Genetic identification and characterization of quantitative trait loci for wheat grain size-related traits independent of grain number per spike

Genetic identification and characterization of quantitative trait loci for wheat grain size-related traits independent of grain number per spike | 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 Genetic identification and characterization of quantitative trait loci for wheat grain size-related traits independent of grain number per spike Tao Li, Yanyan Tang, ZhengXi Lin, Jinghui Wang, Juanyu Zhang, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6202356/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 May, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Thousand-grain weight (TGW), mainly determined by grain length (GL) and width (GW), is an important yield component of wheat. In the study, combined with phenotyping in four field trials and a high-quality genetic map constructed with the Wheat 55K SNP array, a total of seven stable QTLs for TGW, GW and GL were identified in a doubled haploid (DH) population derived from the cross between Chuanmai 42 (CM42) and Kechengmai 4 (K4), in which QTgw.CK4-cib-3D , QGw.CK4-cib-2D and QGl.CK4-cib-5A.1 were novel, and QTgw/Gw.CK4-cib-6A and QGl.CK4-cib-5A.1 were major QTLs explaining more than 10% of the phenotypic variances. The effects of QTgw/Gw.CK4-cib-6A and QGl.CK4-cib-5A.1 on corresponding traitswere further validated in different populations by developing the Kompetitive Allele Specific PCR marker. QTgw/Gw.CK4-cib-6A significantly increased TGW while reducing GNS . Interestingly, the other QTLs for grain size, QGw.CK4-cib-2D , QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 , showed a significant increase in TGW, but did not affect GNS. Moreover, the polymerization of QGw.CK4-cib-2D , QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 had a significant addition effect on TGW without reducing GNS, suggesting that these QTLs can work together as an excellent molecular module to break the trade-off between GNS and TGW in wheat high-yield breeding. By analysis of expression, sequence and function annotation TraesCS5A02G001400, TraesCS5A02G002700 and TraesCS5A02G003400 were predicted as the candidate genes for QGl.CK4-cib-5A.1 . Taken together, the present results lay a foundation for subsequent map-based cloning of these QTL and their utilization in wheat breeding. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Key message Seven stable QTLs for TGW, GW and GL were identified, and two major QTLs were stable in various genetic backgrounds and environments. Introduction Wheat ( Triticum aestivum L.) is one of the most important staple crops, providing approximately one-fifth of the dietary calories for humans (Xu et al. 2019 ). However, the global farmland loss, climate change, and population increase may exacerbate food insufficiency in the future (Ren et al. 2021 ). It was estimated that wheat production must be increased by about 60% by 2050 to ensure global food and nutritional security (Langridge 2013 , Curtis and Halford 2014 ). Therefore, sustained growth in wheat yield is of paramount importance. Grain yield improvement continues to be a huge challenge because it is a complex quantitative trait controlled by polygene and heavily affected by environments (Li et al. 2018 ). Grain yield is mainly determined by three major component traits, viz. the number of spikes per unit area, grain number per spike (GNS), and thousand grain weight (TGW), which show less sensitivity to environment and have higher heritability than grain yield itself. Historically, the three major component traits contributed greatly to the increased wheat yield potential released in the past decades directly and indirectly (Zheng et al. 2011 ; Wu et al. 2014 ; Gao et al. 2015 ; Xu et al. 2019 ; Yang et al. 2020 , 2022 ; Kong et al. 2022 ). TGW is mainly determined by grain size that are usually depicted by grain length (GL), grain width (GW) and grain thickness. Thus, identification of genetic loci controlling TGW and grain size-related traits is essential to elucidate the genetic basis of wheat yield and facilitates the genetic improvement of varieties with high yield. Similar to other yield-related traits, TGW and grain size-related traits are quantitative traits controlled by multiple genes in crops. In rice, many genes involved in determining grain weight and grain size have been reported, such as DLT , GLW7 , GW8 and GS3 mainly regulating GL (Li et al. 2010 ; Mao et al. 2010 ; Tong et al. 2012 ; Wang et al. 2012 ; Si et al. 2016 ), GW2 , GS5 and GW5 mainly controlling GW (Song et al. 2007 ; Weng et al. 2008 ; Li et al. 2011 ), and GSK2 , WTG1 , TGW6 , qTGW3 , GL3 and GS9 contributing to variations of both GL, GW and TGW (Weijers and Friml 2009 ; Ishimaru et al. 2013 ; Huang et al. 2017 ; Hu et al. 2018 ; Xia et al. 2018 ; Zhao et al. 2018 ). However, relatively fewer genes controlling grain weight and grain size have been reported in wheat due to the huge and complicated genome. For example, Tasg-D1 encodes a Ser/Thr protein kinase glycogen synthase kinase3 (STKc_GSK3), which affects grain size by modulating brassinosteroid signaling and is a key gene that determine the round grain of the Indian dwarf wheat ( Triticum sphaerococcum ) (Cheng et al. 2020 ). TGW1 was cloned from the tetraploid wheat, which encodes a 3-ketoacyl-CoA thiolase (KAT-2B) gene and affects TGW by jasmonic acid signaling pathway (Chen et al. 2020 ). In addition, TaGW2 , TaGW7 , TaCKX6 , TaTGW6 , TaGASR7 , TaGL3 , TaGS-D1 , TaSus2 , TaSAP1-A1 and TaTPP-6AL1 obtained by homologous cloning were also involved in the regulation of grain development (Zhang et al. 2012 , 2014 , 2017 ; Chang et al. 2013 ; Dong et al. 2014 ; Hanif et al. 2016 ; Geng et al. 2017 ; Li et al. 2019 ; Wang et al. 2019a ; Yang et al. 2019 ). Quantitative trait loci (QTLs) mapping, the first step towards map-based gene cloning, is an effective approach to parse the genetic basis of complex quantitative traits like TGW and grain size-related traits. With the rapid development of wheat genome sequencing and high-throughput genotyping technologies, numerous QTLs for TGW and grain size-related traits have been identified on all wheat chromosomes to date (Cao et al. 2019 , 2020b ; Wang et al. 2019b ; Duan et al. 2020 ; Liu et al. 2020b ; Isham et al. 2021 ; Li et al. 2022 ). However, only a few were major, environmentally stable, and validated in different genetic backgrounds, which greatly hindered their potential utilization in yield improvement by marker-assisted selection (MAS) breeding. Therefore, continuous detection and validation of novel superior genetic loci are vital for yield improvement in wheat breeding. In this study, we developed a haploid (DH) population derived from the cross between two elite wheat cultivars Chuanmai 42 (CM42) and Kechengmai 4 (K4). Combined with phenotyping in multiple field trials and a high-quality genetic map constructed with the Wheat 55K SNP array, chromosomal regions controlling TGW, GL and GW were identified. The major QTLs were further validated in different populations through developing the Kompetitive Allele Specific PCR (KASP) markers. Moreover, the independent and pyramidal effects of the identified QTL on TGW and GNS were analyzed to evaluate their application potential in wheat breeding. Finally, candidate genes of QGl.CK4-cib-5A.1 were predicted to lay a foundation for subsequent map-based cloning. Materials and Methods Plant materials and field trials A DH population (CK4 population) containing 188 lines was developed based on a cross between wheat varieties CM42 and K4 and used for QTL mapping. The recombinant inbred line (RIL) population (CC population) derived from the cross between CM42 and wheat variety Chuanmai 39 (CM39) was employed to validate the major QTL. The line 2 of the CK4 population containing the major QTL QGl.CK4-cib-5A.1 was backcrossed with K4 to construct the BC 1 F 2 and BC 2 F 2 populations for the validation of the major QTL. All populations and their parents were planted at Shuangliu (SHL, 103˚52'E, 30˚34'N) and Shifang (SHF, 104˚11'E, 31˚6'N) in Sichuan Province, China with a randomized block design. Each line was planted in a five-row plot with a row length of 2.0 m with 50 seeds per row, at a row spacing of 0.3 m. The CK4 population was planted at SHF in 2018 (E1) and 2019 (E3) cropping season and SHL in 2018 (E2) and 2019 (E4) cropping season, the CC populations were planted at SHF and SHL in 2018–2019 cropping season, and the BC 1 F 2 and BC 2 F 2 populations were planted at SHF in 2023 crop seasons with 15 cm spacing between individuals. Field management and disease control were performed according to the common practices for wheat production. Phenotypic evaluation and Statistical analysis At maturity, 10 representative major spikes of each line in the CK4 and CC populations were harvested and manually threshed for evaluating TGW, GL, GW and GNS using the WSeen SC-G software (WSeen Corporation, Hangzhou, China). For the BC 1 F 2 and BC 2 F 2 populations, the main spike of each plant was harvested and manually threshed for evaluating TGW, GL and GW. Best linear unbiased prediction (BLUP) data which were used for combined QTL detection, correlation analysis, and effect analysis were calculated by using the R package “ lme4 ”. Phenotypic correlations, frequency distribution and Student t test were computed in SPSS v20 software (IBM SPSS, Armonk, USA). Broad-sense heritability ( H 2 ) was calculated following the formula: \(\:{H}^{2}={{\sigma\:}}_{\text{g}}^{2}/（{{\sigma\:}}_{\text{g}}^{2}+{{\sigma\:}}_{\text{g}\text{e}}^{2}/\text{n}+{{\sigma\:}}_{\text{g}\text{r}}^{2}/\text{r}{+{\sigma\:}}_{{\epsilon\:}}^{2}/\text{n}\text{r}）\) , where \(\:{{\sigma\:}}_{\text{g}}^{2}\) represents the genotype variance, \(\:{{\sigma\:}}_{\text{g}\text{e}}^{2}\) represents the genotype by environmental effect variance, \(\:\:{{\sigma\:}}_{\text{g}\text{r}}^{2}\) represents the genotype by year's effect variance, \(\:{{\sigma\:}}_{{\epsilon\:}}^{2}\) represents the residual variance, n is the number of environments, and r is the years (Smith et al. 1998 ). Genotyping and genetic map construction Genomic DNA of the CK4 lines and their parents was extracted from young leaves using the Plant Genomic DNA Kit (TransGen Biotech Corporation, Beijing, China). DNA integrity and quantity were checked on agarose gels and spectrophotometry, respectively. Genotyping of the CK4 lines and their parents was performed by China Golden Marker Corporation (Beijing, China) using the wheat 55K SNP array. The homozygous SNPs with polymorphism between parents were selected to construct the high-density genetic linkage map by using the IciMapping v4.2 (Meng et al. 2015 ) and JoinMap v4.0 software. Firstly, the “BIN” function of the IciMapping v4.2 software was used to place the SNPs with no recombination into one bin, and remove the SNPs with a missing rate ≥ 20%. To reduce the complexity of QTL mapping, only one marker was selected as a delegate from each bin to construct the genetic linkage map. Then, the “Grouping” function in JoinMap v4.0 was employed to creat groups with limit of detection (LOD) score values ranging from 2 to 8; Finally, the Kosambi mapping function command in JoinMap v4.0 was used to order the bin markers and calculate genetic distances with the parameters being set as LOD ≥ 5 and round = 3. Lines with a SNP deletion rate of ≥ 20% were removed in the CK4 population, where the heterozygous SNPs were treated as missing. QTL analysis QTL mapping was performed by using the inclusive composite interval mapping (ICIM) function in the Biparental Populations (BIP) module of the IciMapping v4.2 software, where the BLUP data was treated as an additional environment. The parameters were set as follows: walking step = 1 cM, PIN = 0.005, and logarithm of odds (LOD) threshold ≥ 3. QTL that was stably identified in at least three environments and BLUP data, and can explain greater than 10% of phenotypic variation was considered a major QTL. Additionally, the Met (multi-environmental trials) module was utilized to detect interactions between environments and QTLs with the parameters of walking step = 1 cM and LOD ≥ 5. Marker development and major QTL validation According to the results of QTL mapping, the KASP markers closely linked with identified QTL were designed following the specific methods outlined in the KASP primer design manual (Rasheed et al. 2016 ). The KASP markers that were successfully developed and