{"paper_id":"4c42aee2-59cf-41d2-b247-5fa376fb7eb9","body_text":"A Major QTL Cluster of Weak-Gluten on Wheat Chromosome 2D informs Marker-Assisted Selection | 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 A Major QTL Cluster of Weak-Gluten on Wheat Chromosome 2D informs Marker-Assisted Selection Qing Li, Hao Chen, Bingchuan Zhou, Yali Wu, Shasha Gu, Chongjing Xia, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8260532/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Weak-gluten wheat, characterized by low protein content (<11.5%), soft kernels, and weak gluten strength, serves as essential raw material for confectionery products. Despite growing market demand, wheat production over the world faces a critical shortage of high-quality weak-gluten wheat varieties due to insufficient understanding of the genetic architecture underlying key quality traits. Here, we address this knowledge gap through comprehensive quantitative trait locus (QTL) mapping of five critical quality parameters, namely grain protein content (GPC), wet gluten content (WGC), Zeleny sedimentation value (ZSV), test weight (TW), and grain hardness (GH), using a recombinant inbred line (RIL) population of 143 lines derived from the \"Mianmai 902/Taichung 29\" cross evaluated across three diverse environments. Leveraging the Wheat 55K SNP array, we constructed a high-density genetic map comprising 10,132 markers with an average interval of 1.34 cM, identifying 41 QTLs distributed across 16 chromosomes. Most significantly, we discovered a narrow 2.61 Mb interval on chromosome 2DL (34.43-37.04 Mb) containing two co-localized major-effect QTLs, QZStm.swust-2DL (30.28% PVE) and QGHtm.swust-2DL (29.70% PVE), that demonstrate exceptional stability across environments. Within this critical interval, we identified TraesCS2D01G077600LC.1 , a candidate gene encoding lysine synthase that may influence gluten properties through amino acid metabolism. Furthermore, our QTL dosage analysis revealed a quantitative relationship between quality-associated loci and phenotypic expression, enabling predictive breeding strategies. Based on Chinese national standards for weak-gluten wheat, we successfully screened four elite RIL lines (TM77, TM103, TM116, and TM149) that combine superior quality traits with favorable agronomic characteristics. This study provides the first comprehensive QTL atlas specifically for weak-gluten wheat quality traits, delivering validated molecular markers and breeding materials to accelerate the development of high-quality varieties addressing the growing confectionery wheat demand over the world. Weak-gluten wheat Quality traits 55K SNP chip QTL mapping Marker-assisted selection Elite line screening Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Key message Two major QTLs for quality traits were mapped on chromosome 2DL in weak-gluten wheat Mianmai 902, and four elite RIL lines satisfying Chinese national standards were selected. Introduction Weak-gluten wheat is defined as a specialized type of soft wheat. Its defining characteristics include low protein content in the grain, soft kernel texture, and weak gluten strength. This wheat type serves as ideal raw material for confectionery products including biscuits, cakes, and southern Chinese steamed buns (mantou), making it an important food crop with specific market demand (Kumar et al. 2016; Wu et al. 2022) . Key quality indicators for weak-gluten wheat include grain protein content (GPC), wet gluten content (WGC), Zeleny sedimentation value (ZSV), test weight (TW), and grain hardness (GH), all of which significantly influence processing quality and market value (Sun et al. 2010). Grain protein content is fundamentally related to wheat milling quality and represents one of the most genetically complex traits to study due to its polygenic nature and strong environmental interactions (Sun et al. 2010). Gpc-B1 gene, located on chromosome 6B and first cloned from wild emmer wheat ( Triticum turgidum ssp. dicoccoides ), has demonstrated capacity to reduce grain protein content by over 30% (Uauy et al. 2006). Additional regulatory factors, such as HOMEOBOX DOMAIN-2 ( HB-2 ), and several reported QTLs have been identified (Blanco et al. 1996, 2002, 2006; Prasad et al. 1999; Dixon et al. 2022; Zhao et al. 2024). Wet gluten forms a viscoelastic network when proteins in wheat flour absorb water (Wu et al. 2022). This network comprises gliadins, high-molecular-weight glutenin subunits (HMW-GS), and low molecular weight glutenin (LMW-GS). HMW-GS is the key to determine the quality of gluten, with the Dy10-null allele having significant negative effects on sedimentation value while being valuable for weak-gluten wheat development (Wang et al. 2023). It is well established that the three homologous chromosomes 1A, 1B, and 1D each harbor a major locus (Glu-A1, Glu-B1, and Glu-D1, respectively) that controls the composition of HMW-GS. Among these, Glu-D1 exerts the most significant influence on gluten quality. A deletion at the Glu-D1 locus prevents gluten formation, reduces the sedimentation value, and adversely affects processing quality (Shewry et al. 2003). The sedimentation value serves a comprehensive indicator reflecting grain protein quality, dough theological properties, and gluten content (Sun et al. 2012). It correlates strongly with HMW-GS composition (Wang et al. 2023). Research consistently indicate positive correlations among protein content, gluten content, and sedimentation value (Ma et al. 2021). This relationship suggests that quality improvement efforts can effectively target these parameters simultaneously. Grain hardness represents a critical evaluation parameter influencing wheat classification, grading, and processing suitability (Li et al. 2013). The Puroindoline a and b genes ( Pina-D1 and Pinb-D1 ), which are located within the Ha locus on chromosome 5D, encode key proteins that govern this trait (Sourdille et al. 1996; Sun et al. 2010). When both Pina-D1 and Pinb-D1 carry wild-type sequences (alleles), grain exhibits soft texture; the presence of a deletion or mutation in either gene confers hardness (Igrejas et al. 2002; Bhave and Morris 2008; Li et al. 2020). Test weight is a vital quality indicator reflecting wheat grain plumpness and overall quality. Multiple factors influence this trait, with Cabral et al. (2018) identifying multiple QTLs for bulk density including QTwt.crc-4D which colocalizes with the dwarfing gene Rht-D1b , leading to reduced test weight. Significant positive correlations exist between test weight and grain width, but not with grain length, indicating direct influence of grain morphology on test weight (Cabral et al. 2018). Studies also indicate that high temperatures increase protein content while decreasing test weight (White et al. 2022). Despite growing interest, research on genes controlling test weight in wheat remains relatively scarce compared to other quality traits (White et al. 2022), with QTLs associated with test weight mapped across 20 chromosomes, excluding chromosome 6D (Li et al. 2016). Historically, wheat breeding research in China prioritized yield improvement, with dedicated weak-gluten wheat research emerging relatively late. It was not until the late 20th century and early 21st century that weak-gluten wheat gained significant research attention. However, many existing weak-gluten wheat varieties exhibit suboptimal performance in critical quality traits, failing to fully meet national standards for high-quality wheat. This has resulted in a persistent shortage of high-quality weak-gluten wheat resources in China (Liu et al. 2023). Concurrently, rising living standards have driven substantial increases in demand for high-quality weak-gluten wheat, creating an urgent need to develop new high-quality weak-gluten wheat varieties (Wu et al. 2022). Wheat quality traits represent polygenic quantitative characteristics exhibiting continuous phenotypic variation influenced by both genetic and environmental factors, though genetic determinants remain dominant. Elucidating the genetic architecture underlying these traits provides crucial theoretical foundations for efficient quality improvement (Mérida-García et al. 2020; Li et al. 2025). Previous studies have mapped wheat quality QTLs across nearly all 21 chromosomes (Patil et al. 2009; Conti et al. 2011; Li et al. 2012; Kumar et al. 2013; Goel et al. 2019; Hu et al. 2022), yet stable and major-effect QTLs remain relatively scarce (Li et al. 2009, 2025). Research progress has been constrained by the wheat genome's enormous size and complexity (Olmos et al. 2003; Distelfeld et al. 2004; Uauy et al. 2006). Recent advances in genotyping technologies have significantly enhanced QTL mapping efficiency. The Wheat 55K SNP array represents a substantial improvement over traditional genotyping methods, offering higher throughput and greater accuracy for genetic studies (Sun et al. 2010; Liu et al. 2024b). This technology has enabled more comprehensive genetic analyses of wheat quality traits, with multiple research groups successfully applying it to identify QTLs for agronomic and quality characteristics. Despite these advances, comprehensive QTL mapping specifically for weak-gluten wheat quality traits remains limited, creating a critical knowledge gap for breeding programs targeting this specialized market segment. This study addresses this gap by utilizing a recombinant inbred line (RIL) population of 143 lines derived from the \"Mianmai 902/Taichung 29\" cross. We evaluated grain quality traits across three contrasting environments and constructed a high-density genetic map using the 55K SNP chip. Our objectives were to: (1) identify stable and major QTLs controlling weak-gluten wheat quality formation; (2) predict candidate genes within significant QTL regions; and (3) screen superior RILs meeting Chinese national standards for weak-gluten wheat. These findings provide a comprehensive QTL atlas and validated genetic resources for marker-assisted selection breeding of high-quality weak-gluten wheat varieties with direct applications for addressing China's growing demand for premium confectionery wheat. Material and Methods Plant materials and field trials The experimental materials comprised the parental lines Mianmai 902 (MM902, a weak-gluten cultivar) and Taichung 29 (TC29, a spring wheat cultivar highly susceptible to stripe rust), along with 143 recombinant inbred lines (RILs) developed from their cross. Mianmai 902 was developed by Mianyang City Institute of Agricultural Science in 2008 by using Mianmai 37, a short-straw and disease-resistant wheat variety, as the female parent, and MY1848, a line of wheat highly resistant to stripe rust, as the male parent, and then using the F 1 as the female parent, and the high-yielding and disease-resistant wheat germplasm 06-367 (Accession: Mianmai 367) as the male parent, and adopting the genealogy method. It exhibits excellent agronomic traits including high productivity, strong disease resistance, and superior weak-gluten quality characteristics essential for confectionery products. In 2019, it passed the validation of Sichuan Provincial Crop Variety Committee (Sichuan Validated Wheat 20190005). Field trials for this study were carried out during the 2023-2024 growing season across three distinct sites in Sichuan Province, encompassing Qingyi (31°33'N, 104°55'E), Xiaojian (31°47'N, 104°88'E), and Pidu (30°62'N, 104°10'E). The trial was conducted in a completely randomized block design, with two biological replicates per family at each site. Each plot consisted of a single row (1 m length, 0.5 m width) with approximately 100 seeds planted and row spacing of 20 cm. Standard agronomic practices for wheat cultivation in the region were implemented to ensure optimal growth conditions while minimizing experimental artifacts. All plant materials were provided by the Mianyang Academy of Agricultural Sciences. Genotyping and quality trait measurement The leaves of parental Mianmai 902, Taichung29, and 143 family lines were collected in January 2024, and genomic DNA was extracted by a modified CTAB method. Then the DNA concentration and quality were examined using a NanoDrop ND-1000 spectrophotometer (Thermo Scientifc, Wilmington, DE, USA) and the parents and RILs were genotyped using the 55K Illumina® iSelect wheat SNP array (Zhongyujin Marker Biotechnology Co., Ltd., Beijing, China), which consists of 53,063 SNP markers and has become a standard tool for high-resolution genetic mapping in wheat due to its genome specificity and reliability. For quality trait evaluation, approximately 200 plump, visually uniform seeds from each line were analyzed using the 3700 NIR Analyzer (FOSS, Germany) to determine grain protein content, wet gluten content, sedimentation value, grain hardness and test weight. Each quality trait was measured in 3 replicates, with the average value used for analysis. The mean value of each quality trait for each RIL in each environment was calculated from the two field replicates, with each replicate value being the average of three technical measurements. Data analysis The data of quality traits were analyzed using Origin 2021 software (https://www.originlab.com/2021). The variance in the individual environment analysis was calculated using the mean quality values of each RIL at the three experimental sites. ANOVA and correlation analysis were performed using the \"AOV\" tool in IciMapping V4.1(http://www.isbreeding.net/) software. The broad-sense