Assessment of Glutenin Subunit Diversity and its Relation with Dough Properties and Breadmaking Quality of Wheat

Assessment of Glutenin Subunit Diversity and its Relation with Dough Properties and Breadmaking Quality of Wheat | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Glutenin Subunit Diversity and its Relation with Dough Properties and Breadmaking Quality of Wheat Sumit K Singh, Anju Mahendru-Singh, Arvind K Ahalawat, Anirvan Mukherjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8338707/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This study presents assessment of glutenin subunit diversity and its relation with dough properties and breadmaking quality in 18 Indian bread wheat ( Triticum aestivum L.) genotypes. The analysis showed considerable genetic variation and revealed 12 high-molecular-weight glutenin subunit (HMW-GS) alleles at the Glu-1 loci and seven low-molecular-weight glutenin subunit (LMW-GS) alleles at the Glu-3 loci. Grain and flour quality were evaluated using 27 different parameters. The farinograph and alveograph indices showed significant association with traits like protein content, gluten index, dough stability, loaf volume and overall bread quality. The study also identified specific allele combinations that contribute to improved dough strength and extensibility. Genotypes carrying Glu-A1b, Glu-B1c/i, Glu-D1d , and Glu-B3j alleles were consistently associated with superior bread baking characteristics. The findings reinforce Glu-1 scores as robust predictors of baking performance and emphasize the need for trait-based selection strategies that integrate molecular and rheological data for wheat quality improvement. Bread Wheat Glutenin Alleles Rheological Evaluation Farinograph Alveograph Wheat Quality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Wheat ( Triticum aestivum L.) is one of the major commodities for the global agricultural and food security especially in India. With evolving consumer preferences and the rapid expansion of the industrial baking sector, conventional breeding efforts centred solely on maximising yield are proving insufficient. Increasing emphasis on improving grain quality, particularly for bread making applications has become imperative (Singh et al., 2024 ). The bread making potential of wheat largely depends on the two major storage protein fractions glutenins and gliadins, which together network to form gluten and determine essential dough properties such as water absorption, elasticity, strength and gas retention. The glutenins comprise of the high molecular eight gluten subunits (HMW-GS) and the low molecular weight gluten subunits (LMW-GS). These proteins are encoded by the genes located at the Glu-1 and Glu-3 loci. The alleles in these genes differ significantly across Indian wheat cultivars as assessed with biochemical and molecular markers (Rai et al., 2019 ; Singh et al., 2024 ). HMW-GS are reported to play a pivotal role in enhancing dough strength, while LMW-GS predominantly contribute to dough extensibility and influence loaf texture (Rasheed et al, 2014, Franaszek & Salmanowicz, 2021 ). Wheat flour quality is assessed using rheological tools such as farinograph, alveograph, mixograph etc. as per a country’s preference. These instruments simulate dough behaviour under mixing and deformation conditions, which evaluate crucial parameters like dough development time, water absorption, tenacity and extensibility (Cecchini et al., 2021 ). While past studies have explored individual traits, there is a lack of integrated research connecting molecular glutenin profiling with dough rheology and bread making performance (Pourmohammadi et al., 2023). In the present study, the relationships between HMW-GS and LMW-GS glutenin subunits and farinograph and alveograph parameters are assessed to understand their combined influence on bread loaf volume in Indian wheat genotypes to assist breeders in selecting for specific glu alleles or allele combinations along with other economic traits to produce product specific, high yielding cultivars. 2. Materials and Methods 2.1 Experimental Setup and Field Conditions This study examined grain quality traits and rheological parameters along with bread making quality in 18 diverse bread wheat ( Triticum aestivum L.) genotypes The trials were conducted over two consecutive crop seasons (2021–22 and 2022–23), at the experimental farm of the Indian Agricultural Research Institute (ICAR-IARI), New Delhi, positioned at an elevation of 228.61 meters above sea level. Situated within a semi-arid, sub-tropical agro-climatic zone, the region experienced seasonal rainfall levels of 181.5 mm in 2021–22 and 138.4 mm in 2022–23. Sowing was carried out using a randomised complete block design (RCBD) with two replications and 5 row plots of 2.5m each. Uniform fertilisers and irrigations were given across all genotypes to maintain crop health and achieve physiological maturity under optimal conditions. This facilitated reliable comparisons of flour quality traits across genotypic variations. 2.2 Grain Collection, Sample Preparation and Milling Genotypes were harvested at physiological maturity and stored at standard storage environments. Milling was performed using a Quadrumat Senior mill (Brabender, Germany) to produce refined wheat flour (Maida) and was subsequently stored in airtight containers at 4°C until further analysis. Grain Quality Traits: Gluten parameters, including gluten index (GI), wet gluten (WG) and dry gluten (DG) were assessed using the Glutomatic 2200 (Perten Instruments) and the falling number (FN) was determined using the falling number tester apparatus following the standard AACC (2000) procedures. The Sodium Dodecyl Sulfate Sedimentation Volume (SDS-SV) of samples was evaluated using the standard method described by Axford et al. in 1979. 2.3 Grain Hardness Index The grain hardness index (GHI) was evaluated using single-kernel characterization system (SKCS) 4100 (Perten Instruments, Australia), as per the procedure described in AACC (2000) method 55 − 31. 2.4 Glutenin Subunit Extraction and SDS-PAGE Profiling The glutenin subunit composition of high molecular weight gluten subunit (HMW-GS) types was determined using a modified version of the method described by Zhen and Mares 1992 . where 20 mg of flour from each genotype was mixed with 70% ethanol (v/v) for 30 min to wash out gliadins. The mixture was centrifuged at 5000 rpm for 10 min, and the supernatant was discarded. The pellet was treated with an extraction buffer (2% SDS, 1% DTT in 0.5 M Tris-HCl, pH 6.8). The mixture was incubated at 65°C for 30 minutes to solubilize the glutenin fractions followed by vortex for 100 min and centrifuged at 10,000 rpm for 10 min. The supernatant containing glutenin protein was collected and SDS–polyacrylamide gel electrophoresis (SDS-PAGE) analysis was performed on a 12% SDS-PAGE gel, which was run at 100 V for 4–5 hours. After electrophoresis, gels were stained in a solution of 0.1% w/v Commissive brilliant blue R-250, 50% w/v methanol, and 10% w/v trichloroacetic acid. After staining, the gels were rinsed with distilled water and washed on a horizontal shaker until the excess dye was removed. The glutenin subunit bands were identified based on known standards (Payne & Lawrence, 1983 ), using reference cultivars with known HMW-GSs. Glu-1 scores of the studied lines were determined based on the numeric scale developed by Payne et al. for HMW-GS alleles (Payne, 1987). 2.5 Flour Rheology Assessment via Farinograph The rheological properties of the wheat flour samples were evaluated using a two lane Brabender Farinograph (C.W. Brabender Instruments, Germany) following AACC Method 54-21.01 . For each genotype, 300 g of flour (14% moisture basis) was used per test run. The farinograph parameters recorded are Water absorption (WA), dough development time (DDT), dough stability (STAB), degree of softening (DOS), and farinograph quality number (FQN). These parameters helped in understanding of gluten behaviour during mixing, a determining factor in large scale industrial bread production (Rai et al., 2019 ; Singh et al., 2024 ). 2.6 Dough Extensibility Evaluation via Alveograph To further assess dough quality, extensibility and gas retention capacities were measured using a Chopin Alveograph (Tripette & Renaud, France) as per AACC Method 54-30A . Dough samples were prepared under controlled conditions and rested before testing. The alveograph parameters recorded are Tenacity (P), Extensibility (L), P/L ratio, Deformation energy (W, in 10⁻⁴ J). These parameters helped in deeper understanding of the dough’s viscoelastic nature, including extension and extensibility, which directly impact bread texture, crumb structure, and final loaf volume. 2.7 Falling Number (FN) and Stirring Number (SN) Falling Number was measured to assess α-amylase activity in the flour samples using protocol described by Hagberg (1960) and adopted by the AACC International Method (56-81.03). Falling Number (FN) was determined using the Perten Falling Number 1500 system (Perten Instruments AB, Sweden). And, Stirring Number (SN) was measured using the Rapid Visco Analyser (RVA-4500, Perten Instruments), operated under AACC Method 76-21.02. 2.8 Bread-Making and Loaf Volume Estimation To evaluate bread-making performance, a straight-dough method (AACC 2010, 10.09 .01) was employed. Each flour sample was blended with key ingredients: Flour 100gm, Dry yeast 2.5gm Sugar 5.0gm, Salt 2.0gm, Ghee 3.0gm, and Water adjusted according to farinograph water absorption values. The dough was mixed and fermented (for 90 minutes at 30°C) (Dexter et al., 1994 ; Sapirstein et al., 2007 ). After fermentation, dough was gently punched down to release trapped gas. The dough has been allowed to rest and rise after being shaped for the final time before baking. Baking was performed at 220°C for 25 minutes. The loaf volume was measured using the rapeseed displacement technique and recorded in cubic centimetres (cm³) using a loaf volumeter (National Inc.). 3. Results 3.1 Descriptive Analysis The descriptive evaluation of qualitative genetic traits across the wheat genotypes revealed allelic diversity and genetic complexity inherent in the studied population. In the studied genotype (As presented in Table 1 and Fig. 1 ), the number of allelic groups varied appreciably, suggesting substantial genetic heterogeneity across loci. Total Twelve HMW-GS alleles at locus Glu-1 and seven LMW-GS at locus Glu-3 were detected in studied lines (Table 2 ). The allele Glu-A1b (subunit 2*) at the Glu-A1 locus emerged as the most frequently occurring allele (66.7%), followed by alleles Glu-A1a (subunit 1) and Glu-A1c (subunit null), each contributing 16.7%. Similarly, at Glu-1 Locus, Glu-B1u (subunit 7 + 8) allele was found as the predominant type (27.8%) followed by Glu-B1c (subunit 7 + 9) and Glu-B1i (subunit 17 + 18) in 22.2% of cases. Lower frequencies were recorded for alleles Glu-B1f (subunit 13 + 16), Glu-B1e (subunit 20), and Glu-B1a (subunit 7). While at Glu-D1 locus, almost equal split between Glu-D1d (subunit 5 + 10) at 44.4% and Glu-D1a (subunit 2 + 12) at 55.6%. For LMW-GS, Glu-A3 locus was dominated by allele Glu-A3c (72.2%), followed by Glu-A3b allele (22.2%) and Glu-A3f allele (5.6%). But Glu-B3 loci showed substantial diversity with most frequent Glu-B3j allele (33.3%) and alleles Glu-B3b, Glu-B3g and Glu-B3i showed frequencies 22.2% by each. A Burt table (Fig. 2 ) was constructed to explore interactions among alleles and identify dominant co-occurrence frequencies. Notably, Glu-A1 allele 2 (b)* frequently co-occurred with Glu-D1 allele 2 + 12 (6 varieties), Glu-A3 allele c (9 varieties), and Glu-B3 allele j (3 varieties). Table 1 Descriptive statistics of qualitative glutenin markers (Glu-A1, Glu-B1, Glu-D1, Glu-A3 and Glu-B3) across 