polymorphic between parents were used to genotype the validation populations. According to the results of genotyping, phenotypic values of homozygous genotypes of different QTLs were statistically analyzed to evaluate their effects on the corresponding trait in different populations. Candidate gene discovery for major QTL The sequence variations of the candidate genes were analyzed between parental lines based on the whole-genome sequencing data of CM42 and K4 that were previously obtained using the Illumina NovaSeq 6000 platform (Li et al. 2021 ; Zhang et al. 2022 ). Samples of the developing grain in parents were collected in the field 10 days after flowering and quickly stored in liquid nitrogen for RNA extraction. Total RNA was extracted using the Plant RNA Extraction Kit (TransGen Biotech, Beijing, China), which was used to synthesize the first-strand complementary DNA (cDNA) using the TransScript cDNA Synthesis SuperMix (TransGen Biotech, Beijing, China). The RT-qPCR reactions were performed in Bio-Rad CFX96 real-time PCR system with 10ul reaction volumes consisting of 1µl cDNA, 5µl ChamQ Universal SYBR qPCR Master Mix, 0.2µl forward primers, 0.2µl reverse primers, and 3.6µl RNase-free H 2 O. The wheat elongation factor TaEF (GenBank accession number Q03033) was used as the internal reference. Threshold values (CT) included in the analyses were based on three biological and three technical replicates. The relative expression levels were calculated using the comparative ∆∆ CT method. Results Phenotypic evaluation TGW, GW and GL of the CK4 lines and parents were evaluated in four environments, and significant differences ( P < 0.001) were observed between CM42 and K4 in all environments, and CM42 had heavier and bigger grain than K4 (Fig. 1 ; Table 1 ). In the CK4 population, TGW, GW and GL displayed clear transgressive segregation with TGW ranging from 27.65g to 72.77g, GW from 2.5mm to 4.07 and GL from 5.32mm to 8.03mm (Table 1 ). This suggested that the favorable alleles originate from both parents. ANOVA analysis showed that the broad-sense heritability ( H 2 ) of TGW, GW and GL was 0.81, 0.77 and 0.89, respectively, and these traits exhibited significant positive correlations ( P < 0.001) in different environments (Table 1 ; Table 2 ), indicating that they were primarily influenced by genetic factors. The frequency distribution showed that the TGW, GW and GL appeared to be normally distributed over multiple environments, indicating that they were quantitative traits controlled by multiple genes, and suitable for QTL analysis (Fig. 2 ). Moreover, correlation assessment based on BLUP data indicated that TGW, GW and GL were significantly and positively correlated with each other, but negatively correlated with GNS (Table 3 ). Table 1 Phenotypic variation and heritability of TGW, GL and GW in the CK4 population among different environments. Trait Environment Parents The CK4 population H 2 CM42 K4 Range Mean CV(%) TGW E1 54.79*** 48.47 32.96-66.01 49.46 ± 5.14 10.39 0.81 (g) E2 53.06*** 46.74 36.77-64.44 51.58 ± 4.98 9.66 E3 53.02*** 44.41 27.65-56.44 44.32 ± 5.4 12.18 E4 49.32*** 44.23 33.99-59.03 48.03 ± 5.34 11.11 BLUP 53.27 47.95 38.8-58.77 49.8 ± 3.67 7.36 GW E1 3.58*** 3.45 3-3.83 3.5 ± 0.15 4.38 0.77 (mm) E2 3.52*** 3.45 3.02-3.87 3.54 ± 0.15 4.34 E3 3.55*** 3.39 2.5-3.95 3.42 ± 0.27 7.89 E4 3.44** 3.32 2.95-3.91 3.43 ± 0.16 4.79 BLUP 3.57 3.45 3.21-3.78 3.54 ± 0.1 2.86 GL E1 6.87*** 6.48 5.94-7.77 6.71 ± 0.28 4.2 0.89 (mm) E2 6.83*** 6.48 5.89-7.49 6.67 ± 0.28 4.24 E3 7.02*** 6.6 5.32-7.82 6.82 ± 0.49 7.19 E4 7.32*** 6.99 6.09-7.73 6.83 ± 0.27 3.94 BLUP 7.01 6.63 6.05-7.58 6.83 ± 0.25 3.61 Note: CV coefficient of variation; BLUP best linear unbiased prediction; H 2 broad-sense heritability; *** represents significance difference at P < 0.001. Table 2 Correlation coefficients of TGW, GL andGW among different environments. Traits Environments E1 E2 E3 E4 BLUP TGW E1 1 E2 0.73 *** 1 E3 0.72 *** 0.54 *** 1 E4 0.7 *** 0.74 *** 0.58 *** 1 BLUP 0.91 *** 0.86 *** 0.82 *** 0.87 *** 1 GW E1 1 E2 0.66 *** 1 E3 0.38 *** 0.38 *** 1 E4 0.6 *** 0.69 *** 0.47 *** 1 BLUP 0.77 *** 0.8 *** 0.79 *** 0.83 *** 1 GL E1 1 E2 0.9 *** 1 E3 0.5 *** 0.56 *** 1 E4 0.85 *** 0.89 *** 0.61 *** 1 BLUP 0.87 *** 0.91 *** 0.82 *** 0.92 *** 1 Note: *** represents the correlation is significant at the P < 0.001. Table 3 Correlation coefficients between TGW, GL,GW and GNS according to the BLUP data. Traits TGW GW GL GNS TGW 1 GW 0.81 *** 1 GL 0.63 *** 0.29 *** 1 GNS -0.45 *** -0.29 *** -0.39 *** 1 Note: *** represents the correlation is significant at the P < 0.001. High-density genetic linkage map Among the 53,063 SNP markers in the wheat 55K SNP array, 18182 polymorphic SNP between CM42 and K4 were used for subsequent analysis. By performing Bin and grouping functions, 1723 non-redundant bin markers were retained and grouped into 23 linkage groups representing the 21 wheat chromosomes (Table S1 ). Of which, chromosomes 5A and 6D contained two groups. The A, B and D subgenomes contained 650 (37.72%), 629 (36.51%) and 444 (25.77%) bin markers, respectively (Table S1 ). Moreover, five lines of the CK4 population with the missing SNPs ≥ 20% were removed. Finally, a high-density genetic linkage map consisting of 1723 bin markers and 183 lines was constructed for CK4 population, which spanned the total genetic distance of 2790.15 cM with an average interval distance of 1.62 cM per bin marker (Table S1 ). The quality of linkage map was assessed by analyzing the collinearity of genetic positions of SNPs to their physical positions (Mb) in the CS genome. As expected, a high consistence was observed between the genetic and physical map on most chromosomes, suggesting the genetic linkage map constructed in this study was satisfied for QTL analysis. (Figure S1 ). QTL mapping in individual environment Using the ICIM-BIP method, a total of seven stable QTLs for TGW, GL and GW were identified on chromosome 2D, 4B, 5A and 6A, explaining 4.2%-23.28% of the phenotypic variance (Table 4 ). Table 4 Stable QTL for TGW, GL and GW identified in the CK4 population. QTL Environments Position (cM) Left Marker Right Marker LOD PVE(%) Add Physical interval (Mb) QTgw.CK4-cib-3D E2 33 AX-111600316 AX-109928860 4.1 5.65 1.29 391.65-396.37 E3 33 AX-111600316 AX-109928860 2.74 4.63 1.18 BLUP 33 AX-111600316 AX-109928860 3.83 5.66 0.98 QTgw.CK4-cib-4B E2 64 AX-109480649 AX-109839454 5.5 7.49 -1.47 532.2-557.46 E4 64 AX-109480649 AX-109839454 4.4 7.13 -1.48 BLUP 64 AX-109480649 AX-109839454 4.44 6.43 -1.03 QTgw.CK4-cib-6A E1 68 AX-111276079 AX-108958326 9.92 20.21 2.36 455.95-485.16 E2 68 AX-111276079 AX-108958326 13.75 20.79 2.5 E3 69 AX-108958326 AX-110670175 6.76 13.19 2.03 E4 69 AX-108958326 AX-110670175 9.8 18.86 2.42 BLUP 68 AX-111276079 AX-108958326 14.23 23.28 1.99 QGw.CK4-cib-2D E1 94 AX-110010295 AX-109998182 5.4 9.59 -0.05 415.6-431.96 E2 93 AX-110010295 AX-109998182 3.12 4.63 -0.04 BLUP 95 AX-109998182 AX-109330666 4.41 5.14 -0.03 QGw.CK4-cib-6A E1 68 AX-111276079 AX-108958326 6.59 11.8 0.05 455.95-485.16 E2 68 AX-111276079 AX-108958326 8.81 14 0.06 E3 69 AX-108958326 AX-110670175 8.28 15.27 0.07 E4 68 AX-111276079 AX-108958326 8.57 15.31 0.07 BLUP 68 AX-111276079 AX-108958326 12.48 15.79 0.05 QGl.CK4-cib-5A.1 E1 0 AX-108793692 AX-108884876 15.58 16.29 0.12 0.69–2.43 E2 0 AX-108793692 AX-108884876 21.86 22.83 0.15 E4 0 AX-108793692 AX-108884876 12.28 13.67 0.1 BLUP 0 AX-108793692 AX-108884876 20.25 20 0.14 QGl.CK4-cib-5A.2 E1 53 AX-110028038 AX-108776997 9.14 9.11 0.09 256.67-287.81 E2 53 AX-110028038 AX-108776997 9.31 8.44 0.09 E4 53 AX-110028038 AX-108776997 5.9 6.21 0.07 BLUP 53 AX-110028038 AX-108776997 8.5 7.5 0.08 Note: PVE phenotypic variation explained; LOD logarithm of the odd; Add additive effect (Positive values indicate that alleles from CM42 are increasing the trait scores, and negative values indicate that alleles from K4 are increasing the trait scores). Three TGW QTLs were mapped on chromosomes 3D, 4B and 6A, respectively. QTgw.CK4-cib-3D was detected in two environments and the BLUP data, explaining 4.63%-5.66% of the phenotypic variance with the LOD value ranging from 2.74 to 3.93. QTgw.CK4-cib-4B was detected in two environments and the BLUP data and explained 6.43–7.49% of the phenotypic variance with the LOD value ranging from 4.4 to 5.5. The major QTL QTgw.cib.CK4-6A was stably identified in all environments, explaining 13.19%-23.28% of the phenotypic variance with the LOD value ranging from 6.76 to 14.23. The favorable alleles of the QTgw.CK4-cib-3D and QTgw.CK4-cib-6A were derived from CM42, and that of QTgw.CK4-cib-4B was from K4 (Table 4 ). Two GW QTLs were identified on chromosome 2D and 6A, respectively. QGw.CK4-cib-2D was detected in two environments and the BLUP data explaining 4.63%-9.59% of the phenotypic variance, and the LOD values ranged from 3.12 to 5.4; QGw.CK4-cib-6A detected in all environments was the major QTL, and explained 11.8%-15.79% of the phenotypic variance with the LOD value ranging from 6.59 to 12.48. The favorable alleles of the two QTL were contributed by CM42 (Table 4 ). Two GL QTLs, QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 , mapped on chromosome 5A were detected in three environments and the BLUP data. QGl.CK4-cib-5A.1 explaining 13.67%-20% of the phenotypic variance was the major QTL, and the LOD value ranged from 12.28 to 21.86; QGl.cib-5A.2 explained 3.56–9.41% of the phenotypic variance, and the LOD value ranged from 5.9 to 9.31. The favorable alleles of the two QTL were contributed by CM42 (Table 4 ). QTgw.cib.CK4-6A, QGw.CK4-cib-6A and QGl.CK4-cib-5A.1 were major QTLs, of which QTgw.cib.CK4-6A and QGw.CK4-cib-6A were co-located between AX-111276079 and AX-110670175, thus named QTgw/Gw.cib.CK4-6A (Table 4 ). Based on the flanking markers, we further evaluated their effect on corresponding trait in the CK4 population. As shown in Fig. 3 , the allele of CM42 significantly increased TGW by 8.518%-10.29%, and increased GW by 2.9%-5.08% at QTgw/Gw.cib.CK4-6A , and increased GL by 2.83%-3.78% at QGl.CK4-cib-5A.1 . QTL-by-environment interaction analysis Seven QTLs detected by the ICIM-BIP method were also detected by the ICIM-MET method, with the PVE (A) ranging from 2.05%-14.93% and PVE (AbyE) ranging from 0.06%-1.01%. Notably, the PVE (A) of major QTL QTgw.CK4-cib-6A , QGw.CK4-cib-6A and QGl.CK4-cib-5A.1 was 14.51%, 13.4% and 14.93%, while the PVE (AbyE) was only 0.08%, 0.52% and 1.01%, respectively (Table S2). These results suggested that these QTLs were minimally influenced by environmental factors and can be stably expressed in different environments. Validation of the major QTLs in different genetic backgrounds Among the major QTL, QTgw.cib.CK4-6A and QGw.CK4-cib-6A were co-located between AX-111276079 and AX-110670175, corresponding to the physical interval of 455.95Mb-485.16Mb on chromosome 6A; QGl.CK4-cib-5A.1 was located between AX-108793692 and AX-108884876, corresponding to the physical interval of 0.69Mb-2.43Mb on chromosome 5A. The KASP markers K_6A-484416474 and K_5A-2126929 within the physical interval of QTgw/Gw.cib.CK4-6A and QGl.CK4-cib-5A.1 , respectively, were developed and used to trace these QTL in different genetic backgrounds (Table S3). For the QTgw/Gw.cib.CK4-6A , the allele of CM42 significantly increased TGW by 9.4%-13.06%, and increased GW by 2.24%-3.47% among different environments in the CC population (Fig. 4 ). For the QGl.CK4-cib-5A.1 , the allele of CM42 significantly increased GL by 2.93%-3.78% in the CC population, and by 2.64% and 3.25% in the BC 1 F 2 and BC 2 F 2 populations, respectively (Fig. 4 ). The effects of grain size-related QTLs on TGW and GNS QGw.CK4-cib-6A , QGw.CK4-cib-2D , QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 were QTLs for grain size-related traits, thus we further evaluated their effects on TGW and GNS. As shown in the Fig. 5 , the allele of CM42 at QGw.CK4-cib-6A significantly increased TGW by 8.15%, but reduced GNS by 6.56%; the allele of K4 at QGw.CK4-cib-2D significantly increased TGW by 4.33% without affecting the GNS. For the QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 , the alleles of CM42 significantly increased TGW 2.91% and 2.84%, respectively, without affecting the GNS. The effects of QGw.CK4-cib-2D , QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 pyramiding on TGW and GNS As QGw.CK4-cib-2D , QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 significantly increased TGW with no penalty on GNS, we therefore further analyzed their additive effect on TGW and GNS in