The selection and application of tiller number QTLs in modern wheat breeding

The selection and application of tiller number QTLs in modern wheat breeding | 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 The selection and application of tiller number QTLs in modern wheat breeding Xiangjun Lai, Zhiwei Zhu, Yuanfei Zhang, Tian Lu, Jinxia Qin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4226010/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 May, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted 4 You are reading this latest preprint version Abstract Tiller number is a critical factor influencing wheat plant structure and yield potential, yet the genetic underpinnings and implications for tiller breeding selection remain elusive. This study extensively investigates tiller number across 323 wheat accessions within nine diverse environments, unveiling a significant reduction in modern wheat cultivars compared to landraces, demonstrating a prevalent preference for lower tiller numbers in modern breeding. Through genome-wide association study (GWAS), four pivotal quantitative trait loci (QTLs) associated with tiller number were identified, with three extensively selected and preferentially integrated into diverse Chinese agroecological zones. Notably, haplotype analysis revealed that lower tiller haplotypes also have significant genetic effects in enhancing grain number and/or weight. These findings suggest a co-selection of lower tiller numbers and higher spike yield was adopted in modern high-yield breeding programs in China. Additionally, the proposed combinations of these haplotypes aim to optimize tiller numbers for wheat breeding. This study provides novel insights into the genetic basis and selection of tiller number QTLs for modern wheat breeding. Tiller number Genome-wide association study Haplotype selection and application Wheat breeding Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Wheat ( Triticum aestivum L.) is a critical crop for global food security, with production needing to increase by 50% to meet the projected human population demands by 2050 (Ciani et al. 2021 ; Yadav et al. 2019 ). Tillers play a pivotal role in wheat yield potential, directly influencing plant architecture and final grain production (Naruoka et al. 2011 ; Tavakol et al. 2015 ; Wang et al. 2018 ; Xie et al. 2016 ). Less tillers in wheat produced less productive spikes, thus hamper the final yield (Ma et al. 2007 ; Slafer and Andrade 1993 ; Zhang et al. 2022 ). However, excessive tillers can lead to increased water and nutrient absorption for biomass production, potentially reducing the proportion of productive spikes and final yield (Kebrom et al. 2012 ). How the breeding selection on tiller number in modern wheat breeding keeps largely unclear. Tiller number is controlled by genetic factors and strongly influenced by environmental conditions (Song et al. 2023 ; Wang et al. 2019 ; Xu et al. 2017 ). To date, nine important QTLs/genes ( tin1 , tin2 , tin3 , tin4 , tin5 , tin6 , fin , tn1 and dmc ) responsible for tiller inhibition have been identified in wheat (An et al. 2019 ; Dong et al. 2023 ; Kuraparthy et al. 2007 ; Richards and Research 1988; Schoen et al. 2023 ; Si et al. 2022 ; Spielmeyer and Richards 2004 ; Wang et al. 2022c ; Zhang et al. 2013 ), with numerous QTLs for tiller distributed across various wheat chromosomes (Lin et al. 2021 ; Saini et al. 2022 ). Nevertheless, only a few QTLs have been thoroughly investigated for their selection and application in modern cultivars (Wang et al. 2019 ; Xie et al. 2016 ), limiting their integration into breeding practices. Moreover, current studies on selective genetic footprints in modern wheat have predominantly focused on yield-related traits, offering limited insights into the genetic effects of tiller improvement (Cavanagh et al. 2013 ; Niu et al. 2023 ). This has made precise utilization of identified tiller loci challenging in wheat breeding practices. In this study, we identified four tiller number QTLs in a natural wheat population. Modern wheat varieties selected three low-tiller haplotypes to reduce tillering, yet these haplotypes surprisingly exhibited significant potential in increasing grains weight and/or grain numbers. These findings suggest that although lower tillering was favored in current wheat breeding, it was compensated by the development of higher spike yield. These results shed new light on modern wheat breeding selection and offer practical tiller number QTLs for wheat breeding programs. Materials and methods Plant materials and field trials A total of 323 wheat accessions were gathered worldwide (Table S1), comprising 61 landraces (LA), 49 breeding lines (BL), and 213 modern cultivars (MC) (Fig. 1 A, Table S1). These accessions encompassed cultivars from various wheat zones in China and abroad, reflecting a genetically diverse pool of germplasm. All 323 wheat accessions were planted across nine environments from 2018 to 2022 in Yangling (34°28’N, 108°07’E, altitude 517 m) and Chongzhou (30°63’N, 103°67’E, altitude 1300 m) in China (more details in Table S2). The accessions were randomly allocated to plots with row and column spacing of 1 m × 0.2 m, each with three independent replicates. Field management adhered to local wheat production practices. Phenotypic and data analysis Three plants of each plot were selected to assess both the total tiller number (TTN) and productive tiller number (PTN) throughout the wheat filling period. The raw data are available in https://iwheat.net/resource/ for download. The best linear unbiased estimate (BLUE) values for phenotypic data across environments were determined using a mixed linear model (MLM) in R (v3.6.1) with the lme4 package (Bates et al. 2015), which were then utilized for subsequent analysis. Histogram plots were depicted using the R (v3.6.1) package “ggplot” to illustrate the phenotypic distributions of TTN and PTN. Broad sense heritability ( h 2 ) for each trait was calculated using the BLUE values (Li et al. 2015 ). Phenotypic correlations between environments and among traits were computed using the “rcorr” function implemented in the R (v3.6.1) package, utilizing means and BLUE values over environments, respectively. Linkage disequilibrium (LD) and population structure analysis A total of 157,050 high-quality SNPs were employed for both GWAS and LD analyses (Wang et al. 2022b ; Liu et al. 2023a ; Liu et al. 2023b ), with respective densities of 12.97, 15.04, and 3.86 SNPs per Mb in the A, B, and D sub-genomes. To examine LD patterns, a 1% subset of all SNPs was randomly selected using the “--thin 0.01” parameter in PLINK1.9. Pairwise SNPs’ squared correlation coefficient ( r 2 ) was then calculated via PopLDdecay, specifying a “-MaxDist 2000” parameter. The population structure of the 323 collected cultivars was evaluated using unlinked markers ( r 2 = 0) in STRUCTURE software. This involved a burn-in phase of 1000 iterations followed by 1000 Monte Carlo Markov Chain replicates to estimate the optimal subpopulation number (k) within a range of 1–10. Ten replications were performed for each k to gauge the robustness of the inferred population structure. The delta k statistic, which tracks the rate of change in log probability of data between successive values, was utilized to estimate the subpopulation number. Additionally, principal component analysis (PCA) and kinship were computed using GAPIT version 3 in R (v3.6.1) to further analyze population structure, with results compared to those obtained from STRUCTURE. Genome-wide association study (GWAS) GWAS was conducted using SNPs derived from population RNA-seq (Wang et al. 2022b ) to identify marker-trait associations (MTAs) for tiller number. LD analyses and SNP density were previously analyzed (Wang et al. 2022b ; Liu et al. 2023a ; Liu et al. 2023b ). GWAS was implemented with GAPIT version 3 in R (v3.6.1) using the MLM, considering population structure and relative kinship. We employed a resampling-based multiple SNP model, as established in maize GWAS (Tian et al. 2011 ). For this method, 80% of the 323 wheat cultivars were randomly selected without replacement, and forward regression was performed, repeated 100 times. SNPs ( P < 1.0e − 4 ) selected in the regression model in ten or more resamples (RMIP ≥ 10) and detected in at least two environments were considered significant. The median of the P -values across the 100 analyses represented the P -value of the associated SNP. The adjacent associated markers were grouped together as one locus if the inter-marker distance is smaller than the average LD decay (3 Mb) for specific chromosome. The corresponding effect and R 2 (phenotypic variance explained) were estimated and outputted by R (v3.6.1). Validation of QTL genetic effects with segregation populations The F 5:6 population MK95×CS, comprising 115 lines, and the F 2 populations MEX92.1.1.1×Mingguangxiaomai, comprising 168 lines, were utilized for QTL validation. KASP marker development was facilitated using the PolyMarker website ( http://www.polymarker.info/ ), with ten KASP markers successfully developed based on SNP information from population transcriptomic sequencing (further details in Table S5). These markers were then applied to the two validation populations (F 2 population, MEX92.1.1.1×Mingguangxiaomai; F 5:6 population, MK95×CS). The amplification reaction procedures followed those outlined in our previous reports (Liu et al. 2023a ; Liu et al. 2023b ). Evaluation of genetic differentiation Nucleotide diversity ( π value) was calculated using a 500-kb sliding window with VCFtools (v0.1.14) (Danecek et al. 2011 ; Vilella et al. 2005 ) to estimate the degree of variability within each group (LA and MC). The π ratio ( π LA/ π MC) was then determined. QTLs were considered subjected to breeding selection if the SNP detected by GWAS within the selected interval had a π ratio ( π LA/ π MC) > 3. Haplotype analysis QTL data for the candidate genes were extracted