Construction of a genetic linkage map and QTL mapping of the agronomic traits in Foxtail millet (Setaria italica)

Construction of a genetic linkage map and QTL mapping of the agronomic traits in Foxtail millet (Setaria italica) | 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 Construction of a genetic linkage map and QTL mapping of the agronomic traits in Foxtail millet (Setaria italica) Lulu Gao, Qianxue Zhu, Huan Li, Shiyuan Wang, Jiahui Fan, Tianguo Wang, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5061888/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Feb, 2025 Read the published version in BMC Genomics → Version 1 posted 4 You are reading this latest preprint version Abstract Foxtail millet ( Setaria italica ) is one of the most ancient cultivated cereal crops and is ideal for the functional genomics of the Panicoideae crops. In the present study, we generated an F 2 population derived from a cross between an elite foxtail millet variety Jingu28 and a backbone line Ai88 and constructed a genetic linkage map with 213 published SSR markers and two InDel markers. Quantitative trait locus (QTL) mapping identified 46 QTL for 12 agronomic traits, including 13 major effect QTL. Meanwhile, 40 QTL controlling different traits formed 13 co-located QTL clusters. Moreover, one putative candidate gene Seita.9G020100 for qHD9-1 with conserved CCT (constans, constans-like, and timing of chlorophyll A/B binding) motif and a gibberellin biosynthesis related GA20 oxidase gene Seita.5G404900 for qPH5-1 were identified based on homologous gene comparison. The 277 bp insertion/deletion on the promoter of Seita.9G020100 and the one-base (G) insertion/deletion in the third exon of Seita.5G404900 might be candidate functional sites. Furthermore, two markers ( Ghd7InDel and GA20oxSTARP-1 ) were developed based on these two variation sites, respectively. These results will help to elucidate the genetic basis of important agronomic traits in foxtail millet and be useful for marker-assisted selection of varieties with ideal plant architecture and high yield potential. Foxtail millet Genetic linkage map QTL mapping Agronomic traits Candidate gene Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Foxtail millet ( Setaria italica L. Beauv.), domesticated from the wild green foxtail ( Setaria viridis L. Beauv.) about 11,000 years ago in northern China, is one of the most ancient cultivated cereal crops and an important C4 crop [ 1 ]. Moreover, because of its small diploid genome, short growth duration, high inbreeding rate, small morphological stature, easy lab management and excellent drought tolerance, foxtail millet is a model plant for the functional genomic studies of the Panicoideae crops, especially for investigating plant architecture, C4 photosynthesis and drought tolerance [ 2 – 5 ]. However, the molecular and genetic mechanisms underlying agronomic traits of foxtail millet remain unclear. Therefore, it is essential to assess its agronomic traits by developing a genetic linkage map and identifying genes or quantitative trait loci (QTL). In previous studies, many QTL for agronomic traits distributed on 9 chromosomes of foxtail millet were mapped using linked populations. Doust et al . [ 6 ] used the F 2 interspecies population from a cross between S. italica accession B100 and S. viridis accession A10 to conduct QTL analysis and comparative genomics for basal branching (tillering) and axillary branching. Fourteen replicated QTL were detected for the four inflorescence traits (primary branch number and density, spikelet number, and bristle (sterile branchlet) number) in B100×A10 F 2 population [ 7 ]. Mauro-Herrera et al . [ 8 ] identified 16 flowering time QTL in B100×A10 F 7 recombinant inbred lines (RILs). Fang et al . [ 9 ] mapped 29 QTL for 11 traits using a Yugu1 ×Longgu7 F 2 population. Wang et al . [ 10 ] detected 11 major QTL for eight agronomic traits using the F 2 population derived from the cross between Hongmiaozhangu and Changnong35. Zhang et al . [ 11 ] used a foxtail millet population of 439 RILs derived from a cross between Zhanggu and A2 and identified a total of 59 QTL for 14 agronomic traits in plants grown under long- and short-day photoperiods. Ni et al. [ 12 ] used 184 A2×Zhanggu RILs to map QTL for nine agronomic traits, five of which were controlled by a single gene, two QTL were found for plant height, and a candidate gene showed 89% identity to the known rice gibberellin-synthesis gene sd1 , and three QTL were found for the trait of heading date. Odonkor et al . [ 13 ] identified the presence of additive main effect QTL for reduced shattering on chromosomes V and IX using the B100×A10 F 2:3 and RIL populations. Wang et al . [ 14 ] detected fifty-seven QTL related to 11 various agronomic traits using a large F 2 population including 543 plants from a cross between Aininghuang and Jingu 21. Liu et al . [ 15 ] used a 164 RILs from a cross between Longgu7 and Yugu1 and identified forty-seven QTL for four traits of straw weight, panicle weight, grain weight per plant and 1000-grain weight. He et al . [ 16 ] constructed a high-density bin map with 3744 marker bins and identified 26 QTL significantly associated with plant height using a foxtail millet population of 333 RILs derived from a cross between a backbone line Ai 88 and an elite cultivar Liaogu 1. Moreover, Zhi et al . [ 17 ] used this population and detected 159 QTL for panicle architecture and grain yield-related traits. Genome wide association analysis (GWAS) using natural populations has also been used to detect loci associated with important agronomic traits in foxtail millet. Jia et al . [ 18 ] identified 512 loci associated with 47 agronomic traits by GWAS using 916 varieties. Jaiswal et al . [ 19 ] identified 81 MTAs (marker-trait associations) for ten traits across the genome by GWAS in 142 foxtail millet. He et al . [ 20 ] performed an SV-based GWAS (SV-GWAS) for TGW, GW and GL using the 110 high-quality genome sequences and found several significant GWAS signals on chromosomes 1, 3, 4, 5 and 9 for TGW and GW. Furthermore, a total of 1,084 signals were identified to be substantially associated with 128 phenotypes for 60 traits using 1,844 Setaria accessions. Dai et al . [ 21 ] identified eleven QTL for seven traits using two models for genome-wide association studies (RTM- and MLM-GWAS) in 408 diverse foxtail millet accessions. Furthermore, many important functional genes in foxtail millet were cloned. SiAGO1b mutation influenced multiple biological processes, including energy metabolism, cell growth, programmed death and abiotic stress responses in foxtail millet [ 22 ]. A 5.5-kb Copia-like retrotransposon insertion in DWARF1 ( D1 ), which encodes a DELLA protein, mediated the transcriptional reprogramming of D1 leading to a novel N-terminal-deleted truncated DELLA transcript, resulting in dwarf plants [ 23 ]. SiMADS34 , an E‑class MADS‑box transcription factor, regulates inflorescence architecture and grain yield [ 24 ]. Domestication-associated SiPHYC encoding a light receptor repressed flowering and determined Setaria as a short-day plant [ 25 , 26 ]. Li et al . [ 27 ] confirmed that overexpression of the CCT gene SiPRR37 delayed the heading date and increased plant height. SiGW3 negatively regulates grain weight, and the distal 366-bp genomic sequence possibly represses the expression of SiGW3 , thereby increasing grain weight in domesticated foxtail millet [ 20 ]. A RING-type E3 ligase SGD1 and its E2 partner SiUBC32 control grain yield in Setaria italica [ 28 ]. In the present study, an F 2 population derived from a cross between an elite foxtail millet variety Jingu28 and a backbone line Ai88 was generated. Most important agronomic traits varied greatly between the two parents and within the F 2:3 families. A genetic linkage map with 213 published SSR markers and two InDel markers was constructed to explore the genetic control of 12 agronomic traits in the F 2:3 families. QTL mapping identified 46 QTL and two putative candidate genes were identified based on homologous gene comparison. The work will provide insights into the genetic architecture controlling agronomic traits and facilitate the breeding of high-yielding foxtail millet varieties with ideal plant architecture. Materials and methods Mapping population construction Ai88, characterized by a dwarf phenotype with plant height about 90 cm, was developed by the Institute of Millet Crops, Hebei Academy of Agricultural and Forestry Sciences. Jingu28 was developed by Shanxi Academy of Agricultural Sciences. A cross between Jingu28 and Ai88 was made in 2021, and F 1 were grown in the greenhouse in the winter of 2021. F 2 population and F 2:3 families were planted in Shenfeng (116.34′ E, 37.44′ N, Shanxi, China) during the 2022 and 2023 spring cropping season, respectively. Agronomic trait investigation and statistical analysis Agronomic traits were evaluated for parents and F 2:3 families planted in spring 2023 in Taigu, Shanxi. For parents, data were collected on plant height (PH, cm), panicle neck length (PNL, cm), panicle length (PL, cm), panicle diameter (PD, mm), flag leaf length (FLL, cm), flag leaf width (FLW, cm), tiller number (TN), stem node number (SNN), panicle weight (PW, g), grain weight per panicle (GWP, g), primary branch number (PBN), primary branch density (PBD), thousand grain weight (TGW, g), grain length (GL, mm), grain width (GW, mm), stem node length (SNL, cm) and heading date (HD, d). For F 2:3 families, 12 agronomic traits (PH, PNL, PL, PD, FLL, FLW, PW, GWP, TGW, GL, GW and HD) were investigated. We phenotyped at least 10 plants per families for all traits introduced above except HD, and the mean phenotype value was regarded as the corresponding trait value. For F 2 individuals planted in spring 2022 in Taigu, Shanxi, the plants were not emerging consistently and trait data were not measured. The phenotype data were analysed using Microsoft Excel 2021, GraphPad Prism 9.5.0 and IBM SPSS Statistics 19.0. Genetic map construction Total genomic DNA from fresh leaves of the parents and 300 F 2 individuals were extracted according to a modified CTAB method. The published SSR markers were screened for polymorphism between the mapping parents and those showing clear polymorphism and two InDel markers designed based on the results of parental resequencing sequence alignment were used to genotype the F 2 population. JoinMap 4.0 [ 29 ] was used to group and order all markers. The Kosambi mapping function was used to convert recombination frequencies into map distances. QTL mapping WinQTLCart 2.5 [ 30 ] was used to determine QTL location by composite interval mapping (CIM) method and estimate their effects. The threshold for the LOD score was estimated by 1,000 random permutations at a significance level of P = 0.05. The confidence intervals of QTL were determined based on positions ± 2 LOD away from the peaks of the likelihood ratios (LRs). Positive additive effects of QTL indicated that the Jingu28 allele increased the phenotypic value, whereas negative effects indicated that the Ai88 allele increased the phenotypic value. QTL names started with ‘ q ’, followed by a trait abbreviation (e.g. PH for plant height) and the chromosome number, plus − 1, -2, -3 and so on when multiple QTL were detected for one trait on the same chromosome. Graphical representation of the genetic map and QTL bars representing 2-LOD reduction in likelihood was carried out with Map Chart 2.2. Overlapping of genetic regions for QTL controlling different traits was indicative of co-located QTL clusters. Sequence analysis of candidate genes The DNA sequences of