass (3), and GYPP (3) were identified. All the MTAs were identified on A subgenome and B subgenome in which A sub genome (23) followed by B subgenome (16) indicating a potentially greater role of the A subgenome in trait variation. An identical pattern in MTAs observed within the A subgenome for yield and yield-contributing characteristics (Ain et al., 2015 ; Alemu et al., 2021 ; Godoy et al., 2018 ; H. Khan et al., 2022 ).Several significant MTAs for PH were detected on chromosomes 3B and 4A, notably at loci 3B_513710969 and 4A_691403255. These MTAs are novel, as prior studies have predominantly mapped major dwarfing and height-related genes (e.g., Rht-B1, Rht-D1) on chromosomes 4B and 4D (Gao et al., 2015 ; Li et al., 2018 ; Sun et al., 2017 ). The region on 3B is particularly interesting as it has not been commonly linked to height regulation in earlier classical QTL studies, suggesting the possible presence of minor or environment-specific loci. A similar 3B region was only recently suggested to be involved in height plasticity under heat stress (Zanke et al., 2014 ), indicating potential adaptive value of this region. Beyond plant height, novel MTAs were also identified for yield-related traits. For instance, a marker on chromosome 4A (4A_597902329) showed a strong association with GYPP, with a high phenotypic variance explained (R² = 0.14), and was not previously reported in classical studies. Similarly, MTAs on chromosomes 6B and 1B associated with biomass and GYPP were located outside the regions of previously characterized yield QTLs, indicating the discovery of unexplored loci. Importantly, several markers for peduncle length (on 1A and 1B) formed a tight cluster, indicating a potentially strong LD block contributing to trait stability. These novel loci provide promising targets for marker-assisted selection and fine mapping, especially in the context of improving complex traits under climate variability. The identification of novel MTAs for yield related traits in bread wheat bears significant potential for molecular breeding. Markers associated with GPS, SW, and SLPS can be prioritized in marker assisted selection (MAS) pipelines to accelerate the development of high yielding cultivars. The integration of these MTAs to Genomic prediction (GP) can further increase the selection efficiency. This is particularly important for the complex traits like grain yield that are influenced by multiple minor-effect loci and environmental interactions. Lastly, the set of markers identified should be validated and deployed I wheat breeding programs in future. Although, this study was conducted in field conditions but the potential influence of genotype-by-environment (G×E) interactions on trait expression cannot be overlooked. Thus, the validation of these MTAs across multiple year trails and integration of environmental data will be vital for determining the stability and effectiveness. Moreover, the functional validation of candidate genes associated with significant SNPs can enhance understanding of the molecular mechanisms governing yield components. Future studies should also explore the breeding efficiency through the integration of GS and MAS. Combining genome-wide marker data with advanced modeling approaches can accelerate selection cycles and improve the accuracy of predicting superior genotypes. In a nutshell, this reserach identified significant marker-trait associations for eleven agronomic traits in a diverse GWAS panel of bread wheat genotypes. These findings reveal the genetic control of complex yield-related traits and provide novel resources for molecular breeding. Future research should validate these markers across environments and integrate genomic tools with traditional breeding strategies to develop resilient, high-yielding wheat varieties. 4. Conclusion In a conclusion, this research provides significant insights of the key traits that contribute to higher yield in bread wheat and to unveil the genetic architecture using Genome Wide Association Studies. The study identified 39 marker-trait associations (MTAs) to enhance our understanding of yield-related traits, offering targets for molecular breeding to improve wheat productivity and resilience. The study identified key traits for higher wheat yield, including increased plant biomass, heavier spikes, more grains per spike, a higher number of tillers, longer peduncles, and longer spikes. These traits enhance grain production and overall yield potential.39 MTAs has been identified for the traits like Plant Height (PH), Peduncle Length (PL), Tillers, Flag Leaf Length (FLL), Spike Length (SL), Grains per spike (GPS), Biomass, and Grain Yield per Plant (GYPP). Principal component analysis (PCA) helped in grouping of traits based on their genetic relationships, providing insights into how different traits are interconnected. Correlation, step wise regression modelling and structural equation modelling further clarified the relationships between these traits. So, to achieve higher yields, breeders must emphasis on increasing biomass, spike weight, grains per spike, tillers, peduncle length, and spike length. Understanding the genetic markers related with these traits can help breeders to select and grow wheat varieties with higher yield potential. Future research should validate marker-trait associations across diverse environments, explore genotype-by-environment interactions, and integrate genomic selection with traditional breeding to develop resilient wheat varieties. Additionally, studying the biological mechanisms of key genetic regions will improve understanding of trait development and regulation. Abbreviations AHS Agglomerative Hierarchical Clustering BLUP Best Linear Unbiased Prediction DArT Diversity Array Technology FLL Flag Leaf Length FLW Flag Leaf Width GBS Genotyping By Sequencing GCV Genotypic Coefficient of Variation GDP Gross Domestic Product GWAS Genome Wide Association Study GPS Grain Per Spike GYPP Grain Yield Per Plant KASP Kompetitive Allele-Specific PCR KPS Kernels Per Spike KNPS Kernel Number Per Spike MAS Marker Assisted Selection MAF Minor Allele Frequency MLM Mixed Linear Model MTA Marker Trait Association NGS Next Generation Screening PCA Principal Component Analysis PCV Phenotypic Coefficient of Variation PES Percentage of Effective Spike PH Plant Height PL Peduncle Length PM Plant Morphology QTL Quantitative Trait Locus RCBD Randomized Complete Block Design RIL Recombinant Inbred Lines SEM Structure Equation Model SL Spike Length SNP Single Nucleotide Polymorphisms SSR Simple Sequence Repeats SW Spike Weight SLPS Spikelets Per Spike TKW Thousand Kernal Weight YCT Yield Component Trait Declarations Author contributions Conceptualization of the research Muhammad Jamil; design of the experiment Muhammad Jamil and Waseem Ahmad; contribution of the experimental material Muhammad Jamil, Waseem Ahmad and Areeba Fatima; execution of the field experiment and data collection Waseem Ahmad and Areeba Fatima and Sana Rasheed; analysis of the data and interpretation Muhammad Jamil; preparation of the manuscript Muhammad Jamil and Waseem Ahmad. All authors read and approved the final manuscript. Acknowledgement The authors acknowledge Awais Rasheed, department of plant sciences, Quaid-i-Azam University, Islamabad on providing germplasm and genotyping facility for this research work. Funding The authors didn’t receive any funding from government or funding agencies. Data availability All the datasets are available and can be provided by corresponding author upon request. Conflict of interest The authors declare no conflicts of interest. References Ahmad, W., Bibi, N., Sanwal, M., Ahmed, R., Jamil, M., Kalsoom, R., Arif, M., & Fahad, S. (2024). Cereal Crops in the Era of Climate Change: An Overview. In S. Fahad, S. Saud, T. Nawaz, L. Gu, M. Ahmad, & R. Zhou (Eds.), Environment, Climate, Plant and Vegetation Growth (pp. 609-630). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-69417-2_21 Ain, Q.-u., Rasheed, A., Anwar, A., Mahmood, T., Imtiaz, M., Mahmood, T., Xia, X., He, Z., & Quraishi, U. M. (2015). Genome-wide association for grain yield under rainfed conditions in historical wheat cultivars from Pakistan. Frontiers in Plant Science , 6 , 743. Alemu, A., Suliman, S., Hagras, A., Thabet, S., Al-Abdallat, A., Abdelmula, A. A., & Tadesse, W. (2021). Multi-model genome-wide association and genomic prediction analysis of 16 agronomic, physiological and quality related traits in ICARDA spring wheat. Euphytica , 217 , 1-22. An, Q., Li, C., Li, H., Zheng, Q., Li, B., & Li, Z. (2022). An analysis of the genetic relation between photosynthesis and yield-related traits in Wheat. Agriculture , 12 (4), 560. Chidzanga, C., Mullan, D., Roy, S., Baumann, U., & Garcia, M. (2022). Nested association mapping-based GWAS for grain yield and related traits in wheat grown under diverse Australian environments. Theoretical and Applied Genetics , 135 (12), 4437-4456. Eltaher, S., Baenziger, P. S., Belamkar, V., Emara, H. A., Nower, A. A., Salem, K. F., Alqudah, A. M., & Sallam, A. (2021). GWAS revealed effect of genotype× environment interactions for grain yield of Nebraska winter wheat. BMC genomics , 22 , 1-14. Gao, F., Wen, W., Liu, J., Rasheed, A., Yin, G., Xia, X., Wu, X., & He, Z. (2015). Genome-wide linkage mapping of QTL for yield components, plant height and yield-related physiological traits in the Chinese wheat cross Zhou 8425B/Chinese Spring. Frontiers in Plant Science , 6 , 1099. Godoy, J., Gizaw, S., Chao, S., Blake, N., Carter, A., Cuthbert, R., Dubcovsky, J., Hucl, P., Kephart, K., & Pozniak, C. (2018). Genome‐wide association study of agronomic traits in a spring‐planted North American elite hard red spring wheat panel. Crop Science , 58 (5), 1838-1852. Isham, K., Wang, R., Zhao, W., Wheeler, J., Klassen, N., Akhunov, E., & Chen, J. (2021). QTL mapping for grain yield and three yield components in a population derived from two high-yielding spring wheat cultivars. Theoretical and Applied Genetics , 134 , 2079-2095. Jamil, M., Ahmad, W., Sanwal, M., & Maqsood, M. F. (2025). Gene editing and GWAS for digital imaging analysis of wheat grain weight, size and shape are inevitable to enhance the yield. Cereal Research Communications , 1-20. Jamil, M., Ali, A., Gul, A., Ghafoor, A., Napar, A. A., Ibrahim, A. M., Naveed, N. H., Yasin, N. A., & Mujeeb-Kazi, A. (2019). Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat. BMC plant biology , 19 , 1-18. Jia, Y., Zhang, Y., Sun, Y., Ma, C., Bai, Y., Zhang, H., Hou, J., Wang, Y., Ji, W., & Bai, H. (2024). QTL Mapping of Yield-Related Traits in Tetraploid Wheat Based on Wheat55K SNP Array. Plants , 13 (10), 1285. Jin, J., Liu, D., Qi, Y., Ma, J., & Zhen, W. (2020). Major QTL for seven yield-related traits in common wheat (Triticum aestivum L.). Frontiers in Genetics , 11 , 1012. Kang, C.-S., Mo, Y.-J., Kim, K.-M., Kim, K.-H., Chun, J.-B., Park, C.-S., & Cho, S.-W. (2020). Mapping quantitative trait loci for yield potential traits in wheat recombinant inbred lines. Agronomy , 11 (1), 22. Khan, Kainat, Z., Maqbool, S., Mehwish, A., Ahmad, S., Suleman, H. M., Mahmood, Z., Ali, M., Aziz, A., & Rasheed, A. (2022). Genome-wide association for heat tolerance at seedling stage in historical spring wheat cultivars. Frontiers in Plant Science , 13 , 972481. Khan, H., Krishnappa, G., Kumar, S., Mishra, C. N., Krishna, H., Devate, N. B., Rathan, N. D., Parkash, O., Yadav, S. S., & Srivastava, P. (2022). Genome-wide association study for grain yield and component traits in bread wheat (Triticum aestivum L.). Frontiers in genetics , 13 , 982589. Knežević, D., Radosavac, A., & Zelenika, M. (2015). Variability of grain weight per spike in wheat grown in different ecological conditions. Acta Agriculturae Serbica , 20 (39), 85-95. Kumar, R., Das, S. P., Choudhury, B. U., Kumar, A., Prakash, N. R., Verma, R., Chakraborti, M., Devi, A. G., Bhattacharjee, B., & Das, R. (2024). Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing. Biological Research , 57 (1), 80. Kumar, S., Singh, P. N., Gaur, S. C., Quatadah, S. M., Singh, K., Gautam, U., Singh, S. K., Yadav, M., Gaur, A. K., & Kumar, K. (2025). Assessment of Genetic Variability, Correlation and Path Analysis in Bread Wheat (Triticum aestivum L.) Genotypes for Yield and its Attributing Traits. Journal of Advances in Biology & Biotechnology , 28 (1), 612-620. Li, F., Wen, W., He, Z., Liu, J., Jin, H., Cao, S., Geng, H., Yan, J., Zhang, P., & Wan, Y. (2018). Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers. Theoretical and Applied Genetics , 131 , 1903-1924. Li, F., Wen, W., Liu, J., Zhang, Y., Cao, S., He, Z., Rasheed, A., Jin, H., Zhang, C., & Yan, J. (2019). Genetic architecture of grain yield in bread wheat based on genome-wide association studies. BMC plant biology , 19 , 1-19. Li, T., Li, Q., Wang, J., Yang, Z., Tang, Y., Su, Y., Zhang, J., Qiu, X., Pu, X., & Pan, Z. (2022). High-resolution detection of quantitative trait loci for seven important yield-related traits in wheat (Triticum aestivum L.) using a high-density SLAF-seq genetic map. BMC Genomic Data , 23 (1), 37. Li, X., Xu, X., Liu, W., Li, X., Yang, X., Ru, Z., & Li, L. (2020). Dissection of superior alleles for yield-related traits and their distribution in important cultivars of wheat by association mapping. Frontiers in plant science , 11 , 175. Liu, H., Shi, Z., Ma, F., Xu, Y., Han, G., Zhang, J., Liu, D., & An, D. (2022). Identification and validation of plant height, spike length and spike compactness loci in common wheat (Triticum aestivum L.). BMC plant biology , 22 (1), 568. Liu, J., Xu, Z., Fan, X., Zhou, Q., Cao, J., Wang, F., Ji, G., Yang, L., Feng, B., & Wang, T. (2018). A genome-wide association study of wheat spike related traits in China. Frontiers in Plant Science , 9 , 1584. Luo, X., Yang, Y., Lin, X., & Xiao, J. (2023). Deciphering spike architecture formation towards yield improvement in wheat. Journal of genetics and genomics , 50 (11), 835-845. Ma, F., Xu, Y., Wang, R., Tong, Y., Zhang, A., Liu, D., & An, D. (2023). Identification of major QTLs for yield-related traits with improved genetic map in wheat. Frontiers in plant science , 14 , 1138696. Mehvish, A., Aziz, A., Bukhari, B., Qayyum, H., Mahmood, Z., Baber, M., Sajjad, M., Pang, X., & Wang, F. (2023). Identification of Single-Nucleotide Polymorphisms (SNPs) Associated with Heat Tolerance at the Reproductive Stage in Synthetic Hexaploid Wheats Using GWAS. Plants , 12 (8), 1610. Russell, B., Brown-Guedira, G., Sneller, C. H., & Mohammadi, M. (2020). Transferability of marker trait associations in wheat is disturbed mainly by genotype× year interaction. Crop Breeding, Genetics and Genomics , 4 (3). Saini, & Kumar, S. (2024). Applying genomics resources to accelerate the development of climate resilient crops. In Adapting to Climate Change in Agriculture-Theories and Practices: Approaches for Adapting to Climate Change in Agriculture in India (pp. 43-120). Springer. Sheoran, S., Jaiswal, S., Raghav, N., Sharma, R., Sabhyata, Gaur, A., Jaisri, J., Tandon, G., Singh, S., & Sharma, P. (2022). Genome-wide association study and post-genome-wide association study analysis for spike fertility and yield related traits in bread wheat. Frontiers in Plant Science , 12 , 820761. Sun, C., Zhang, F., Yan, X., Zhang, X., Dong, Z., Cui, D., & Chen, F. (2017). Genome‐wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China. Plant biotechnology journal , 15 (8), 953-969. Team, R. C. (2020). RA language and environment for statistical computing, R Foundation for Statistical. Computing . Tesfaye, K. (2021). Climate change in the hottest wheat regions. Nature Food , 2 (1), 8-9. https://doi.org/10.1038/s43016-020-00218-0 Turuspekov, Y., Baibulatova, A., Yermekbayev, K., Tokhetova, L., Chudinov, V., Sereda, G., Ganal, M., Griffiths, S., & Abugalieva, S. (2017). GWAS for plant growth stages and yield components in spring wheat (Triticum aestivum L.) harvested in three regions of Kazakhstan. BMC plant biology , 17 , 1-11. Tyrka, M., Krajewski, P., Bednarek, P. T., Rączka, K., Drzazga, T., Matysik, P., Martofel, R., Woźna-Pawlak, U., Jasińska, D., & Niewińska, M. (2023). Genome-wide association mapping in elite winter wheat breeding for yield improvement. Journal of Applied Genetics , 64 (3), 377-391. Ullah, M. I., Mahpara, S., Bibi, R., Shah, R. U., Ullah, R., Abbas, S., Ullah, M. I., Hassan, A. M., El-Shehawi, A. M., & Brestic, M. (2021). Grain yield and correlated traits of bread wheat lines: Implications for yield improvement. Saudi Journal of Biological Sciences , 28 (10), 5714-5719. Wang, J., & Zhang, Z. (2021). GAPIT version 3: boosting power and accuracy for genomic association and prediction. Genomics, proteomics & bioinformatics , 19 (4), 629-640. Wang, S.-X., Zhu, Y.-L., Zhang, D.-X., Shao, H., Liu, P., Hu, J.-B., Zhang, H., Zhang, H.-P., Chang, C., & Lu, J. (2017). Genome-wide association study for grain yield and related traits in elite wheat varieties and advanced lines using SNP markers. PloS one , 12 (11), e0188662. Würschum, T., Leiser, W. L., Langer, S. M., Tucker, M. R., & Longin, C. F. H. (2018). Phenotypic and genetic analysis of spike and kernel characteristics in wheat reveals long-term genetic trends of grain yield components. Theoretical and Applied Genetics , 131 , 2071-2084. Xu, H., Wang, Z., Wang, F., Hu, X., Ma, C., Jiang, H., Xie, C., Gao, Y., Ding, G., & Zhao, C. (2024). Genome-wide association study and genomic selection of spike-related traits in bread wheat. Theoretical and Applied Genetics , 137 (6), 131. Zanke, C. D., Ling, J., Plieske, J., Kollers, S., Ebmeyer, E., Korzun, V., Argillier, O., Stiewe, G., Hinze, M., & Neumann, K. (2014). Whole genome association mapping of plant height in winter wheat (Triticum aestivum L.). PLoS One , 9 (11), e113287. Zhang, H., Chen, J., Li, R., Deng, Z., Zhang, K., Liu, B., & Tian, J. (2016). Conditional QTL mapping of three yield components in common wheat (Triticum aestivum L.). The Crop Journal , 4 (3), 220-228. Zhao, J., Sun, L., Gao, H., Hu, M., Mu, L., Cheng, X., Wang, J., Zhao, Y., Li, Q., & Wang, P. (2023). Genome-wide association study of yield-related traits in common wheat (Triticum aestivum L.) under normal and drought treatment conditions. Frontiers in Plant Science , 13 , 1098560. 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-6466875","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452443775,"identity":"6b2cc35e-e73e-4f62-8f1f-5da2db29f95f","order_by":0,"name":"Muhammad Jamil","email":"","orcid":"https://orcid.org/0000-0002-5416-5471","institution":"The Islamia University of Bahawalpur Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Jamil","suffix":""},{"id":452443776,"identity":"a9b085c6-7e92-445b-910a-a37e78df61b2","order_by":1,"name":"Waseem Ahmad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYHACNiBmlmOTf3wAyJCQIVqLMR9DWgJICw/RWhLnMeQYgHiEtRgcP/zs0Y0aa2M2hjOfX92oseBhYD98dANeLWfSzI1zjqXLsTH2brPOOQZ0GE9a2g18WswOJJhJ57AdNmZj5t1mnMMG1CLBY4Zfy/nn36Rz/h1ObGPjeWac848YLTdyzKRz24BaeHiYH+e2EaHF/sabMuncvnRjNgk2M+bcPgkeNkJ+kexP3yad881aTn4G8+PPOd/q5PjZDx/DqwUZsEmASWKVgwDzB1JUj4JRMApGwcgBAA2sQ3MTNoOwAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0008-7796-5501","institution":"Avignon Universite","correspondingAuthor":true,"prefix":"","firstName":"Waseem","middleName":"","lastName":"Ahmad","suffix":""},{"id":452443777,"identity":"04b6578f-5137-4ef6-8d2b-18be54a97d42","order_by":2,"name":"Areeba Fatima","email":"","orcid":"","institution":"The Islamia University of Bahawalpur Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Areeba","middleName":"","lastName":"Fatima","suffix":""},{"id":452443778,"identity":"3c20f2d2-3dfc-4773-aa49-533b6befd3c2","order_by":3,"name":"Sana Rasheed","email":"","orcid":"","institution":"The Islamia University of Bahawalpur Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Sana","middleName":"","lastName":"Rasheed","suffix":""}],"badges":[],"createdAt":"2025-04-17 01:00:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6466875/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6466875/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82345381,"identity":"a91c78a3-5032-45f2-9010-a65363695244","added_by":"auto","created_at":"2025-05-09 09:58:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127371,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelogram of 11 Traits of 150 Genotypes Showing Pearson’s Correlation Coefficient (r) which is significant (P\u0026lt;.05) above 0.16 and below -0.16. Traits have been ordered according to Agglomerative Hierarchical Clustering (AHC) and classified into four groups.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6466875/v1/1b063be4989234254beac439.png"},{"id":82345389,"identity":"1b1a64cb-e6ba-4b30-b7bd-df7f96a0b755","added_by":"auto","created_at":"2025-05-09 09:58:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":441919,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) Of 11 Traits Studied in 150 Wheat Genotypes Shows Eigenvalue Analysis (\u003cstrong\u003ea\u003c/strong\u003e), Percentage Contribution (\u003cstrong\u003eb\u003c/strong\u003e) of each Trait to the First Four Principal Components (PCs), Score Plot (\u003cstrong\u003ec\u003c/strong\u003e) Displaying Two-Dimensional Spread of 150 Wheat Genotypes, and the Loading Plot (\u003cstrong\u003ed\u003c/strong\u003e) of Traits as Vector Lengths Classified into Four Colors Representing their Respective PCs. Eigenvalue and Variation Percentage Explained by PC1 and PC2 are also Given Along the x and y-Axis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6466875/v1/9234b49b3c10d166f8f24f20.png"},{"id":82346280,"identity":"846e2ee0-086a-4180-92a6-a6ecf409b131","added_by":"auto","created_at":"2025-05-09 10:14:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":157720,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the whole standardized data unraveling the k-mean based clustering of traits and genotypes\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6466875/v1/ddb3e71a8f9404fef2979970.png"},{"id":82345383,"identity":"66a13230-6536-4ea2-82c8-74754ae587ab","added_by":"auto","created_at":"2025-05-09 09:58:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51899,"visible":true,"origin":"","legend":"\u003cp\u003ePareto Chart of Potential Predictors Identified Through Stepwise Regression Model\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6466875/v1/cce89bcd1eea1f487a2cf640.png"},{"id":82345382,"identity":"9dba559c-f8ae-42cd-9068-ef86a2109c14","added_by":"auto","created_at":"2025-05-09 09:58:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":651103,"visible":true,"origin":"","legend":"\u003cp\u003ePath Diagram Generated After Structure Equation Model Showing Direct and Indirect Effects of Traits Contributing to Biomass, Yield and Spike Weight. \u003cstrong\u003ePH\u003c/strong\u003e: Plant Height; \u003cstrong\u003ePL\u003c/strong\u003e: Peduncle Length; \u003cstrong\u003eGPS\u003c/strong\u003e: Grains Per Spike; \u003cstrong\u003eSL\u003c/strong\u003e: Spike Length; \u003cstrong\u003eGYPP\u003c/strong\u003e: Grain Yield Per Plant; \u003cstrong\u003eSW\u003c/strong\u003e: Spike Weight; \u003cstrong\u003eSLPS\u003c/strong\u003e: Spikelets Per Spike\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6466875/v1/523c6edb3ef3988923959079.png"},{"id":88333933,"identity":"2c6d6476-e750-434d-b3a4-ff513fda33c0","added_by":"auto","created_at":"2025-08-05 11:37:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2514147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6466875/v1/586a7dea-2ce6-4851-af31-a75a07be3c1f.pdf"}],"financialInterests":"","formattedTitle":"Unveiling the genetic Landscape of agronomic traits in bread wheat through Genome wide association Studies","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBread wheat \u003cem\u003e(Triticum aestivum L.)\u003c/em\u003e is one of the largest cultivated food crops holding a strategic role in providing essential nutrients to global human population. It is cultivated in an area of approximately 200\u0026nbsp;million hectares and fulfils one fifth of the calorie needs of the global population (Tesfaye, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To meet the food demand of projected more than 9\u0026nbsp;billion population of the globe in 2050, the annual increase in wheat production needs to be increased by its current level of 1\u0026ndash;1.6% (H. Khan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Challenges like climate change, population increase, desertification, evolving pest and disease pressure have negatively correlated to the grain yield of many regions in the past few decades (Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conventional breeding techniques have contributed to wheat improvement, but progress has been slow due to the complex genetic nature of these traits, which are controlled by multiple genes and influenced by environmental conditions. Breeders and Geneticist are mainly concerned with the parameters like the grain yield and grain quality in wheat. The low heritability and complex genetic landscape of wheat has made yield improvement a challenging objective of wheat breeding program (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Increasing wheat yield and improving key agronomic traits, such as plant height, grain yield, spike weight, and flag leaf morphology, remain essential for sustainable wheat production.\u003c/p\u003e \u003cp\u003eGiven the global challenges of food security and climate change, optimizing wheat yield and adaptability is more urgent than ever. It is also important to understand the relationship between agronomic traits which ultimately affects grain yield. Grain yield is a complex polygenic trait result of the combination of many physiological and morphological traits and highly influenced by environmental changes (Chidzanga et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Grain yield is highly influenced by spike number per unit area (SN), kernel number per spike (KNS) and thousand-kernel weight (TKW) (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, grain yield depends upon many other factors like grain shape, spike architecture, plant height (PH), and flag leaf related traits. But these traits impact the grain yield by inducing changes in the grain filling, photosynthetic efficiency, days to heading and translocation of dry matter (Jamil et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Many of the yield component parameters compared to GY are more easily selectable and have significant heritability(h\u003csup\u003e2\u003c/sup\u003e), especially in the early phases of breeding cycles (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The selection of these yield components shuns the yield plateaus and provide new opportunities to enhance the genetic gains. The complex and quantitative nature of grain yield and component traits have multiple gene inheritance and affected by genotype x environment interaction. Researchers believes that the potential strategy to improve wheat yield can be achieved by unveiling the genetic landscape of wheat and effective application of molecular breeding technique like marker assisted selection.