18 wheat genotypes. Variable Categories Frequencies % Glu-A1 1 (a) 3 16.6667 2* (b) 12 66.6667 null (c) 3 16.6667 Glu-B1 7 + 9 (c) 4 22.2222 7 + 8 (u) 5 27.7778 7 (a) 2 11.1111 13 + 16 (f) 1 5.5556 17 + 18 (i) 4 22.2222 20 (e) 2 11.112 Glu-D1 2 + 12 (a) 10 55.5556 5 + 10 (d) 8 44.4444 Glu-A3 b 4 22.2222 c 13 72.2222 f 1 5.5556 Glu-B3 b 4 22.2222 g 4 22.2222 i 4 22.2222 j 6 33.3333 The multivariate contingency matrix was displayed in a 3D bar chart (Fig. 1 : 3D view of the Burt table ). This helped to describe the interaction between the genetic structure of quality traits and the overlap in allele frequencies across the studied genotypes. In the studied genotype, Glu-A1b (subunit 2*) frequently cooccurred with Glu-D1a (subunit 2 + 12), Glu-A3c , and Glu-B3j , suggesting frequent allelic associations among certain varieties. The Glu-B1u (subunit 7 + 8) also commonly cooccurred with Glu-D1d (subunit 5 + 10) and Glu-B3b , which formed another prominent interaction cluster. Table 2 Descriptive summary of quantitative grain, dough rheology and breadmaking quality traits evaluated in 18 wheat genotypes. Statistic No. of observations Minimum Maximum Mean Variance (n-1) Standard deviation (n-1) Glu-1 Score 18 4 10 7.83 3.32 1.82 H.I. 18 65.67 90.01 77.34 37.32 6.11 Protein 18 8.65 12.02 10.71 0.88 0.94 SDS-SV 18 36 56 43.11 27.16 5.21 GI 18 44 100 73.06 503.11 22.43 F.W.A 18 61.8 67.3 63.88 3.08 1.76 D.D.T. 18 1.9 37 7.46 61.77 7.86 STAB 18 2.6 13.4 7.11 11.78 3.43 D.O.S. 18 6 129 46.67 979.06 31.29 F.Q.N. 18 48 200 96.5 2239.44 47.32 F. A.T. 18 0.58 7.48 2.15 3.56 1.89 F.D.T. 18 4.28 19.54 9.2522 22.73 4.77 DOUGH WT. 18 161.4 171 167.46 3.72 1.93 BREAD WEIGHT 18 151 159.3 154.75 5.8 2.41 LOAF VOLUME 18 500 610 565.56 1026.14 32.04 BREAD QUALITY SCORE 18 6 8.67 7.5 0.62 0.79 A comprehensive descriptive analysis of 27 quantitative quality related parameters revealed considerable variability among the 18 wheat genotypes examined (Table-2). All traits were fully recorded without any missing data, which ensured reliability in statistical evaluations. The Glu-1 score was observed in a broad range from 4.00 to 10.00, with a mean value of 7.94 (SD = 1.70), while hardness index (HI), varied between 65.67 to 90.01 (Mean = 77.34, SD = 6.11). Grain protein (P%) content showed moderate variation, averaging 12.39% (SD = 0.94). The Falling Number of flour (FN) ranged widely from 483 to 967 (mean = 759.72, SD = 110.31), and the Stirring Number of flour (SN) from 1443.00 to 2173.00 (mean = 1761.56, SD = 194.59). The farinograph water absorption (FWA) was recorded as the mean value of 63.48% (SD = 2.66).. Dough rheology parameters such as dough development time (DDT) (M = 7.46 min, SD = 7.86) and dough stability (STAB) (M = 7.11 min, SD = 3.43) exhibited wide fluctuations, reinforcing the genotypic differences in gluten strength and dough-handling properties (Aaliya et al., 2021; Tozatti et al., 2020). Evaluations of gluten quality highlighted substantial variations in wet gluten (M = 27.22%, SD = 3.62), dry gluten (M = 9.30%, SD = 1.19), and gluten index (M = 73.06, SD = 22.43). The alveograph (Alve) with W values (baking strength) ranging from 128.00 to 257.00 (M = 173.78, SD = 43.18) and P/L ratios from 0.58 to 3.89 (M = 1.59, SD = 0.92). The descriptive statistics of bread baking quality traits and other studied traits are also represented in Table-2). To better understand how allelic differences correspond with bread baking quality traits, grouped boxplots were employed to visualise the distribution of quantitative traits across allelic categories (Fig. 2 ). 3.2 Correlation Analysis Pearson correlation matrix (Table-3) was generated at 95% significance level (α = 0.05). This statistical approach helped identify significant associations between variables, shedding light on the underlying physiological and biochemical traits that govern wheat quality. The grain hardness index demonstrated positive correlation with bread weight (Wt) (r = 0.68), dough Wt (r = 0.54), gluten index (GI) content (r = 0.41), farinograph degree of softening (D.O.S) (r = 0.42) and Alve_P (r = 0.37). Protein content (P%) showed positive correlations with multiple quality-related traits, including Wet Gluten (WG%) (r = 0.613), Dry Gluten (DG%) (r = 0.691), SDS-SV (ml) (r = 0.480). Protein (P%) found correlated with dough stability (STAB; r = 0.408), farinograph arrival time (F.A.T.; r = 0.339), and farinograph departure time (F.D.T.; r = 0.240). Moreover, P% also showed positive correlations with bread weight (r = 0.511) and dough weight (r = 0.386). Dough Stability (STAB) known as an indicator of overall dough strength, showed strong positive correlations with F.Q.N. (r = 0.915), F.A.T. (r = 0.566), and F.D.T. (r = 0.944). It also showed a strong negative relationship with D.O.S. (r = -0.864). Falling Number (FN), which indicates α-amylase activity, showed a positive correlation with degree of softening (D.O.S) (r = 0.417) and a negative association with D.D.T (r = -0.394). Stirring Number (SN) showed positive correlations with SDS-SV (r = 0.383), STAB (r = 0.495), F.Q.N. (r = 0.385), F.D.T. (r = 0.401) and negative association with D.O.S. (r = -0.394). The Alveograph parameters provide significant understanding of the rheological properties of dough. Alveograph P (Alv_P) found positively correlated with F.W.A (r = 0.763) and Alveograph P/L ratio (Alve_P/L) (r = 0.876), supports its role in dough strength and elasticity. Alve_P/L also showed a strong positive association with F.W.A (r = 0.846). Alveograph Ie (Alve_le) found negatively correlated with Alve_P/L (r = -0.865) and Alv_P (r = -0.686). 3.3 Principal Component Analysis (PCA) of Quality Traits The PCA plot showed how the different flour and dough quality parameters interacted and contributed to the overall variation in the data. In this analysis, the first five principal components collectively accounted for 83.46% of the total variance in the dataset (Fig. 5 ). The first component (PC1) explained 36.46%, followed by PC2 (21.38%), PC3 (12.89%), PC4 (7.11%), and PC5 (5.62%). The steep decline in the eigenvalues after PC2 showed a natural “elbow” in the curve (Fig. 5 ), which confirms that PC1 and PC2 together possess the major trait variation and are appropriate for biplot construction and interpretation. The variable factor plot (Fig. 6 , top right) shows the traits in principal component space with their vector length. Variables such as P (%), WG (%), DG (%), bread weight, dough weight, and H.I loaded strongly and positively on PC1. These traits clustered directionally and are represented by long vector lengths, which indicates that these traits give both high contributions to the component and have strong inter-correlations. This suggests that PC1 reflects a latent dimension tied to compositional density and gluten/protein load, which are key factors in dough structure and bread volume. While PC2 was shaped largely by indicators of dough strength and baking quality. Traits including falling number (FN), loaf volume (LV), dough stability (STAB), and gluten index (GI) showed high positive loadings on PC2. While these traits are distinct from those defining PC1 in physiological function, their separation suggests they offer complementary to diversity in quality character. The genotype distribution biplot (Fig. 6 , top left) revealed clear clustering of genotypes based on trait profiles. A group comprising DBW 14, PBW 343, HD 2864, DBW 39, and PBW 175 was located in the upper-right quadrant, adjacent to vectors such as protein content, GI, LV, dough weight, and bread weight. While, genotypes such as PBW 590, HD 2888, and C 306 were positioned negatively along PC1 and/or PC2. Intermediate genotypes like CBW38, HD 2985, and HD 2967 occupied central positions, reflecting balanced trait profiles. Further resolution of genotype-trait interactions was studied through the grouped PCA plots in the lower panels of Fig. 6 . The bottom left panel shows a Glu-1 score category biplot of traits overlaid with confidence ellipses, showing natural groupings among quality parameters. GI, FN, STAB, and LV formed a clear cluster. While traits such as development time (D.D.T.), water absorption (F.W.A), and alveograph parameters (Alv_P, Alv_P/L, Alv_W) projected orthogonally or negatively along PC1. The bottom right panel of Fig. 6 mixes genotype labels with Glu-1 score categories, overlaid with coloured ellipses. Genotypes with high Glu-1 scores tended to cluster near the origin or along the positive axes of PC1 and PC2. These regions overlapped with key quality traits, including GI, FN, STAB, LV, and bread weight. Genotypes such as HD 2864, HD 2643, PBW 343, and PBW 175, were centrally located within the high-density ellipses. While genotypes like PBW 590 and C 306, which appeared at the periphery or outside these ellipses, corresponded to lower Glu-1 scores and distinct phenotypic traits. 3.4 Multiple Correspondence Analysis (MCA) of Glutenin Alleles Multiple Correspondence Analysis (MCA) was performed to investigate allelic variation at Glu-1 and Glu-3 loci, which contribute to genotypic diversity among wheat cultivars. The eigenvalue distribution (Fig. 7 ) indicates that first two MCA dimensions (Dim.) contributed the largest share with 30.7% of total variance, where first Dimension 1 (Dim.1) contributed the largest share with 16.2% of variance, while the second dimension 2 (Dim.2) explained 14.5%. Subsequent dimension contributed gradually smaller shares like Dim.3, Dim.4, and Dim.5, accounting 12.6%, 11.6%, and 9.5% respectively, cumulative explained variance of (64.40%) (Table 4). Thus, these dimensions reflected the most structurally informative aspects of the genotype allele relationship. The MCA individual-genotype map (Fig. 8 , left) showed groups of genotypes with specific allelic composition. Genotypes like HD 2888, C 306, and NI 5439 appeared at the edge of the Dim.1 and Dim.2 plane, indicating their distinct allelic compositions. For example, C 306 and HD 2888 have the alleles such as Glu-B3i , Glu-A3f , and the uncommon Glu-A1c allele. However, more centrally clustered genotypes such as DBW 17, PBW 343, and HD 2987 exhibited common and frequent allele combinations, such as Glu-B3c and Glu-D1a . The MCA variable contribution plot (Fig. 8 , right) further detailed the alignment of allelic categories along the principal dimensions. The Glu-A1c allele and Glu-B1e allele contributed strongly along Dim.1 and Dim.2 (contributions: 10.16 and 8.13, respectively). Similarly, Glu-B1f and Glu-B1j alleles also had substantial loadings and contributed to distinct positioning of genotypes such as CBW 38 and NI 5439. Dimensional contributions calculated from the MCA variable table showed that alleles such as Glu-A1c (Dim.1 = 1.40, Dim.2 = 1.19) and Glu-B1f (Dim.1 = -1.38, Dim.2 = -0.40) had the highest coordinate values. Whereas, more frequent alleles like Glu-A1b and Glu-B1c contributed less to variability and clustered near the origin. 4. Discussion The present study aimed to understand the correlations and associations between glutenin alleles and various quality and baking parameters. 