candidate gene Seita.5G404900 and Seita.9G020100 were amplified from both Jingu28 and Ai88 using the primer pairs shown in Table S4. PCR products were separated by 1% agarose gel electrophoresis and the target DNA fragments were sequenced at Beijing Tsingke Biotech Co., Ltd.. The comparison and analysis of the sequencing results were performed using DNAMAN version 6.0 software. Development of the semi-thermal asymmetric reverse PCR (STARP) marker A semi-thermal asymmetric reverse PCR (STARP) marker GA20oxSTARP-1 was designed based on the single-base insertion/deletion in the third exon of Seita.5G404900 . The primers included two asymmetrically modified allele-specific (AMAS) primers (GA20oxSTARP-1 F1 and GA20oxSTARP-1 F2) and their same reverse primer (GA20oxSTARP-1 R). GA20oxSTARP-1 F1 was designed to amplify the Ai88 allele. Meanwhile, GA20oxSTARP-1 F2 could amplify the Jingu28 allele uniquely with 10 bp insertion (ACGACACGAC) at 5′ terminus. The nucleotides substitution principle followed the suggestion of Long et al. [ 31 ]. The three primers were mixed in a ratio of 1:1:2 (GA20oxSTARP-1 F1: GA20oxSTARP-1 F2: GA20oxSTARP-1 R) and diluted. Allelic variation analysis The 257 foxtail millet accessions randomly selected from 916 foxtail millet accessions published by Jia et al . [ 18 ] were used for allelic variation analysis. The 257 foxtail millet accessions were planted in spring 2023 in Taigu, Shanxi, and each variety was planted for four rows. The heading date and the plant height were investigated. Results Phenotypic evaluation The phenotypic values of important agronomic traits between Jingu28 and Ai88 were compared. For plant height (PH), panicle neck length (PNL), panicle length (PL), flag leaf length (FLL), thousand grain weight (TGW) and stem node length (SNL), Jingu28 were significantly higher than Ai88. In contrast, Jingu28 were significantly lower than Ai88 for panicle diameter (PD), stem node number (SNN), panicle weight (PW), grain weight per panicle (GWP), primary branch number (PBN), primary branch density (PBD) and grain width (GW). However, there were no significant differences in flag leaf width (FLW), tiller number (TN) and grain length (GL) between Jingu28 and Ai88. In addition, the heading date (HD) Jingu28 was 89 days, approximately two days later than Ai88 (Fig. 1 , Table S1 ). The phenotype of twelve traits (PH, PNL, PL, PD, FLL, FLW, PW, GWP, TGW, GL, GW and HD) in the F 2:3 families crossed by Jingu28 and Ai88 had widespread variations and showed continuous distributions (normal distributions), indicating that they are governed by multiple genes (Fig. 2 , Table S2 ). Correlation analysis among 12 agronomic traits We analysed the correlation among the 12 agronomic traits in the F 2:3 families using the Pearson correlation coefficient. The trait PH was positively correlated with PNL, PL, FLL, PW, GWP, TGW and HD, respectively, and negatively correlated with FLW. The trait PNL was positively correlated with PL, FLL, PW and GWP, respectively. The trait PL was positively correlated with PD, FLL, FLW, PW and HD, respectively. The trait PD was positively correlated with FLW. The trait FLL was positively correlated with FLW and HD, respectively, and negatively correlated with GW. The trait FLW was negatively correlated with GWP and TGW, respectively, and positively correlated with HD. The trait PW was positively correlated with GWP, TGW and GL, respectively. The trait GWP was positively correlated with TGW and negatively correlated with HD. The trait TGW was positively correlated with GL and GW, respectively. The trait GL was positively correlated with GW. No significant correlations were detected for other traits. Moreover, the correlation coefficients between PL and FLL (0.75), GL and GW (0.64), TGW and GW (0.63), PH and PNL (0.57), FLL and FLW (0.54) were relatively high (Fig. 3 ). The results showed that there were significant or extremely significant correlations among some of the 12 traits and indicated that some traits with a strong correlation each other might be affected by the same loci or closely linked loci. Genetic linkage map 517 published SSR markers on nine chromosomes of foxtail millet were used to screen for polymorphism between parents. 213 polymorphic SSR markers and two InDel markers designed based on the parental resequencing date were used to genotype the F 2 population. The 215 markers were mapped into nine chromosomes, covering 1492.5 cM, with average distance of 6.94 cM between adjacent markers and ranged from 5.50 to 10.24 cM for all nine chromosomes (Fig. 4 , Table 1 ). Makers were not evenly distributed over chromosomes. For example, Chr9 was mapped with 36 markers, whereas Chr8 was mapped with only 12 markers. The longest chromosome in terms of genetic distance was Chr3, which spanned 223.7 cM, and the shortest was Chr8, which spanned only 122.8 cM. Six large gaps (> 30 cM) were identified on Chr1 (30.6 cM between SICAAS1031 and SICAAS1054), Chr2 (39.1 cM between SICAAS2024 and S184), Chr3 (39.7 cM between SICAAS3005 and SICAAS3008), Chr4 (38.2 cM between SICAAS4017 and S231), Chr5 (31.0 cM between SICAAS5039 and b223) and Chr8 (51.8 cM between SICAAS8027 and SiGMS12819) (Fig. 4 ). Table 1 Summary of marker distribution and genetic distance of nine chromosomes Numbers of markers Genetic distance (cM) Average interval (cM) 29 163.4 5.63 26 220.8 8.49 29 223.7 7.71 17 150.1 8.83 25 138.6 5.55 23 126.4 5.50 18 130.2 7.24 12 122.8 10.24 36 216.4 6.01 215 1492.5 6.94 QTL mapping for 12 agronomic traits QTL mapping was performed using the genotypes of F 2 population and phenotypes of F 2:3 families. In total, 46 QTL on eight chromosomes except Chr8 for 12 traits were detected, with the phenotypic variation explained (PVE) of 0.52–37.81%. The LOD values of these QTL ranged from 3.62 to 49.62 (Fig. 4 , Table 2 ). Table 2 QTL analysis for 12 agronomic traits Trait QTL Chr. Nearest marker Position (cM) Genetic interval (cM) Flanking marker Physical interval (Mb) LOD Additive effect Dominance effect PVE (%) PH qPH1 1 19 85.4 71.5–94.6 SICAAS1020-b243 19.87–34.63 6.19 5.40 0.51 3.71 qPH2-1 2 11 92.4 78.2–97.7 SICAAS2041-b239 8.10-28.52 3.62 4.31 -0.46 2.72 qPH2-2 2 13 118.3 110.3-136.9 p67-p56 32.04–41.26 5.17 5.05 0.62 3.06 qPH2-3 2 24 219.6 206.5-220.6 SICAAS2024-ZQXInDel2-1 46.59–49.16 12.10 8.19 0.35 8.17 qPH5-1 5 23 103.4 102.4-105.1 SICAAS5036-SICAAS5039 43.12–43.79 49.62 17.47 6.29 29.55 qPH5-2 5 24 109.6 107.6-115.7 SICAAS5039-b223 43.79–45.75 40.32 17.05 6.08 27.25 qPH9-1 9 2 12.9 2.7–24 SICAAS9002-SICAAS9008 0.21–1.84 7.52 5.81 4.19 2.70 qPH9-2 9 28 140.1 138.8-154.4 SICAAS9082-m2 37.30–44.00 4.42 -5.44 3.24 5.49 PNL qPNL2 2 18 142.1 133.4-152.3 SICAAS2045-b169 38.20-44.71 4.49 0.30 1.34 0.53 qPNL5 5 21 102 96.7-105.3 SICAAS5035-SICAAS5039 40.92–43.79 16.84 1.96 1.13 9.64 PL qPL3 3 28 200.2 183-215.6 b163-SICAAS3052 47.64–50.46 4.83 0.75 0.34 2.62 qPL4-1 4 8 73.5 68.5–75.8 b255-SICAAS4060 6.83–17.35 6.20 0.87 0.02 5.10 qPL4-2 4 10 81.9 75.8–85.3 SICAAS4060-SICAAS4036 17.35–32.75 6.16 0.91 0.02 5.46 qPL5 5 2 8 0-12.3 S262-S264 3.49–5.41 8.31 1.05 0.16 6.15 qPL9-1 9 2 21.9 8.9–30.3 b130-SICAAS9008 0.51–1.84 7.16 1.05 -0.17 8.82 qPL9-2 9 4 35.2 30.3–45.1 SICAAS9008-SICAAS9019 1.84–3.90 7.49 0.95 -0.08 6.93 qPL9-3 9 23 131.1 127.3-132.7 SICAAS9110-SICAAS9053 15.49–22.18 4.93 -0.96 0.76 8.58 PD qPD1-1 1 21 94.4 92.4–101 SICAAS1071-S121 31.97–38.89 4.73 -1.29 -0.24 5.24 qPD1-2 1 24 109.2 101-123.2 S121-SICAAS1051 38.89–40.49 4.70 -1.25 -0.93 2.77 qPD2 2 14 126.7 110.4-138.2 p67-p56 32.04–41.26 3.74 -1.08 -0.51 2.77 qPD9 9 11 71.5 53.3–80.1 b248-SICAAS9036 2.88–9.70 5.07 1.25 -0.05 6.96 FLL qFLL2-1 2 18 155.3 143.4-163.8 SICAAS2016-b169 41.26–44.71 5.01 1.14 1.68 0.52 qFLL2-2 2 25 220.6 217.2-220.7 SICAAS2024-ZQXInDel2-1 46.59–49.16 37.22 4.79 0.43 35.21 qFLL4 4 10 82.9 77.9–96.4 SICAAS4034-SICAAS4004 30.40-35.65 4.31 1.32 -0.22 4.05 qFLL6 6 5 47.4 38.1–54.4 S297-SICAAS6096 2.18–4.37 9.90 -2.10 -0.03 9.19 qFLL9 9 2 6.9 0-27.7 SICAAS9002-SICAAS9008 0.21–1.84 4.15 1.27 0.20 2.87 FLW qFLW1-1 1 21 93.4 89.4-100.5 SICAAS1071-S121 31.97–38.89 9.23 -0.09 0.02 11.04 qFLW1-2 1 24 103.2 101-116.4 S121-SICAAS1051 38.89–40.49 8.16 -0.08 0.03 10.61 qFLW2 2 25 220.6 219.5-220.7 SICAAS2024-ZQXInDel2-1 46.59–49.16 16.83 0.13 0.03 13.80 qFLW5-1 5 20 90.7 86.7–93 SiGMS8556-SICAAS5035 39.89–40.92 15.86 -0.12 -0.04 12.91 qFLW5-2 5 23 103.4 96.3-105.3 SICAAS5035-SICAAS5039 40.92–43.79 20.36 -0.13 -0.06 12.46 PW qPW5 5 23 105.6 101.4–116 SICAAS5035-b223 40.92–45.75 16.41 2.12 0.71 14.42 qPW7 7 11 75.1 66.2–89.6 SICAAS7081-SICAAS7041 23.27–31.22 4.08 1.05 -0.69 8.14 qPW9-1 9 12 73.1 63.9–80.8 SICAAS9024-SICAAS9036 5.23–9.70 4.36 1.04 -0.47 7.38 qPW9-2 9 13 87.8 80.8–89.2 SICAAS9036-b187 9.70-10.77 3.96 1.02 0.20 3.88 qPW9-3 9 21 126.6 110.1-129.1 p95x-SICAAS9052 12.28–17.44 4.09 -1.13 -0.01 4.94 GWP qGWP5 5 23 103.6 95.6-122.4 SICAAS5035-b223 40.92–45.75 4.62 0.96 0.41 4.28 TGW qTGW2 2 11 93.4 88.4–97.4 b242-b239 25.50-28.52 4.16 0.10 0.01 6.58 GL qGL2 2 12 103.7 88.8-109.7 b242-p67 25.50-32.04 4.83 0.04 0.0004 8.55 GW qGW2 2 23 216.5 193.1-220.6 SICAAS2024-ZQXInDel2-1 46.59–49.16 3.81 -0.03 0.0013 6.39 HD qHD2 2 23 217.5 208.2-220.6 SICAAS2024-ZQXInDel2-1 46.59–49.16 18.12 3.95 -0.34 22.48 qHD5 5 24 124.6 107.6-136.6 SICAAS5039-b223 43.79–45.75 3.75 -1.50 -1.45 1.61 qHD6 6 5 42.4 25.6–54.3 SICAAS6005-SICAAS6096 1.46–4.37 3.79 -1.47 0.11 3.62 qHD9-1 9 2 19.9 12.6–28.8 b130-SICAAS9008 0.51–1.84 21.64 4.40 -0.93 37.81 qHD9-2 9 5 37.6 34.2–40.7 SICAAS9009-SICAAS9014 2.02–2.88 7.26 2.25 -1.47 11.60 qHD9-3 9 27 139.8 138.8-151.9 SICAAS9082-m2 37.30–44.00 10.76 -2.90 -0.04 10.21 Plant height Eight QTL related to PH were detected and explained 2.70–29.55% of the phenotypic variation. Of these, the additive effects of qPH1 , qPH2-1 , qPH2-2 , qPH2-3 , qPH5-1 , qPH5-2 and qPH9-1 were from Jingu28, while qPH9-2 was from Ai88. And two major QTL (PVE > 10), qPH5-1 and qPH5-2 , were detected on Chr5 with LOD values of 49.62 and 40.32, respectively. Their additive effects were 17.47 and 17.05, respectively (Fig. 4 , Table 2 ). Panicle neck length Two QTL ( qPNL2 and qPNL5 ) for PNL were detected and explained 0.53% and 9.64% of the phenotypic variation, respectively. The additive effects of these two QTL were both from Jingu28 (Fig. 4 , Table 2 ). Panicle length Seven QTL on Chr3, Chr4, Chr5 and Chr9 were found to be related to PL, accounting for 2.62%-8.82% of the phenotypic variation. The additive effect of qPL9-3 was from Ai88, while the others ( qPL3 , qPL4-1 , qPL4-2 , qPL5 , qPL9-1 and qPL9-2 ) were from Jingu28 (Fig. 4 , Table 2 ). Panicle diameter There were four QTL located on Chr1, Chr2 and Chr9 affecting PD, with LOD values range from 3.74 to 5.07. The PVE of these QTL was 2.77–6.96%. The additive effects of qPD1-1 , qPD1-2 and qPD2 were from Ai88, while qPD9 was from Jingu28 (Fig. 4 , Table 2 ). Flag leaf length For FLL, five QTL with a PVE from 0.52 to 35.21% were found on Chr2, Chr4, Chr6 and Chr9, of which one major QTL, qFLL2-2 , with a LOD value of 37.22 and a PVE of 35.21%, was detected. The additive effects of qFLL2-1 , qFLL2-2 , qFLL4 and qFLL9 were from Jingu28, while qFLL6 was from Ai88 (Fig. 4 , Table 2 ). Flag leaf width A total of five major QTL for FLW with the PVE from 10.61 to 13.80% were located on Chr1, Chr2 and Chr5. The LOD values of these QTL were range from 8.16 to 20.36. The additive effects of qFLW1-1 , qFLW1-2 , qFLW5-1 and qFLW5-1 were from Ai88, while qFLW2 was from Jingu28 (Fig. 4 , Table 2 ). Panicle weight On Chr5, Chr7 and Chr9, five QTL controlling PW with the PVE of 3.88–14.42% were detected, of which one major QTL, qPW5 , could explain 14.22% of the