\u003c/p\u003e \u003cp\u003eWith the advancement of genomic tools, many techniques and approaches have been adopted to fully promote sustainable production to cope up with the issue of food insecurity. Among the approaches, genome-wide association studies (GWAS) have emerged as a powerful approach to dissect the genetic basis of complex agronomic traits (Jamil et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Identification of genetic loci associated with key agronomic traits is critical for developing high-yielding and stress-resilient wheat varieties. GWAS provides a high-resolution approach to uncover genetic variants linked to desirable traits, allowing breeders to integrate favorable alleles into breeding programs efficiently (Kumar et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By mapping trait-associated loci, researchers can develop molecular markers for early selection, reducing breeding cycles and increasing genetic gain.\u003c/p\u003e \u003cp\u003eIn a last decade, researchers have speedup their efforts to decipher the genetic basis of agronomic traits in bread wheat. Quantitative trait loci (QTL) mapping based on bi parental population has been extensively applied to identify QTLs related to yield components related traits due to low-cost (Gao et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Isham et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jia et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).The drawbacks of using QTL mapping are low markers density and low resolutions of one or more cross overs. The reduction in the cost of High throughput genotyping has led to emerge genome wide association studies (GWAS) as a powerful and robust genotyping approach to elucidate the genetic basis of grain yield and components traits in wheat (Jamil et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A huge shift in studies have been reported over the last few years in which QTLs controlling grain yield related traits has been identified (Chidzanga et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Eltaher et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; H. Khan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mehvish et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Turuspekov et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tyrka et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Th main reason behind the success of GWAS is the use of high-density markers SNP genotyping platform like Affymetrix and Illumina which are offering rich resource high-throughput genotyping data for studying the diverse panels of wheat (Saini \u0026amp; Kumar, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile previous studies have identified several genomic regions associated with key agronomic traits in bread wheat, gaps remain in understanding their stability across diverse genetic backgrounds and environments. Despite various GWAS efforts in bread wheat, many underlying loci associated with important agronomic traits remain unidentified or poorly characterized. Many identified loci have not been validated in multiple populations, and their functional roles are still unknown. Moreover, the complex interaction between these QTLs, pleotropic effect on other agronomic traits and epistatic effect makes it difficult to unveil the genetic landscape of grain yield in bread wheat. Consequently, there is a need of continued research to advance our understanding of the genetic factors affecting grain yield and to apply these findings in developing novel breeding strategies. A deeper understanding of the genetic architecture of these traits can enhance marker-assisted breeding and accelerate wheat improvement programs.\u003c/p\u003e \u003cp\u003eThis research provides a comprehensive understanding of the genomic landscape of agronomic traits in bread wheat by conducting a large-scale GWAS using diverse genetic material. By analyzing a broad panel of wheat genotypes, this study will identify stable and novel SNPs associated with plant height, grain yield, spike morphology, and flag leaf characteristics. Pakistan has developed a number of new wheat varieties, but more high-yielding varieties are still needed. Wheat production has increased by 35 to 50 percent after the introduction of newly engineered genotypes (Ullah et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study is designed to use GWAS to describe genomic regions associated with wheat yield and its contributing traits. Moreover, clustering of wheat germplasm by genotypic analysis was also performed. The identified SNP markers will be evaluated for their potential use in marker-assisted selection, facilitating their application in wheat breeding programs. By addressing these knowledge gaps, this research will contribute to the development of improved wheat varieties with enhanced yield potential and optimized agronomic traits.\u003c/p\u003e \u003cp\u003eThe objectives of our study are: 1) to study phenotypic evaluation of bread wheat varieties: 2) to determine the yield contributor of wheat: and 3) To identify the genomic regions contributing to the yield and major agronomic traits.\u003c/p\u003e"},{"header":"2. Material and Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant material and field trails\u003c/h2\u003e \u003cp\u003eThe experiment used diverse panel of 150 wheat genotypes taken from the department of plant sciences, Quaid-e-Azam University, Islamabad, Pakistan. These wheat lines with broad genetic background were grown in the field area of the Islamia University of Bahawalpur during the cropping season Dec to April. The field trails were conducted for two years, 2022-23 and 2023-24 in randomized complete block design with three replicates. To account for environmental variation across years, we calculated Best Linear Unbiased Estimates (BLUEs) for each genotype using data from both years. In a set of 150 genotypes, each was planted one foot apart from other in a single block and there were three replicates representing three blocks. Standard agronomic practice was adopted according to the local practices of the region. The soil consisted of clay, loam, and sand. Genotypes were planted in one-meter beds, with irrigation, weed control, and crop protection measures applied as needed. Urea granules were applied during tillering, heading, and grain filling, while nets were used at the booting stage to prevent lodging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Phenotyping\u003c/h2\u003e \u003cp\u003eEleven phenotypic traits, grain yield per plant (GYPP), spike weight (SW), spikelet per spike (SLPS), grains per spike (GPS), spike length (SL), plant height (PH), tillers, peduncle length (PL), flag leaf length (FLL), flag leaf width (FLW) and biomass were assessed in this diverse panel. All plants were harvested in each plot at the physiological maturity. Grain yield per plant (GYPP) was recorded at harvest by weighing the total grain yield per plant after drying. Spike weight (SW) was determined by drying 10 spikes per plot at 75\u0026deg;C for 48 hours and then taking their average weight. The number of spikelets per spike (SLPS) was counted from 10 randomly selected spikes per plot, while grains per spike (GPS) were counted after threshing the same sample. Spike length (SL) was measured from the base to the tip, excluding awns, in 10 spikes per plot. Plant height (PH) was recorded as the distance from the soil surface to the tip of the spike (excluding awns). Tillers per plant were counted considering only productive tillers. Peduncle length (PL), representing the uppermost internode length, was also measured. Flag leaf length (FLL) was measured from the base to the tip in 5 flag leaves per plant at the mid-grain filling stage, while flag leaf width (FLW) was measured at its widest point using a caliper.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted to examine the relationships between grain yield and other agronomic traits in bread wheat. Descriptive analysis, ANOVA, correlation analysis and heritability estimates were conducted in the R statistical package (Team, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Pearson\u0026rsquo;s correlation coefficients (\u0026#119903;) were calculated to determine the associations among traits, with traits grouped using agglomerative hierarchical clustering (AHC). Heatmap of the whole standardized data was used in unraveling the k-mean based clustering of traits (PH, PL, tillers, biomass, SW, GYPP, GPS, SL, SLPS, FLW and FLL) and genotypes. Principal Component Analysis (PCA) was performed to evaluate trait contributions to variation across 150 wheat genotypes, including eigenvalue analysis, variance percentage, score plots, and loading plots. Stepwise multiple regression analysis was used to identify the optimal combination of traits contributing to grain yield. Structural Equation Modeling (SEM) was used to assess direct and indirect effects of traits on yield, biomass, and spike weight. These comprehensive statistical approaches provided insights into the genetic architecture and key determinants of yield in bread wheat.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Genotyping and SNP filtering\u003c/h2\u003e \u003cp\u003eThe extraction of DNA from the leaves of young seedlings was taken by DNA extraction kit from Prima Scientific (Bangkok, Thailand). The genotyping of wheat panel was performed using Illumina Infinium Wheat 37K SNP Array as done by (Khan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A total no of 37,401 Single Nucleotide Polymorphism (SNP) markers were initially analyzed, covering all 21 chromosomes of the wheat genome. A pipeline of TASSEL 5 software was used for SNP calling against the whole wheat (CS) genome assembly (WGA 0.4, International Wheat Genome Sequencing Consortium). The monomorphic SNPs with minor allele frequency (MAF) of \u0026lt;\u0026thinsp;5% missing values of \u0026gt;\u0026thinsp;10% and heterozygote frequency\u0026thinsp;\u0026gt;\u0026thinsp;20% were removed from analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Genome-Wide Association Study (GWAS)\u003c/h2\u003e \u003cp\u003eGenome-wide association study (GWAS) was conducted by means of a mixed linear model (MLM) to identify genomic regions related with eleven phenotypic traits in 150 bread wheat genotypes. The GWAS was performed using the average BLUEs derived from two years of field data to minimize the influence of year-specific environmental effects. 37,401 single nucleotide polymorphisms (SNPs) were used for analysis with a minor allele frequency (MAF) greater than 0.05. The phenotypic values of eleven agronomic traits of bread wheat of 150 diverse genotypes along with corresponding genotyping data were used in GWAS analysis. Significant no of MTAs were identified using the BLINK (Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway) model implemented in Genome Association and Prediction Integrated Tool (GAPIT) version 3.0 in the R software package (Wang \u0026amp; Zhang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Determining the correct p-value threshold for statistical significance is critical to differentiate the true positives from false positives. To determine the statistical significance threshold in GWAS, Bonferroni correction has been employed. To estimate Bonferroni correction, α was set to 0.05 which is divided by the total number of SNPs. The Bonferroni-corrected SNPs were considered for significant association and R2 was used to describe the percentage variation explained (PVE) by significant MTAs.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Descriptive statistics of eleven agronomic traits\u003c/h2\u003e\n \u003cp\u003eThe descriptive statistical analysis of eleven agronomic traits in 150 wheat genotypes revealed substantial variation, indicating significant genetic diversity within the population (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Plant height ranged from 55 cm to 112 cm, with a mean value of 81.46 cm. More than 50% of the genotypes exhibited heights between 75 and 90 cm, suggesting a balanced distribution of medium-statured plants. Peduncle length varied from 0 to 19 cm, with an average of 8.92 cm, and the majority of genotypes fell within the 6 to 11 cm range. The number of tillers per plant exhibited wide variation, ranging from 2 to 25, with a mean of 15.05, indicating genotypic differences in tillering ability, a key yield component.