Fine mapping of QGPC.caas-7AL for grain protein content in bread wheat

Fine mapping of QGPC.caas-7AL for grain protein content in bread wheat | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fine mapping of QGPC.caas-7AL for grain protein content in bread wheat Dehui Zhao, Jianqi Zeng, Hui Jin, Dan Liu, Li Yang, Xianchun Xia, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4242047/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2024 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Wheat grain protein content (GPC) is important for end-use quality. Identification of genetic loci for GPC is helpful to create new varieties with good processing quality and nutrients. Zhongmai 578 (ZM578) and Jimai 22 (JM22) are two elite wheat varieties with different contents of GPC. In the present study, 262 recombinant inbred lines (RILs) derived from a cross between ZM578 and JM22 were used to map the GPC with high-density wheat Illumina iSelect 50K single-nucleotide polymorphism (SNP) array. Seven quantitative trait loci (QTL) were identified for GPC on chromosomes 3AS, 3AL 3BS, 4AL, 5BS, 5DL and 7AL by inclusive composite interval mapping, designated as QGPC.caas-3AS , QGPC.caas-3AL , QGPC.caas-3BS , QGPC.caas-4AL , QGPC.caas-5BS , QGPC.caas-5DL and QGPC.caas-7AL , respectively. Among these, alleles for increasing GPC at QGPC.caas-3AS , QGPC.caas-3BS , QGPC.caas-4AL and QGPC.caas-7AL loci were contributed by ZM578, whereas those at the other three loci were from JM22. The stable QTL QGPC.caas-7AL was fine mapped to a 1.82 Mb physical interval using secondary populations from six heterozygous recombinant plants obtained by selfing a residual RIL. Four genes were predicted as candidates of QGPC.caas-7AL based on sequence polymorphism and expression patterns. The near-isogenic lines (NILs) with the favorable allele at the QGPC.caas-7AL locus increased farinograph stability time, extension area, extensibility and maximum resistance by 19.6%, 6.3%, 6.0% and 20.3%, respectively. Kompetitive allele-specific PCR (KASP) marker for QGPC.caas-7AL was developed and validated in a diverse panel of 166 Chinese wheat cultivars. These results provide further insight into the genetic basis of GPC, and the fine-mapped QGPC.caas-7AL will be an attractive target for map-based cloning and marker-assisted selection in wheat breeding programs. Figures Figure 1 Figure 2 Figure 3 Figure 4 Key message We identified a stable major QTL QGPC.caas-7AL for grain protein content using genome-wide linkage mapping, narrowed it down into a 1.82 Mb physical interval and further predicted its candidate genes. Introduction Bread wheat ( Triticum aestivum L.) is one of the most important food crops worldwide, providing approximately 20% of the calories and 25% of the protein for humans (FAO, http://www.fao.org/faostat/en/). The end-use quality is heavily influenced by grain protein content (GPC) in wheat (Payne, 1987). The demand for high-quality wheat as a source of protein is expected to increase significantly in the near future. Thus, improving quality potential is a major breeding objective. Identification and mining of loci for GPC can provide genetic resources and tools to improve grain quality. GPC is a typical quantitative trait conditioned by multi-genes and environmental factors (Balyan et al. 2013), and the quantitative trait loci (QTL) for GPC have been reported on all wheat chromosomes (Groos et al. 2003; Kunert et al. 2007; Ma et al. 2012; Quraishi et al. 2017; Fatiukha et al. 2020; Jiang et al. 2021). Echeverry-Solarte et al. (2015) identified 11 GPC QTL on nine chromosomes in 163 RILs derived from two widely divergent parents, and QGPC.ndsu.1A.1 and QGPC.ndsu.6B.1 showed major effects, with 16.5% and 16.9% of the phenotypic variances explained (PVE). Zou et al. (2017) identified two QTL associated with GPC in 167 RILs derived from ‘Attila’ and ‘CDC Go’, and QGpc.dms-2D and QGpc.dms-4B explained 13.4% and 6.3% of the phenotypic variances, respectively. Fatiukha et al. (2020) mapped 12 GPC QTL on ten chromosomes using 208 RILs derived from an elite durum cultivar Svevo and a wild emmer wheat accession Y12-3, and four major stable QTL were detected on chromosomes 4BS, 5AS, 6BS and 7BL, respectively. The QTL on chromosome 6BS had similar physical position as Gpc-B1 (Uauy et al. 2006). Jiang et al. (2021) identified 17 GPC QTL on ten chromosomes in 282 RILs derived from the cross of Ningmai 9 and Yangmai 158, explaining 3.9–9.2% of the phenotypic variances. Lou et al. (2021) identified ten stable GPC QTL on six chromosomes in 486 wheat accessions under normal and late sowing conditions, and QLGPC.cau-1B detected under two growing conditions explained 3.5% and 4.2% of the phenotypic variances, respectively. A genome-wide association study (GWAS) of 189 wheat accessions identified 37 single-nucleotide polymorphisms (SNPs) significantly associated with GPC, some of which also exhibited a concurrent effect on gluten content (Suliman et al. 2021). Although many studies focused on the identification of GPC loci, the validation and breeding application were seldom reported such as Gpc-B1 and Homeobox domain-2 ( HB-2 ) (Safdar et al. 2023). The Yellow and Huai River Valleys Wheat Zone (YHRVWZ) is a major wheat-producing region in China with potentially good pan bread and/or noodle quality (He et al. 2002; 2004). Although the quality traits, including GPC, have received more attention in recent years, improving wheat quality is a major challenge in the future (He et al. 2018). In this study, Jimai 22 (JM22) is an elite wheat cultivar in the YHRVWZ with high yield and middle gluten strength, developed by the Crop Research Institute, Shandong Academy of Agricultural Sciences. Zhongmai 578 (ZM578) derived from Zhongmai 255/Jimai 22 is a variety with high yield and strong gluten strength, developed by the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences. The objectives of the present study were to: (1) identify loci controlling GPC in the ZM578/JM22 RIL population, (2) fine mapping QGPC.caas-7AL by secondary populations derived from a residual heterozygous RIL, and (3) develop and validate KASP markers for breeding high-quality wheat varieties by marker-assisted selection (MAS). Materials and methods Plant materials Two hundred and sixty-two F 5 RILs from the ZM578/JM22 cross were used for QTL mapping. A diverse panel of 166 wheat cultivars (Li et al. 2019 ) was used to validate the effects of QTL. The line RIL267 with a residual heterozygous genotype within the marker interval of QGPC.caas-7AL was self-pollinated, generating six heterozygous recombinant plants (named as RL1 to RL6); 55 homozygous plants from RIL267 with 29 having 7A + allele ( QGPC.caas-7AL favorable allele, e.g. ZM578 genotype) and 26 having 7A − allele ( GPC.caas-7AL unfavorable allele, e.g. JM22 genotype) were used for Farinograph and Extensograph tests by genotypes, respectively, for a preliminary evaluation of the phenotypic effects of QGPC.caas-7AL on major dough rheological quality parameters. The ZM578/JM22 RILs and parents were sown in four environments including Luoyang, Shangqiu, Xinxiang in Henan province and Gaoyi in Hebei province during the 2020–2021 cropping season. The field trials were conducted in randomized complete blocks with three replications. Each line was grown in a one-row plot in 1-m length spacing 25 cm between rows with 30 seeds per row. The 166 wheat cultivars were sown in three 1.5-m rows spacing 20 cm apart in 50 seeds per row, in randomized complete blocks with three replications at Suixi in Anhui province and Anyang in Henan province during 2012–2013 and 2013–2014 cropping seasons. All the field trials were managed according to local practices. The progeny from RIL267 and RL1 to RL6 were sown in 3.0-m rows spacing 25 cm apart with 30 seeds per row at Xinxiang, Henan province during 2021–2022 and 2022–2023 cropping seasons. Phenotyping of GPC and quality characteristics Mature grains were harvested for GPC testing. The GPC was measured using a Perten DA7200 device (Perten Instruments AB, Kungens Kurva, Sweden) according to the AACC method 39–10. The grain samples of two homozygous genotypes were milled into flour using a Buhler experimental mill. Hard wheat sample was tempered to around 16% moisture content. Farinograph and Extensograph values were determined using AACC methods 56-81B and 54 − 21, respectively. Molecular genotyping Genomic DNA was extracted from young leaves using the CTAB method (Porebski et al. 1997 ). The RILs and parents were genotyped using the wheat Illumina assay 50K SNP array containing 55,224 SNPs developed in collaboration by CAAS and the Capital-Bio, Beijing, China ( https://www.capitalbiotech.com/ ). Map construction and QTL analysis For genetic map construction, the monomorphic markers between parents and the markers with high missing values (more than 20.0%) or minor allele frequency (MAF) less than 0.3 were removed at first, and the remaining 9,661 high-quality polymorphic SNP markers were used for subsequent analysis. The BIN function in IciMapping v4.1 was used to remove redundant markers (Meng et al. 2015 ). Linkage analysis of the 1,501 non-redundant markers was performed with JoinMap v4.0 using the regression mapping algorithm. The high-density genetic linkage map was described in a previous study (Liu et al. 2023 ). QTL analysis was conducted by inclusive composite interval mapping (ICIM) using IciMapping 4.1 (Meng et al. 2015 ). The walking step for QTL detection was set as 0.1 cM, and the threshold of LOD scores was set as 2.5. Physical positions of mapped SNPs were obtained by blasting the flanking sequences of SNPs against the reference genome sequence of Chinese Spring (IWGSC v1.1, https://urgi.versailles.inra.fr/blast_iwgsc/ ). Conversion of SNPs to KASP markers Array-based SNP markers closely linked to