from the obtained QTL dataset, which only included biallelic QTLs. Haplotype analysis was conducted in R (v3.6.1) to determine if the loci could induce phenotypic changes using Student’s t -test. The results were visualized using GraphPad Prism (v8.4.2). Statistics analysis Chi-square tests were conducted using the contingency table method with the chisq.test() function in R (v3.6.1). ANCOVA was employed to analyze interaction effects between different haplotype combinations, with population structure serving as a covariate (Westfall et al. 1999 ). Multiple comparative analyses were performed using the two-sided Dunn’s Kruskal–Wallis test ( P < 0.05). Results Changes in tiller number of modern cultivars during wheat improvement A total of 323 bread wheat accessions were collected around world, comprising 61 LA, 213 MC, and 49 BL (Fig. 1 A, Table S1). Phenotypic data for TTN and PTN were collected from nine environments (Table S1 and S2). Both traits exhibited significantly positive correlations across the nine environments. TTN and PTN demonstrated similar broad-sense heritability ( h 2 ) values to thousand grain weight (TGW) and grain number per spike (GNS), but lower than that of plant height (PH) (Table 1 ). This suggests that yield-related traits have higher environmental sensitivity compared to PH. Table 1 The correlation and broad sense heritability of TTN, PTN, TGW, GNS and PH in different environments. Trait Range of Correlation Coefficient (r) Median (r) h 2 ( %) TTN 0.20** ~ 0.82** 0.47** 88.3 PTN 0.13* ~ 0.81** 0.39** 85.3 TGW 0.27** ~ 0.77** 0.47** 82.0 GNS 0.21**~0.7788 0.47** 78.0 PH 0.44**~0.91** 0.85** 96.6 * P < 0.05; ** P < 0.01. TGW, thousand grain weight; GNS, grain number per spike; PH, plant height. h 2 , broad sense heritability. Further analysis of these multi-environmental data revealed significant reductions in both TTN and PTN in MC compared to LA (Fig. 1 B). Additionally, PTN exhibited a more pronounced decline than TTN, resulting in a significant reduction in the ratio of productive tillers in MC compared to LA (Fig. 1 B). Furthermore, tiller number showed substantial reductions in both Chinese modern cultivars (CMC) and introduced modern cultivars (IMC), with a greater reduction observed in CMC (Fig. 1 C). Taken together, tiller number is significantly reduced in modern wheat cultivars. Genome-wide association study of tiller number Analysis of population structure, kinship and PCA unveiled three subgroups of the population (Fig. 2 A-D), which is consistent with the classification of LA, MC, and BL. TTN and PTN exhibited continuous variation in the natural population (Fig. 2 E; Table S3), indicating the quantitative nature of both traits. A total of 166 SNPs were identified to be significantly associated with TTN and PTN ( P < 10 − 4 ) in more than two environments through resampling GWAS analysis (Table S4), leading to the identification of 15 QTLs (Table 2 ). Table 2 The stable QTLs of TTN and PTN identified in resampling GWAS. Trait QTL Chr Pos (Mbp) P value R 2 (%) RMIP Env QTLs detected in previous studies QTL Pos(Mbp) R² (%) Related traits TTN QTTN-2D.1 2D 10.7 3.58E-05 29.0-57.7 11–12 E9, BLUE AX-94675758 9.9 1.44 TTN, PTN QTTN-2D.2 2D 13.7–14.9 4.01E-05 25.3–61.4 10–64 E1, E5, E6, E8, E9, BLUE QTTN-2D.3 2D 16.3 2.91E-05 31.2–42.7 16–25 E5, E9 tin6 16.2–18.3 TTN, PTN QTTN-3D 3D 151.4 4.31E-05 37.1–61.3 14–21 E8, BLUE QTTN-4A.1 4A 196.6 2.78E-05 29.1–49.1 43–79 E8, E9 QTTN-4A.2 4A 387.9 2.73E-05 29.4–47.2 13–58 E8, E9 QTTN-4A.3 4A 450.7 3.64E-05 28.5–49.9 26–29 E8, E9 QTTN-5A 5A 684.3-685.7 3.81E-05 24.8–61.5 12–64 E6, E8, E9, BLUE Qltn.sicau-5A 688.1 8.6 TTN QTTN-6B.1 6B 250.7 1.95E-05 34.1–46.9 38–39 E3, E8 QTTN-6B.2 6B 446.1 3.67E-05 27.8–43.9 16–24 E5, E9 QTTN-6B.3 6B 503.5-514.1 1.29E-05 26.5–50 10–85 E8, E9 PTN QPTN-2D 2D 13.7–14.2 3.04E-05 18.8–60.3 10–68 E1, E5, E6, E8, E9, BLUE QPTN-5A 5A 684.3-685.7 3.45E-05 19.1–59.0 11–37 E6, E8, E9, BLUE Qltn.sicau-5A 688.1 8.6 TTN QPTN-6A 6A 16.5 3.59E-05 28.9–59.3 13 E8, BLUE MQTL6A-1 16.57–18.71 14.22 TTN, PTN QPTN-6B 6B 444.9 2.62E-05 26.3–40.1 22–72 E3, E5 Chr., Chromosome; Pos, Physical position of the QTL in the reference genome; Env, Environments; RMIP, the times of SNPs were selected in the regression model. Notably, three QTLs for TTN and PTN, QTTN-2D.1 , QTTN-5A and QPTN-6A , had been previously reported and were physically close to the QTLs identified in our study, indicating the reliability and stability of these QTLs in controlling tillering. QTTN-2D.1 , associated with both TTN and PTN, was approximately 1 Mb physically distant from the QTL we identified (Kumar et al. 2020 ). QTTN-5A , detected in both TTN and PTN, and was renamed as QT(P)TN-5A in this study (Wang et al. 2016 ). Another QTL, QPTN-6A , was found to affect both TTN and PTN and shared a similar physical location with our mapping (Bilgrami et al. 2020 ). Additionally, a novel QTL on Chromosome 2D exhibited a strong effect on both TTN and PTN, detected consistently in five environments, and was designated as QT(P)TN-2D.2 . Consequently, the four QTLs were selected for verification (Table S5) and subsequent analyses related to breeding selection and application. Effects of tiller number QTLs in different genetic backgrounds Within the LD block, two major haplotypes were identified in each of the four QTLs (Fig. 2 F and 2 G), with haplotype B exhibiting a significant effect in reducing TTN and/or PTN (Fig. 2 H-K), reflecting the selection for lower tillers in modern wheat cultivars. Therefore, we defined haplotype B, which significantly reduced the number of tillers, as the superior haplotype. To validate the role of the four QTLs in regulating tiller number, ten KASP markers were developed based on SNP information (Tables S5). The effects of QT(P)TN-2D.2 on regulating TTN and PTN were confirmed in an F 2 population (F 2 , MEX92.1.1.1×Mingguangxiaomai, Fig. 3 A and 3 B), while the effects of QT(P)TN-5A and QPTN-6A on regulating TTN and PTN were confirmed in an F 5:6 population (F 5:6 , MK95×CS, Fig. 3 A, 3 C and 3 D). Notably, the tiller number of heterozygous QT(P)TN-2D.2 Hap A/B was kept with that of QT(P)TN-2D.2 Hap A , but significantly higher than that of QT(P)TN-2D.2 Hap B in the F 2 population (Fig. 3 B), indicating that the low-tiller number is a recessive trait. Haplotypic selection analysis of tiller number QTLs in modern wheat cultivars Breeding selection signatures were determined based on the π ratio ( π LA/ π MC) for the LA and MC groups along the chromosomes of four QTLs using SNP profiles (Fig. 4 A). Compared to LA, haplotype B of QTTN-2D.1 , QT(P)TN-2D.2 and QT(P)TN-5A were positively selected in MC (Fig. 4 A). Further analysis revealed an increasing application frequencies of haplotype B of these three QTLs in CMC, especially in the Yellow and Huai wheat zone (Y&H) (Fig. 4 B). However, the application frequency of haplotype B of QPTN-6A only increased in IMC compared to LA (Fig. 4 B). These results support the selection for lower-tiller number in modern wheat breeding programs, but different tillering QTLs were adopted in CMC and IMC. Consistent with the above findings, the haplotype B of three QTLs accumulated more frequently in cultivars released after the year 2000 in Chinese Y&H (Fig. 4 C), resulting in a significant declined in tiller number (Fig. 4 D). These results suggest that superior alleles controlling low-tiller number are more preferred in Chinese modern wheat breeding. Analysis of haplotypic combinations for tiller number QTLs across different wheat zones To evaluate the accumulation of superior alleles for tillering during breeding and improvement, the number of haplotype B was counted for four QTLs in 323 wheat accessions. Most accessions regulated TTN and PTN by accumulating three and two elite haplotype B, respectively (Fig. 5 A). Notably, there was no difference in PTN between accessions with two or three superior alleles (Fig. 5 B), suggesting that pyramidalization of two superior alleles is sufficient to achieve an appropriate tiller number in modern wheat breeding. The selection of pairwise combinations of haplotypes in different wheat zones was further investigated in 213 MC, and demonstrated that the QTTN-2D.1b , QT(P)TN-2D.2b and QT(P)TN-5Ab exhibited significant additive effects when combined in pairs (Fig. S1). Further analysis found that the haplotypes combinations for lower tillers were positively selected in both CMC and IMC (Fig. 5 C) and manifested stability tiller number (variation coefficients of 4.8% and 5.1% for TTN and PTN, respectively) across different wheat zones in China, but lower than that in IMC (Fig. 5 D). These findings highlight the importance of pyramiding superior alleles for regulating tiller number and facilitating future wheat breeding efforts. Pleiotropic effects of four low-tiller number QTLs Tiller number directly influences wheat yield, a primary breeding target in modern wheat breeding. Therefore, the correlations between three key spike yield-related traits and TTN, PTN were evaluated in our population. Results showed significant negative correlations between GNS, GW, and TGW with TTN and PTN (Fig. 6 A). Further analysis revealed the negative correlations between TGW and TTN, PTN in MC, while no significant correlations were observed in LA (Fig. 6 B), indicating a co-selection of these traits in modern wheat breeding process. Phenotypic analysis and resampling GWAS confirmed the significant genetic effects of QTTN-2D.1b , QT(P)TN-2D.2b and QT(P)TN-5Ab in enhancing GNS, GW and TGW (Fig. 6 C). These findings suggest that there is synergistic selection for low tiller number and high spike yield in modern wheat breeding. Additional agronomic traits were investigated in our population for assessing the pleiotropy of these four QTLs. The results showed that QTTN-2D.1b , QT(P)TN-2D.2b and QT(P)TN-5Ab had significant effects in reducing PH, peduncle length (PL), flag leaf angle (FLAN) and flag leaf length (FLL) (Fig. S2; Table S6), suggesting co-selection