\u003c/p\u003e\n \u003cp\u003eFlag leaf length and width, crucial for photosynthetic efficiency, showed ranges of 13\u0026ndash;33 cm and 1\u0026ndash;2.3 cm, with mean values of 24 cm and 1.58 cm, respectively. Spike-related traits, such as spike length and spikelets per spike, varied from 7 to 15 cm and 15 to 25, with means of 11.13 cm and 19.61, respectively. Significant variation in spike weight (4\u0026ndash;100, mean 37.89) and grains per spike (12.50\u0026ndash;65.45, mean 38.37) suggests diverse genetic potential for grain production. Biomass ranged from 9 to 157, averaging 63.51, reflecting the capacity for dry matter accumulation. Grain yield per plant, the most crucial economic trait, exhibited a broad range (4\u0026ndash;61) with an average of 24.24, underscoring the potential for selecting high-yielding genotypes. These findings highlight the genetic variability among the studied wheat genotypes, providing insights for breeding programs focused on improving yield and agronomic performance.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of the studied traits of 150 wheat genotypes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\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\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin-Max\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1(25%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3(75%)\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\u003ePlant Height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.46\u0026thinsp;\u0026plusmn;\u0026thinsp;12.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeduncle Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTillers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.05\u0026thinsp;\u0026plusmn;\u0026thinsp;5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlag Leaf Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026ndash;33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlag Leaf width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpike Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026ndash;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpikelet per Spike\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpike Weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.89\u0026thinsp;\u0026plusmn;\u0026thinsp;14.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrains per spike\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.50-65.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.37\u0026thinsp;\u0026plusmn;\u0026thinsp;11.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiomass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9-157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.507\u0026thinsp;\u0026plusmn;\u0026thinsp;25.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrain yield per plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGYPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026ndash;61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.24\u0026thinsp;\u0026plusmn;\u0026thinsp;10.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Pearson\u0026rsquo;s correlation coefficient analysis\u003c/h2\u003e\n \u003cp\u003ePearson correlation analysis among the 11 agronomic traits in bread wheat revealed varying degrees of association, with correlation coefficients ranging from \u0026minus;\u0026thinsp;0.03 to 0.89. Many trait associations exhibited moderate to strong correlations, while a few showed weak or negligible relationships. Eleven variables have been classified to Agglomerative Hierarchical Clustering (AHC) in to four clusters \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. First cluster have two variables that are plant height and peduncle length. Second cluster have four variables that are tillers, grain yield per plant, spike weight and biomass. Third cluster have two variables that are flag leaf length and flag leaf width. Fourth cluster have three variables that are \u0026ldquo;grains per spike, spike length and spikelets per spike\u0026rdquo;. There are 55 pair-wise correlations among 11 traits. In first cluster, Plant height is in significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) positive correlation with peduncle length (0.61), GYPP (0.18), SW (0.19) and biomass (0.38). Peduncle length is in significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) positive correlation with tillers (0.22) and biomass (0.24). Higher the peduncle length, higher will be the tillers and biomass. Peduncle length is negatively correlated with GPS, FLW and SLPS. Tillers are in highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) positive correlation with GYPP (0.35), SW (0.36) and Biomass (0.4). Its means that if we increase tillers than yield and biomass should be increase. Tillers are negatively correlated with FLL and FLW.\u003c/p\u003e\n \u003cp\u003eIn the second cluster, grain yield per plant is in significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) positive correlation with SW (0.85), biomass (0.86), GPS (0.36) and SLPS (0.2). Grain yield per plant is negatively correlated with FLW. Spike weight is in significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) positive correlation with biomass (0.89), GPS (0.21) and SLPS (0.24). Spike weight is not negatively correlated with any variable or trait. Biomass is in significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) positive correlation with SLPS (0.2). Biomass is negatively correlated with FLW. Flag leaf length is in significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) positive correlation with FLW (0.2). Flag leaf length is negatively correlated with GPS. It means by increasing GPS, flag leaf length will decrease. Flag leaf width is in not significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) positive correlation with any other trait. It is negatively correlated with GPS. GPS, SL and SLPS have not significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) positive and negative correlation with any other trait as shown in (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Principal Component Analysis of Eleven Traits Across 150 Genotypes\u003c/h2\u003e\n \u003cp\u003ePrincipal Component Analysis (PCA) was performed to explore the patterns of variation among all the studied agronomic traits in bread wheat. PCA of 11 traits across 150 wheat genotypes identified four significant principal components (PCs) explaining 72% of the total variance (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). PC1 had the highest eigenvalue (3.27) and explained 30% of the variance, followed by PC2 (1.77, 16%), PC3 (1.44, 14%), and PC4 (1.23, 12%). These components collectively summarized the variation in key traits, with PC1 associated with grain yield per plant, biomass, tillers, and spike weight, while PC2 captured plant height, peduncle length, and grains per spike. PC3 explained spikelets per spike, flag leaf length, and flag leaf width, whereas PC4 mainly represented spike length.\u003c/p\u003e\n \u003cp\u003eTrait contributions (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb) varied across PCs, with plant height mainly explained by PC2 (27%) and PC4 (6%), while peduncle length was largely associated with PC2 (35%). Grain yield per plant showed the highest contribution in PC1 (25%), with additional minor influences from PC2, PC3, and PC4. The clustering analysis grouped genotypes into four clusters (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec) based on PCA scores. Cluster 1 (blue) contained 28 genotypes with high grain yield, biomass, tillers, and spike weight, while Cluster 2 (red) included 65 genotypes with dominant plant height and peduncle length. Cluster 3 (green) comprised 18 genotypes with high spikelets per spike, flag leaf length, and flag leaf width, whereas Cluster 4 (orange) contained 39 genotypes characterized by spike length. The loading plot (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed) further illustrated trait associations, confirming strong correlations among yield-related traits along PC1 and plant structural traits along PC2, highlighting the significance of these traits in explaining genetic variability among wheat genotypes.\u003c/p\u003e\n \u003cp\u003ePrincipal Component Analysis (PCA) Of 11 Traits Studied in 150 Wheat Genotypes Shows Eigenvalue Analysis (\u003cstrong\u003ea\u003c/strong\u003e), Percentage Contribution (\u003cstrong\u003eb\u003c/strong\u003e) of each Trait to the First Four Principal Components (PCs), Score Plot (\u003cstrong\u003ec\u003c/strong\u003e) Displaying Two-Dimensional Spread of 150 Wheat Genotypes, and the Loading Plot (\u003cstrong\u003ed\u003c/strong\u003e) of Traits as Vector Lengths Classified into Four Colors Representing their Respective PCs. Eigenvalue and Variation Percentage Explained by PC1 and PC2 are also Given Along the x and y-Axis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Agglomerative Hierarchical Clustering (AHC)\u003c/h2\u003e\n \u003cp\u003eThe heatmap was created by clustering of traits and genotypes (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Each line on the right side of map (y-axis) represents one genotype. Eleven traits are on the x-axis. A color key is on the right side. Higher the expression value of any particular trait the color would be blue as the expression would be low, so the color is orange. Blue lines in the map shows higher values and orange lines shows the lower values. These lines in the map actually shows the genotype behavior with respect to the traits. Cluster in the bottom shows that these genotypes have low expressions of the studied traits.\u003c/p\u003e\n \u003cp\u003eThe clustering dendrograms along both axes reflect the similarity patterns: Tillers, biomass, spike weight and grain yield grouped in single cluster is the indication of their close association. Peduncle length was higher in top cluster of genotypes and lower in the bottom. For tillers, genotypes that are located at upper side have high expression, then the genotypes in the center. Biomass have low expression genotypes at the bottom and highly expressed genotypes are present in the upper end and in center. Spike weight has highly expressed genotypes at the top. Grain yield per plant was lower in the bottom cluster than the others. Notably, traits like GYPP, SW, and GPS, which are central to yield, cluster together and may guide selection criteria in breeding programs.\u003c/p\u003e\n \u003cp\u003eFLL, FLW, SLPS, and SL, suggest potential correlation or shared genetic regulation, while genotypes forming distinct clusters indicate phenotypically similar groups. The application of k-means clustering, superimposed with hierarchical clustering, provides a refined stratification of data, allowing breeders and researchers to identify high-performing or genetically diverse genotypes based on multi-trait performance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Step-wise Multiple Regression Modeling\u003c/h2\u003e\n \u003cp\u003eUsing the data from 150 wheat genotypes the eleven traits in this study were subjected to a stepwise regression analysis to determine the significant variables contributing to the variation in GYPP (dependent variable) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe selection terms in stepwise regression used 0.15 alpha to enter and to remove.\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\text{G}\\text{Y}\\text{P}\\text{P}=\\:-2.03-0.067\\text{*}\\text{P}\\text{H}+0.16\\text{*}\\text{S}\\text{W}+0.22\\text{*}\\text{G}\\text{P}\\text{S}+0.27\\text{*}\\text{b}\\text{i}\\text{o}\\text{m}\\text{a}\\text{s}\\text{s}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere GYPP is grain yield (g) per plant, PH: plant height (cm), SW: spike weight (g), GPS: grains per spike. With this regression equation, grain yield per plant (GYPP) is the dependent variable predicted through step-wise selected potential independent variables PH, SW, GPS and biomass. It suggests that for each unit increase in plant height (PH), the grain yield per plant decreases by 0.067 units, indicating a negative association. Conversely, spike weight (SW) positively influences grain yield per plant, with each unit increase in spike weight resulting in a 0.16 unit increase in GYPP. Similarly, an increase in grains per spike (GPS) enhances the grain yield per plant by 0.22 units for each additional grain per spike. Biomass also plays a significant role, contributing to an increase of 0.27 units in grain yield per plant for each unit increase in biomass.\u003c/p\u003e\n \u003cp\u003eThe constant term (-2.03) represents the baseline grain yield per plant when all predictor variables are zero, setting a foundational reference point for the model. The Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the results of a multiple regression analysis, indicating that the model describes 83.91% of the change in the dependent variable, with an adjusted R-squared of 83.47%. The standard error of the regression is 4.18, and the model has a predicted R- squared of 82.14%, demonstrating strong predictive power. The Corrected Akaike Information Criterion (AICc) and Bayesian Information Criterion (BIC)\u0026rdquo; are 862.53 and 880, respectively, suggesting a good model fit.