4.1 Allelic Diversity at Glutenin Loci in Indian genotypes and Its Functional Implications In this Study, analysis of 18 Indian bread wheat genotypes was examined for glutenin subunit composition, dough rheology, and bread-making quality traits. Twelve high-molecular-weight glutenin subunit (HMW-GS) alleles across the Glu-1 loci and seven low-molecular-weight glutenin subunit (LMW-GS) alleles at Glu-3 confirms the genetic diversity of the studied Indian wheat population (Table-1). Systematic statistical approach involving multilocus co-occurrence (Burt table figure-1) and allele-specific phenotypic variability (Boxplots Fig. 2 ) was performed to understand influence of glutenin allelic combinations on wheat bread making quality. The Burt contingency table visualized the frequency of glutenin allele combinations (Glu-A1, Glu-B1, Glu-D1, Glu-A3, Glu-B3). Bars with greater height represented more frequent combinations in the studied genotypes. The most prominent peaks with presence in 9 genotypes, showed allelic combination of Glu-A1b (2*) + Glu-A3c + Glu-B3j + Glu-D1a (2 + 12). The second peak was observed for Glu-D1d (5 + 10) + Glu-B1 7 + 8 + Glu-B3b/g . while, alleles such as Glu-A1c (null), Glu-B1e (20), and Glu-A3f showed low-frequency bars. Boxplot (Figure-2) of Phenotypic traits such as loaf volume, bread weight, dough weight, and bread quality score in relation to different combinations of HMW-GS and LMW-GS offers valuable information for influence of Glu loci on bread quality. Among the Glu-A1 alleles, 2* was found as the most favorable, showing superior performance across all four measured traits, particularly in loaf volume and bread quality score (Figure-2). Genotypes carrying this allele have higher median values in bread quality score, loaf volume and dough weight. These findings are consistent with those of Zhang et al. (2022), who highlighted 2* as a beneficial allele for enhancing gluten strength and dough functionality. The Glu-B1 locus alleles Glu- B1i (17 + 18), Glu- B1c (7 + 9), and Glu- B1b (7 + 8) showed their strong association with improved dough weight and loaf volume. While allele Glu-B1e (20) consistently underperformed across all traits, which suggests its negative effect on gluten quality for bread making. The alleles like Glu-B1c (7 + 9) and Glu-B1i (17 + 18) align with superior performance, promoting polymeric protein formation and enhancing dough elasticity was also documented by Gianibelli et al. (2005). While, Glu-D1 locus, the Glu-D1d (5 + 10) allele clearly excelled out the Glu-D1a (2 + 12) allele for higher overall bread quality scores. Genotypes carrying Glu-D1d (5 + 10) showed tighter distribution by providing stronger and more extensible dough. These observations are supported by earlier studies (e.g., Gupta et al., 1994). The LMW-GS loci Glu-A3 and Glu-B3 are considered secondary contributors, but have significant impacts on quality traits. Within Glu-A3, allele b excelled in higher loaf volume and bread quality scores, and bread weight, which suggests a positive role in strengthening the gluten matrix. While Glu-B3 alleles g and j were consistently linked to better loaf volume and bread quality score, reinforcing their importance in dough baking performance. The Glu-1 score showed a strong and predictable relationship with all quality traits assessed. The Genotypes scores which have a Glu-1 Score greater than 7, consistently showed better loaf volumes, and the Bread Quality Score confirms their role in improved dough strength and bread making. This robust correlation supports the findings of (Payne et al., 1987; Vancini et al., 2019), who identified the Glu-1 score as a reliable predictor of baking quality. 4.2. Correlation among Wheat Quality Trait s The present study highlighted correlations among the physiochemical and rheological traits, influencing the biochemical and functional basis of wheat processing quality. Grain hardness (HI) in this study showed correlation with multiple traits, including positive correlation with DG, D.O.S, Alv_P, dough weight, and bread weight, while negative with GI, Alve_Ie, and BQS. Grain hardness (HI) plays a crucial role in starch damage during milling, which promotes water absorption followed by an influence on flour characteristics, particle size dynamics, and protein-starch interrelationship, which plays a crucial role in baking and end use quality. These findings confirm the established role of grain hardness in flour refinement and dough behavior (Campbell et al., 2007). Protein content (P%) showed strong positive correlations with WG, DG, and SDS-SV, confirming its significant role in dough formation and strength. High protein levels facilitate the development of a cohesive gluten matrix, essential for maintaining dough elasticity & extensibility and gas retention during fermentation (Shewry & Halford, 2002). This association between protein and dough strength supports its role as a potential predictor of breadmaking quality. Though the observed variability among high protein genotypes suggests that protein quantity alone does not promise performance consistency. Falling number (FN) found positively associated with dough development time (r = 0.394) and negatively with degree of softening (r = 0.417), showing that low enzymatic activity enhances dough stability, and high α-amylase weakens dough structure by degrading starch excessively. Similar patterns were reported in studies exploring enzyme impacts on dough rheology (Barrera et al., 2016 ). Stirring Number (SN) is inversely related to the α-amylase activity in flour, which means higher SN values indicate lower enzymatic degradation of starch, thus greater viscosity stability. This behavior mirrors the observation of Falling Number tests, and both parameters are important in finding sprout-damaged potential of the grain. In the current study, SN values were found positively correlated with STAB and negatively with DOS, supporting the idea that low enzymatic activity favors dough strength and structural integrity (Liu et al., 2023). Positive correlation of SN with SDS-SV, wet gluten, and dry gluten content was observed, the latter are the indicators of protein quality and gluten matrix potential of dough. This correlation suggests that high SN flours preserve the integrity of gluten forming proteins by having low α-amylase activity during dough formation. These findings are consistent with previous work on gluten performance under variable enzyme conditions (Shewry & Halford, 2002). Positive correlations between SN and both STAB and SDS-SV suggest that flour with higher SN values tends to produce doughs with more consistent gluten networks. One possible explanation for these rheological benefits is that a more intact starch phase interacts more effectively with gluten proteins, enhancing elasticity and network formation. This interaction becomes critical in industrial baking, where predictable mixing and expansion behaviour are essential. High SN values found negatively correlated with bread weight and Alve_W (dough's baking strength), which reflects that flours with optimized SN are good for bread structural uniformity and volume. However, extreme SN values may indicate overly low enzyme activity, which could affect Maillard reactions and crust formation. Among the Farinograph traits, dough stability (STAB) showed positive correlations with protein %, FN and SN support the interpretation that stronger gluten matrices resist breakdown during mixing. While STAB and degree of softening (D.O.S.) have an inverse correlation represent its positive connection with the mixing tolerance of dough. Farinograph arrival time (F.A.T.) and departure time (F.D.T.), which together define the dough development property, showed positive correlation with both STAB and sedimentation volume. These associations suggest that flours with longer development and stability times generally form cohesive gluten networks, leading to better gas retention and higher loaf volumes (Shewry & Halford, 2002). The correlations observed in this study align with industrial expectations for bread flours, which are high water absorption (≥ 60%), dough development time ≥ 3 minutes, and STAB ≥ 8 minutes (Sumit Kr Singh et.al. 2024 ). Alveograph data added further nuance to the study and offered details of dough strength and extensibility. In alveograph traits, dough tenacity (Alv_P) showed a clear positive correlation with both the Alve_P/L & Alve_G and negative correlation with Alve_L & Alve_le indicates that a strong gluten network maintains dough resilience and stronger doughs resist deformation but are less extensible. This observation aligns with the findings of Delcour & Hoseney, 2010 and Rojas et al., 1999. 4.5. Principal Component Analysis (PCA) PCA effectively reduced traits dimensionality and revealed major contributors to wheat quality variability. The first three principal components explained over 70% of the total variation, with PC1 reflecting dough strength attributes—mainly farinograph traits (STAB, F.Q.N., F.D.T.) and alveograph tenacity (Alv_P, Alve_P/L). PC2 captured protein-related traits (P%, DG%, WG%) and end-use characteristics like bread and dough weight. These findings underscore the close alignment between gluten composition and baking performance, consistent with Shewry & Halford (2002) and Rakszegi et al. (2019). The first three principal components explained over 70% of the total variation. The PC1 found associated with farinographic and alveograph parameters including dough stability (STAB), farinograph quality number (F.Q.N.), departure time (F.D.T.), Alveograph P (Alv_P), and Alve_P/L ratio. This shows that PC1 reflects dough strength and its resistance to deformation. Whereas, PC2 possess mainly protein related traits, including protein percentage (P%), dry gluten (DG%), wet gluten (WG%), and bread weight, which describe its inclination towards compositional attributes and influence on baking performance. These findings align with earlier reports emphasizing the association among gluten composition, gluten strength and farinograph indices for baking quality (Shewry & Halford, 2002; Rakszegi et al., 2019). This analysis also showed a cluster of traits (Figure) related to gluten composition (WG%, DG%, SDS-SV) with P% and baking traits such as dough weight and bread weight. This highlights the shared biochemical basis of gluten polymerization and water absorption influenced by the interactions between gluten proteins and water molecules. On the other hand, traits such as Alve_Ie and Alve_L found opposite to Alve_P/L and Alve_P ratio, confirm the antagonist relationship between dough strength and extensibility (Delcour & Hoseney, 2010). Genotypes such as HD 2643, DBW 14, and PBW 343 aligned closely with PC1 and PC2 vectors and represented their superior dough strength and gluten quality for bread making. These genotypes clustered within the inner ellipse of the Glu-1 score filtered biplot, which signifies their stability and desirable rheological parameters. While genotypes like PBW 590 and CBW 38 were observed far from the central cluster, which indicates their lower gluten strength and potential suitability for end-products required low gluten strength like, biscuits and cookies. These biplot findings are consistent with Glu-1 scoring systems for genotype classification in bread wheat (Feldman & Sears, 1981; Zhao et al., 2021). 