heritability ( H 2 ) of each grain quality trait was estimated using the following formula: H 2 =σ g 2 /（σ g 2 +σ 2 ge /e+σε 2 /re）. σ g 2 =genetic variance; σ 2 ge = variance of genotype×environment interaction; σ ε 2 =error variance; r = replications per environment; e = the number of environments. The high-density genetic map and QTL mapping Before constructing the linkage map, the redundant SNP markers were sorted out, and the SNP markers without polymorphism between parents were first eliminated, and then the markers with a missing rate of more than 20% were deleted by using the \"BIN\" tool in ICIMaping V4.2 software. In this study, IciMaping V4.2 (http://www.isbreeding.net/) software was used to detect and locate potential QTLs by composite interval plotting method, and based on software recommendations or common practices, the software parameters are set as follows: Step=1 cM, the value of the input variable (PIN) p=0.0001, and LOD=2.5. When the LOD value is higher than 2.5, a QTL is considered to exist, and the contribution rate and additive effect of each QTL are calculated. QTL naming rules: For example, QGHtm.swust-2DL , where GH represents the quality-related trait (grain hardness) in this study, tm represents the MM902/TC29 population, swust represents the affiliation name, and 2DL represents the chromosome information. Identification of candidate genes To identify quality-related candidate genes within significant QTL regions, physical map alignments were performed against the IWGSC RefSeq v1.0 reference genome. Candidate genes within the confidence intervals of QTL clusters were retrieved from the IWGSC RefSeq v1.0 annotation. Genes annotated with functions potentially related to grain quality were prioritized as candidates. Results Phenotypic evaluation and correlation analysis Phenotypic evaluation across three diverse environments (Qingyi Town, Xiaojian Town, and Pidu District) revealed significant differences between parental lines for key quality traits. Mianmai 902 (MM902), the weak-gluten wheat parent, consistently exhibited lower grain protein content (GPC), wet gluten content (WGC), grain hardness (GH), and Zeleny sedimentation value (ZSV) compared to Taichung 29 (TC29), with the exception of test weight (TW), which showed environment-dependent variation (Table 1). The recombinant inbred line (RIL) population displayed continuous phenotypic distributions for all five quality traits, characteristic of polygenic inheritance (Figure 1). In the RIL population, the variation of GPC in the three environments (Qingyi Town, Xiaojian Town, and Pidu District) ranged from 8.01% to 15.23%, and the coefficient of variation ranged from 7.51% to 11.76%; the variation of WGC ranged from 20.07% to 41.83%, and the coefficient of variation ranged from 6.1% to 12.45%; the minimum value of the TW was detected in Qingyi (706.83 g/L), and the maximum value appeared in Pidu (834.03 g/L), and the coefficient of variation of this trait ranged from 1.76% to 2.28%. and the coefficient of variation for this trait was smaller than that of the other four traits; the range of the variation of the GH was 29.4-67.62, and the range of the coefficient of variation was 8.14%-12.55%; the range of the variation of the ZSV was 23.92mL-58.67mL, and the range of the coefficient of variation was 9.6%-16.62% (Table 1).The variation of individual traits in the three environments (Qingyi Town, Xiaojian Town, and Pidu District) ranged from 1.76% to 16.62%. A notable observation was the widespread transgressive segregation, which was predominantly favorable, for almost all quality traits in each environment. Concurrently, the broad-sense heritability (hB2) estimates for these traits varied between 59.83% and 71.78% (Table 1). The five grain quality traits in the RIL population were all continuously distributed, which was consistent with the phenotypic characteristics of quantitative traits and was suitable for QTL mapping analysis (Figure. 1). Correlation analysis revealed significant relationships among quality parameters (Figure 2). Strong positive correlations were observed between GPC and WGC (r = 0.87, P < 0.001 in Qingyi), GPC and ZSV (r = 0.78-0.82, P < 0.001 across environments), and WGC and ZSV (r = 0.81-0.85, P < 0.001). Notably, TW showed negative correlations with GH (r = -0.26, P < 0.01 in Qingyi and Pidu), suggesting an inverse relationship between grain plumpness and hardness that has practical implications for weak-gluten wheat breeding. High-Density Genetic Map Construction and QTL Identification Genotyping with the Wheat 55K SNP array identified 39,110 polymorphic SNP markers between parental lines. A total of 10,132 molecular markers with known chromosome position information were obtained. The 5B chromosome had the largest number of markers with 900 markers, and the 4D chromosome with the least number of markers had 175 markers. The 10,132 SNP markers were used to construct 21 linkage groups, and the average distance between adjacent markers was 1.34 cM. The length of the single chromosome spectrum ranged from 311.42 cM (chromosome 1B) to 1247.9476 cM (chromosome 6A), and this genetic map was used for the mapping of QTLs for quality traits. Composite interval mapping identified 41 QTLs, distributed over 16 chromosomes (all wheat chromosomes excluding 1A, 3A, 4B, 6B, and 7B) (Table 2). Individual QTLs explained 0.15%-30.28% of phenotypic variation (PVE), with an average of 5.52%. Thirty QTLs showed additive effects derived from MM902 (the weak-gluten parent), while 11 showed effects from TC29, reflecting the genetic architecture underlying weak-gluten characteristics. Applying rigorous criteria (LOD > 2.5 and PVE > 10%), we identified eight major-effect QTLs. Seven of these originated from MM902, consistent with its weak-gluten phenotype, while one derived from TC29. Eight QTLs demonstrated stability across multiple environments, with two particularly significant loci meeting both stability and major-effect criteria: QGHtm.swust-2DL and QZStm.swust-2DL 0 (Figure 3). For the GPC, three QTLs were detected on chromosomes 2DL, 5DL and 7AL, explaining 7.22%-13.46% of PVE. QGPCtm.swust-7AL (8.34% PVE) was detected in Qingyi environment and additive effects were derived from MM902. QGPCtm.swust-2DL and QGPCtm.swust-5DL (13.46% and 7.22% PVE) were detected in Pidu environment, with additive effects derived from MM902 and TC29. Thus QGPCtm.swust-2DL was the major QTL (Table 2, Figure 3). For the WGC, two QTLs were detected on chromosomes 2DL and 7DL. QWGCtm.swust-2DL was detected in Pidu environment, explaining 23.13% phenotypic variation and the additive effects derived from MM902, which was the major QTL . QWGCtm.swust-7DL was detected in the Qingyi environment, explaining 11.1% phenotypic variation.(Table 2, Figure 3). For the GH, a total of 12 QTLs were detected on chromosomes 1BS, 1DL, 2AL, 2BL, 3BL, 5DL, 6AL, 6DL, 2DL, 4DL, 4AS, and 7DL, each individual QTL explained 0.16%-29.7% phenotypic variation. QGHtm.swust-1BS, QGHtm.swust-2BL, QGHtm.swust-5DL, QGHtm.swust-6DL, and QGHtm.swust-2DL were all detected in two environments, explaining phenotypic variations of 3.7%-4.0%, 0.16%-3.3%, 0.15%-3.27%, 0.15%-3.68%, and 0.12%-29.7%. Among them, QGHtm.swust-2DL was the major and stable QTL, with additive effects derived from MM902. The remaining QTLs were detected in only a single environment, explaining phenotypic variation ranging from 0.16% to 10.49%. Among them, QGHtm.swust-7DL is a major QTL, with additive effects derived from MM902 (Table 2, Table S1, Figure 3). For the TW, a total of 18 QTLs were detected on chromosomes 2AL, 2DS, 3DL, 4AS, 4DL, 5BS, 5DL, 6AL, 6DL and 7DS, each individual QTL explained 0.44%-10.58% phenotypic variation. QTWtm.swust-4AS was detected in two environments, and explained 0.45%-4.11% phenotypic variation, with additive effects derived from MM902. The other QTLs were detected in one environment, explaining phenotypic variations ranging from 0.44% to 10.58%. The additive effects were all from MM902, and QTWtm.swust-4DL.1 was the major QTL. (Table 2, Table S1, Figure 3) For the ZSV, six QTLs were detected on chromosomes 1DL, 2DL, 4DS, 5AL, 6AL and 7AS, each individual QTL explained 4.79%-30.28% phenotypic variation. QZStm.swust-2DL and QZStm.swust-6AL were detected in two environments, and explained phenotypic variations were 30.28% and 7.37%, respectively. QZStm.swust-2DL was a stable and dominant QTL, with additive effects all derived from MM902. The other QTLs were detected in one environment, including QZStm.swust-1DL , QZStm.swust-4DS , QZStm.swust-5AL , and QZStm.swust-7AS . The phenotypic variations explained by these three QTL were 4.79%, 6.80%, 6.07%, and 8.73%, respectively. Among them, the additive effects of QZStm.swust-5AL were derived from TC29, and the others were derived from MM902 (Table 2, Figure 3) Additive effects of QTLs on Quality Traits By integrating phenotypic data of quality traits from field trials, analyze the QTLs located for each trait separately, and RILs were classified based on QTL numbers. Grain protein content (GPC) was categorized into three groups (0–2), wet gluten content (WGC) into two groups (0–1), grain hardness into six groups (0–5+), test weight (TW) into six groups (0–5+), and sedimentation value (ZSV) into four groups (0–4) (Figure 3). RILs without QTLs exhibited higher values for all quality traits, while lines accumulating multiple QTLs showed progressively lower trait values. Specifically, GPC and WGC decreased significantly with increasing QTL number, shifting the population from medium-gluten toward the weak-gluten classification standard. Although TW decreased slightly with higher QTL counts, it remained within premium wheat quality parameters (Figure 4). This quantitative relationship provides breeders with a predictive framework for selecting appropriate QTL combinations to achieve desired quality profiles. QTL clusters and candidate genes Analysis of QTL distribution revealed four significant clusters where multiple quality traits co-localized, on chromosomes 2D, 2A, 5D, and 6A (Table 3, Fig. 5). Due to the smaller interval of the QTL cluster on chromosome 2D, candidate genes were predicted at the QTL cluster region on chromosome 2D. The QTL cluster on chromosome 2D contained one major QTL, QWGCtm.swust-2DL , and one stable and major QTL, QGHtm.swust-2DL , both with additive effects derived from Mianmiai 902. This QTL cluster between markers AX-110072786 -AX-111956072, corresponding to the 34.43Mb-37.04Mb region with an approximate physical interval of 2.61Mb. Alignment with the wheat reference genome (IWGSC RefSeq v1.0) revealed 48 high-confidence genes and 63 low-confidence genes within this cluster (Table 3). Within this critical interval, TraesCS2D01G077600LC.1 emerged as a promising candidate gene. This locus encodes lysine synthase, an enzyme involved in amino acid biosynthesis that may influence gluten protein composition by altering the synthesis ratio of amino acids. The resulting changes in protein composition could directly impact gluten elasticity and sedimentation value, key determinants of weak-gluten characteristics. This represents a novel potential mechanism for weak-gluten wheat development that warrants further experimental validation. Elite Line Selection for Weak-Gluten Wheat Breeding In this study, we measured and analyzed the quality data of 143 family lines in three different environments (Qingyi Town, Xiaojian Town, and Pidu District), and found that the proportion of lines meeting the quality standard for weak-gluten wheat in different environments showed fluctuations, indicating that the quality traits of weak-gluten wheat can be affected by the environment. Based on China's national quality standard for weak gluten wheat, six excellent lines were screened, and all five quality traits (GPC < 11.5%, WGC < 22%, GH < 45, ZSV < 30 mL, TW > 750 g/L) of these six lines met the standard for weak gluten wheat (Table 4). Notably, line TM116 satisfied all five quality parameters in two environments, demonstrating exceptional phenotypic stability. In this study, the agronomic trait data of these 143 family lines were also measured and analyzed in three different environments (Qingyi Town, Xiaojian Town, and Pidu District), and the individual traits showed a continuous distribution in all three environments. Mianmai 902, the parent of this study, as a high quality weak gluten wheat variety, has excellent agronomic traits. In this paper, we refer to the agronomic traits of Mianmai 902 and the main domestic varieties (such as Zhengmai 9023, Chuan Mai 42, etc.), and select the plants with plant heights of 70 cm-100 cm, spike lengths of more than or equal to 9 cm, the number of spikelets of more than 17, and the number of tillers of more than four. Based on the above criteria, the lines screened above for quality traits were screened for agronomic traits, and based on the mean values of these lines in the three environments, TM115 did not meet the criteria for plant height, and TM207 did not meet the criteria for spike length and spikelet number. The remaining family lines TM77, TM103, TM116 and TM149 met the criteria (Table 5). Crucially, lines TM77 and TM116 harbored both major and stable QTLs ( QGHtm.swust-2DL and QZStm.swust-2DL ), while TM103 and TM149 contained QGHtm.swust-2DL . The superior performance of TM116, which carries both major QTLs and exhibits stable quality across environments, makes it particularly valuable for breeding programs targeting high-quality