QGPC.caas-3BS and QGPC.caas-7AL were converted into KASP markers. Allele-specific and common reverse primers for each KASP marker were designed using PolyMarker (Ramirez-Gonzalez et al. 2015 , http://www.polymarker.info/ ). KASP assays were performed in a 4 µl reaction volume containing 2 µl 2 × KASP Master Mix, 0.045 µl KASP primer mix and 2 µl genomic DNA at 30 ng/µl (Chandra et al. 2017 ). PCR products were detected with a PHERAstarplus SNP genotyping instrument (LGC Science Shanghai Ltd., China), using single excitation light and double emission light for fluorescence detection. Gel free fluorescence signal scanning and allele separation were automatically analyzed by the KlusterCaller™ 2.24.0.11 (LGC, Hoddesdon, UK). Statistical analysis Analysis of variance (ANOVA), phenotypic correlation coefficients, and Student’s t -tests were conducted with SAS 9.2 (SAS Institute Inc, Cary, NC). PROC MIXED was used in ANOVA to evaluate the contributions of genotype (lines) and environment, where genotype, environment, genotype × environment interaction, and replicate nested in environments were all considered as random effects. In parallel, a model considering genotype as fixed effect was fitted for estimating the best linear unbiased estimate (BLUE) of genotype across environments. Adjusted means of each genotype for GPC in individual and across environments were separately computed with PROC MIXED. Broad-sense heritability ( H b 2 ) was calculated using the formula: H b 2 = σ g 2 /( σ g 2 + σ ge 2 / r + σ ε 2 / re ), where σ g 2 , σ ge 2 and σ ε 2 are estimates of genotype, genotype × environment interaction and residual error variances, respectively, and e and r are the number of environments and replicate in environment, respectively. Cultivars with homozygous genotype from the validation panel were used to verify QTL effects. The difference of GPC between two classes of homozygous genotypes (ZM578and JM22 genotypes) were calculated by PROC MIXED, treating genotype as fixed effect, with lines nested in genotype, and environment, their related interaction and replicates nested in environments as random. For the statistical analyses in progeny tests, phenotypic difference of GPC between two homozygous genotypes were determined by Student’s t -tests. Results Phenotypic evaluation The mean values on an across-environment GPC were 15.3% and 13.6% for ZM578 and JM22, respectively (Table S1 ). ZM578 showed a higher GPC than JM22 in all environments. The GPC in 262 RILs ranged from 11.8–17.7% in Luoyang, 12.2–19.0% in Shangqiu, 12.1–18.3% in Xinxiang, and 12.5–18.5% in Gaoyi (Table S1 ). Continuous distribution and transgressive segregation were observed for GPC, indicative of polygenic inheritance (Table S1 , Fig. S1 ). Pearson’s correlation coefficients for GPC of the mapping population ranged from 0.66 to 0.88 among four environments ( P < 0.001) (Table S2 ). ANOVA showed that genotype, environment, and genotype × environment interaction effects were all significant, with genotype contributing the largest effect. A high H b 2 (0.92) for GPC was obtained across four environments (Table S3). Linkage map construction Totally, 9,354 high-quality SNPs in the wheat Illumina iSelect 50K SNP array were used for linkage analysis and map construction. A high-density linkage map spanning 2413.84 cM on all wheat chromosomes except 6B was constructed using 1507 representative bin markers. The average chromosome length was 114.94 cM, ranging from 10.77 cM (1B) to 227.24 cM (5D). The details for the linkage map were shown in Liu et al. ( 2023 ). QTL mapping of GPC Both the GPC values in four environments and the BLUE value were used for QTL mapping. Seven QTL for GPC were detected in multiple environments on chromosomes 3AS, 3AL 3BS, 4AL, 5BS, 5DL and 7AL, designated QGPC.caas-3AS , QGPC.caas-3AL , QGPC.caas-3BS , QGPC.caas-4AL , QGPC.caas-5BS , QGPC.caas-5DL and QGPC.caas-7AL , respectively (Table 1 , Fig. 1 ). Of these, the increasing GPC alleles (favorable alleles) at QGPC.caas-3AS , QGPC.caas-3BS , QGPC.caas-4AL and QGPC.caas-7AL were contributed by ZM578, whereas the other three QTL were from JM22. There was a linear relationship between phenotypes and the number of favorable alleles (Fig. 2 ), with the addition of favorable alleles additively contributing to enhanced GPC. The GPC of the genotype with six favorable alleles was 2.3% higher than that of the genotype with one favorable allele. Table 1 QTL for grain protein content detected by inclusive composite interval mapping (ICIM) in the Zhongmai 578/Jimai 22 RIL population QTL Environment Genetic position (cM) Physical interval (Mb) α Flanking marker LOD PVE (%) β ADD γ QGPC.caas-3AS E2/E3/E4/BLUE δ 5.75–6.25 67.77–68.30 AX-111570945 ~ AX-86175555 4.78–14.40 6.8–10.2 0.28 ~ 0.34 QGPC.caas-3AL E1/E2/E3/BLUE 53.25–66.25 650.42–686.69 AX-95659056 ~ AX-109344781 5.21–5.88 3.6–6.6 -0.23 ~ -0.20 QGPC.caas-3BS E3/E4/BLUE 31.75–32.75 20.72–21.17 AX-95233993 ~ AX-110403140 3.40–3.82 2.6–5.0 0.16 ~ 0.25 QGPC.caas-4AL E2/E3/BLUE 0–0.75 442.91–467.26 AX-111535294 ~ AX-108927364 2.50–3.59 3.8–6.1 0.18 ~ 0.27 QGPC.caas-5BS E2/E3/E4/BLUE 0–2.25 46.71–49.57 AX-109852325 ~ AX-110621719 3.85–10.85 5.4–7.4 -0.29 ~ -0.23 QGPC.caas-5DL E1/E2/E3/BLUE 2.75–22 324.33–369.20 AX-111624154 ~ AX-110867187 3.44–9.71 3.9–6.7 -0.28 ~ -0.18 QGPC.caas-7AL E1/E2/E3/BLUE 155.75–166.25 670.33–675.50 AX-110942203 ~ AX-109534708 5.06–9.63 4.7–10.2 0.20 ~ 0.29 α Physical positions (Mb) were obtained by blasting SNP flanking sequences against the reference genome sequence of Chinese Spring (IWGSC v1.1, https://urgi.versailles.inra.fr/blast_iwgsc/ ). β Phenotypic variance explained by the QTL. γ Estimated additive effect of the QTL; positive values indicate that favorable alleles came from Zhongmai 578, whereas negative values indicate that favorable alleles were derived from Jimai22. δ E1 to E4, and BLUE indicate the growing season of 2020–2021 at Luoyang, Shangqiu, Xinxiang and Gaoyi, and best linear unbiased estimate across four environments, respectively. Validation of QTL A diverse panel of 166 cultivars was used to validate the effects of the QGPC.caas-3BS and QGPC.caas-7AL (Fig. 3 , Tables S4 and S5). The TT allele (ZM578 genotype) had a significantly ( P < 0.05) higher GPC than the CC allele (JM22 genotype) for QGPC.caas-3BS in four environments and BLUE, with GPC differences between two genotypes ranging from 0.4–0.5%. Likewise, the GG allele (ZM578 genotype) had significantly ( P < 0.05) higher GPC than the AA allele (JM22 genotype) at the QGPC.caas-7AL locus in three environments and BLUE, with GPC differences from 0.3–0.5% between two genotypes. Effect of favorable GPC alleles on thousand-kernel weight (TKW) The increasing GPC allele at QGPC.caas-5DL was contributed by JM22, whereas that at QGPC.caas-7AL was from ZM578. The favorable alleles for GPC at these two loci also had significant and positive effects on TKW (Liu et al. 2023 ), indicating that both QTL regions were not only for GPC, but also associated with TKW. These QTL regions can be simultaneously used to improve wheat yield and quality for developing wheat cultivars with high yield and good quality by MAS. Fine mapping of QGPC.caas-7AL RIL267 with a heterozygous genotype in the mapping interval of QGPC.caas-7AL was successfully screened from the RIL population (Fig. 4 b). A significant difference in GPC was detected between the 7A+ (29 plants) and 7A− (26 plants) genotypes from self-pollinated progenies of RIL267 (Table S6). Six plants with recombination types were identified using KASP markers (Fig. 4 c). Subsequently, these six recombinants (designated as RL1 to RL6) were planted to generate secondary mapping populations (Fig. 4 c). Within each secondary population, homozygous non-recombinant lines, viz. 7A + NILs and 7A − NILs, were genotyped with molecular markers and phenotypically evaluated. Significant differences in GPC were detected between 7A + and 7A − NILs within RL1, RL5 and RL6 ( P < 0.05), whereas non-significant effects for GPC were observed within populations from RL2, RL3 and RL4 (Fig. 4 c). Based on these results, QGPC.caas-7AL was mapped to a 1.82 Mb interval flanked by Kasp-7A1 and Kasp-7A3 (Fig. 4 c). To further analyze the genetic effect of QGPC.caas-7AL on quality characteristics, the Farinograph and Extensograph between 7A + and 7A − homozygous lines were investigated by mixing 7A+ (29 plants) and 7A− (26 plants) genotypes, respectively. Compared with 7A − homozygous lines, the 7A + homozygous lines increased farinograph stability time, extension area, extensibility and maximum resistance by 19.6%, 26.3%, 6.0% and 20.3%, respectively (Table S6). These demonstrated that the 7A + allele had a positive effect on processing quality. Discussion The quality improvement is considered one of the top priorities in wheat breeding. While breeders focus on improvement of grain yield, especially in the later cycles of breeding programs, they also pay high attention to the baking and milling quality of wheat flour. Alongside the consideration of biotic and abiotic stress resistance, both grain yield and quality parameters form the foundation for the success of an experimental line in the wheat market. Many quality traits are significantly influenced by environmental factors, crop management practices, and soil fertility. Nevertheless, a large variation among different wheat lines indicates the presence of underlying genetic factors controlling the quality traits, and GPC is one of the most important parameters influencing wheat end-use quality (Payne, 1987 ). Harnessing this genetic variation to enhance end-use quality is an important approach that aligns with sustainable agricultural practices. Thus, we conducted a genome-wide linkage mapping for GPC in the ZM578/JM22 RILs and developed available KASP markers for wheat breeding. Comparisons