of tillering, spike yield, and plant structure in modern wheat breeding. Discussion Tiller number plays a crucial role in wheat yield and plant architecture (Wang et al. 2019 ), yet the tiller breeding selection in modern wheat remains largely unclear. A previous study showed that tiller numbers decreased during wheat domestication to transition to a compact plant structure, facilitating easier management in terms of crop protection, irrigation, and fertilizer application (Zhang et al. 2019 ). Our findings suggest that modern wheat breeding continues to prioritize this reduction trend, especially in China (Fig. 1 B). Consistently, only the lower tiller haplotypes of tiller QTLs were selected in modern wheat breeding (Fig. 4 A), providing the genetic basis for the decrease in tiller numbers in MC. These findings suggest that a breeding selection for lower tillers occurred in modern wheat breeding practices, similarly to the perspectives in modern rice breeding, which tends to favor varieties with moderate to reduced tillers (Jiao et al. 2010 ; Springer 2010 ; Wang et al. 2022a ). Less tillers may allow wheat to focus its nutrients on limited flower and grain development, thereby enhancing grain weight and number (Ren et al. 2018 ; Xu et al. 2017 ). In this study, a significant negative correlation between spike yield traits (grain number and grain weight) and tiller number in wheat cultivars was observed (Fig. 6 A), supporting the notion that lower tiller numbers may contribute to grain yield improvement, as previous evidences showing that certain genes can increase wheat yield by reducing tiller number and simultaneously enhancing grain size and number (Gupta et al. 2023 ; Jun et al. 2011; Niu et al. 2023 ). In this study, all three selected haplotypes of lower tillers had a significant genetic effect in enhancing spike yield (Fig. 6 C), indicating a co-selection of lower tillers but higher spike yield in modern high-yield breeding. Considering the continuous improvement in yield as a primary breeding objective, with a focus on selecting grain and spike traits (Gegas et al. 2010 ; Luo et al. 2023 ), the low tiller count in co-selection would be a by-product of the breeding selection for higher grain weight and/or more grain number. Compared to CMC, the IMC showed less reduction in tiller number, indicating that reducing tillers may not be the only option to enhance yield in world-wide modern wheat breeding. In fact, previous investigations also showed that tiller number is indeed increased in the MC of the United States (Smith et al. 2020 ; Niu et al. 2023 ). This divergent selection on tiller number suggests that having more tillers could also be another option to increase modern wheat yield, which is regarded as beneficial for varieties to adapt to diverse environmental challenges with more tillers (Smith et al. 2020 ; Niu et al. 2023 ). A higher utilization frequency of haplotypes of lower tillers is observed in the varieties released after the year 2000 in the Y&H, resulting in around a 10 percent reduction in tiller number (Fig. 4 D), suggesting that the reduction of tiller number may still be a key selection direction in current wheat breeding. However, insufficient tillers in wheat can lead to a reduction in leaf area, impacting nutrient uptake, overall plant growth, and hampering the final yield (Slafer and Andrade 1993 ). It would not be a remarkable reduction in tiller numbers in practice theory. However, the selection for low tiller numbers may keep continue if the increase in spike yield outweighs the decrease yield caused by tiller reduction by the same gene or genomic region. Declarations Authors’ contribution statement Shengbao Xu and Jinxia Qin designed the concept and experiments. Xiangjun Lai, Yuanfei Zhang, Tian Lu performed field investigation. Xiangjun Lai, Zhiwei Zhu analyzed the data. Xiangjun Lai, Jinxia Qin and Shengbao Xu wrote the manuscript. All the authors were involved in the revision of the manuscript and approved the final manuscript. Acknowledgments This research was supported by “Integration of Two Chains” Key Research and Development Projects of Shaanxi Province “Wheat Seed Industry Innovation Project” and Key R&D of Yangling Seed Industry Innovation Center (Ylzy-xm-01). Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability statement All raw data, models, or code generated or used during the study are available from the corresponding author by request. The phenotypes and SNP data are available in https://iwheat.net/resource/ for free download. 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Theor Appl Genet 129(3):603–612 Wang Z, Shi H, Yu S, Zhou W, Li J, Liu S, Deng M, Ma J, Wei Y, Zheng Y, Liu Y (2019) Comprehensive transcriptomics, proteomics, and metabolomics analyses of the mechanisms regulating tiller production in low-tillering wheat. Theor Appl Genet 132(8):2181–2193 Wang Z, Wu F, Chen X, Zhou W, Shi H, Lin Y, Hou S, Yu S, Zhou H, Li C, Liu Y (2022c) Fine mapping of the tiller inhibition gene TIN4 contributing to ideal plant architecture in common wheat. Theor Appl Genet 135(2):527–535 Westfall PH, Hochberg Y, Rom D, Wolfinger R, Tobias R (1999) Advances in multiple comparisons and multiple tests using the SAS system. SAS Institutex, NC Xie Q, Mayes S, Sparkes DL (2016) Optimizing tiller production and survival for grain yield improvement in a bread wheat × spelt mapping population. Ann Bot 117(1):51–66 Xu T, Bian N, Wen M, Xiao J, Yuan C, Cao A, Zhang S, Wang X, Wang H (2017) Characterization of a common wheat ( Triticum aestivum L.) high-tillering dwarf mutant. Theor Appl Genet 130(3):483–494 Yadav OP, Singh DV, Dhillon BS, Mohapatra TJC (2019) India's evergreen revolution in cereals. Curr Sci 116(11):1805–1808 Zhang J, Wu J, Liu W, Lu X, Yang X, Breeding AGJM (2013) Genetic mapping of a fertile tiller inhibition gene, ftin , in wheat. Mol Breed 31(2):441–449 Zhang X, Jia H, Li T, Wu J, Nagarajan R, Lei L, Powers C, Kan CC, Hua W, Liu Z, Chen C, Carver BF, Yan L (2022) TaCol-B5 modifies spike architecture and enhances grain yield in wheat. Science 376:180–183 Zhang X, Lin Z, Wang J, Liu H, Zhou L, Zhong S, Li Y, Zhu C, Liu J, Lin Z (2019) The tin1 gene retains the function of promoting tillering in maize. Nat Commun 10(1):5608 Supplementary Files SupplementaryFigs.docx Supplementary Fig. 1 The interactions and combinations analysis of four tiller number QTLs. Supplementary Fig. 2 The effects of four low-tiller number QTLs on different agronomic traits. SupplementaryTables.xlsx Supplemental Table 1. The information and phenotypes of 323 wheat accessions in eight environments. Supplemental Table 2. The information of wheat cultivars sowing. Supplemental Table 3. The phenotypic data statistics of total tiller number and productive tiller number observed in nine environments. Supplemental Table 4. Resampling GWAS result. Supplemental Table 5. Primers of KASP markers for four QTLs. Supplemental Table 6. The interactions effects of four tiller number QTLs on different agronomic traits. Cite Share Download PDF Status: Published Journal Publication published 24 May, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted Reviewers agreed at journal 15 May, 2024 Reviewers invited by journal 13 May, 2024 Editor assigned by journal 08 Apr, 2024 First submitted to journal 05 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-4226010","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301824275,"identity":"3a75386b-c13e-4673-9d24-7a0e61b0dc4d","order_by":0,"name":"Xiangjun Lai","email":"","orcid":"","institution":"Northwest A\u0026F University College of Agronomy","correspondingAuthor":false,"prefix":"","firstName":"Xiangjun","middleName":"","lastName":"Lai","suffix":""},{"id":301824276,"identity":"c7322fba-6877-4157-8440-8bdddf64a1d7","order_by":1,"name":"Zhiwei Zhu","email":"","orcid":"","institution":"Northwest A\u0026F University College of Agronomy","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Zhu","suffix":""},{"id":301824277,"identity":"8a088cd1-3097-4c99-99d1-da2b71983624","order_by":2,"name":"Yuanfei Zhang","email":"","orcid":"","institution":"Northwest A\u0026F University College of Agronomy","correspondingAuthor":false,"prefix":"","firstName":"Yuanfei","middleName":"","lastName":"Zhang","suffix":""},{"id":301824278,"identity":"cff40dc0-eca6-4400-ab03-4bb24de0d3fd","order_by":3,"name":"Tian Lu","email":"","orcid":"","institution":"Northwest A\u0026F University College of Agronomy","correspondingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"Lu","suffix":""},{"id":301824279,"identity":"9b4c6402-957a-4a9d-bb38-d4ae0a445d09","order_by":4,"name":"Jinxia Qin","email":"","orcid":"","institution":"Northwest A\u0026F University College of Agronomy","correspondingAuthor":false,"prefix":"","firstName":"Jinxia","middleName":"","lastName":"Qin","suffix":""},{"id":301824280,"identity":"6d396366-284b-4fa6-9e0f-a16e72143996","order_by":5,"name":"Shengbao Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYHACNmaGCgjrAwMDM7FazoAZjDOI18LYRooWgxvpzx4XzruTOH9G8sMGhgrrxAb2swfwapGckWNuPHPbs8QNN9IMGxjOpCc28OQl4NXCL5HDJs277XDuBokE8weMbYcTGyR4DPB7RCL9mTTvnMO582ekf2xg/EeEFn6JBDNp3obDuQ03cgwbGBuI0CLZ88ZMesaxw/UbzrwpbEg4lm7cxpODX4vBcaDDCmoOG8u3p29s+FBjLdvPfga/FlSQAPIdCepHwSgYBaNgFOAAAMfJRrQi4ZbLAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2167-5341","institution":"Northwest A\u0026F University College of Agronomy","correspondingAuthor":true,"prefix":"","firstName":"Shengbao","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-04-06 06:34:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4226010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4226010/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-025-04908-w","type":"published","date":"2025-05-24T15:57:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56929541,"identity":"3d01280c-465e-4679-9739-b272f2a3b181","added_by":"auto","created_at":"2024-05-22 09:27:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe changes in tiller number during wheat improvement.