weak-gluten wheat. Discussion Significance of High-Density QTL Mapping for Weak-Gluten Wheat Quality Traits This study addresses a key gap in the genetics of weak-gluten wheat quality by constructing a high-density genetic map (Wheat 55K SNP array) and performing a comprehensive QTL analysis. We detected a total of 41 QTLs on 16 chromosomes. A major finding was the identification of two stable, major-effect QTLs, QZStm.swust-2DL and QGHtm.swust-2DL , which explain 30.28% and 29.70% of the phenotypic variance, respectively. This work constitutes a key advancement for marker-assisted breeding in weak-gluten wheat. These findings substantially advance our understanding beyond previous studies that typically reported fewer QTLs with lower phenotypic variance explained (Li et al. 2025). The comprehensive QTL atlas generated here provides unprecedented resolution for marker-assisted selection (MAS) in weak-gluten wheat breeding programs, directly addressing China's urgent need for high-quality weak-gluten varieties that currently face market shortages (Liu et al. 2023). Our approach demonstrates how high-throughput genotyping technologies have transformed wheat QTL mapping from earlier studies that relied on genetic maps with only several hundred markers (Groos et al. 2003; Sun et al. 2010) to contemporary high-resolution analyses. The 10,132 SNP markers comprising our genetic map represent a 20- to 50-fold increase in density compared to historical studies, enabling more precise QTL localization. This technological advancement is particularly crucial for wheat quality traits, which have historically received less research attention than agronomic traits and disease resistance despite their economic importance (Li et al. 2025). Chromosome 2D QTL Cluster: A Novel Genetic Resource for Weak-Gluten Wheat Development The most significant finding of this study is the identification of a narrow 2.61 Mb interval on chromosome 2DL (34.43-37.04 Mb) containing two major co-localized QTLs: QWGCtm.swust-2DL (23.13% PVE) and QGHtm.swust-2DL (29.70% PVE). This cluster represents a potential pleiotropic locus or tightly linked gene complex with exceptional value for breeding programs targeting weak-gluten wheat. Unlike previously reported QTLs, this locus demonstrates remarkable stability across environments while explaining nearly 30% of phenotypic variation for critical quality traits, substantially higher than typical QTLs for wheat quality traits which often explain less than 10% of variation (Li et al. 2009). Our comparative analysis reveals that this QTL cluster occupies a distinct physical position from previously reported loci. While Lou et al (2021) identified a grain hardness QTL on chromosome 2D, their locus is far from our discovery. Similarly, Guo et al. (2020) reported sedimentation value QTLs on chromosome 2D, but none overlap with our 28.88-37.04 Mb interval. This spatial separation, combined with the exceptional effect size and stability, strongly suggests we have identified a novel genetic determinant of weak-gluten characteristics. The candidate gene TraesCS2D01G077600LC.1 within this interval encodes lysine synthase, which may influence gluten protein composition by altering amino acid synthesis ratios. This represents a previously uncharacterized mechanism for weak-gluten development that warrants functional validation. Unlike the well-studied Glu-1 loci that directly affect glutenin composition (Shewry et al. 2003), this novel locus appears to modulate quality through amino acid metabolism, potentially offering breeders a complementary approach to traditional glutenin-based selection. Trait Correlations and Their Breeding Implications Our results confirm the well-established positive correlations among GPC, WGC, and ZSV (r = 0.78-0.87, P < 0.001), consistent with the biochemical relationship between protein content and gluten functionality (Ma et al. 2021). However, we identified a previously underappreciated negative correlation between test weight (TW) and grain hardness (GH) (r = -0.26, P < 0.01), which has significant implications for breeding strategies. This inverse relationship likely stems from the physiological connection between grain moisture content and hardness, softer grains typically retain more moisture, increasing test weight while decreasing hardness. This finding challenges conventional breeding approaches that often select for high test weight without considering its potential trade-offs with desired softness in weak-gluten wheat. The QTL additive analysis provides a powerful framework for precision breeding (Figure 4). RILs accumulating quality-associated QTLs showed progressive reduction in GPC and WGC, shifting the population toward weak-gluten classification standards while maintaining acceptable test weight. This quantitative relationship enables breeders to strategically select appropriate QTL combinations to achieve targeted quality profiles without compromising other essential traits. Notably, lines carrying both QZStm.swust-2DL and QGHtm.swust-2DL (such as TM116) consistently met Chinese national standards for weak-gluten wheat across multiple environments, demonstrating the practical utility of these markers. Comparative Analysis with Previous QTL Studies Our findings both complement and extend previous wheat quality QTL research. While Zhao et al. (2024) identified seven GPC QTLs using a 50K SNP array, our study detected three GPC QTLs, including a novel major-effect locus QGPCtm.swust-2DL (13.46% PVE) at 45.52-46.87 Mb on chromosome 2D. This position differs substantially from the QGPC.caas-5DL and QGPC.caas-7AL loci reported by Zhao et al. (2024), and from the chromosome 2D QTL mapped by Semagn et al. (2021), suggesting a previously uncharacterized genetic determinant of protein content. Similarly, the QWGCtm.swust-7DL (11.10% PVE) on chromosome 7D represents a novel locus distinct from those reported by Pu et al. (2022). The physical interval (28.18-52.28 Mb) does not overlap with previously documented wet gluten QTLs, highlighting the value of our multi-environment approach in uncovering stable genetic factors. For grain hardness, the QGHtm.swust-7DL (10.49% PVE) at 41.41-42.11 Mb on chromosome 7D appears to be a novel locus, as it occupies a different position than Q.HI.scau-7D reported by Li et al. (2013). Additionally, our identification of QGHtm.swust-5DL on the same chromosome as the Puroindoline genes ( Pina and Pinb ) but at a distinct physical location suggests potential new regulatory mechanisms beyond the well-characterized Ha locus. Practical Applications and Future Directions The four elite RILs identified in this study (TM77, TM103, TM116, and TM149) represent immediately applicable genetic resources for weak-gluten wheat breeding programs. Notably, TM116 combines exceptional quality stability with favorable agronomic traits and carries both major QTLs on chromosome 2DL. This line could serve as a valuable parental material for developing new weak-gluten varieties that meet Chinese national standards while maintaining yield potential. Our findings directly address the critical shortage of high-quality weak-gluten wheat resources in China (Liu et al. 2023) by providing validated molecular markers and breeding materials. The chromosome 2DL QTL cluster, in particular, offers unprecedented opportunities for marker-assisted selection with potential to reduce breeding cycles by 30-50% compared to conventional phenotypic selection. Given that weak-gluten wheat commands premium prices in confectionery markets (Kumar et al. 2016; Wu et al. 2022), the economic impact of implementing these markers could be substantial. Future research should prioritize fine-mapping of the chromosome 2DL interval to identify the causal gene(s) and validate their function through transgenic approaches. The candidate gene TraesCS2D01G01G077600LC.1 warrants particular attention, as its lysine synthase function suggests a novel mechanism for modulating gluten properties. Additionally, investigating gene-by-environment interactions for the major QTLs across diverse agroecological zones would enhance the robustness of marker-assisted breeding strategies. Declarations Author contribution statement LQ participated in data collection and analysis and drafted the manuscript. CH participated in data collection, data analysis, and manuscript revision. ZBC, WYL, and GSS participated in data collection and organization. RY and YGQprovided plant materials and guided the testing of relevant quality indicators. LX and ZSH guided field cultivation and related surveys. XCJ and ZXL participated in manuscript revision and provided relevant suggestions. Acknowledgments This work was supported by National Natural Science Foundation of China (No. 32572773), Crop Germplasm Innovation and Genetic Improvement Key Laboratory of Sichuan Province (myzdsys24-02), the earmarked fund for CARS-03 (No. CARS-03-82), the Key Research and Development Support Program of the Chengdu Municipal Science and Technology Bureau (Grant No. 2024-YF05-01670-SN). Conflict of interest The authors declare that they have no conflict of interest. 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Trait a Env b Parents c RILs H 2 ( %) f TC29 MM902 D-value AV±SD d CV e Min Max GPC PD 13.35 11.5 1.85 12.14±0.08 7.51 9.99 15.09 71.78% QY 13.39 10.79 2.6 11.19±0.11 11.76 8.01 15.23 XJ 12.908 10.45 2.458 11.46±0.09 9.58 8.72 14.59 WGC PD 33.04 28.64 4.4 35.30±0.18 9.92 20.97 41.83 66.85% QY 37.07 28.52 8.55 29.28±0.24 12.45 20.07 38.62 XJ 40.36 33.24 7.12 30.95±0.32 6.1 31.93 41.1 TW PD 800.44 778.41 22.03 796.4±1.51 2.28 736.64 834.03 59.83% QY 790.24 770.07 20.17 781.01±1.46 1.76 706.83 810.41 XJ 796.17 767.23 28.94 795.35±1.17 2.24 752.53 827.36 GH PD 50.04 43.69 6.35 46.21±0.48 12.55 31.02 62.22 70.38% QY 57.82 40.96 16.86 45.37±0.44 11.70 29.40 60.15 XJ 50.83 43.39 7.44 48.24±0.33 8.14 37.63 61.16 ZSV PD 46.12 36.39 9.73 39.04±0.54 16.62 28.24 58.67 69.14% QY 55.53 35.06 20.47 40.43±0.54 16.02 23.92 56.14 XJ 50.6 39.74 10.86 42.92±0.34 9.60 31.79 53.37 a Grain protein content (GPC, %); Wet gluten content, (WGC, %); Test Weight (TW, g/L); Grain Hardness (GH); Zeleny Sedimentation Value (ZSV, mL). b PD, Pidu District; XJ, Xiaojian Town; QY, Qingyi Town. c TC29, Taichung29; MM902, Mianmai902 ; D-value, The difference between TC29 and MM902. d SD, Standard deviation. e CV, (Coefficient of variation, %) = SD/average * 100. f H 2 (%), broad-sense heritability. Table 2 Summary of stable or major QTLs identified on the quality traits of the Taichung29/MM902 recombinant inbred line (RIL) populations examined in 2023. Trait a QTL Env b Chr LeftMarker RightMarker LOD c PVE(%) d Add e GPC QGPCtm.swust-2DL PD 2DL AX-109785183 AX-110384776 4.74 13.46 0.33 WGC QWGCtm.swust-2DL PD 2DL AX-110072786 AX-111956072 8.91 23.13 1.43 QWGCtm.swust-7DL QY 7DL AX-111473400 AX-109276227 3.68 11.10 1.63 GH QGHtm.swust-1BS XJ 1BS AX-109913678 AX-110041309 3.69 3.95 -4.70 QY 1BS AX-109913678 AX-110041309 2.90 3.70 4.10 QGHtm.swust-2BL XJ 2BL AX-111502586 AX-110928542 3.05 0.16 -5.81 QY 2BL AX-111502586 AX-110928542 2.83 3.30 4.47 QGHtm.swust-5DL XJ 5DL AX-109459390 AX-111481379 2.77 0.15 -5.68 QY 5DL AX-109459390 AX-111481379 2.60 3.27 4.40 QGHtm.swust-6DL XJ 6DL AX-109035604 AX-109077353 3.56 0.15 -6.01 QY 6DL AX-109035604 AX-109077353 3.00 3.68 4.29 QGHtm.swust-2DL PD 2DL AX-110072786 AX-111956072 13.65 29.70 3.38 QY 2DL AX-110072786 AX-111956072 3.62 1.02 1.71 QGHtm.swust-7DL QY 7DL AX-110830564 AX-110888456 3.23 10.49 1.50 TW QTWtm.swust-4AS PD 4AS AX-111272311 AX-110541026 2.71 4.11 19.64 QY 4AS AX-111272311 AX-110541026 5.48 0.45 29.72 QTWtm.swust-4DL.1 XJ 4DL AX-110912249 AX-110832233 3.50 10.58 20.23 QTWtm.swust-6DL.2 PD 6DL AX-89564650 AX-89535748 3.19 9.89 5.71 ZSV QZStm.swust-2DL PD 2DL AX-111956072 AX-111707760 10.91 30.28 3.61 QY 2DL AX-111956072 AX-111707760 3.85 11.66 2.42 QZStm.swust-6AL PD 6AL AX-109041968 AX-111585865 4.11 7.37 1.79 QY 6AL AX-109041968 AX-111585865 4.11 7.37 1.79 a Grain protein content (GPC, %); Wet gluten content, (WGC, %); Test Weight (TW, g/L); Grain Hardness (GH); Zeleny Sedimentation Value (ZSV, mL). b PD, Pidu District; XJ, Xiaojian Town; QY, Qingyi Town.. c LOD, logarithm of odds score. d PVE, percentage of the phenotypic variance explained by the individual QTLs. e Add, additive effect ofthe grain yield allele. Table 3. QTL clusters for two traits in two environments. Cluster Chr Marker interval QTL hysical position (bp) High confidence genes Low confidence genes 1 2D AX-110072786 - AX-111956072 QWGCtm.swust-2DL 34428838-37041235 ID=TraesCS2D01G080700 ID=TraesCS2D01G085400.1 ID=TraesCS2D01G076900LC.1 - ID=TraesCS2D01G083100LC.1 QGHtm.swust-2DL 2 2A AX-111570872 - AX-110492596 QGHtm.swust-2AL 124363723-651597865 ID=TraesCS2A01G169500.1 - ID=TraesCS2A01G397700.1 ID=TraesCS2A01G179600LC.1 - ID=TraesCS2A01G553700LC.1 QTWtm.swust-2AL.2 3 5D AX-109459390 - AX-111481379 QGHtm.swust-5DL 459210485-495064805 ID=TraesCS5D01G390900.1 - ID=TraesCS5D01G443800.1 ID=TraesCS5D01G482500LC.1 - ID=TraesCS5D01G526800LC.1 QTWtm.swust-5DL.1 4 6A AX-108891954 - AX-110469098 QGHtm.swust-6AL 233809045-58754970 ID=TraesCS6A01G189000.1 - ID=TraesCS6A01G356000.1 ID=TraesCS6A01G287100LC.1 - ID=TraesCS6A01G556100LC.1 QTWtm.swust-6AL.2 Table 4. RIL population lines meeting five quality standards for Weak-Gluten wheat Lines Environment a GPC b WGC c TW d GH e ZSV f TM77 PD 10.05 23.91 781.50 46.76 28.70 TM103 QY 9.2611 23.51 779.65 34.26 29.66 TM115 QY 8.4025 20.96 783.47 35.16 26.84 TM116 PD 10.448 20.97 755.33 36.90 28.80 QY 9.4248 23.94 784.20 36.57 27.93 TM149 PD 11.622 24.39 793.35 37.65 28.24 TM207 PD 10.509 25.39 781.77 41.17 29.04 a PD, Pidu District; XJ, Xiaojian Town; QY, Qingyi Town.. b-f Grain protein content (GPC, %); Wet gluten content, (WGC, %); Test Weight (TW, g/L); Grain Hardness (GH); Zeleny Sedimentation Value (ZSV, mL). Table 5 . Agronomic traits of lines in the RIL population that meet five quality requirements for weak-gluten wheat Lines Environment a PH b SL c SNS d KNPS e TN f TM77 XJ 81.7 12.2 19 72 6 PD 86.0 12.2 18 68 6 QY 69.7 10.4 18 49 4 MEAN 79.1 11.6 18 63 6 TM103 XJ 76.7 10.7 20 55 7 PD 75.3 10.8 17 47 7 QY 76.0 8.8 16 43 3 MEAN 76.0 10.1 17 48 6 TM116 XJ 95.0 10.8 19 72 6 PD 90.7 10.0 20 57 4 QY 91.0 10.3 18 47 5 MEAN 92.2 10.3 19 59 5 TM149 XJ 84.0 8.5 18 67 8 PD 79.7 9.6 20 58 9 QY 71.6 9.0 18 52 6 MEAN 78.4 9.0 19 59 8 a PD, Pidu District; XJ, Xiaojian Town; QY, Qingyi Town. b-f PH, Plant height; SL, Spike length; SNS, Spikelet number per spike; KNPS, Kernel number per spike; TN, Tiller number. Additional Declarations No competing interests reported. 