4.6. Multiple Correspondence Analysis (MCA): Glu-1 Allelic Differentiation and Quality Traits The Multiple Correspondence Analysis (MCA) provided the dissection of categorical variation in glutenin allele compositions and their influence on quality traits. The first two dimensions, Dim 1 (16.2%) and Dim 2 (14.5%), together explained 30.7% of the total inertia. MCA biplots showed that HMW-GS alleles such as Glu-A1b (2*), Glu- B1i (17 + 18), and Glu-D1d (5 + 10) aligned with genotypes like HD 2967 and HD 2985, which confirm its linkage with superior gluten strength and bread making quality traits. These alleles are widely recognized for enhancing viscoelastic dough properties due to their high expression and disulfide-bonding potential (Payne et al., 1987; Branlard et al., 2001). While genotypes such as C 306 and CBW 38 showed allelic combinations like Glu-A1c (null) and Glu-B1j , and distant from these quality trait vectors. This reflects their association with weaker gluten networks. The Glu-A1c (null) allele is mainly associated with distinct overall gluten strength (Juhász et al., 2013; Shewry et al., 2009). Genotypes such as C 306 and CBW 38 showed allelic combinations like Glu-A1c (null) and Glu-B1j , and distant from quality trait vectors. This reflects their association with weaker gluten networks. The Glu-A1c (null) allele is mainly associated with the absence of HMW-GS at the Glu-A1 loci and thus reduces the overall gluten strength (Juhász et al., 2013; Shewry et al., 2009). The Glu-b3 alleles Glu-B3j demonstrated a small discriminative influence when paired with dominant Glu-1 alleles for gluten strength and the bread quality traits. The findings mainly involve combinations linking subunits 5 + 10 ( Glu-D1d ) with superior viscoelastic properties compared to 2 + 12 ( Glu-D1a ), (Liu et al., 2011; Zhao et al., 2021). This finding provides an idea regarding genotypes harbouring HMW-GS 5 + 10 mostly clusters with high STAB, FQN, and SDS-SV, reinforcing its breeding relevance. This MCA plot also revealed an interesting fact that some genotypes like CBW 38, HD 2987 and HD2643, with good reported allelic combinations for bread making quality, did not cluster tightly with trait vectors. This suggests possible G×E interactions or the presence of epistatic modifiers influencing expression. Conclusion This study confirms that genetic variation at glutenin loci has a direct impact on the functional quality of wheat flour, particularly for breadmaking in Indian wheat genotypes. The combination of high- and low-molecular-weight glutenin subunits plays a key role in shaping dough properties like stability and loaf volume. Genotypes with Glu-1 scores higher than seven consistently showed better breadmaking performance, which aligns with both their molecular composition and dough characteristics. These findings are valuable for Indian breeding programs aiming at market-driven wheat improvement. The integration of molecular profiling and comprehensive rheological assessment provides a robust framework for identifying superior wheat cultivars tailored for industrial applications. Combining genetic markers with detailed rheological analysis offers an approach to improve food quality and security through more targeted wheat breeding strategies. Declarations Author Contribution S.K.S. conceived the study, designed the experiments, performed data analysis and prepared the main manuscript text. A.M.S. supervised glutenin profiling, contributed to rheological data interpretation and assisted in manuscript revision. A.K.A. coordinated field experiments, managed sample collection and A.M. assisted in statistical analyses, provided support in reviewing and editing the manuscript. All authors reviewed and approved the final manuscript. Data Availability The datasets generated and/or analysed during the current study are available from the corresponding author (S.K.S.) on reasonable request. References Aydoğan S, Akçacik ŞAHİNM, HAMZAOĞLU AG, S., Taner S (2015) Relationships between Farinograph Parameters and Bread Volume, Physicochemical Traits in Bread Wheat Flours. 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Foods 13(7) Article 7. https://doi.org/10.3390/foods13071125 Zhen Z, Mares D (1992) A simple extraction and one-step SDS–PAGE system for separating HMW And LMW glutenin subunits of wheat and high molecular weight proteins of rye. J Cereal Sci 15(1):63–78. https://doi.org/10.1016/S0733-5210(09)80057-X Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Jan, 2026 Reviewers agreed at journal 17 Jan, 2026 Reviewers invited by journal 15 Jan, 2026 Editor assigned by journal 12 Dec, 2025 Submission checks completed at journal 12 Dec, 2025 First submitted to journal 11 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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14:21:49","extension":"xml","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136252,"visible":true,"origin":"","legend":"","description":"","filename":"233a4d8341994364a2d2a14de4c0e0521structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8338707/v1/3dd2e84d3c20bc9baa7f2d30.xml"},{"id":100692971,"identity":"dc12dca2-b4da-4557-9c3c-c32ee0310e04","added_by":"auto","created_at":"2026-01-20 14:24:28","extension":"html","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148745,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8338707/v1/7380195c3acf7cf069f27278.html"},{"id":100693391,"identity":"11521c6a-dd0b-4d31-887c-1819101da8a2","added_by":"auto","created_at":"2026-01-20 14:28:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69490,"visible":true,"origin":"","legend":"\u003cp\u003e3D Burt matrix of glutenin loci showing inter-category co-distributions. A three-dimensional bar plot depicting the multivariate Burt contingency matrix for glutenin allele categories (Glu-A1, Glu-B1, Glu-D1, Glu-A3, Glu-B3) across 18 wheat genotypes. Taller bars indicate more frequent co-occurring allele combinations.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8338707/v1/2e1e9641d4da8314bf4313fe.jpg"},{"id":100692841,"identity":"1ac20da2-be63-4449-9091-07b009924264","added_by":"auto","created_at":"2026-01-20 14:22:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104806,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot comparison of quantitative wheat quality traits across allelic categories of glutenin markers. Grouped boxplots representing the distribution of key quantitative traits stratified by alleles at Glu-1 and Glu-3 loci.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8338707/v1/5606bbc5c846213d1d023104.jpg"},{"id":100693029,"identity":"7741c855-ec52-4600-95aa-1a8310a9364a","added_by":"auto","created_at":"2026-01-20 14:25:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125728,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation matrix (α = 0.05) showing significant associations among 27 studied quantitative quality traits in 18 wheat genotypes. The heatmap presents pairwise Pearson correlation coefficients, where green cells represent high positive correlations and red cells denote significant high negative correlations.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8338707/v1/1d4d8906bf099e2db31c8d77.jpg"},{"id":100692817,"identity":"5decba29-3ac4-49e2-b3c5-36e0b01da8b5","added_by":"auto","created_at":"2026-01-20 14:22:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46939,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot showing the variance explained by principal components. A scree plot displaying the eigenvalues and proportion of variance explained by each of the first several principal components. PC1 and PC2 together explain the major share (57.84%) of total variability, with a clear “elbow point” after PC2.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8338707/v1/a674e43f0de574755bd863ce.jpg"},{"id":100692972,"identity":"fd909795-92eb-4b5d-a3eb-a6c80d7b6eda","added_by":"auto","created_at":"2026-01-20 14:24:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110020,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) biplots of wheat genotypes and quality traits. Top left: genotype distribution biplot showing clustering based on trait profiles. Top right: variable factors (Studied traits) plot highlighting trait loadings on PC1 and PC2. Bottom panels: grouped PCA plots showing Glu-1 score categories and genotype clustering with confidence ellipses.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8338707/v1/fb47b527df8710ad6c05e673.jpg"},{"id":100693325,"identity":"f10e48ac-cd47-4cc6-ab21-1193e813d05a","added_by":"auto","created_at":"2026-01-20 14:27:49","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":46993,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot of explained variance by MCA dimensions. Eigenvalue distribution of Multiple Correspondence Analysis (MCA) showing Dim.1 and Dim.2 together account for 30.7% of total variance.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8338707/v1/fb772a769047397a345b5cae.jpg"},{"id":100692943,"identity":"11ea8f5f-7bab-4b57-b211-6ab4cfdae176","added_by":"auto","created_at":"2026-01-20 14:24:12","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":93655,"visible":true,"origin":"","legend":"\u003cp\u003eMCA biplots of genotypes and glutenin allele categories (left) and variable contribution plot (right). (Left) MCA map illustrating genotype separation along Dim.1 and Dim.2 based on HMW-GS and LMW-GS allelic composition. (Right) Variable contribution plot showing how glutenin alleles contribute to dimensional variance and discriminate genotype groups. Alleles with high contribution vectors indicate stronger roles in defining genomic structure and associated quality traits.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8338707/v1/ce6e85fad2ccd21d7e320379.jpg"},{"id":100700662,"identity":"c384ff0c-4df2-42af-ac35-9bacb3421548","added_by":"auto","created_at":"2026-01-20 15:55:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1546649,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8338707/v1/6169bd3e-50ab-4922-869e-385839902d5a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Glutenin Subunit Diversity and its Relation with Dough Properties and Breadmaking Quality of Wheat","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is one of the major commodities for the global agricultural and food security especially in India. With evolving consumer preferences and the rapid expansion of the industrial baking sector, conventional breeding efforts centred solely on maximising yield are proving insufficient. Increasing emphasis on improving grain quality, particularly for bread making applications has become imperative (Singh et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The bread making potential of wheat largely depends on the two major storage protein fractions glutenins and gliadins, which together network to form gluten and determine essential dough properties such as water absorption, elasticity, strength and gas retention. The glutenins comprise of the high molecular eight gluten subunits (HMW-GS) and the low molecular weight gluten subunits (LMW-GS). These proteins are encoded by the genes located at the \u003cem\u003eGlu-1\u003c/em\u003e and \u003cem\u003eGlu-3\u003c/em\u003e loci. The alleles in these genes differ significantly across Indian wheat cultivars as assessed with biochemical and molecular markers (Rai et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHMW-GS are reported to play a pivotal role in enhancing dough strength, while LMW-GS predominantly contribute to dough extensibility and influence loaf texture (Rasheed et al, 2014, Franaszek \u0026amp; Salmanowicz, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Wheat flour quality is assessed using rheological tools such as farinograph, alveograph, mixograph etc. as per a country\u0026rsquo;s preference. These instruments simulate dough behaviour under mixing and deformation conditions, which evaluate crucial parameters like dough development time, water absorption, tenacity and extensibility (Cecchini et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While past studies have explored individual traits, there is a lack of integrated research connecting molecular glutenin profiling with dough rheology and bread making performance (Pourmohammadi et al., 2023).\u003c/p\u003e \u003cp\u003eIn the present study, the relationships between HMW-GS and LMW-GS glutenin subunits and farinograph and alveograph parameters are assessed to understand their combined influence on bread loaf volume in Indian wheat genotypes to assist breeders in selecting for specific glu alleles or allele combinations along with other economic traits to produce product specific, high yielding cultivars.