phenotypic variation, with the LOD value of 16.41. The additive effect of qPW9-3 was from Ai88, while the other four QTL ( qPW5 , qPW7 , qPW9-1 and qPW9-2 ) were from Jingu28 (Fig. 4 , Table 2 ). Grain weight per panicle, thousand grain weight, grain length and grain width For GWP, TGW, GL and GW, only one QTL for each trait was detected, which was qGWP5 , qTGW2, qGL2 and qGW2 , respectively. These four QTL accounted for 4.28%, 6.58%, 8.55% and 6.39% of the phenotypic variation, respectively. The additive effects of qGWP5 , qTGW2 and qGL2 were from Jingu28, while qGW2 was from Ai88 (Fig. 4 , Table 2 ). Heading date Six QTL on Chr2, Chr5, Chr6 and Chr9 for HD were detected, with LOD values range from 3.75 to 21.64, accounting for 1.61–37.81% of the phenotypic variation. Among them, qHD2 , qHD9-1 , qHD9-2 and qHD9-3 were four major QTL, with PVE of 22.48%, 37.81%, 11.60% and 10.21%, respectively. The additive effects of qHD2 , qHD9-1 and qHD9-2 were from Jingu28, while qHD5 , qHD6 and qHD9-3 were from Ai88 (Fig. 4 , Table 2 ). QTL clusters for multiple traits In this study, 40 out of 46 QTL controlling different traits formed 13 co-located QTL clusters (Table S3 ). Of these, seven were double co-located QTL ( qPD1-2 / qFLW1-2 , qPL4-2 / qFLL4 , qFLL6 / qHD6 , qPL9-2 / qHD9-2 , qPD9 / qPW9-1 , qPL9-3 / qPW9-3 and qPH9-2 / qHD9-3 ) and two were triple co-located QTL ( qPH1 / qPD1-1 / qFLW1-1 and qPH2-1 / qTGW2 / qGL2 ). There were two quadruple co-located QTL clusters ( qPH2-2 / qPNL2 / qPD2 / qFLL2-1 and qPH9-1 / qPL9-1 / qFLL9 / qHD9-1 ), one quintuple co-located QTL cluster ( qPH2-3 / qFLL2-2 / qFLW2 / qGW2 / qHD2 ) and one septuple co-located QTL cluster ( qPH5-1 / qPH5-2 / qPNL5 / qFLW5-2 / qPW5 / qGWP5 / qHD5 ). Intriguingly, the quintuple co-located QTL cluster ( qPH2-3 / qFLL2-2 / qFLW2 / qGW2 / qHD2 , Chr2: 46.59–49.16 Mb) has large effects, explaining 8.17% ( qPH2-3 ), 35.21% ( qFLL2-2 ), 13.80% ( qFLW2 ), 6.39% ( qGW2 ) and 22.48% ( qHD2 ) of the phenotypic variation. Moreover, the septuple co-located QTL cluster ( qPH5-1 / qPH5-2 / qPNL5 / qFLW5-2 / qPW5 / qGWP5 / qHD5 ) contained two main effect QTL for PH, explaining 29.55% ( qPH5-1 ) and 27.25% ( qPH5-1 ) of the phenotypic variation. The quadruple co-located QTL cluster ( qPH9-1 / qPL9-1 / qFLL9 / qHD9-1 ) contained one main effect QTL for HD, with a PVE of 37.81% (Table S3 ). Candidate genes analysis For heading date, there was a major QTL, qHD9-1 , with very large effect on chromosome 9, which was also included in the quadruple co-located QTL cluster ( qPH9-1 / qPL9-1 / qFLL9 / qHD9-1 ), suggesting that it might be a polytropic QTL. Moreover, the additive effect of qHD9-1 was from Jingu28. And then we analyzed the candidate interval (Chr9: 0.51–1.84 Mb) of this locus and found that there was a candidate gene Seita.9G020100 (Chr9: 1,056,892-1,059,177 bp) with conserved CCT (constans, constans-like, and timing of chlorophyll A/B binding) motif, homologous with transcription factor Ghd7 in rice. Ghd7 encoding a CCT domain protein is a central regulator of growth, development, and stress responses, as well as a negative regulator of heading date in rice [ 32 , 33 ]. Therefore, we guessed that the gene Seita.9G020100 might be the casual gene for qHD9-1 and sequenced the promoter and gene sequence of Seita.9G020100 in Jingu28 and Ai88. Sequence alignment showed that there was a 277 bp deletion and one base (T) insertion on the promoter and a 5 bp (AATTA) deletion in the intron of Jingu 28, compared with Ai88 (Fig. 5 a, S1 ). Previous studies have shown that the insertion/deletion in the promoter may affect gene expression and thus lead to phenotypic changes [ 34 , 35 ]. And then, we guess that the 277 bp deletion in the promoter of Seita.9G020100 in Jingu28 changed gene expression and delayed flowering, thus increasing the heading date. Based on the 277 bp insertion/deletion, we designed an InDel marker Ghd7InDel . Ghd7InDel was used to amplify 257 foxtail millet accessions randomly selected from 916 foxtail millet accessions published by Jia et al . [ 18 ] and the result showed that 104 foxtail millet accessions have the 277 bp deletion (Allele Jingu28 ) and 153 have not the 277 bp deletion (Allele Ai88 , Fig. 5 b, Table S5). The average heading time of foxtail millet accessions with Allele Jingu28 was significantly later than that with Allele Ai88 in 2023 in Taigu, Shanxi Province (Fig. 5 c, Table S5). For plant height, there were two major QTL qPH5-1 and qPH5-2 on chromosome 5, explaining 29.55% and 27.25% of the phenotypic variation, respectively, which were also included in the septuple co-located QTL cluster ( qPH5-1 / qPH5-2 / qPNL5 / qFLW5-2 / qPW5 / qGWP5 / qHD5 ). We analyzed the candidate genes of the QTL qPH5-1 (Chr5: 43.12–43.79 Mb) and found a gibberellin biosynthesis related GA20 oxidase gene Seita.5G404900 , which is a homologue of the rice ‘green revolution’ gene OsSD1 ( LOC_Os01g66100 ). Furthermore, sequence alignment of the Seita.5G404900 between Jingu28 and Ai88 showed that there was a SNP in the first exon, nine SNPs and a four-base insertion/deletion in the second intron and a one-base (G) insertion/deletion in the third exon (Fig. 6 a, S2 ). The SNP in the first exon was a nonsense mutation that did not cause a change in amino acid sequence. The one-base deletion in the third exon of Ai88, compared with Jingu28, led to the frameshift mutation. Therefore, we hypothesized that the single-base deletion in the third exon affected the function of the gibberellin biosynthesis related GA20 oxidase gene Seita.5G404900 , thereby reducing the plant height of the Ai88. A semi-thermal asymmetric reverse PCR (STARP) marker GA20oxSTARP-1 was designed based on the single-base insertion/deletion in the third exon and used to amplify 257 foxtail millet accessions randomly selected from 916 foxtail millet accessions published by Jia et al . [ 18 ]. The result showed that 254 foxtail millet accessions have not the single-base deletion (Allele Jingu28 ) and three have the single-base deletion (Allele Ai88 ), which were Ci348 (Baimi1), Ci736 (Zituigu) and Ci937 (C193, Fig. 6 b, Table S5). The average plant height of foxtail millet accessions with Allele Jingu28 was significantly higher than that with Allele Ai88 in 2023 in Taigu, Shanxi Province (Fig. 6 c, Table S5). Discussion Comparison the QTL identified in this study with previous studies In the present study, we used the F 2 mapping population and constructed a genetic map containing 213 SSR markers and two InDel markers, covering the whole genome with a genetic length of 1492.5 cM, with average distance of 6.94 cM between adjacent markers (Fig. 4 , Table 1 ). In total, we identified 46 QTL for 12 agronomic traits using the genotypes of F 2 population and phenotypes of F 2:3 families. The additive effect of 15 QTL were from Ai88, by contrast, 31 QTL were from Jingu28. Furthermore, 13 major effect QTL, namely qPH5-1 , qPH5-2 , qFLL2-2 , qFLW1-1 , qFLW1-2 , qFLW2 , qFLW5-1 , qFLW5-2 , qPW5 , qHD2 , qHD9-1 , qHD9-2 , and qHD9-3 , with a PVE of more than 10%, were detected (Fig. 4 , Table 2 ). For plant height, eight QTL ( qPH1 , qPH2-1 , qPH2-2 , qPH2-3 , qPH5-1 , qPH5-2 , qPH9-1 and qPH9-2 ) on chromosome 1, 2, 5 and 9 were detected. Among them, qPH2-2 might be a novel QTL. The qPH1 (19.87–34.63 Mb) was overlapped with qPH1-1 (29,694,706 − 29,725,881 bp) and qPH1-2 (31,837,278 − 31,865,079 bp) identified by Wang et al . [ 14 ], qPH1.1 - qPH1.3 mapped by He et al . [ 16 ], the QTL on chromosome I (31,946,156 − 42,023,774 bp) mapped by Gao et al . [ 36 ] and qph1 (bin162, 32,124,053 − 32,154,417 bp) detected by Zhang et al . [ 11 ] under a long-day photoperiod. The qPH2-1 (8.10–28.52 Mb) in this study was in accordance with qPH2 (26,040,234 − 28,913,785 bp) discovered by He et al . [ 16 ]. The qPH5-1 (43.12–43.79 Mb) was overlapped with qph5 (bin1188, 43,657,914 − 43,708,214 bp) under a long-day photoperiod and qph5 (bin1186, 43,191,338 − 43,553,311 bp) under a short-day photoperiod identified by Zhang et al . [ 11 ]. Zhu et al . [ 37 ] performed bulk segregant analysis using an F 2 population crossed by a semi-dwarf line 263A and an elite high-stalk breeding variety Chuang 29 and led to the identification of a 16.2-Mb region (30.7–32.6 Mb, 32.7–38.5 Mb, 38.7–41.2 Mb, and 41.3–47.3 Mb) on chromosome 5 related to plant height and they found a gibberellin biosynthesis related GA20 oxidase gene ( Seita.5G404900 ), which had a single-base at the third exon, leading to the frameshift mutation at 263A. Interestingly, qPH5-1 (43.12–43.79 Mb) and qPH5-2 (43.79–45.75 Mb) in this study were overlapped with the interval on chromosome 5 published by Zhu et al . (2022) and we also thought that Seita.5G404900 might be the candidate gene of the QTL qPH5-1 , because a one-base deletion existed in the third exon of Ai88, compared with Jingu28, leading to the frameshift mutation. Furthermore, qPH5-2 (43.79–45.75 Mb) was overlapped with qPH5-3 (45,014,767 − 45,037,138 bp) identified by Wang et al . [ 14 ]. The qPH9-1 (0.21–1.84 Mb) was in accordance with qph9 (bin1774, 1,559,957–1,859,997 bp) under a long-day photoperiod mapped by Zhang et al . [ 11 ]. Moreover, qPH9-2 (37.30–44.00 Mb) identified in our study was overlapped with qPH9.2 - qPH9.5 ( qPH9.2 , 35,624,887 − 41,536,123 bp; qPH9.3 , 41,536,123 − 42,609,213 bp; qPH9.4 , 42,767,054 − 43,430,752 bp; qPH9.5 , 42,855,389 − 43,953,050 bp) discovered by He et al . [ 16 ]. Interestingly, five QTL ( qPH2-1 , qPH2-3 , qPH5-2 , qPH9-1 and qPH9-2 ) were detected by Dai et al . [ 21 ] (Table S6). For panicle neck length, two QTL ( qPNL2 and qPNL5 ) were detected in our study. In fact, we thought three QTL for PNL should be detected, because one QTL near the end of chromosome 2 reached the LOD threshold of 3.6, however, the WinQTLCart software didn’t detect the locus for having not enough molecular marker. The qPNL5 (40.92–43.79 Mb) was in accordance with qnl5 (bin1188, 43,657,914 − 43,708,214 bp) under a long-day photoperiod by Zhang et al . [ 11 ] and qPNL2 might be a novel QTL (Table S6). In the present study, seven QTL ( qPL3 , qPL4-1 , qPL4-2 , qPL5 , qPL9-1 , qPL9-2 and qPL9-3 ) on Chr3, Chr4, Chr5 and Chr9 were found to be related to PL and only the additive effect of qPL9-3 was from Ai88. Like panicle neck length, one QTL for PL near the end of chromosome 2 reached the LOD threshold of 3.6, however, the WinQTLCart software also didn’t detect the locus for having not enough molecular marker. Therefore, eight QTL for PL should be detected. The qPL4-1 (6.83–17.35 Mb), qPL4-2 (17.35–32.75 Mb), qPL5 (3.49–5.41 Mb) and qPL9-1 (0.51–1.84 Mb) in our study were overlapped with qpl4-1 (bin837, 7,479,756–7,510,803 bp), qpl4-2 (bin870, 28,284,656 − 29,738,603 bp), qpl5-1 (bin987, 4,176,711–4,313,654 bp) and qpl9 (bin1771, 758,712–1,360,456 bp) mapped by Zhang et al . [ 11 ], respectively. The qPL3 and qPL9-3 were also identified by Jia et al . [ 18 ]. In addition, qPL3 , qPL4-1 , qPL4-2 and qPL5 have been detected by Zhi et al . [ 17 ] and qPL3 , qPL4-2 and qPL9-3 have been mapped by Dai et al . [ 21 ]. The qPL9-2 (1.84–3.90 Mb) might be a novel QTL (Table S6). There were four QTL ( qPD1-1 , qPD1-2 , qPD2 and qPD9 ) located on Chr1, Chr2 and Chr9 affecting PD in this study and qPD1-2 might be a novel QTL. The qPD2 (32.04–41.26 Mb) and qPD9 (2.88–9.70 Mb) were overlapped with qpd2 (bin392, 31,510,348 − 32,450,713 bp) and qpd9-1 (bin1796, 3,617,446–3,678,459 bp) detected by Zhang et al . [ 11 ], respectively. The qPD2 was also identified by Zhi et al . [ 17 ]. Three QTL ( qPD1-1 , qPD2 and qPD9 ) were mapped by Dai et al . [ 21 ] (Table S6). For flag leaf length, five