\u003c/p\u003e\n \u003cp\u003eThe regression coefficients for PH (-0.0664), SW (0.1564), GPS (0.2229), and biomass (0.2709) are all statistically significant, with PH, GPS, and biomass having p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This indicates that an increase in PH significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;.05) decreases GYPP, while increase in SW, GPS, and biomass increase GYPP. The Analysis of Variance (ANOVA) table demonstrated that the overall model is highly significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;.0001), and PH, SW, GPS, and biomass significantly contributing to the model. SW and biomass have the highest contributions, explaining 69.68% and 7.41% of the variation, respectively. The model\u0026apos;s fit is further confirmed by the high F-value (189.06) and low error contribution (16.09%), indicating that the predictors effectively explain the variation in the dependent variable. The Durbin-Watson statistic of 1.92 suggests no autocorrelation in the residuals, further validating the model\u0026apos;s reliability.\u003c/p\u003e\n \u003cp\u003eThe Pareto chart of potential predictors identified through the stepwise regression model (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) provides a clear visualization of the most influential factors affecting grain yield per plant (GYPP). The chart ranks predictor variables by their standardized effects, highlighting their relative importance. The y-axis lists predictors labeled from A to K, including PH (A), SW (H), GPS (J), and biomass (K). The x-axis represents the standardized effect size, with larger values indicating stronger influences on GYPP. A red dashed line at 1.447 marks the statistical significance threshold at the \u0026alpha;\u0026thinsp;=\u0026thinsp;0.15, with bars extending beyond this line indicating significant predictors.\u003c/p\u003e\n \u003cp\u003eBiomass (K) has the largest standardized effect at 8.17, making it the most influential predictor on GYPP. Grains per spike (GPS - J) follows, with a substantial effect of 6.9. Spike weight (SW - H) also significantly impacts GYPP with a standardized effect of 2.85. Lastly, the plant height (PH - A) shows a significant effect on GYPP with an effect size of 2.04. This Pareto chart underscores the importance of biomass, GPS, SW, and PH in predicting GYPP, with biomass and GPS being the most critical factors. The chart provides a clear guide for prioritizing these variables in further agricultural analysis and practice to optimize grain yield per plant.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModel Summary, Coefficients and Analysis of Variance of Stepwise Regression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR-sq\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR-sq(adj)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePRESS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR-sq(pred)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAICc\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBIC\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\u003e4.18348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2816.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e862.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE Coef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-7.72, 3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.1306, -0.0022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\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\u003e0.1564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0479, 0.2648)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.1590, 0.2868)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebiomass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.2053, 0.3364)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdj SS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eContribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdj MS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13235.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3308.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\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\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e832.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e832.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebiomass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1168.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1168.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2537.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eDurbin-Watson Statistic=1.92;\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003e3.6 Structure Equation Modeling (SEM)\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003cp\u003eOut of eleven variables, biomass is variable that has been predicted by peduncle length, tillers and plant height (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). So, peduncle length, tillers and plant height have direct impact on biomass and at the same time plant height and tillers have indirect impact on grain yield per plant. Biomass has through impact on grain yield per plant. Biomass was predicted by three variables (plant height, tillers and peduncle length). The effect of peduncle length on biomass was 0.38; it means, one-unit change in peduncle length, biomass would be changes 0.38 times. Biomass was significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001) predicted by tillers with effect of 1.61. It means, one-unit change in tillers, 1.61 times change in biomass would be observed. Tillers are correlated positively with biomass. It indicates that by increasing tillers, biomass would be increased. Biomass was also significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001) predicted by plant height with the effect of 0.75. It means, one-unit change in plant height, biomass would be changes 0.75 times.\u003c/p\u003e\n \u003cp\u003eArrow indicates the dependence of biomass on peduncle length, tillers and plant height. Plant height and tillers have indirect impact on grain yield per plant. Grain yield per plant was significantly (p-value .001) predicted by plant height and tillers (0.13, 0.28) respectively. So, they were positively associated with grain yield per plant. Grains per spike, spike length and spikelets per spike\u0026rdquo; have straight impact on Spike weight. Arrow indicates the dependence of spike weight on spike length, spikelets per spike and grains per spike. Spike weight was significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001) predicted by grains per spike with the effect of 0.24. It means, one-unit change in grains per spike, spike weight would be changes up to 0.24 times. Spike weight is positively correlated with grains per spike. If we increase grains per spike, spike weight would be increased. Spike weight was predicted by spike length and was 0.74. It means, one-unit change in spike length, spike weight would be changes 0.74 times.\u003c/p\u003e\n \u003cp\u003eSpike weight was predicted by spikelets per spike and was 0.71. It means, one unit change in spikelets per spike, spike weight would be changes 0.71 times. Spikelets per spike have direct impact on spike length. Arrow indicates the dependence of spikelets per spike, on spike length. Spikelets per spike was significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001) predicted by spike length and the effect was 0.33. It means, one unit change in spike length, spikelets per spike would be changes 0.33** times. Spikelets per spike and spike length are positively correlated. Grain yield per plant and spike weight are correlated with one another (77.81). It means if we want to maximize the yield, spike weight must be increased, which depends on spike length, spikelets per spike and grains per spike. For higher yield, biomass should be higher which depends on plant height, peduncle length and tillers\u0026rdquo;. With this path analysis the role of individual character is vital in defining the grain yield as well as spike weight was observed.\u003c/p\u003e\n \u003cp\u003ePath Diagram Generated After Structure Equation Model Showing Direct and Indirect Effects of Traits Contributing to Biomass, Yield and Spike Weight. PH: Plant Height; PL: Peduncle Length; GPS: Grains Per Spike; SL: Spike Length; GYPP: Grain Yield Per Plant; SW: Spike Weight; SLPS: Spikelets Per Spike\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Genome-wide Association Study (GWAS)\u003c/h2\u003e\n \u003cp\u003eA genome-wide association study (GWAS) was conducted using a mixed linear model (MLM) to identify genomic regions associated with eleven phenotypic traits in 150 bread wheat genotypes. The analysis used 37,401 single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) greater than 0.05. A total of 39 genomic regions also known as marker trait associations (MTAs) were identified (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The first column specifies the trait being analyzed, such as \u0026ldquo;Plant Height (PH), Peduncle Length (PL), Tillers, Flag Leaf Length (FLL), Spike Length (SL), Grains per spike (GPS), Biomass, and Grain Yield per Plant (GYPP)\u0026rdquo;. Second column lists the genetic marker associated with each trait, typically identified by its position on a specific chromosome. Third one is (Chr) Chromosome where the marker is located. Fourth one indicates p-value obtained from the statistical model. A lower p-value (e.g., 2.77E-04) indicates stronger evidence against the null hypothesis (no association). R2 is proportion of variance explained by the marker-trait association. Allele indicates the allele associated with the trait. For example, \u0026apos;T\u0026apos; or \u0026apos;C\u0026apos; are alleles, and \u0026apos;Minor\u0026apos; or \u0026apos;Major\u0026apos; denotes the effect allele. Effects positive values show an increase in the trait value associated with that allele, while negative values show a decrease. MAF is the frequency of the less common allele in the population being studied. For Plant Height (PH), marker 3B_513710969 on chromosome 3B has a significant association (p\u0026thinsp;=\u0026thinsp;2.77E-04) with a minor allele T having an effect size of 9.16, whereas the major allele C has an effect size of 18.52. A minor allele T might increase Plant Height by 9.16 units, while the major allele C increases it by 18.52 units, both alleles having a minor allele frequency (MAF) of 0.46.\u003c/p\u003e\n \u003cp\u003eThe Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e reveals significant associations between PH and several genetic markers located on chromosome 3B (markers 3B_513710969, 3B_513710985, 3B_513711040, 3B_513712090, 3B_513712139) and chromosome 4A (marker 4A_691119517, 4A_691119528, 4A_691403255). These markers demonstrate low p-values (2.77E-04 to 2.86E-04) and a consistent R2 value of 0.12. Allele effects vary across markers, with alleles like \u0026apos;T\u0026apos;, \u0026apos;C\u0026apos;, \u0026apos;G\u0026apos;, and \u0026apos;A\u0026apos; influencing PH positively. These findings are crucial for breeding programs aiming to optimize crop architecture for improved yield and adaptability, ensuring sustainable agricultural yield. The MLM analysis for peduncle length identifies several genetic markers on chromosomes 1A and 1B significantly associated with peduncle length variations. These markers exhibit low p-values (ranging from 1.91E-04 to 4.15E-04) and moderate R2 values (0.11 to 0.12). Allele effects vary; for instance, alleles like \u0026apos;G\u0026apos;, \u0026apos;T\u0026apos;, \u0026apos;C\u0026apos;, and \u0026apos;A\u0026apos; influence peduncle length positively, highlighting genetic diversity in controlling this trait.\u003c/p\u003e\n \u003cp\u003eTillering, represented in the analysis by genetic marker 3B_717368919, plays a major role in determination of yield. The marker shows a significant association with tillers (p\u0026thinsp;=\u0026thinsp;3.33E-04, R2\u0026thinsp;=\u0026thinsp;0.12), with the minor allele \u0026apos;T\u0026apos; contributing positively by increasing tiller numbers (effect size of 6.79), while the major allele \u0026apos;C\u0026apos; has a lesser effect (1.23). Flag leaf length has genetic marker 3A_589180579 shows a strong association (p\u0026thinsp;=\u0026thinsp;3.44E-05, R2\u0026thinsp;=\u0026thinsp;0.15), with alleles \u0026apos;C\u0026apos; and \u0026apos;G\u0026apos; exerting contrasting effects of -1.33 units and \u0026minus;\u0026thinsp;6.62 units, respectively. The low MAF of 0.11 indicates the less frequent occurrence of these alleles. Spike length influences grain yield and is influenced by genetic markers on chromosomes 1A and 6B. The markers (1A_502117641, 1A_502117650, 6B_477210508) show significant associations with SL (p-values ranging from 3.87E-04 to 5.53E-04) and varying R2 values (0.11 to 0.25). Allelic effects vary across these markers (\u0026apos;C\u0026apos;, \u0026apos;T\u0026apos;) and influence SL positively. Understanding these genetic determinants helps in breeding crops with optimal spike length for improved grain production. Grain per spike show genetic marker 2B_75613744 exhibits a significant association with (p\u0026thinsp;=\u0026thinsp;3.20E-04, R2\u0026thinsp;=\u0026thinsp;0.11), where alleles \u0026apos;A\u0026apos; and \u0026apos;G\u0026apos; influence GPS positively by 0.99 and 8.33 respectively. The MAF of 0.40 indicates a relatively common occurrence of these alleles.