with previous reports GPC QTL were reported previously on all wheat chromosomes. In this study, we detected seven GPC QTL on chromosomes 3AS, 3AL, 3BS, 4AL, 5BS, 5DL and 7AL. Of these, QGPC.caas-3AS was located in a 0.5 cM genetic region (67.77–68.30 Mb), flanked by AX-111570945 and AX-86175555 . Markers AX-95659056 and AX-109344781 flanking QGPC.caas-3AL were located in 650.42 and 686.69 Mb, respectively. Zhang et al. ( 2011 ) identified a GPC locus in 90.07–425.22 Mb. QGpc.caas-3A was linked with marker wms155 at 702.96 Mb (Li et al. 2012 ). Jiang et al. ( 2021 ) identified two QTL for GPC in 624.54–686.72 Mb and 697.25–700.95 Mb. Lou et al ( 2021 ) mapped three GPC QTL at 191.53 Mb, 369.40 Mb and 484.64 Mb on chromosome 3A. These results suggest that QGPC.caas-3AS may be a new locus, but QGPC.caas-3AL was mapped in a similar location to Qgpc-3A (Jiang et al. 2021 ). QGPC.caas-3BS was located in a 1.0 cM interval that corresponds to 20.72–21.17 Mb in 3BS. QProtein.caas-3B linked with Xwmc3 and Xbarc68.1 at 398.38 Mb to 586.52 Mb in the PH82-2/Neixiang 188 RIL population (Zhang et al. 2011 ). Jiang et al. ( 2021 ) detected three QTL at 24.94–101.31 Mb, 40.25–49.86 Mb and 146.89–153.61 Mb on 3BS in Ningmai 9/Yangmai 158 RIL population. These indicate that QGPC.caas-3BS is likely to be new. QGPC.caas-4AL , flanked by AX-111535294 and AX-108927364 , was located at 442.91–467.26 Mb. Wang et al. ( 2012 ) detected a QTL for GPC close to Xwmc516 at 12.20 Mb on 4A. Liu et al. ( 2017 ) identified a locus for GPC in 621.70–673.41 Mb on 4A. Thus, QGPC.caas-4AL may be a new locus. The GPC QTL on chromosome 5BS in this study was located in a 2.25 cM interval that corresponds to 46.71–49.57 Mb. Previously, Zou et al. ( 2017 ) identified a QTL for GPC in 678.52–691.13 Mb on chromosome 5B. Jiang et al. ( 2021 ) reported three QTL ( Qgpc-5B.1 , Qgpc-5B.2 and Qgpc-5B.3 ) for GPC in 4.86–8.92 Mb, 545.04–546.83 Mb and 682.70–697.61 Mb, respectively. Therefore, QGPC.caas-5BS also is likely to be a new locus. The QGPC.caas-5DL located in the interval of 324.33 to 369.20 Mb. QProtein.caas-5D for GPC was mapped in 5.60–5.82 Mb on chromosome 5DS by Zhang et al. ( 2011 ). Li et al. ( 2012 ) detected a QTL for GPC linked to wms272 at 562.34 Mb. Their positions are different from QGPC.caas-5DL. Thus, QGPC.caas-5DL is likely a new GPC QTL. QGPC.caas-7AL was located in 670.33–672.15 Mb. Liu et al. ( 2017 ) identified a SNP at 708.63 Mb, exhibiting the strongest association with GPC. Jiang et al. ( 2021 ) reported Qgpc-7A.1 (205.46–210.86 Mb) and Qgpc-7A.2 (670.78–679.84 Mb) for GPC, respectively. QGPC.caas-7AL was mapped in a similar location to Qgpc-7A.2 . Potential candidate genes for QGPC.caas-7AL We have narrowed down the QGPC.caas-7AL to a 1.82 Mb physical interval with 48 high-confidence annotated genes. Synteny analysis of genome sequences of Chinese Spring and other wheat varieties showed that the physical interval of QGPC.caas-7AL was highly conserved among different wheat genomes (Fig. S2 ). Previous studies have shown that protein accumulation during grain development is conferred by regulation at the transcriptional level and post-transcriptional level, which involves in some transcription factors (TFs), such as MYB TFs, AP2, and NAC TFs, as well as some important protein modifications, such as N-glycosylation, folding and assembly of proteins, and protein bodies. TraesCS7A02G476200 , TraesCS7A02G476300 , TraesCS7A02G477700 , TraesCS7A02G479100 and TraesCS7A02G479200 , annotated as bHLH transcription factor, transcription factor, encoding Myb/SANT-like DNA-binding domain protein, plant transcription factor family protein, and Thioredoxin, respectively, may be the potential candidate genes for QGPC.caas-7AL (Table S7). Furthermore, we analyzed the expression patterns of these five genes on the wheat expVIP expression platform ( http://www.wheat-expression.com/ ) and Wheat eFPBrowser ( http://bar.utoronto.ca/efp_wheat/cgi-bin/efpWeb.cgi ), and found that only TraesCS7A02G479200 was expressed in the developing grains. Based on the resequencing data of two parents, missense mutation was found in the promoter region of TraesCS7A02G476200 , TraesCS7A02G476300 and TraesCS7A02G479100 (Table S8). Thus, TraesCS7A02G476200 , TraesCS7A02G476300 , TraesCS7A02G479100 and TraesCS7A02G479200 were considered candidate genes for QGPC.caas-7AL . It is a challenge to functionally validate four genes through transgenic experiments, so we will continue to further narrow down the target interval and reduce the number of potential causal genes by physical mapping and deep sequencing. Furthermore, the 7A + allele at QGPC.caas-7AL had a positive effect on quality parameters. The KASP marker of QGPC.caas-7AL was validated to be significantly related with GPC in 166 cultivars. The favorable allele of QGPC.caas-7AL , which has pleiotropic effect on TKW, can be traced back to Sunstate (Liu et al. 2023 ). Therefore, QGPC.caas-7AL may be of great value in MAS breeding programs. Negative correlation between yield traits and GPC were reported previously (Yagdi et al. 2007 ; Blanco et al. 2012). In this study, QGPC.caas-5DL and QGPC.caas-7AL have pleiotropic effects on TKW (Liu et al. 2023 ), which are attributed to either a single gene with pleiotropic effects on both traits or two closely linked genes with separate effects. Thus, these QTL could be used in breeding for improvement of both GPC and grain related traits. Applications in wheat quality breeding Grain yield and quality improvement are important targets in wheat breeding programs. Simultaneous improvement of both grain yield and GPC is a challenging task due to a negative correlation between grain yield and GPC. To ensure food security, genetic improvement in grain yield was a principal target in wheat breeding programs during the last 50 years in China (Gao et al. 2017 ). In recent years, high GPC received more attention in China markets. There is a rising demand for flour with high GPC to produce high-quality steamed bread. In traditional wheat breeding strategy, a high GPC parent from other countries was used to improve local varieties, and quality testing is usually performed after the agronomic selection in field, leading to some lines with excellent quality being abandoned in early breeding generations. In addition, most of the quality traits, including GPC, are sensitive to environments and show significant differences among years or locations (He et al. 2004 ; Jiang et al. 2021 ). Therefore, the evaluation of GPC in single environments may be inaccurate. MAS is independent of the environment and has a great potential in wheat quality improvement. KASP marker is a high-throughput SNP genotyping platform that offers low cost, high throughput, and easy operability in MAS and fine mapping of genes. In this study, two parents of the RILs showed good and acceptable performance in GPC with 15.3% and 13.6% for ZM578 and JM22, respectively. However, some derived RILs pyramiding favorable GPC alleles from two parents showed high GPC, which is consistent with previous studies (Fatiukha et al. 2020 ; Jiang et al. 2021 ). Therefore, it is feasible to improve GPC by exploring and pyramiding the increasing GPC-alleles in local varieties. In this study, two KASP markers for QGPC.caas-3BS and QGPC.caas-7AL , respectively, were successfully developed and validated in a diverse panel of 166 cultivars. Cultivars with the favorable allele at the QGPC.caas-3BS and QGPC.caas-7AL locus increased GPC by 0.4–0.5% and 0.3–0.5%, respectively. Thus, these KASP markers can be used for MAS in wheat breeding. Several accessions with favorable alleles at two loci and high GPC, such as Xiaoyan 54, Zhong 892, Shanmai 94, Norin 67, Shanyou 205, Nidera Baguette 20, Barra and Sunstate, and the high-GPC RILs in the population can be good parental lines for wheat breeding. Declarations Data availability The datasets generated during the current study are available from the corresponding author on reasonable request. Acknowledgements This work was funded by the Natural Science Foundation of China (32272182), the Core Research Budget of the Non-profit Governmental Research Institutions (S2022ZD04), the CAAS Agricultural Science and Technology Innovation Program (CAAS-ZDRW202002), the Doctoral Research Launch Fund of Henan University of Science and Technology (13480100), and the Luoyang Core Technology Research Public Welfare Program (2302034A). Author contribution statement DZ performed the experiment and wrote the paper. DZ, JZ, HJ, DL, YT, WZ and CW assisted in the field trials. DZ, JZ, HJ, DL and LY participated in the trait evaluation. XX, CS, ZH and JL provided extensive revision of the manuscript. YZ designed the experiment and wrote the paper. All authors read the final version of the manuscript and approved for publication. Conflicts of interest All authors declare that they have no conflicts of interest. 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Mol Breed 37:92 Echeverry-Solarte M, Kumai A, Kianian S, Simsek S, Alamri MS, Mantovani EE, McClean PE, Deckard EL, Elias E, Schatz B, Xu SS, Mergoum M (2015) New QTL alleles for quality-related traits in spring wheat revealed by RIL population derived from supernumerary × non-supernumerary spikelet genotypes. Theor Appl Genet 128:893-912 Fatiukha A, Filler N, Lupo I, Lidzbarsky G, Klymiuk V, Korol AB, Pozniak C, Fahima T, Krugman T (2020) Grain protein content and thousand kernel weight QTLs identified in a durum × wild emmer wheat mapping population tested in five environments. Theor Appl Genet 133:119-131 Gao FM, Ma DY, Yin GH, Rasheed A, Dong Y, Xiao YG, Wu XX, Xia XC, He ZH (2017) Genetic progress in grain yield and physiological traits in Chinese wheat cultivars of Southern Yellow and Huai valley winter wheat zone since 1950. 