QTL ( qFLL2-1 , qFLL2-2 , qFLL4 , qFLL6 and qFLL9 ) with a PVE from 0.52 to 35.21% were detected and qFLL2-1 and qFLL6 might be two novel QTL. Three QTL ( qFLL2-2 , qFLL4 and qFLL9 ) were detected by Zhang et al . [ 11 ]. Moreover, qFLL2-2 was also identified by Jia et al . [ 18 ]. For flag leaf width, five major QTL in the present study were located on Chr1, Chr2 and Chr5 and four QTL ( qFLW1-1 , qFLW2 , qFLW5-1 and qFLW5-2 ) among them might be novel QTL. The qFLW1-2 was detected by Jia et al . [ 18 ] (Table S6). In our study, five QTL ( qPW5 , qPW7 , qPW9-1 , qPW9-2 and qPW9-3 ) controlling panicle weight were detected, of which qPW9-2 and qPW9-3 might be two novel QTL. The qPW5 , qPW7 and qPW9-1 were identified by Zhang et al . [ 11 ], Wang et al . [ 14 ], Liu et al . [ 15 ], Zhi et al . [ 17 ] and Dai et al . [ 21 ]. For grain weight per panicle, thousand grain weight, grain length and grain width, only one QTL for each trait was detected in the present study, which was qGWP5 , qTGW2, qGL2 and qGW2 , respectively. The qGWP5 and qTGW2 were overlapped with qGW5 (41,302,002–41,350,538 bp) identified by Wang et al . [ 14 ] and qTGW2.1 (28,215,787 − 28,932,236 bp) mapped by Zhi et al . [ 17 ], respectively. The qGL2 and qGW2 were two novel QTL (Table S6). Six QTL ( qHD2 , qHD5 , qHD6 , qHD9-1 , qHD9-2 and qHD9-3 ) on Chr2, Chr5, Chr6 and Chr9 for HD were detected. Three QTL, qHD2 , qHD9-1 and qHD9-3 were identified by Jia et al . [ 18 ] and qHD5 , qHD6 and qHD9-2 might be novel QTL (Table S6). Co-located QTL clusters for different traits It is a widespread phenomenon that many QTL controlling different traits were co-located in the same intervals of the genome. For example, Zhi et al . [ 17 ] identified 34 co-located QTL clusters controlling agronomic traits related to panicle architecture and grain yield. In the present study, 13 co-located QTL clusters distributed on Chr1, Chr2, Chr4, Chr5, Chr6 and Chr9 were formed, including seven double co-located QTL ( qPD1-2 / qFLW1-2 , qPL4-2 / qFLL4 , qFLL6 / qHD6 , qPL9-2 / qHD9-2 , qPD9 / qPW9-1 , qPL9-3 / qPW9-3 and qPH9-2 / qHD9-3 ), two triple co-located QTL ( qPH1 / qPD1-1 / qFLW1-1 and qPH2-1 / qTGW2 / qGL2 ), two quadruple co-located QTL clusters ( qPH2-2 / qPNL2 / qPD2 / qFLL2-1 and qPH9-1 / qPL9-1 / qFLL9 / qHD9-1 ), one quintuple co-located QTL cluster ( qPH2-3 / qFLL2-2 / qFLW2 / qGW2 / qHD2 ) and one septuple co-located QTL cluster ( qPH5-1 / qPH5-2 / qPNL5 / qFLW5-2 / qPW5 / qGWP5 / qHD5 , Table S3 ). In fact, the quintuple co-located QTL cluster ( qPH2-3 / qFLL2-2 / qFLW2 / qGW2 / qHD2 ) should be one septuple co-located QTL cluster containing QTL for PNL and PL, because one locus controlling PNL and PL near the end of chromosome 2 reached the LOD threshold of 3.6, however, the WinQTLCart software didn’t detect the QTL for having not enough molecular marker (Fig. S3 ). Co-location of QTL for different traits in this study was consistent with significant correlations between these traits. Co-located QTL may be conferred by pleiotropic genes that play important roles in the network of agronomic and yield development of foxtail millet, or by closely-linked alleles. Candidate genes located at some QTL Flowering time (also known as heading date in cereals) is a critical determinant of regional adaptability of plants, and thus crop yields. In this study, six QTL ( qHD2 , qHD5 , qHD6 , qHD9-1 , qHD9-2 and qHD9-3 ) on Chr2, Chr5, Chr6 and Chr9 for HD were detected. Among them, qHD2 , qHD9-1 , qHD9-2 and qHD9-3 were four major QTL, with PVE of 22.48%, 37.81%, 11.60% and 10.21%, respectively (Fig. 4 , Table 2 ). For qHD9-1 , the gene Seita.9G020100 (Chr9: 1,056,892-1,059,177 bp) with conserved CCT (constans, constans-like, and timing of chlorophyll A/B binding) motif might be the candidate gene for it, which was homologous with transcription factor Ghd7 in rice and CONSTANS-like 1 ( COL1 , AT5G15850 ) in Arabidopsis . In rice, Ghd7 encoding a CCT domain protein is a negative regulator of heading date and the down-regulation of Ghd7 can promote early flowering [ 32 , 33 , 38 ]. Furthermore, we guess that the 277 bp deletion in the promoter of Seita.9G020100 in Jingu28 changed gene expression and increased the heading date. However, it needs to be further verified by some molecular biology experiments, for instance, expression analysis, promoter activity analysis, gene overexpression or knockout and so on. Moreover, we found that the average heading time of foxtail millet accessions with Allele Jingu28 was significantly later than that with Allele Ai88 (Fig. 5 c), showing that the 277 bp insertion/deletion might be an important functional site and the InDel marker Ghd7InDel could be used for marker-assisted selection (MAS) in foxtail millet. In rice, Ghd7 is also a central regulator of growth, development and stress responses and the interaction between Hd1 and Ghd7 is important for controlling yield traits in rice [ 32 , 33 , 38 ]. Interestingly, the qHD9-1 in our study was contained in the quadruple co-located QTL cluster ( qPH9-1 / qPL9-1 / qFLL9 / qHD9-1 ), indicating that Seita.9G020100 might affect multiple traits in foxtail millet. For qHD2 , one gene Seita.2G444300 was existed in the candidate interval, which is the homologous gene of PSEUDO-RESPONSE REGULATOR 37 ( PRR37 ) in rice and PRR7 in Arabidopsis . PRR37 encodes a CCT domain-containing protein and was a core rice gene controlling photoperiod sensitivity. Under long days conditions, PRR37 suppressed flowering and increased plant height, the number of spikelets per panicle, and yield [ 39 – 41 ]. PRR7 , a central component of the Arabidopsis clock, was directly involved in the repression of master regulators of plant growth, light signaling and stress responses [ 42 – 45 ]. In foxtail millet, it was confirmed that overexpression of Seita.2G444300 ( SiPRR37 ) delayed the heading date and increased plant height. Therefore, we guess that Seita.2G444300 ( SiPRR37 ) might be responsible for the qHD2 or qPH2-3 / qFLL2-2 / qFLW2/ q GW2 / qHD2 . Plant height is an important trait that determines tradeoffs between competition and the distribution of resource, which is crucial for yield potential. Therefore, understanding the genetic basis of plant height in foxtail millet would help to design foxtail millet cultivars with ideal plant architecture and high grain yield potential. In the present study, eight QTL ( qPH1 , qPH2-1 , qPH2-2 , qPH2-3 , qPH5-1 , qPH5-2 , qPH9-1 and qPH9-2 ) were detected. For qPH1 , its dwarf allele was from Ai88. The LOD value of qPH1 was 6.19, explaining 3.71% of the phenotypic variations. It localized to a 14.76-Mb interval flanked by markers SICAAS1020 and b243 (Fig. 4 , Table 2 ). The candidate genes of the mapping interval of qPH1 were analyzed and we found that Seita.1G242300 is an orthologue gene to rice LOC_Os02g41954 and Arabidopsis AT4G21200 . LOC_Os02g41954 and AT4G21200 encode gibberellin 2-beta-dioxygenase 7 (ATGA2ox7) and gibberellin 2-oxidase 8 (ATGA2ox8), respectively. Arabidopsis thaliana gibberellin 2-oxidase 8 (AtGA2ox8) catalyzes the 2β-hydroxylation of the C 20 -GA precursors GA 12 and GA 53 . Increased expression of either ATGA2ox7 or ATGA2ox8 caused a dwarf phenotype in both Arabidopsis and tobacco, while loss of function ATGA2ox7 ATGA2ox8 double mutants had higher levels of active GAs and displayed phenotypes associated with excess GAs [ 46 ]. For qPH5-1 , it explained 29.55% of the phenotypic variation. We found a gibberellin biosynthesis related GA20 oxidase gene Seita.5G404900 existed in the candidate interval of qPH5-1 , which is a homologue of the rice ‘green revolution’ gene OsSD1 ( LOC_Os01g66100 ). In rice, the semi-dwarf mutant sd1 is the result of a deficiency of active GAs in the elongating stem caused by the loss of function of SD1 [ 47 – 49 ]. In the present study, we found that there was a one-base (G) deletion in the third exon of Seita.5G404900 in Ai88 (Fig. 6 a, S2 ), leading to the frameshift mutation, which might affect the function of the gibberellin biosynthesis related GA20 oxidase gene Seita.5G404900 , thereby reducing the plant height of the Ai88. In addition, a semi-thermal asymmetric reverse PCR (STARP) marker GA20oxSTARP-1 was designed based on the single-base insertion/deletion in the third exon and used to allelic variation analysis. The result showed that there were only three foxtail millet accessions with the single-base deletion (Allele Ai88 ) in the 257 foxtail millet accessions randomly selected from 916 foxtail millet accessions published by Jia et al . [ 18 ], which were Ci348 (Baimi1), Ci736 (Zituigu) and Ci937 (C193, Fig. 6 b, Table S5), respectively, indicating the single-base deletion was a rare variation. Moreover, the average plant height of foxtail millet accessions with Allele Ai88 was significantly lower than that with Allele Jingu28 (Fig. 6 c), indicating this variation and the marker GA20oxSTARP-1 could be used for molecular breeding of plant height in foxtail millet. Conclusion In this study, a genetic linkage map with 213 published SSR markers and two InDel markers was constructed. QTL mapping identified 46 QTL for 12 agronomic traits. The 277 bp insertion/deletion on the promoter of Seita.9G020100 and the one-base (G) insertion/deletion in the third exon of Seita.5G404900 might be candidate functional sites for major QTL qHD9-1 and qPH5-1 , respectively. Abbreviations QTL : Quantitative trait locus/ Quantitative trait loci SSR : Simple repeat sequence InDel : Insertion-deletion RIL : Recombinant inbred line GWAS : Genome wide association analysis MTA : Marker-trait association SV : Structural variation RTM : Restricted two-stage multi-locus multi-allele MLM : Mixed linear model CTAB : Cetyltrimethyl ammonium bromide CIM : Composite interval mapping LOD : Logarithm of odds PCR : Polymerase chain reaction cM : Centimorgan Mb : Mega base pairs STARP : Semi-thermal asymmetric reverse PCR Chr : Chromosome PVE : Phenotypic variation explained SNP : Single nucleotide polymorphism GA : Gibberellic acid Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and material All data generated or analysed during this study are included in this published article and its additional flies. Competing interests The authors declare no competing interests Funding This work was supported by Special Plan for Scientific and Technological Innovation Talent Team of Shanxi Province (202204051002036), Shanxi Agricultural University Doctoral Research Initiation Project (2021BQ23, 2021BQ24), Award Scientific Program for Excellent Doctors in Shanxi Province (SXBYKY2021070, SXBYKY2021059), and Natural Science Foundation of Shanxi Province (202203021222169, 20210302123382). Authors’ contributions LLG, GHY and JGW designed the experiments. LLG, GHY, QXZ and HL performed experiments. SYW, JHF, TGW, LJY, YQZ, YXM and LC collected the phenotype. LLG and GHY wrote the manuscript. XRL, SQD, XQC and XYY helped revise the manuscript. XMD provided the 257 foxtail millet accessions. All authors reviewed and approved the final manuscript. 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Proceedings of the National Academy of Sciences of the United States of America. 