Characterization of a stable QTL for flag leaf width and its genetic effects on yield-related traits

Characterization of a stable QTL for flag leaf width and its genetic effects on yield-related traits | 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 Characterization of a stable QTL for flag leaf width and its genetic effects on yield-related traits tao sun, Haotian Cao, Yahui Tong, Qiwei Yang, Meishan Zhou, Xinzhi Yang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7921960/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Flag leaf width (FLW) is an important controller of flag leaf size in wheat ( Triticum aestivum L.) and is closely related to yield-related traits. Results In this study, wheat Kenong 9204 (KN9204) × Jing 411 recombinant inbred mapping populations (KJ-RILs) were used as materials in a quantitative trait locus (QTL) analysis, and a major and stable QTL for FLW, qFlw-4B , was detected in multiple environments on chromosome 4B. KJ-RILs and a natural mapping population consisting of 314 breeding varieties/advanced lines were also utilized to further investigate the genetic and selection effects of qFlw-4B in wheat breeding. Compared with the Jing 411 haplotype ( Hap-J411 ), the KN9204 haplotype of the qFlw-4B region, Hap-KN9204 , significantly increased FLW as well as improved yield-related traits, such as spikelet number per spike, kernel number per spike, and spike number per plant in both KJ-RILs and the natural populations. The selection effect revealed that the superior haplotype of qFlw-4B had a relatively high selection intensity in both domestically and internationally bred varieties, and its selection utilization rate gradually increased. In addition, the InDel marker 4BFLW-290 targeting qFlw-4B was developed. This study was an important reference for the utilization of qFlw-4B in wheat molecular breeding. Conclusion A major stable QTL for FLW was identified in wheat, and its genetic effects on yield related-traits, as well as its potential use value in molecular breeding programs, were characterized. In addition, an InDel marker closely linked to the stable major QTL was developed. This study enhanced the understanding of the potential genetic mechanisms underlying wheat FLW and provided crucial information for the future genetic improvement and molecular breeding of wheat varieties. Wheat (Triticum aestivum L.) Flag leaf width Superior haplotype Molecular markers Genetic effect analysis Breeding selection effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Wheat ( Triticum aestivum L.) is an important global food crop, with a wide range of cultivation, large trade volume, and a large population coverage, making it a “strategic pillar“ for food security[ 1 , 2 ]. The global population is estimated to reach 9.6 billion by 2050[ 3 ]. To meet the growing demand for food from the increasing population, wheat breeders must maintain a genetic gain of 2.4% per year[ 4 ]. The topmost leaf of wheat plants, known as the flag leaf, serves a key function in providing photoassimilates to the developing grains after anthesis and accounts for roughly 45%–58% of the total photosynthetic capacity[ 5 ]. It also produces 41%−43% of the required carbohydrates during grain filling[ 6 ]. The orientation and size of a flag leaf are important in plant breeding, because they affect plant canopy morphology and photosynthetic efficiency[ 7 ]. The size of the flag leaf, consisting of leaf length, width, and area, is an extremely important factor that determines leaf structure and yield potential[ 8 ]. FLW serves as a crucial constituent of leaf morphology in wheat and exerts a profound impact on yield[ 9 ]. The FLW is usually closely correlated with the photosynthetic rate, thousand kernel weight (TKW), and grain yield per plant (GYPP)[ 10 ]. Thus, FLW is a vital component of the ideal plant and its optimization will improve plant architecture. Consequently, breeding wheat with an optimal FLW has been proposed as a viable approach for increasing grain yields[ 11 ]. FLW is a complex agronomic characteristic that is regulated by both genotype and environment. With the availability of molecular markers and genetic maps, many quantitative trait loci (QTLs) related to FLW have been documented in rice and wheat [ 12 – 18 ]. For example, qFlw7.2 , a new major QTL for FLW in rice, has been fine mapped within a 45.30 − 53.34 cM region on chromosome 7[ 19 ]. Additionally, qFlw4 for FLW in rice has been fine-mapped to an interval of 74.8 kb[ 20 ], whereas the narrow leaf genes Nal1 , Nal7 , and Nal9 [ 20 – 21 ], narrow-curly leaf genes Nal2 and Nal3 [ 22 – 23 ], and narrow-rolled leaf gene Nrl1 have all been cloned in rice[ 24 ]. Recently, there has been significant research on QTLs of FLW in wheat. Yan et al. reported the fine mapping of qFlw-6A to a 500-kb region on 6A[ 25 ], and Zhao et al. (2022) reported the fine mapping of qFlw-5B to a 2.5-Mb region on 5B[ 9 ]. Qiao et al. precisely localized QFlw.sxau-6BL to a 661.13 − 662.40 Mb region on 6B[ 26 ]. These studies on FLW-related genes/QTLs have provided an important theoretical basis for improved FLW breeding; however, there are limited studies on the genetic and breeding selection effects of FLW-related genes/QTLs. In this study, one major stable FLW-related QTL, qFlw-4B , was identified in wheat Kenong 9204 (KN9204) × Jing 411 (J411) recombinant inbred mapping populations (KJ-RILs). The objectives of this study were therefore to verify the genetic effects of qFlw-4B in KJ-RILs and the natural mapping population, and to determine the effects of qFlw-4B breeding selection in different regions and ages. In addition, an InDel marker for qFlw-4B was developed. Methods Plant materials and growth conditions The KJ-RIL population, consisting of 188 lines, was constructed using the single seed descent method. The KJ 129 line was not used in this study due to genotypic deletion of most of the SNP markers. The FLW of the near-isogenic line NIL-KN9204 for the qFlw-4B region was significantly greater than that of the near-isogenic line NIL-J411. KJ-RILs and their parents were grown in eight different locations, years, and environments with different nitrogen (N) application treatments. The LN (Low nitrogen) environments were located in the following places during the designated seasons: 2011–2012 in Shijiazhuang (E1-LN), 2012–2013 in Shijiazhuang (E3-LN), 2012–2013 in Beijing (E5-LN), and 2012–2013 in Xinxiang (E7-LN). The HN (High nitrogen) environments were located in the following places during the designated seasons: 2011–2012 in Shijiazhuang (E2-HN), 2012–2013 in Shijiazhuang (E4-HN), 2012–2013 in Beijing (E6-HN), and 2012–2013 in Xinxiang (E8-HN). Field settings and corresponding data on soil N levels in eight environments have been described in detail in a previous study[ 27 ]. A natural population consisting of 314 breeding varieties/advanced lines was planted in 12 environments (location and season), specifically: 2018 − 2019 Qixia (E1), 2019 − 2020 Weifang (E2), 2019 − 2020 Laishan Pula Valley (E3), 2020 − 2021 Shijiazhuang (E4), 2020 − 2021 Weifang (E5), 2020 − 2021 Lai Shan Pula Valley (E6), 2020 − 2021 Zhifu (E7), 2021 − 2022 Shijiazhuang (E8), 2021 − 2022 Laishan Pula Valley (E9), 2021 − 2022 Zhifu (E10), 2022 − 2023 Lai Shan Muyu Village (E11), and 2022 − 2023 Lai Shan Pula Valley (E12). The KJ-RIL trial used a randomized block design with two replicates per treatment. The experimental plots consisted of three rows, each 1.5 m long with a row spacing of 0.25 m. Thirty seeds were sown per row, and field management was conducted according to local standards. Natural group materials were cultivated in a random complete block design, with two replicates at each location. Each variety was planted in three rows, each row 1.5 m long, with 20 cm spacing. Fifteen seeds were sown manually in each row. Crop management followed local agricultural practices. Phenotypic evaluation and data analysis The width of the widest part of the wheat flag leaf was determined at the heading stage. Spikelet number per spike (SNPS), together with other yield-related traits, such as kernel number per spike (KNPS), spike length (SL), plant height (PH), TKW, kernel weight per spike (KWPS), spike number per plant (SNPP), grain width (GW), grain length (GL), and GYPP, were evaluated as the methods described in our previous studies of Cui et al.(2013), Zhang et al. (2017) and Fan et al. (2019) [ 28 – 30 ]. Data on FLW and other yield-related traits for the 187 KJ-RILs under four LN and four HN environmental conditions were imported into QGA Sation 2.0[ 31 ], and the best linear unbiased estimation (BLUE) was computed to obtain two datasets, LN (BLUE-LN) and HN (BLUE-HN). BLUE values for yield-related traits in the 12 environments in which the natural mapping population was grown were calculated in the same way. Similarly, the broad-sense heritability (H 2 ) values of FLW in LN and HN environments for the KJ-RIL populations were calculated using QGA Sation 2.0. SPSS 25.0 software was employed to carry out one-way analysis of variance and phenotypic correlation analysis between FLW and other traits. Association analysis was conducted on genotype and phenotype data derived from KJ-RILs and natural mapping populations, aiming to clarify the genetic impacts of qFlw-4B on yield-associated traits. Genotype information obtainment and QTL identification Referring to the method of Stacey et al [ 32 ], young leaves of all materials from KJ-RILs and natural mapping populations were separately sampled, and DNA was extracted using the CTAB method. The KJ-RIL and natural mapping populations were then genotyped using 660 K and 55 K SNP chips, respectively. To obtain the physical location of each SNP in the KN 9204 genome assembly, an analysis was performed using BLAST software ( ftp://ftp.ncbi.nlm.nih.gov/blast/ExecuTABLEs/release/ ). Based on the KJ-RIL mapping population, and combined with the 660K SNP array as well as the previous research work of our team, a high-density linkage map containing 119,566 loci was finally constructed[ 33 ]. The QTL detection analysis was conducted via physical mapping rather than genetic linkage mapping, employing the BIP module of IciMapping V4.2 ( ftp://ftp.ncbi.nlm.nih.gov/blast/execuTABLEs/release/ ). This analysis utilized 1,000 alignments with a 1.0 Mb steps, with P = 0.001 in stepwise regression, and adopted a Logarithm of Odds (LOD) threshold of 2.5 for QTL localization. Design of InDel molecular markers Using the 10× genome resequencing of KN9204 and J411 (data not disclosed), an InDel in the KN9204 genome located at 31,224,659 was selected, and primer 4BFLW-290 was designed (Table S1 ). InDel PCR primers with polymorphisms were designed in Primer Server of Wheat Omics 2.1 ( http://202.194.139.32/ ). Results Phenotypic analysis of FLW in the KJ-RILs The analyses of phenotypic data from KJ-RILs under different environmental conditions are shown in Table 1 . The coefficient of variation of FLW in the KJ-RIL population ranged from 9.33% to 10.85%, indicating that its phenotypic variation was relatively stable, and its generalized heritability was 0.62 − 0.84. The mean values of FLW in the KJ-RILs ranged from 1.20 cm to 1.59 cm, and the absolute values of kurtosis and skewness were less than 1, based on a correlation analysis. The FLW phenotypic data from the 187 KJ-RIL samples exhibited an approximately normal distribution (Fig. 1 ). In addition, the FLWs of KJ-RILs were higher under most of the HN conditions than in the LN conditions, indicating that N levels affect FLW. Table 1 Phenotypic analysis of FLW in KJ-RILs under different environmental conditions Environment Parents KJ-RIL KN9204 J411 Min Max Mean SD CV(%) SK KUR H 2 E1(LN) 1.48 1.25 1.20 1.66 0.78 0.13 10.79 0.08 -0.12 0.70 E2(HN) 1.44 1.13 1.31 1.76 0.82 0.14 10.85 0.12 -0.01 0.68 E3(LN) 1.43 1.36 1.06 1.74 1.35 0.13 9.49 0.17 -0.45 0.69 E4(HN) 1.79 1.56 1.18 2.30 1.59 0.17 10.66 0.19 -0.55 0.73 E5(LN) 1.54 1.41 1.43 1.80 1.16 0.13 9.16 0.35 -0.20 0.84 E6(HN) 1.65 1.55 1.22 2.10 1.58 0.17 10.51 0.21 -0.36 0.74 E7(LN) 1.68 1.56 1.59 2.06 1.20 0.15 9.33 0.47 0.02 0.62 E8(HN) 1.68 1.57 1.55 2.04 1.12 0.16 10.44 0.42 -0.20 0.68 SD: standard deviation. CV: coefficient of variation. H 2 : broad-sense heritability. The correlation analysis indicated that under LN conditions, FLW showed significant positive correlations with PH, SL, SNPP, SNPS, KNPS, KWPS, GL, and GW. Under HN conditions, FLW exhibited significant positive correlations with SL, SNPP, SNPS, KNPS, KWPS, GL, and GYPP, while showing significant negative correlations with PH, GW, and TKW (Table 2 ). Table 2 Correlation analysis between FLW and yield-related traits in the KJ-RILs Condition PH SL SNPP SNPS KNPS KWPS GL GW TKW GYPP FLW LN 0.23 ༊༊ 0.31 ༊༊ 0.43 ༊༊ 0.21 ༊༊ 0.17 ༊༊ 0.24 ༊༊ 0.28 ༊༊ 0.07 ༊ 0.02 0.39 HN -0.06 ༊ 0.44 ༊༊ 0.09 ༊༊ 0.25 ༊༊ 0.48 ༊༊ 0.42 ༊༊ 0.21 ༊༊ -0.12 ༊༊ -0.22 ༊༊ 0.16 ༊༊ PH: plant height, SL: spike length, SNPP: spike number per plant, SNPS: spikelet number per spike, KNPS: kernel number per spike, KWPS: kernel weight per spike, GL: grain length, GW: grain weight, TKW: thousand kernel weight, GYPP: grain yield per plant. The asterisks *, ** represent P < 0.05 and P < 0.01, respectively. QTL detection of qFlw-4B The QTL analysis showed that qFlw-4B was consistently detected in E1–E8, as well as in LN BLUE and HN-BLUE datasets (Table 3 ; Fig. 2 ), explained 0.34%–18.45% of the FLW phenotypic variation in the KJ-RILs, with LOD values of 4.42 − 12.17. The beneficial alleles derived from KN9204 enhanced FLW, and the