\u003c/p\u003e\n \u003cp\u003eBiomass production is a key determinant of overall crop yield. Genetic markers on chromosomes 1B, 4B, and 5A show significant associations with (p-values ranging from 1.27E-04 to 4.04E-04), with moderate R2 values (0.11 to 0.13). Allelic effects (\u0026apos;C\u0026apos;, \u0026apos;T\u0026apos;, \u0026apos;G\u0026apos; and \u0026apos;A\u0026apos;) vary, influencing biomass positively or negatively. Grain yield per plant is a fundamental trait determining crop productivity. Genetic markers on chromosomes 4A and 6B exhibit significant associations with GYPP (p-values ranging from 2.46E-04 to 5.28E- 05) and varying R2 values (0.12 to 0.14). Allelic effects are diverse, impacting GYPP positively or negatively. This analysis provides crucial insights into the genetic markers influencing important agronomic characteristics, aiding in future breeding efforts to enhance crop yield.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e \u0026nbsp;\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\u003eMarker-trait Association Obtained after Mixed Linear Model (MLM)\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\u0026nbsp;\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\" colspan=\"2\"\u003e\n \u003cp\u003eMinor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMajor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\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\u003eChr\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAF\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\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B_513710969\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\u003e2.77E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B_513710985\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\u003e2.77E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B_513711040\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\u003e2.77E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B_513712090\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\u003e2.77E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B_513712139\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\u003e2.77E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B_625593951\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\u003e3.24E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4A_691119517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4A_691119528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4A_691403255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\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\u003e1A_115371695\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\u003e2.74E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\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\u003e1A_142330282\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\u003e2.74E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\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\u003e1A_166070521\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\u003e3.78E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\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\u003e1A_195192114\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\u003e2.74E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\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\u003e1A_225085343\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\u003e2.74E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\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\u003e1A_242118598\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\u003e2.74E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\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\u003e1A_249081921\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\u003e3.78E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\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\u003e1A_249082698\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\u003e3.78E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\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\u003e1A_249082795\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\u003e3.78E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\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\u003e1A_249144497\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\u003e3.78E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\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\u003e1A_249144590\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\u003e3.78E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\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\u003e1A_249776740\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\u003e3.78E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\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\u003e1A_252356259\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\u003e3.79E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\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\u003e1A_252356260\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\u003e3.79E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\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\u003e1A_252356373\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\u003e3.78E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\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\u003e1B_289096605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.99E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\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\u003e1B_311150885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\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\u003e1B_503816495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.15E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etillers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B_717368919\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\u003e3.33E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3A_589180579\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\u003e3.44E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\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\u003e1A_502117641\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\u003e5.53E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\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\u003e1A_502117650\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\u003e5.53E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\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\u003e6B_477210508\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\u003e3.87E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2B_75613744\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\u003e3.20E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebiomass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1B_526272352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.04E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-76.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebiomass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4B_326084122\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\u003e3.57E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-75.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebiomass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5A_647969803\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\u003e1.27E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-51.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGYPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4A_597902329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.28E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-38.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGYPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6B_442751575\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\u003e2.64E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGYPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6B_442751636\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\u003e2.46E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eChr: Chromosome; p: p-value of model; MAF: Minor allele frequency\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThe present study tested the genotypic diversity of germplasm to determine the association of yield related traits in bread wheat and dissected the genetic basis of variation in agronomic traits. Our work highlights the importance of biomass, GPS, SW, and PH in predicting GYPP, with biomass and GPS being the most critical factors. It informs us to prioritize these variables in further agricultural analysis and practice to optimize grain yield per plant. The comprehensive phenotypic evaluation coupled with high-density genotyping, enabled identified 39 marker-trait associations (MTAs) linked to yield and related traits in a diverse panel of bread wheat genotypes using genome-wide association studies (GWAS). Our findings not only validate previously reported QTLs but also uncover potentially novel loci, reiterating the effectiveness of using diverse germplasm for dissecting complex traits in wheat.\u003c/p\u003e \u003cp\u003eThe phenotypic data shows a significant variation among all the 150 genotypes of all the studied agronomic traits, showcasing the broad genetic diversity of the bread wheat panel. Grain yield per plant (GYPP) being the significant indicator of yield directly be predicted by biomass, spike weight (SW), spikelets per spike (SLPS) and grains per spike (GPS). It has been well documented that higher biomass correlates with increased grain yield due to the allocation of photosynthates to grains (An et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Russell et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).On the other hand, spike weight (SW) is directly impacted by Grains per spike, spike length and spikelets per spike (Knežević et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Structural Equation Modeling further confirmed the direct and indirect effects of these traits on GYPP, emphasizing their potential use as selection indices in breeding programs. These results align with previous reports highlighting the importance of spike-related traits in determining wheat yield (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sheoran et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; W\u0026uuml;rschum et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, traits such as flag leaf area (FLL) and flag leaf width (FLW), which influence photosynthetic capacity, were moderately correlated with biomass and yield, further supporting their role in enhancing source capacity during grain filling.\u003c/p\u003e \u003cp\u003eA total of 39 Bonferroni-corrected MTAs was identified for PH (9), PL (18), tillers (1), FLL (1), SL (3), GPS (1), biomass (3), and GYPP (3) were identified. All the MTAs were identified on A subgenome and B subgenome in which A sub genome (23) followed by B subgenome (16) indicating a potentially greater role of the A subgenome in trait variation. An identical pattern in MTAs observed within the A subgenome for yield and yield-contributing characteristics (Ain et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Alemu et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Godoy et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; H. Khan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Several significant MTAs for PH were detected on chromosomes 3B and 4A, notably at loci 3B_513710969 and 4A_691403255. These MTAs are novel, as prior studies have predominantly mapped major dwarfing and height-related genes (e.g., Rht-B1, Rht-D1) on chromosomes 4B and 4D (Gao et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The region on 3B is particularly interesting as it has not been commonly linked to height regulation in earlier classical QTL studies, suggesting the possible presence of minor or environment-specific loci. A similar 3B region was only recently suggested to be involved in height plasticity under heat stress (Zanke et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), indicating potential adaptive value of this region.