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Indian J Agri Sci 77:565-8 Zhang Y, Tang J, Zhang Y, Yan J, Xiao Y, Zhang Y, Xia X, He Z (2011) QTL mapping for quantities of protein fractions in bread wheat ( Triticum aestivum L.). Theor Appl Genet 122:971-987 Zou J, Semagn K, Iqbal M, Chen H, Asif M, N’Diaye A, Navabi A, Perez-Lara E, Pozniak C, Yang R-C, Randhawa H, Spaner D (2017) QTLs associated with agronomic traits in the Attila x CDC Go spring wheat population evaluated under conventional management. PLoS One 12:e0171528 Supplementary Files SupplementaryFigures.docx SupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 06 Nov, 2024 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Major revisions 05 Aug, 2024 Reviewers agreed at journal 11 May, 2024 Reviewers invited by journal 10 May, 2024 Editor assigned by journal 10 Apr, 2024 First submitted to journal 09 Apr, 2024 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. 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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-4242047","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301119150,"identity":"2be21f8d-635d-4636-bd6c-103440870a91","order_by":0,"name":"Dehui Zhao","email":"","orcid":"","institution":"College of Agriculture, Henan University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dehui","middleName":"","lastName":"Zhao","suffix":""},{"id":301119151,"identity":"03350e49-55f7-43e4-9bde-67ba44a3f404","order_by":1,"name":"Jianqi Zeng","email":"","orcid":"","institution":"Chinese 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12:57:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4242047/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4242047/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-024-04769-9","type":"published","date":"2024-11-06T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56843366,"identity":"544ee8f2-a936-4fd4-855b-e97d411b872c","added_by":"auto","created_at":"2024-05-21 07:26:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":556720,"visible":true,"origin":"","legend":"\u003cp\u003eLOD contours obtained by inclusive composite interval mapping of QTL for grain protein content in the Zhongmai 578/Jimai 22 RIL population. E1 to E4 and BLUE indicate the growing season of 2020–2021 at Luoyang, Shangqiu, Xinxiang and Gaoyi, and best linear unbiased estimate across four environments, respectively.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4242047/v1/c99c735fb0034fe6af521eea.png"},{"id":56843370,"identity":"b7e47b8d-dbcb-453a-9a5a-8485807d1dd4","added_by":"auto","created_at":"2024-05-21 07:26:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50695,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regressions between the number of favorable alleles and best linear unbiased estimate (BLUE) across four environments of grain protein content in the Zhongmai 578/Jimai 22 RIL population. Number of lines carrying the corresponding number of favorable alleles are shown in brackets. X and Y in the equations represent the number of favorable alleles and BLUE of grain protein content.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4242047/v1/1c260fe270ba9e36681c6391.png"},{"id":56843367,"identity":"e8566b4f-b8e5-4c33-8f66-778f284e1762","added_by":"auto","created_at":"2024-05-21 07:26:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46777,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of \u003cem\u003eQGPC.caas-3BS \u003c/em\u003eand\u003cem\u003e QGPC.caas-7AL\u003c/em\u003e in the diverse panel of 166 Chinese wheat varieties. E5 to E8 and BLUE indicate the growing season of 2012–2013 at Anyang and Suixi, the growing season of 2013–2014 at Anyang and Suixi, and best linear unbiased estimate across four environments, respectively. ns, * and ** indicate no significant, significant at \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 and \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, respectively (Student’s \u003cem\u003et\u003c/em\u003e-test).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4242047/v1/a06ae7d8cf16dc4be1b27a69.png"},{"id":56843365,"identity":"af86582c-a3e6-40b8-b52c-3aeb02dc3e23","added_by":"auto","created_at":"2024-05-21 07:26:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":129354,"visible":true,"origin":"","legend":"\u003cp\u003eFine mapping of the \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e locus. a) Genetic map of chromosome 7A showing QTL with red color, b) Residual heterozygous line (RIL267), c) the \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e map of 1.82 Mb interval on chromosome 7A. Left side are the six markers for screening recombinants (upside) and the graphical genotypes of six recombinants (downside). Arrows represent the 1.82 Mb interval of \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e mapped. Right side is the comparison of GPC between Zhongmai 578 (ZM578) and Jimai 22 (JM22) genotypes within each secondary population. The values of GPC were the mean value (mean ± SD) of the homozygous plants in 7A+ and 7A− NILs within RL1–RL6. n represent the number of homozygous plants. Blue, black and gray bars represent ZM578, heterozygous and JM22 genotypes, respectively. ns, * and ** indicate no significant, significant at \u003cem\u003eP\u003c/em\u003e \u0026lt;0.05 and \u003cem\u003eP \u003c/em\u003e\u0026lt;0.01, respectively (Student’s \u003cem\u003et\u003c/em\u003e-test).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4242047/v1/c65527887548135402d4883a.png"},{"id":68750101,"identity":"0f482331-e9d0-447e-be4a-ffd9e99c2db0","added_by":"auto","created_at":"2024-11-11 16:09:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1436130,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4242047/v1/0c5b4a76-ade3-47f1-9b0f-165e6636ffc3.pdf"},{"id":56843364,"identity":"1a489992-0322-4378-9c28-d769ed05de63","added_by":"auto","created_at":"2024-05-21 07:26:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":470798,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4242047/v1/80f7889bd3f5b5893b0e4014.docx"},{"id":56843369,"identity":"da44c6a2-76e8-4d21-8b4a-3001246cfb1e","added_by":"auto","created_at":"2024-05-21 07:26:06","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":43090,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4242047/v1/f3e2328ae23bb760cd20478e.xlsx"}],"financialInterests":"","formattedTitle":"Fine mapping of QGPC.caas-7AL for grain protein content in bread wheat","fulltext":[{"header":"Key message","content":"\u003cp\u003e\u003cstrong\u003eWe identified a stable major QTL \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e for grain protein content using genome-wide linkage mapping, narrowed it down into a 1.82 Mb physical interval and further predicted its candidate genes.\u003c/strong\u003e\u003c/p\u003e\n"},{"header":"Introduction","content":"\u003cp\u003eBread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is one of the most important food crops worldwide, providing approximately 20% of the calories and 25% of the protein for humans (FAO, http://www.fao.org/faostat/en/). The end-use quality is heavily influenced by grain protein content (GPC) in wheat (Payne, 1987). The demand for high-quality wheat as a source of protein is expected to increase significantly in the near future. Thus, improving quality potential is a major breeding objective. Identification and mining of loci for GPC can provide genetic resources and tools to improve grain quality.\u003c/p\u003e\n\u003cp\u003eGPC is a typical quantitative trait conditioned by multi-genes and environmental factors (Balyan et al. 2013), and the quantitative trait loci (QTL) for GPC have been reported on all wheat chromosomes (Groos et al. 2003; Kunert et al. 2007; Ma et al. 2012; Quraishi et al. 2017; Fatiukha et al. 2020; Jiang et al. 2021). Echeverry-Solarte et al. (2015) identified 11 GPC QTL on nine chromosomes in 163 RILs derived from two widely divergent parents, and \u003cem\u003eQGPC.ndsu.1A.1\u0026nbsp;\u003c/em\u003eand \u003cem\u003eQGPC.ndsu.6B.1\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eshowed major effects, with 16.5% and 16.9% of the phenotypic variances explained (PVE). Zou et al. (2017) identified two QTL associated with GPC in 167 RILs derived from \u0026lsquo;Attila\u0026rsquo; and \u0026lsquo;CDC Go\u0026rsquo;, and \u003cem\u003eQGpc.dms-2D\u003c/em\u003e and \u003cem\u003eQGpc.dms-4B\u003c/em\u003e explained 13.4% and 6.3% of the phenotypic variances, respectively. Fatiukha et al. (2020) mapped 12 GPC QTL on ten chromosomes using 208 RILs derived from an elite durum cultivar Svevo and a wild emmer wheat accession Y12-3, and four major stable QTL were detected on chromosomes 4BS, 5AS, 6BS and 7BL, respectively. The QTL on chromosome 6BS had similar physical position as \u003cem\u003eGpc-B1\u003c/em\u003e (Uauy et al. 2006). Jiang et al. (2021) identified 17 GPC QTL on ten chromosomes in 282 RILs derived from the cross of Ningmai 9 and Yangmai 158, explaining 3.9\u0026ndash;9.2% of the phenotypic variances. Lou et al. (2021) identified ten stable GPC QTL on six chromosomes in 486 wheat accessions under normal and late sowing conditions, and \u003cem\u003eQLGPC.cau-1B\u003c/em\u003e detected under two growing conditions explained 3.5% and 4.2% of the phenotypic variances, respectively. A genome-wide association study (GWAS) of 189 wheat accessions identified 37 single-nucleotide polymorphisms (SNPs) significantly associated with GPC, some of which also exhibited a concurrent effect on gluten content (Suliman et al. 2021). Although many studies focused on the identification of GPC loci, the validation and breeding application were seldom reported such as \u003cem\u003eGpc-B1\u0026nbsp;\u003c/em\u003eand \u003cem\u003eHomeobox domain-2\u003c/em\u003e (\u003cem\u003eHB-2\u003c/em\u003e) (Safdar et al. 