2002;99(13):9043-9048. Additional Declarations No competing interests reported. Supplementary Files SupplementalTableS1S6.xls Table S1 Statistical analysis of parental phenotype data Table S2 Descriptive statistics of the 12 agronomic traits in F 2:3 families Table S3 Co-located QTL clusters identified for different traits Table S4 List of primers in this study Table S5 The list of 257 foxtail millet accessions ( Setaria italica ) Table S6 Comparasin of QTL in this study with the previous studies Fig.S1.jpg Supplementary information Fig. S1 The genomic sequence alignment of Seita.9G020100 in Ai88 and Jingu28. The start codon (ATG) and stop codon (TAG) were marked with red lines. The green box marked the exons. Fig.S2.jpg Fig. S2 The genomic sequence alignment of Seita.5G404900 in Jingu28 and Ai88. The start codon (ATG) and stop codon (TAG) were marked with red lines. The green box marked the exons. Fig.S3.jpg Fig. S3 QTL mapping result for 12 agronomic traits on chromosome 2. UncroppedGelsandBlotsimageofFig.5b.tif UncroppedGelsandBlotsimageofFig.6b.jpg Cite Share Download PDF Status: Published Journal Publication published 17 Feb, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 18 Sep, 2024 Editor assigned by journal 13 Sep, 2024 Submission checks completed at journal 13 Sep, 2024 First submitted to journal 10 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5061888","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":355946166,"identity":"2b61822c-7d08-4c43-bf3a-d10f5b4dc7db","order_by":0,"name":"Lulu Gao","email":"","orcid":"","institution":"Shanxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Gao","suffix":""},{"id":355946167,"identity":"afe5f025-f090-412f-9580-7926efb7411e","order_by":1,"name":"Qianxue Zhu","email":"","orcid":"","institution":"Shanxi Agricultural 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panicle neck length (i), panicle length (j), panicle diameter (k), flag leaf length (l), flag leaf width (m), tiller number (n), stem node number (o), panicle weight (p), grain weight per panicle (q), primary branch number (r), primary branch density (s), thousand grain weight (t), grain length (u), grain width (v) and stem node length (w). Differences between genotypes were assessed with Student’s \u003cem\u003et\u003c/em\u003e test. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/efcfc369f52162cfd3345fe8.png"},{"id":69337115,"identity":"abb2ad49-199f-41f5-9ccc-ea8a2839115c","added_by":"auto","created_at":"2024-11-19 10:13:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":877148,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of 12 agronomic traits in F\u003csub\u003e2:3\u003c/sub\u003e families.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/69edaf6b6c42c4eab3d7f6ad.png"},{"id":69337332,"identity":"aacd0f0e-5f2b-4916-b5c1-15177bb6bb35","added_by":"auto","created_at":"2024-11-19 10:21:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":651929,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis among 12 agronomic traits in F\u003csub\u003e2:3\u003c/sub\u003e families. The statistical method Pearson correlation coefficient was used. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/49dd7479f8318b88429cb64b.png"},{"id":69336357,"identity":"8bac956f-1440-4bfc-a7c5-6f6663c23fb4","added_by":"auto","created_at":"2024-11-19 10:05:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7202884,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic linkage map and QTL controlling agronomic trait. On each chromosome, the name of each marker is shown on the right. The number on the left indicates the genetic location (cM).\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/f207073186c9e167d0ce260f.png"},{"id":69337116,"identity":"3baa8c1d-fc06-42da-ad42-629d8af29113","added_by":"auto","created_at":"2024-11-19 10:13:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2820895,"visible":true,"origin":"","legend":"\u003cp\u003eThe sequence comparison of \u003cem\u003eSeita.9G020100 \u003c/em\u003ebetween Jingu28 and Ai88 and allelic variation analysis of the 277 bp insertion/deletion. a The structure diagram of \u003cem\u003eSeita.9G020100 \u003c/em\u003eand sequence comparison between Jingu28 and Ai88. The number above the arrow indicates the position relative to the ATG in Ai88. The bases before and after the slash represent the sequences in Ai88 and Jingu28, respectively. b Agarose gel electrophoresis of some foxtail millet materials amplified by InDel marker \u003cem\u003eGhd7InDel\u003c/em\u003e designed based on the 277 bp insertion/deletion. c Comparison of heading time between Allele\u003csup\u003eJingu28\u003c/sup\u003e (with the 277 bp deletion) and Allele\u003csup\u003eAi88 \u003c/sup\u003e(without the 277 bp deletion).\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/be6bc2bb9c3b73c3bddf1936.png"},{"id":69337333,"identity":"7d8f35ba-bdd2-43da-91b4-9d226114ac87","added_by":"auto","created_at":"2024-11-19 10:21:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2079480,"visible":true,"origin":"","legend":"\u003cp\u003eThe sequence comparison of \u003cem\u003eSeita.5G404900 \u003c/em\u003ebetween Jingu28 and Ai88 and allelic variation analysis of the one-base insertion/deletion in the third exon. a The structure diagram of \u003cem\u003eSeita.5G404900 \u003c/em\u003eand sequence comparison between Jingu28 and Ai88. The number above the arrow indicates the position relative to the ATG in Jingu28. The bases before and after the slash represent the sequences in Jingu28 and Ai88, respectively. b Agarose gel electrophoresis of some foxtail millet materials amplified by the STARP marker \u003cem\u003eGA20oxSTARP-1\u003c/em\u003e designed based on the single-base insertion/deletion in the third exon of \u003cem\u003eSeita.5G404900\u003c/em\u003e. c Comparison of plant height between Allele\u003csup\u003eJingu28\u003c/sup\u003e (without one-base deletion) and Allele\u003csup\u003eAi88\u003c/sup\u003e (with one-base deletion).\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/77cb5aa8cf7ddefb06a48d68.png"},{"id":77052513,"identity":"a8a07a22-1dc0-45c9-9485-009ffc1a752d","added_by":"auto","created_at":"2025-02-24 16:13:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18675039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/751cf588-1144-49c8-9e72-d45aca6eb2d4.pdf"},{"id":69336361,"identity":"6d15b2e7-72be-4dd4-acb8-47f129ea47e1","added_by":"auto","created_at":"2024-11-19 10:05:27","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49162,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1 Statistical analysis of parental phenotype data\u003c/p\u003e\n\u003cp\u003eTable S2 Descriptive statistics of the 12 agronomic traits in F\u003csub\u003e2:3\u003c/sub\u003e families\u003c/p\u003e\n\u003cp\u003eTable S3 Co-located QTL clusters identified for different traits\u003c/p\u003e\n\u003cp\u003eTable S4 List of primers in this study\u003c/p\u003e\n\u003cp\u003eTable S5 The list of 257 foxtail millet accessions (\u003cem\u003eSetaria italica\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003eTable S6 Comparasin of QTL in this study with the previous studies\u003c/p\u003e","description":"","filename":"SupplementalTableS1S6.xls","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/f966fbd99771f3fe2242c13e.xls"},{"id":69336356,"identity":"2fec8b0f-d261-4dcb-8165-8c555d513398","added_by":"auto","created_at":"2024-11-19 10:05:27","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1750048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig. S1 The genomic sequence alignment of \u003cem\u003eSeita.9G020100\u003c/em\u003ein Ai88 and Jingu28. The start codon (ATG) and stop codon (TAG) were marked with red lines. The green box marked the exons.\u003c/p\u003e","description":"","filename":"Fig.S1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/5de29fe25ecbb41318f87f88.jpg"},{"id":69337119,"identity":"d2c627b1-7f2b-4c29-adc9-71edbb8dba74","added_by":"auto","created_at":"2024-11-19 10:13:28","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2923853,"visible":true,"origin":"","legend":"\u003cp\u003eFig. S2 The genomic sequence alignment of \u003cem\u003eSeita.5G404900\u003c/em\u003ein Jingu28 and Ai88. The start codon (ATG) and stop codon (TAG) were marked with red lines. The green box marked the exons.\u003c/p\u003e","description":"","filename":"Fig.S2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/1fc3f4a3756ac00290445192.jpg"},{"id":69336362,"identity":"92d3e2db-2e4e-412a-81ac-fb1e8e6ea265","added_by":"auto","created_at":"2024-11-19 10:05:27","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":598363,"visible":true,"origin":"","legend":"\u003cp\u003eFig. S3 QTL mapping result for 12 agronomic traits on chromosome 2.\u003c/p\u003e","description":"","filename":"Fig.S3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/0889a24a78d7ca478f3249c5.jpg"},{"id":69336364,"identity":"f45e2578-e5cc-40c8-adcc-d3aee5d1980d","added_by":"auto","created_at":"2024-11-19 10:05:27","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":10093470,"visible":true,"origin":"","legend":"","description":"","filename":"UncroppedGelsandBlotsimageofFig.5b.tif","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/a29d01af74c443e81c968e36.tif"},{"id":69336365,"identity":"f540b6c9-0b23-499d-9180-70eef53e0553","added_by":"auto","created_at":"2024-11-19 10:05:27","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":380622,"visible":true,"origin":"","legend":"","description":"","filename":"UncroppedGelsandBlotsimageofFig.6b.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5061888/v1/767e58b993d0dee90a8fcb12.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction of a genetic linkage map and QTL mapping of the agronomic traits in Foxtail millet (Setaria italica)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFoxtail millet (\u003cem\u003eSetaria italica\u003c/em\u003e L. Beauv.), domesticated from the wild green foxtail (\u003cem\u003eSetaria viridis\u003c/em\u003e L. Beauv.) about 11,000 years ago in northern China, is one of the most ancient cultivated cereal crops and an important C4 crop [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Moreover, because of its small diploid genome, short growth duration, high inbreeding rate, small morphological stature, easy lab management and excellent drought tolerance, foxtail millet is a model plant for the functional genomic studies of the \u003cem\u003ePanicoideae\u003c/em\u003e crops, especially for investigating plant architecture, C4 photosynthesis and drought tolerance [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the molecular and genetic mechanisms underlying agronomic traits of foxtail millet remain unclear. Therefore, it is essential to assess its agronomic traits by developing a genetic linkage map and identifying genes or quantitative trait loci (QTL).\u003c/p\u003e \u003cp\u003eIn previous studies, many QTL for agronomic traits distributed on 9 chromosomes of foxtail millet were mapped using linked populations. Doust \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] used the F\u003csub\u003e2\u003c/sub\u003e interspecies population from a cross between \u003cem\u003eS. italica\u003c/em\u003e accession B100 and \u003cem\u003eS. viridis\u003c/em\u003e accession A10 to conduct QTL analysis and comparative genomics for basal branching (tillering) and axillary branching. Fourteen replicated QTL were detected for the four inflorescence traits (primary branch number and density, spikelet number, and bristle (sterile branchlet) number) in B100\u0026times;A10 F\u003csub\u003e2\u003c/sub\u003e population [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Mauro-Herrera \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] identified 16 flowering time QTL in B100\u0026times;A10 F\u003csub\u003e7\u003c/sub\u003e recombinant inbred lines (RILs). Fang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] mapped 29 QTL for 11 traits using a Yugu1 \u0026times;Longgu7 F\u003csub\u003e2\u003c/sub\u003e population. \u003cem\u003eWang et al\u003c/em\u003e. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] detected 11 major QTL for eight agronomic traits using the F\u003csub\u003e2\u003c/sub\u003e population derived from the cross between Hongmiaozhangu and Changnong35. Zhang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] used a foxtail millet population of 439 RILs derived from a cross between Zhanggu and A2 and identified a total of 59 QTL for 14 agronomic traits in plants grown under long- and short-day photoperiods. Ni et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] used 184 A2\u0026times;Zhanggu RILs to map QTL for nine agronomic traits, five of which were controlled by a single gene, two QTL were found for plant height, and a candidate gene showed 89% identity to the known rice gibberellin-synthesis gene \u003cem\u003esd1\u003c/em\u003e, and three QTL were found for the trait of heading date. Odonkor \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] identified the presence of additive main effect QTL for reduced shattering on chromosomes V and IX using the B100\u0026times;A10 F\u003csub\u003e2:3\u003c/sub\u003e and RIL populations. Wang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] detected fifty-seven QTL related to 11 various agronomic traits using a large F\u003csub\u003e2\u003c/sub\u003e population including 543 plants from a cross between Aininghuang and Jingu 21. Liu \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] used a 164 RILs from a cross between Longgu7 and Yugu1 and identified forty-seven QTL for four traits of straw weight, panicle weight, grain weight per plant and 1000-grain weight. He \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] constructed a high-density bin map with 3744 marker bins and identified 26 QTL significantly associated with plant height using a foxtail millet population of 333 RILs derived from a cross between a backbone line Ai 88 and an elite cultivar Liaogu 1. Moreover, Zhi \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] used this population and detected 159 QTL for panicle architecture and grain yield-related traits.\u003c/p\u003e \u003cp\u003eGenome wide association analysis (GWAS) using natural populations has also been used to detect loci associated with important agronomic traits in foxtail millet. Jia \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] identified 512\u0026emsp;loci\u0026emsp;associated\u0026emsp;with\u0026emsp;47\u0026emsp;agronomic\u0026emsp;traits\u0026emsp;by GWAS using 916 varieties. Jaiswal \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] identified 81 MTAs (marker-trait associations) for ten traits across the genome by GWAS in 142 foxtail millet. He \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] performed an SV-based GWAS (SV-GWAS) for TGW, GW and GL using the 110 high-quality genome sequences and found several significant GWAS signals on chromosomes 1, 3, 4, 5 and 9 for TGW and GW. Furthermore, a total of 1,084 signals were identified to be substantially associated with 128 phenotypes for 60 traits using 1,844 Setaria accessions. Dai \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] identified eleven QTL for seven traits using two models for genome-wide association studies (RTM- and MLM-GWAS) in 408 diverse foxtail millet accessions.\u003c/p\u003e \u003cp\u003eFurthermore, many important functional genes in foxtail millet were cloned. \u003cem\u003eSiAGO1b\u003c/em\u003e mutation influenced multiple biological processes, including energy metabolism, cell growth, programmed death and abiotic stress responses in foxtail millet [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A 5.5-kb Copia-like retrotransposon insertion in \u003cem\u003eDWARF1\u003c/em\u003e (\u003cem\u003eD1\u003c/em\u003e), which encodes a DELLA protein, mediated the transcriptional reprogramming of \u003cem\u003eD1\u003c/em\u003e leading to a novel N-terminal-deleted truncated DELLA transcript, resulting in dwarf plants [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. \u003cem\u003eSiMADS34\u003c/em\u003e, an E‑class MADS‑box transcription factor, regulates inflorescence architecture and grain yield [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Domestication-associated \u003cem\u003eSiPHYC\u003c/em\u003e encoding a light receptor repressed flowering and determined \u003cem\u003eSetaria\u003c/em\u003e as a short-day plant [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Li \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] confirmed that overexpression of the CCT gene \u003cem\u003eSiPRR37\u003c/em\u003e delayed the heading date and increased plant height. \u003cem\u003eSiGW3\u003c/em\u003e negatively regulates grain weight, and the distal 366-bp genomic sequence possibly represses the expression of \u003cem\u003eSiGW3\u003c/em\u003e, thereby increasing grain weight in domesticated foxtail millet [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A RING-type E3 ligase SGD1 and its E2 partner SiUBC32 control grain yield in \u003cem\u003eSetaria italica\u003c/em\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, an F\u003csub\u003e2\u003c/sub\u003e population derived from a cross between an elite foxtail millet variety Jingu28 and a backbone line Ai88 was generated. Most important agronomic traits varied greatly between the two parents and within the F\u003csub\u003e2:3\u003c/sub\u003e families. A genetic linkage map with 213 published SSR markers and two InDel markers was constructed to explore the genetic control of 12 agronomic traits in the F\u003csub\u003e2:3\u003c/sub\u003e families. QTL mapping identified 46 QTL and two putative candidate genes were identified based on homologous gene comparison. The work will provide insights into the genetic architecture controlling agronomic traits and facilitate the breeding of high-yielding foxtail millet varieties with ideal plant architecture.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMapping population construction\u003c/h2\u003e \u003cp\u003eAi88, characterized by a dwarf phenotype with plant height about 90 cm, was developed by the Institute of Millet Crops, Hebei Academy of Agricultural and Forestry Sciences. Jingu28 was developed by Shanxi Academy of Agricultural Sciences. A cross between Jingu28 and Ai88 was made in 2021, and F\u003csub\u003e1\u003c/sub\u003e were grown in the greenhouse in the winter of 2021. F\u003csub\u003e2\u003c/sub\u003e population and F\u003csub\u003e2:3\u003c/sub\u003e families were planted in Shenfeng (116.34\u0026prime; E, 37.44\u0026prime; N, Shanxi, China) during the 2022 and 2023 spring cropping season, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAgronomic trait investigation and statistical analysis\u003c/h2\u003e \u003cp\u003eAgronomic traits were evaluated for parents and F\u003csub\u003e2:3\u003c/sub\u003e families planted in spring 2023 in Taigu, Shanxi. For parents, data were collected on plant height (PH, cm), panicle neck length (PNL, cm), panicle length (PL, cm), panicle diameter (PD, mm), flag leaf length (FLL, cm), flag leaf width (FLW, cm), tiller number (TN), stem node number (SNN), panicle weight (PW, g), grain weight per panicle (GWP, g), primary branch number (PBN), primary branch density (PBD), thousand grain weight (TGW, g), grain length (GL, mm), grain width (GW, mm), stem node length (SNL, cm) and heading date (HD, d). For F\u003csub\u003e2:3\u003c/sub\u003e families, 12 agronomic traits (PH, PNL, PL, PD, FLL, FLW, PW, GWP, TGW, GL, GW and HD) were investigated. We phenotyped at least 10 plants per families for all traits introduced above except HD, and the mean phenotype value was regarded as the corresponding trait value. For F\u003csub\u003e2\u003c/sub\u003e individuals planted in spring 2022 in Taigu, Shanxi, the plants were not emerging consistently and trait data were not measured.\u003c/p\u003e \u003cp\u003eThe phenotype data were analysed using Microsoft Excel 2021, GraphPad Prism 9.5.0 and IBM SPSS Statistics 19.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenetic map construction\u003c/h2\u003e \u003cp\u003eTotal genomic DNA from fresh leaves of the parents and 300 F\u003csub\u003e2\u003c/sub\u003e individuals were extracted according to a modified CTAB method. The published SSR markers were screened for polymorphism between the mapping parents and those showing clear polymorphism and two InDel markers designed based on the results of parental resequencing sequence alignment were used to genotype the F\u003csub\u003e2\u003c/sub\u003e population. JoinMap 4.0 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] was used to group and order all markers. The Kosambi mapping function was used to convert recombination frequencies into map distances.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQTL mapping\u003c/h2\u003e \u003cp\u003eWinQTLCart 2.5 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] was used to determine QTL location by composite interval mapping (CIM) method and estimate their effects. The threshold for the LOD score was estimated by 1,000 random permutations at a significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05. The confidence intervals of QTL were determined based on positions\u0026thinsp;\u0026plusmn;\u0026thinsp;2 LOD away from the peaks of the likelihood ratios (LRs). Positive additive effects of QTL indicated that the Jingu28 allele increased the phenotypic value, whereas negative effects indicated that the Ai88 allele increased the phenotypic value. QTL names started with \u0026lsquo;\u003cem\u003eq\u003c/em\u003e\u0026rsquo;, followed by a trait abbreviation (e.g. PH for plant height) and the chromosome number, plus \u0026minus;\u0026thinsp;1, -2, -3 and so on when multiple QTL were detected for one trait on the same chromosome. Graphical representation of the genetic map and QTL bars representing 2-LOD reduction in likelihood was carried out with Map Chart 2.2. Overlapping of genetic regions for QTL controlling different traits was indicative of co-located QTL clusters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSequence analysis of candidate genes\u003c/h2\u003e \u003cp\u003eThe DNA sequences of candidate gene \u003cem\u003eSeita.5G404900\u003c/em\u003e and \u003cem\u003eSeita.9G020100\u003c/em\u003e were amplified from both Jingu28 and Ai88 using the primer pairs shown in Table S4. PCR products were separated by 1% agarose gel electrophoresis and the target DNA fragments were sequenced at Beijing Tsingke Biotech Co., Ltd.. The comparison and analysis of the sequencing results were performed using DNAMAN version 6.0 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of the semi-thermal asymmetric reverse PCR (STARP) marker\u003c/h2\u003e \u003cp\u003eA semi-thermal asymmetric reverse PCR (STARP) marker \u003cem\u003eGA20oxSTARP-1\u003c/em\u003e was designed based on the single-base insertion/deletion in the third exon of \u003cem\u003eSeita.5G404900\u003c/em\u003e. The primers included two asymmetrically modified allele-specific (AMAS) primers (GA20oxSTARP-1 F1 and GA20oxSTARP-1 F2) and their same reverse primer (GA20oxSTARP-1 R). GA20oxSTARP-1 F1 was designed to amplify the Ai88 allele. Meanwhile, GA20oxSTARP-1 F2 could amplify the Jingu28 allele uniquely with 10 bp insertion (ACGACACGAC) at 5\u0026prime; terminus. The nucleotides substitution principle followed the suggestion of Long et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The three primers were mixed in a ratio of 1:1:2 (GA20oxSTARP-1 F1: GA20oxSTARP-1 F2: GA20oxSTARP-1 R) and diluted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAllelic variation analysis\u003c/h2\u003e \u003cp\u003eThe 257 foxtail millet accessions randomly selected from 916 foxtail millet accessions published by Jia \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] were