the mapping population. As shown in the Fig. 6 , polymerizing QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 increased TGW by 5.71% with no significant effect on GNS, and when QGw.CK4-cib-2D , QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 were pyramided, the TGW was increased by 9.55% with no significant effect on GNS. Candidate genes prediction of QGl.CK4-cib-5A.1 Potential candidate genes of the major QTL were analyzed using the following approaches. Firstly, genes within the physical interval of major QTL were extracted from IWGSC RefSeq v1.1 annotation ( https://wheat-urgi.versailles.inra.fr/ ); then, spatiotemporal expression patterns of these genes were analyzed in the Hexaploid Wheat Expression Database ( http://202.194.139.32/expression/wheat.html ); finally, sequence and expression difference of potential candidate genes were analyzed between parents. Among the major QTL, QTgw.cib.CK4-6A and QGw.CK4-cib-6A were co-located in a ~ 30Mb physical interval, which needs to be further narrowed for candidate gene analysis. For the QGl.CK4-cib-5A.1 , it was located between 0.69Mb and 2.43Mb on chromosome 5A (Fig. 7 ). A BLAST search on the CS physical map disclosed 27 genes within this interval (Table S4). Temporal and spatial expression analysis of these genes using public expression data showed that TraesCS5A02G001400, TraesCS5A02G001500, TraesCS5A02G002700, TraesCS5A02G003300 and TraesCS5A02G003400 exhibited high expression in grain (Fig. 7 ), suggesting that they may be related to grain development. Thus, we further analyzed their expression between the parental lines in the grain 10 days after flowering (Table S3). Significant differential expressions were detected in TraesCS5A02G001400, TraesCS5A02G001500, TraesCS5A02G002700 and TraesCS5A02G003400 (Fig. 7 ). In addition, non-synonymous SNP mutations or indel were identified in the CDS of TraesCS5A02G001400, TraesCS5A02G002700, TraesCS5A02G003300 and TraesCS5A02G003400 (Figure S2). Notably, TraesCS5A02G001400, TraesCS5A02G002700 and TraesCS5A02G003400 both had sequence variations causing amino acid change and significant differential expressions between parental lines, suggesting that they likely are the key candidate genes of QTgw/Gw.CK4-cib-5A.1. Discussion Grain weight, an important component of wheat yield, is a complex trait determined by multiple QTLs/genes. QTL analysis is a well-established and widely-used method for dissecting the genetic basis of complex traits in crops (Yang et al. 2012 ). In this study, seven stable QTLs for TGW, GL and GW were identified in a DH population derived from the cross between CM42 and K4 (Table 4 ). CM42 is an elite wheat variety with large grain and long spike, and had a high yield potential in Sichuan and the Yangzi River region of China (Yang et al. 2009 ). Over the past few decades, more than thirty wheat varieties have been bred using CM42 as one of the parents. Among the QTL detected in this study, the favorable alleles of five QTLs were contributed by the parent CM42 (Table 4 ). The polymerization of these QTLs in CM42 partially explains its large grain and high yield. Comparison of QTLs detected in this study to those reported previously To date, numerous QTLs/genes associated with grain weight and size have been reported in different genetic populations. To further discern relationships between QTL detected in this study and those reported previously, we compared their physical intervals on the CS genome. QTgw.CK4-cib-3D was located between AX-111600316 and AX-111600316 with an interval of 391.65Mb-396.37Mb on the chromosome arm 3DL (Table 4 ), which was physically separated from the cloned gene of TGW on chromosome 3D, including TaLAX1 at 456.53Mb and TaERF3-3D at 434.210Mb (He et al. 2021 ; Jia et al. 2021 ), respectively. Moreover, there were no reported QTL for TGW that was overlapped with it, indicating that QTgw.CK4-cib-3D may be a novel QTL for TGW. QTgw.CK4-cib-4B was located between AX-109480649 and AX-109839454 with an interval of 532.2Mb-557.46Mb on the chromosome arm 4BL (Table 4 ), which was overlapped with that of QTKW.ndsu.4B.2 (Guan et al. 2020 ) and Qtgw.ahau-4B.1 (Cao et al. 2020a ), indicating that they are likely alleles. QTgw/Gw.CK4-cib-6A was located in the interval of 455.95Mb-485.16Mb on the chromosome arm 6AL (Table 4 ), which was physically separated from the cloned genes of TGW on chromosome 6A, including TaGW2 at 237.76Mb (Zhang et al. 2018 ), KAT-2A at 606.97Mb (Chen et al. 2020 ), TaPIN1-6A at 543.39Mb (Yao et al. 2021 ) and TaPRR1-A1 at 429.39Mb (Sun et al. 2020 ), but overlapped with the QTKW-6A.1 detected by Lee et al ( 2014 ). Although it is not clear whether the two loci are the same, QTgw/Gw.CK4-cib-6A is worth further fine mapping and cloning due to its strong genetic effect on TGW. For the QGw.CK4-cib-2D , it was located between AX-110010295 and AX-109330666, corresponding to the physical interval of 415.6Mb-431.96Mb on the chromosome arm 2DL (Table 4 ). DA1 and TaGW7 located at 8.29Mb and 128.39Mb on chromosome 2D, respectively, were physically separated from it (Wang et al. 2019a ; Liu et al. 2020a ; Mora-Ramirez et al. 2021 ). In addition, there was no reported QTL for GW in this interval, indicating that QGw.CK4-cib-2D may be a novel QTL for GW. QGl.CK4-cib-5A.1 was located in the interval of 0.69Mb-2.43Mb on the chromosome arm 5AS (Table 4 ), where no QTL and cloned gene of grain size have been reported in the previous study, suggesting that it is likely a novel QTL for GL. QGl.CK4-cib-5A.2 was located between AX-110028038 and AX-108776997, corresponding to the physical interval of 256.67Mb-287.81Mb on the chromosome arm 5AL (Table 4 ). QGl.cau-5A.1 was detected in the vicinity of this region (Wu et al. 2015 ), indicating that they are likely alleles. Application in wheat high-yield breeding GNS and TGW, as the important components of wheat yield, are usually mutually restrictive. An increase in one of them may lead to a decrease in another, which in turn limits the overall improvement of wheat yield (Zhai et al. 2018 ; Isham et al. 2021 ). Thus, how to overcome the trade-off between GNS and TGW is a critical goal in high-yield wheat breeding. In this study, QTgw/Gw.CK4-cib-6A significantly increased TGW by 8.15%, but reduced GNS by 6.56% (Fig. 5 ), which impairs its contribution to grain yield and limited its application in wheat high-yield breeding. Thus, QTgw/Gw.CK4-cib-6A needs to be utilized in combination with other GNS-related QTLs in the wheat breeding practice. Interestingly, the other QTL for grain size, QGw.CK4-cib-2D , QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 , showed a significant increase in TGW without a significant decrease in GNS (Fig. 5 ), suggesting that they may be excellent QTLs to break the trade-off between GNS and TGW in wheat high-yield breeding. GL and GW are important grain size-related traits and significantly correlated with TGW, which is consistent with the results of this study (Table 3 ), and indicated that optimization of both GW and GL could improve TGW. In addition, previous studies have shown that GL and GW are genetically independent and controlled by distinct genetic components (Gegas et al. 2010 ; Cristina et al. 2016 ; Kumar et al. 2016 ). Therefore, pyramiding multiple loci of GW and GL may be an effective approach to combine their positive roles in optimization of grain size. In this study, the polymerization of QGw.CK4-cib-2D , QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 showed a significant additive effect on TGW, which resulted in an increase of TGW by 9.55%, but did not reduce GNS (Fig. 6 ). The results suggested that the combination of these QTLs may be favorable for optimizing grain size, and has potential application value in wheat high-yield breeding. Potential candidate gene of QGl.CK4-cib-5A.1 Within the physical interval of QGl.CK4-cib-5A.1 , there were 27 predicted genes in the CS genome. Analysis of expression patterns, sequence differences and expression levels indicated that TraesCS5A02G001400, TraesCS5A02G002700 and TraesCS5A02G003400 may be key candidate genes for QGl.CK4-cib-5A.1 (Fig. 7 , Figure S2). TraesCS5A02G001400 encodes a NONPHOTOTROPIC HYPOCOTYL 3 (NPH3) domain-containing protein that is an essential signaling component for phototropism and plays a fundamental role in plant growth and development through regulating the polar transport of auxin (Furutani et al. 2007 ; Roberts et al. 2011 ). TraesCS5A02G002700 encodes a sucrose phosphatase (SPP) protein that is homologous to rice OSPP1 and is a key regulatory enzyme in the pathway of sucrose biosynthesis. Sucrose is the main carbohydrate product of photosynthesis in plants, which is transported long distance from source leaves to roots, flowers and grains to support their growth and development (Chen et al. 2005 ). Jing et al. ( 2022 ) found that TaSPP had a significant correlation with wheat grain size by haplotype analysis. TraesCS5A02G003400 encodes a nodulin-like domain-containing protein that is involved in the catabolism of sucrose and starch (Panahabadi et al. 2021 ). Khan et al. ( 2007 ) found that mutations in this gene in Arabidopsis reduced the amount of starch present in tissues. Taken together, these findings provide a foundation for the subsequent fine mapping and map-based cloning of QGl.CK4-cib-5A.1 . Conclusion In the present study, seven stable QTLs for TGW, GW and GL were identified on chromosome 2D, 4B, 5A and 6A. Of them, QTgw.CK4-cib-3D , QGw.CK4-cib-2D and QGl.CK4-cib-5A.1 were likely novel, and the QTgw/Gw.CK4-cib-6A and QGl.CK4-cib-5A.1 were major validated in different populations by developing KASP markers. QTgw/Gw.CK4-cib-6A significantly increased TGW, but reduced GNS. Interestingly, QGw.CK4-cib-2D , QGl.CK4-cib-5A.1 and QGl.CK4-cib-5A.2 , showed a significant increase in TGW, but did not affect GNS. Moreover, the polymerization of them had a significant addition effect on TGW without reducing GNS. Furthermore, the candidate genes of QGl.CK4-cib-5A.1 were predicted by expression, sequence and function annotation analysis. These results lay a foundation for subsequent map-based cloning of these QTL and their further utilization in wheat breeding aiming to yield improvement. Declarations Authorship contribution TL undertook the field trials, data analysis and KASP markers development, and drafted this manuscript. YT, ZL, JW, JZ, QL and FH participated in phenotyping. JL, and HZ discussed results and revised the manuscript. ZL, JL and WY provided us the parental lines. GD bred the K4, assisted in field trials, discussed results and revised the manuscript. HL designed the experiments, guided the entire study, participated in data analysis, discussed results and revised the manuscript. Conflict of interest All authors declare that they have no conflict of interest. Ethical standards The authors declare that this research has no human and animal participants and that the experiments comply with the current laws of China. Funding This work was supported by the National Key R&D Program of China (2024YFD1201200), Natural Science Foundation of Sichuan Province (2023NSFSC1169), the National Natural Science Foundation of China (32301790, 32272125) and Sichuan Provincial Agricultural Department Innovative Research Team (SCCXTD-2024-11). Acknowledgments We thank anonymous reviewers and editors for their critical reading and revisions of this article. References Cao J, Shang Y, Xu D, et al (2020a) Identification and Validation of New Stable QTLs for Grain Weight and Size by Multiple Mapping Models in Common Wheat. Front Genet 11:. https://doi.org/10.3389/fgene.2020.584859 Cao P, Liang X, Zhao H, et al (2019) Identification of the quantitative trait loci controlling spike-related traits in hexaploid wheat (Triticum aestivum L.). Planta 250:1967–1981. https://doi.org/10.1007/s00425-019-03278-0 Cao S, Xu D, Hanif M, et al (2020b) Genetic architecture underpinning yield component traits in wheat. 