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Yellow and Huai River Valleys Wheat Zone (YHRVWZ) is a major wheat-producing region in China with potentially good pan bread and/or noodle quality (He et al. 2002; 2004). Although the quality traits, including GPC, have received more attention in recent years, improving wheat quality is a major challenge in the future (He et al. 2018). In this study, Jimai 22 (JM22) is an elite wheat cultivar in the YHRVWZ with high yield and middle gluten strength, developed by the Crop Research Institute, Shandong Academy of Agricultural Sciences. Zhongmai 578 (ZM578) derived from Zhongmai 255/Jimai 22 is a variety with high yield and strong gluten strength, developed by the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences.\u0026nbsp;The objectives of the present study were to: (1) identify loci controlling GPC in the ZM578/JM22 RIL population, (2)\u0026nbsp;fine mapping \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e by secondary populations derived from a residual heterozygous RIL, and (3) develop and validate KASP markers for breeding high-quality wheat varieties by marker-assisted selection (MAS).\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003eTwo hundred and sixty-two F\u003csub\u003e5\u003c/sub\u003e RILs from the ZM578/JM22 cross were used for QTL mapping. A diverse panel of 166 wheat cultivars (Li et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was used to validate the effects of QTL. The line RIL267 with a residual heterozygous genotype within the marker interval of \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e was self-pollinated, generating six heterozygous recombinant plants (named as RL1 to RL6); 55 homozygous plants from RIL267 with 29 having 7A\u0026thinsp;+\u0026thinsp;allele (\u003cem\u003eQGPC.caas-7AL\u003c/em\u003e favorable allele, e.g. ZM578 genotype) and 26 having 7A\u0026thinsp;\u0026minus;\u0026thinsp;allele (\u003cem\u003eGPC.caas-7AL\u003c/em\u003e unfavorable allele, e.g. JM22 genotype) were used for Farinograph and Extensograph tests by genotypes, respectively, for a preliminary evaluation of the phenotypic effects of \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e on major dough rheological quality parameters.\u003c/p\u003e \u003cp\u003eThe ZM578/JM22 RILs and parents were sown in four environments including Luoyang, Shangqiu, Xinxiang in Henan province and Gaoyi in Hebei province during the 2020\u0026ndash;2021 cropping season. The field trials were conducted in randomized complete blocks with three replications. Each line was grown in a one-row plot in 1-m length spacing 25 cm between rows with 30 seeds per row. The 166 wheat cultivars were sown in three 1.5-m rows spacing 20 cm apart in 50 seeds per row, in randomized complete blocks with three replications at Suixi in Anhui province and Anyang in Henan province during 2012\u0026ndash;2013 and 2013\u0026ndash;2014 cropping seasons. All the field trials were managed according to local practices. The progeny from RIL267 and RL1 to RL6 were sown in 3.0-m rows spacing 25 cm apart with 30 seeds per row at Xinxiang, Henan province during 2021\u0026ndash;2022 and 2022\u0026ndash;2023 cropping seasons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhenotyping of GPC and quality characteristics\u003c/h2\u003e \u003cp\u003eMature grains were harvested for GPC testing. The GPC was measured using a Perten DA7200 device (Perten Instruments AB, Kungens Kurva, Sweden) according to the AACC method 39\u0026ndash;10. The grain samples of two homozygous genotypes were milled into flour using a Buhler experimental mill. Hard wheat sample was tempered to around 16% moisture content. Farinograph and Extensograph values were determined using AACC methods 56-81B and 54\u0026thinsp;\u0026minus;\u0026thinsp;21, respectively.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eMolecular genotyping\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from young leaves using the CTAB method (Porebski et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The RILs and parents were genotyped using the wheat Illumina assay 50K SNP array containing 55,224 SNPs developed in collaboration by CAAS and the Capital-Bio, Beijing, China (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.capitalbiotech.com/\u003c/span\u003e\u003cspan address=\"https://www.capitalbiotech.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMap construction and QTL analysis\u003c/h2\u003e \u003cp\u003eFor genetic map construction, the monomorphic markers between parents and the markers with high missing values (more than 20.0%) or minor allele frequency (MAF) less than 0.3 were removed at first, and the remaining 9,661 high-quality polymorphic SNP markers were used for subsequent analysis. The BIN function in IciMapping v4.1 was used to remove redundant markers (Meng et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Linkage analysis of the 1,501 non-redundant markers was performed with JoinMap v4.0 using the regression mapping algorithm. The high-density genetic linkage map was described in a previous study (Liu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). QTL analysis was conducted by inclusive composite interval mapping (ICIM) using IciMapping 4.1 (Meng et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The walking step for QTL detection was set as 0.1 cM, and the threshold of LOD scores was set as 2.5. Physical positions of mapped SNPs were obtained by blasting the flanking sequences of SNPs against the reference genome sequence of Chinese Spring (IWGSC v1.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://urgi.versailles.inra.fr/blast_iwgsc/\u003c/span\u003e\u003cspan address=\"https://urgi.versailles.inra.fr/blast_iwgsc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConversion of SNPs to KASP markers\u003c/h2\u003e \u003cp\u003eArray-based SNP markers closely linked to \u003cem\u003eQGPC.caas-3BS\u003c/em\u003e and \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e were converted into KASP markers. Allele-specific and common reverse primers for each KASP marker were designed using PolyMarker (Ramirez-Gonzalez et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.polymarker.info/\u003c/span\u003e\u003cspan address=\"http://www.polymarker.info/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). KASP assays were performed in a 4 \u0026micro;l reaction volume containing 2 \u0026micro;l 2 \u0026times; KASP Master Mix, 0.045 \u0026micro;l KASP primer mix and 2 \u0026micro;l genomic DNA at 30 ng/\u0026micro;l (Chandra et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). PCR products were detected with a PHERAstarplus SNP genotyping instrument (LGC Science Shanghai Ltd., China), using single excitation light and double emission light for fluorescence detection. Gel free fluorescence signal scanning and allele separation were automatically analyzed by the KlusterCaller\u0026trade; 2.24.0.11 (LGC, Hoddesdon, UK).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAnalysis of variance (ANOVA), phenotypic correlation coefficients, and Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests were conducted with SAS 9.2 (SAS Institute Inc, Cary, NC). PROC MIXED was used in ANOVA to evaluate the contributions of genotype (lines) and environment, where genotype, environment, genotype \u0026times; environment interaction, and replicate nested in environments were all considered as random effects. In parallel, a model considering genotype as fixed effect was fitted for estimating the best linear unbiased estimate (BLUE) of genotype across environments. Adjusted means of each genotype for GPC in individual and across environments were separately computed with PROC MIXED. Broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) was calculated using the formula: \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e/(\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003ege\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e/\u003cem\u003er\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eε\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e/\u003cem\u003ere\u003c/em\u003e), where \u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e, \u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003ege\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eε\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e are estimates of genotype, genotype \u0026times; environment interaction and residual error variances, respectively, and \u003cem\u003ee\u003c/em\u003e and \u003cem\u003er\u003c/em\u003e are the number of environments and replicate in environment, respectively. Cultivars with homozygous genotype from the validation panel were used to verify QTL effects. The difference of GPC between two classes of homozygous genotypes (ZM578and JM22 genotypes) were calculated by PROC MIXED, treating genotype as fixed effect, with lines nested in genotype, and environment, their related interaction and replicates nested in environments as random. For the statistical analyses in progeny tests, phenotypic difference of GPC between two homozygous genotypes were determined by Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic evaluation\u003c/h2\u003e \u003cp\u003eThe mean values on an across-environment GPC were 15.3% and 13.6% for ZM578 and JM22, respectively (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). ZM578 showed a higher GPC than JM22 in all environments. The GPC in 262 RILs ranged from 11.8\u0026ndash;17.7% in Luoyang, 12.2\u0026ndash;19.0% in Shangqiu, 12.1\u0026ndash;18.3% in Xinxiang, and 12.5\u0026ndash;18.5% in Gaoyi (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Continuous distribution and transgressive segregation were observed for GPC, indicative of polygenic inheritance (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Pearson\u0026rsquo;s correlation coefficients for GPC of the mapping population ranged from 