used for allelic variation analysis. The 257 foxtail millet accessions were planted in spring 2023 in Taigu, Shanxi, and each variety was planted for four rows. The heading date and the plant height were investigated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic evaluation\u003c/h2\u003e \u003cp\u003eThe phenotypic values of important agronomic traits between Jingu28 and Ai88 were compared. For plant height (PH), panicle neck length (PNL), panicle length (PL), flag leaf length (FLL), thousand grain weight (TGW) and stem node length (SNL), Jingu28 were significantly higher than Ai88. In contrast, Jingu28 were significantly lower than Ai88 for panicle diameter (PD), stem node number (SNN), panicle weight (PW), grain weight per panicle (GWP), primary branch number (PBN), primary branch density (PBD) and grain width (GW). However, there were no significant differences in flag leaf width (FLW), tiller number (TN) and grain length (GL) between Jingu28 and Ai88. In addition, the heading date (HD) Jingu28 was 89 days, approximately two days later than Ai88 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe phenotype of twelve traits (PH, PNL, PL, PD, FLL, FLW, PW, GWP, TGW, GL, GW and HD) in the F\u003csub\u003e2:3\u003c/sub\u003e families crossed by Jingu28 and Ai88 had widespread variations and showed continuous distributions (normal distributions), indicating that they are governed by multiple genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis among 12 agronomic traits\u003c/h2\u003e \u003cp\u003eWe analysed the correlation among the 12 agronomic traits in the F\u003csub\u003e2:3\u003c/sub\u003e families using the Pearson correlation coefficient. The trait PH was positively correlated with PNL, PL, FLL, PW, GWP, TGW and HD, respectively, and negatively correlated with FLW. The trait PNL was positively correlated with PL, FLL, PW and GWP, respectively. The trait PL was positively correlated with PD, FLL, FLW, PW and HD, respectively. The trait PD was positively correlated with FLW. The trait FLL was positively correlated with FLW and HD, respectively, and negatively correlated with GW. The trait FLW was negatively correlated with GWP and TGW, respectively, and positively correlated with HD. The trait PW was positively correlated with GWP, TGW and GL, respectively. The trait GWP was positively correlated with TGW and negatively correlated with HD. The trait TGW was positively correlated with GL and GW, respectively. The trait GL was positively correlated with GW. No significant correlations were detected for other traits. Moreover, the correlation coefficients between PL and FLL (0.75), GL and GW (0.64), TGW and GW (0.63), PH and PNL (0.57), FLL and FLW (0.54) were relatively high (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results showed that there were significant or extremely significant correlations among some of the 12 traits and indicated that some traits with a strong correlation each other might be affected by the same loci or closely linked loci.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenetic linkage map\u003c/h2\u003e \u003cp\u003e517 published SSR markers on nine chromosomes of foxtail millet were used to screen for polymorphism between parents. 213 polymorphic SSR markers and two InDel markers designed based on the parental resequencing date were used to genotype the F\u003csub\u003e2\u003c/sub\u003e population. The 215 markers were mapped into nine chromosomes, covering 1492.5 cM, with average distance of 6.94 cM between adjacent markers and ranged from 5.50 to 10.24 cM for all nine chromosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Makers were not evenly distributed over chromosomes. For example, Chr9 was mapped with 36 markers, whereas Chr8 was mapped with only 12 markers. The longest chromosome in terms of genetic distance was Chr3, which spanned 223.7 cM, and the shortest was Chr8, which spanned only 122.8 cM. Six large gaps (\u0026gt;\u0026thinsp;30 cM) were identified on Chr1 (30.6 cM between SICAAS1031 and SICAAS1054), Chr2 (39.1 cM between SICAAS2024 and S184), Chr3 (39.7 cM between SICAAS3005 and SICAAS3008), Chr4 (38.2 cM between SICAAS4017 and S231), Chr5 (31.0 cM between SICAAS5039 and b223) and Chr8 (51.8 cM between SICAAS8027 and SiGMS12819) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of marker distribution and genetic distance of nine chromosomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumbers of markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenetic distance (cM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage interval (cM)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e220.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e223.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e126.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e216.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1492.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eQTL mapping for 12 agronomic traits\u003c/h2\u003e \u003cp\u003eQTL mapping was performed using the genotypes of F\u003csub\u003e2\u003c/sub\u003e population and phenotypes of F\u003csub\u003e2:3\u003c/sub\u003e families. In total, 46 QTL on eight chromosomes except Chr8 for 12 traits were detected, with the phenotypic variation explained (PVE) of 0.52\u0026ndash;37.81%. The LOD values of these QTL ranged from 3.62 to 49.62 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQTL analysis for 12 agronomic traits\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNearest marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePosition (cM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenetic interval (cM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFlanking marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhysical interval (Mb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAdditive effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDominance effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePVE (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71.5\u0026ndash;94.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS1020-b243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.87\u0026ndash;34.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPH2-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.2\u0026ndash;97.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS2041-b239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.10-28.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPH2-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e110.3-136.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep67-p56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32.04\u0026ndash;41.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPH2-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e219.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e206.5-220.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS2024-ZQXInDel2-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.59\u0026ndash;49.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPH5-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.4-105.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS5036-SICAAS5039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.12\u0026ndash;43.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e49.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e17.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e29.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPH5-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e107.6-115.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS5039-b223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.79\u0026ndash;45.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e17.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e27.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPH9-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.7\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS9002-SICAAS9008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.21\u0026ndash;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPH9-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138.8-154.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS9082-m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.30\u0026ndash;44.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPNL2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e133.4-152.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS2045-b169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.20-44.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPNL5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.7-105.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS5035-SICAAS5039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40.92\u0026ndash;43.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e9.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPL3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e183-215.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eb163-SICAAS3052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47.64\u0026ndash;50.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPL4-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.5\u0026ndash;75.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eb255-SICAAS4060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.83\u0026ndash;17.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPL4-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.8\u0026ndash;85.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS4060-SICAAS4036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.35\u0026ndash;32.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPL5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0-12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS262-S264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.49\u0026ndash;5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPL9-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.9\u0026ndash;30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eb130-SICAAS9008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.51\u0026ndash;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPL9-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.3\u0026ndash;45.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS9008-SICAAS9019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.84\u0026ndash;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPL9-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e127.3-132.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS9110-SICAAS9053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.49\u0026ndash;22.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPD1-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.4\u0026ndash;101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS1071-S121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31.97\u0026ndash;38.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPD1-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e101-123.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS121-SICAAS1051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.89\u0026ndash;40.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e110.4-138.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep67-p56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32.04\u0026ndash;41.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPD9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.3\u0026ndash;80.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eb248-SICAAS9036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.88\u0026ndash;9.