corresponding additive effect values fell within the range of 0.03 to 0.06. For qFlw-4B , it was physically localized to an 18-Mb interval on chromosome KN4B (spanning positions 27.70 − 45.70 Mb), which is flanked by the molecular markers AX-95143370 and AX-110527790 . Table 3 Using IciMapping v4.2 software, the putative additive QTL qFlw-4B could be consistently identified in the KJ-RILs population across 10 different datasets Environments Position (Mb) a Left Marker Right Marker LOD b PVE (%) c Add d E1(LN) 27.70 AX-95143370 AX109899078 5.02 9.52 0.04 E2(HN) 29.70 AX-109988872 AX109284839 11.23 0.34 0.06 E3(LN) 45.70 AX-110713957 AX-110527790 11.19 14.59 0.04 E4(HN) 45.70 AX-110713957 AX-110527790 8.68 9.77 0.05 E5(LN) 44.70 AX-110713957 AX-110527790 4.98 12.73 0.06 E6(HN) 29.70 AX-109988872 AX109284839 12.17 15.84 0.05 E7(LN) 30.70 AX-109988872 AX109284839 9.73 16.49 0.05 E8(HN) 29.70 AX-109988872 AX109284839 8.89 18.45 0.06 BLUE-LN 29.70 AX-109988872 AX109284839 4.98 11.31 0.03 BLUE-HN 29.70 AX-109988872 AX109284839 4.42 10.40 0.04 ཁ The physical position of the LOD peak in the KN9204 genome. ག Logarithm of the odds score. གྷ Rates of the phenotypic variances explained by the QTL. ང Additive effect on FLW. PCR (InDel) markers In this study, the InDel marker 4BFLW-290 (31,224,659), which is closely related to qFlw-4B , was developed for the identification of FLW, and it stably amplified clear and specific bands in the 188 KJ-RILs (Fig. 3 ). It can stably amplify clear fragments of 210 bp and 244 bp from KN9204 and J411, respectively. The length of the aforementioned PCR products is completely consistent with the DNA resequencing results of the two parental materials. This InDel marker was genotyped in the 187 KJ-RIL populations, revealing that the KN9204 haplotype of the qFlw-4B region, Hap-KN9204 , significantly outperformed the J411 haplotype of the region, Hap-J411 , across all eight environments. Therefore, 4BFLW-290 may be used in further wheat molecular breeding. Genetic impacts of qFlw-4B on traits associated with yield within the KJ-RIL genetic mapping population The LOD peak position showed that AX-95143370 (KN4B: 26.28 Mb), AX-109899078 (KN4B: 27.70 Mb), AX-109988872 (KN4B: 29.70 Mb), AX-109284839 (KN4B: 30.70 Mb), 4BFLW-290 (KN4B: 31.22 Mb), AX-110713957 (KN4B: 44.70 Mb), AX-110527790 (KN4B: 45.70 Mb), and 4BFLW-290 were the closest linkage markers for qFlw-4B . Therefore, these markers were used to analyze the genetic effects of qFlw-4B on yield-related traits. In eight environmental datasets, the haplotype Hap-KN9204 significantly increased FLW (Fig. 4), with the mean elevation rate of this trait reaching 8.82%. It only significantly increased SNPS and KNPS in four environments, with average increases of 2.28% and 6.25%, respectively, whereas it increased SNPP, SL, and GL in no more than three environments. However, it significantly decreased PH in all eight environments, with an average decrease of 13.48%, significantly decreased TKW in four of the eight environments, with an average decrease of 6.86%, and significantly decreased GW in two of the eight environments, with an average decrease of 2.80%. Exploration of the pyramiding effect of qFlw-4B under diverse genetic backgrounds Beyond qFlw-4B , we further detected 2 stable additive QTLs governing the FLW trait within the KJ genetic population, specifically qFlw-5B and qFlw-6B [ 30 ]. The positive alleles of qFlw-5B and qFlw-6B associated with the increased FLW were from KN9204 and J411, respectively. A pyramiding effect analysis for these two QTLs and qFlw-4B was performed. Based on the genotypes of markers tightly linked to these three QTLs, the KJ-RILs mapping population was divided into eight groups. Under both LN and HN nitrogen conditions, as the count of positive alleles in plants rose, the magnitude of FLW increase showed a growing trend and this observation validated the notable additive effects of all three QTLs (Fig. 5 a). Relative to the phenotypic performance of the three negative allele pyramided type (BBA), the pyramid combination of three beneficial QTL alleles (AAB) elevated FLW by 15.15% and 15.49% under LN and HN conditions, correspondingly. To further clarify the additive effect of qFlw-4B , we split the KJ-RILs population into four subgroups according to the genotype of markers that are tightly linked with qFlw-4B , qFlw-5B , and qFlw-6B . The additive effect of qFlw-4B fell within the range of 0.03 to 0.16 (Fig. 5 b-e). In the AA (+ −) and AB (+ +) genetic backgrounds, the additive effect of qFlw-4B on the FLW trait reached a significant level (Fig. 5 b, c). When both qFlw-5B and qFlw-6B carried negative alleles, the additive effect was at its lowest under both LN and HN conditions. In contrast, the highest additive effect under the LN and HN conditions was observed when these two loci harbored positive alleles. Genetic effects analysis of qFlw-4B on wheat yield-related traits based on a natural mapping population Three SNP molecular markers, AX-109526283 (G/A, KN4B: 27.93 Mb), AX-109580651 (G/A, KN4B: 29.70 Mb), and AX-109347500 (C/T, KN4B: 45.97 Mb), adjacent to the LOD peak position for qFlw-4B were used to genotype the 314 accessions in the natural mapping population. In the natural population haplotype Hap-GG-GG-CC was identical to KN9204, defining it as a superior haplotype. Hap-AA-AA-CC was identical to J411, defining it as a non-superior haplotype. Hap-GG-GG-TT, Hap-AA-AA-TT, Hap-AA-GG-TT, Hap-AA-GG-TT, and Hap-AA-GG-CC represent recombination types (Table S2). Lines with heterozygous genotypes at any of the three loci were defined as heterozygous. Lines with deletions at any of the three loci were defined as deletion types. Out of 314 natural populations, 48.41% had the superior haplotype (Hap-GG-GG-CC), whereas 13.69% had the non-superior haplotype (Hap-AA-AA-CC), and 24.52% had recombinant haplotypes. The percentages of heterozygous and deletion types were 7.32% and 6.05%, respectively (Table 4 ). Consequently, a correlation analysis of qFlw-4B superior and non-superior haplotypes with yield-related traits in natural populations was performed. The superior haplotype Hap-GG-GG-CC significantly increased FLW, FLL, KNPS, SNPS, SNPP, GYPP, FSN, and SL. However, it had a significant negative effect on GL, and had no significant effects on TKW, PH, and GW (Fig. 6 ). The analysis results were consistent with those of the KJ-RIL population, suggesting that qFlw-4B has effects on yield-related traits. Table 4 Haplotype analysis of qFlw-4B based on a natural mapping population Genotype Quantity Proportion (%) Superior haplotype 152 48.41% Non-superior haplotype 43 13.69% Recombinants 77 24.52% Heterozygous 23 7.32% Deletion 19 6.05% Analysis of selection effects of qFlw-4B in wheat breeding The breeding-related selection effects of the qFlw-4B haplotype were analyzed based in six varieties (lines), which were divided into six subgroups based on their spatial distribution, Shanxi, Qinghai, Henan, Sichuan, Shandong, and foreign regions. The highest utilization rates of the superior haplotype (Hap-GG-GG-CC) in the six subgroups were 52.63%, 42.11%, 41.18%, 39.39%, 30.43%, and 65.75%, respectively. Thus, the utilization rate of the superior haplotype in foreign varieties was much higher than in domestic varieties. The utilization of recombinant haplotypes was second only to the superior haplotype in the provinces of Qinghai, Shandong, Henan, and Sichuan (Fig. 7 a). Selection effects of qFlw-4B in wheat breeding efforts were analyzed over a time span using 187 varieties that were traceable in the natural mapping population. The superior haplotype (Hap-GG-GG-CC) had a high seed value throughout the wheat breeding process, and the utilization rate was progressively higher in the 1990s, 2000s, and 2010s, reaching 50% in the latter. Thus, the seed value of the superior haplotype was gradually being exploited. On the contrary, the utilization rate of the non-superior haplotype has been decreasing year by year (Fig. 7 b). The temporal and spatial results indicated that the positive allele of qFlw-4B occupied a more important position in wheat breeding work and possessed greater seed value and breeding potential. Discussion Comparison with previous studies In previous studies, multiple QTLs associated with FLW have been detected[ 34 – 42 ]. Many of these QTLs were detected on chromosome 4B. Chen et al. [ 35 ] detected one QTL associated with FLW at 28.6 − 43.54 Mb on chromosome 4B. Wang et al. [ 40 ] located a QTL associated with FLW at 56.5 − 59.5 Mb on chromosome 4B in six environments and BLUE datasets. Liu et al. [ 41 ] detected two QTLs on chromosome 4B, QFLW-4B.1 and QFLW-4B.2 , with physical locations spanning 38.80 − 75.74 Mb. Zhang et al. [ 42 ], using RILs derived from the common wheat Yannong15 and SN304, identified three QTL associated with FLW within the 38.6 − 53.6 Mb interval on chromosome 4B. In this study, qFlw-4B was localized in the interval 27.70 − 45.70 Mb on chromosome 4B. This result is consistent with the above studies. Thus, qFlw-4B has been detected in several mapping populations; consequently, we speculated it was a stable QTL having significant breeding value. Many other yield-related QTLs that co-localize with qFlw-4B have been identified in previous studies. Wen et al. [ 43 ] detected QTLs for SN, GNS, PH, and TGW in the 20.0 − 38.0 Mb interval of chromosome 4B. Li et al. [ 44 ] detected a major QTL for spike number per unit area in the interval of 24.91–38.36 Mb on chromosome 4BS. Xu et al. [ 45 ] localized a TKW primary QTL in the range of 19.7 − 38.2 Mb on chromosome 4B. Chen et al. [ 46 ] localized three yield-associated OTLs ( QTgw.cau-4 B-2 , QGw.cau-4B , and QTgw.cau-4B-1 ) on chromosome 4B in the 20 − 35-Mb range. Several QTLs associated with yield traits, such as PH, ears per plant, sterile spikelet number, grain number per spike, grain weight per spike, and floret number per spikelet, were detected at the same position of chromosome 4B by Liu et al. [ 47 ]. The above results suggest that QTL clusters with pleiotropic effects on yield traits may exist near qFlw-4B . The identification and utilization of superior alleles of qFlw-4B may be a highly effective way to improve wheat yield. Relationship between qFlw-4B and yield-related traits For typical gramineous crops, such as rice and wheat, there is a close correlation between the size of their flag leaves and yield-related traits[ 48 – 52 ]. Liu et al. [ 7 ] demonstrated that FLW positively correlates with traits such as SL, KNPS, and KWPS, while it negatively correlates with TKW, consistent with the findings of our study in HN. Zhao et al.’s [ 17 ] research indicates that FLW is positively correlated with yield traits such as KNPS and KWPS, whereas it shows no significant correlation with TKW, consistent with our findings in LN. However, they reported that FLW is negatively correlated with SNPP, which is contrary to our results. Recently, Zhao et al. [ 9 ] further discovered that FLW exerts positive effects on TKW and KNPS, while producing negative effects on SNPP and SL. Their findings on KNPS align with those of this study, whereas the results for TKW, SNPP, and SL are inconsistent. For qFlw-4B , the superior genotype Hap-KN9204 significantly increased FLW, KNPS, SNPP, and SNPS in both KJ-RILs and in the natural population, but the effects on PH and TKW, which may be influenced by genetic background, were inconsistent. Selective utilization and potential application of qFlw-4B The selection effect analysis revealed that Hap-GG-GG-CC (Hap-KN9204) of qFlw-4B was the dominant haplotype used in breeding efforts. The superior haplotype of qFlw-4B underwent strongly selection from the 1990s (37.04%−50%) (Fig. 7 b). This may be because qFlw-4B exerted positive effects on most yield components, including SL, SNPS, SNPP, and KNPS, thereby significantly increasing the yield of the variety. This suggested that the superior haplotype of qFlw-4B had some potential for application in modern breeding work. It may also be convenient to develop the InDel marker for application in qFlw-4B -related breeding. At present, it provides an important resource for the molecular-assisted breeding of wheat. Declarations Ethics approval and consent to participate These experiments complied with the ethical standards in China. Clinical Trial Not applicable. Consent to Publish declaration Not applicable. Availability of data and materials Data is provided within the manuscript or supplementary information files Declarations Conflict of interest The authors declare that they have no conflict of interest. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 32472134), the Foundation of Shandong Province, China (Grant No. ZR2022MC119), the Shandong Provincial Key Research (Grant No. 2024LZGCQY012, 2022LZG002-2), the Shandong Provincial Fund for Excellent Young Scholars (Grant No. ZR2022YQ19) Authors’ contributions FC, CZ and YW analyzed the data and drafted the manuscript. TS, HG, HX, ZZ, HC, YT, ZZ and MT performed phenotype evaluation. RQ, HS and DL helped design the study and revised the manuscript. All of the authors have read and approved the final version of the manuscript. Acknowledgements Not applicable. References Ma J, Tian T, Wang P, Liu Y, Zhang P, Chen T, Guo L, Zhang Y, Wu Y, Shahinnia F, Yang D. Identification of quantitative trait loci and candidate genes underlying kernel traits of wheat ( Triticum aestivum L.) in response to drought stress. Theor Appl Genet. 