0.66 to 0.88 among four environments (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). ANOVA showed that genotype, environment, and genotype \u0026times; environment interaction effects were all significant, with genotype contributing the largest effect. A high \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (0.92) for GPC was obtained across four environments (Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLinkage map construction\u003c/h2\u003e \u003cp\u003eTotally, 9,354 high-quality SNPs in the wheat Illumina iSelect 50K SNP array were used for linkage analysis and map construction. A high-density linkage map spanning 2413.84 cM on all wheat chromosomes except 6B was constructed using 1507 representative bin markers. The average chromosome length was 114.94 cM, ranging from 10.77 cM (1B) to 227.24 cM (5D). The details for the linkage map were shown in Liu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eQTL mapping of GPC\u003c/h2\u003e \u003cp\u003eBoth the GPC values in four environments and the BLUE value were used for QTL mapping. Seven QTL for GPC were detected in multiple environments on chromosomes 3AS, 3AL 3BS, 4AL, 5BS, 5DL and 7AL, designated \u003cem\u003eQGPC.caas-3AS\u003c/em\u003e, \u003cem\u003eQGPC.caas-3AL\u003c/em\u003e, \u003cem\u003eQGPC.caas-3BS\u003c/em\u003e, \u003cem\u003eQGPC.caas-4AL\u003c/em\u003e, \u003cem\u003eQGPC.caas-5BS\u003c/em\u003e, \u003cem\u003eQGPC.caas-5DL\u003c/em\u003e and \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of these, the increasing GPC alleles (favorable alleles) at \u003cem\u003eQGPC.caas-3AS\u003c/em\u003e, \u003cem\u003eQGPC.caas-3BS\u003c/em\u003e, \u003cem\u003eQGPC.caas-4AL\u003c/em\u003e and \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e were contributed by ZM578, whereas the other three QTL were from JM22. There was a linear relationship between phenotypes and the number of favorable alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with the addition of favorable alleles additively contributing to enhanced GPC. The GPC of the genotype with six favorable alleles was 2.3% higher than that of the genotype with one favorable allele.\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\u003eQTL for grain protein content detected by inclusive composite interval mapping (ICIM) in the Zhongmai 578/Jimai 22 RIL population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenetic position (cM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhysical interval (Mb) \u003csup\u003eα\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFlanking marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePVE (%)\u003csup\u003eβ\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eADD\u003csup\u003eγ\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\u003e\u003cem\u003eQGPC.caas-3AS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2/E3/E4/BLUE\u003csup\u003eδ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.75\u0026ndash;6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.77\u0026ndash;68.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-111570945\u0026thinsp;~\u0026thinsp;AX-86175555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.78\u0026ndash;14.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.8\u0026ndash;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.28\u0026thinsp;~\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQGPC.caas-3AL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1/E2/E3/BLUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.25\u0026ndash;66.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e650.42\u0026ndash;686.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-95659056\u0026thinsp;~\u0026thinsp;AX-109344781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.21\u0026ndash;5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.6\u0026ndash;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.23 ~ -0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQGPC.caas-3BS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3/E4/BLUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.75\u0026ndash;32.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.72\u0026ndash;21.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-95233993\u0026thinsp;~\u0026thinsp;AX-110403140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.40\u0026ndash;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.6\u0026ndash;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.16\u0026thinsp;~\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQGPC.caas-4AL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2/E3/BLUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e442.91\u0026ndash;467.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-111535294\u0026thinsp;~\u0026thinsp;AX-108927364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.50\u0026ndash;3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.8\u0026ndash;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.18\u0026thinsp;~\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQGPC.caas-5BS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2/E3/E4/BLUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.71\u0026ndash;49.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-109852325\u0026thinsp;~\u0026thinsp;AX-110621719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.85\u0026ndash;10.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.4\u0026ndash;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.29 ~ -0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQGPC.caas-5DL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1/E2/E3/BLUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.75\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e324.33\u0026ndash;369.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-111624154\u0026thinsp;~\u0026thinsp;AX-110867187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.44\u0026ndash;9.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.9\u0026ndash;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.28 ~ -0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQGPC.caas-7AL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1/E2/E3/BLUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155.75\u0026ndash;166.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e670.33\u0026ndash;675.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-110942203\u0026thinsp;~\u0026thinsp;AX-109534708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.06\u0026ndash;9.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.7\u0026ndash;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.20\u0026thinsp;~\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eα\u003c/sup\u003e Physical positions (Mb) were obtained by blasting SNP flanking sequences against the reference genome sequence of Chinese Spring (IWGSC v1.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://urgi.versailles.inra.fr/blast_iwgsc/\u003c/span\u003e\u003cspan address=\"https://urgi.versailles.inra.fr/blast_iwgsc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eβ\u003c/sup\u003e Phenotypic variance explained by the QTL.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eγ\u003c/sup\u003e Estimated additive effect of the QTL; positive values indicate that favorable alleles came from Zhongmai 578, whereas negative values indicate that favorable alleles were derived from Jimai22.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eδ\u003c/sup\u003e E1 to E4, and BLUE indicate the growing season of 2020\u0026ndash;2021 at Luoyang, Shangqiu, Xinxiang and Gaoyi, and best linear unbiased estimate across four environments, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eValidation of QTL\u003c/h2\u003e \u003cp\u003eA diverse panel of 166 cultivars was used to validate the effects of the \u003cem\u003eQGPC.caas-3BS\u003c/em\u003e and \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Tables S4 and S5). The TT allele (ZM578 genotype) had a significantly (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) higher GPC than the CC allele (JM22 genotype) for \u003cem\u003eQGPC.caas-3BS\u003c/em\u003e in four environments and BLUE, with GPC differences between two genotypes ranging from 0.4\u0026ndash;0.5%. Likewise, the GG allele (ZM578 genotype) had significantly (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) higher GPC than the AA allele (JM22 genotype) at the \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e locus in three environments and BLUE, with GPC differences from 0.3\u0026ndash;0.5% between two genotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEffect of favorable GPC alleles on thousand-kernel weight (TKW)\u003c/h2\u003e \u003cp\u003eThe increasing GPC allele at \u003cem\u003eQGPC.caas-5DL\u003c/em\u003e was contributed by JM22, whereas that at \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e was from ZM578. The favorable alleles for GPC at these two loci also had significant and positive effects on TKW (Liu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), indicating that both QTL regions were not only for GPC, but also associated with TKW. These QTL regions can be simultaneously used to improve wheat yield and quality for developing wheat cultivars with high yield and good quality by MAS.