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Mol Breed 34:1097–1107. https://doi.org/10.1007/s11032-014-0102-7 Zhao DS, Li QF, Zhang CQ, et al (2018) GS9 acts as a transcriptional activator to regulate rice grain shape and appearance quality. Nat Commun 9:. https://doi.org/10.1038/s41467-018-03616-y Zheng TC, Zhang XK, Yin GH, et al (2011) Genetic gains in grain yield, net photosynthesis and stomatal conductance achieved in Henan Province of China between 1981 and 2008. F Crop Res 122:225–233. https://doi.org/10.1016/j.fcr.2011.03.015 Supplementary Files FigureS1.tif.jpg Figure S1. Schematic representation of the syntenic relationships between a given marker in wheat genetic and physical maps. FigureS2.tif.jpg Figure S2. CDS alignment of TraesCS5A02G001400 (a), TraesCS5A02G001500 (c, d), TraesCS5A02G002700 (b), TraesCS5A02G003300 (e) and TraesCS5A02G003400 (f) between parents. SupplementaryTables.docx Cite Share Download PDF Status: Published Journal Publication published 25 May, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Accept 21 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers invited by journal 17 Apr, 2025 Editor assigned by journal 15 Apr, 2025 First submitted to journal 15 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6202356","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444283349,"identity":"64a3fcdb-cc27-4f6d-84ba-f999986baeb8","order_by":0,"name":"Tao Li","email":"","orcid":"","institution":"Chinese Academy of Science Chengdu Institute of Biology","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Li","suffix":""},{"id":444283350,"identity":"e6988159-9a43-4c8f-ba7d-8a68acdbe0a8","order_by":1,"name":"Yanyan Tang","email":"","orcid":"","institution":"Chinese Academy of Science Chengdu Institute of 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Li","suffix":""},{"id":444283360,"identity":"45e0c47e-883b-4a67-9463-02da1626f31a","order_by":11,"name":"Wuyun Yang","email":"","orcid":"","institution":"Sichuan Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wuyun","middleName":"","lastName":"Yang","suffix":""},{"id":444283361,"identity":"e05e767d-7842-4023-8337-d63286c56bf6","order_by":12,"name":"Guangbin Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYDCCA0DM2GDDwCZBopY00rUcZmAgWgvf8d7DL7/uOC/PJ93A+OEHg10eQS2SZ86lWcueuW3YJnOAWbKHIbmYoBaDGzlmxpJttxPYJBIYpIHuTGwgqOX+G5CWcyAtzL+J03KDx/jhx7YDIC1sxNkieSbHjJnxTDLQLwfbLHsMkglr4Tt+xvjjzx128vKzmw/f+FFhR1gLELBJ84BpRqBiAyLUAwHzxx/EKRwFo2AUjIKRCgCIzT3cbl+ZAgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0050-0713","institution":"CIB: Chinese Academy of Science Chengdu Institute of Biology","correspondingAuthor":true,"prefix":"","firstName":"Guangbin","middleName":"","lastName":"Deng","suffix":""},{"id":444283362,"identity":"f96f85f4-413d-452a-a494-ef97dfce0da9","order_by":13,"name":"Hai Long","email":"","orcid":"","institution":"Chinese Academy of Science Chengdu Institute of Biology","correspondingAuthor":false,"prefix":"","firstName":"Hai","middleName":"","lastName":"Long","suffix":""}],"badges":[],"createdAt":"2025-03-11 11:17:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6202356/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6202356/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-025-04912-0","type":"published","date":"2025-05-25T15:57:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80875802,"identity":"0ca96166-ecdf-4534-b6d0-715fb704da0d","added_by":"auto","created_at":"2025-04-18 06:27:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":595116,"visible":true,"origin":"","legend":"\u003cp\u003eGrain size of Chuanmai 42, Kechengmai 4, and some representative lines of the CK4 population.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6202356/v1/6124b45a8798a3ec1d4653c6.jpg"},{"id":80875052,"identity":"a381a9e8-5bd5-423f-913e-cddc1fa90edd","added_by":"auto","created_at":"2025-04-18 06:19:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1900442,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of TGW (a), GL (b) and GW (c) in different environments.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6202356/v1/031617bb8c35866554ff64b2.jpg"},{"id":80875811,"identity":"76341bc8-e2a6-485d-bee9-283e2e09bf3d","added_by":"auto","created_at":"2025-04-18 06:27:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1515414,"visible":true,"origin":"","legend":"\u003cp\u003eThe interval of major QTL \u003cem\u003eQTgw/Gw.cib-CK4-6A \u003c/em\u003e(\u003cstrong\u003ea\u003c/strong\u003e)\u003cem\u003e \u003c/em\u003eand \u003cem\u003eQGl.cib-CK4-5A.1 \u003c/em\u003e(\u003cstrong\u003eb\u003c/strong\u003e)\u003cem\u003e \u003c/em\u003eon the genetic linkage map, and their effects on corresponding traits in different environments. *** represents significance difference at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6202356/v1/2970235c943391e411da443f.jpg"},{"id":80875055,"identity":"a9fba475-c8c9-4962-9f6a-c10e1cfbbd59","added_by":"auto","created_at":"2025-04-18 06:19:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1142450,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of major QTL \u003cem\u003eQTgw/Gw.cib-CK4-6A\u003c/em\u003e and\u003cem\u003e QGl.cib-CK4-5A.1\u003c/em\u003e in different populations. (\u003cstrong\u003ea\u003c/strong\u003e) The effect of \u003cem\u003eQTgw/Gw.cib-CK4-6A \u003c/em\u003eon GW in the CC population among different environments; (\u003cstrong\u003eb\u003c/strong\u003e) The effect of \u003cem\u003eQTgw/Gw.cib-CK4-6A \u003c/em\u003eon TGW in CC population among different environments; (\u003cstrong\u003ec\u003c/strong\u003e) The effect of \u003cem\u003eQGl.cib-CK4-5A.1 \u003c/em\u003eon GL in the CC population among different environments; (\u003cstrong\u003ed\u003c/strong\u003e) The effect of \u003cem\u003eQGl.cib-CK4-5A.1 \u003c/em\u003eon GL in the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population;\u003cem\u003e \u003c/em\u003e(\u003cstrong\u003ee\u003c/strong\u003e) The effect of \u003cem\u003eQGl.cib-CK4-5A.1 \u003c/em\u003eon GL in the BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population; *, ** and *** represent significant difference at \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, 0.01 and 0.001, respectively.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6202356/v1/0cb740f871b982a3a1dc722f.jpg"},{"id":80875804,"identity":"d69eab20-2c09-4014-a11e-08cacf7e3cf4","added_by":"auto","created_at":"2025-04-18 06:27:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":525336,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eGw.cib-CK4-6A\u003c/em\u003e (\u003cstrong\u003ea\u003c/strong\u003e), \u003cem\u003eGw.cib-CK4-2D \u003c/em\u003e(\u003cstrong\u003eb\u003c/strong\u003e), \u003cem\u003eQGl.cib-CK4-5A.1 \u003c/em\u003e(\u003cstrong\u003ec\u003c/strong\u003e) and \u003cem\u003eQGl.cib-CK4-5A.2 \u003c/em\u003e(\u003cstrong\u003ed\u003c/strong\u003e)\u003cem\u003e \u003c/em\u003eon\u003cem\u003e \u003c/em\u003eTGW and GNS. *, ** and *** represent significant difference at \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, 0.01 and 0.001, respectively; ns represents no significant difference.\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6202356/v1/41e507e24585ed048fffd382.jpg"},{"id":80875815,"identity":"429035d8-7a48-4cb2-bc47-493d463e3aa4","added_by":"auto","created_at":"2025-04-18 06:27:43","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":357324,"visible":true,"origin":"","legend":"\u003cp\u003ePyramiding effects of \u003cem\u003eGw.cib-CK4-2D\u003c/em\u003e, \u003cem\u003eQGl.cib-CK4-5A.1\u003c/em\u003e and \u003cem\u003eQGl.cib-CK4-5A.2 \u003c/em\u003eon\u003cem\u003e \u003c/em\u003eTGW (\u003cstrong\u003ea\u003c/strong\u003e) and GNS (\u003cstrong\u003eb\u003c/strong\u003e). “+” indicates alleles increasing TGW, while “-” indicates alleles reducing TGW; * and ** represent significant difference at \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 and 0.01, respectively; ns represents no significant difference.\u003c/p\u003e","description":"","filename":"Figure6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6202356/v1/ed0ac22a41e44ceb09e0c3da.jpg"},{"id":80875808,"identity":"4879cb15-9e42-4db9-8f27-f3f8bf0356c3","added_by":"auto","created_at":"2025-04-18 06:27:42","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":689888,"visible":true,"origin":"","legend":"\u003cp\u003eCandidate genes analysis of \u003cem\u003eQGl.CK4-cib-5A.1. \u003c/em\u003e(\u003cstrong\u003ea\u003c/strong\u003e) the genetic interval of \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e; (\u003cstrong\u003eb\u003c/strong\u003e) The physical interval of \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e; (\u003cstrong\u003ec\u003c/strong\u003e) Genes within the \u003cem\u003eQGl.cib-5A.1\u003c/em\u003einterval and their expression pattern in grain; (\u003cstrong\u003ed\u003c/strong\u003e) Expression analysis of TraesCS5A02G001400, TraesCS5A02G001500, TraesCS5A02G002700, TraesCS5A02G003300 and TraesCS5A02G003400 in grain between parental lines. *** represent significant difference at \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001; ns represents no significant difference.\u003c/p\u003e","description":"","filename":"Figure7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6202356/v1/8f3fdee67bd7c3a2fbd62c70.jpg"},{"id":83460644,"identity":"bb68f477-a749-490a-a315-c63d4d5570e8","added_by":"auto","created_at":"2025-05-26 16:13:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8145225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6202356/v1/9d0b8f5c-3e2f-4ffb-8285-d94cb1980070.pdf"},{"id":80875054,"identity":"742cfffd-4437-4c7d-8ddb-a773a49c96c6","added_by":"auto","created_at":"2025-04-18 06:19:42","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1597178,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1.\u003c/strong\u003e Schematic representation of the syntenic relationships between a given marker in wheat genetic and physical maps.\u003c/p\u003e","description":"","filename":"FigureS1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6202356/v1/1807b715f4cb007825e0efd6.jpg"},{"id":80876035,"identity":"6537e4db-4d3f-409c-ab2d-55f17acf2181","added_by":"auto","created_at":"2025-04-18 06:35:42","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3367050,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2.\u003c/strong\u003e CDS alignment of TraesCS5A02G001400 (\u003cstrong\u003ea\u003c/strong\u003e), TraesCS5A02G001500 (\u003cstrong\u003ec\u003c/strong\u003e,\u003cstrong\u003e d\u003c/strong\u003e), TraesCS5A02G002700 (\u003cstrong\u003eb\u003c/strong\u003e), TraesCS5A02G003300 (\u003cstrong\u003ee\u003c/strong\u003e) and TraesCS5A02G003400 (\u003cstrong\u003ef\u003c/strong\u003e) between parents.\u003c/p\u003e","description":"","filename":"FigureS2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6202356/v1/540b11d9d0bffc1451a95329.jpg"},{"id":80875050,"identity":"ad677c78-3b01-40dd-80d4-2be056278fde","added_by":"auto","created_at":"2025-04-18 06:19:41","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":34035,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6202356/v1/7cb04b2607a0511a5d2a674b.docx"}],"financialInterests":"","formattedTitle":"Genetic identification and characterization of quantitative trait loci for wheat grain size-related traits independent of grain number per spike","fulltext":[{"header":"Key message","content":"\u003cp\u003eSeven stable QTLs for TGW, GW and GL were identified, and two major QTLs were stable in various genetic backgrounds and environments.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is one of the most important staple crops, providing approximately one-fifth of the dietary calories for humans (Xu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the global farmland loss, climate change, and population increase may exacerbate food insufficiency in the future (Ren et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It was estimated that wheat production must be increased by about 60% by 2050 to ensure global food and nutritional security (Langridge \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Curtis and Halford \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, sustained growth in wheat yield is of paramount importance.\u003c/p\u003e \u003cp\u003eGrain yield improvement continues to be a huge challenge because it is a complex quantitative trait controlled by polygene and heavily affected by environments (Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Grain yield is mainly determined by three major component traits, viz. the number of spikes per unit area, grain number per spike (GNS), and thousand grain weight (TGW), which show less sensitivity to environment and have higher heritability than grain yield itself. Historically, the three major component traits contributed greatly to the increased wheat yield potential released in the past decades directly and indirectly (Zheng et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kong et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). TGW is mainly determined by grain size that are usually depicted by grain length (GL), grain width (GW) and grain thickness. Thus, identification of genetic loci controlling TGW and grain size-related traits is essential to elucidate the genetic basis of wheat yield and facilitates the genetic improvement of varieties with high yield.