2025; 19;13: 216. Li T, Tang Y, Lin Z, Chen B, Wang J, Li Q, Huang F, Zhang J, Liang J, Zhang H, Liu Z, Li J, Yang W, Deng G, Long H. Identification and development of KASP markers for genetic loci controlling plant height in bread wheat and evaluation their effects using near isogenic lines. BMC Plant Biol. 2025; 25: 832. Turhan S, Taspinar MS, Yigider E, Aydin M, Agar G. The role of long terminal repeat (LTR) responses to drought in seleniumtreated wheat. Environ Eng Manag J. 2021; 20. Molero G, Joynson R, Pinera-Chavez FJ et al. Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential. Plant Biotechnol J. 2019; 17:1276-1288. Araus JL, Tapia L. Photosynthetic gas exchange characteristics of wheat flag leaf blades and sheaths during grain filling: the case of a spring crop grown under Mediterranean climate conditions. Plant Physiology. 1987; 85: 667-673. Kumar P, Gill HS, Singh M, Kaur K, Koupal D, Talukder S, Bernardo A, Amand PS, Bai G, Sehgal SK. Characterization of flag leaf morphology identifies a major genomic region controlling flag leaf angle in the US winter wheat ( Triticum aestivum L.). Theor Appl Genet. 2024; 137: 205. Zhou X, Qiang C, Chen L, Qing D, Huang J, Li J, Pan Y. The Landscape of Presence/Absence Variations during the Improvement of Rice. Genes (Basel). 2024; 15: 645. Guitman MR, Arnozis PA, Barneix AJ. Effect of source-sink relations and nitrogen nutrition on senescence and N remobilization in the flag leaf of wheat. Physiol Plant. 1991;82: 278-84. Zhao C, Liu X, Liu H, Kong W, Zhao Z, Zhang S, Wang S, Chen Y, Wu Y, Sun H, Qin R, Cui F. Fine mapping of QFlw-5B , a major QTL for flag leaf width in common wheat ( Triticum aestivum L . ). Theor Appl Genet. 2022; 135: 2531-2541. Khaliq I, Irshad A, Ahsan M. Awns and flag leaf contribution towards grain yield in spring wheat ( Triticum aestivum L . ). Cereal Res Commun. 2008; 36:65-76. Morinaka Y, Sakamoto T, Inukai Y, Agetsuma M, Kitano H, Ashikari M, Matsuoka M. Morphological alteration caused by brassinosteroid insensitivity increases the biomass and grain production of rice. Plant Physiol. 2006; 141:924-931. Mei HW, Li ZK, Shu QY, Guo LB, Wang YP, Yu XQ, Ying CS, Luo LJ. Gene actions of QTLs affecting several agronomic traits resolved in a recombinant inbred rice population and two testcross populations. Theor Appl Genet. 2003; 110:649-659. Wang X, Chen L, Zhao Z, Jiang N, Khan NU, Hu Q, Liu R, Liu Z, Qian X, Zhu X, Sun X, Li J, Zhang H, Li D, Xu P, Pan Y, Li Z, Zhang Z. Exploring genomic regions and genes modulating plant height and flag leaf morphology in rice. Plant J. 2025; 122: e70145. Kulkarni SR, Balachandran SM, Ulaganathan K, Balakrishnan D, Praveen M, Prasad ASH, Fiyaz RA, Senguttuvel P, Sinha P, Kale RR, Rekha G, Kousik MBVN, Harika G, Anila M, Punniakoti E, Dilip T, Hajira SK, Pranathi K, Das MA, Shaik M, Chaitra K, Rao PK, Gangurde SS, Pandey MK, Sundaram RM. Molecular mapping of QTLs for yield related traits in recombinant inbred line (RIL) population derived from the popular rice hybrid KRH-2 and their validation through SNP genotyping. Sci Rep. 2020; 10: 13695. Fan X, Cui F, Zhao C, Zhang W, Yang L, Zhao X, Han J, Su Q, Ji J, Zhao Z, Tong Y, Li J. QTLs for flag leaf size and their influence on yield-related traits in wheat ( Triticum aestivum L . ). Mol Breed. 2015; 35:24. Jia Y, Xu N, Wu J, Wang C, Huang M, Li Y. Genome-wide association study, linkage mapping and transcriptomic analysis revealed candidate genes with the flag leaf traits associated with nitrogen use efficiency in wheat. BMC Genomics. 2025; 26: 833. Zhao C, Bao Y, Wang X, Yu H, Ding A, Guan C, Cui J, Wu Y, Sun H, Li X, Zhai D, Li L, Wang H, Cui F. QTL for flag leaf size and their influence on yield-related traits in wheat. Euphytica.2018; 214: 209. Yang D, Liu Y, Cheng H, Chang L, Chen J, Chai S, Li M. Genetic dissection of flag leaf morphology in wheat ( Triticum aestivum L.) under diverse water regimes. BMC Genet. 2016; 17: 94. Zhang B, Ye W, Ren D, Tian P, Peng Y, Gao Y, Ruan B, Wang L, Zhang G, Guo L, Qian Q, Gao Z. Genetic analysis of flag leaf size and candidate genes determination of a major QTL for flag leaf width in rice. Rice (N Y). 2015. Chen M, Luo J, Shao G, Wei X, Tang S, Sheng Z, Song J, Hu P. Fine mapping of a major QTL for flag leaf width in rice, qFLW4 , which might be caused by alternative splicing of NAL1 . Plant Cell Rep. 2012; 31: 863-872. Qi J, Qian Q, Bu Q, Li S, Chen Q, Sun J, Liang W, Zhou Y, Chu C, Li X, Ren F, Palme K, Zhao B, Chen J, Chen M, Li C. Mutation of the rice narrow leaf1 gene, which encodes a novel protein, affects vein patterning and polar auxin transport. Plant Physiol1. 2008; 47:1947-1959. Cho SH, Yoo SC, Zhang H, Pandeya D, Koh HJ, Hwang JY, Kim GT, Paek NC. The rice narrow leaf2 and narrow leaf3 loci encode WUSCHEL-related homeobox 3A ( OsWOX3A ) and function in leaf, spikelet, tiller and lateral root development. New Phytol. 2013; 198:1071-1084. Ishiwata A, Ozawa M, Nagasaki H, Kato M, Noda Y, Yamaguchi T, Nosaka M, Shimizu-Sato S, Nagasaki A, Maekawa M, Hirano HY, Sato Y. Two WUSCHEL-related homeobox genes, narrow leaf 2 and narrow leaf 3, control leaf width in rice. Plant Cell Physiol. 2013; 54: 779-792. Hu J, Zhu L, Zeng D, Gao Z, Guo L, Fang Y, Zhang G, Dong G, Yan M, Liu J, Qian Q. Identification and characterization of narrow and rolled leaf 1, a novel gene regulating leaf morphology and plant architecture in rice. Plant Mol Biol. 2010; 73: 283-292. Yan X, Wang S, Yang B, Zhang W, Cao Y, Shi Y, Sun D, Jing R. QTL mapping for flag leaf-related traits and genetic effect of QFLW-6A on flag leaf width using two related introgression line populations in wheat. PLoS ONE. 2020; 15: e0229912. Qiao L, Zheng X, Zhao J, Wu B, Hao Y, Li X, Helal MMU, Zheng J. Genetic dissection of flag leaf morphology traits and fine mapping of a novel QTL ( Qflw.sxau-6BL ) in bread wheat ( Triticum aestivum L . ). Theor Appl Genet. 2025; 138: 21. Cui F, Fan X, Zhao C, Chen M, Ji J, Zhang W, Li J. A novel genetic map of wheat: utility for mapping QTL for yield under different nitrogen treatments. BMC Gene. 2014; 15:57. Cui F, Zhao C, Li J, Ding A, Li X, Bao Y, Li J, Ji J, Wang H. Kernel weight per spike: What contributes to it at the individual QTL level Mol Breed. 2013; 31: 265-278. Zhang N, Fan X, Cui F, Zhao C, Zhang W, Zhao X, Yang L, Pan R, Chen M, Han J, Ji J, Liu D, Zhao Z, Tong Y, Zhang A, Wang T, Li J. Characterization of the temporal and spatial expression of wheat ( Triticum aestivum L . ) plant height at the QTL level and their influence on yield related traits. Theor Appl Genet. 2017; 130: 1235-1252. Fan X, Cui F, Ji J, Zhang W, Zhang X, Liu J, Meng D, Tong Y, Wang Y, Wang T, Li J. Dissection of pleiotropic QTL regions controlling wheat spike characteristics under different nitrogen treatments using traditional and conditional QTL mapping. Front Plant Sci. 2019; 10:187. Chen G, Zhu Z, Zhang F, Zhu J. Quantitative genetic analysis station for the genetic analysis of complex traits. Chin Sci Bull. 2012; 57:2721-2726. Stacey J, Isaac PG. Isolation of DNA from plants. Methods Mol Biol. 1994; 28:9-15. Cui F, Zhang N, Fan X, Zhang W, Zhao C, Yang L, Pan R, Chen M, Han J, Zhao X, Ji J, Tong Y, Zhang H, Jia J, Zhao G, Li J. Utilization of a Wheat660K SNP array-derived high-density genetic map for high-resolution mapping of a major QTL for kernel number. Sci Rep. 2017; 7: 3788. Ma J, Tu Y, Zhu J, Luo W, Liu H, Li C, Li S, Liu J, Ding P, Habib A, Mu Y, Tang H, Liu Y, Jiang Q, Chen G, Wang J, Li W, Pu Z, Zheng Y, Wei Y, Kang H, Chen G, Lan X. Flag leaf size and posture of bread wheat: genetic dissection, QTL validation and their relationships with yield-related traits. Theor Appl Genet. 2020; 133: 297-315. Chen L, Xu Z, Fan X, Zhou Q, Yu Q, Liu X, Liao S, Jiang C, Lin D, Ma F, Feng B, Wang T. Genetic dissection of quantitative trait loci for flag leaf size in bread wheat ( Triticum aestivum L. ). Front Plant Sci. 2022; 13: 1047899. Jin J, Liu D, Qi Y, Ma J, Zhen W. Major QTL for Seven Yield-Related Traits in Common Wheat ( Triticum aestivum L.). Front Genet. 2020; 11: 1012. Tu Y, Liu H, Liu J, Tang H, Mu Y, Deng M, Jiang Q, Liu Y, Chen G, Wang J, Qi P, Pu Z, Chen G, Peng Y, Jiang Y, Xu Q, Kang H, Lan X, Wei Y, Zheng Y, Ma J. QTL mapping and validation of bread wheat flag leaf morphology across multiple environments in different genetic backgrounds. Theor Appl Genet. 2021; 134: 261-278 Wu Q, Chen Y, Fu L, Zhou S, Chen J, Zhao X, et al. QTL mapping of flag leaf traits in common wheat using an integrated high-density SSR and SNP genetic linkage map. Euphytica.2016; 208, 337-351. Wang Y, Qiao L, Yang C, Li X, Zhao J, Wu B, Zheng X, Li P, Zheng J. Identification of genetic loci for flag-leaf-related traits in wheat ( Triticum aestivum L . ) and their effects on grain yield. Front Plant Sci.2022; 13: 990287. Wang J, Liu H, Zhao C, Tang H, Mu Y, Xu Q, Deng M, Jiang Q, Chen G, Qi P, Wang J, Jiang Y, Chen S, Wei Y, Zheng Y, Lan X, Ma J. Mapping and validation of major and stable QTL for flag leaf size from tetraploid wheat. Plant Genome.2022; 15: e20252. Liu K, Xu H, Liu G, Guan P, Zhou X, Peng H, Yao Y, Ni Z, Sun Q, Du J. QTL mapping of flag leaf-related traits in wheat ( Triticum aestivum L . ). Theor Appl Genet. 2018; 131: 839-849. Zhang X, Xing P, Lin C, Wang H, Bao Y, Li X. QTL mapping for the flag leaf-related traits using RILs derived from Trititrigia germplasm line SN304 and wheat cultivar Yannong15 in multiple environments. BMC Plant Biol. 2024; 24: 297. Wen S, Zhang M, Tu K, Fan C, Tian S, Bi C, Chen Z, Zhao H, Wei C, Shi X, Yu J, Sun Q, You M. A major quantitative trait loci cluster controlling three components of yield and plant height identified on chromosome 4B of common wheat. Front Plant Sci. 2022; 12: 799520. Li J, Wen S, Fan C, Zhang M, Tian S, Kang W, Zhao W, Bi C, Wang Q, Lu S, Guo W, Ni Z, Xie C, Sun Q, You M. Characterization of a major quantitative trait locus on the short arm of chromosome 4B for spike number per unit area in common wheat ( Triticum aestivum L . ). Theor Appl Genet. 2020; 133: 2259-2269. Xu D, Wen W, Fu L, Li F, Li J, Xie L, Xia X, Ni Z, He Z, Cao S. Genetic dissection of a major QTL for kernel weight spanning the Rht-B1 locus in bread wheat. Theor Appl Genet. 2019; 132: 3191-3200. Chen Z, Cheng X, Chai L, Wang Z, Bian R, Li J, Zhao A, Xin M, Guo W, Hu Z, Peng H, Yao Y, Sun Q, Ni Z. Dissection of genetic factors underlying grain size and fine mapping of QTgw.cau-7D in common wheat ( Triticum aestivum L.). Theor Appl Genet. 2020; 133: 149-162. Liu G, Jia L, Lu L, Qin D, Zhang J, Guan P, Ni Z, Yao Y, Sun Q, Peng H. Mapping QTLs of yield-related traits using RIL population derived from common wheat and Tibetan semi-wild wheat. Theor Appl Genet. 2014; 127: 2415-32. Schierenbeck M, Alqudah AM, Thabet SG, Avogadro EG, Dietz JI, Simón MR, Börner A. Natural allelic variation confers diversity in the regulation of flag leaf traits in wheat. Sci Rep. 2024; 14: 13316. Wang P, Zhou G, Cui K, Li Z, Yu S. Clustered QTL for source leaf size and yield traits in rice ( Oryza sativa L.). Mol Breed. 2012; 29: 99-113. Wang P, Zhou G, Yu H, Yu S. Fine mapping a major QTL for flag leaf size and yield-related traits in rice. Theor Appl Genet. 2011; 123: 1319-1330. Liu B, Sun M, Wang K, Bian Y, Che Y, Liu J, Luo X, Liu S, Xie L, Li L, Qu K, Chao Y, Che R, Ye X, Xia X, Mao L, He Z, Li A, Cao S. Wheat domestication gene Q interplays with TaARF12 to antagonistically modulate plant architecture by integrating multiple hormone homeostasis. New Phytol. 2025; 24: 741-757. Huang Y, Kong Z, Wu X, Cheng R, Yu D, Ma Z. Characterization of three wheat grain weight QTLs that differentially affect kernel dimensions. Theor Appl Genet. 2015;128: 2437-45. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 20 Nov, 2025 Reviews received at journal 19 Nov, 2025 Reviews received at journal 17 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor invited by journal 02 Nov, 2025 Editor assigned by journal 02 Nov, 2025 Submission checks completed at journal 01 Nov, 2025 First submitted to journal 01 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7921960","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543618059,"identity":"65adb318-c527-4da2-ac24-a09e1b413991","order_by":0,"name":"tao sun","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"tao","middleName":"","lastName":"sun","suffix":""},{"id":543618060,"identity":"388d6a49-9e46-4bb6-9d90-712e50f407cc","order_by":1,"name":"Haotian Cao","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Haotian","middleName":"","lastName":"Cao","suffix":""},{"id":543618061,"identity":"ff0cdadc-dd73-46b3-9da6-4b490997ae20","order_by":2,"name":"Yahui Tong","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Yahui","middleName":"","lastName":"Tong","suffix":""},{"id":543618062,"identity":"ed9110c9-aa7e-47b8-8e05-08a4db02758d","order_by":3,"name":"Qiwei Yang","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Qiwei","middleName":"","lastName":"Yang","suffix":""},{"id":543618063,"identity":"f3c3d500-922e-4297-b840-292a14e553a3","order_by":4,"name":"Meishan Zhou","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Meishan","middleName":"","lastName":"Zhou","suffix":""},{"id":543618064,"identity":"9f1bcbad-52d8-4d50-98d2-715db772826a","order_by":5,"name":"Xinzhi Yang","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Xinzhi","middleName":"","lastName":"Yang","suffix":""},{"id":543618065,"identity":"afbdb6b2-b547-49a8-a9fc-d9bcba81bfe0","order_by":6,"name":"Caining Wang","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Caining","middleName":"","lastName":"Wang","suffix":""},{"id":543618066,"identity":"565621fa-8a47-4338-8cb5-842f8a58197e","order_by":7,"name":"Xinwen Zhang","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Xinwen","middleName":"","lastName":"Zhang","suffix":""},{"id":543618067,"identity":"30000887-3c8d-4c09-b6cd-1fbc028f97f6","order_by":8,"name":"Han Sun","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Sun","suffix":""},{"id":543618068,"identity":"3dd9a2c3-c337-44a7-8c6f-9e808c6c86b0","order_by":9,"name":"Yongzhen Wu","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Yongzhen","middleName":"","lastName":"Wu","suffix":""},{"id":543618069,"identity":"42209eb0-8003-4f5c-9553-b0afa1f56566","order_by":10,"name":"Ran Qin","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Qin","suffix":""},{"id":543618070,"identity":"f689891b-2f7a-480e-ba20-7a6714427b76","order_by":11,"name":"Chunhua Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYFCCBBBxQI6NvfkAaVqM+XiOJZCmJXGeRI4CcRrk25OfSf6ouZPexpDDwPCjYhthLQZnnplJSBx7ltvGcPYAY8+Z20RokUgwkzBsOJzbxtiXwMzYRoQW+Rnp3yQSGw6nszHzGBCnheFGjpnEwYbDCWxsxGoxOPOm2LLh2GHDNh62hINE+UW+PX3jzR81h+Xl5z8++OBHBTEOQwYHSFQ/CkbBKBgFowAXAABd0D2NT3vJsAAAAABJRU5ErkJggg==","orcid":"","institution":"Ludong University","correspondingAuthor":true,"prefix":"","firstName":"Chunhua","middleName":"","lastName":"Zhao","suffix":""},{"id":543618071,"identity":"9e6c86fa-688e-4c6a-bbab-1d93433b4133","order_by":12,"name":"Fa Cui","email":"","orcid":"","institution":"Ludong University","correspondingAuthor":false,"prefix":"","firstName":"Fa","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2025-10-22 09:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7921960/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7921960/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96243509,"identity":"2f30e3a1-3e48-4b46-96f4-e11964d767b1","added_by":"auto","created_at":"2025-11-19 07:16:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2091896,"visible":true,"origin":"","legend":"","description":"","filename":"CharacterizationofastableQTLforflagleafwidthanditsgeneticeffectsonyieldrelatedtraits.docx","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/de89419134175de8813fe3f4.docx"},{"id":96243226,"identity":"6c9c1619-acc0-4d02-9278-72a55b212603","added_by":"auto","created_at":"2025-11-19 07:15:52","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12660,"visible":true,"origin":"","legend":"","description":"","filename":"bdc912079ff84fd398c1603d3b937ce9.json","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/39beae1e0609954c77a6faee.json"},{"id":96243459,"identity":"9c762ba6-7b02-4f3a-8769-c82908186fa1","added_by":"auto","created_at":"2025-11-19 07:16:26","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26037,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/77a510871cb81648e380c440.xlsx"},{"id":96243134,"identity":"ffe3e6d7-2263-471c-9623-fd44af9600fd","added_by":"auto","created_at":"2025-11-19 07:15:41","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170595,"visible":true,"origin":"","legend":"","description":"","filename":"bdc912079ff84fd398c1603d3b937ce91enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/56239f72b0f9957bd9138442.xml"},{"id":95915392,"identity":"40acfa0f-6717-496c-ad9f-821f3e44fc1f","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"emf","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2621700,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/a65dcfb60d7cacdd36920546.emf"},{"id":95915396,"identity":"3d14e889-109d-45a3-89be-ff0ea15b2da9","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"emf","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1517976,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.emf","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/3be1d4ed83e606a3e67bd028.emf"},{"id":96243684,"identity":"eeb2dfb6-d0d5-4cea-9973-1fd32f948a1d","added_by":"auto","created_at":"2025-11-19 07:16:51","extension":"emf","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1721464,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.emf","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/d3a7dd69bc1cdc894a8196d2.emf"},{"id":96243777,"identity":"cb57c0f6-a514-4a07-85f2-3f35b9bed928","added_by":"auto","created_at":"2025-11-19 07:17:01","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":850267,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/2d7b344c9d2bb68ea852256e.jpeg"},{"id":96243031,"identity":"8014514f-7df2-4f9a-bfa2-1188a49438e5","added_by":"auto","created_at":"2025-11-19 07:15:16","extension":"emf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":470008,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.emf","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/6d81c4fec117309a14116efc.emf"},{"id":96244887,"identity":"72ae450d-2a0e-446a-8f8d-df08cd05dc6e","added_by":"auto","created_at":"2025-11-19 07:19:30","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":322820,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/ce827912afd2ac278b6a179c.jpeg"},{"id":95915387,"identity":"b8fe636d-8429-41d0-b76f-8b1b42000f55","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68564,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/48ca2210a60cdf4e455e9817.jpeg"},{"id":96243061,"identity":"55da6e30-c26c-44c0-a1dc-7f1d7079a6a4","added_by":"auto","created_at":"2025-11-19 07:15:22","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":569105,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/7895a76288c42a60b207c4e9.png"},{"id":96242888,"identity":"d785f299-7edf-491b-931a-1b291e5e4a82","added_by":"auto","created_at":"2025-11-19 07:14:46","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":232018,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/0adacf1c4c2bc6f34c6b6559.png"},{"id":96242907,"identity":"b83f96de-3073-432b-84e2-e19c0bb4f34f","added_by":"auto","created_at":"2025-11-19 07:14:50","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":241424,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/16b9d7dbe3537ec1e1c967a9.png"},{"id":95915389,"identity":"f955b972-1b8c-47a2-90f2-4ad6a68982a0","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":145138,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/04cc73ac89c52cbef6d1c11d.png"},{"id":96243907,"identity":"e6e46502-049e-4a2c-8db7-54cd1e0a8a65","added_by":"auto","created_at":"2025-11-19 07:17:17","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":365940,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/935ffdc1429cf5f8a9d48176.png"},{"id":95915398,"identity":"b78ec626-463c-4595-ada6-07d608ff9067","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50046,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/7435a2c93c594f9277b1fdc6.png"},{"id":95915394,"identity":"dea29442-2e39-4eb3-b5f7-03066d718926","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23133,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/9f61387687f7d58fdbfc06ce.png"},{"id":95915400,"identity":"b0237765-6668-41ba-952d-0aef90aa0b0c","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":169023,"visible":true,"origin":"","legend":"","description":"","filename":"bdc912079ff84fd398c1603d3b937ce91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/33dea63a06bc0d57e83952c4.xml"},{"id":95915403,"identity":"94c9812b-5dcb-4f5b-9e03-c2e713486d70","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179907,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/5291576e213ccebf2517e0bf.html"},{"id":95915378,"identity":"729c2660-355a-43b4-ae20-ae1b74d47bb2","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136819,"visible":true,"origin":"","legend":"\u003cp\u003eAn analysis of frequency distributions, correlations, and fitting curves of FLWs within KJ-RIL populations across varying environmental conditions. \u003csup\u003e***\u003c/sup\u003eRepresents a significance level of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/815bb91ef978e6f4f674243b.png"},{"id":96243535,"identity":"108c6ba6-3281-47a3-b705-99b8d18e0164","added_by":"auto","created_at":"2025-11-19 07:16:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33283,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of LOD values of \u003cem\u003eqFlw-4B\u003c/em\u003e across ten datasets. The abscissa represents the physical position of the SNP markers used for QTL mapping analysis, and the ordinate denotes the corresponding LOD values. The markers in red were used in the subsequent genetic effect analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/3b822fba80b44d8d851fd94f.png"},{"id":95915376,"identity":"e7eb7ee2-fe30-4017-9c0b-29e01809c26b","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73907,"visible":true,"origin":"","legend":"\u003cp\u003eObtained \u003cem\u003e4BFLW-290\u003c/em\u003e PCR-amplified products via PCR amplification in the KJ-RILs. M: DNA marker. K: KN9204. J: J411. Numbers 1–24 represent the amplified fragments in the KJ-RILs.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/a1174d79aa50ee989e81801b.png"},{"id":96243347,"identity":"44bfb173-d341-4a39-a887-3e32ca6ac66c","added_by":"auto","created_at":"2025-11-19 07:16:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35549,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic effects of \u003cem\u003eqFlw-4B\u003c/em\u003e on yield-related traits in the eight environments. FLW: Flag leaf width, SNPS: spikelet number per spike, KNPS: kernel number per spike, SNPP: spike number per plant, GL: grain length, SL: spike length, PH: plant height, GW: grain width, and TKW: thousand kernel weight. \u003csup\u003e*\u003c/sup\u003eSignificant difference at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003ehighly significant difference at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01. The asterisks *, ** represent P\u0026lt;0.05 and P\u0026lt;0.01, respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/7591e073bd9f65f4ef37b22b.png"},{"id":96243089,"identity":"82dc0a9a-e3ad-4915-8d3e-67c563b9a8e1","added_by":"auto","created_at":"2025-11-19 07:15:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":54241,"visible":true,"origin":"","legend":"\u003cp\u003eGene pyramiding effect assessment of \u003cem\u003eqFlw-4B\u003c/em\u003e across diverse genetic backgrounds under LN and HN growth environments​. (a)Left: The Arabic numerals above each bar represent the level of FLW, the Arabic numerals below each bar represent the number of positive alleles, the Arabic numerals at the very bottom of the bar chart represent the number of samples.Right: Sources of the positive versus negative alleles for the three QTLs. (b–e) The additive genetic effects of \u003cem\u003eqFlw-4B\u003c/em\u003e across diverse genetic backgrounds under the conditions of both HN and LN. The asterisks *, ** represent P\u0026lt;0.05 and P\u0026lt;0.01, respectively.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/a726263f05ea4f18f21bec54.png"},{"id":95915384,"identity":"b3e8578b-99a2-458f-91f6-2336e09d1a0e","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":35309,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic effects of \u003cem\u003eqFlw-4B\u003c/em\u003e on yield-related traits were analyzed in 314 natural populations. FLW: flag leaf width, FLL: flag leaf length, KNPS: kernel number per spike, SNPS: spikelet number per spike, SNPP: spike number per plant, TKW: thousand kernel weight, GYPP: grain yield per plant, FSN: fertile spikelet number per spike, PH: plant height, SL: spike length, GL: grain length, and GW: grain width. The asterisks *, **, and NS represent P \u0026lt; 0.05, P \u0026lt; 0.01, and no significant difference, respectively.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/c1f8055a3876c4b2baac3eab.png"},{"id":96243716,"identity":"18b994e4-27ac-4a7a-b924-f4b5f5458720","added_by":"auto","created_at":"2025-11-19 07:16:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":67335,"visible":true,"origin":"","legend":"\u003cp\u003eSelection effects of \u003cem\u003eqFlw-4B\u003c/em\u003ehaplotypes in the breeding of varieties/advanced lines analyzed based on spatial distribution and time span. Superior haplotype (hap): Hap-GG-GG-CC; non-superior haplotype: Hap-AA-AA-CC; recombination type: Hap-GG-GG-TT, Hap-AA-AA-TT, Hap-AA-GG-TT, Hap-AA-GG-TT, and Hap-AA-GG-CC. Lines with heterozygous genotypes at any of the three loci were defined as heterozygous. Lines with deletions at any of the three loci were defined as deletion types.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/e34c8a85abff4f3bad187add.png"},{"id":96255335,"identity":"72396344-d579-45fd-88d0-52e3e2cc2548","added_by":"auto","created_at":"2025-11-19 07:48:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1445553,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/84caed79-06e8-4d66-b5fd-038561f64503.pdf"},{"id":95915382,"identity":"8433e951-909f-4076-9766-ad3e45b5c0d4","added_by":"auto","created_at":"2025-11-14 11:29:50","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26037,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7921960/v1/ca459f237f133af57f374732.