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFine mapping of\u003c/b\u003e \u003cb\u003eQGPC.caas-7AL\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRIL267 with a heterozygous genotype in the mapping interval of \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e was successfully screened from the RIL population (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). A significant difference in GPC was detected between the 7A+ (29 plants) and 7A\u0026minus; (26 plants) genotypes from self-pollinated progenies of RIL267 (Table S6). Six plants with recombination types were identified using KASP markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Subsequently, these six recombinants (designated as RL1 to RL6) were planted to generate secondary mapping populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Within each secondary population, homozygous non-recombinant lines, viz. 7A\u0026thinsp;+\u0026thinsp;NILs and 7A\u0026thinsp;\u0026minus;\u0026thinsp;NILs, were genotyped with molecular markers and phenotypically evaluated. Significant differences in GPC were detected between 7A\u0026thinsp;+\u0026thinsp;and 7A\u0026thinsp;\u0026minus;\u0026thinsp;NILs within RL1, RL5 and RL6 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas non-significant effects for GPC were observed within populations from RL2, RL3 and RL4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Based on these results, \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e was mapped to a 1.82 Mb interval flanked by \u003cem\u003eKasp-7A1\u003c/em\u003e and \u003cem\u003eKasp-7A3\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eTo further analyze the genetic effect of \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e on quality characteristics, the Farinograph and Extensograph between 7A\u0026thinsp;+\u0026thinsp;and 7A\u0026thinsp;\u0026minus;\u0026thinsp;homozygous lines were investigated by mixing 7A+ (29 plants) and 7A\u0026minus; (26 plants) genotypes, respectively. Compared with 7A\u0026thinsp;\u0026minus;\u0026thinsp;homozygous lines, the 7A\u0026thinsp;+\u0026thinsp;homozygous lines increased farinograph stability time, extension area, extensibility and maximum resistance by 19.6%, 26.3%, 6.0% and 20.3%, respectively (Table S6). These demonstrated that the 7A\u0026thinsp;+\u0026thinsp;allele had a positive effect on processing quality.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe quality improvement is considered one of the top priorities in wheat breeding. While breeders focus on improvement of grain yield, especially in the later cycles of breeding programs, they also pay high attention to the baking and milling quality of wheat flour. Alongside the consideration of biotic and abiotic stress resistance, both grain yield and quality parameters form the foundation for the success of an experimental line in the wheat market. Many quality traits are significantly influenced by environmental factors, crop management practices, and soil fertility. Nevertheless, a large variation among different wheat lines indicates the presence of underlying genetic factors controlling the quality traits, and GPC is one of the most important parameters influencing wheat end-use quality (Payne, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Harnessing this genetic variation to enhance end-use quality is an important approach that aligns with sustainable agricultural practices. Thus, we conducted a genome-wide linkage mapping for GPC in the ZM578/JM22 RILs and developed available KASP markers for wheat breeding.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComparisons with previous reports\u003c/h2\u003e \u003cp\u003eGPC QTL were reported previously on all wheat chromosomes. In this study, we detected seven GPC QTL on chromosomes 3AS, 3AL, 3BS, 4AL, 5BS, 5DL and 7AL. Of these, \u003cem\u003eQGPC.caas-3AS\u003c/em\u003e was located in a 0.5 cM genetic region (67.77\u0026ndash;68.30 Mb), flanked by \u003cem\u003eAX-111570945\u003c/em\u003e and \u003cem\u003eAX-86175555\u003c/em\u003e. Markers \u003cem\u003eAX-95659056\u003c/em\u003e and \u003cem\u003eAX-109344781\u003c/em\u003e flanking \u003cem\u003eQGPC.caas-3AL\u003c/em\u003e were located in 650.42 and 686.69 Mb, respectively. Zhang et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) identified a GPC locus in 90.07\u0026ndash;425.22 Mb. \u003cem\u003eQGpc.caas-3A\u003c/em\u003e was linked with marker \u003cem\u003ewms155\u003c/em\u003e at 702.96 Mb (Li et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Jiang et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) identified two QTL for GPC in 624.54\u0026ndash;686.72 Mb and 697.25\u0026ndash;700.95 Mb. Lou et al (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) mapped three GPC QTL at 191.53 Mb, 369.40 Mb and 484.64 Mb on chromosome 3A. These results suggest that \u003cem\u003eQGPC.caas-3AS\u003c/em\u003e may be a new locus, but \u003cem\u003eQGPC.caas-3AL\u003c/em\u003e was mapped in a similar location to \u003cem\u003eQgpc-3A\u003c/em\u003e (Jiang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eQGPC.caas-3BS\u003c/em\u003e was located in a 1.0 cM interval that corresponds to 20.72\u0026ndash;21.17 Mb in 3BS. \u003cem\u003eQProtein.caas-3B\u003c/em\u003e linked with \u003cem\u003eXwmc3\u003c/em\u003e and \u003cem\u003eXbarc68.1\u003c/em\u003e at 398.38 Mb to 586.52 Mb in the PH82-2/Neixiang 188 RIL population (Zhang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Jiang et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) detected three QTL at 24.94\u0026ndash;101.31 Mb, 40.25\u0026ndash;49.86 Mb and 146.89\u0026ndash;153.61 Mb on 3BS in Ningmai 9/Yangmai 158 RIL population. These indicate that \u003cem\u003eQGPC.caas-3BS\u003c/em\u003e is likely to be new.\u003c/p\u003e \u003cp\u003e \u003cem\u003eQGPC.caas-4AL\u003c/em\u003e, flanked by \u003cem\u003eAX-111535294\u003c/em\u003e and \u003cem\u003eAX-108927364\u003c/em\u003e, was located at 442.91\u0026ndash;467.26 Mb. Wang et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) detected a QTL for GPC close to \u003cem\u003eXwmc516\u003c/em\u003e at 12.20 Mb on 4A. Liu et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) identified a locus for GPC in 621.70\u0026ndash;673.41 Mb on 4A. Thus, \u003cem\u003eQGPC.caas-4AL\u003c/em\u003e may be a new locus.\u003c/p\u003e \u003cp\u003eThe GPC QTL on chromosome 5BS in this study was located in a 2.25 cM interval that corresponds to 46.71\u0026ndash;49.57 Mb. Previously, Zou et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) identified a QTL for GPC in 678.52\u0026ndash;691.13 Mb on chromosome 5B. Jiang et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported three QTL (\u003cem\u003eQgpc-5B.1\u003c/em\u003e, \u003cem\u003eQgpc-5B.2\u003c/em\u003e and \u003cem\u003eQgpc-5B.3\u003c/em\u003e) for GPC in 4.86\u0026ndash;8.92 Mb, 545.04\u0026ndash;546.83 Mb and 682.70\u0026ndash;697.61 Mb, respectively. Therefore, \u003cem\u003eQGPC.caas-5BS\u003c/em\u003e also is likely to be a new locus.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eQGPC.caas-5DL\u003c/em\u003e located in the interval of 324.33 to 369.20 Mb. \u003cem\u003eQProtein.caas-5D\u003c/em\u003e for GPC was mapped in 5.60\u0026ndash;5.82 Mb on chromosome 5DS by Zhang et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Li et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) detected a QTL for GPC linked to \u003cem\u003ewms272\u003c/em\u003e at 562.34 Mb. Their positions are different from \u003cem\u003eQGPC.caas-5DL.\u003c/em\u003e Thus, \u003cem\u003eQGPC.caas-5DL\u003c/em\u003e is likely a new GPC QTL.\u003c/p\u003e \u003cp\u003e \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e was located in 670.33\u0026ndash;672.15 Mb. Liu et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) identified a SNP at 708.63 Mb, exhibiting the strongest association with GPC. Jiang et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported \u003cem\u003eQgpc-7A.1\u003c/em\u003e (205.46\u0026ndash;210.86 Mb) and \u003cem\u003eQgpc-7A.2\u003c/em\u003e (670.78\u0026ndash;679.84 Mb) for GPC, respectively. \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e was mapped in a similar location to \u003cem\u003eQgpc-7A.2\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePotential candidate genes for\u003c/b\u003e \u003cb\u003eQGPC.caas-7AL\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe have narrowed down the \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e to a 1.82 Mb physical interval with 48 high-confidence annotated genes. Synteny analysis of genome sequences of Chinese Spring and other wheat varieties showed that the physical interval of \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e was highly conserved among different wheat genomes (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Previous studies have shown that protein accumulation during grain development is conferred by regulation at the transcriptional level and post-transcriptional level, which involves in some transcription factors (TFs), such as MYB TFs, AP2, and NAC TFs, as well as some important protein modifications, such as N-glycosylation, folding and assembly of proteins, and protein bodies. \u003cem\u003eTraesCS7A02G476200\u003c/em\u003e, \u003cem\u003eTraesCS7A02G476300\u003c/em\u003e, \u003cem\u003eTraesCS7A02G477700\u003c/em\u003e, \u003cem\u003eTraesCS7A02G479100\u003c/em\u003e and \u003cem\u003eTraesCS7A02G479200\u003c/em\u003e, annotated as bHLH transcription factor, transcription factor, encoding Myb/SANT-like DNA-binding domain protein, plant transcription factor family protein, and Thioredoxin, respectively, may be the potential candidate genes for \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e (Table S7). Furthermore, we analyzed the expression patterns of these five genes on the wheat expVIP expression platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.wheat-expression.com/\u003c/span\u003e\u003cspan address=\"http://www.wheat-expression.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Wheat eFPBrowser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bar.utoronto.ca/efp_wheat/cgi-bin/efpWeb.cgi\u003c/span\u003e\u003cspan address=\"http://bar.utoronto.ca/efp_wheat/cgi-bin/efpWeb.