\u003c/strong\u003e(A) The population of the 323 wheat accessions. LA, landraces; MC, modern cultivars; BL, breeding lines. Y\u0026amp;H, Yellow and Huai wheat zone; YTR, Yangtze River wheat zone; OTW, Other wheat zone in China; IMC, Introduced modern cultivars. The numbers and percentage on the circular pie chart indicate the count and proportion of wheat cultivars. (B) The differences in tiller numberand ratio of productive tiller between landraces (LA) and modern cultivars (MC). \u003cem\u003eP\u003c/em\u003e indicates the \u003cem\u003eP\u003c/em\u003e-value based on the Student’s \u003cem\u003et-\u003c/em\u003etest. The percentage represents the ratio of (the tiller number of MC - the tiller number of LA)/the tiller number of LA × 100%. The numbers at the bottom of the box-plot indicate the corresponding phenotypic BLUE values. (C) The total tiller number and productive tiller number in LA and MC across different wheat regions. Different letters indicate statistical significance, determined using a two-sided Dunn’s Kruskal–Wallis test (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4226010/v1/c36343aaf7bc4ae2483670c1.png"},{"id":56929542,"identity":"515d0993-7e79-43b5-89db-c9d16dd83af0","added_by":"auto","created_at":"2024-05-22 09:27:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96720,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGWAS results and haplotype analysis of four low-tiller number QTLs. \u003c/strong\u003e(A-B) Population structure (A) and kinship (B) of the wheat accessions inferred using different numbers of clusters. At K = 3, the wheat accessions were separated. (C) Principle component analysis (PCA) of the genotype data from 323 accessions. (D) Decay of linkage disequilibrium (LD) in the wheat genome. (E) The phenotypic histogram plot of total tiller number and productive tiller number. (F-G) The manhattan plots and Q-Q plots of total tiller number (F) and productive tiller number (G) in BLUE. The candidate QTLs are marked above the significantly associated peaks. The gray line represents the significance threshold (−log\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003ep\u003c/em\u003e)＞4). (H-K) Linkage disequilibrium (LD) heatmap and haplotype analysis of \u003cem\u003eQTTN-2D.1 \u003c/em\u003e(H), \u003cem\u003eQT(P)TN-2D.2 \u003c/em\u003e(I), \u003cem\u003eQT(P)TN-5A \u003c/em\u003e(J) and \u003cem\u003eQPTN-6A \u003c/em\u003e(K). Hap A, haplotype A; Hap B, haplotype B; Hap C, haplotype C. N indicates the number of accessions in each haplotype. TTN, total tiller number; PTN, productive tiller number. \u003cem\u003eP\u003c/em\u003e indicates the \u003cem\u003eP\u003c/em\u003e-value based on the Student’s \u003cem\u003et-\u003c/em\u003etest.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4226010/v1/c0506b08c8e7c93db20eeea4.png"},{"id":56929537,"identity":"09962c5c-3900-48d4-b1d3-08761f0226dc","added_by":"auto","created_at":"2024-05-22 09:27:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe validation of low-tiller number QTLs. \u003c/strong\u003e(A) The genotyping of \u003cem\u003eQT(P)TN-2D.2\u003c/em\u003e, \u003cem\u003eQT(P)TN-5A\u003c/em\u003e and \u003cem\u003eQPTN-6A\u003c/em\u003e using KASP assay. Blue dots represent homozygous Hap A; red dots represent homozygous Hap B; green dots represent heterozygous Hap A/B; black dots represent the NTC (non-template control). (B) The validation of \u003cem\u003eQT(P)TN-2D.2\u003c/em\u003e in an F\u003csub\u003e2\u003c/sub\u003e population (MEX92.1.1.1×Mingguangxiaomai). (C-D) The validation of \u003cem\u003eQT(P)TN-5A \u003c/em\u003e(C) and \u003cem\u003eQPTN-6A \u003c/em\u003e(D) in an F\u003csub\u003e5:6\u003c/sub\u003e population (F\u003csub\u003e5:6, \u003c/sub\u003eMK95×CS). \u003cem\u003eP\u003c/em\u003e indicates the \u003cem\u003eP\u003c/em\u003e-value based on the Student’s \u003cem\u003et-\u003c/em\u003etest. The numbers at the bottom of the box-plot indicate the corresponding phenotypic BLUE values of tiller number. The numbers under the box-plot indicate the number of wheat accessions with indicated haplotype. MK147, CS (Chinese spring), MEX92.1.1.1 and Mingguangxiaomai are the parents of populations.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4226010/v1/c5bec5050b3a80c0dda98fc4.png"},{"id":56930299,"identity":"dc99e9e7-39c3-47a3-9a5e-23a62bcaeaa4","added_by":"auto","created_at":"2024-05-22 09:35:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe haplotypic selection analysis of four tiller number QTLs in modern cultivars. \u003c/strong\u003e(A) \u003cem\u003eπ \u003c/em\u003estatistics for selection analysis. The location corresponding to those QTLs are bracketed within two red dotted lines. (B) The haplotypic distribution of four QTLs in different wheat zones. Hap A, haplotype A; Hap B, haplotype B. The numbers in the bar indicate the number of wheat accessions with indicated haplotype. * indicates a significant increase in Chi-square test under the background of LA (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01), and only modern cultivars from different wheat zones were used for Chi-square test. (C) The application of three low-tiller number QTLs in Yellow and Huai wheat zone (Y\u0026amp;H) before and after the year 2000. Two modern cultivars were not included because of their unclear age. B-2000, the year from 1960’s to 2000; P-2000, the year after 2000. (D) The total tiller number and productive tiller number in Yellow and Huai wheat zone (Y\u0026amp;H) before and after the year 2000. Different letters indicate statistical significance, determined using two-sided Dunn’s Kruskal–Wallis test (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4226010/v1/dae18c6c3a8e18b0d27fc904.png"},{"id":56929540,"identity":"286462cc-b17d-43c4-9152-5f700eef305c","added_by":"auto","created_at":"2024-05-22 09:27:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":59711,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe interactions and combinations analysis of four low-tiller number QTLs. \u003c/strong\u003e(A) The accumulation of haplotype B at multiple loci in wheat population. The numbers in the bar-plot indicate the number of wheat cultivars. (B) The tiller number of cultivars with different number of haplotype B. The numbers at the bottom of the box-plot represent average values and standard error of tiller number. Different letters indicate statistical significance, which is determined by two-sided Dunn’s Kruskal–Wallis test (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). (C) The distribution of haplotypic combinations of four tiller number QTLs in different wheat zones. \u003cem\u003e2D.1a2D.2a\u003c/em\u003e refers \u003cem\u003eQTTN-2D.1a\u003c/em\u003e and \u003cem\u003eQT(P)TN-2D.2a. 2D.1a2D.2b\u003c/em\u003e refers \u003cem\u003eQTTN-2D.1a \u003c/em\u003eand \u003cem\u003eQT(P)TN-2D.2b. 2D.1b2D.2a \u003c/em\u003erefers \u003cem\u003eQTTN-2D.1b\u003c/em\u003e and \u003cem\u003eQT(P)TN-2D.2a.\u003c/em\u003e \u003cem\u003e2D.1b2D.2b\u003c/em\u003e refers \u003cem\u003eQTTN-2D.1b\u003c/em\u003e and \u003cem\u003eQT(P)TN-2D.2b.\u003c/em\u003e The same abbreviation rule for other genotypes. Y\u0026amp;H, Yellow and Huai wheat zone; YTR, Yangtze River wheat zone; OTW, Other wheat zone in China; IMC, Introduced abroad modern cultivars. The number in parentheses indicates the number of wheat cultivars. (D) The tiller number of haplotypic combinations of four tiller number QTLs in different wheat zones.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4226010/v1/a9cfd34d8b84bf657d705b63.png"},{"id":56929538,"identity":"1f60ba28-8b3b-43f5-b952-5a36ce16a3e8","added_by":"auto","created_at":"2024-05-22 09:27:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70902,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effects of four low-tiller number QTLs on yield traits. \u003c/strong\u003e(A) The correlations of tiller number and yield components traits. TGW, thousand grain weight; GNS, grain number per spike; GW, grain width; TTN, total tiller number; PTN, productive tiller number. (B) The correlations of tiller number and yield components traits in landraces (LA) and modern cultivars (MC). (C) The effects of four low-tiller number QTLs on yield traits. G indicates resampling GWAS on yield traits and plant structure traits. * and ** indicate Student's \u003cem\u003et\u003c/em\u003e-test \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 and \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, respectively. The numbers under box indicate the average value of haplotype A or haplotype B. The percentage present the ratio of (the phenotypic of haplotype B-the phenotypic of haplotype A)/the phenotypic of haplotype A×100%.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4226010/v1/e06901a624293ae421dce654.png"},{"id":83460632,"identity":"7e661de1-87fc-4b1d-b5a2-d8afabffc6ba","added_by":"auto","created_at":"2025-05-26 16:13:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1747468,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4226010/v1/ea24a3bb-6c23-461e-beed-6e0f445eafd0.pdf"},{"id":56929543,"identity":"176b4cb8-fd4c-41ce-84ec-e2af7cad442b","added_by":"auto","created_at":"2024-05-22 09:27:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2468912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. 1\u003c/strong\u003e The interactions and combinations analysis of four tiller number QTLs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Fig. 2 \u003c/strong\u003eThe effects of four low-tiller number QTLs on different agronomic traits.\u003c/p\u003e","description":"","filename":"SupplementaryFigs.docx","url":"https://assets-eu.researchsquare.com/files/rs-4226010/v1/f4997962468bd30133a90f25.docx"},{"id":56929535,"identity":"52290ab6-19eb-4f92-b422-baeb448eb8eb","added_by":"auto","created_at":"2024-05-22 09:27:28","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":105006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table 1.