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of a stable QTL for flag leaf width and its genetic effects on yield-related traits","fulltext":[{"header":"Background","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is an important global food crop, with a wide range of cultivation, large trade volume, and a large population coverage, making it a \u0026ldquo;strategic pillar\u0026ldquo; for food security[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The global population is estimated to reach 9.6\u0026nbsp;billion by 2050[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To meet the growing demand for food from the increasing population, wheat breeders must maintain a genetic gain of 2.4% per year[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe topmost leaf of wheat plants, known as the flag leaf, serves a key function in providing photoassimilates to the developing grains after anthesis and accounts for roughly 45%\u0026ndash;58% of the total photosynthetic capacity[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It also produces 41%\u0026minus;43% of the required carbohydrates during grain filling[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The orientation and size of a flag leaf are important in plant breeding, because they affect plant canopy morphology and photosynthetic efficiency[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The size of the flag leaf, consisting of leaf length, width, and area, is an extremely important factor that determines leaf structure and yield potential[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFLW serves as a crucial constituent of leaf morphology in wheat and exerts a profound impact on yield[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The FLW is usually closely correlated with the photosynthetic rate, thousand kernel weight (TKW), and grain yield per plant (GYPP)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Thus, FLW is a vital component of the ideal plant and its optimization will improve plant architecture. Consequently, breeding wheat with an optimal FLW has been proposed as a viable approach for increasing grain yields[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFLW is a complex agronomic characteristic that is regulated by both genotype and environment. With the availability of molecular markers and genetic maps, many quantitative trait loci (QTLs) related to FLW have been documented in rice and wheat [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For example, \u003cem\u003eqFlw7.2\u003c/em\u003e, a new major QTL for FLW in rice, has been fine mapped within a 45.30\u0026thinsp;\u0026minus;\u0026thinsp;53.34 cM region on chromosome 7[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, \u003cem\u003eqFlw4\u003c/em\u003e for FLW in rice has been fine-mapped to an interval of 74.8 kb[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], whereas the narrow leaf genes \u003cem\u003eNal1\u003c/em\u003e, \u003cem\u003eNal7\u003c/em\u003e, and \u003cem\u003eNal9\u003c/em\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], narrow-curly leaf genes \u003cem\u003eNal2\u003c/em\u003e and \u003cem\u003eNal3\u003c/em\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and narrow-rolled leaf gene \u003cem\u003eNrl1\u003c/em\u003e have all been cloned in rice[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecently, there has been significant research on QTLs of FLW in wheat. Yan et al. reported the fine mapping of \u003cem\u003eqFlw-6A\u003c/em\u003e to a 500-kb region on 6A[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and Zhao et al. (2022) reported the fine mapping of \u003cem\u003eqFlw-5B\u003c/em\u003e to a 2.5-Mb region on 5B[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Qiao et al. precisely localized \u003cem\u003eQFlw.sxau-6BL\u003c/em\u003e to a 661.13\u0026thinsp;\u0026minus;\u0026thinsp;662.40 Mb region on 6B[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These studies on FLW-related genes/QTLs have provided an important theoretical basis for improved FLW breeding; however, there are limited studies on the genetic and breeding selection effects of FLW-related genes/QTLs.\u003c/p\u003e\u003cp\u003eIn this study, one major stable FLW-related QTL, \u003cem\u003eqFlw-4B\u003c/em\u003e, was identified in wheat Kenong 9204 (KN9204) \u0026times; Jing 411 (J411) recombinant inbred mapping populations (KJ-RILs). The objectives of this study were therefore to verify the genetic effects of \u003cem\u003eqFlw-4B\u003c/em\u003e in KJ-RILs and the natural mapping population, and to determine the effects of \u003cem\u003eqFlw-4B\u003c/em\u003e breeding selection in different regions and ages. In addition, an InDel marker for \u003cem\u003eqFlw-4B\u003c/em\u003e was developed.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant materials and growth conditions\u003c/h2\u003e\u003cp\u003eThe KJ-RIL population, consisting of 188 lines, was constructed using the single seed descent method. The KJ 129 line was not used in this study due to genotypic deletion of most of the SNP markers. The FLW of the near-isogenic line NIL-KN9204 for the \u003cem\u003eqFlw-4B\u003c/em\u003e region was significantly greater than that of the near-isogenic line NIL-J411. KJ-RILs and their parents were grown in eight different locations, years, and environments with different nitrogen (N) application treatments. The LN (Low nitrogen) environments were located in the following places during the designated seasons: 2011\u0026ndash;2012 in Shijiazhuang (E1-LN), 2012\u0026ndash;2013 in Shijiazhuang (E3-LN), 2012\u0026ndash;2013 in Beijing (E5-LN), and 2012\u0026ndash;2013 in Xinxiang (E7-LN). The HN (High nitrogen) environments were located in the following places during the designated seasons: 2011\u0026ndash;2012 in Shijiazhuang (E2-HN), 2012\u0026ndash;2013 in Shijiazhuang (E4-HN), 2012\u0026ndash;2013 in Beijing (E6-HN), and 2012\u0026ndash;2013 in Xinxiang (E8-HN). Field settings and corresponding data on soil N levels in eight environments have been described in detail in a previous study[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA natural population consisting of 314 breeding varieties/advanced lines was planted in 12 environments (location and season), specifically: 2018\u0026thinsp;\u0026minus;\u0026thinsp;2019 Qixia (E1), 2019\u0026thinsp;\u0026minus;\u0026thinsp;2020 Weifang (E2), 2019\u0026thinsp;\u0026minus;\u0026thinsp;2020 Laishan Pula Valley (E3), 2020\u0026thinsp;\u0026minus;\u0026thinsp;2021 Shijiazhuang (E4), 2020\u0026thinsp;\u0026minus;\u0026thinsp;2021 Weifang (E5), 2020\u0026thinsp;\u0026minus;\u0026thinsp;2021 Lai Shan Pula Valley (E6), 2020\u0026thinsp;\u0026minus;\u0026thinsp;2021 Zhifu (E7), 2021\u0026thinsp;\u0026minus;\u0026thinsp;2022 Shijiazhuang (E8), 2021\u0026thinsp;\u0026minus;\u0026thinsp;2022 Laishan Pula Valley (E9), 2021\u0026thinsp;\u0026minus;\u0026thinsp;2022 Zhifu (E10), 2022\u0026thinsp;\u0026minus;\u0026thinsp;2023 Lai Shan Muyu Village (E11), and 2022\u0026thinsp;\u0026minus;\u0026thinsp;2023 Lai Shan Pula Valley (E12).\u003c/p\u003e\u003cp\u003eThe KJ-RIL trial used a randomized block design with two replicates per treatment. The experimental plots consisted of three rows, each 1.5 m long with a row spacing of 0.25 m. Thirty seeds were sown per row, and field management was conducted according to local standards. Natural group materials were cultivated in a random complete block design, with two replicates at each location. Each variety was planted in three rows, each row 1.5 m long, with 20 cm spacing. Fifteen seeds were sown manually in each row. Crop management followed local agricultural practices.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhenotypic evaluation and data analysis\u003c/h3\u003e\n\u003cp\u003eThe width of the widest part of the wheat flag leaf was determined at the heading stage. Spikelet number per spike (SNPS), together with other yield-related traits, such as kernel number per spike (KNPS), spike length (SL), plant height (PH), TKW, kernel weight per spike (KWPS), spike number per plant (SNPP), grain width (GW), grain length (GL), and GYPP, were evaluated as the methods described in our previous studies of Cui et al.(2013), Zhang et al. (2017) and Fan et al. (2019) [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eData on FLW and other yield-related traits for the 187 KJ-RILs under four LN and four HN environmental conditions were imported into QGA Sation 2.0[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and the best linear unbiased estimation (BLUE) was computed to obtain two datasets, LN (BLUE-LN) and HN (BLUE-HN). BLUE values for yield-related traits in the 12 environments in which the natural mapping population was grown were calculated in the same way. Similarly, the broad-sense heritability (H\u003csup\u003e2\u003c/sup\u003e) values of FLW in LN and HN environments for the KJ-RIL populations were calculated using QGA Sation 2.0. SPSS 25.0 software was employed to carry out one-way analysis of variance and phenotypic correlation analysis between FLW and other traits. Association analysis was conducted on genotype and phenotype data derived from KJ-RILs and natural mapping populations, aiming to clarify the genetic impacts of \u003cem\u003eqFlw-4B\u003c/em\u003e on yield-associated traits.\u003c/p\u003e\n\u003ch3\u003eGenotype information obtainment and QTL identification\u003c/h3\u003e\n\u003cp\u003eReferring to the method of Stacey et al [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], young leaves of all materials from KJ-RILs and natural mapping populations were separately sampled, and DNA was extracted using the CTAB method. The KJ-RIL and natural mapping populations were then genotyped using 660 K and 55 K SNP chips, respectively. To obtain the physical location of each SNP in the KN 9204 genome assembly, an analysis was performed using BLAST software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.ncbi.nlm.nih.gov/blast/ExecuTABLEs/release/\u003c/span\u003e\u003cspan address=\"http://ftp://ftp.ncbi.nlm.nih.gov/blast/ExecuTABLEs/release/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Based on the KJ-RIL mapping population, and combined with the 660K SNP array as well as the previous research work of our team, a high-density linkage map containing 119,566 loci was finally constructed[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The QTL detection analysis was conducted via physical mapping rather than genetic linkage mapping, employing the BIP module of IciMapping V4.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.ncbi.nlm.nih.gov/blast/execuTABLEs/release/\u003c/span\u003e\u003cspan address=\"http://ftp://ftp.ncbi.nlm.nih.gov/blast/execuTABLEs/release/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This analysis utilized 1,000 alignments with a 1.0 Mb steps, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 in stepwise regression, and adopted a Logarithm of Odds (LOD) threshold of 2.5 for QTL localization.\u003c/p\u003e\n\u003ch3\u003eDesign of InDel molecular markers\u003c/h3\u003e\n\u003cp\u003eUsing the 10\u0026times; genome resequencing of KN9204 and J411 (data not disclosed), an InDel in the KN9204 genome located at 31,224,659 was selected, and primer \u003cem\u003e4BFLW-290\u003c/em\u003e was designed (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). InDel PCR primers with polymorphisms were designed in Primer Server of Wheat Omics 2.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://202.194.139.32/\u003c/span\u003e\u003cspan address=\"http://202.194.139.32/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePhenotypic analysis of FLW in the KJ-RILs\u003c/h2\u003e\u003cp\u003eThe analyses of phenotypic data from KJ-RILs under different environmental conditions are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The coefficient of variation of FLW in the KJ-RIL population ranged from 9.33% to 10.85%, indicating that its phenotypic variation was relatively stable, and its generalized heritability was 0.62\u0026thinsp;\u0026minus;\u0026thinsp;0.84. The mean values of FLW in the KJ-RILs ranged from 1.20 cm to 1.59 cm, and the absolute values of kurtosis and skewness were less than 1, based on a correlation analysis. The FLW phenotypic data from the 187 KJ-RIL samples exhibited an approximately normal distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, the FLWs of KJ-RILs were higher under most of the HN conditions than in the LN conditions, indicating that N levels affect FLW.