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and found that only \u003cem\u003eTraesCS7A02G479200\u003c/em\u003e was expressed in the developing grains. Based on the resequencing data of two parents, missense mutation was found in the promoter region of \u003cem\u003eTraesCS7A02G476200\u003c/em\u003e, \u003cem\u003eTraesCS7A02G476300\u003c/em\u003e and \u003cem\u003eTraesCS7A02G479100\u003c/em\u003e (Table S8). Thus, \u003cem\u003eTraesCS7A02G476200\u003c/em\u003e, \u003cem\u003eTraesCS7A02G476300\u003c/em\u003e, \u003cem\u003eTraesCS7A02G479100\u003c/em\u003e and \u003cem\u003eTraesCS7A02G479200\u003c/em\u003e were considered candidate genes for \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e. It is a challenge to functionally validate four genes through transgenic experiments, so we will continue to further narrow down the target interval and reduce the number of potential causal genes by physical mapping and deep sequencing. Furthermore, the 7A\u0026thinsp;+\u0026thinsp;allele at \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e had a positive effect on quality parameters. The KASP marker of \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e was validated to be significantly related with GPC in 166 cultivars. The favorable allele of \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e, which has pleiotropic effect on TKW, can be traced back to Sunstate (Liu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e may be of great value in MAS breeding programs.\u003c/p\u003e \u003cp\u003eNegative correlation between yield traits and GPC were reported previously (Yagdi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Blanco et al. 2012). In this study, \u003cem\u003eQGPC.caas-5DL\u003c/em\u003e and \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e have pleiotropic effects on TKW (Liu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which are attributed to either a single gene with pleiotropic effects on both traits or two closely linked genes with separate effects. Thus, these QTL could be used in breeding for improvement of both GPC and grain related traits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eApplications in wheat quality breeding\u003c/h2\u003e \u003cp\u003eGrain yield and quality improvement are important targets in wheat breeding programs. Simultaneous improvement of both grain yield and GPC is a challenging task due to a negative correlation between grain yield and GPC. To ensure food security, genetic improvement in grain yield was a principal target in wheat breeding programs during the last 50 years in China (Gao et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In recent years, high GPC received more attention in China markets. There is a rising demand for flour with high GPC to produce high-quality steamed bread. In traditional wheat breeding strategy, a high GPC parent from other countries was used to improve local varieties, and quality testing is usually performed after the agronomic selection in field, leading to some lines with excellent quality being abandoned in early breeding generations. In addition, most of the quality traits, including GPC, are sensitive to environments and show significant differences among years or locations (He et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the evaluation of GPC in single environments may be inaccurate. MAS is independent of the environment and has a great potential in wheat quality improvement. KASP marker is a high-throughput SNP genotyping platform that offers low cost, high throughput, and easy operability in MAS and fine mapping of genes. In this study, two parents of the RILs showed good and acceptable performance in GPC with 15.3% and 13.6% for ZM578 and JM22, respectively. However, some derived RILs pyramiding favorable GPC alleles from two parents showed high GPC, which is consistent with previous studies (Fatiukha et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, it is feasible to improve GPC by exploring and pyramiding the increasing GPC-alleles in local varieties. In this study, two KASP markers for \u003cem\u003eQGPC.caas-3BS\u003c/em\u003e and \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e, respectively, were successfully developed and validated in a diverse panel of 166 cultivars. Cultivars with the favorable allele at the \u003cem\u003eQGPC.caas-3BS\u003c/em\u003e and \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e locus increased GPC by 0.4\u0026ndash;0.5% and 0.3\u0026ndash;0.5%, respectively. Thus, these KASP markers can be used for MAS in wheat breeding. Several accessions with favorable alleles at two loci and high GPC, such as Xiaoyan 54, Zhong 892, Shanmai 94, Norin 67, Shanyou 205, Nidera Baguette 20, Barra and Sunstate, and the high-GPC RILs in the population can be good parental lines for wheat breeding.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e This work was funded by the Natural Science Foundation of China (32272182), the Core Research Budget of the Non-profit Governmental Research Institutions (S2022ZD04), the CAAS Agricultural Science and Technology Innovation Program (CAAS-ZDRW202002), the Doctoral Research Launch Fund of Henan University of Science and Technology (13480100), and the Luoyang Core Technology Research Public Welfare Program (2302034A).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e DZ performed the experiment and wrote the paper. DZ, JZ, HJ, DL, YT, WZ and CW assisted in the field trials. DZ, JZ, HJ, DL and LY participated in the trait evaluation. XX, CS, ZH and JL provided extensive revision of the manuscript. YZ designed the experiment and wrote the paper. All authors read the final version of the manuscript and approved for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e All authors declare that they have no conflicts of interest.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBalyan HS, Gupta PK, Kumar S, Dhariwal R, Jaiswal V, Tyagi S, Agarwal P, Gahlaut V, Kumari S (2013) Genetic improvement of grain protein content and other health-related constituents of wheat grain. 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PLoS One 12:e0171528\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4242047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4242047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWheat grain protein content (GPC) is important for end-use quality. Identification of genetic loci for GPC is helpful to create new varieties with good processing quality and nutrients. Zhongmai 578 (ZM578) and Jimai 22 (JM22) are two elite wheat varieties with different contents of GPC. In the present study, 262 recombinant inbred lines (RILs) derived from a cross between ZM578 and JM22 were used to map the GPC with high-density wheat Illumina iSelect 50K single-nucleotide polymorphism (SNP) array. Seven quantitative trait loci (QTL) were identified for GPC on chromosomes 3AS, 3AL 3BS, 4AL, 5BS, 5DL and 7AL by inclusive composite interval mapping, designated as \u003cem\u003eQGPC.caas-3AS\u003c/em\u003e, \u003cem\u003eQGPC.caas-3AL\u003c/em\u003e, \u003cem\u003eQGPC.caas-3BS\u003c/em\u003e, \u003cem\u003eQGPC.caas-4AL\u003c/em\u003e, \u003cem\u003eQGPC.caas-5BS\u003c/em\u003e, \u003cem\u003eQGPC.caas-5DL\u003c/em\u003e and \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e, respectively. Among these, alleles for increasing GPC at \u003cem\u003eQGPC.caas-3AS\u003c/em\u003e, \u003cem\u003eQGPC.caas-3BS\u003c/em\u003e, \u003cem\u003eQGPC.caas-4AL\u003c/em\u003e and \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e loci were contributed by ZM578, whereas those at the other three loci were from JM22.\u003cem\u003e \u003c/em\u003eThe stable QTL \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e was fine mapped to a 1.82 Mb physical interval using secondary populations from six heterozygous recombinant plants obtained by selfing a residual RIL. Four genes were predicted as candidates of \u003cem\u003eQGPC.caas-7AL\u003c/em\u003ebased on sequence polymorphism and expression patterns. The near-isogenic lines (NILs) with the favorable allele at the \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e locus increased farinograph stability time, extension area, extensibility and maximum resistance by 19.6%, 6.3%, 6.0% and 20.3%, respectively. Kompetitive allele-specific PCR (KASP) marker for \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e was developed and validated in a diverse panel of 166 Chinese wheat cultivars. These results provide further insight into the genetic basis of GPC, and the fine-mapped \u003cem\u003eQGPC.caas-7AL\u003c/em\u003e will be an attractive target for map-based cloning and marker-assisted selection in wheat breeding programs.\u003c/p\u003e","manuscriptTitle":"Fine mapping of QGPC.caas-7AL for grain protein content in bread wheat","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-21 07:26:02","doi":"10.21203/rs.3.rs-4242047/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-08-06T02:47:50+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-05-11T07:42:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-10T15:59:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-10T13:11:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2024-04-09T08:56:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ef67e49a-3e4c-4b8d-8df4-8e993f687c97","owner":[],"postedDate":"May 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-11T16:04:52+00:00","versionOfRecord":{"articleIdentity":"rs-4242047","link":"https://doi.org/10.1007/s00122-024-04769-9","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2024-11-06 15:57:27","publishedOnDateReadable":"November 6th, 2024"},"versionCreatedAt":"2024-05-21 07:26:02","video":"","vorDoi":"10.1007/s00122-024-04769-9","vorDoiUrl":"https://doi.org/10.1007/s00122-024-04769-9","workflowStages":[]},"version":"v1","identity":"rs-4242047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4242047","identity":"rs-4242047","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}