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental Setup and Field Conditions\u003c/h2\u003e \u003cp\u003eThis study examined grain quality traits and rheological parameters along with bread making quality in 18 diverse bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) genotypes The trials were conducted over two consecutive crop seasons (2021\u0026ndash;22 and 2022\u0026ndash;23), at the experimental farm of the Indian Agricultural Research Institute (ICAR-IARI), New Delhi, positioned at an elevation of 228.61 meters above sea level. Situated within a semi-arid, sub-tropical agro-climatic zone, the region experienced seasonal rainfall levels of 181.5 mm in 2021\u0026ndash;22 and 138.4 mm in 2022\u0026ndash;23.\u003c/p\u003e \u003cp\u003eSowing was carried out using a randomised complete block design (RCBD) with two replications and 5 row plots of 2.5m each. Uniform fertilisers and irrigations were given across all genotypes to maintain crop health and achieve physiological maturity under optimal conditions. This facilitated reliable comparisons of flour quality traits across genotypic variations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Grain Collection, Sample Preparation and Milling\u003c/h2\u003e \u003cp\u003eGenotypes were harvested at physiological maturity and stored at standard storage environments. Milling was performed using a Quadrumat Senior mill (Brabender, Germany) to produce refined wheat flour (Maida) and was subsequently stored in airtight containers at 4\u0026deg;C until further analysis.\u003c/p\u003e \u003cp\u003eGrain Quality Traits:\u003c/p\u003e \u003cp\u003eGluten parameters, including gluten index (GI), wet gluten (WG) and dry gluten (DG) were assessed using the Glutomatic 2200 (Perten Instruments) and the falling number (FN) was determined using the falling number tester apparatus following the standard AACC (2000) procedures. The Sodium Dodecyl Sulfate Sedimentation Volume (SDS-SV) of samples was evaluated using the standard method described by Axford et al. in 1979.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Grain Hardness Index\u003c/h2\u003e \u003cp\u003eThe grain hardness index (GHI) was evaluated using single-kernel characterization system (SKCS) 4100 (Perten Instruments, Australia), as per the procedure described in AACC (2000) method 55\u0026thinsp;\u0026minus;\u0026thinsp;31.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Glutenin Subunit Extraction and SDS-PAGE Profiling\u003c/h2\u003e \u003cp\u003eThe glutenin subunit composition of high molecular weight gluten subunit (HMW-GS) types was determined using a modified version of the method described by Zhen and Mares \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1992\u003c/span\u003e. where 20 mg of flour from each genotype was mixed with 70% ethanol (v/v) for 30 min to wash out gliadins. The mixture was centrifuged at 5000 rpm for 10 min, and the supernatant was discarded. The pellet was treated with an extraction buffer (2% SDS, 1% DTT in 0.5 M Tris-HCl, pH 6.8). The mixture was incubated at 65\u0026deg;C for 30 minutes to solubilize the glutenin fractions followed by vortex for 100 min and centrifuged at 10,000 rpm for 10 min. The supernatant containing glutenin protein was collected and SDS\u0026ndash;polyacrylamide gel electrophoresis (SDS-PAGE) analysis was performed on a 12% SDS-PAGE gel, which was run at 100 V for 4\u0026ndash;5 hours. After electrophoresis, gels were stained in a solution of 0.1% w/v Commissive brilliant blue R-250, 50% w/v methanol, and 10% w/v trichloroacetic acid. After staining, the gels were rinsed with distilled water and washed on a horizontal shaker until the excess dye was removed. The glutenin subunit bands were identified based on known standards (Payne \u0026amp; Lawrence, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), using reference cultivars with known HMW-GSs. Glu-1 scores of the studied lines were determined based on the numeric scale developed by Payne et al. for HMW-GS alleles (Payne, 1987).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Flour Rheology Assessment via Farinograph\u003c/h2\u003e \u003cp\u003eThe rheological properties of the wheat flour samples were evaluated using a two lane Brabender Farinograph (C.W. Brabender Instruments, Germany) following \u003cb\u003eAACC Method 54-21.01\u003c/b\u003e. For each genotype, 300 g of flour (14% moisture basis) was used per test run. The farinograph parameters recorded are Water absorption (WA), dough development time (DDT), dough stability (STAB), degree of softening (DOS), and farinograph quality number (FQN). These parameters helped in understanding of gluten behaviour during mixing, a determining factor in large scale industrial bread production (Rai et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Dough Extensibility Evaluation via Alveograph\u003c/h2\u003e \u003cp\u003eTo further assess dough quality, extensibility and gas retention capacities were measured using a Chopin Alveograph (Tripette \u0026amp; Renaud, France) as per \u003cb\u003eAACC Method 54-30A\u003c/b\u003e. Dough samples were prepared under controlled conditions and rested before testing. The alveograph parameters recorded are Tenacity (P), Extensibility (L), P/L ratio, Deformation energy (W, in 10⁻⁴ J). These parameters helped in deeper understanding of the dough\u0026rsquo;s viscoelastic nature, including extension and extensibility, which directly impact bread texture, crumb structure, and final loaf volume.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Falling Number (FN) and Stirring Number (SN)\u003c/h2\u003e \u003cp\u003eFalling Number was measured to assess α-amylase activity in the flour samples using protocol described by Hagberg (1960) and adopted by the AACC International Method (56-81.03). Falling Number (FN) was determined using the Perten Falling Number 1500 system (Perten Instruments AB, Sweden). And, Stirring Number (SN) was measured using the Rapid Visco Analyser (RVA-4500, Perten Instruments), operated under AACC Method 76-21.02.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Bread-Making and Loaf Volume Estimation\u003c/h2\u003e \u003cp\u003eTo evaluate bread-making performance, a straight-dough method (AACC 2010, 10.09 .01) was employed. Each flour sample was blended with key ingredients: Flour 100gm, Dry yeast 2.5gm Sugar 5.0gm, Salt 2.0gm, Ghee 3.0gm, and Water adjusted according to farinograph water absorption values. The dough was mixed and fermented (for 90 minutes at 30\u0026deg;C) (Dexter et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Sapirstein et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). After fermentation, dough was gently punched down to release trapped gas. The dough has been allowed to rest and rise after being shaped for the final time before baking. Baking was performed at 220\u0026deg;C for 25 minutes. The loaf volume was measured using the rapeseed displacement technique and recorded in cubic centimetres (cm\u0026sup3;) using a loaf volumeter (National Inc.).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive Analysis\u003c/h2\u003e \u003cp\u003eThe descriptive evaluation of qualitative genetic traits across the wheat genotypes revealed allelic diversity and genetic complexity inherent in the studied population. In the studied genotype (As presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the number of allelic groups varied appreciably, suggesting substantial genetic heterogeneity across loci.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTotal Twelve HMW-GS alleles at locus Glu-1 and seven LMW-GS at locus Glu-3 were detected in studied lines (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The allele \u003cem\u003eGlu-A1b\u003c/em\u003e (subunit 2*) at the Glu-A1 locus emerged as the most frequently occurring allele (66.7%), followed by alleles \u003cem\u003eGlu-A1a\u003c/em\u003e (subunit 1) and \u003cem\u003eGlu-A1c\u003c/em\u003e (subunit null), each contributing 16.7%. Similarly, at Glu-1 Locus, \u003cem\u003eGlu-B1u\u003c/em\u003e (subunit 7\u0026thinsp;+\u0026thinsp;8) allele was found as the predominant type (27.8%) followed by \u003cem\u003eGlu-B1c\u003c/em\u003e (subunit 7\u0026thinsp;+\u0026thinsp;9) and \u003cem\u003eGlu-B1i\u003c/em\u003e (subunit 17\u0026thinsp;+\u0026thinsp;18) in 22.2% of cases. Lower frequencies were recorded for alleles \u003cem\u003eGlu-B1f\u003c/em\u003e (subunit 13\u0026thinsp;+\u0026thinsp;16), \u003cem\u003eGlu-B1e\u003c/em\u003e (subunit 20), and \u003cem\u003eGlu-B1a\u003c/em\u003e (subunit 7). While at Glu-D1 locus, almost equal split between \u003cem\u003eGlu-D1d\u003c/em\u003e (subunit 5\u0026thinsp;+\u0026thinsp;10) at 44.4% and \u003cem\u003eGlu-D1a\u003c/em\u003e (subunit 2\u0026thinsp;+\u0026thinsp;12) at 55.6%. For LMW-GS, Glu-A3 locus was dominated by allele \u003cem\u003eGlu-A3c\u003c/em\u003e (72.2%), followed by\u003cem\u003eGlu-A3b\u003c/em\u003e allele (22.2%) and \u003cem\u003eGlu-A3f\u003c/em\u003e allele (5.6%). But Glu-B3 loci showed substantial diversity with most frequent \u003cem\u003eGlu-B3j\u003c/em\u003e allele (33.3%) and alleles \u003cem\u003eGlu-B3b, Glu-B3g and Glu-B3i\u003c/em\u003e showed frequencies 22.2% by each. A Burt table (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was constructed to explore interactions among alleles and identify dominant co-occurrence frequencies. Notably, Glu-A1 allele 2 (b)* frequently co-occurred with Glu-D1 allele 2\u0026thinsp;+\u0026thinsp;12 (6 varieties), Glu-A3 allele c (9 varieties), and Glu-B3 allele j (3 varieties).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of qualitative glutenin markers (Glu-A1, Glu-B1, Glu-D1, Glu-A3 and Glu-B3) across 18 wheat genotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequencies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu-A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.6667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2* (b)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.6667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enull (c)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.6667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu-B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026thinsp;+\u0026thinsp;9 (c)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.2222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026thinsp;+\u0026thinsp;8 (u)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.7778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.1111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026thinsp;+\u0026thinsp;16 (f)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u0026thinsp;+\u0026thinsp;18 (i)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.2222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu-D1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026thinsp;+\u0026thinsp;12 (a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.5556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026thinsp;+\u0026thinsp;10 (d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.4444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu-A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.2222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.2222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu-B3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.2222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.2222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.2222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ej\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.3333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe multivariate contingency matrix was displayed in a 3D bar chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cem\u003e3D view of the Burt table\u003c/em\u003e). This helped to describe the interaction between the genetic structure of quality traits and the overlap in allele frequencies across the studied genotypes. In the studied genotype, \u003cem\u003eGlu-A1b\u003c/em\u003e (subunit 2*) frequently cooccurred with \u003cem\u003eGlu-D1a\u003c/em\u003e (subunit 2\u0026thinsp;+\u0026thinsp;12), \u003cem\u003eGlu-A3c\u003c/em\u003e, and \u003cem\u003eGlu-B3j\u003c/em\u003e, suggesting frequent allelic associations among certain varieties. The \u003cem\u003eGlu-B1u\u003c/em\u003e (subunit 7\u0026thinsp;+\u0026thinsp;8) also commonly cooccurred with \u003cem\u003eGlu-D1d\u003c/em\u003e (subunit 5\u0026thinsp;+\u0026thinsp;10) and \u003cem\u003eGlu-B3b\u003c/em\u003e, which formed another prominent interaction cluster.