\u003c/p\u003e \u003cp\u003eBeyond plant height, novel MTAs were also identified for yield-related traits. For instance, a marker on chromosome 4A (4A_597902329) showed a strong association with GYPP, with a high phenotypic variance explained (R\u0026sup2; = 0.14), and was not previously reported in classical studies. Similarly, MTAs on chromosomes 6B and 1B associated with biomass and GYPP were located outside the regions of previously characterized yield QTLs, indicating the discovery of unexplored loci. Importantly, several markers for peduncle length (on 1A and 1B) formed a tight cluster, indicating a potentially strong LD block contributing to trait stability. These novel loci provide promising targets for marker-assisted selection and fine mapping, especially in the context of improving complex traits under climate variability.\u003c/p\u003e \u003cp\u003eThe identification of novel MTAs for yield related traits in bread wheat bears significant potential for molecular breeding. Markers associated with GPS, SW, and SLPS can be prioritized in marker assisted selection (MAS) pipelines to accelerate the development of high yielding cultivars. The integration of these MTAs to Genomic prediction (GP) can further increase the selection efficiency. This is particularly important for the complex traits like grain yield that are influenced by multiple minor-effect loci and environmental interactions. Lastly, the set of markers identified should be validated and deployed I wheat breeding programs in future.\u003c/p\u003e \u003cp\u003eAlthough, this study was conducted in field conditions but the potential influence of genotype-by-environment (G\u0026times;E) interactions on trait expression cannot be overlooked. Thus, the validation of these MTAs across multiple year trails and integration of environmental data will be vital for determining the stability and effectiveness. Moreover, the functional validation of candidate genes associated with significant SNPs can enhance understanding of the molecular mechanisms governing yield components. Future studies should also explore the breeding efficiency through the integration of GS and MAS. Combining genome-wide marker data with advanced modeling approaches can accelerate selection cycles and improve the accuracy of predicting superior genotypes.\u003c/p\u003e \u003cp\u003eIn a nutshell, this reserach identified significant marker-trait associations for eleven agronomic traits in a diverse GWAS panel of bread wheat genotypes. These findings reveal the genetic control of complex yield-related traits and provide novel resources for molecular breeding. Future research should validate these markers across environments and integrate genomic tools with traditional breeding strategies to develop resilient, high-yielding wheat varieties.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn a conclusion, this research provides significant insights of the key traits that contribute to higher yield in bread wheat and to unveil the genetic architecture using Genome Wide Association Studies. The study identified 39 marker-trait associations (MTAs) to enhance our understanding of yield-related traits, offering targets for molecular breeding to improve wheat productivity and resilience. The study identified key traits for higher wheat yield, including increased plant biomass, heavier spikes, more grains per spike, a higher number of tillers, longer peduncles, and longer spikes. These traits enhance grain production and overall yield potential.39 MTAs has been identified for the traits like Plant Height (PH), Peduncle Length (PL), Tillers, Flag Leaf Length (FLL), Spike Length (SL), Grains per spike (GPS), Biomass, and Grain Yield per Plant (GYPP). Principal component analysis (PCA) helped in grouping of traits based on their genetic relationships, providing insights into how different traits are interconnected. Correlation, step wise regression modelling and structural equation modelling further clarified the relationships between these traits. So, to achieve higher yields, breeders must emphasis on increasing biomass, spike weight, grains per spike, tillers, peduncle length, and spike length. Understanding the genetic markers related with these traits can help breeders to select and grow wheat varieties with higher yield potential. Future research should validate marker-trait associations across diverse environments, explore genotype-by-environment interactions, and integrate genomic selection with traditional breeding to develop resilient wheat varieties. Additionally, studying the biological mechanisms of key genetic regions will improve understanding of trait development and regulation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAgglomerative Hierarchical Clustering\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBLUP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBest Linear Unbiased Prediction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDArT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiversity Array Technology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFLL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFlag Leaf Length\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFLW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFlag Leaf Width\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenotyping By Sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenotypic Coefficient of Variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGross Domestic Product\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome Wide Association Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGrain Per Spike\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGYPP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGrain Yield Per Plant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKASP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKompetitive Allele-Specific PCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKernels Per Spike\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKNPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKernel Number Per Spike\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMarker Assisted Selection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinor Allele Frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMixed Linear Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMarker Trait Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNext Generation Screening\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhenotypic Coefficient of Variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePercentage of Effective Spike\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlant Height\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeduncle Length\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlant Morphology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative Trait Locus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomized Complete Block Design\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRecombinant Inbred Lines\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStructure Equation Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpike Length\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Nucleotide Polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSimple Sequence Repeats\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpike Weight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSLPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpikelets Per Spike\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTKW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThousand Kernal Weight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eYCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eYield Component Trait\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization of the research Muhammad Jamil; design of the experiment Muhammad Jamil and Waseem Ahmad; contribution of the experimental material Muhammad Jamil, Waseem Ahmad and Areeba Fatima; execution of the field experiment and data collection Waseem Ahmad and Areeba Fatima and Sana Rasheed; analysis of the data and interpretation Muhammad Jamil; preparation of the manuscript Muhammad Jamil and Waseem Ahmad. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge Awais Rasheed, department of plant sciences, Quaid-i-Azam University, Islamabad on providing germplasm and genotyping facility for this research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors didn\u0026rsquo;t receive any funding from government or funding agencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the datasets are available and can be provided by corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhmad, W., Bibi, N., Sanwal, M., Ahmed, R., Jamil, M., Kalsoom, R., Arif, M., \u0026amp; Fahad, S. (2024). Cereal Crops in the Era of Climate Change: An Overview. In S. Fahad, S. Saud, T. Nawaz, L. Gu, M. Ahmad, \u0026amp; R. Zhou (Eds.), \u003cem\u003eEnvironment, Climate, Plant and Vegetation Growth\u003c/em\u003e (pp. 609-630). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-69417-2_21\u003c/li\u003e\n \u003cli\u003eAin, Q.-u., Rasheed, A., Anwar, A., Mahmood, T., Imtiaz, M., Mahmood, T., Xia, X., He, Z., \u0026amp; Quraishi, U. M. (2015). Genome-wide association for grain yield under rainfed conditions in historical wheat cultivars from Pakistan. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;6\u003c/em\u003e, 743.\u003c/li\u003e\n \u003cli\u003eAlemu, A., Suliman, S., Hagras, A., Thabet, S., Al-Abdallat, A., Abdelmula, A. A., \u0026amp; Tadesse, W. (2021). Multi-model genome-wide association and genomic prediction analysis of 16 agronomic, physiological and quality related traits in ICARDA spring wheat. \u003cem\u003eEuphytica\u003c/em\u003e,\u003cem\u003e\u0026nbsp;217\u003c/em\u003e, 1-22.\u003c/li\u003e\n \u003cli\u003eAn, Q., Li, C., Li, H., Zheng, Q., Li, B., \u0026amp; Li, Z. (2022). An analysis of the genetic relation between photosynthesis and yield-related traits in Wheat. \u003cem\u003eAgriculture\u003c/em\u003e,\u003cem\u003e\u0026nbsp;12\u003c/em\u003e(4), 560.\u003c/li\u003e\n \u003cli\u003eChidzanga, C., Mullan, D., Roy, S., Baumann, U., \u0026amp; Garcia, M. (2022). Nested association mapping-based GWAS for grain yield and related traits in wheat grown under diverse Australian environments. \u003cem\u003eTheoretical and Applied Genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;135\u003c/em\u003e(12), 4437-4456.\u003c/li\u003e\n \u003cli\u003eEltaher, S., Baenziger, P. S., Belamkar, V., Emara, H. A., Nower, A. A., Salem, K. F., Alqudah, A. M., \u0026amp; Sallam, A. (2021). GWAS revealed effect of genotype\u0026times; environment interactions for grain yield of Nebraska winter wheat. \u003cem\u003eBMC genomics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;22\u003c/em\u003e, 1-14.\u003c/li\u003e\n \u003cli\u003eGao, F., Wen, W., Liu, J., Rasheed, A., Yin, G., Xia, X., Wu, X., \u0026amp; He, Z. (2015). Genome-wide linkage mapping of QTL for yield components, plant height and yield-related physiological traits in the Chinese wheat cross Zhou 8425B/Chinese Spring. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;6\u003c/em\u003e, 1099.\u003c/li\u003e\n \u003cli\u003eGodoy, J., Gizaw, S., Chao, S., Blake, N., Carter, A., Cuthbert, R., Dubcovsky, J., Hucl, P., Kephart, K., \u0026amp; Pozniak, C. (2018). Genome‐wide association study of agronomic traits in a spring‐planted North American elite hard red spring wheat panel. \u003cem\u003eCrop Science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;58\u003c/em\u003e(5), 1838-1852.\u003c/li\u003e\n \u003cli\u003eIsham, K., Wang, R., Zhao, W., Wheeler, J., Klassen, N., Akhunov, E., \u0026amp; Chen, J. (2021). QTL mapping for grain yield and three yield components in a population derived from two high-yielding spring wheat cultivars. \u003cem\u003eTheoretical and Applied Genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;134\u003c/em\u003e, 2079-2095.\u003c/li\u003e\n \u003cli\u003eJamil, M., Ahmad, W., Sanwal, M., \u0026amp; Maqsood, M. F. (2025). Gene editing and GWAS for digital imaging analysis of wheat grain weight, size and shape are inevitable to enhance the yield. \u003cem\u003eCereal Research Communications\u003c/em\u003e, 1-20.\u003c/li\u003e\n \u003cli\u003eJamil, M., Ali, A., Gul, A., Ghafoor, A., Napar, A. A., Ibrahim, A. M., Naveed, N. H., Yasin, N. A., \u0026amp; Mujeeb-Kazi, A. (2019). Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat. \u003cem\u003eBMC plant biology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;19\u003c/em\u003e, 1-18.\u003c/li\u003e\n \u003cli\u003eJia, Y., Zhang, Y., Sun, Y., Ma, C., Bai, Y., Zhang, H., Hou, J., Wang, Y., Ji, W., \u0026amp; Bai, H. (2024). QTL Mapping of Yield-Related Traits in Tetraploid Wheat Based on Wheat55K SNP Array. \u003cem\u003ePlants\u003c/em\u003e,\u003cem\u003e\u0026nbsp;13\u003c/em\u003e(10), 1285.\u003c/li\u003e\n \u003cli\u003eJin, J., Liu, D., Qi, Y., Ma, J., \u0026amp; Zhen, W. (2020). Major QTL for seven yield-related traits in common wheat (Triticum aestivum L.). \u003cem\u003eFrontiers in Genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;11\u003c/em\u003e, 1012.\u003c/li\u003e\n \u003cli\u003eKang, C.-S., Mo, Y.-J., Kim, K.-M., Kim, K.-H., Chun, J.-B., Park, C.-S., \u0026amp; Cho, S.