\u003c/p\u003e \u003cp\u003eSimilar to other yield-related traits, TGW and grain size-related traits are quantitative traits controlled by multiple genes in crops. In rice, many genes involved in determining grain weight and grain size have been reported, such as \u003cem\u003eDLT\u003c/em\u003e, \u003cem\u003eGLW7\u003c/em\u003e, \u003cem\u003eGW8\u003c/em\u003e and \u003cem\u003eGS3\u003c/em\u003e mainly regulating GL (Li et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mao et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tong et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Si et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), \u003cem\u003eGW2\u003c/em\u003e, \u003cem\u003eGS5\u003c/em\u003e and \u003cem\u003eGW5\u003c/em\u003e mainly controlling GW (Song et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Weng et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and \u003cem\u003eGSK2\u003c/em\u003e, \u003cem\u003eWTG1\u003c/em\u003e, \u003cem\u003eTGW6\u003c/em\u003e, \u003cem\u003eqTGW3\u003c/em\u003e, \u003cem\u003eGL3\u003c/em\u003e and \u003cem\u003eGS9\u003c/em\u003e contributing to variations of both GL, GW and TGW (Weijers and Friml \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ishimaru et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xia et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, relatively fewer genes controlling grain weight and grain size have been reported in wheat due to the huge and complicated genome. For example, \u003cem\u003eTasg-D1\u003c/em\u003e encodes a Ser/Thr protein kinase glycogen synthase kinase3 (STKc_GSK3), which affects grain size by modulating brassinosteroid signaling and is a key gene that determine the round grain of the Indian dwarf wheat (\u003cem\u003eTriticum sphaerococcum\u003c/em\u003e) (Cheng et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eTGW1\u003c/em\u003e was cloned from the \u003cem\u003etetraploid\u003c/em\u003e wheat, which encodes a 3-ketoacyl-CoA thiolase (KAT-2B) gene and affects TGW by jasmonic acid signaling pathway (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, \u003cem\u003eTaGW2\u003c/em\u003e, \u003cem\u003eTaGW7\u003c/em\u003e, \u003cem\u003eTaCKX6\u003c/em\u003e, \u003cem\u003eTaTGW6\u003c/em\u003e, \u003cem\u003eTaGASR7\u003c/em\u003e, \u003cem\u003eTaGL3\u003c/em\u003e, \u003cem\u003eTaGS-D1\u003c/em\u003e, \u003cem\u003eTaSus2\u003c/em\u003e, \u003cem\u003eTaSAP1-A1\u003c/em\u003e and \u003cem\u003eTaTPP-6AL1\u003c/em\u003e obtained by homologous cloning were also involved in the regulation of grain development (Zhang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chang et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dong et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hanif et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Geng et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQuantitative trait loci (QTLs) mapping, the first step towards map-based gene cloning, is an effective approach to parse the genetic basis of complex quantitative traits like TGW and grain size-related traits. With the rapid development of wheat genome sequencing and high-throughput genotyping technologies, numerous QTLs for TGW and grain size-related traits have been identified on all wheat chromosomes to date (Cao et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Duan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Isham et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, only a few were major, environmentally stable, and validated in different genetic backgrounds, which greatly hindered their potential utilization in yield improvement by marker-assisted selection (MAS) breeding. Therefore, continuous detection and validation of novel superior genetic loci are vital for yield improvement in wheat breeding.\u003c/p\u003e \u003cp\u003eIn this study, we developed a haploid (DH) population derived from the cross between two elite wheat cultivars Chuanmai 42 (CM42) and Kechengmai 4 (K4). Combined with phenotyping in multiple field trials and a high-quality genetic map constructed with the Wheat 55K SNP array, chromosomal regions controlling TGW, GL and GW were identified. The major QTLs were further validated in different populations through developing the Kompetitive Allele Specific PCR (KASP) markers. Moreover, the independent and pyramidal effects of the identified QTL on TGW and GNS were analyzed to evaluate their application potential in wheat breeding. Finally, candidate genes of \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e were predicted to lay a foundation for subsequent map-based cloning.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and field trials\u003c/h2\u003e \u003cp\u003eA DH population (CK4 population) containing 188 lines was developed based on a cross between wheat varieties CM42 and K4 and used for QTL mapping. The recombinant inbred line (RIL) population (CC population) derived from the cross between CM42 and wheat variety Chuanmai 39 (CM39) was employed to validate the major QTL. The line 2 of the CK4 population containing the major QTL \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e was backcrossed with K4 to construct the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e and BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e populations for the validation of the major QTL. All populations and their parents were planted at Shuangliu (SHL, 103˚52'E, 30˚34'N) and Shifang (SHF, 104˚11'E, 31˚6'N) in Sichuan Province, China with a randomized block design. Each line was planted in a five-row plot with a row length of 2.0 m with 50 seeds per row, at a row spacing of 0.3 m. The CK4 population was planted at SHF in 2018 (E1) and 2019 (E3) cropping season and SHL in 2018 (E2) and 2019 (E4) cropping season, the CC populations were planted at SHF and SHL in 2018\u0026ndash;2019 cropping season, and the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e and BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e populations were planted at SHF in 2023 crop seasons with 15 cm spacing between individuals. Field management and disease control were performed according to the common practices for wheat production.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhenotypic evaluation and Statistical analysis\u003c/h3\u003e\n\u003cp\u003eAt maturity, 10 representative major spikes of each line in the CK4 and CC populations were harvested and manually threshed for evaluating TGW, GL, GW and GNS using the WSeen SC-G software (WSeen Corporation, Hangzhou, China). For the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e and BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e populations, the main spike of each plant was harvested and manually threshed for evaluating TGW, GL and GW.\u003c/p\u003e \u003cp\u003eBest linear unbiased prediction (BLUP) data which were used for combined QTL detection, correlation analysis, and effect analysis were calculated by using the R package \u0026ldquo;\u003cem\u003elme4\u003c/em\u003e\u0026rdquo;. Phenotypic correlations, frequency distribution and Student \u003cem\u003et\u003c/em\u003e test were computed in SPSS v20 software (IBM SPSS, Armonk, USA). Broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) was calculated following the formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}^{2}={{\\sigma\\:}}_{\\text{g}}^{2}/（{{\\sigma\\:}}_{\\text{g}}^{2}+{{\\sigma\\:}}_{\\text{g}\\text{e}}^{2}/\\text{n}+{{\\sigma\\:}}_{\\text{g}\\text{r}}^{2}/\\text{r}{+{\\sigma\\:}}_{{\\epsilon\\:}}^{2}/\\text{n}\\text{r}）\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\sigma\\:}}_{\\text{g}}^{2}\\)\u003c/span\u003e\u003c/span\u003e represents the genotype variance, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\sigma\\:}}_{\\text{g}\\text{e}}^{2}\\)\u003c/span\u003e\u003c/span\u003e represents the genotype by environmental effect variance, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{{\\sigma\\:}}_{\\text{g}\\text{r}}^{2}\\)\u003c/span\u003e\u003c/span\u003e represents the genotype by year's effect variance, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\sigma\\:}}_{{\\epsilon\\:}}^{2}\\)\u003c/span\u003e\u003c/span\u003e represents the residual variance, n is the number of environments, and r is the years (Smith et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGenotyping and genetic map construction\u003c/h3\u003e\n\u003cp\u003eGenomic DNA of the CK4 lines and their parents was extracted from young leaves using the Plant Genomic DNA Kit (TransGen Biotech Corporation, Beijing, China). DNA integrity and quantity were checked on agarose gels and spectrophotometry, respectively. Genotyping of the CK4 lines and their parents was performed by China Golden Marker Corporation (Beijing, China) using the wheat 55K SNP array. The homozygous SNPs with polymorphism between parents were selected to construct the high-density genetic linkage map by using the IciMapping v4.2 (Meng et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and JoinMap v4.0 software. Firstly, the \u0026ldquo;BIN\u0026rdquo; function of the IciMapping v4.2 software was used to place the SNPs with no recombination into one bin, and remove the SNPs with a missing rate\u0026thinsp;\u0026ge;\u0026thinsp;20%. To reduce the complexity of QTL mapping, only one marker was selected as a delegate from each bin to construct the genetic linkage map. Then, the \u0026ldquo;Grouping\u0026rdquo; function in JoinMap v4.0 was employed to creat groups with limit of detection (LOD) score values ranging from 2 to 8; Finally, the Kosambi mapping function command in JoinMap v4.0 was used to order the bin markers and calculate genetic distances with the parameters being set as LOD\u0026thinsp;\u0026ge;\u0026thinsp;5 and round\u0026thinsp;=\u0026thinsp;3. Lines with a SNP deletion rate of \u0026ge;\u0026thinsp;20% were removed in the CK4 population, where the heterozygous SNPs were treated as missing.\u003c/p\u003e\n\u003ch3\u003eQTL analysis\u003c/h3\u003e\n\u003cp\u003eQTL mapping was performed by using the inclusive composite interval mapping (ICIM) function in the Biparental Populations (BIP) module of the IciMapping v4.2 software, where the BLUP data was treated as an additional environment. The parameters were set as follows: walking step\u0026thinsp;=\u0026thinsp;1 cM, PIN\u0026thinsp;=\u0026thinsp;0.005, and logarithm of odds (LOD) threshold\u0026thinsp;\u0026ge;\u0026thinsp;3. QTL that was stably identified in at least three environments and BLUP data, and can explain greater than 10% of phenotypic variation was considered a major QTL. Additionally, the Met (multi-environmental trials) module was utilized to detect interactions between environments and QTLs with the parameters of walking step\u0026thinsp;=\u0026thinsp;1 cM and LOD\u0026thinsp;\u0026ge;\u0026thinsp;5.\u003c/p\u003e\n\u003ch3\u003eMarker development and major QTL validation\u003c/h3\u003e\n\u003cp\u003eAccording to the results of QTL mapping, the KASP markers closely linked with identified QTL were designed following the specific methods outlined in the KASP primer design manual (Rasheed et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The KASP markers that were successfully developed and polymorphic between parents were used to genotype the validation populations. According to the results of genotyping, phenotypic values of homozygous genotypes of different QTLs were statistically analyzed to evaluate their effects on the corresponding trait in different populations.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCandidate gene discovery for major QTL\u003c/h2\u003e \u003cp\u003eThe sequence variations of the candidate genes were analyzed between parental lines based on the whole-genome sequencing data of CM42 and K4 that were previously obtained using the Illumina NovaSeq 6000 platform (Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Samples of the developing grain in parents were collected in the field 10 days after flowering and quickly stored in liquid nitrogen for RNA extraction. Total RNA was extracted using the Plant RNA Extraction Kit (TransGen Biotech, Beijing, China), which was used to synthesize the first-strand complementary DNA (cDNA) using the TransScript cDNA Synthesis SuperMix (TransGen Biotech, Beijing, China). The RT-qPCR reactions were performed in Bio-Rad CFX96 real-time PCR system with 10ul reaction volumes consisting of 1\u0026micro;l cDNA, 5\u0026micro;l ChamQ Universal SYBR qPCR Master Mix, 0.2\u0026micro;l forward primers, 0.2\u0026micro;l reverse primers, and 3.6\u0026micro;l RNase-free H\u003csub\u003e2\u003c/sub\u003eO. The wheat elongation factor \u003cem\u003eTaEF\u003c/em\u003e (GenBank accession number Q03033) was used as the internal reference. Threshold values (CT) included in the analyses were based on three biological and three technical replicates. The relative expression levels were calculated using the comparative \u003csup\u003e∆∆\u003c/sup\u003eCT method.