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive summary of quantitative grain, dough rheology and breadmaking quality traits evaluated in 18 wheat genotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of observations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariance (n-1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard deviation (n-1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu-1 Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH.I.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e503.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF.W.A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.D.T.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.O.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e979.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF.Q.N.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2239.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF. A.T.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF.D.T.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.2522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOUGH WT.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e167.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBREAD WEIGHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e159.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e154.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOAF VOLUME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e565.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1026.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBREAD QUALITY SCORE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA comprehensive descriptive analysis of 27 quantitative quality related parameters revealed considerable variability among the 18 wheat genotypes examined (Table-2). All traits were fully recorded without any missing data, which ensured reliability in statistical evaluations. The Glu-1 score was observed in a broad range from 4.00 to 10.00, with a mean value of 7.94 (SD\u0026thinsp;=\u0026thinsp;1.70), while hardness index (HI), varied between 65.67 to 90.01 (Mean\u0026thinsp;=\u0026thinsp;77.34, SD\u0026thinsp;=\u0026thinsp;6.11). Grain protein (P%) content showed moderate variation, averaging 12.39% (SD\u0026thinsp;=\u0026thinsp;0.94). The Falling Number of flour (FN) ranged widely from 483 to 967 (mean\u0026thinsp;=\u0026thinsp;759.72, SD\u0026thinsp;=\u0026thinsp;110.31), and the Stirring Number of flour (SN) from 1443.00 to 2173.00 (mean\u0026thinsp;=\u0026thinsp;1761.56, SD\u0026thinsp;=\u0026thinsp;194.59). The farinograph water absorption (FWA) was recorded as the mean value of 63.48% (SD\u0026thinsp;=\u0026thinsp;2.66).. Dough rheology parameters such as dough development time (DDT) (M\u0026thinsp;=\u0026thinsp;7.46 min, SD\u0026thinsp;=\u0026thinsp;7.86) and dough stability (STAB) (M\u0026thinsp;=\u0026thinsp;7.11 min, SD\u0026thinsp;=\u0026thinsp;3.43) exhibited wide fluctuations, reinforcing the genotypic differences in gluten strength and dough-handling properties (Aaliya et al., 2021; Tozatti et al., 2020). Evaluations of gluten quality highlighted substantial variations in wet gluten (M\u0026thinsp;=\u0026thinsp;27.22%, SD\u0026thinsp;=\u0026thinsp;3.62), dry gluten (M\u0026thinsp;=\u0026thinsp;9.30%, SD\u0026thinsp;=\u0026thinsp;1.19), and gluten index (M\u0026thinsp;=\u0026thinsp;73.06, SD\u0026thinsp;=\u0026thinsp;22.43). The alveograph (Alve) with W values (baking strength) ranging from 128.00 to 257.00 (M\u0026thinsp;=\u0026thinsp;173.78, SD\u0026thinsp;=\u0026thinsp;43.18) and P/L ratios from 0.58 to 3.89 (M\u0026thinsp;=\u0026thinsp;1.59, SD\u0026thinsp;=\u0026thinsp;0.92). The descriptive statistics of bread baking quality traits and other studied traits are also represented in Table-2). To better understand how allelic differences correspond with bread baking quality traits, grouped boxplots were employed to visualise the distribution of quantitative traits across allelic categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation Analysis\u003c/h2\u003e \u003cp\u003ePearson correlation matrix (Table-3) was generated at 95% significance level (α\u0026thinsp;=\u0026thinsp;0.05). This statistical approach helped identify significant associations between variables, shedding light on the underlying physiological and biochemical traits that govern wheat quality. The grain hardness index demonstrated positive correlation with bread weight (Wt) (r\u0026thinsp;=\u0026thinsp;0.68), dough Wt (r\u0026thinsp;=\u0026thinsp;0.54), gluten index (GI) content (r\u0026thinsp;=\u0026thinsp;0.41), farinograph degree of softening (D.O.S) (r\u0026thinsp;=\u0026thinsp;0.42) and Alve_P (r\u0026thinsp;=\u0026thinsp;0.37). Protein content (P%) showed positive correlations with multiple quality-related traits, including Wet Gluten (WG%) (r\u0026thinsp;=\u0026thinsp;0.613), Dry Gluten (DG%) (r\u0026thinsp;=\u0026thinsp;0.691), SDS-SV (ml) (r\u0026thinsp;=\u0026thinsp;0.480).\u003c/p\u003e \u003cp\u003eProtein (P%) found correlated with dough stability (STAB; r\u0026thinsp;=\u0026thinsp;0.408), farinograph arrival time (F.A.T.; r\u0026thinsp;=\u0026thinsp;0.339), and farinograph departure time (F.D.T.; r\u0026thinsp;=\u0026thinsp;0.240). Moreover, P% also showed positive correlations with bread weight (r\u0026thinsp;=\u0026thinsp;0.511) and dough weight (r\u0026thinsp;=\u0026thinsp;0.386). Dough Stability (STAB) known as an indicator of overall dough strength, showed strong positive correlations with F.Q.N. (r\u0026thinsp;=\u0026thinsp;0.915), F.A.T. (r\u0026thinsp;=\u0026thinsp;0.566), and F.D.T. (r\u0026thinsp;=\u0026thinsp;0.944). It also showed a strong negative relationship with D.O.S. (r = -0.864). Falling Number (FN), which indicates α-amylase activity, showed a positive correlation with degree of softening (D.O.S) (r\u0026thinsp;=\u0026thinsp;0.417) and a negative association with D.D.T (r = -0.394). Stirring Number (SN) showed positive correlations with SDS-SV (r\u0026thinsp;=\u0026thinsp;0.383), STAB (r\u0026thinsp;=\u0026thinsp;0.495), F.Q.N. (r\u0026thinsp;=\u0026thinsp;0.385), F.D.T. (r\u0026thinsp;=\u0026thinsp;0.401) and negative association with D.O.S. (r = -0.394).\u003c/p\u003e \u003cp\u003eThe Alveograph parameters provide significant understanding of the rheological properties of dough. Alveograph P (Alv_P) found positively correlated with F.W.A (r\u0026thinsp;=\u0026thinsp;0.763) and Alveograph P/L ratio (Alve_P/L) (r\u0026thinsp;=\u0026thinsp;0.876), supports its role in dough strength and elasticity. Alve_P/L also showed a strong positive association with F.W.A (r\u0026thinsp;=\u0026thinsp;0.846). Alveograph Ie (Alve_le) found negatively correlated with Alve_P/L (r = -0.865) and Alv_P (r = -0.686).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Principal Component Analysis (PCA) of Quality Traits\u003c/h2\u003e \u003cp\u003eThe PCA plot showed how the different flour and dough quality parameters interacted and contributed to the overall variation in the data. In this analysis, the first five principal components collectively accounted for 83.46% of the total variance in the dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The first component (PC1) explained 36.46%, followed by PC2 (21.38%), PC3 (12.89%), PC4 (7.11%), and PC5 (5.62%). The steep decline in the eigenvalues after PC2 showed a natural \u0026ldquo;elbow\u0026rdquo; in the curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which confirms that PC1 and PC2 together possess the major trait variation and are appropriate for biplot construction and interpretation.\u003c/p\u003e \u003cp\u003eThe variable factor plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003e, top right) shows the traits in principal component space with their vector length. Variables such as P (%), WG (%), DG (%), bread weight, dough weight, and H.I loaded strongly and positively on PC1. These traits clustered directionally and are represented by long vector lengths, which indicates that these traits give both high contributions to the component and have strong inter-correlations. This suggests that PC1 reflects a latent dimension tied to compositional density and gluten/protein load, which are key factors in dough structure and bread volume. While PC2 was shaped largely by indicators of dough strength and baking quality. Traits including falling number (FN), loaf volume (LV), dough stability (STAB), and gluten index (GI) showed high positive loadings on PC2. While these traits are distinct from those defining PC1 in physiological function, their separation suggests they offer complementary to diversity in quality character.\u003c/p\u003e \u003cp\u003eThe genotype distribution biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003e, top left) revealed clear clustering of genotypes based on trait profiles. A group comprising DBW 14, PBW 343, HD 2864, DBW 39, and PBW 175 was located in the upper-right quadrant, adjacent to vectors such as protein content, GI, LV, dough weight, and bread weight. While, genotypes such as PBW 590, HD 2888, and C 306 were positioned negatively along PC1 and/or PC2. Intermediate genotypes like CBW38, HD 2985, and HD 2967 occupied central positions, reflecting balanced trait profiles.\u003c/p\u003e \u003cp\u003eFurther resolution of genotype-trait interactions was studied through the grouped PCA plots in the lower panels of Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The bottom left panel shows a Glu-1 score category biplot of traits overlaid with confidence ellipses, showing natural groupings among quality parameters. GI, FN, STAB, and LV formed a clear cluster. While traits such as development time (D.D.T.), water absorption (F.W.A), and alveograph parameters (Alv_P, Alv_P/L, Alv_W) projected orthogonally or negatively along PC1.\u003c/p\u003e \u003cp\u003eThe bottom right panel of Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003e mixes genotype labels with Glu-1 score categories, overlaid with coloured ellipses. Genotypes with high Glu-1 scores tended to cluster near the origin or along the positive axes of PC1 and PC2. These regions overlapped with key quality traits, including GI, FN, STAB, LV, and bread weight. Genotypes such as HD 2864, HD 2643, PBW 343, and PBW 175, were centrally located within the high-density ellipses. While genotypes like PBW 590 and C 306, which appeared at the periphery or outside these ellipses, corresponded to lower Glu-1 scores and distinct phenotypic traits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Multiple Correspondence Analysis (MCA) of Glutenin Alleles\u003c/h2\u003e \u003cp\u003eMultiple Correspondence Analysis (MCA) was performed to investigate allelic variation at Glu-1 and Glu-3 loci, which contribute to genotypic diversity among wheat cultivars. The eigenvalue distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003e) indicates that first two MCA dimensions (Dim.) contributed the largest share with 30.7% of total variance, where first Dimension 1 (Dim.1) contributed the largest share with 16.2% of variance, while the second dimension 2 (Dim.2) explained 14.5%. Subsequent dimension contributed gradually smaller shares like Dim.3, Dim.4, and Dim.5, accounting 12.6%, 11.6%, and 9.5% respectively, cumulative explained variance of (64.40%) (Table\u0026nbsp;4). Thus, these dimensions reflected the most structurally informative aspects of the genotype allele relationship.