-W. (2020). Mapping quantitative trait loci for yield potential traits in wheat recombinant inbred lines. \u003cem\u003eAgronomy\u003c/em\u003e,\u003cem\u003e\u0026nbsp;11\u003c/em\u003e(1), 22.\u003c/li\u003e\n \u003cli\u003eKhan, Kainat, Z., Maqbool, S., Mehwish, A., Ahmad, S., Suleman, H. M., Mahmood, Z., Ali, M., Aziz, A., \u0026amp; Rasheed, A. (2022). Genome-wide association for heat tolerance at seedling stage in historical spring wheat cultivars. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;13\u003c/em\u003e, 972481.\u003c/li\u003e\n \u003cli\u003eKhan, H., Krishnappa, G., Kumar, S., Mishra, C. N., Krishna, H., Devate, N. B., Rathan, N. D., Parkash, O., Yadav, S. S., \u0026amp; Srivastava, P. (2022). Genome-wide association study for grain yield and component traits in bread wheat (Triticum aestivum L.). \u003cem\u003eFrontiers in genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;13\u003c/em\u003e, 982589.\u003c/li\u003e\n \u003cli\u003eKnežević, D., Radosavac, A., \u0026amp; Zelenika, M. (2015). Variability of grain weight per spike in wheat grown in different ecological conditions. \u003cem\u003eActa Agriculturae Serbica\u003c/em\u003e,\u003cem\u003e\u0026nbsp;20\u003c/em\u003e(39), 85-95.\u003c/li\u003e\n \u003cli\u003eKumar, R., Das, S. P., Choudhury, B. U., Kumar, A., Prakash, N. R., Verma, R., Chakraborti, M., Devi, A. G., Bhattacharjee, B., \u0026amp; Das, R. (2024). Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing. \u003cem\u003eBiological Research\u003c/em\u003e,\u003cem\u003e\u0026nbsp;57\u003c/em\u003e(1), 80.\u003c/li\u003e\n \u003cli\u003eKumar, S., Singh, P. N., Gaur, S. C., Quatadah, S. M., Singh, K., Gautam, U., Singh, S. K., Yadav, M., Gaur, A. K., \u0026amp; Kumar, K. (2025). Assessment of Genetic Variability, Correlation and Path Analysis in Bread Wheat (Triticum aestivum L.) Genotypes for Yield and its Attributing Traits. \u003cem\u003eJournal of Advances in Biology \u0026amp; Biotechnology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;28\u003c/em\u003e(1), 612-620.\u003c/li\u003e\n \u003cli\u003eLi, F., Wen, W., He, Z., Liu, J., Jin, H., Cao, S., Geng, H., Yan, J., Zhang, P., \u0026amp; Wan, Y. (2018). Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers. \u003cem\u003eTheoretical and Applied Genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;131\u003c/em\u003e, 1903-1924.\u003c/li\u003e\n \u003cli\u003eLi, F., Wen, W., Liu, J., Zhang, Y., Cao, S., He, Z., Rasheed, A., Jin, H., Zhang, C., \u0026amp; Yan, J. (2019). Genetic architecture of grain yield in bread wheat based on genome-wide association studies. \u003cem\u003eBMC plant biology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;19\u003c/em\u003e, 1-19.\u003c/li\u003e\n \u003cli\u003eLi, T., Li, Q., Wang, J., Yang, Z., Tang, Y., Su, Y., Zhang, J., Qiu, X., Pu, X., \u0026amp; Pan, Z. (2022). High-resolution detection of quantitative trait loci for seven important yield-related traits in wheat (Triticum aestivum L.) using a high-density SLAF-seq genetic map. \u003cem\u003eBMC Genomic Data\u003c/em\u003e,\u003cem\u003e\u0026nbsp;23\u003c/em\u003e(1), 37.\u003c/li\u003e\n \u003cli\u003eLi, X., Xu, X., Liu, W., Li, X., Yang, X., Ru, Z., \u0026amp; Li, L. (2020). Dissection of superior alleles for yield-related traits and their distribution in important cultivars of wheat by association mapping. \u003cem\u003eFrontiers in plant science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;11\u003c/em\u003e, 175.\u003c/li\u003e\n \u003cli\u003eLiu, H., Shi, Z., Ma, F., Xu, Y., Han, G., Zhang, J., Liu, D., \u0026amp; An, D. (2022). Identification and validation of plant height, spike length and spike compactness loci in common wheat (Triticum aestivum L.). \u003cem\u003eBMC plant biology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;22\u003c/em\u003e(1), 568.\u003c/li\u003e\n \u003cli\u003eLiu, J., Xu, Z., Fan, X., Zhou, Q., Cao, J., Wang, F., Ji, G., Yang, L., Feng, B., \u0026amp; Wang, T. (2018). A genome-wide association study of wheat spike related traits in China. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;9\u003c/em\u003e, 1584.\u003c/li\u003e\n \u003cli\u003eLuo, X., Yang, Y., Lin, X., \u0026amp; Xiao, J. (2023). Deciphering spike architecture formation towards yield improvement in wheat. \u003cem\u003eJournal of genetics and genomics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;50\u003c/em\u003e(11), 835-845.\u003c/li\u003e\n \u003cli\u003eMa, F., Xu, Y., Wang, R., Tong, Y., Zhang, A., Liu, D., \u0026amp; An, D. (2023). Identification of major QTLs for yield-related traits with improved genetic map in wheat. \u003cem\u003eFrontiers in plant science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;14\u003c/em\u003e, 1138696.\u003c/li\u003e\n \u003cli\u003eMehvish, A., Aziz, A., Bukhari, B., Qayyum, H., Mahmood, Z., Baber, M., Sajjad, M., Pang, X., \u0026amp; Wang, F. (2023). Identification of Single-Nucleotide Polymorphisms (SNPs) Associated with Heat Tolerance at the Reproductive Stage in Synthetic Hexaploid Wheats Using GWAS. \u003cem\u003ePlants\u003c/em\u003e,\u003cem\u003e\u0026nbsp;12\u003c/em\u003e(8), 1610.\u003c/li\u003e\n \u003cli\u003eRussell, B., Brown-Guedira, G., Sneller, C. H., \u0026amp; Mohammadi, M. (2020). Transferability of marker trait associations in wheat is disturbed mainly by genotype\u0026times; year interaction. \u003cem\u003eCrop Breeding, Genetics and Genomics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;4\u003c/em\u003e(3).\u003c/li\u003e\n \u003cli\u003eSaini, \u0026amp; Kumar, S. (2024). Applying genomics resources to accelerate the development of climate resilient crops. In \u003cem\u003eAdapting to Climate Change in Agriculture-Theories and Practices: Approaches for Adapting to Climate Change in Agriculture in India\u003c/em\u003e (pp. 43-120). Springer.\u003c/li\u003e\n \u003cli\u003eSheoran, S., Jaiswal, S., Raghav, N., Sharma, R., Sabhyata, Gaur, A., Jaisri, J., Tandon, G., Singh, S., \u0026amp; Sharma, P. (2022). Genome-wide association study and post-genome-wide association study analysis for spike fertility and yield related traits in bread wheat. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;12\u003c/em\u003e, 820761.\u003c/li\u003e\n \u003cli\u003eSun, C., Zhang, F., Yan, X., Zhang, X., Dong, Z., Cui, D., \u0026amp; Chen, F. (2017). Genome‐wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China. \u003cem\u003ePlant biotechnology journal\u003c/em\u003e,\u003cem\u003e\u0026nbsp;15\u003c/em\u003e(8), 953-969.\u003c/li\u003e\n \u003cli\u003eTeam, R. C. (2020). RA language and environment for statistical computing, R Foundation for Statistical. \u003cem\u003eComputing\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eTesfaye, K. (2021). Climate change in the hottest wheat regions. \u003cem\u003eNature Food\u003c/em\u003e,\u003cem\u003e\u0026nbsp;2\u003c/em\u003e(1), 8-9. https://doi.org/10.1038/s43016-020-00218-0\u003c/li\u003e\n \u003cli\u003eTuruspekov, Y., Baibulatova, A., Yermekbayev, K., Tokhetova, L., Chudinov, V., Sereda, G., Ganal, M., Griffiths, S., \u0026amp; Abugalieva, S. (2017). GWAS for plant growth stages and yield components in spring wheat (Triticum aestivum L.) harvested in three regions of Kazakhstan. \u003cem\u003eBMC plant biology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;17\u003c/em\u003e, 1-11.\u003c/li\u003e\n \u003cli\u003eTyrka, M., Krajewski, P., Bednarek, P. T., Rączka, K., Drzazga, T., Matysik, P., Martofel, R., Woźna-Pawlak, U., Jasińska, D., \u0026amp; Niewińska, M. (2023). Genome-wide association mapping in elite winter wheat breeding for yield improvement. \u003cem\u003eJournal of Applied Genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;64\u003c/em\u003e(3), 377-391.\u003c/li\u003e\n \u003cli\u003eUllah, M. I., Mahpara, S., Bibi, R., Shah, R. U., Ullah, R., Abbas, S., Ullah, M. I., Hassan, A. M., El-Shehawi, A. M., \u0026amp; Brestic, M. (2021). Grain yield and correlated traits of bread wheat lines: Implications for yield improvement. \u003cem\u003eSaudi Journal of Biological Sciences\u003c/em\u003e,\u003cem\u003e\u0026nbsp;28\u003c/em\u003e(10), 5714-5719.\u003c/li\u003e\n \u003cli\u003eWang, J., \u0026amp; Zhang, Z. (2021). GAPIT version 3: boosting power and accuracy for genomic association and prediction. \u003cem\u003eGenomics, proteomics \u0026amp; bioinformatics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;19\u003c/em\u003e(4), 629-640.\u003c/li\u003e\n \u003cli\u003eWang, S.-X., Zhu, Y.-L., Zhang, D.-X., Shao, H., Liu, P., Hu, J.-B., Zhang, H., Zhang, H.-P., Chang, C., \u0026amp; Lu, J. (2017). Genome-wide association study for grain yield and related traits in elite wheat varieties and advanced lines using SNP markers. \u003cem\u003ePloS one\u003c/em\u003e,\u003cem\u003e\u0026nbsp;12\u003c/em\u003e(11), e0188662.\u003c/li\u003e\n \u003cli\u003eW\u0026uuml;rschum, T., Leiser, W. L., Langer, S. M., Tucker, M. R., \u0026amp; Longin, C. F. H. (2018). Phenotypic and genetic analysis of spike and kernel characteristics in wheat reveals long-term genetic trends of grain yield components. \u003cem\u003eTheoretical and Applied Genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;131\u003c/em\u003e, 2071-2084.\u003c/li\u003e\n \u003cli\u003eXu, H., Wang, Z., Wang, F., Hu, X., Ma, C., Jiang, H., Xie, C., Gao, Y., Ding, G., \u0026amp; Zhao, C. (2024). Genome-wide association study and genomic selection of spike-related traits in bread wheat. \u003cem\u003eTheoretical and Applied Genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;137\u003c/em\u003e(6), 131.\u003c/li\u003e\n \u003cli\u003eZanke, C. D., Ling, J., Plieske, J., Kollers, S., Ebmeyer, E., Korzun, V., Argillier, O., Stiewe, G., Hinze, M., \u0026amp; Neumann, K. (2014). Whole genome association mapping of plant height in winter wheat (Triticum aestivum L.). \u003cem\u003ePLoS One\u003c/em\u003e,\u003cem\u003e\u0026nbsp;9\u003c/em\u003e(11), e113287.\u003c/li\u003e\n \u003cli\u003eZhang, H., Chen, J., Li, R., Deng, Z., Zhang, K., Liu, B., \u0026amp; Tian, J. (2016). Conditional QTL mapping of three yield components in common wheat (Triticum aestivum L.). \u003cem\u003eThe Crop Journal\u003c/em\u003e,\u003cem\u003e\u0026nbsp;4\u003c/em\u003e(3), 220-228.\u003c/li\u003e\n \u003cli\u003eZhao, J., Sun, L., Gao, H., Hu, M., Mu, L., Cheng, X., Wang, J., Zhao, Y., Li, Q., \u0026amp; Wang, P. (2023). Genome-wide association study of yield-related traits in common wheat (Triticum aestivum L.) under normal and drought treatment conditions. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;13\u003c/em\u003e, 1098560.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bread wheat, GWAS, Agronomic traits, MTAs, SNP markers, Yield","lastPublishedDoi":"10.21203/rs.3.rs-6466875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6466875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCritical understanding of the genetic basis of yield-related traits through the identification of quantitative trait loci (QTLs) is essential for accelerating wheat improvement. The aim of this study was to evaluate diverse genotypes for yield-related traits and conducting GWAS to pinpoint genomic regions responsible for these traits. A field trial with 150 diverse bread wheat genotypes was conducted to evaluate eleven yield-related traits. in RCBD design with three replicates. Statistical analysis included Pearson\u0026rsquo;s correlation, step-wise multiple regression, structural equation modeling, and principal component analysis (PCA) were performed to assess trait relationships. The genome-wide association studies (GWAS) panel was genotyped using a 37K SNP array to identify trait associations. A total of 37,401 single nucleotide polymorphisms (SNPs) were analyzed to recognize 39 marker-trait associations (MTAs) across the panel. Nine MTAs for PH, 18 for PL, one for tillers, one for FLL, three for SL, one for GPS, three for biomass, and three for GYPP were identified. The most important traits contributing to yield were biomass, spike weight, plant height, peduncle length and tillers. Genotypic analysis for the clustering of wheat germplasm in this study revealed significant clusters among the wheat lines. This study provides perceptive of the genetic framework underlying key agronomic traits in bread wheat. The identification of genomic regions related with those traits offers critical insights for breeding programs aimed at improving wheat yield. Future research should validate markers across environments and integrate genomic selection and Marker assisted breeding for resilient wheat.\u003c/p\u003e","manuscriptTitle":"Unveiling the genetic Landscape of agronomic traits in bread wheat through Genome wide association Studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 09:58:23","doi":"10.21203/rs.3.rs-6466875/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 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-05T11:29:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 09:58:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6466875","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6466875","identity":"rs-6466875","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}