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Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shasha\",\"middleName\":\"\",\"lastName\":\"Gu\",\"suffix\":\"\"},{\"id\":563773753,\"identity\":\"f42e82d3-274b-4472-acef-edda6a98d149\",\"order_by\":5,\"name\":\"Chongjing Xia\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Southwest University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chongjing\",\"middleName\":\"\",\"lastName\":\"Xia\",\"suffix\":\"\"},{\"id\":563773754,\"identity\":\"527e94ad-7485-4e37-8404-c5fe09fd8c1f\",\"order_by\":6,\"name\":\"Xin Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Southwest University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xin\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":563773756,\"identity\":\"fed21d5f-8fe8-42fa-a902-b5cd1bc02040\",\"order_by\":7,\"name\":\"Shouhang Zheng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Mianyang 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09:11:32\",\"extension\":\"png\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":131926,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8260532/v1/d28f6ce9a4dfc91aa614cdaf.png\"},{\"id\":99217702,\"identity\":\"306982f6-7b13-4b73-b7c1-55090f2680ef\",\"added_by\":\"auto\",\"created_at\":\"2025-12-30 09:11:32\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":155608,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFrequency distribution of quality-related traits in the Taichung29/Mianmai902 RIL across three different environments. (a) Grain protein content (GPC, %), (b) Wet gluten content (WGC, %), (c) Test Weight (TW,g/L), (d) Grain Hardness (GH), (e) Zeleny Sedimentation Value (ZSV,mL). The green bars represent the XJ (Xiaojian) environment, the light purple bars represent the PD (Pidu) environment, and the dark purple bars represent the QY (Qingyi) environment.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8260532/v1/459d5e04b7d4adde56e9ca6d.png\"},{\"id\":99217704,\"identity\":\"c529ed1f-c091-4999-8ce7-79a0c6cd9f47\",\"added_by\":\"auto\",\"created_at\":\"2025-12-30 09:11:32\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":296768,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe pearson correlation coefficients (r) between investigated quality traits under XJ (Xiaojian), PD (Pidu), and QY (Qingyi) environments. GPC(%): Grain Protein Content, WGC(%): Wet Gluten Content, TW(g/L): Test Weight, GH: Grain Hardness, ZSV (mL): Zeleny Sedimentation Value. Purple indicates a positive correlation between two indicators, while blue indicates a negative correlation between two indicators. Significant differences are indicated as follows: *P ≤0.05, **P ≤0.01, and ***P ≤0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8260532/v1/7fda919746c2313f1488f6b6.png\"},{\"id\":99217703,\"identity\":\"be08bc52-d888-4da4-803a-1d1db325fe00\",\"added_by\":\"auto\",\"created_at\":\"2025-12-30 09:11:32\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":188928,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMapping of nine major quantitative trait loci (QTLs) related to quality traits. (a) for Grain protein content (GPC, %):\\u003cem\\u003eQGPCtm.swust-2DL\\u003c/em\\u003e; (b-c) for Wet gluten content (WGC, %):\\u003cem\\u003eQWGCtm.swust-2DL\\u003c/em\\u003e,\\u003cem\\u003eQWGCtm.swust-7DL\\u003c/em\\u003e; (d-i) Grain Hardness (GH):\\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e,\\u003cem\\u003eQGHtm.swust-7DL\\u003c/em\\u003e,\\u003cem\\u003eQGHtm.swust-1BS\\u003c/em\\u003e,\\u003cem\\u003eQGHtm.swust-2BL\\u003c/em\\u003e,\\u003cem\\u003eQGHtm.swust-5DL\\u003c/em\\u003e,\\u003cem\\u003eQGHtm.swust-6DL\\u003c/em\\u003e; (j-l) for Test Weight (TW,g/L):\\u003cem\\u003eQTWtm.swust-4DL.1\\u003c/em\\u003e,\\u003cem\\u003eQTWtm.swust-6DL.2\\u003c/em\\u003e, QTWtm.swust-4AS;(m-n) Zeleny Sedimentation Value (ZSV,mL):\\u003cem\\u003eQZStm.swust-2DL\\u003c/em\\u003e,\\u003cem\\u003eQZStm.swust-6AL\\u003c/em\\u003e.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8260532/v1/d2b84f4263ed941ad56f82e9.png\"},{\"id\":99217711,\"identity\":\"6207c9d5-bf73-4c2d-968b-2af30763b721\",\"added_by\":\"auto\",\"created_at\":\"2025-12-30 09:11:32\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":91469,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEffects of individual QTL and their combinations on the quality traits in three environments, XJ (Mianyang), QY (Mianyang) and PD (Pidu). (a) Grain protein content , (b) Wet gluten content, (c) Grain Hardness, (d) Test Weight , (e) Zeleny Sedimentation. Light purple boxes represent the PD environment, dark purple boxes represent the QY environment, and blue boxes represent the XJ environment. Box plots indicate the quality traits associated with the identified QTL and their combination.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8260532/v1/31f8801af976e89e8687e46f.png\"},{\"id\":99318147,\"identity\":\"84c8d1d5-61d7-499c-8317-80e8b8c6b0d1\",\"added_by\":\"auto\",\"created_at\":\"2025-12-31 16:31:39\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":517931,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDistribution of QTLs on chromosomes. The left side of the chromosome shows genetic distance(cM), while the right side displays markers. Ellipses represent WGC, stars denote GPC, squares indicate GH, circles signify ZSV, and triangles denote TW.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8260532/v1/fd0c08190958d4262ff7ec50.png\"},{\"id\":99217719,\"identity\":\"ea365ac9-d7ff-461c-9f21-ac18a602e322\",\"added_by\":\"auto\",\"created_at\":\"2025-12-30 09:11:33\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":5713,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFrequency distribution of agronomic traits in the Taichung29/Mianmai902 RIL across three different environments in 2023. (a) Plant height, (b) Spike length, (c) Spikelet number per spike, (d) Kernel number per spike, (e) Tiller number. The green bars represent the XJ (Xiaojian) environment, the light purple bars represent the PD (Pidu) environment, and the dark purple bars represent the QY (Qingyi) environment.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"placeholderimage.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8260532/v1/75cff08a4db2bf0f8a9d6060.png\"},{\"id\":99789958,\"identity\":\"a0cb4304-f405-49d6-8019-330d4eaa4eef\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 12:51:15\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2469550,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8260532/v1/98ae4b13-eb21-4c60-8f03-98f1c55f2277.pdf\"},{\"id\":99217709,\"identity\":\"4512483f-bd2b-46e4-bb97-2040fb293715\",\"added_by\":\"auto\",\"created_at\":\"2025-12-30 09:11:32\",\"extension\":\"xlsx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":14547,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarymaterial.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8260532/v1/c28102c98330f203b091f904.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A Major QTL Cluster of Weak-Gluten on Wheat Chromosome 2D informs Marker-Assisted Selection\",\"fulltext\":[{\"header\":\"Key message \",\"content\":\"\\u003cp\\u003eTwo major QTLs for quality traits were mapped on chromosome 2DL in weak-gluten wheat Mianmai 902, and four elite RIL lines satisfying Chinese national standards were selected.\\u003c/p\\u003e\"},{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eWeak-gluten wheat is defined as a specialized type of soft wheat. Its defining characteristics include low protein content in the grain, soft kernel texture, and weak gluten strength. This wheat type serves as ideal raw material for confectionery products including biscuits, cakes, and southern Chinese steamed buns (mantou), making it an important food crop with specific market demand (Kumar et al. 2016; Wu et al. 2022) . Key quality indicators for weak-gluten wheat include grain protein content (GPC), wet gluten content (WGC), Zeleny sedimentation value (ZSV), test weight (TW), and grain hardness (GH), all of which significantly influence processing quality and market value (Sun et al. 2010).\\u003c/p\\u003e\\n\\u003cp\\u003eGrain protein content is fundamentally related to wheat milling quality and represents one of the most genetically complex traits to study due to its polygenic nature and strong environmental interactions (Sun et al. 2010). \\u003cem\\u003eGpc-B1\\u003c/em\\u003e gene, located on chromosome 6B and first cloned from wild emmer wheat (\\u003cem\\u003eTriticum turgidum\\u003c/em\\u003e ssp. \\u003cem\\u003edicoccoides\\u003c/em\\u003e), has demonstrated capacity to reduce grain protein content by over 30% (Uauy et al. 2006). Additional regulatory factors, such as \\u003cem\\u003eHOMEOBOX DOMAIN-2\\u003c/em\\u003e (\\u003cem\\u003eHB-2\\u003c/em\\u003e), and several reported QTLs have been identified (Blanco et al. 1996, 2002, 2006; Prasad et al. 1999; Dixon et al. 2022; Zhao et al. 2024).\\u003c/p\\u003e\\n\\u003cp\\u003eWet gluten forms a viscoelastic network when proteins in wheat flour absorb water (Wu et al. 2022). This network comprises gliadins, high-molecular-weight glutenin subunits (HMW-GS), and low molecular weight glutenin (LMW-GS). HMW-GS is the key to determine the quality of gluten, with the Dy10-null allele having significant negative effects on sedimentation value while being valuable for weak-gluten wheat development (Wang et al. 2023). It is well established that the three homologous chromosomes 1A, 1B, and 1D each harbor a major locus (Glu-A1, Glu-B1, and Glu-D1, respectively) that controls the composition of HMW-GS. Among these, \\u003cem\\u003eGlu-D1\\u003c/em\\u003e exerts the most significant influence on gluten quality. A deletion at the \\u003cem\\u003eGlu-D1\\u003c/em\\u003e locus prevents gluten formation, reduces the sedimentation value, and adversely affects processing quality (Shewry et al. 2003).\\u003c/p\\u003e\\n\\u003cp\\u003eThe sedimentation value serves a comprehensive indicator reflecting grain protein quality, dough theological properties, and gluten content (Sun et al. 2012). It correlates strongly with HMW-GS composition (Wang et al. 2023). Research consistently indicate positive correlations among protein content, gluten content, and sedimentation value\\u0026nbsp;(Ma et al. 2021). This relationship suggests that quality improvement efforts can effectively target these parameters simultaneously.\\u003c/p\\u003e\\n\\u003cp\\u003eGrain hardness represents a critical evaluation parameter influencing wheat classification, grading, and processing suitability (Li et al. 2013). The Puroindoline a and b genes (\\u003cem\\u003ePina-D1\\u003c/em\\u003e and \\u003cem\\u003ePinb-D1\\u003c/em\\u003e), which are located within the \\u003cem\\u003eHa\\u003c/em\\u003e locus on chromosome 5D, encode key proteins that govern this trait (Sourdille et al. 1996; Sun et al. 2010). When both \\u003cem\\u003ePina-D1\\u003c/em\\u003e and \\u003cem\\u003ePinb-D1\\u003c/em\\u003e carry wild-type sequences (alleles), grain exhibits soft texture; the presence of a deletion or mutation in either gene confers hardness (Igrejas et al. 2002; Bhave and Morris 2008; Li et al. 2020).\\u003c/p\\u003e\\n\\u003cp\\u003eTest weight is a vital quality indicator reflecting wheat grain plumpness and overall quality. Multiple factors influence this trait, with Cabral et al. (2018) identifying multiple QTLs for bulk density including \\u003cem\\u003eQTwt.crc-4D\\u003c/em\\u003e which colocalizes with the dwarfing gene \\u003cem\\u003eRht-D1b\\u003c/em\\u003e, leading to reduced test weight. Significant positive correlations exist between test weight and grain width, but not with grain length, indicating direct influence of grain morphology on test weight (Cabral et al. 2018). Studies also indicate that high temperatures increase protein content while decreasing test weight (White et al. 2022). Despite growing interest, research on genes controlling test weight in wheat remains relatively scarce compared to other quality traits (White et al. 2022), with QTLs associated with test weight mapped across 20 chromosomes, excluding chromosome 6D (Li et al. 2016).