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePhenotypic analysis of FLW in KJ-RILs under different environmental conditions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eParents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c11\" namest=\"c4\"\u003e\u003cp\u003eKJ-RIL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKN9204\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJ411\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCV(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSK\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eKUR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE1(LN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE2(HN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE3(LN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE4(HN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE5(LN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE6(HN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE7(LN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE8(HN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSD: standard deviation. CV: coefficient of variation. H\u003csup\u003e2\u003c/sup\u003e: broad-sense heritability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe correlation analysis indicated that under LN conditions, FLW showed significant positive correlations with PH, SL, SNPP, SNPS, KNPS, KWPS, GL, and GW. Under HN conditions, FLW exhibited significant positive correlations with SL, SNPP, SNPS, KNPS, KWPS, GL, and GYPP, while showing significant negative correlations with PH, GW, and TKW (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation analysis between FLW and yield-related traits in the KJ-RILs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCondition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSNPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSNPS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKNPS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKWPS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eGW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eTKW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eGYPP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFLW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.23\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.24\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.28\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.07\u003csup\u003e༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.06\u003csup\u003e༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.48\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.42\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.21\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.12\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-0.22\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.16\u003csup\u003e༊༊\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePH: plant height, SL: spike length, SNPP: spike number per plant, SNPS: spikelet number per spike, KNPS: kernel number per spike, KWPS: kernel weight per spike, GL: grain length, GW: grain weight, TKW: thousand kernel weight, GYPP: grain yield per plant. The asterisks *, ** represent P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQTL detection of\u003c/b\u003e \u003cb\u003eqFlw-4B\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe QTL analysis showed that \u003cem\u003eqFlw-4B\u003c/em\u003e was consistently detected in E1\u0026ndash;E8, as well as in LN BLUE and HN-BLUE datasets (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), explained 0.34%\u0026ndash;18.45% of the FLW phenotypic variation in the KJ-RILs, with LOD values of 4.42\u0026thinsp;\u0026minus;\u0026thinsp;12.17. The beneficial alleles derived from KN9204 enhanced FLW, and the corresponding additive effect values fell within the range of 0.03 to 0.06. For \u003cem\u003eqFlw-4B\u003c/em\u003e, it was physically localized to an 18-Mb interval on chromosome KN4B (spanning positions 27.70\u0026thinsp;\u0026minus;\u0026thinsp;45.70 Mb), which is flanked by the molecular markers \u003cem\u003eAX-95143370\u003c/em\u003e and \u003cem\u003eAX-110527790\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUsing IciMapping v4.2 software, the putative additive QTL \u003cem\u003eqFlw-4B\u003c/em\u003e could be consistently identified in the KJ-RILs population across 10 different datasets\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironments\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePosition (Mb)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLeft Marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRight Marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLOD\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePVE (%)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdd\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE1(LN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAX-95143370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAX109899078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE2(HN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAX-109988872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAX109284839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE3(LN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAX-110713957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAX-110527790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE4(HN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAX-110713957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAX-110527790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE5(LN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAX-110713957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAX-110527790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE6(HN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAX-109988872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAX109284839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE7(LN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAX-109988872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAX109284839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE8(HN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAX-109988872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAX109284839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBLUE-LN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAX-109988872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAX109284839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBLUE-HN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAX-109988872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAX109284839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003eཁ\u003c/sup\u003eThe physical position of the LOD peak in the KN9204 genome.\u003c/p\u003e\u003cp\u003e\u003csup\u003eག\u003c/sup\u003eLogarithm of the odds score.\u003c/p\u003e\u003cp\u003e\u003csup\u003eགྷ\u003c/sup\u003eRates of the phenotypic variances explained by the QTL.\u003c/p\u003e\u003cp\u003e\u003csup\u003eང\u003c/sup\u003eAdditive effect on FLW.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePCR (InDel) markers\u003c/h3\u003e\n\u003cp\u003eIn this study, the InDel marker \u003cem\u003e4BFLW-290\u003c/em\u003e (31,224,659), which is closely related to \u003cem\u003eqFlw-4B\u003c/em\u003e, was developed for the identification of FLW, and it stably amplified clear and specific bands in the 188 KJ-RILs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It can stably amplify clear fragments of 210 bp and 244 bp from KN9204 and J411, respectively. The length of the aforementioned PCR products is completely consistent with the DNA resequencing results of the two parental materials. This InDel marker was genotyped in the 187 KJ-RIL populations, revealing that the KN9204 haplotype of the \u003cem\u003eqFlw-4B\u003c/em\u003e region, \u003cem\u003eHap-KN9204\u003c/em\u003e, significantly outperformed the J411 haplotype of the region, \u003cem\u003eHap-J411\u003c/em\u003e, across all eight environments. Therefore, \u003cem\u003e4BFLW-290\u003c/em\u003e may be used in further wheat molecular breeding.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic impacts of\u003c/b\u003e \u003cb\u003eqFlw-4B\u003c/b\u003e \u003cb\u003eon traits associated with yield within the KJ-RIL genetic mapping population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe LOD peak position showed that \u003cem\u003eAX-95143370\u003c/em\u003e (KN4B: 26.28 Mb), \u003cem\u003eAX-109899078\u003c/em\u003e (KN4B: 27.70 Mb), \u003cem\u003eAX-109988872\u003c/em\u003e (KN4B: 29.70 Mb), \u003cem\u003eAX-109284839\u003c/em\u003e (KN4B: 30.70 Mb), \u003cem\u003e4BFLW-290\u003c/em\u003e (KN4B: 31.22 Mb), \u003cem\u003eAX-110713957\u003c/em\u003e (KN4B: 44.70 Mb), \u003cem\u003eAX-110527790\u003c/em\u003e (KN4B: 45.70 Mb), and \u003cem\u003e4BFLW-290\u003c/em\u003e were the closest linkage markers for \u003cem\u003eqFlw-4B\u003c/em\u003e. Therefore, these markers were used to analyze the genetic effects of \u003cem\u003eqFlw-4B\u003c/em\u003e on yield-related traits.\u003c/p\u003e\u003cp\u003eIn eight environmental datasets, the haplotype \u003cem\u003eHap-KN9204\u003c/em\u003e significantly increased FLW (Fig.\u0026nbsp;4), with the mean elevation rate of this trait reaching 8.82%. It only significantly increased SNPS and KNPS in four environments, with average increases of 2.28% and 6.25%, respectively, whereas it increased SNPP, SL, and GL in no more than three environments. However, it significantly decreased PH in all eight environments, with an average decrease of 13.48%, significantly decreased TKW in four of the eight environments, with an average decrease of 6.86%, and significantly decreased GW in two of the eight environments, with an average decrease of 2.80%.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExploration of the pyramiding effect of\u003c/b\u003e \u003cb\u003eqFlw-4B\u003c/b\u003e \u003cb\u003eunder diverse genetic backgrounds\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBeyond \u003cem\u003eqFlw-4B\u003c/em\u003e, we further detected 2 stable additive QTLs governing the FLW trait within the KJ genetic population, specifically \u003cem\u003eqFlw-5B\u003c/em\u003e and \u003cem\u003eqFlw-6B\u003c/em\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The positive alleles of \u003cem\u003eqFlw-5B\u003c/em\u003e and \u003cem\u003eqFlw-6B\u003c/em\u003e associated with the increased FLW were from KN9204 and J411, respectively. A pyramiding effect analysis for these two QTLs and \u003cem\u003eqFlw-4B\u003c/em\u003e was performed. Based on the genotypes of markers tightly linked to these three QTLs, the KJ-RILs mapping population was divided into eight groups. Under both LN and HN nitrogen conditions, as the count of positive alleles in plants rose, the magnitude of FLW increase showed a growing trend and this observation validated the notable additive effects of all three QTLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Relative to the phenotypic performance of the three negative allele pyramided type (BBA), the pyramid combination of three beneficial QTL alleles (AAB) elevated FLW by 15.15% and 15.49% under LN and HN conditions, correspondingly.\u003c/p\u003e\u003cp\u003eTo further clarify the additive effect of \u003cem\u003eqFlw-4B\u003c/em\u003e, we split the KJ-RILs population into four subgroups according to the genotype of markers that are tightly linked with \u003cem\u003eqFlw-4B\u003c/em\u003e, \u003cem\u003eqFlw-5B\u003c/em\u003e, and \u003cem\u003eqFlw-6B\u003c/em\u003e. The additive effect of \u003cem\u003eqFlw-4B\u003c/em\u003e fell within the range of 0.03 to 0.16 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb-e). In the AA (+ \u0026minus;) and AB (+ +) genetic backgrounds, the additive effect of \u003cem\u003eqFlw-4B\u003c/em\u003e on the FLW trait reached a significant level (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, c).\u003c/p\u003e\u003cp\u003eWhen both \u003cem\u003eqFlw-5B\u003c/em\u003e and \u003cem\u003eqFlw-6B\u003c/em\u003e carried negative alleles, the additive effect was at its lowest under both LN and HN conditions. In contrast, the highest additive effect under the LN and HN conditions was observed when these two loci harbored positive alleles.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic effects analysis of\u003c/b\u003e \u003cb\u003eqFlw-4B\u003c/b\u003e \u003cb\u003eon wheat yield-related traits based on a natural mapping population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThree SNP molecular markers, \u003cem\u003eAX-109526283\u003c/em\u003e (G/A, KN4B: 27.93 Mb), \u003cem\u003eAX-109580651\u003c/em\u003e (G/A, KN4B: 29.70 Mb), and \u003cem\u003eAX-109347500\u003c/em\u003e (C/T, KN4B: 45.97 Mb), adjacent to the LOD peak position for \u003cem\u003eqFlw-4B\u003c/em\u003e were used to genotype the 314 accessions in the natural mapping population. In the natural population haplotype Hap-GG-GG-CC was identical to KN9204, defining it as a superior haplotype. Hap-AA-AA-CC was identical to J411, defining it as a non-superior haplotype. Hap-GG-GG-TT, Hap-AA-AA-TT, Hap-AA-GG-TT, Hap-AA-GG-TT, and Hap-AA-GG-CC represent recombination types (Table S2). Lines with heterozygous genotypes at any of the three loci were defined as heterozygous. Lines with deletions at any of the three loci were defined as deletion types. Out of 314 natural populations, 48.41% had the superior haplotype (Hap-GG-GG-CC), whereas 13.69% had the non-superior haplotype (Hap-AA-AA-CC), and 24.52% had recombinant haplotypes. The percentages of heterozygous and deletion types were 7.32% and 6.05%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConsequently, a correlation analysis of \u003cem\u003eqFlw-4B\u003c/em\u003e superior and non-superior haplotypes with yield-related traits in natural populations was performed. The superior haplotype Hap-GG-GG-CC significantly increased FLW, FLL, KNPS, SNPS, SNPP, GYPP, FSN, and SL. However, it had a significant negative effect on GL, and had no significant effects on TKW, PH, and GW (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The analysis results were consistent with those of the KJ-RIL population, suggesting that \u003cem\u003eqFlw-4B\u003c/em\u003e has effects on yield-related traits.