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic evaluation\u003c/h2\u003e \u003cp\u003eTGW, GW and GL of the CK4 lines and parents were evaluated in four environments, and significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were observed between CM42 and K4 in all environments, and CM42 had heavier and bigger grain than K4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the CK4 population, TGW, GW and GL displayed clear transgressive segregation with TGW ranging from 27.65g to 72.77g, GW from 2.5mm to 4.07 and GL from 5.32mm to 8.03mm (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This suggested that the favorable alleles originate from both parents. ANOVA analysis showed that the broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) of TGW, GW and GL was 0.81, 0.77 and 0.89, respectively, and these traits exhibited significant positive correlations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in different environments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that they were primarily influenced by genetic factors. The frequency distribution showed that the TGW, GW and GL appeared to be normally distributed over multiple environments, indicating that they were quantitative traits controlled by multiple genes, and suitable for QTL analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, correlation assessment based on BLUP data indicated that TGW, GW and GL were significantly and positively correlated with each other, but negatively correlated with GNS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhenotypic variation and heritability of TGW, GL and GW in the CK4 population among different environments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eParents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eThe CK4 population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCM42\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCV(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.79***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e32.96-66.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e49.46\u0026thinsp;\u0026plusmn;\u0026thinsp;5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.06***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e36.77-64.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e51.58\u0026thinsp;\u0026plusmn;\u0026thinsp;4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.02***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e27.65-56.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e44.32\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e33.99-59.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e48.03\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e38.8-58.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.58***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e3-3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e3.02-3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.55***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e2.5-3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e2.95-3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e3.21-3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.87***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e5.94-7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e6.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.83***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e5.89-7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.02***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e5.32-7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e6.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e6.09-7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e6.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e6.05-7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e6.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: \u003cem\u003eCV\u003c/em\u003e coefficient of variation; \u003cem\u003eBLUP\u003c/em\u003e best linear unbiased prediction; \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e broad-sense heritability; \u003cem\u003e***\u003c/em\u003e represents significance difference at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficients of TGW, GL andGW among different environments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: *** represents the correlation is significant at the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficients between TGW, GL,GW and GNS according to the BLUP data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGNS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *** represents the correlation is significant at the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHigh-density genetic linkage map\u003c/h2\u003e \u003cp\u003eAmong the 53,063 SNP markers in the wheat 55K SNP array, 18182 polymorphic SNP between CM42 and K4 were used for subsequent analysis. By performing Bin and grouping functions, 1723 non-redundant bin markers were retained and grouped into 23 linkage groups representing the 21 wheat chromosomes (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Of which, chromosomes 5A and 6D contained two groups. The A, B and D subgenomes contained 650 (37.72%), 629 (36.51%) and 444 (25.77%) bin markers, respectively (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Moreover, five lines of the CK4 population with the missing SNPs\u0026thinsp;\u0026ge;\u0026thinsp;20% were removed. Finally, a high-density genetic linkage map consisting of 1723 bin markers and 183 lines was constructed for CK4 population, which spanned the total genetic distance of 2790.15 cM with an average interval distance of 1.62 cM per bin marker (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The quality of linkage map was assessed by analyzing the collinearity of genetic positions of SNPs to their physical positions (Mb) in the CS genome. As expected, a high consistence was observed between the genetic and physical map on most chromosomes, suggesting the genetic linkage map constructed in this study was satisfied for QTL analysis. (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eQTL mapping in individual environment\u003c/h2\u003e \u003cp\u003eUsing the ICIM-BIP method, a total of seven stable QTLs for TGW, GL and GW were identified on chromosome 2D, 4B, 5A and 6A, explaining 4.2%-23.28% of the phenotypic variance (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStable QTL for TGW, GL and GW identified in the CK4 population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\u003eEnvironments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePosition (cM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeft Marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRight 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(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePhysical interval (Mb)\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\u003eQTgw.CK4-cib-3D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-111600316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-109928860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e391.65-396.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-111600316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-109928860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-111600316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-109928860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQTgw.CK4-cib-4B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-109480649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-109839454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e532.2-557.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-109480649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-109839454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-109480649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-109839454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQTgw.CK4-cib-6A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-111276079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108958326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e455.95-485.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-111276079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108958326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-108958326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-110670175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-108958326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-110670175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-111276079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108958326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-110010295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-109998182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e415.6-431.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-110010295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-109998182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-109998182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-109330666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQGw.CK4-cib-6A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-111276079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108958326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e455.95-485.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-111276079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108958326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-108958326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-110670175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-111276079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108958326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-111276079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108958326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-108793692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108884876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.69\u0026ndash;2.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-108793692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108884876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-108793692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108884876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-108793692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108884876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-110028038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108776997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e256.67-287.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-110028038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108776997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-110028038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108776997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-110028038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAX-108776997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: \u003cem\u003ePVE\u003c/em\u003e phenotypic variation explained; \u003cem\u003eLOD\u003c/em\u003e logarithm of the odd; \u003cem\u003eAdd\u003c/em\u003e additive effect (Positive values indicate that alleles from CM42 are increasing the trait scores, and negative values indicate that alleles from K4 are increasing the trait scores).