\u003c/strong\u003e The information and phenotypes of 323 wheat accessions in eight environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Table 2.\u003c/strong\u003e The information of wheat cultivars sowing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Table 3.\u003c/strong\u003e The phenotypic data statistics of total tiller number and productive tiller number observed in nine environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Table 4.\u003c/strong\u003e Resampling GWAS result.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Table 5.\u003c/strong\u003e Primers of KASP markers for four QTLs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Table 6. \u003c/strong\u003eThe interactions effects of four tiller number QTLs on different agronomic traits.\u003c/p\u003e","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4226010/v1/41690f16b12812b0a571edb7.xlsx"}],"financialInterests":"","formattedTitle":"The selection and application of tiller number QTLs in modern wheat breeding","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is a critical crop for global food security, with production needing to increase by 50% to meet the projected human population demands by 2050 (Ciani et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yadav et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Tillers play a pivotal role in wheat yield potential, directly influencing plant architecture and final grain production (Naruoka et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tavakol et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Less tillers in wheat produced less productive spikes, thus hamper the final yield (Ma et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Slafer and Andrade \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, excessive tillers can lead to increased water and nutrient absorption for biomass production, potentially reducing the proportion of productive spikes and final yield (Kebrom et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). How the breeding selection on tiller number in modern wheat breeding keeps largely unclear.\u003c/p\u003e \u003cp\u003eTiller number is controlled by genetic factors and strongly influenced by environmental conditions (Song et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To date, nine important QTLs/genes (\u003cem\u003etin1\u003c/em\u003e, \u003cem\u003etin2\u003c/em\u003e, \u003cem\u003etin3\u003c/em\u003e, \u003cem\u003etin4\u003c/em\u003e, \u003cem\u003etin5\u003c/em\u003e, \u003cem\u003etin6\u003c/em\u003e, \u003cem\u003efin\u003c/em\u003e, \u003cem\u003etn1\u003c/em\u003e and \u003cem\u003edmc\u003c/em\u003e) responsible for tiller inhibition have been identified in wheat (An et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dong et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kuraparthy et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Richards and Research 1988; Schoen et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Si et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Spielmeyer and Richards \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), with numerous QTLs for tiller distributed across various wheat chromosomes (Lin et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Saini et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nevertheless, only a few QTLs have been thoroughly investigated for their selection and application in modern cultivars (Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), limiting their integration into breeding practices. Moreover, current studies on selective genetic footprints in modern wheat have predominantly focused on yield-related traits, offering limited insights into the genetic effects of tiller improvement (Cavanagh et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Niu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This has made precise utilization of identified tiller loci challenging in wheat breeding practices.\u003c/p\u003e \u003cp\u003eIn this study, we identified four tiller number QTLs in a natural wheat population. Modern wheat varieties selected three low-tiller haplotypes to reduce tillering, yet these haplotypes surprisingly exhibited significant potential in increasing grains weight and/or grain numbers. These findings suggest that although lower tillering was favored in current wheat breeding, it was compensated by the development of higher spike yield. These results shed new light on modern wheat breeding selection and offer practical tiller number QTLs for wheat breeding programs.\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 total of 323 wheat accessions were gathered worldwide (Table S1), comprising 61 landraces (LA), 49 breeding lines (BL), and 213 modern cultivars (MC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Table S1). These accessions encompassed cultivars from various wheat zones in China and abroad, reflecting a genetically diverse pool of germplasm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll 323 wheat accessions were planted across nine environments from 2018 to 2022 in Yangling (34\u0026deg;28\u0026rsquo;N, 108\u0026deg;07\u0026rsquo;E, altitude 517 m) and Chongzhou (30\u0026deg;63\u0026rsquo;N, 103\u0026deg;67\u0026rsquo;E, altitude 1300 m) in China (more details in Table S2). The accessions were randomly allocated to plots with row and column spacing of 1 m \u0026times; 0.2 m, each with three independent replicates. Field management adhered to local wheat production practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic and data analysis\u003c/h2\u003e \u003cp\u003eThree plants of each plot were selected to assess both the total tiller number (TTN) and productive tiller number (PTN) throughout the wheat filling period. The raw data are available in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iwheat.net/resource/\u003c/span\u003e\u003cspan address=\"https://iwheat.net/resource/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e for download. The best linear unbiased estimate (BLUE) values for phenotypic data across environments were determined using a mixed linear model (MLM) in R (v3.6.1) with the lme4 package (Bates et al. 2015), which were then utilized for subsequent analysis. Histogram plots were depicted using the R (v3.6.1) package \u0026ldquo;ggplot\u0026rdquo; to illustrate the phenotypic distributions of TTN and PTN. Broad sense heritability (\u003cem\u003eh\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) for each trait was calculated using the BLUE values (Li et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Phenotypic correlations between environments and among traits were computed using the \u0026ldquo;rcorr\u0026rdquo; function implemented in the R (v3.6.1) package, utilizing means and BLUE values over environments, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLinkage disequilibrium (LD) and population structure analysis\u003c/h2\u003e \u003cp\u003eA total of 157,050 high-quality SNPs were employed for both GWAS and LD analyses (Wang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e), with respective densities of 12.97, 15.04, and 3.86 SNPs per Mb in the A, B, and D sub-genomes. To examine LD patterns, a 1% subset of all SNPs was randomly selected using the \u0026ldquo;--thin 0.01\u0026rdquo; parameter in PLINK1.9. Pairwise SNPs\u0026rsquo; squared correlation coefficient (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) was then calculated via PopLDdecay, specifying a \u0026ldquo;-MaxDist 2000\u0026rdquo; parameter.\u003c/p\u003e \u003cp\u003eThe population structure of the 323 collected cultivars was evaluated using unlinked markers (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0) in STRUCTURE software. This involved a burn-in phase of 1000 iterations followed by 1000 Monte Carlo Markov Chain replicates to estimate the optimal subpopulation number (k) within a range of 1\u0026ndash;10. Ten replications were performed for each k to gauge the robustness of the inferred population structure. The delta k statistic, which tracks the rate of change in log probability of data between successive values, was utilized to estimate the subpopulation number. Additionally, principal component analysis (PCA) and kinship were computed using GAPIT version 3 in R (v3.6.1) to further analyze population structure, with results compared to those obtained from STRUCTURE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide association study (GWAS)\u003c/h2\u003e \u003cp\u003eGWAS was conducted using SNPs derived from population RNA-seq (Wang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e) to identify marker-trait associations (MTAs) for tiller number. LD analyses and SNP density were previously analyzed (Wang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). GWAS was implemented with GAPIT version 3 in R (v3.6.1) using the MLM, considering population structure and relative kinship. We employed a resampling-based multiple SNP model, as established in maize GWAS (Tian et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For this method, 80% of the 323 wheat cultivars were randomly selected without replacement, and forward regression was performed, repeated 100 times. SNPs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.0e\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) selected in the regression model in ten or more resamples (RMIP\u0026thinsp;\u0026ge;\u0026thinsp;10) and detected in at least two environments were considered significant. The median of the \u003cem\u003eP\u003c/em\u003e-values across the 100 analyses represented the \u003cem\u003eP\u003c/em\u003e-value of the associated SNP.\u003c/p\u003e \u003cp\u003eThe adjacent associated markers were grouped together as one locus if the inter-marker distance is smaller than the average LD decay (3 Mb) for specific chromosome. The corresponding effect and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (phenotypic variance explained) were estimated and outputted by R (v3.6.