\\u003c/p\\u003e\\n\\u003cp\\u003eHistorically, wheat breeding research in China prioritized yield improvement, with dedicated weak-gluten wheat research emerging relatively late. It was not until the late 20th century and early 21st century that weak-gluten wheat gained significant research attention. However, many existing weak-gluten wheat varieties exhibit suboptimal performance in critical quality traits, failing to fully meet national standards for high-quality wheat. This has resulted in a persistent shortage of high-quality weak-gluten wheat resources in China (Liu et al. 2023). Concurrently, rising living standards have driven substantial increases in demand for high-quality weak-gluten wheat, creating an urgent need to develop new high-quality weak-gluten wheat varieties (Wu et al. 2022).\\u003c/p\\u003e\\n\\u003cp\\u003eWheat quality traits represent polygenic quantitative characteristics exhibiting continuous phenotypic variation influenced by both genetic and environmental factors, though genetic determinants remain dominant. Elucidating the genetic architecture underlying these traits provides crucial theoretical foundations for efficient quality improvement (Mérida-García et al. 2020; Li et al. 2025). Previous studies have mapped wheat quality QTLs across nearly all 21 chromosomes (Patil et al. 2009; Conti et al. 2011; Li et al. 2012; Kumar et al. 2013; Goel et al. 2019; Hu et al. 2022), yet stable and major-effect QTLs remain relatively scarce (Li et al. 2009, 2025). Research progress has been constrained by the wheat genome's enormous size and complexity (Olmos et al. 2003; Distelfeld et al. 2004; Uauy et al. 2006).\\u003c/p\\u003e\\n\\u003cp\\u003eRecent advances in genotyping technologies have significantly enhanced QTL mapping efficiency. The Wheat 55K SNP array represents a substantial improvement over traditional genotyping methods, offering higher throughput and greater accuracy for genetic studies (Sun et al. 2010; Liu et al. 2024b). This technology has enabled more comprehensive genetic analyses of wheat quality traits, with multiple research groups successfully applying it to identify QTLs for agronomic and quality characteristics. Despite these advances, comprehensive QTL mapping specifically for weak-gluten wheat quality traits remains limited, creating a critical knowledge gap for breeding programs targeting this specialized market segment.\\u003c/p\\u003e\\n\\u003cp\\u003eThis study addresses this gap by utilizing a recombinant inbred line (RIL) population of 143 lines derived from the \\\"Mianmai 902/Taichung 29\\\" cross. We evaluated grain quality traits across three contrasting environments and constructed a high-density genetic map using the 55K SNP chip. Our objectives were to: (1) identify stable and major QTLs controlling weak-gluten wheat quality formation; (2) predict candidate genes within significant QTL regions; and (3) screen superior RILs meeting Chinese national standards for weak-gluten wheat. These findings provide a comprehensive QTL atlas and validated genetic resources for marker-assisted selection breeding of high-quality weak-gluten wheat varieties with direct applications for addressing China's growing demand for premium confectionery wheat.\\u003c/p\\u003e\"},{\"header\":\"Material and Methods\",\"content\":\"\\u003ch2\\u003ePlant materials and field trials\\u003c/h2\\u003e\\n\\u003cp\\u003eThe experimental materials comprised the parental lines Mianmai 902 (MM902, a weak-gluten cultivar) and Taichung 29 (TC29, a spring wheat cultivar highly susceptible to stripe rust), along with 143 recombinant inbred lines (RILs) developed from their cross.\\u0026nbsp;Mianmai 902 was developed by Mianyang City Institute of Agricultural Science in 2008 by using Mianmai 37, a short-straw and disease-resistant wheat variety, as the female parent, and MY1848, a line of wheat highly resistant to stripe rust, as the male parent, and then using the F\\u003csub\\u003e1\\u003c/sub\\u003e as the female parent, and the high-yielding and disease-resistant wheat germplasm 06-367 (Accession: Mianmai 367) as the male parent, and adopting the genealogy method. It exhibits excellent agronomic traits including high productivity, strong disease resistance, and superior weak-gluten quality characteristics essential for confectionery products. In 2019, it passed the validation of Sichuan Provincial Crop Variety Committee (Sichuan Validated Wheat 20190005).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eField trials for this study were carried out during the 2023-2024 growing season across three distinct sites in Sichuan Province, encompassing Qingyi (31°33'N, 104°55'E), Xiaojian (31°47'N, 104°88'E), and Pidu (30°62'N, 104°10'E). The trial was conducted in a completely randomized block design, with two biological replicates per family at each site. Each plot consisted of a single row (1 m length, 0.5 m width) with approximately 100 seeds planted and row spacing of 20 cm. Standard agronomic practices for wheat cultivation in the region were implemented to ensure optimal growth conditions while minimizing experimental artifacts. All plant materials were provided by the Mianyang Academy of Agricultural Sciences.\\u003c/p\\u003e\\n\\u003ch2\\u003eGenotyping and quality trait measurement\\u003c/h2\\u003e\\n\\u003cp\\u003eThe leaves of parental Mianmai 902, Taichung29, and 143 family lines were collected in January 2024, and genomic DNA was extracted by a modified CTAB method. Then the DNA concentration and quality were examined using a NanoDrop ND-1000 spectrophotometer (Thermo Scientifc, Wilmington, DE, USA) and the parents and RILs were genotyped using the 55K Illumina® iSelect wheat SNP array (Zhongyujin Marker Biotechnology Co., Ltd., Beijing, China), which consists of 53,063 SNP markers and has become a standard tool for high-resolution genetic mapping in wheat due to its genome specificity and reliability.\\u003c/p\\u003e\\n\\u003cp\\u003eFor quality trait evaluation, approximately 200 plump, visually uniform seeds from each line were analyzed using the 3700 NIR Analyzer (FOSS, Germany) to determine grain protein content, wet gluten content, sedimentation value, grain hardness and test weight. Each quality trait was measured in 3 replicates, with the average value used for analysis. The mean value of each quality trait for each RIL in each environment was calculated from the two field replicates, with each replicate value being the average of three technical measurements.\\u003c/p\\u003e\\n\\u003ch2\\u003eData analysis\\u003c/h2\\u003e\\n\\u003cp\\u003eThe data of quality traits were analyzed using Origin 2021 software (https://www.originlab.com/2021). The variance in the individual environment analysis was calculated using the mean quality values of each RIL at the three experimental sites. ANOVA and correlation analysis were performed using the \\\"AOV\\\" tool in IciMapping V4.1(http://www.isbreeding.net/) software. The broad-sense heritability (\\u003cem\\u003eH\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/em\\u003e) of each grain quality trait was estimated using the following formula:\\u003cem\\u003eH\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/em\\u003e =σ\\u003csub\\u003eg\\u003c/sub\\u003e\\u003csup\\u003e2\\u003c/sup\\u003e/（σ\\u003csub\\u003eg\\u003c/sub\\u003e\\u003csup\\u003e2\\u003c/sup\\u003e+σ\\u003csup\\u003e2\\u003c/sup\\u003e\\u003csub\\u003ege\\u003c/sub\\u003e/e+σε\\u003csup\\u003e2\\u003c/sup\\u003e/re）. σ\\u003csub\\u003eg\\u003c/sub\\u003e\\u003csup\\u003e2\\u003c/sup\\u003e=genetic variance; σ\\u003csup\\u003e2\\u003c/sup\\u003e\\u003csub\\u003ege\\u0026nbsp;\\u003c/sub\\u003e= variance of genotype×environment interaction; σ\\u003csub\\u003eε\\u003c/sub\\u003e\\u003csup\\u003e2\\u003c/sup\\u003e=error variance; r = replications per environment; e = the number of environments.\\u003c/p\\u003e\\n\\u003ch2\\u003eThe high-density genetic map and QTL mapping\\u003c/h2\\u003e\\n\\u003cp\\u003eBefore constructing the linkage map, the redundant SNP markers were sorted out, and the SNP markers without polymorphism between parents were first eliminated, and then the markers with a missing rate of more than 20% were deleted by using the \\\"BIN\\\" tool in ICIMaping V4.2 software. In this study, IciMaping V4.2 (http://www.isbreeding.net/) software was used to detect and locate potential QTLs by composite interval plotting method, and based on software recommendations or common practices, the software parameters are set as follows: Step=1 cM, the value of the input variable (PIN) p=0.0001, and LOD=2.5. When the LOD value is higher than 2.5, a QTL is considered to exist, and the contribution rate and additive effect of each QTL are calculated. QTL naming rules: For example, \\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e, where GH represents the quality-related trait (grain hardness) in this study, tm represents the MM902/TC29 population, swust represents the affiliation name, and 2DL represents the chromosome information.\\u003c/p\\u003e\\n\\u003ch2\\u003eIdentification of candidate genes\\u003c/h2\\u003e\\n\\u003cp\\u003eTo identify quality-related candidate genes within significant QTL regions, physical map alignments were performed against the IWGSC RefSeq v1.0 reference genome. Candidate genes within the confidence intervals of QTL clusters were retrieved from the IWGSC RefSeq v1.0 annotation. Genes annotated with functions potentially related to grain quality were prioritized as candidates.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003ch2\\u003ePhenotypic evaluation and correlation analysis\\u003c/h2\\u003e\\n\\u003cp\\u003ePhenotypic evaluation across three diverse environments (Qingyi Town, Xiaojian Town, and Pidu District)\\u0026nbsp;revealed significant differences between parental lines for key quality traits. Mianmai 902 (MM902), the weak-gluten wheat parent, consistently exhibited lower grain protein content (GPC), wet gluten content (WGC), grain hardness (GH), and Zeleny sedimentation value (ZSV) compared to Taichung 29 (TC29), with the exception of test weight (TW), which showed environment-dependent variation (Table 1). The recombinant inbred line (RIL) population displayed continuous phenotypic distributions for all five quality traits, characteristic of polygenic inheritance (Figure 1).\\u003c/p\\u003e\\n\\u003cp\\u003eIn the RIL population, the variation of GPC in the three environments (Qingyi Town, Xiaojian Town, and Pidu District) ranged from 8.01% to 15.23%, and the coefficient of variation ranged from 7.51% to 11.76%; the variation of WGC ranged from 20.07% to 41.83%, and the coefficient of variation ranged from 6.1% to 12.45%; the minimum value of the TW was detected in Qingyi (706.83 g/L), and the maximum value appeared in Pidu (834.03 g/L), and the coefficient of variation of this trait ranged from 1.76% to 2.28%. and the coefficient of variation for this trait was smaller than that of the other four traits; the range of the variation of the GH was 29.4-67.62, and the range of the coefficient of variation was 8.14%-12.55%; the range of the variation of the ZSV was 23.92mL-58.67mL, and the range of the coefficient of variation was 9.6%-16.62% (Table 1).The variation of individual traits in the three environments (Qingyi Town, Xiaojian Town, and Pidu District) ranged from 1.76% to 16.62%. A notable observation was the widespread transgressive segregation, which was predominantly favorable, for almost all quality traits in each environment. Concurrently, the broad-sense heritability (hB2) estimates for these traits varied between 59.83% and 71.78% (Table 1). The five grain quality traits in the RIL population were all continuously distributed, which was consistent with the phenotypic characteristics of quantitative traits and was suitable for QTL mapping analysis (Figure. 1).\\u003c/p\\u003e\\n\\u003cp\\u003eCorrelation analysis revealed significant relationships among quality parameters (Figure 2). Strong positive correlations were observed between GPC and WGC (r = 0.87, P \\u0026lt; 0.001 in Qingyi), GPC and ZSV (r = 0.78-0.82, P \\u0026lt; 0.001 across environments), and WGC and ZSV (r = 0.81-0.85, P \\u0026lt; 0.001). Notably, TW showed negative correlations with GH (r = -0.26, P \\u0026lt; 0.01 in Qingyi and Pidu), suggesting an inverse relationship between grain plumpness and hardness that has practical implications for weak-gluten wheat breeding.\\u003c/p\\u003e\\n\\u003ch2\\u003eHigh-Density Genetic Map Construction and QTL Identification\\u003c/h2\\u003e\\n\\u003cp\\u003eGenotyping with the Wheat 55K SNP array identified 39,110 polymorphic SNP markers between parental lines. A total of 10,132 molecular markers with known chromosome position information were obtained. The 5B chromosome had the largest number of markers with 900 markers, and the 4D chromosome with the least number of markers had 175 markers. The 10,132 SNP markers were used to construct 21 linkage groups, and the average distance between adjacent markers was 1.34 cM. The length of the single chromosome spectrum ranged from 311.42 cM (chromosome 1B) to 1247.9476 cM (chromosome 6A), and this genetic map was used for the mapping of QTLs for quality traits.\\u003c/p\\u003e\\n\\u003cp\\u003eComposite interval mapping identified 41 QTLs, distributed over 16 chromosomes (all wheat chromosomes excluding 1A, 3A, 4B, 6B, and 7B) (Table 2). Individual QTLs explained 0.15%-30.28% of phenotypic variation (PVE), with an average of 5.52%. Thirty QTLs showed additive effects derived from MM902 (the weak-gluten parent), while 11 showed effects from TC29, reflecting the genetic architecture underlying weak-gluten characteristics.