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHaplotype analysis of \u003cem\u003eqFlw-4B\u003c/em\u003e based on a natural mapping population\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProportion (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuperior haplotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.41%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-superior haplotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.69%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecombinants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.52%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeterozygous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.32%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeletion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.05%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of selection effects of\u003c/b\u003e \u003cb\u003eqFlw-4B\u003c/b\u003e \u003cb\u003ein wheat breeding\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe breeding-related selection effects of the \u003cem\u003eqFlw-4B\u003c/em\u003e haplotype were analyzed based in six varieties (lines), which were divided into six subgroups based on their spatial distribution, Shanxi, Qinghai, Henan, Sichuan, Shandong, and foreign regions. The highest utilization rates of the superior haplotype (Hap-GG-GG-CC) in the six subgroups were 52.63%, 42.11%, 41.18%, 39.39%, 30.43%, and 65.75%, respectively. Thus, the utilization rate of the superior haplotype in foreign varieties was much higher than in domestic varieties. The utilization of recombinant haplotypes was second only to the superior haplotype in the provinces of Qinghai, Shandong, Henan, and Sichuan (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eSelection effects of \u003cem\u003eqFlw-4B\u003c/em\u003e in wheat breeding efforts were analyzed over a time span using 187 varieties that were traceable in the natural mapping population. The superior haplotype (Hap-GG-GG-CC) had a high seed value throughout the wheat breeding process, and the utilization rate was progressively higher in the 1990s, 2000s, and 2010s, reaching 50% in the latter. Thus, the seed value of the superior haplotype was gradually being exploited. On the contrary, the utilization rate of the non-superior haplotype has been decreasing year by year (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). The temporal and spatial results indicated that the positive allele of \u003cem\u003eqFlw-4B\u003c/em\u003e occupied a more important position in wheat breeding work and possessed greater seed value and breeding potential.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eComparison with previous studies\u003c/h2\u003e\u003cp\u003eIn previous studies, multiple QTLs associated with FLW have been detected[\u003cspan additionalcitationids=\"CR35 CR36 CR37 CR38 CR39 CR40 CR41\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Many of these QTLs were detected on chromosome 4B. Chen et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] detected one QTL associated with FLW at 28.6\u0026thinsp;\u0026minus;\u0026thinsp;43.54 Mb on chromosome 4B. Wang et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] located a QTL associated with FLW at 56.5\u0026thinsp;\u0026minus;\u0026thinsp;59.5 Mb on chromosome 4B in six environments and BLUE datasets. Liu et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] detected two QTLs on chromosome 4B, \u003cem\u003eQFLW-4B.1\u003c/em\u003e and \u003cem\u003eQFLW-4B.2\u003c/em\u003e, with physical locations spanning 38.80\u0026thinsp;\u0026minus;\u0026thinsp;75.74 Mb. Zhang et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], using RILs derived from the common wheat Yannong15 and SN304, identified three QTL associated with FLW within the 38.6\u0026thinsp;\u0026minus;\u0026thinsp;53.6 Mb interval on chromosome 4B. In this study, \u003cem\u003eqFlw-4B\u003c/em\u003e was localized in the interval 27.70\u0026thinsp;\u0026minus;\u0026thinsp;45.70 Mb on chromosome 4B. This result is consistent with the above studies. Thus, \u003cem\u003eqFlw-4B\u003c/em\u003e has been detected in several mapping populations; consequently, we speculated it was a stable QTL having significant breeding value.\u003c/p\u003e\u003cp\u003eMany other yield-related QTLs that co-localize with \u003cem\u003eqFlw-4B\u003c/em\u003e have been identified in previous studies. Wen et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] detected QTLs for SN, GNS, PH, and TGW in the 20.0\u0026thinsp;\u0026minus;\u0026thinsp;38.0 Mb interval of chromosome 4B. Li et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] detected a major QTL for spike number per unit area in the interval of 24.91\u0026ndash;38.36 Mb on chromosome 4BS. Xu et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] localized a TKW primary QTL in the range of 19.7\u0026thinsp;\u0026minus;\u0026thinsp;38.2 Mb on chromosome 4B. Chen et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] localized three yield-associated OTLs (\u003cem\u003eQTgw.cau-4 B-2\u003c/em\u003e, \u003cem\u003eQGw.cau-4B\u003c/em\u003e, and \u003cem\u003eQTgw.cau-4B-1\u003c/em\u003e) on chromosome 4B in the 20\u0026thinsp;\u0026minus;\u0026thinsp;35-Mb range. Several QTLs associated with yield traits, such as PH, ears per plant, sterile spikelet number, grain number per spike, grain weight per spike, and floret number per spikelet, were detected at the same position of chromosome 4B by Liu et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The above results suggest that QTL clusters with pleiotropic effects on yield traits may exist near \u003cem\u003eqFlw-4B\u003c/em\u003e. The identification and utilization of superior alleles of \u003cem\u003eqFlw-4B\u003c/em\u003e may be a highly effective way to improve wheat yield.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelationship between\u003c/b\u003e \u003cb\u003eqFlw-4B\u003c/b\u003e \u003cb\u003eand yield-related traits\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor typical gramineous crops, such as rice and wheat, there is a close correlation between the size of their flag leaves and yield-related traits[\u003cspan additionalcitationids=\"CR49 CR50 CR51\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Liu et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] demonstrated that FLW positively correlates with traits such as SL, KNPS, and KWPS, while it negatively correlates with TKW, consistent with the findings of our study in HN. Zhao et al.\u0026rsquo;s [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] research indicates that FLW is positively correlated with yield traits such as KNPS and KWPS, whereas it shows no significant correlation with TKW, consistent with our findings in LN. However, they reported that FLW is negatively correlated with SNPP, which is contrary to our results. Recently, Zhao et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] further discovered that FLW exerts positive effects on TKW and KNPS, while producing negative effects on SNPP and SL. Their findings on KNPS align with those of this study, whereas the results for TKW, SNPP, and SL are inconsistent.\u003c/p\u003e\u003cp\u003eFor \u003cem\u003eqFlw-4B\u003c/em\u003e, the superior genotype Hap-KN9204 significantly increased FLW, KNPS, SNPP, and SNPS in both KJ-RILs and in the natural population, but the effects on PH and TKW, which may be influenced by genetic background, were inconsistent.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSelective utilization and potential application of\u003c/b\u003e \u003cb\u003eqFlw-4B\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe selection effect analysis revealed that \u003cem\u003eHap-GG-GG-CC\u003c/em\u003e (Hap-KN9204) of \u003cem\u003eqFlw-4B\u003c/em\u003e was the dominant haplotype used in breeding efforts. The superior haplotype of \u003cem\u003eqFlw-4B\u003c/em\u003e underwent strongly selection from the 1990s (37.04%\u0026minus;50%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). This may be because \u003cem\u003eqFlw-4B\u003c/em\u003e exerted positive effects on most yield components, including SL, SNPS, SNPP, and KNPS, thereby significantly increasing the yield of the variety. This suggested that the superior haplotype of \u003cem\u003eqFlw-4B\u003c/em\u003e had some potential for application in modern breeding work. It may also be convenient to develop the InDel marker for application in \u003cem\u003eqFlw-4B\u003c/em\u003e-related breeding. At present, it provides an important resource for the molecular-assisted breeding of wheat.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese experiments complied with the ethical standards in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations Conflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 32472134), the Foundation of Shandong Province, China (Grant No. ZR2022MC119), the Shandong Provincial Key Research (Grant No. 2024LZGCQY012, 2022LZG002-2), the Shandong Provincial Fund for Excellent Young Scholars (Grant No. ZR2022YQ19)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFC, CZ and YW analyzed the data and drafted the manuscript. TS, HG, HX, ZZ, HC, YT, ZZ and MT performed phenotype evaluation. RQ, HS and DL helped design the study and revised the manuscript. All of the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMa J, Tian T, Wang P, Liu Y, Zhang P, Chen T, Guo L, Zhang Y, Wu Y, Shahinnia F, Yang D. Identification of quantitative trait loci and candidate genes underlying kernel traits of wheat (\u003cem\u003eTriticum aestivum\u0026nbsp;\u003c/em\u003eL.) in response to drought stress. Theor Appl Genet. 2025; 19;13: 216.\u003c/li\u003e\n \u003cli\u003eLi T, Tang Y, Lin Z, Chen B, Wang J, Li Q, Huang F, Zhang J, Liang J, Zhang H, Liu Z, Li J, Yang W, Deng G, Long H. Identification and development of KASP markers for genetic loci controlling plant height in bread wheat and evaluation their effects using near isogenic lines. BMC Plant Biol. 2025; 25: 832.\u003c/li\u003e\n \u003cli\u003eTurhan S, Taspinar MS, Yigider E, Aydin M, Agar G. The role of long terminal repeat (LTR) responses to drought in seleniumtreated wheat.\u0026nbsp;Environ Eng Manag J. 2021; 20.\u003c/li\u003e\n \u003cli\u003eMolero G, Joynson R, Pinera-Chavez FJ et\u0026nbsp;al. Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential. Plant Biotechnol J. 2019; 17:1276-1288.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Araus JL, Tapia L. Photosynthetic gas exchange characteristics of wheat flag leaf blades and sheaths during grain filling: the case of a spring crop grown under Mediterranean climate conditions. Plant Physiology. 1987; 85: 667-673.\u003c/li\u003e\n \u003cli\u003eKumar P, Gill HS, Singh M, Kaur K, Koupal D, Talukder S, Bernardo A, Amand PS, Bai G, Sehgal SK. Characterization of flag leaf morphology identifies a major genomic region controlling flag leaf angle in the US winter wheat (\u003cem\u003eTriticum aestivum\u0026nbsp;\u003c/em\u003eL.). Theor Appl Genet. 2024; 137: 205.\u003c/li\u003e\n \u003cli\u003eZhou X, Qiang C, Chen L, Qing D, Huang J, Li J, Pan Y. The Landscape of Presence/Absence Variations during the Improvement of Rice. Genes (Basel). 2024; 15: 645.\u003c/li\u003e\n \u003cli\u003eGuitman MR, Arnozis PA, Barneix AJ. Effect of source-sink relations and nitrogen nutrition on senescence and N remobilization in the flag leaf of wheat. Physiol Plant. 1991;82: 278-84.\u003c/li\u003e\n \u003cli\u003eZhao C, Liu X, Liu H, Kong W, Zhao Z, Zhang S, Wang S, Chen Y, Wu Y, Sun H, Qin R, Cui F. Fine mapping of \u003cem\u003eQFlw-5B\u003c/em\u003e, a major QTL for flag leaf width in common wheat (\u003cem\u003eTriticum aestivum\u0026nbsp;\u003c/em\u003eL\u003cem\u003e.\u003c/em\u003e). Theor Appl Genet. 2022; 135: 2531-2541.\u003c/li\u003e\n \u003cli\u003eKhaliq I, Irshad A, Ahsan M. Awns and flag leaf contribution towards grain yield in spring wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L\u003cem\u003e.\u003c/em\u003e). Cereal Res Commun. 2008; 36:65-76.\u003c/li\u003e\n \u003cli\u003eMorinaka Y, Sakamoto T, Inukai Y, Agetsuma M, Kitano H, Ashikari M, Matsuoka M. Morphological alteration caused by brassinosteroid insensitivity increases the biomass and grain production of rice. Plant Physiol. 2006; 141:924-931.\u003c/li\u003e\n \u003cli\u003eMei HW, Li ZK, Shu QY, Guo LB, Wang YP, Yu XQ, Ying CS, Luo LJ. Gene actions of QTLs affecting several agronomic traits resolved in a recombinant inbred rice population and two testcross populations. Theor Appl Genet. 2003; 110:649-659.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang X, Chen L, Zhao Z, Jiang N, Khan NU, Hu Q, Liu R, Liu Z, Qian X, Zhu X, Sun X, Li J, Zhang H, Li D, Xu P, Pan Y, Li Z, Zhang Z. Exploring genomic regions and genes modulating plant height and flag leaf morphology in rice. Plant J. 2025; 122: e70145.\u003c/li\u003e\n \u003cli\u003eKulkarni SR, Balachandran SM, Ulaganathan K, Balakrishnan D, Praveen M, Prasad ASH, Fiyaz RA, Senguttuvel P, Sinha P, Kale RR, Rekha G, Kousik MBVN, Harika G, Anila M, Punniakoti E, Dilip T, Hajira SK, Pranathi K, Das MA, Shaik M, Chaitra K, Rao PK, Gangurde SS, Pandey MK, Sundaram RM. Molecular mapping of QTLs for yield related traits in recombinant inbred line (RIL) population derived from the popular rice hybrid KRH-2 and their validation through SNP genotyping. Sci Rep. 2020; 10: 13695.\u003c/li\u003e\n \u003cli\u003eFan X, Cui F, Zhao C, Zhang W, Yang L, Zhao X, Han J, Su Q, Ji J, Zhao Z, Tong Y, Li J. QTLs for flag leaf size and their influence on yield-related traits in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L\u003cem\u003e.\u003c/em\u003e). Mol\u0026nbsp;Breed. 2015; 35:24.\u003c/li\u003e\n \u003cli\u003eJia Y, Xu N, Wu J, Wang C, Huang M, Li Y. Genome-wide association study, linkage mapping and transcriptomic analysis revealed candidate genes with the flag leaf traits associated with nitrogen use efficiency in wheat. BMC Genomics. 2025; 26: 833.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhao C, Bao Y, Wang X, Yu H, Ding A, Guan C, Cui J, Wu Y, Sun H, Li X, Zhai D, Li L, Wang H, Cui F. QTL for flag leaf size and their influence on yield-related traits in wheat. Euphytica.2018; 214: 209.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYang D, Liu Y, Cheng H, Chang L, Chen J, Chai S, Li M. Genetic dissection of flag leaf morphology in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) under diverse water regimes. BMC Genet. 2016; 17: 94.