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eValidation of QTL genetic effects with segregation populations\u003c/h2\u003e \u003cp\u003eThe F\u003csub\u003e5:6\u003c/sub\u003e population MK95\u0026times;CS, comprising 115 lines, and the F\u003csub\u003e2\u003c/sub\u003e populations MEX92.1.1.1\u0026times;Mingguangxiaomai, comprising 168 lines, were utilized for QTL validation. KASP marker development was facilitated using the PolyMarker website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.polymarker.info/\u003c/span\u003e\u003cspan address=\"http://www.polymarker.info/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with ten KASP markers successfully developed based on SNP information from population transcriptomic sequencing (further details in Table S5). These markers were then applied to the two validation populations (F\u003csub\u003e2\u003c/sub\u003e population, MEX92.1.1.1\u0026times;Mingguangxiaomai; F\u003csub\u003e5:6\u003c/sub\u003e population, MK95\u0026times;CS). The amplification reaction procedures followed those outlined in our previous reports (Liu et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of genetic differentiation\u003c/h2\u003e \u003cp\u003eNucleotide diversity (\u003cem\u003eπ\u003c/em\u003e value) was calculated using a 500-kb sliding window with VCFtools (v0.1.14) (Danecek et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Vilella et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) to estimate the degree of variability within each group (LA and MC). The π ratio (\u003cem\u003eπ\u003c/em\u003eLA/\u003cem\u003eπ\u003c/em\u003eMC) was then determined. QTLs were considered subjected to breeding selection if the SNP detected by GWAS within the selected interval had a \u003cem\u003eπ\u003c/em\u003e ratio (\u003cem\u003eπ\u003c/em\u003eLA/\u003cem\u003eπ\u003c/em\u003eMC)\u0026thinsp;\u0026gt;\u0026thinsp;3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eHaplotype analysis\u003c/h2\u003e \u003cp\u003eQTL data for the candidate genes were extracted from the obtained QTL dataset, which only included biallelic QTLs. Haplotype analysis was conducted in R (v3.6.1) to determine if the loci could induce phenotypic changes using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test. The results were visualized using GraphPad Prism (v8.4.2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistics analysis\u003c/h2\u003e \u003cp\u003eChi-square tests were conducted using the contingency table method with the \u003cem\u003echisq.test()\u003c/em\u003e function in R (v3.6.1). ANCOVA was employed to analyze interaction effects between different haplotype combinations, with population structure serving as a covariate (Westfall et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Multiple comparative analyses were performed using the two-sided Dunn\u0026rsquo;s Kruskal\u0026ndash;Wallis test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eChanges in tiller number of modern cultivars during wheat improvement\u003c/h2\u003e \u003cp\u003eA total of 323 bread wheat accessions were collected around world, comprising 61 LA, 213 MC, and 49 BL (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Table S1). Phenotypic data for TTN and PTN were collected from nine environments (Table S1 and S2). Both traits exhibited significantly positive correlations across the nine environments. TTN and PTN demonstrated similar broad-sense heritability (\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) values to thousand grain weight (TGW) and grain number per spike (GNS), but lower than that of plant height (PH) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This suggests that yield-related traits have higher environmental sensitivity compared to PH.\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\u003eThe correlation and broad sense heritability of TTN, PTN, TGW, GNS and PH in different environments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange of Correlation Coefficient (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e(\u003c/em\u003e%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20** ~ 0.82**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13* ~ 0.81**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27** ~ 0.77**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21**~0.7788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44**~0.91**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01. TGW, thousand grain weight; GNS, grain number per spike; PH, plant height. \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e, broad sense heritability.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurther analysis of these multi-environmental data revealed significant reductions in both TTN and PTN in MC compared to LA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Additionally, PTN exhibited a more pronounced decline than TTN, resulting in a significant reduction in the ratio of productive tillers in MC compared to LA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Furthermore, tiller number showed substantial reductions in both Chinese modern cultivars (CMC) and introduced modern cultivars (IMC), with a greater reduction observed in CMC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Taken together, tiller number is significantly reduced in modern wheat cultivars.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide association study of tiller number\u003c/h2\u003e \u003cp\u003eAnalysis of population structure, kinship and PCA unveiled three subgroups of the population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D), which is consistent with the classification of LA, MC, and BL. TTN and PTN exhibited continuous variation in the natural population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE; Table S3), indicating the quantitative nature of both traits. A total of 166 SNPs were identified to be significantly associated with TTN and PTN (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) in more than two environments through resampling GWAS analysis (Table S4), leading to the identification of 15 QTLs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eThe stable QTLs of TTN and PTN identified in resampling GWAS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePos\u003c/p\u003e \u003cp\u003e(Mbp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRMIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003eQTLs detected in previous studies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePos(Mbp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR\u0026sup2; (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eRelated traits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eTTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eQTTN-2D.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.58E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e29.0-57.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eE9, BLUE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eAX-94675758\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e9.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eTTN, PTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQTTN-2D.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.7\u0026ndash;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.01E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.3\u0026ndash;61.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE1, E5, E6, E8, E9, BLUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQTTN-2D.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.91E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.2\u0026ndash;42.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE5, E9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003etin6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.2\u0026ndash;18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTTN, PTN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQTTN-3D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.31E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.1\u0026ndash;61.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE8, BLUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQTTN-4A.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.78E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.1\u0026ndash;49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE8, E9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQTTN-4A.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e387.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.73E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.4\u0026ndash;47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026ndash;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE8, E9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQTTN-4A.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e450.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.64E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.5\u0026ndash;49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE8, E9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eQTTN-5A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e684.3-685.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.81E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e24.8\u0026ndash;61.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e12\u0026ndash;64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eE6, E8, E9, BLUE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eQltn.sicau-5A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e688.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e8.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eTTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQTTN-6B.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.95E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.1\u0026ndash;46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE3, E8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQTTN-6B.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e446.