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe MCA individual-genotype map (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, left) showed groups of genotypes with specific allelic composition. Genotypes like HD 2888, C 306, and NI 5439 appeared at the edge of the Dim.1 and Dim.2 plane, indicating their distinct allelic compositions. For example, C 306 and HD 2888 have the alleles such as \u003cem\u003eGlu-B3i\u003c/em\u003e, \u003cem\u003eGlu-A3f\u003c/em\u003e, and the uncommon \u003cem\u003eGlu-A1c\u003c/em\u003e allele. However, more centrally clustered genotypes such as DBW 17, PBW 343, and HD 2987 exhibited common and frequent allele combinations, such as \u003cem\u003eGlu-B3c\u003c/em\u003e and \u003cem\u003eGlu-D1a\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe MCA variable contribution plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, right) further detailed the alignment of allelic categories along the principal dimensions. The \u003cem\u003eGlu-A1c\u003c/em\u003e allele and \u003cem\u003eGlu-B1e\u003c/em\u003e allele contributed strongly along Dim.1 and Dim.2 (contributions: 10.16 and 8.13, respectively). Similarly, \u003cem\u003eGlu-B1f\u003c/em\u003e and \u003cem\u003eGlu-B1j\u003c/em\u003e alleles also had substantial loadings and contributed to distinct positioning of genotypes such as CBW 38 and NI 5439.\u003c/p\u003e \u003cp\u003eDimensional contributions calculated from the MCA variable table showed that alleles such as \u003cem\u003eGlu-A1c\u003c/em\u003e (Dim.1\u0026thinsp;=\u0026thinsp;1.40, Dim.2\u0026thinsp;=\u0026thinsp;1.19) and \u003cem\u003eGlu-B1f\u003c/em\u003e (Dim.1 = -1.38, Dim.2 = -0.40) had the highest coordinate values. Whereas, more frequent alleles like \u003cem\u003eGlu-A1b\u003c/em\u003e and \u003cem\u003eGlu-B1c\u003c/em\u003e contributed less to variability and clustered near the origin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study aimed to understand the correlations and associations between glutenin alleles and various quality and baking parameters.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Allelic Diversity at Glutenin Loci in Indian genotypes and Its Functional Implications\u003c/h2\u003e \u003cp\u003eIn this Study, analysis of 18 Indian bread wheat genotypes was examined for glutenin subunit composition, dough rheology, and bread-making quality traits. Twelve high-molecular-weight glutenin subunit (HMW-GS) alleles across the Glu-1 loci and seven low-molecular-weight glutenin subunit (LMW-GS) alleles at Glu-3 confirms the genetic diversity of the studied Indian wheat population (Table-1). Systematic statistical approach involving multilocus co-occurrence (Burt table figure-1) and allele-specific phenotypic variability (Boxplots Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was performed to understand influence of glutenin allelic combinations on wheat bread making quality.\u003c/p\u003e \u003cp\u003eThe Burt contingency table visualized the frequency of glutenin allele combinations (Glu-A1, Glu-B1, Glu-D1, Glu-A3, Glu-B3). Bars with greater height represented more frequent combinations in the studied genotypes. The most prominent peaks with presence in 9 genotypes, showed allelic combination of \u003cem\u003eGlu-A1b\u003c/em\u003e (2*)\u0026thinsp;+\u0026thinsp;\u003cem\u003eGlu-A3c\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eGlu-B3j\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eGlu-D1a\u003c/em\u003e (2\u0026thinsp;+\u0026thinsp;12). The second peak was observed for \u003cem\u003eGlu-D1d\u003c/em\u003e (5\u0026thinsp;+\u0026thinsp;10)\u0026thinsp;+\u0026thinsp;Glu-B1 7\u0026thinsp;+\u0026thinsp;8\u0026thinsp;+\u0026thinsp;\u003cem\u003eGlu-B3b/g\u003c/em\u003e. while, alleles such as \u003cem\u003eGlu-A1c\u003c/em\u003e (null), \u003cem\u003eGlu-B1e\u003c/em\u003e (20), and \u003cem\u003eGlu-A3f\u003c/em\u003e showed low-frequency bars.\u003c/p\u003e \u003cp\u003eBoxplot (Figure-2) of Phenotypic traits such as loaf volume, bread weight, dough weight, and bread quality score in relation to different combinations of HMW-GS and LMW-GS offers valuable information for influence of Glu loci on bread quality. Among the Glu-A1 alleles, 2* was found as the most favorable, showing superior performance across all four measured traits, particularly in loaf volume and bread quality score (Figure-2). Genotypes carrying this allele have higher median values in bread quality score, loaf volume and dough weight. These findings are consistent with those of Zhang et al. (2022), who highlighted 2* as a beneficial allele for enhancing gluten strength and dough functionality. The Glu-B1 locus alleles Glu-\u003cem\u003eB1i\u003c/em\u003e (17\u0026thinsp;+\u0026thinsp;18), Glu-\u003cem\u003eB1c\u003c/em\u003e (7\u0026thinsp;+\u0026thinsp;9), and Glu-\u003cem\u003eB1b\u003c/em\u003e (7\u0026thinsp;+\u0026thinsp;8) showed their strong association with improved dough weight and loaf volume. While allele \u003cem\u003eGlu-B1e\u003c/em\u003e (20) consistently underperformed across all traits, which suggests its negative effect on gluten quality for bread making. The alleles like Glu-B1c (7\u0026thinsp;+\u0026thinsp;9) and \u003cem\u003eGlu-B1i\u003c/em\u003e (17\u0026thinsp;+\u0026thinsp;18) align with superior performance, promoting polymeric protein formation and enhancing dough elasticity was also documented by Gianibelli et al. (2005). While, Glu-D1 locus, the \u003cem\u003eGlu-D1d\u003c/em\u003e (5\u0026thinsp;+\u0026thinsp;10) allele clearly excelled out the \u003cem\u003eGlu-D1a\u003c/em\u003e (2\u0026thinsp;+\u0026thinsp;12) allele for higher overall bread quality scores. Genotypes carrying \u003cem\u003eGlu-D1d\u003c/em\u003e (5\u0026thinsp;+\u0026thinsp;10) showed tighter distribution by providing stronger and more extensible dough. These observations are supported by earlier studies (e.g., Gupta et al., 1994).\u003c/p\u003e \u003cp\u003eThe LMW-GS loci Glu-A3 and Glu-B3 are considered secondary contributors, but have significant impacts on quality traits. Within Glu-A3, allele b excelled in higher loaf volume and bread quality scores, and bread weight, which suggests a positive role in strengthening the gluten matrix. While Glu-B3 alleles g and j were consistently linked to better loaf volume and bread quality score, reinforcing their importance in dough baking performance.\u003c/p\u003e \u003cp\u003eThe Glu-1 score showed a strong and predictable relationship with all quality traits assessed. The Genotypes scores which have a Glu-1 Score greater than 7, consistently showed better loaf volumes, and the Bread Quality Score confirms their role in improved dough strength and bread making. This robust correlation supports the findings of (Payne et al., 1987; Vancini et al., 2019), who identified the Glu-1 score as a reliable predictor of baking quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e4.2. Correlation among Wheat Quality Trait\u003c/b\u003es\u003c/h2\u003e \u003cp\u003eThe present study highlighted correlations among the physiochemical and rheological traits, influencing the biochemical and functional basis of wheat processing quality. Grain hardness (HI) in this study showed correlation with multiple traits, including positive correlation with DG, D.O.S, Alv_P, dough weight, and bread weight, while negative with GI, Alve_Ie, and BQS. Grain hardness (HI) plays a crucial role in starch damage during milling, which promotes water absorption followed by an influence on flour characteristics, particle size dynamics, and protein-starch interrelationship, which plays a crucial role in baking and end use quality. These findings confirm the established role of grain hardness in flour refinement and dough behavior (Campbell et al., 2007). Protein content (P%) showed strong positive correlations with WG, DG, and SDS-SV, confirming its significant role in dough formation and strength. High protein levels facilitate the development of a cohesive gluten matrix, essential for maintaining dough elasticity \u0026amp; extensibility and gas retention during fermentation (Shewry \u0026amp; Halford, 2002). This association between protein and dough strength supports its role as a potential predictor of breadmaking quality. Though the observed variability among high protein genotypes suggests that protein quantity alone does not promise performance consistency. Falling number (FN) found positively associated with dough development time (r\u0026thinsp;=\u0026thinsp;0.394) and negatively with degree of softening (r\u0026thinsp;=\u0026thinsp;0.417), showing that low enzymatic activity enhances dough stability, and high α-amylase weakens dough structure by degrading starch excessively. Similar patterns were reported in studies exploring enzyme impacts on dough rheology (Barrera et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStirring Number (SN) is inversely related to the α-amylase activity in flour, which means higher SN values indicate lower enzymatic degradation of starch, thus greater viscosity stability. This behavior mirrors the observation of Falling Number tests, and both parameters are important in finding sprout-damaged potential of the grain. In the current study, SN values were found positively correlated with STAB and negatively with DOS, supporting the idea that low enzymatic activity favors dough strength and structural integrity (Liu et al., 2023).\u003c/p\u003e \u003cp\u003ePositive correlation of SN with SDS-SV, wet gluten, and dry gluten content was observed, the latter are the indicators of protein quality and gluten matrix potential of dough. This correlation suggests that high SN flours preserve the integrity of gluten forming proteins by having low α-amylase activity during dough formation. These findings are consistent with previous work on gluten performance under variable enzyme conditions (Shewry \u0026amp; Halford, 2002). Positive correlations between SN and both STAB and SDS-SV suggest that flour with higher SN values tends to produce doughs with more consistent gluten networks. One possible explanation for these rheological benefits is that a more intact starch phase interacts more effectively with gluten proteins, enhancing elasticity and network formation. This interaction becomes critical in industrial baking, where predictable mixing and expansion behaviour are essential. High SN values found negatively correlated with bread weight and Alve_W (dough's baking strength), which reflects that flours with optimized SN are good for bread structural uniformity and volume. However, extreme SN values may indicate overly low enzyme activity, which could affect Maillard reactions and crust formation.\u003c/p\u003e \u003cp\u003eAmong the Farinograph traits, dough stability (STAB) showed positive correlations with protein %, FN and SN support the interpretation that stronger gluten matrices resist breakdown during mixing. While STAB and degree of softening (D.O.S.) have an inverse correlation represent its positive connection with the mixing tolerance of dough. Farinograph arrival time (F.A.T.) and departure time (F.D.T.), which together define the dough development property, showed positive correlation with both STAB and sedimentation volume. These associations suggest that flours with longer development and stability times generally form cohesive gluten networks, leading to better gas retention and higher loaf volumes (Shewry \u0026amp; Halford, 2002). The correlations observed in this study align with industrial expectations for bread flours, which are high water absorption (\u0026ge;\u0026thinsp;60%), dough development time\u0026thinsp;\u0026ge;\u0026thinsp;3 minutes, and STAB\u0026thinsp;\u0026ge;\u0026thinsp;8 minutes (Sumit Kr Singh et.al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlveograph data added further nuance to the study and offered details of dough strength and extensibility. In alveograph traits, dough tenacity (Alv_P) showed a clear positive correlation with both the Alve_P/L \u0026amp; Alve_G and negative correlation with Alve_L \u0026amp; Alve_le indicates that a strong gluten network maintains dough resilience and stronger doughs resist deformation but are less extensible. This observation aligns with the findings of Delcour \u0026amp; Hoseney, 2010 and Rojas et al., 1999.