\\u003c/p\\u003e\\n\\u003cp\\u003eApplying rigorous criteria (LOD \\u0026gt; 2.5 and PVE \\u0026gt; 10%), we identified eight major-effect QTLs. Seven of these originated from MM902, consistent with its weak-gluten phenotype, while one derived from TC29. Eight QTLs demonstrated stability across multiple environments, with two particularly significant loci meeting both stability and major-effect criteria: \\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e and \\u003cem\\u003eQZStm.swust-2DL\\u003c/em\\u003e0 (Figure 3).\\u003c/p\\u003e\\n\\u003cp\\u003eFor the GPC, three QTLs were detected on chromosomes 2DL, 5DL and 7AL, explaining 7.22%-13.46% of PVE. \\u003cem\\u003eQGPCtm.swust-7AL\\u0026nbsp;\\u003c/em\\u003e(8.34% PVE) was detected in Qingyi environment and additive effects were derived from MM902. \\u003cem\\u003eQGPCtm.swust-2DL\\u003c/em\\u003e and \\u003cem\\u003eQGPCtm.swust-5DL\\u003c/em\\u003e (13.46% and 7.22% PVE) were detected in Pidu environment, with additive effects derived from MM902 and TC29. Thus \\u003cem\\u003eQGPCtm.swust-2DL\\u003c/em\\u003e was the major QTL (Table 2, Figure 3).\\u003c/p\\u003e\\n\\u003cp\\u003eFor the WGC, two QTLs were detected on chromosomes 2DL and 7DL. \\u003cem\\u003eQWGCtm.swust-2DL\\u0026nbsp;\\u003c/em\\u003ewas detected in Pidu environment, explaining 23.13% phenotypic variation and the additive effects derived from MM902, which was the major QTL . \\u003cem\\u003eQWGCtm.swust-7DL\\u003c/em\\u003e was detected in the Qingyi environment, explaining 11.1% phenotypic variation.(Table 2, Figure 3).\\u003c/p\\u003e\\n\\u003cp\\u003eFor the GH, a total of 12 QTLs were detected on chromosomes 1BS, 1DL, 2AL, 2BL, 3BL, 5DL, 6AL, 6DL, 2DL, 4DL, 4AS, and 7DL, each individual QTL explained\\u0026nbsp;0.16%-29.7% phenotypic variation. \\u003cem\\u003eQGHtm.swust-1BS, QGHtm.swust-2BL, QGHtm.swust-5DL, QGHtm.swust-6DL, and QGHtm.swust-2DL\\u003c/em\\u003e were all detected in two environments, explaining\\u0026nbsp;phenotypic variations of 3.7%-4.0%, 0.16%-3.3%, 0.15%-3.27%, 0.15%-3.68%, and 0.12%-29.7%. Among them, \\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e was the major and stable QTL, with additive effects derived from\\u0026nbsp;MM902. The remaining QTLs were detected in only a single environment, explaining phenotypic variation ranging from 0.16% to 10.49%. Among them, \\u003cem\\u003eQGHtm.swust-7DL\\u003c/em\\u003e is a major QTL, with additive effects derived from MM902 (Table 2, Table S1, Figure 3).\\u003c/p\\u003e\\n\\u003cp\\u003eFor the TW, a total of 18 QTLs were detected on chromosomes 2AL, 2DS, 3DL, 4AS, 4DL, 5BS, 5DL, 6AL, 6DL and 7DS, each individual QTL explained\\u0026nbsp;0.44%-10.58% phenotypic variation. \\u003cem\\u003eQTWtm.swust-4AS\\u003c/em\\u003e was detected in two environments, and explained\\u0026nbsp;0.45%-4.11% phenotypic variation, with additive effects derived from MM902. The other QTLs were detected in one environment, explaining\\u0026nbsp;phenotypic variations ranging from 0.44% to 10.58%. The additive effects were all from MM902, and \\u003cem\\u003eQTWtm.swust-4DL.1\\u003c/em\\u003e was the major QTL. (Table 2, Table S1, Figure 3)\\u003c/p\\u003e\\n\\u003cp\\u003eFor the ZSV, six QTLs were detected on chromosomes 1DL, 2DL, 4DS, 5AL, 6AL and 7AS, each individual QTL explained\\u0026nbsp;4.79%-30.28% phenotypic variation. \\u003cem\\u003eQZStm.swust-2DL\\u003c/em\\u003e and \\u003cem\\u003eQZStm.swust-6AL\\u003c/em\\u003e were detected in two environments, and explained phenotypic variations were 30.28% and 7.37%, respectively. \\u003cem\\u003eQZStm.swust-2DL\\u003c/em\\u003e was a stable and dominant QTL, with additive effects all derived from MM902. The other QTLs were detected in one environment, including \\u003cem\\u003eQZStm.swust-1DL\\u003c/em\\u003e, \\u003cem\\u003eQZStm.swust-4DS\\u003c/em\\u003e, \\u003cem\\u003eQZStm.swust-5AL\\u003c/em\\u003e, and \\u003cem\\u003eQZStm.swust-7AS\\u003c/em\\u003e. The phenotypic variations explained by these three QTL were 4.79%, 6.80%, 6.07%, and 8.73%, respectively. Among them, the additive effects of \\u003cem\\u003eQZStm.swust-5AL\\u003c/em\\u003e were derived from TC29, and the others were derived from MM902 (Table 2, Figure 3)\\u003c/p\\u003e\\n\\u003ch2\\u003eAdditive effects of QTLs on Quality Traits\\u003c/h2\\u003e\\n\\u003cp\\u003eBy integrating phenotypic data of quality traits from field trials, analyze the QTLs located for each trait separately, and RILs were classified based on QTL numbers. Grain protein content (GPC) was categorized into three groups (0–2), wet gluten content (WGC) into two groups (0–1), grain hardness into six groups (0–5+), test weight (TW) into six groups (0–5+), and sedimentation value (ZSV) into four groups (0–4) (Figure 3). RILs without QTLs exhibited higher values for all quality traits, while lines accumulating multiple QTLs showed progressively lower trait values. Specifically, GPC and WGC decreased significantly with increasing QTL number, shifting the population from medium-gluten toward the weak-gluten classification standard. Although TW decreased slightly with higher QTL counts, it remained within premium wheat quality parameters (Figure 4). This quantitative relationship provides breeders with a predictive framework for selecting appropriate QTL combinations to achieve desired quality profiles.\\u003c/p\\u003e\\n\\u003ch2\\u003eQTL clusters and candidate genes\\u003c/h2\\u003e\\n\\u003cp\\u003eAnalysis of QTL distribution revealed four significant clusters where multiple quality traits co-localized, on chromosomes 2D, 2A, 5D, and 6A\\u0026nbsp;(Table 3, Fig. 5). Due to the smaller interval of the QTL cluster on chromosome 2D, candidate genes were predicted at the QTL cluster region on chromosome 2D. The QTL cluster on chromosome 2D contained one major QTL, \\u003cem\\u003eQWGCtm.swust-2DL\\u003c/em\\u003e, and one stable and major QTL, \\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e, both with additive effects derived from Mianmiai 902. This QTL cluster between markers\\u003cem\\u003e\\u0026nbsp;AX-110072786\\u003c/em\\u003e -AX-111956072, corresponding to the 34.43Mb-37.04Mb region with an approximate physical interval of 2.61Mb. Alignment with the wheat reference genome (IWGSC RefSeq v1.0) revealed 48 high-confidence genes and 63 low-confidence genes within this cluster (Table 3).\\u003c/p\\u003e\\n\\u003cp\\u003eWithin this critical interval, \\u003cem\\u003eTraesCS2D01G077600LC.1\\u003c/em\\u003e emerged as a promising candidate gene. This locus encodes lysine synthase, an enzyme involved in amino acid biosynthesis that may influence gluten protein composition by altering the synthesis ratio of amino acids. The resulting changes in protein composition could directly impact gluten elasticity and sedimentation value, key determinants of weak-gluten characteristics. This represents a novel potential mechanism for weak-gluten wheat development that warrants further experimental validation.\\u003c/p\\u003e\\n\\u003ch2\\u003eElite Line Selection for Weak-Gluten Wheat Breeding\\u003c/h2\\u003e\\n\\u003cp\\u003eIn this study, we measured and analyzed the quality data of 143 family lines in three different environments\\u0026nbsp;(Qingyi Town, Xiaojian Town, and Pidu District), and found that the proportion of lines meeting the quality standard for weak-gluten wheat in different environments showed fluctuations, indicating that the quality traits of weak-gluten wheat can be affected by the environment. Based on China's national quality standard for weak gluten wheat, six excellent lines were screened, and all five quality traits (GPC \\u0026lt; 11.5%, WGC \\u0026lt; 22%, GH \\u0026lt; 45, ZSV \\u0026lt; 30 mL, TW \\u0026gt; 750 g/L) of these six lines met the standard for weak gluten wheat (Table 4). Notably, line TM116 satisfied all five quality parameters in two environments, demonstrating exceptional phenotypic stability.\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, the agronomic trait data of these 143 family lines were also measured and analyzed in three different environments\\u0026nbsp;(Qingyi Town, Xiaojian Town, and Pidu District), and the individual traits showed a continuous distribution in all three environments. Mianmai 902, the parent of this study, as a high quality weak gluten wheat variety, has excellent agronomic traits. In this paper, we refer to the agronomic traits of Mianmai 902 and the main domestic varieties (such as Zhengmai 9023, Chuan Mai 42, etc.), and select the plants with plant heights of 70 cm-100 cm, spike lengths of more than or equal to 9 cm, the number of spikelets of more than 17, and the number of tillers of more than four. Based on the above criteria, the lines screened above for quality traits were screened for agronomic traits, and based on the mean values of these lines in the three environments, TM115 did not meet the criteria for plant height, and TM207 did not meet the criteria for spike length and spikelet number. The remaining family lines TM77, TM103, TM116 and TM149 met the criteria (Table 5).\\u003c/p\\u003e\\n\\u003cp\\u003eCrucially, lines TM77 and TM116 harbored both major and stable QTLs (\\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e and \\u003cem\\u003eQZStm.swust-2DL\\u003c/em\\u003e), while TM103 and TM149 contained \\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e. The superior performance of TM116, which carries both major QTLs and exhibits stable quality across environments, makes it particularly valuable for breeding programs targeting high-quality weak-gluten wheat.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eSignificance of High-Density QTL Mapping for Weak-Gluten Wheat Quality Traits\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study addresses a key gap in the genetics of weak-gluten wheat quality by constructing a high-density genetic map (Wheat 55K SNP array) and performing a comprehensive QTL analysis. We detected a total of 41 QTLs on 16 chromosomes. A major finding was the identification of two stable, major-effect QTLs, \\u003cem\\u003eQZStm.swust-2DL\\u003c/em\\u003e and \\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e, which explain 30.28% and 29.70% of the phenotypic variance, respectively. This work constitutes a key advancement for marker-assisted breeding in weak-gluten wheat. These findings substantially advance our understanding beyond previous studies that typically reported fewer QTLs with lower phenotypic variance explained (Li et al. 2025). The comprehensive QTL atlas generated here provides unprecedented resolution for marker-assisted selection (MAS) in weak-gluten wheat breeding programs, directly addressing China's urgent need for high-quality weak-gluten varieties that currently face market shortages (Liu et al. 2023).\\u003c/p\\u003e\\n\\u003cp\\u003eOur approach demonstrates how high-throughput genotyping technologies have transformed wheat QTL mapping from earlier studies that relied on genetic maps with only several hundred markers (Groos et al. 2003; Sun et al. 2010) to contemporary high-resolution analyses. The 10,132 SNP markers comprising our genetic map represent a 20- to 50-fold increase in density compared to historical studies, enabling more precise QTL localization. This technological advancement is particularly crucial for wheat quality traits, which have historically received less research attention than agronomic traits and disease resistance despite their economic importance (Li et al. 2025).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eChromosome 2D QTL Cluster: A Novel Genetic Resource for Weak-Gluten Wheat Development\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe most significant finding of this study is the identification of a narrow 2.61 Mb interval on chromosome 2DL (34.43-37.04 Mb) containing two major co-localized QTLs: \\u003cem\\u003eQWGCtm.swust-2DL\\u003c/em\\u003e (23.13% PVE) and \\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e (29.70% PVE). This cluster represents a potential pleiotropic locus or tightly linked gene complex with exceptional value for breeding programs targeting weak-gluten wheat. Unlike previously reported QTLs, this locus demonstrates remarkable stability across environments while explaining nearly 30% of phenotypic variation for critical quality traits, substantially higher than typical QTLs for wheat quality traits which often explain less than 10% of variation (Li et al. 2009).\\u003c/p\\u003e\\n\\u003cp\\u003eOur comparative analysis reveals that this QTL cluster occupies a distinct physical position from previously reported loci. While\\u0026nbsp;Lou et al\\u0026nbsp;(2021) identified a grain hardness QTL on chromosome 2D, their locus is far from our discovery. Similarly, Guo et al. (2020) reported sedimentation value QTLs on chromosome 2D, but none overlap with our 28.88-37.04 Mb interval. This spatial separation, combined with the exceptional effect size and stability, strongly suggests we have identified a novel genetic determinant of weak-gluten characteristics.\\u003c/p\\u003e\\n\\u003cp\\u003eThe candidate gene \\u003cem\\u003eTraesCS2D01G077600LC.1\\u003c/em\\u003e within this interval encodes lysine synthase, which may influence gluten protein composition by altering amino acid synthesis ratios. This represents a previously uncharacterized mechanism for weak-gluten development that warrants functional validation. Unlike the well-studied \\u003cem\\u003eGlu-1\\u003c/em\\u003e loci that directly affect glutenin composition (Shewry et al. 2003), this novel locus appears to modulate quality through amino acid metabolism, potentially offering breeders a complementary approach to traditional glutenin-based selection.