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.67E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.8\u0026ndash;43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE5, E9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQTTN-6B.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e503.5-514.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.5\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u0026ndash;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE8, E9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQPTN-2D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.7\u0026ndash;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.04E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.8\u0026ndash;60.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u0026ndash;68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE1, E5, E6, E8, E9, BLUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eQPTN-5A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e684.3-685.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.45E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e19.1\u0026ndash;59.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e11\u0026ndash;37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eE6, E8, E9, BLUE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eQltn.sicau-5A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e688.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e8.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eTTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eQPTN-6A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e16.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.59E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e28.9\u0026ndash;59.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eE8, BLUE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eMQTL6A-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e16.57\u0026ndash;18.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e14.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eTTN, PTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQPTN-6B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e444.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.62E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.3\u0026ndash;40.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\u0026ndash;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE3, E5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eChr., Chromosome; Pos, Physical position of the QTL in the reference genome; Env, Environments; RMIP, the times of SNPs were selected in the regression model.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotably, three QTLs for TTN and PTN, \u003cem\u003eQTTN-2D.1\u003c/em\u003e, \u003cem\u003eQTTN-5A\u003c/em\u003e and \u003cem\u003eQPTN-6A\u003c/em\u003e, had been previously reported and were physically close to the QTLs identified in our study, indicating the reliability and stability of these QTLs in controlling tillering. \u003cem\u003eQTTN-2D.1\u003c/em\u003e, associated with both TTN and PTN, was approximately 1 Mb physically distant from the QTL we identified (Kumar et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eQTTN-5A\u003c/em\u003e, detected in both TTN and PTN, and was renamed as \u003cem\u003eQT(P)TN-5A\u003c/em\u003e in this study (Wang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Another QTL, \u003cem\u003eQPTN-6A\u003c/em\u003e, was found to affect both TTN and PTN and shared a similar physical location with our mapping (Bilgrami et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, a novel QTL on Chromosome 2D exhibited a strong effect on both TTN and PTN, detected consistently in five environments, and was designated as \u003cem\u003eQT(P)TN-2D.2\u003c/em\u003e. Consequently, the four QTLs were selected for verification (Table S5) and subsequent analyses related to breeding selection and application.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEffects of tiller number QTLs in different genetic backgrounds\u003c/h2\u003e \u003cp\u003eWithin the LD block, two major haplotypes were identified in each of the four QTLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG), with haplotype B exhibiting a significant effect in reducing TTN and/or PTN (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH-K), reflecting the selection for lower tillers in modern wheat cultivars. Therefore, we defined haplotype B, which significantly reduced the number of tillers, as the superior haplotype.\u003c/p\u003e \u003cp\u003eTo validate the role of the four QTLs in regulating tiller number, ten KASP markers were developed based on SNP information (Tables S5). The effects of \u003cem\u003eQT(P)TN-2D.2\u003c/em\u003e on regulating TTN and PTN were confirmed in an F\u003csub\u003e2\u003c/sub\u003e population (F\u003csub\u003e2\u003c/sub\u003e, MEX92.1.1.1\u0026times;Mingguangxiaomai, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), while the effects of \u003cem\u003eQT(P)TN-5A\u003c/em\u003e and \u003cem\u003eQPTN-6A\u003c/em\u003e on regulating TTN and PTN were confirmed in an F\u003csub\u003e5:6\u003c/sub\u003e population (F\u003csub\u003e5:6\u003c/sub\u003e, MK95\u0026times;CS, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Notably, the tiller number of heterozygous \u003cem\u003eQT(P)TN-2D.2\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap A/B\u003c/em\u003e\u003c/sup\u003e was kept with that of \u003cem\u003eQT(P)TN-2D.2\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap A\u003c/em\u003e\u003c/sup\u003e, but significantly higher than that of \u003cem\u003eQT(P)TN-2D.2\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap B\u003c/em\u003e\u003c/sup\u003e in the F\u003csub\u003e2\u003c/sub\u003e population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), indicating that the low-tiller number is a recessive trait.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHaplotypic selection analysis of tiller number QTLs in modern wheat cultivars\u003c/h2\u003e \u003cp\u003eBreeding selection signatures were determined based on the \u003cem\u003eπ\u003c/em\u003e ratio (\u003cem\u003eπ\u003c/em\u003eLA/\u003cem\u003eπ\u003c/em\u003eMC) for the LA and MC groups along the chromosomes of four QTLs using SNP profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Compared to LA, haplotype B of \u003cem\u003eQTTN-2D.1\u003c/em\u003e, \u003cem\u003eQT(P)TN-2D.2\u003c/em\u003e and \u003cem\u003eQT(P)TN-5A\u003c/em\u003e were positively selected in MC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Further analysis revealed an increasing application frequencies of haplotype B of these three QTLs in CMC, especially in the Yellow and Huai wheat zone (Y\u0026amp;H) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). However, the application frequency of haplotype B of \u003cem\u003eQPTN-6A\u003c/em\u003e only increased in IMC compared to LA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These results support the selection for lower-tiller number in modern wheat breeding programs, but different tillering QTLs were adopted in CMC and IMC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsistent with the above findings, the haplotype B of three QTLs accumulated more frequently in cultivars released after the year 2000 in Chinese Y\u0026amp;H (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), resulting in a significant declined in tiller number (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). These results suggest that superior alleles controlling low-tiller number are more preferred in Chinese modern wheat breeding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of haplotypic combinations for tiller number QTLs across different wheat zones\u003c/h2\u003e \u003cp\u003eTo evaluate the accumulation of superior alleles for tillering during breeding and improvement, the number of haplotype B was counted for four QTLs in 323 wheat accessions. Most accessions regulated TTN and PTN by accumulating three and two elite haplotype B, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Notably, there was no difference in PTN between accessions with two or three superior alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), suggesting that pyramidalization of two superior alleles is sufficient to achieve an appropriate tiller number in modern wheat breeding.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe selection of pairwise combinations of haplotypes in different wheat zones was further investigated in 213 MC, and demonstrated that the \u003cem\u003eQTTN-2D.1b\u003c/em\u003e, \u003cem\u003eQT(P)TN-2D.2b\u003c/em\u003e and \u003cem\u003eQT(P)TN-5Ab\u003c/em\u003e exhibited significant additive effects when combined in pairs (Fig. S1). Further analysis found that the haplotypes combinations for lower tillers were positively selected in both CMC and IMC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) and manifested stability tiller number (variation coefficients of 4.8% and 5.1% for TTN and PTN, respectively) across different wheat zones in China, but lower than that in IMC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). These findings highlight the importance of pyramiding superior alleles for regulating tiller number and facilitating future wheat breeding efforts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePleiotropic effects of four low-tiller number QTLs\u003c/h2\u003e \u003cp\u003eTiller number directly influences wheat yield, a primary breeding target in modern wheat breeding. Therefore, the correlations between three key spike yield-related traits and TTN, PTN were evaluated in our population. Results showed significant negative correlations between GNS, GW, and TGW with TTN and PTN (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Further analysis revealed the negative correlations between TGW and TTN, PTN in MC, while no significant correlations were observed in LA (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), indicating a co-selection of these traits in modern wheat breeding process. Phenotypic analysis and resampling GWAS confirmed the significant genetic effects of \u003cem\u003eQTTN-2D.1b\u003c/em\u003e, \u003cem\u003eQT(P)TN-2D.2b\u003c/em\u003e and \u003cem\u003eQT(P)TN-5Ab\u003c/em\u003e in enhancing GNS, GW and TGW (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). These findings suggest that there is synergistic selection for low tiller number and high spike yield in modern wheat breeding.