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThree TGW QTLs were mapped on chromosomes 3D, 4B and 6A, respectively. \u003cem\u003eQTgw.CK4-cib-3D\u003c/em\u003e was detected in two environments and the BLUP data, explaining 4.63%-5.66% of the phenotypic variance with the LOD value ranging from 2.74 to 3.93. \u003cem\u003eQTgw.CK4-cib-4B\u003c/em\u003e was detected in two environments and the BLUP data and explained 6.43\u0026ndash;7.49% of the phenotypic variance with the LOD value ranging from 4.4 to 5.5. The major QTL \u003cem\u003eQTgw.cib.CK4-6A\u003c/em\u003e was stably identified in all environments, explaining 13.19%-23.28% of the phenotypic variance with the LOD value ranging from 6.76 to 14.23. The favorable alleles of the \u003cem\u003eQTgw.CK4-cib-3D\u003c/em\u003e and \u003cem\u003eQTgw.CK4-cib-6A\u003c/em\u003e were derived from CM42, and that of \u003cem\u003eQTgw.CK4-cib-4B\u003c/em\u003e was from K4 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwo GW QTLs were identified on chromosome 2D and 6A, respectively. \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e was detected in two environments and the BLUP data explaining 4.63%-9.59% of the phenotypic variance, and the LOD values ranged from 3.12 to 5.4; \u003cem\u003eQGw.CK4-cib-6A\u003c/em\u003e detected in all environments was the major QTL, and explained 11.8%-15.79% of the phenotypic variance with the LOD value ranging from 6.59 to 12.48. The favorable alleles of the two QTL were contributed by CM42 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwo GL QTLs, \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e, mapped on chromosome 5A were detected in three environments and the BLUP data. \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e explaining 13.67%-20% of the phenotypic variance was the major QTL, and the LOD value ranged from 12.28 to 21.86; \u003cem\u003eQGl.cib-5A.2\u003c/em\u003e explained 3.56\u0026ndash;9.41% of the phenotypic variance, and the LOD value ranged from 5.9 to 9.31. The favorable alleles of the two QTL were contributed by CM42 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eQTgw.cib.CK4-6A, QGw.CK4-cib-6A\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e were major QTLs, of which \u003cem\u003eQTgw.cib.CK4-6A\u003c/em\u003e and \u003cem\u003eQGw.CK4-cib-6A\u003c/em\u003e were co-located between AX-111276079 and AX-110670175, thus named \u003cem\u003eQTgw/Gw.cib.CK4-6A\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Based on the flanking markers, we further evaluated their effect on corresponding trait in the CK4 population. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the allele of CM42 significantly increased TGW by 8.518%-10.29%, and increased GW by 2.9%-5.08% at \u003cem\u003eQTgw/Gw.cib.CK4-6A\u003c/em\u003e, and increased GL by 2.83%-3.78% at \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQTL-by-environment interaction analysis\u003c/h2\u003e \u003cp\u003eSeven QTLs detected by the ICIM-BIP method were also detected by the ICIM-MET method, with the PVE (A) ranging from 2.05%-14.93% and PVE (AbyE) ranging from 0.06%-1.01%. Notably, the PVE (A) of major QTL \u003cem\u003eQTgw.CK4-cib-6A\u003c/em\u003e, \u003cem\u003eQGw.CK4-cib-6A\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e was 14.51%, 13.4% and 14.93%, while the PVE (AbyE) was only 0.08%, 0.52% and 1.01%, respectively (Table S2). These results suggested that these QTLs were minimally influenced by environmental factors and can be stably expressed in different environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the major QTLs in different genetic backgrounds\u003c/h2\u003e \u003cp\u003eAmong the major QTL, \u003cem\u003eQTgw.cib.CK4-6A\u003c/em\u003e and \u003cem\u003eQGw.CK4-cib-6A\u003c/em\u003e were co-located between AX-111276079 and AX-110670175, corresponding to the physical interval of 455.95Mb-485.16Mb on chromosome 6A; \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e was located between AX-108793692 and AX-108884876, corresponding to the physical interval of 0.69Mb-2.43Mb on chromosome 5A. The KASP markers \u003cem\u003eK_6A-484416474\u003c/em\u003e and \u003cem\u003eK_5A-2126929\u003c/em\u003e within the physical interval of \u003cem\u003eQTgw/Gw.cib.CK4-6A\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e, respectively, were developed and used to trace these QTL in different genetic backgrounds (Table S3). For the \u003cem\u003eQTgw/Gw.cib.CK4-6A\u003c/em\u003e, the allele of CM42 significantly increased TGW by 9.4%-13.06%, and increased GW by 2.24%-3.47% among different environments in the CC population (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For the \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e, the allele of CM42 significantly increased GL by 2.93%-3.78% in the CC population, and by 2.64% and 3.25% in the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e and BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e populations, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe effects of grain size-related QTLs on TGW and GNS\u003c/h2\u003e \u003cp\u003e \u003cem\u003eQGw.CK4-cib-6A\u003c/em\u003e, \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e, \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e were QTLs for grain size-related traits, thus we further evaluated their effects on TGW and GNS. As shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the allele of CM42 at \u003cem\u003eQGw.CK4-cib-6A\u003c/em\u003e significantly increased TGW by 8.15%, but reduced GNS by 6.56%; the allele of K4 at \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e significantly increased TGW by 4.33% without affecting the GNS. For the \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e, the alleles of CM42 significantly increased TGW 2.91% and 2.84%, respectively, without affecting the GNS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe effects of\u003c/b\u003e \u003cb\u003eQGw.CK4-cib-2D\u003c/b\u003e, \u003cb\u003eQGl.CK4-cib-5A.1\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eQGl.CK4-cib-5A.2\u003c/b\u003e \u003cb\u003epyramiding on TGW and GNS\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAs \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e, \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e significantly increased TGW with no penalty on GNS, we therefore further analyzed their additive effect on TGW and GNS in the mapping population. As shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e, polymerizing \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e increased TGW by 5.71% with no significant effect on GNS, and when \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e, \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e were pyramided, the TGW was increased by 9.55% with no significant effect on GNS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCandidate genes prediction of\u003c/b\u003e \u003cb\u003eQGl.CK4-cib-5A.1\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePotential candidate genes of the major QTL were analyzed using the following approaches. Firstly, genes within the physical interval of major QTL were extracted from IWGSC RefSeq v1.1 annotation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wheat-urgi.versailles.inra.fr/\u003c/span\u003e\u003cspan address=\"https://wheat-urgi.versailles.inra.fr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); then, spatiotemporal expression patterns of these genes were analyzed in the Hexaploid Wheat Expression Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://202.194.139.32/expression/wheat.html\u003c/span\u003e\u003cspan address=\"http://202.194.139.32/expression/wheat.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); finally, sequence and expression difference of potential candidate genes were analyzed between parents. Among the major QTL, \u003cem\u003eQTgw.cib.CK4-6A\u003c/em\u003e and \u003cem\u003eQGw.CK4-cib-6A\u003c/em\u003e were co-located in a\u0026thinsp;~\u0026thinsp;30Mb physical interval, which needs to be further narrowed for candidate gene analysis. For the \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e, it was located between 0.69Mb and 2.43Mb on chromosome 5A (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). A BLAST search on the CS physical map disclosed 27 genes within this interval (Table S4). Temporal and spatial expression analysis of these genes using public expression data showed that TraesCS5A02G001400, TraesCS5A02G001500, TraesCS5A02G002700, TraesCS5A02G003300 and TraesCS5A02G003400 exhibited high expression in grain (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e), suggesting that they may be related to grain development. Thus, we further analyzed their expression between the parental lines in the grain 10 days after flowering (Table S3). Significant differential expressions were detected in TraesCS5A02G001400, TraesCS5A02G001500, TraesCS5A02G002700 and TraesCS5A02G003400 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In addition, non-synonymous SNP mutations or indel were identified in the CDS of TraesCS5A02G001400, TraesCS5A02G002700, TraesCS5A02G003300 and TraesCS5A02G003400 (Figure S2). Notably, TraesCS5A02G001400, TraesCS5A02G002700 and TraesCS5A02G003400 both had sequence variations causing amino acid change and significant differential expressions between parental lines, suggesting that they likely are the key candidate genes of \u003cem\u003eQTgw/Gw.CK4-cib-5A.1.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGrain weight, an important component of wheat yield, is a complex trait determined by multiple QTLs/genes. QTL analysis is a well-established and widely-used method for dissecting the genetic basis of complex traits in crops (Yang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this study, seven stable QTLs for TGW, GL and GW were identified in a DH population derived from the cross between CM42 and K4 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). CM42 is an elite wheat variety with large grain and long spike, and had a high yield potential in Sichuan and the Yangzi River region of China (Yang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Over the past few decades, more than thirty wheat varieties have been bred using CM42 as one of the parents. Among the QTL detected in this study, the favorable alleles of five QTLs were contributed by the parent CM42 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The polymerization of these QTLs in CM42 partially explains its large grain and high yield.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eComparison of QTLs detected in this study to those reported previously\u003c/h2\u003e \u003cp\u003eTo date, numerous QTLs/genes associated with grain weight and size have been reported in different genetic populations. To further discern relationships between QTL detected in this study and those reported previously, we compared their physical intervals on the CS genome. \u003cem\u003eQTgw.CK4-cib-3D\u003c/em\u003e was located between AX-111600316 and AX-111600316 with an interval of 391.65Mb-396.37Mb on the chromosome arm 3DL (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which was physically separated from the cloned gene of TGW on chromosome 3D, including \u003cem\u003eTaLAX1\u003c/em\u003e at 456.53Mb and \u003cem\u003eTaERF3-3D\u003c/em\u003e at 434.210Mb (He et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jia et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), respectively. Moreover, there were no reported QTL for TGW that was overlapped with it, indicating that \u003cem\u003eQTgw.CK4-cib-3D\u003c/em\u003e may be a novel QTL for TGW. \u003cem\u003eQTgw.CK4-cib-4B\u003c/em\u003e was located between AX-109480649 and AX-109839454 with an interval of 532.2Mb-557.46Mb on the chromosome arm 4BL (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which was overlapped with that of \u003cem\u003eQTKW.ndsu.4B.2\u003c/em\u003e (Guan et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and \u003cem\u003eQtgw.ahau-4B.1\u003c/em\u003e (Cao et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e), indicating that they are likely alleles. \u003cem\u003eQTgw/Gw.CK4-cib-6A\u003c/em\u003e was located in the interval of 455.95Mb-485.16Mb on the chromosome arm 6AL (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which was physically separated from the cloned genes of TGW on chromosome 6A, including \u003cem\u003eTaGW2\u003c/em\u003e at 237.76Mb (Zhang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), \u003cem\u003eKAT-2A\u003c/em\u003e at 606.97Mb (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), \u003cem\u003eTaPIN1-6A\u003c/em\u003e at 543.39Mb (Yao et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and \u003cem\u003eTaPRR1-A1\u003c/em\u003e at 429.39Mb (Sun et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but overlapped with the \u003cem\u003eQTKW-6A.1\u003c/em\u003e detected by Lee et al (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although it is not clear whether the two loci are the same, \u003cem\u003eQTgw/Gw.CK4-cib-6A\u003c/em\u003e is worth further fine mapping and