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTrait Correlations and Their Breeding Implications\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eOur results confirm the well-established positive correlations among GPC, WGC, and ZSV (r = 0.78-0.87, P \\u0026lt; 0.001), consistent with the biochemical relationship between protein content and gluten functionality (Ma et al. 2021). However, we identified a previously underappreciated negative correlation between test weight (TW) and grain hardness (GH) (r = -0.26, P \\u0026lt; 0.01), which has significant implications for breeding strategies. This inverse relationship likely stems from the physiological connection between grain moisture content and hardness, softer grains typically retain more moisture, increasing test weight while decreasing hardness. This finding challenges conventional breeding approaches that often select for high test weight without considering its potential trade-offs with desired softness in weak-gluten wheat.\\u003c/p\\u003e\\n\\u003cp\\u003eThe QTL additive analysis provides a powerful framework for precision breeding (Figure 4). RILs accumulating quality-associated QTLs showed progressive reduction in GPC and WGC, shifting the population toward weak-gluten classification standards while maintaining acceptable test weight. This quantitative relationship enables breeders to strategically select appropriate QTL combinations to achieve targeted quality profiles without compromising other essential traits. Notably, lines carrying both \\u003cem\\u003eQZStm.swust-2DL\\u003c/em\\u003e and \\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e (such as TM116) consistently met Chinese national standards for weak-gluten wheat across multiple environments, demonstrating the practical utility of these markers.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eComparative Analysis with Previous QTL Studies\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eOur findings both complement and extend previous wheat quality QTL research. While Zhao et al. (2024) identified seven GPC QTLs using a 50K SNP array, our study detected three GPC QTLs, including a novel major-effect locus \\u003cem\\u003eQGPCtm.swust-2DL\\u003c/em\\u003e (13.46% PVE) at 45.52-46.87 Mb on chromosome 2D. This position differs substantially from the \\u003cem\\u003eQGPC.caas-5DL\\u003c/em\\u003e and \\u003cem\\u003eQGPC.caas-7AL\\u003c/em\\u003e loci reported by Zhao et al. (2024), and from the chromosome 2D QTL mapped by Semagn et al. (2021), suggesting a previously uncharacterized genetic determinant of protein content.\\u003c/p\\u003e\\n\\u003cp\\u003eSimilarly, the \\u003cem\\u003eQWGCtm.swust-7DL\\u003c/em\\u003e (11.10% PVE) on chromosome 7D represents a novel locus distinct from those reported by Pu et al. (2022). The physical interval (28.18-52.28 Mb) does not overlap with previously documented wet gluten QTLs, highlighting the value of our multi-environment approach in uncovering stable genetic factors.\\u003c/p\\u003e\\n\\u003cp\\u003eFor grain hardness, the \\u003cem\\u003eQGHtm.swust-7DL\\u003c/em\\u003e (10.49% PVE) at 41.41-42.11 Mb on chromosome 7D appears to be a novel locus, as it occupies a different position than \\u003cem\\u003eQ.HI.scau-7D\\u003c/em\\u003e reported by Li et al. (2013). Additionally, our identification of \\u003cem\\u003eQGHtm.swust-5DL\\u003c/em\\u003e on the same chromosome as the Puroindoline genes (\\u003cem\\u003ePina\\u003c/em\\u003e and \\u003cem\\u003ePinb\\u003c/em\\u003e) but at a distinct physical location suggests potential new regulatory mechanisms beyond the well-characterized \\u003cem\\u003eHa\\u003c/em\\u003e locus.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePractical Applications and Future Directions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe four elite RILs identified in this study (TM77, TM103, TM116, and TM149) represent immediately applicable genetic resources for weak-gluten wheat breeding programs. Notably, TM116 combines exceptional quality stability with favorable agronomic traits and carries both major QTLs on chromosome 2DL. This line could serve as a valuable parental material for developing new weak-gluten varieties that meet Chinese national standards while maintaining yield potential.\\u003c/p\\u003e\\n\\u003cp\\u003eOur findings directly address the critical shortage of high-quality weak-gluten wheat resources in China (Liu et al. 2023) by providing validated molecular markers and breeding materials. The chromosome 2DL QTL cluster, in particular, offers unprecedented opportunities for marker-assisted selection with potential to reduce breeding cycles by 30-50% compared to conventional phenotypic selection. Given that weak-gluten wheat commands premium prices in confectionery markets (Kumar et al. 2016; Wu et al. 2022), the economic impact of implementing these markers could be substantial.\\u003c/p\\u003e\\n\\u003cp\\u003eFuture research should prioritize fine-mapping of the chromosome 2DL interval to identify the causal gene(s) and validate their function through transgenic approaches. The candidate gene \\u003cem\\u003eTraesCS2D01G01G077600LC.1\\u003c/em\\u003e warrants particular attention, as its lysine synthase function suggests a novel mechanism for modulating gluten properties. Additionally, investigating gene-by-environment interactions for the major QTLs across diverse agroecological zones would enhance the robustness of marker-assisted breeding strategies.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor contribution statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eLQ participated in data collection and analysis and drafted the manuscript. CH participated in data collection, data analysis, and manuscript revision. ZBC, WYL, and GSS participated in data collection and organization. RY and YGQprovided plant materials and guided the testing of relevant quality indicators. LX and ZSH guided field cultivation and related surveys. XCJ and ZXL participated in manuscript revision and provided relevant suggestions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by National Natural Science Foundation of China (No.\\u0026nbsp;32572773), Crop Germplasm Innovation and Genetic Improvement Key Laboratory of Sichuan Province (myzdsys24-02), the earmarked fund for CARS-03 (No. CARS-03-82), the Key Research and Development Support Program of the Chengdu Municipal Science and Technology Bureau (Grant No. 2024-YF05-01670-SN).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no conflict of interest.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eBhave M, Morris CF (2008) Molecular genetics of puroindolines and related genes: Allelic diversity in wheat and other grasses. 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Theor Appl Genet 137:261. https://doi.org/10.1007/s00122-024-04769-9\\u003c/li\\u003e\\n\\u003cli\\u003eZhou X, Wang Y, Luo Y, et al (2024) Genome-wide mapping of quantitative trait loci conferring resistance to stripe rust in spring wheat line PI 660072. Theor Appl Genet 137(11):255. https://doi.org/10.1007/s00122-024-04760-4\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable 1.\\u003c/strong\\u003e Statistical analysis of grain quality traits in MM902,TC29 and their recombinant inbred line (RIL) populations in 2023.\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"104%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTrait\\u003c/strong\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEnv\\u003c/strong\\u003eb\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 30px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eParents\\u003c/strong\\u003ec\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n 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10px;\\\"\\u003e\\n \\u003cp\\u003e35.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e20.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 1px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e40.43\\u0026plusmn;0.54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e16.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e23.92\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e56.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003eXJ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e50.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e39.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e10.86\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 1px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e42.92\\u0026plusmn;0.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e9.60\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e31.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e53.37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e Grain protein content (GPC, %); Wet gluten content, (WGC, %); Test Weight (TW, g/L); Grain Hardness (GH); Zeleny Sedimentation Value (ZSV, mL).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e PD, Pidu District; XJ, Xiaojian Town; QY, Qingyi Town.\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ec\\u0026nbsp;\\u003c/sup\\u003eTC29, Taichung29; MM902, Mianmai902 ; D-value, The difference between TC29 and MM902.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ed\\u003c/sup\\u003e SD, Standard deviation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ee\\u003c/sup\\u003e CV, (Coefficient of variation, %) = SD/average * 100.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ef\\u003c/sup\\u003e \\u003cem\\u003eH\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/em\\u003e(%), broad-sense heritability.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2 Summary of stable or major QTLs identified on the quality traits of the Taichung29/MM902 recombinant inbred line (RIL) populations examined in 2023.\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"103%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003eTrait\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003eQTL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003eEnv\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Chr\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003eLeftMarker\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n 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\\u003cp\\u003e2DL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-109785183\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-110384776\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e4.74\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e13.46\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.33\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003eWGC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQWGCtm.swust-2DL\\u003c/em\\u003e\\u003c/p\\u003e\\n 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\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e4.11\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e19.64\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003eQY\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e4AS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-111272311\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-110541026\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e5.48\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e0.45\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e29.72\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQTWtm.swust-4DL.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003eXJ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e4DL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-110912249\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-110832233\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e3.50\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e10.58\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e20.23\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQTWtm.swust-6DL.2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003ePD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e6DL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-89564650\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-89535748\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e3.19\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e9.89\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e5.71\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003eZSV\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQZStm.swust-2DL\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003ePD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e2DL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-111956072\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-111707760\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e10.91\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e30.28\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e3.61\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003eQY\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e2DL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-111956072\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-111707760\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e3.85\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e11.66\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e2.42\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQZStm.swust-6AL\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003ePD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e6AL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-109041968\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-111585865\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e4.11\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e7.37\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e1.79\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003eQY\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e6AL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-109041968\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-111585865\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e4.11\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e7.37\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e1.79\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e Grain protein content (GPC, %); Wet gluten content, (WGC, %); Test Weight (TW, g/L); Grain Hardness (GH); Zeleny Sedimentation Value (ZSV, mL).