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditional agronomic traits were investigated in our population for assessing the pleiotropy of these four QTLs. The results showed that \u003cem\u003eQTTN-2D.1b\u003c/em\u003e, \u003cem\u003eQT(P)TN-2D.2b\u003c/em\u003e and \u003cem\u003eQT(P)TN-5Ab\u003c/em\u003e had significant effects in reducing PH, peduncle length (PL), flag leaf angle (FLAN) and flag leaf length (FLL) (Fig. S2; Table S6), suggesting co-selection of tillering, spike yield, and plant structure in modern wheat breeding.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTiller number plays a crucial role in wheat yield and plant architecture (Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), yet the tiller breeding selection in modern wheat remains largely unclear. A previous study showed that tiller numbers decreased during wheat domestication to transition to a compact plant structure, facilitating easier management in terms of crop protection, irrigation, and fertilizer application (Zhang et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our findings suggest that modern wheat breeding continues to prioritize this reduction trend, especially in China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Consistently, only the lower tiller haplotypes of tiller QTLs were selected in modern wheat breeding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), providing the genetic basis for the decrease in tiller numbers in MC. These findings suggest that a breeding selection for lower tillers occurred in modern wheat breeding practices, similarly to the perspectives in modern rice breeding, which tends to favor varieties with moderate to reduced tillers (Jiao et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Springer \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLess tillers may allow wheat to focus its nutrients on limited flower and grain development, thereby enhancing grain weight and number (Ren et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, a significant negative correlation between spike yield traits (grain number and grain weight) and tiller number in wheat cultivars was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), supporting the notion that lower tiller numbers may contribute to grain yield improvement, as previous evidences showing that certain genes can increase wheat yield by reducing tiller number and simultaneously enhancing grain size and number (Gupta et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jun et al. 2011; Niu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, all three selected haplotypes of lower tillers had a significant genetic effect in enhancing spike yield (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), indicating a co-selection of lower tillers but higher spike yield in modern high-yield breeding. Considering the continuous improvement in yield as a primary breeding objective, with a focus on selecting grain and spike traits (Gegas et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the low tiller count in co-selection would be a by-product of the breeding selection for higher grain weight and/or more grain number.\u003c/p\u003e \u003cp\u003eCompared to CMC, the IMC showed less reduction in tiller number, indicating that reducing tillers may not be the only option to enhance yield in world-wide modern wheat breeding. In fact, previous investigations also showed that tiller number is indeed increased in the MC of the United States (Smith et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Niu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This divergent selection on tiller number suggests that having more tillers could also be another option to increase modern wheat yield, which is regarded as beneficial for varieties to adapt to diverse environmental challenges with more tillers (Smith et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Niu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA higher utilization frequency of haplotypes of lower tillers is observed in the varieties released after the year 2000 in the Y\u0026amp;H, resulting in around a 10 percent reduction in tiller number (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), suggesting that the reduction of tiller number may still be a key selection direction in current wheat breeding. However, insufficient tillers in wheat can lead to a reduction in leaf area, impacting nutrient uptake, overall plant growth, and hampering the final yield (Slafer and Andrade \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). It would not be a remarkable reduction in tiller numbers in practice theory. However, the selection for low tiller numbers may keep continue if the increase in spike yield outweighs the decrease yield caused by tiller reduction by the same gene or genomic region.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShengbao\u0026nbsp;Xu\u0026nbsp;and\u0026nbsp;Jinxia Qin\u0026nbsp;designed the concept and experiments.\u0026nbsp;Xiangjun Lai, Yuanfei Zhang, Tian Lu\u0026nbsp;performed field investigation.\u0026nbsp;Xiangjun Lai, Zhiwei Zhu analyzed the data. Xiangjun Lai,\u0026nbsp;Jinxia Qin\u0026nbsp;and\u0026nbsp;Shengbao\u0026nbsp;Xu\u0026nbsp;wrote the manuscript.\u0026nbsp;All the authors were involved in the revision of the manuscript and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by\u0026nbsp;\u0026ldquo;Integration of Two Chains\u0026rdquo; Key Research and Development Projects of Shaanxi Province \u0026ldquo;Wheat Seed Industry Innovation Project\u0026rdquo; and Key R\u0026amp;D of Yangling Seed Industry Innovation Center (Ylzy-xm-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw data, models, or code generated or used during the study are available from the corresponding author by request. The phenotypes and SNP data are available in https://iwheat.net/resource/ for free download.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAn J, Niu H, Ni Y, Jiang Y, Zheng Y, He R, Li J, Jiao Z, Zhang J, Li H, Li Q, Niu J (2019) The miRNA-mRNA networks involving abnormal energy and hormone metabolisms restrict tillering in a wheat mutant \u003cem\u003edmc\u003c/em\u003e. Int J Mol Sci 20(18):4586\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates D, M\u0026auml;chler M, Bolker B, Walker SJS (2014) Computing: fitting linear mixed-effects models using lme4. arXiv1406(1):133\u0026ndash;199\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilgrami SS, Ramandi HD, Shariati V, Razavi K, Tavakol E, Fakheri BA, Mahdi Nezhad N, Ghaderian M (2020) Detection of genomic regions associated with tiller number in Iranian bread wheat under different water regimes using genome-wide association study. 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Nat Commun 10(1):5608\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Tiller number, Genome-wide association study, Haplotype selection and application, Wheat breeding","lastPublishedDoi":"10.21203/rs.3.rs-4226010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4226010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTiller number is a critical factor influencing wheat plant structure and yield potential, yet the genetic underpinnings and implications for tiller breeding selection remain elusive. This study extensively investigates tiller number across 323 wheat accessions within nine diverse environments, unveiling a significant reduction in modern wheat cultivars compared to landraces, demonstrating a prevalent preference for lower tiller numbers in modern breeding. Through genome-wide association study (GWAS), four pivotal quantitative trait loci (QTLs) associated with tiller number were identified, with three extensively selected and preferentially integrated into diverse Chinese agroecological zones. Notably, haplotype analysis revealed that lower tiller haplotypes also have significant genetic effects in enhancing grain number and/or weight. These findings suggest a co-selection of lower tiller numbers and higher spike yield was adopted in modern high-yield breeding programs in China. Additionally, the proposed combinations of these haplotypes aim to optimize tiller numbers for wheat breeding. This study provides novel insights into the genetic basis and selection of tiller number QTLs for modern wheat breeding.\u003c/p\u003e","manuscriptTitle":"The selection and application of tiller number QTLs in modern wheat breeding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-22 09:27:23","doi":"10.21203/rs.3.rs-4226010/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-05-15T07:45:15+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-13T09:58:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-08T13:43:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2024-04-06T02:34:23+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":"be4500bf-09a9-4365-85a6-940e97196b2d","owner":[],"postedDate":"May 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:08:57+00:00","versionOfRecord":{"articleIdentity":"rs-4226010","link":"https://doi.org/10.1007/s00122-025-04908-w","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2025-05-24 15:57:35","publishedOnDateReadable":"May 24th, 2025"},"versionCreatedAt":"2024-05-22 09:27:23","video":"","vorDoi":"10.1007/s00122-025-04908-w","vorDoiUrl":"https://doi.org/10.1007/s00122-025-04908-w","workflowStages":[]},"version":"v1","identity":"rs-4226010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4226010","identity":"rs-4226010","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}