\u003c/li\u003e\n \u003cli\u003eZhang B, Ye W, Ren D, Tian P, Peng Y, Gao Y, Ruan B, Wang L, Zhang G, Guo L, Qian Q, Gao Z. Genetic analysis of flag leaf size and candidate genes determination of a major QTL for flag leaf width in rice. Rice (N Y). 2015.\u003c/li\u003e\n \u003cli\u003eChen M, Luo J, Shao G, Wei X, Tang S, Sheng Z, Song J, Hu P. Fine mapping of a major QTL for flag leaf width in rice, \u003cem\u003eqFLW4\u003c/em\u003e, which might be caused by alternative splicing of \u003cem\u003eNAL1\u003c/em\u003e. Plant Cell Rep. 2012; 31: 863-872.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eQi J, Qian Q, Bu Q, Li S, Chen Q, Sun J, Liang W, Zhou Y, Chu C, Li X, Ren F, Palme K, Zhao B, Chen J, Chen M, Li C. Mutation of the rice narrow leaf1 gene, which encodes a novel protein, affects vein patterning and polar auxin transport. Plant Physiol1. 2008; 47:1947-1959.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCho SH, Yoo SC, Zhang H, Pandeya D, Koh HJ, Hwang JY, Kim GT, Paek NC. The rice narrow leaf2 and narrow leaf3 loci encode WUSCHEL-related homeobox\u0026nbsp;3A (\u003cem\u003eOsWOX3A\u003c/em\u003e) and function in leaf, spikelet, tiller and lateral root development. New Phytol. 2013; 198:1071-1084.\u003c/li\u003e\n \u003cli\u003eIshiwata A, Ozawa M, Nagasaki H, Kato M, Noda Y, Yamaguchi T, Nosaka M, Shimizu-Sato S, Nagasaki A, Maekawa M, Hirano HY, Sato Y. Two WUSCHEL-related homeobox genes, narrow leaf 2 and narrow leaf 3, control leaf width in rice. Plant Cell Physiol. 2013; 54: 779-792.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHu J, Zhu L, Zeng D, Gao Z, Guo L, Fang Y, Zhang G, Dong G, Yan M, Liu J, Qian Q. Identification and characterization of narrow and rolled leaf 1, a novel gene regulating leaf morphology and plant architecture in rice. Plant Mol Biol. 2010; 73: 283-292.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYan X, Wang S, Yang B, Zhang W, Cao Y, Shi Y, Sun D, Jing R. QTL mapping for flag leaf-related traits and genetic effect of \u003cem\u003eQFLW-6A\u003c/em\u003e on flag leaf width using two related introgression line populations in wheat. PLoS ONE. 2020; 15: e0229912.\u003c/li\u003e\n \u003cli\u003eQiao L, Zheng X, Zhao J, Wu B, Hao Y, Li X, Helal MMU, Zheng J. Genetic dissection of flag leaf morphology traits and fine mapping of a novel QTL (\u003cem\u003eQflw.sxau-6BL\u003c/em\u003e) in bread wheat (\u003cem\u003eTriticum aestivum\u0026nbsp;\u003c/em\u003eL\u003cem\u003e.\u003c/em\u003e). Theor Appl Genet. 2025; 138: 21.\u003c/li\u003e\n \u003cli\u003eCui F, Fan X, Zhao C, Chen M, Ji J, Zhang W, Li J. A novel genetic map of wheat: utility for mapping QTL for yield under different nitrogen treatments. BMC Gene. 2014; 15:57.\u003c/li\u003e\n \u003cli\u003eCui F, Zhao C, Li J, Ding A, Li X, Bao Y, Li J, Ji J, Wang H. Kernel weight per spike: What contributes to it at the individual QTL level Mol Breed. 2013; 31: 265-278.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang N, Fan X, Cui F, Zhao C, Zhang W, Zhao X, Yang L, Pan R, Chen M, Han J, Ji J, Liu D, Zhao Z, Tong Y, Zhang A, Wang T, Li J. Characterization of the temporal and spatial expression of wheat (\u003cem\u003eTriticum aestivum\u0026nbsp;\u003c/em\u003eL\u003cem\u003e.\u003c/em\u003e) plant height at the QTL level and their influence on yield related traits. Theor Appl Genet. 2017; 130: 1235-1252.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFan X, Cui F, Ji J, Zhang W, Zhang X, Liu J, Meng D, Tong Y, Wang Y, Wang T, Li J. Dissection of pleiotropic QTL regions controlling wheat spike characteristics under different nitrogen treatments using traditional and conditional QTL mapping. Front Plant Sci. 2019; 10:187.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eChen G, Zhu Z, Zhang F, Zhu J. Quantitative genetic analysis station for the genetic analysis of complex traits. Chin Sci Bull. 2012; 57:2721-2726.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Stacey J, Isaac PG. Isolation of DNA from plants. Methods Mol Biol. 1994; 28:9-15.\u003c/li\u003e\n \u003cli\u003eCui F, Zhang N, Fan X, Zhang W, Zhao C, Yang L, Pan R, Chen M, Han J, Zhao X, Ji J, Tong Y, Zhang H, Jia J, Zhao G, Li J. Utilization of a Wheat660K SNP array-derived high-density genetic map for high-resolution mapping of a major QTL for kernel number. Sci Rep. 2017; 7: 3788.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMa J, Tu Y, Zhu J, Luo W, Liu H, Li C, Li S, Liu J, Ding P, Habib A, Mu Y, Tang H, Liu Y, Jiang Q, Chen G, Wang J, Li W, Pu Z, Zheng Y, Wei Y, Kang H, Chen G, Lan X. Flag leaf size and posture of bread wheat: genetic dissection, QTL validation and their relationships with yield-related traits. Theor Appl Genet. 2020; 133: 297-315.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eChen L, Xu Z, Fan X, Zhou Q, Yu Q, Liu X, Liao S, Jiang C, Lin D, Ma F, Feng B, Wang T. Genetic dissection of quantitative trait loci for flag leaf size in bread wheat (\u003cem\u003eTriticum aestivum L.\u003c/em\u003e). Front Plant Sci. 2022; 13: 1047899.\u003c/li\u003e\n \u003cli\u003eJin J, Liu D, Qi Y, Ma J, Zhen W. Major QTL for Seven Yield-Related Traits in Common Wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.). Front Genet. 2020; 11: 1012.\u003c/li\u003e\n \u003cli\u003eTu Y, Liu H, Liu J, Tang H, Mu Y, Deng M, Jiang Q, Liu Y, Chen G, Wang J, Qi P, Pu Z, Chen G, Peng Y, Jiang Y, Xu Q, Kang H, Lan X, Wei Y, Zheng Y, Ma J. QTL mapping and validation of bread wheat flag leaf morphology across multiple environments in different genetic backgrounds. Theor Appl Genet. 2021; 134: 261-278\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWu Q, Chen Y, Fu L, Zhou S, Chen J, Zhao X, et al. QTL mapping of flag leaf traits in common wheat using an integrated high-density SSR and SNP genetic linkage map. Euphytica.2016; 208, 337-351.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang Y, Qiao L, Yang C, Li X, Zhao J, Wu B, Zheng X, Li P, Zheng J. Identification of genetic loci for flag-leaf-related traits in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L\u003cem\u003e.\u003c/em\u003e) and their effects on grain yield. Front Plant Sci.2022; 13: 990287.\u003c/li\u003e\n \u003cli\u003eWang J, Liu H, Zhao C, Tang H, Mu Y, Xu Q, Deng M, Jiang Q, Chen G, Qi P, Wang J, Jiang Y, Chen S, Wei Y, Zheng Y, Lan X, Ma J. Mapping and validation of major and stable QTL for flag leaf size from tetraploid wheat. Plant Genome.2022; 15: e20252.\u003c/li\u003e\n \u003cli\u003eLiu K, Xu H, Liu G, Guan P, Zhou X, Peng H, Yao Y, Ni Z, Sun Q, Du J. QTL mapping of flag leaf-related traits in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L\u003cem\u003e.\u003c/em\u003e). Theor Appl Genet. 2018; 131: 839-849.\u003c/li\u003e\n \u003cli\u003eZhang X, Xing P, Lin C, Wang H, Bao Y, Li X. QTL mapping for the flag leaf-related traits using RILs derived from Trititrigia germplasm line SN304 and wheat cultivar Yannong15 in multiple environments. BMC Plant Biol. 2024; 24: 297.\u003c/li\u003e\n \u003cli\u003eWen S, Zhang M, Tu K, Fan C, Tian S, Bi C, Chen Z, Zhao H, Wei C, Shi X, Yu J, Sun Q, You M. A major quantitative trait loci cluster controlling three components of yield and plant height identified on chromosome 4B of common wheat. Front Plant Sci. 2022; 12: 799520.\u003c/li\u003e\n \u003cli\u003eLi J, Wen S, Fan C, Zhang M, Tian S, Kang W, Zhao W, Bi C, Wang Q, Lu S, Guo W, Ni Z, Xie C, Sun Q, You M. Characterization of a major quantitative trait locus on the short arm of chromosome 4B for spike number per unit area in common wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L\u003cem\u003e.\u003c/em\u003e). Theor Appl Genet. 2020; 133: 2259-2269.\u003c/li\u003e\n \u003cli\u003eXu D, Wen W, Fu L, Li F, Li J, Xie L, Xia X, Ni Z, He Z, Cao S. Genetic dissection of a major QTL for kernel weight spanning the \u003cem\u003eRht-B1\u003c/em\u003e locus in bread wheat. Theor Appl Genet. 2019; 132: 3191-3200.\u003c/li\u003e\n \u003cli\u003eChen Z, Cheng X, Chai L, Wang Z, Bian R, Li J, Zhao A, Xin M, Guo W, Hu Z, Peng H, Yao Y, Sun Q, Ni Z. Dissection of genetic factors underlying grain size and fine mapping of \u003cem\u003eQTgw.cau-7D\u003c/em\u003e in common wheat (\u003cem\u003eTriticum aestivum\u0026nbsp;\u003c/em\u003eL.). Theor Appl Genet. 2020; 133: 149-162.\u003c/li\u003e\n \u003cli\u003eLiu G, Jia L, Lu L, Qin D, Zhang J, Guan P, Ni Z, Yao Y, Sun Q, Peng H. Mapping QTLs of yield-related traits using RIL population derived from common wheat and Tibetan semi-wild wheat. Theor Appl Genet. 2014; 127: 2415-32.\u003c/li\u003e\n \u003cli\u003eSchierenbeck M, Alqudah AM, Thabet SG, Avogadro EG, Dietz JI, Sim\u0026oacute;n MR, B\u0026ouml;rner A. Natural allelic variation confers diversity in the regulation of flag leaf traits in wheat. Sci Rep. 2024; 14: 13316.\u003c/li\u003e\n \u003cli\u003eWang P, Zhou G, Cui K, Li Z, Yu S. Clustered QTL for source leaf size and yield traits in rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.). Mol Breed. 2012; 29: 99-113.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang P, Zhou G, Yu H, Yu S. Fine mapping a major QTL for flag leaf size and yield-related traits in rice. Theor Appl Genet. 2011; 123: 1319-1330.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu B, Sun M, Wang K, Bian Y, Che Y, Liu J, Luo X, Liu S, Xie L, Li L, Qu K, Chao Y, Che R, Ye X, Xia X, Mao L, He Z, Li A, Cao S. Wheat domestication gene Q interplays with TaARF12 to antagonistically modulate plant architecture by integrating multiple hormone homeostasis. New Phytol. 2025; 24: 741-757.\u003c/li\u003e\n \u003cli\u003eHuang Y, Kong Z, Wu X, Cheng R, Yu D, Ma Z. Characterization of three wheat grain weight QTLs that differentially affect kernel dimensions. Theor Appl Genet. 2015;128: 2437-45.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wheat (Triticum aestivum L.), Flag leaf width, Superior haplotype, Molecular markers, Genetic effect analysis, Breeding selection effect","lastPublishedDoi":"10.21203/rs.3.rs-7921960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7921960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eFlag leaf width (FLW) is an important controller of flag leaf size in wheat (\u003cem\u003eTriticum aestivum \u003c/em\u003eL.) and is closely related to yield-related traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e In this study, wheat Kenong 9204 (KN9204) × Jing 411 recombinant inbred mapping populations (KJ-RILs) were used as materials in a quantitative trait locus (QTL) analysis, and a major and stable QTL for FLW, \u003cem\u003eqFlw-4B\u003c/em\u003e, was detected in multiple environments on chromosome 4B. KJ-RILs and a natural mapping population consisting of 314 breeding varieties/advanced lines were also utilized to further investigate the genetic and selection effects of \u003cem\u003eqFlw-4B\u003c/em\u003e in wheat breeding. Compared with the Jing 411 haplotype (\u003cem\u003eHap-J411\u003c/em\u003e), the KN9204 haplotype of the \u003cem\u003eqFlw-4B \u003c/em\u003eregion, \u003cem\u003eHap-KN9204\u003c/em\u003e, significantly increased FLW as well as improved yield-related traits, such as spikelet number per spike, kernel number per spike, and spike number per plant in both KJ-RILs and the natural populations. The selection effect revealed that the superior haplotype of \u003cem\u003eqFlw-4B \u003c/em\u003ehad a relatively high selection intensity in both domestically and internationally bred varieties, and its selection utilization rate gradually increased. In addition, the InDel marker \u003cem\u003e4BFLW-290\u003c/em\u003e targeting \u003cem\u003eqFlw-4B\u003c/em\u003ewas developed. This study was an important reference for the utilization of \u003cem\u003eqFlw-4B\u003c/em\u003e in wheat molecular breeding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e A major stable QTL for FLW was identified in wheat, and its genetic effects on yield related-traits, as well as its potential use value in molecular breeding programs, were characterized. In addition, an InDel marker closely linked to the stable major QTL was developed. This study enhanced the understanding of the potential genetic mechanisms underlying wheat FLW and provided crucial information for the future genetic improvement and molecular breeding of wheat varieties.\u003c/p\u003e","manuscriptTitle":"Characterization of a stable QTL for flag leaf width and its genetic effects on yield-related traits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 11:29:45","doi":"10.21203/rs.3.rs-7921960/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"45819069252499217947503997515586653831","date":"2025-11-20T07:58:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T09:58:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T07:07:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297763002554090824102571966352630051615","date":"2025-11-14T05:09:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151673175062771283956164892183067021519","date":"2025-11-05T06:16:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T17:01:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-02T10:46:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-02T10:43:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-01T12:31:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-11-01T12:25:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2326e17b-8e38-40b8-b07e-b587eff0b982","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-14T11:29:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 11:29:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7921960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7921960","identity":"rs-7921960","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}