cloning due to its strong genetic effect on TGW. For the \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e, it was located between AX-110010295 and AX-109330666, corresponding to the physical interval of 415.6Mb-431.96Mb on the chromosome arm 2DL (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eDA1\u003c/em\u003e and \u003cem\u003eTaGW7\u003c/em\u003e located at 8.29Mb and 128.39Mb on chromosome 2D, respectively, were physically separated from it (Wang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Mora-Ramirez et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, there was no reported QTL for GW in this interval, indicating that \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e may be a novel QTL for GW. \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e was located in the interval of 0.69Mb-2.43Mb on the chromosome arm 5AS (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), where no QTL and cloned gene of grain size have been reported in the previous study, suggesting that it is likely a novel QTL for GL. \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e was located between AX-110028038 and AX-108776997, corresponding to the physical interval of 256.67Mb-287.81Mb on the chromosome arm 5AL (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eQGl.cau-5A.1\u003c/em\u003e was detected in the vicinity of this region (Wu et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), indicating that they are likely alleles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eApplication in wheat high-yield breeding\u003c/h2\u003e \u003cp\u003eGNS and TGW, as the important components of wheat yield, are usually mutually restrictive. An increase in one of them may lead to a decrease in another, which in turn limits the overall improvement of wheat yield (Zhai et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Isham et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, how to overcome the trade-off between GNS and TGW is a critical goal in high-yield wheat breeding. In this study, \u003cem\u003eQTgw/Gw.CK4-cib-6A\u003c/em\u003e significantly increased TGW by 8.15%, but reduced GNS by 6.56% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which impairs its contribution to grain yield and limited its application in wheat high-yield breeding. Thus, \u003cem\u003eQTgw/Gw.CK4-cib-6A\u003c/em\u003e needs to be utilized in combination with other GNS-related QTLs in the wheat breeding practice. Interestingly, the other QTL for grain size, \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e, \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e, showed a significant increase in TGW without a significant decrease in GNS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting that they may be excellent QTLs to break the trade-off between GNS and TGW in wheat high-yield breeding.\u003c/p\u003e \u003cp\u003eGL and GW are important grain size-related traits and significantly correlated with TGW, which is consistent with the results of this study (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and indicated that optimization of both GW and GL could improve TGW. In addition, previous studies have shown that GL and GW are genetically independent and controlled by distinct genetic components (Gegas et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cristina et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, pyramiding multiple loci of GW and GL may be an effective approach to combine their positive roles in optimization of grain size. In this study, the polymerization of \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e, \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e showed a significant additive effect on TGW, which resulted in an increase of TGW by 9.55%, but did not reduce GNS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The results suggested that the combination of these QTLs may be favorable for optimizing grain size, and has potential application value in wheat high-yield breeding.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePotential candidate gene of\u003c/b\u003e \u003cb\u003eQGl.CK4-cib-5A.1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWithin the physical interval of \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e, there were 27 predicted genes in the CS genome. Analysis of expression patterns, sequence differences and expression levels indicated that TraesCS5A02G001400, TraesCS5A02G002700 and TraesCS5A02G003400 may be key candidate genes for \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Figure S2). TraesCS5A02G001400 encodes a NONPHOTOTROPIC HYPOCOTYL 3 (NPH3) domain-containing protein that is an essential signaling component for phototropism and plays a fundamental role in plant growth and development through regulating the polar transport of auxin (Furutani et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Roberts et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). TraesCS5A02G002700 encodes a sucrose phosphatase (SPP) protein that is homologous to rice \u003cem\u003eOSPP1\u003c/em\u003e and is a key regulatory enzyme in the pathway of sucrose biosynthesis. Sucrose is the main carbohydrate product of photosynthesis in plants, which is transported long distance from source leaves to roots, flowers and grains to support their growth and development (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Jing et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that \u003cem\u003eTaSPP\u003c/em\u003e had a significant correlation with wheat grain size by haplotype analysis. TraesCS5A02G003400 encodes a nodulin-like domain-containing protein that is involved in the catabolism of sucrose and starch (Panahabadi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Khan et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) found that mutations in this gene in \u003cem\u003eArabidopsis\u003c/em\u003e reduced the amount of starch present in tissues. Taken together, these findings provide a foundation for the subsequent fine mapping and map-based cloning of \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the present study, seven stable QTLs for TGW, GW and GL were identified on chromosome 2D, 4B, 5A and 6A. Of them, \u003cem\u003eQTgw.CK4-cib-3D\u003c/em\u003e, \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e were likely novel, and the \u003cem\u003eQTgw/Gw.CK4-cib-6A\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e were major validated in different populations by developing KASP markers. \u003cem\u003eQTgw/Gw.CK4-cib-6A\u003c/em\u003e significantly increased TGW, but reduced GNS. Interestingly, \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e, \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e, showed a significant increase in TGW, but did not affect GNS. Moreover, the polymerization of them had a significant addition effect on TGW without reducing GNS. Furthermore, the candidate genes of \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e were predicted by expression, sequence and function annotation analysis. These results lay a foundation for subsequent map-based cloning of these QTL and their further utilization in wheat breeding aiming to yield improvement.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eAuthorship contribution\u003c/h2\u003e \u003cp\u003eTL undertook the field trials, data analysis and KASP markers development, and drafted this manuscript. YT, ZL, JW, JZ, QL and FH participated in phenotyping. JL, and HZ discussed results and revised the manuscript. ZL, JL and WY provided us the parental lines. GD bred the K4, assisted in field trials, discussed results and revised the manuscript. HL designed the experiments, guided the entire study, participated in data analysis, discussed results and revised the manuscript.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eAll authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical standards\u003c/h2\u003e \u003cp\u003eThe authors declare that this research has no human and animal participants and that the experiments comply with the current laws of China.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2024YFD1201200), Natural Science Foundation of Sichuan Province (2023NSFSC1169), the National Natural Science Foundation of China (32301790, 32272125) and Sichuan Provincial Agricultural Department Innovative Research Team (SCCXTD-2024-11).\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe thank anonymous reviewers and editors for their critical reading and revisions of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCao J, Shang Y, Xu D, et al (2020a) Identification and Validation of New Stable QTLs for Grain Weight and Size by Multiple Mapping Models in Common Wheat. 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F Crop Res 122:225\u0026ndash;233. https://doi.org/10.1016/j.fcr.2011.03.015\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":false,"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-6202356/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6202356/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThousand-grain weight (TGW), mainly determined by grain length (GL) and width (GW), is an important yield component of wheat. In the study, combined with phenotyping in four field trials and a high-quality genetic map constructed with the Wheat 55K SNP array, a total of seven stable QTLs for TGW, GW and GL were identified in a doubled haploid (DH) population derived from the cross between Chuanmai 42 (CM42) and Kechengmai 4 (K4), in which \u003cem\u003eQTgw.CK4-cib-3D\u003c/em\u003e, \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e were novel, and \u003cem\u003eQTgw/Gw.CK4-cib-6A\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.1 \u003c/em\u003ewere major QTLs explaining more than 10% of the phenotypic variances. The effects of \u003cem\u003eQTgw/Gw.CK4-cib-6A\u003c/em\u003e and \u003cem\u003eQGl.CK4-cib-5A.1 \u003c/em\u003eon corresponding traitswere further validated in different populations by developing the Kompetitive Allele Specific PCR marker. \u003cem\u003eQTgw/Gw.CK4-cib-6A \u003c/em\u003esignificantly increased TGW while reducing GNS\u003cem\u003e.\u003c/em\u003e Interestingly, the other QTLs for grain size, \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e, \u003cem\u003eQGl.CK4-cib-5A.1 \u003c/em\u003eand \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003e, showed a significant increase in TGW, but did not affect GNS. Moreover, the polymerization of \u003cem\u003eQGw.CK4-cib-2D\u003c/em\u003e, \u003cem\u003eQGl.CK4-cib-5A.1 \u003c/em\u003eand \u003cem\u003eQGl.CK4-cib-5A.2\u003c/em\u003ehad a significant addition effect on TGW without reducing GNS, suggesting that these QTLs can work together as an excellent molecular module to break the trade-off between GNS and TGW in wheat high-yield breeding. By analysis of expression, sequence and function annotation TraesCS5A02G001400, TraesCS5A02G002700 and TraesCS5A02G003400 were predicted as the candidate genes for \u003cem\u003eQGl.CK4-cib-5A.1\u003c/em\u003e. Taken together, the present results lay a foundation for subsequent map-based cloning of these QTL and their utilization in wheat breeding.\u003c/p\u003e","manuscriptTitle":"Genetic identification and characterization of quantitative trait loci for wheat grain size-related traits independent of grain number per spike","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-18 06:19:37","doi":"10.21203/rs.3.rs-6202356/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2025-04-21T12:30:33+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-04-17T09:12:26+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-17T09:06:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-16T03:34:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2025-04-15T10:41:05+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":"96d4a908-772c-453d-992f-49b859fc62b6","owner":[],"postedDate":"April 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:09:30+00:00","versionOfRecord":{"articleIdentity":"rs-6202356","link":"https://doi.org/10.1007/s00122-025-04912-0","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2025-05-25 15:57:55","publishedOnDateReadable":"May 25th, 2025"},"versionCreatedAt":"2025-04-18 06:19:37","video":"","vorDoi":"10.1007/s00122-025-04912-0","vorDoiUrl":"https://doi.org/10.1007/s00122-025-04912-0","workflowStages":[]},"version":"v1","identity":"rs-6202356","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6202356","identity":"rs-6202356","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}