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Principal Component Analysis (PCA)\u003c/h2\u003e \u003cp\u003ePCA effectively reduced traits dimensionality and revealed major contributors to wheat quality variability. The first three principal components explained over 70% of the total variation, with PC1 reflecting dough strength attributes\u0026mdash;mainly farinograph traits (STAB, F.Q.N., F.D.T.) and alveograph tenacity (Alv_P, Alve_P/L). PC2 captured protein-related traits (P%, DG%, WG%) and end-use characteristics like bread and dough weight. These findings underscore the close alignment between gluten composition and baking performance, consistent with Shewry \u0026amp; Halford (2002) and Rakszegi et al. (2019).\u003c/p\u003e \u003cp\u003eThe first three principal components explained over 70% of the total variation. The PC1 found associated with farinographic and alveograph parameters including dough stability (STAB), farinograph quality number (F.Q.N.), departure time (F.D.T.), Alveograph P (Alv_P), and Alve_P/L ratio. This shows that PC1 reflects dough strength and its resistance to deformation. Whereas, PC2 possess mainly protein related traits, including protein percentage (P%), dry gluten (DG%), wet gluten (WG%), and bread weight, which describe its inclination towards compositional attributes and influence on baking performance. These findings align with earlier reports emphasizing the association among gluten composition, gluten strength and farinograph indices for baking quality (Shewry \u0026amp; Halford, 2002; Rakszegi et al., 2019). This analysis also showed a cluster of traits (Figure) related to gluten composition (WG%, DG%, SDS-SV) with P% and baking traits such as dough weight and bread weight. This highlights the shared biochemical basis of gluten polymerization and water absorption influenced by the interactions between gluten proteins and water molecules. On the other hand, traits such as Alve_Ie and Alve_L found opposite to Alve_P/L and Alve_P ratio, confirm the antagonist relationship between dough strength and extensibility (Delcour \u0026amp; Hoseney, 2010). Genotypes such as HD 2643, DBW 14, and PBW 343 aligned closely with PC1 and PC2 vectors and represented their superior dough strength and gluten quality for bread making. These genotypes clustered within the inner ellipse of the Glu-1 score filtered biplot, which signifies their stability and desirable rheological parameters. While genotypes like PBW 590 and CBW 38 were observed far from the central cluster, which indicates their lower gluten strength and potential suitability for end-products required low gluten strength like, biscuits and cookies. These biplot findings are consistent with Glu-1 scoring systems for genotype classification in bread wheat (Feldman \u0026amp; Sears, 1981; Zhao et al., 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Multiple Correspondence Analysis (MCA): Glu-1 Allelic Differentiation and Quality Traits\u003c/h2\u003e \u003cp\u003eThe Multiple Correspondence Analysis (MCA) provided the dissection of categorical variation in glutenin allele compositions and their influence on quality traits. The first two dimensions, Dim 1 (16.2%) and Dim 2 (14.5%), together explained 30.7% of the total inertia. MCA biplots showed that HMW-GS alleles such as \u003cem\u003eGlu-A1b\u003c/em\u003e (2*), Glu-\u003cem\u003eB1i\u003c/em\u003e (17\u0026thinsp;+\u0026thinsp;18), and \u003cem\u003eGlu-D1d\u003c/em\u003e (5\u0026thinsp;+\u0026thinsp;10) aligned with genotypes like HD 2967 and HD 2985, which confirm its linkage with superior gluten strength and bread making quality traits. These alleles are widely recognized for enhancing viscoelastic dough properties due to their high expression and disulfide-bonding potential (Payne et al., 1987; Branlard et al., 2001). While genotypes such as C 306 and CBW 38 showed allelic combinations like \u003cem\u003eGlu-A1c\u003c/em\u003e (null) and \u003cem\u003eGlu-B1j\u003c/em\u003e, and distant from these quality trait vectors. This reflects their association with weaker gluten networks. The \u003cem\u003eGlu-A1c\u003c/em\u003e (null) allele is mainly associated with distinct overall gluten strength (Juh\u0026aacute;sz et al., 2013; Shewry et al., 2009). Genotypes such as C 306 and CBW 38 showed allelic combinations like \u003cem\u003eGlu-A1c\u003c/em\u003e (null) and \u003cem\u003eGlu-B1j\u003c/em\u003e, and distant from quality trait vectors. This reflects their association with weaker gluten networks. The \u003cem\u003eGlu-A1c\u003c/em\u003e (null) allele is mainly associated with the absence of HMW-GS at the Glu-A1 loci and thus reduces the overall gluten strength (Juh\u0026aacute;sz et al., 2013; Shewry et al., 2009). The Glu-b3 alleles \u003cem\u003eGlu-B3j\u003c/em\u003e demonstrated a small discriminative influence when paired with dominant Glu-1 alleles for gluten strength and the bread quality traits. The findings mainly involve combinations linking subunits 5\u0026thinsp;+\u0026thinsp;10 (\u003cem\u003eGlu-D1d\u003c/em\u003e) with superior viscoelastic properties compared to 2\u0026thinsp;+\u0026thinsp;12 (\u003cem\u003eGlu-D1a\u003c/em\u003e), (Liu et al., 2011; Zhao et al., 2021). This finding provides an idea regarding genotypes harbouring HMW-GS 5\u0026thinsp;+\u0026thinsp;10 mostly clusters with high STAB, FQN, and SDS-SV, reinforcing its breeding relevance. This MCA plot also revealed an interesting fact that some genotypes like CBW 38, HD 2987 and HD2643, with good reported allelic combinations for bread making quality, did not cluster tightly with trait vectors. This suggests possible G\u0026times;E interactions or the presence of epistatic modifiers influencing expression.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study confirms that genetic variation at glutenin loci has a direct impact on the functional quality of wheat flour, particularly for breadmaking in Indian wheat genotypes. The combination of high- and low-molecular-weight glutenin subunits plays a key role in shaping dough properties like stability and loaf volume. Genotypes with Glu-1 scores higher than seven consistently showed better breadmaking performance, which aligns with both their molecular composition and dough characteristics. These findings are valuable for Indian breeding programs aiming at market-driven wheat improvement. The integration of molecular profiling and comprehensive rheological assessment provides a robust framework for identifying superior wheat cultivars tailored for industrial applications. Combining genetic markers with detailed rheological analysis offers an approach to improve food quality and security through more targeted wheat breeding strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.K.S. conceived the study, designed the experiments, performed data analysis and prepared the main manuscript text. A.M.S. supervised glutenin profiling, contributed to rheological data interpretation and assisted in manuscript revision. A.K.A. coordinated field experiments, managed sample collection and A.M. assisted in statistical analyses, provided support in reviewing and editing the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author (S.K.S.) on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAydoğan S, Ak\u0026ccedil;acik ŞAHİNM, HAMZAOĞLU AG, S., Taner S (2015) Relationships between Farinograph Parameters and Bread Volume, Physicochemical Traits in Bread Wheat Flours. J Bahri Dagdas Crop Res 3(1):14\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzadfar E, Elhami Rad AH, Sharifi A, Armin M (2023) Effect of Olive Pomace Fiber on the Baking Properties of Wheat Flour and Flat Bread (Barbari Bread) Quality. 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J Cereal Sci 15(1):63\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0733-5210(09)80057-X\u003c/span\u003e\u003cspan address=\"10.1016/S0733-5210(09)80057-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-molecular-biology-reporter","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pmbr","sideBox":"Learn more about [Plant Molecular Biology Reporter](http://link.springer.com/journal/11105)","snPcode":"11105","submissionUrl":"https://submission.nature.com/new-submission/11105/3","title":"Plant Molecular Biology Reporter","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bread Wheat, Glutenin Alleles, Rheological Evaluation, Farinograph, Alveograph, Wheat Quality","lastPublishedDoi":"10.21203/rs.3.rs-8338707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8338707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents assessment of glutenin subunit diversity and its relation with dough properties and breadmaking quality in 18 Indian bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) genotypes. The analysis showed considerable genetic variation and revealed 12 high-molecular-weight glutenin subunit (HMW-GS) alleles at the \u003cem\u003eGlu-1\u003c/em\u003e loci and seven low-molecular-weight glutenin subunit (LMW-GS) alleles at the \u003cem\u003eGlu-3\u003c/em\u003e loci. Grain and flour quality were evaluated using 27 different parameters. The farinograph and alveograph indices showed significant association with traits like protein content, gluten index, dough stability, loaf volume and overall bread quality. The study also identified specific allele combinations that contribute to improved dough strength and extensibility. Genotypes carrying \u003cem\u003eGlu-A1b, Glu-B1c/i, Glu-D1d\u003c/em\u003e, and \u003cem\u003eGlu-B3j\u003c/em\u003e alleles were consistently associated with superior bread baking characteristics. The findings reinforce Glu-1 scores as robust predictors of baking performance and emphasize the need for trait-based selection strategies that integrate molecular and rheological data for wheat quality improvement.\u003c/p\u003e","manuscriptTitle":"Assessment of Glutenin Subunit Diversity and its Relation with Dough Properties and Breadmaking Quality of Wheat","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 11:59:47","doi":"10.21203/rs.3.rs-8338707/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-24T10:57:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265671661798906612604016632972955593231","date":"2026-01-17T10:11:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-16T02:23:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-12T11:22:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-12T11:22:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Molecular Biology Reporter","date":"2025-12-11T16:17:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-molecular-biology-reporter","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pmbr","sideBox":"Learn more about [Plant Molecular Biology Reporter](http://link.springer.com/journal/11105)","snPcode":"11105","submissionUrl":"https://submission.nature.com/new-submission/11105/3","title":"Plant Molecular Biology Reporter","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"74cfa98d-c757-4079-bea7-4a789595169b","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T11:59:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 11:59:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8338707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8338707","identity":"rs-8338707","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}