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e PD, Pidu District; XJ, Xiaojian Town; QY, Qingyi Town..\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ec\\u003c/sup\\u003e LOD, logarithm of odds score.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ed\\u003c/sup\\u003e PVE, percentage of the phenotypic variance explained by the individual QTLs.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ee\\u003c/sup\\u003e Add, additive effect ofthe grain yield allele.\\u003cbr\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3.\\u003c/strong\\u003e QTL clusters for two traits in two environments.\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"101%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCluster\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eChr\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMarker interval\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eQTL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ehysical position (bp)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHigh confidence genes\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLow confidence genes\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e2D\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-110072786\\u003c/em\\u003e-\\u003cem\\u003eAX-111956072\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQWGCtm.swust-2DL\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e34428838-37041235\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eID=TraesCS2D01G080700\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eID=TraesCS2D01G085400.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eID=TraesCS2D01G076900LC.1\\u003c/em\\u003e-\\u003cem\\u003eID=TraesCS2D01G083100LC.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e2A\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-111570872\\u003c/em\\u003e-\\u003cem\\u003eAX-110492596\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQGHtm.swust-2AL\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e124363723-651597865\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eID=TraesCS2A01G169500.1\\u003c/em\\u003e-\\u003cem\\u003eID=TraesCS2A01G397700.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eID=TraesCS2A01G179600LC.1\\u003c/em\\u003e-\\u003cem\\u003eID=TraesCS2A01G553700LC.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQTWtm.swust-2AL.2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e5D\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-109459390\\u003c/em\\u003e-\\u003cem\\u003eAX-111481379\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQGHtm.swust-5DL\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e459210485-495064805\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eID=TraesCS5D01G390900.1\\u003c/em\\u003e-\\u003cem\\u003eID=TraesCS5D01G443800.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eID=TraesCS5D01G482500LC.1\\u003c/em\\u003e-\\u003cem\\u003eID=TraesCS5D01G526800LC.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQTWtm.swust-5DL.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e6A\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAX-108891954\\u003c/em\\u003e-\\u003cem\\u003eAX-110469098\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQGHtm.swust-6AL\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e233809045-58754970\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eID=TraesCS6A01G189000.1\\u003c/em\\u003e-\\u003cem\\u003eID=TraesCS6A01G356000.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eID=TraesCS6A01G287100LC.1\\u003c/em\\u003e-\\u003cem\\u003eID=TraesCS6A01G556100LC.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eQTWtm.swust-6AL.2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTable 4.\\u0026nbsp;\\u003c/strong\\u003eRIL population lines meeting five quality standards for Weak-Gluten wheat\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLines\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEnvironment\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGPC\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eWGC\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTW\\u003csup\\u003ed\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGH\\u003csup\\u003ee\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eZSV\\u003csup\\u003ef\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003eTM77\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003ePD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e10.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e23.91\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e781.50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e46.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e28.70\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003eTM103\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003eQY \\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e9.2611\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e23.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e779.65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e34.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e29.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003eTM115\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003eQY\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e8.4025\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e20.96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e783.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e35.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e26.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003eTM116 \\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003ePD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e10.448\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e20.97\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e755.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e36.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e28.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003eQY\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e9.4248\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e23.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e784.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e36.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e27.93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003eTM149\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003ePD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e11.622\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e24.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e793.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e37.65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e28.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003eTM207\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003ePD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e10.509\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e25.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e781.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e41.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e29.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e PD, Pidu District; XJ, Xiaojian Town; QY, Qingyi Town..\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003eb-f\\u003c/sup\\u003e Grain protein content (GPC, %); Wet gluten content, (WGC, %); Test Weight (TW, g/L); Grain Hardness (GH); Zeleny Sedimentation Value (ZSV, mL).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 5 .\\u0026nbsp;\\u003c/strong\\u003eAgronomic traits of lines in the RIL population that meet five quality requirements for weak-gluten wheat\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLines\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEnvironment\\u003c/strong\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePH\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSL\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSNS\\u003csup\\u003ed\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eKNPS\\u003csup\\u003ee\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTN\\u003csup\\u003ef\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003eTM77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003eXJ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e81.7\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n 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\\u003cp\\u003e10.3\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e19\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e59\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e5\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003eTM149\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003eXJ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e84.0\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n 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\\u003cp\\u003e9.0\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e19\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e59\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e8\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e PD, Pidu District; XJ, Xiaojian Town; QY, Qingyi Town.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003eb-f\\u003c/sup\\u003e PH, Plant height; SL, Spike length; SNS, Spikelet number per spike; KNPS, Kernel number per spike; TN, Tiller number.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Weak-gluten wheat, Quality traits, 55K SNP chip, QTL mapping, Marker-assisted selection, Elite line screening\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8260532/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8260532/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eWeak-gluten wheat, characterized by low protein content (\\u0026lt;11.5%), soft kernels, and weak gluten strength, serves as essential raw material for confectionery products. Despite growing market demand, wheat production over the world faces a critical shortage of high-quality weak-gluten wheat varieties due to insufficient understanding of the genetic architecture underlying key quality traits. Here, we address this knowledge gap through comprehensive quantitative trait locus (QTL) mapping of five critical quality parameters, namely grain protein content (GPC), wet gluten content (WGC), Zeleny sedimentation value (ZSV), test weight (TW), and grain hardness (GH), using a recombinant inbred line (RIL) population of 143 lines derived from the \\\"Mianmai 902/Taichung 29\\\" cross evaluated across three diverse environments. Leveraging the Wheat 55K SNP array, we constructed a high-density genetic map comprising 10,132 markers with an average interval of 1.34 cM, identifying 41 QTLs distributed across 16 chromosomes. Most significantly, we discovered a narrow 2.61 Mb interval on chromosome 2DL (34.43-37.04 Mb) containing two co-localized major-effect QTLs, \\u003cem\\u003eQZStm.swust-2DL\\u003c/em\\u003e (30.28% PVE) and \\u003cem\\u003eQGHtm.swust-2DL\\u003c/em\\u003e(29.70% PVE), that demonstrate exceptional stability across environments. Within this critical interval, we identified \\u003cem\\u003eTraesCS2D01G077600LC.1\\u003c/em\\u003e, a candidate gene encoding lysine synthase that may influence gluten properties through amino acid metabolism. Furthermore, our QTL dosage analysis revealed a quantitative relationship between quality-associated loci and phenotypic expression, enabling predictive breeding strategies. Based on Chinese national standards for weak-gluten wheat, we successfully screened four elite RIL lines (TM77, TM103, TM116, and TM149) that combine superior quality traits with favorable agronomic characteristics. This study provides the first comprehensive QTL atlas specifically for weak-gluten wheat quality traits, delivering validated molecular markers and breeding materials to accelerate the development of high-quality varieties addressing the growing confectionery wheat demand over the world.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A Major QTL Cluster of Weak-Gluten on Wheat Chromosome 2D informs Marker-Assisted Selection\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-30 09:11:27\",\"doi\":\"10.21203/rs.3.rs-8260532/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"642acbbc-490c-4d64-a3e9-335a0d7e6194\",\"owner\":[],\"postedDate\":\"December 30th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-04T08:39:37+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-30 09:11:27\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8260532\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8260532\",\"identity\":\"rs-8260532\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}