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqFLL2-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e155.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e143.4-163.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS2016-b169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41.26\u0026ndash;44.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqFLL2-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e217.2-220.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS2024-ZQXInDel2-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.59\u0026ndash;49.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e35.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqFLL4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.9\u0026ndash;96.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS4034-SICAAS4004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.40-35.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqFLL6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.1\u0026ndash;54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS297-SICAAS6096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.18\u0026ndash;4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e9.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqFLL9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0-27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS9002-SICAAS9008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.21\u0026ndash;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqFLW1-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89.4-100.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS1071-S121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31.97\u0026ndash;38.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e11.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqFLW1-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e101-116.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS121-SICAAS1051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.89\u0026ndash;40.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqFLW2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e219.5-220.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS2024-ZQXInDel2-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.59\u0026ndash;49.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e13.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqFLW5-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86.7\u0026ndash;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSiGMS8556-SICAAS5035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39.89\u0026ndash;40.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e12.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqFLW5-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.3-105.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS5035-SICAAS5039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40.92\u0026ndash;43.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e12.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPW5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e101.4\u0026ndash;116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS5035-b223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40.92\u0026ndash;45.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPW7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.2\u0026ndash;89.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS7081-SICAAS7041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.27\u0026ndash;31.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPW9-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.9\u0026ndash;80.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS9024-SICAAS9036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.23\u0026ndash;9.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPW9-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.8\u0026ndash;89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS9036-b187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.70-10.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqPW9-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e110.1-129.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep95x-SICAAS9052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.28\u0026ndash;17.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqGWP5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.6-122.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS5035-b223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40.92\u0026ndash;45.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.28\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqTGW2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.4\u0026ndash;97.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eb242-b239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.50-28.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqGL2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.8-109.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eb242-p67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.50-32.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqGW2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e216.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e193.1-220.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS2024-ZQXInDel2-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.59\u0026ndash;49.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqHD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e217.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e208.2-220.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS2024-ZQXInDel2-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.59\u0026ndash;49.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e22.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqHD5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e107.6-136.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS5039-b223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.79\u0026ndash;45.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqHD6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.6\u0026ndash;54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS6005-SICAAS6096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.46\u0026ndash;4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqHD9-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.6\u0026ndash;28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eb130-SICAAS9008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.51\u0026ndash;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e21.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e37.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqHD9-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.2\u0026ndash;40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS9009-SICAAS9014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.02\u0026ndash;2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e11.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqHD9-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e138.8-151.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSICAAS9082-m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.30\u0026ndash;44.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePlant height\u003c/h2\u003e \u003cp\u003eEight QTL related to PH were detected and explained 2.70\u0026ndash;29.55% of the phenotypic variation. Of these, the additive effects of \u003cem\u003eqPH1\u003c/em\u003e, \u003cem\u003eqPH2-1\u003c/em\u003e, \u003cem\u003eqPH2-2\u003c/em\u003e, \u003cem\u003eqPH2-3\u003c/em\u003e, \u003cem\u003eqPH5-1\u003c/em\u003e, \u003cem\u003eqPH5-2\u003c/em\u003e and \u003cem\u003eqPH9-1\u003c/em\u003e were from Jingu28, while \u003cem\u003eqPH9-2\u003c/em\u003e was from Ai88. And two major QTL (PVE\u0026thinsp;\u0026gt;\u0026thinsp;10), \u003cem\u003eqPH5-1\u003c/em\u003e and \u003cem\u003eqPH5-2\u003c/em\u003e, were detected on Chr5 with LOD values of 49.62 and 40.32, respectively. Their additive effects were 17.47 and 17.05, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePanicle neck length\u003c/h2\u003e \u003cp\u003eTwo QTL (\u003cem\u003eqPNL2\u003c/em\u003e and \u003cem\u003eqPNL5\u003c/em\u003e) for PNL were detected and explained 0.53% and 9.64% of the phenotypic variation, respectively. The additive effects of these two QTL were both from Jingu28 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePanicle length\u003c/h2\u003e \u003cp\u003eSeven QTL on Chr3, Chr4, Chr5 and Chr9 were found to be related to PL, accounting for 2.62%-8.82% of the phenotypic variation. The additive effect of \u003cem\u003eqPL9-3\u003c/em\u003e was from Ai88, while the others (\u003cem\u003eqPL3\u003c/em\u003e, \u003cem\u003eqPL4-1\u003c/em\u003e, \u003cem\u003eqPL4-2\u003c/em\u003e, \u003cem\u003eqPL5\u003c/em\u003e, \u003cem\u003eqPL9-1\u003c/em\u003e and \u003cem\u003eqPL9-2\u003c/em\u003e) were from Jingu28 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePanicle diameter\u003c/h2\u003e \u003cp\u003eThere were four QTL located on Chr1, Chr2 and Chr9 affecting PD, with LOD values range from 3.74 to 5.07. The PVE of these QTL was 2.77\u0026ndash;6.96%. The additive effects of \u003cem\u003eqPD1-1\u003c/em\u003e, \u003cem\u003eqPD1-2\u003c/em\u003e and \u003cem\u003eqPD2\u003c/em\u003e were from Ai88, while \u003cem\u003eqPD9\u003c/em\u003e was from Jingu28 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFlag leaf length\u003c/h2\u003e \u003cp\u003eFor FLL, five QTL with a PVE from 0.52 to 35.21% were found on Chr2, Chr4, Chr6 and Chr9, of which one major QTL, \u003cem\u003eqFLL2-2\u003c/em\u003e, with a LOD value of 37.22 and a PVE of 35.21%, was detected. The additive effects of \u003cem\u003eqFLL2-1\u003c/em\u003e, \u003cem\u003eqFLL2-2\u003c/em\u003e, \u003cem\u003eqFLL4\u003c/em\u003e and \u003cem\u003eqFLL9\u003c/em\u003e were from Jingu28, while \u003cem\u003eqFLL6\u003c/em\u003e was from Ai88 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFlag leaf width\u003c/h2\u003e \u003cp\u003eA total of five major QTL for FLW with the PVE from 10.61 to 13.80% were located on Chr1, Chr2 and Chr5. The LOD values of these QTL were range from 8.16 to 20.36. The additive effects of \u003cem\u003eqFLW1-1\u003c/em\u003e, \u003cem\u003eqFLW1-2\u003c/em\u003e, \u003cem\u003eqFLW5-1\u003c/em\u003e and \u003cem\u003eqFLW5-1\u003c/em\u003e were from Ai88, while \u003cem\u003eqFLW2\u003c/em\u003e was from Jingu28 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePanicle weight\u003c/h2\u003e \u003cp\u003eOn Chr5, Chr7 and Chr9, five QTL controlling PW with the PVE of 3.88\u0026ndash;14.42% were detected, of which one major QTL, \u003cem\u003eqPW5\u003c/em\u003e, could explain 14.22% of the phenotypic variation, with the LOD value of 16.41. The additive effect of \u003cem\u003eqPW9-3\u003c/em\u003e was from Ai88, while the other four QTL (\u003cem\u003eqPW5\u003c/em\u003e, \u003cem\u003eqPW7\u003c/em\u003e, \u003cem\u003eqPW9-1\u003c/em\u003e and \u003cem\u003eqPW9-2\u003c/em\u003e) were from Jingu28 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eGrain weight per panicle, thousand grain weight, grain length and grain width\u003c/h2\u003e \u003cp\u003eFor GWP, TGW, GL and GW, only one QTL for each trait was detected, which was \u003cem\u003eqGWP5\u003c/em\u003e, \u003cem\u003eqTGW2, qGL2\u003c/em\u003e and \u003cem\u003eqGW2\u003c/em\u003e, respectively. These four QTL accounted for 4.28%, 6.58%, 8.55% and 6.39% of the phenotypic variation, respectively. The additive effects of \u003cem\u003eqGWP5\u003c/em\u003e, \u003cem\u003eqTGW2\u003c/em\u003e and \u003cem\u003eqGL2\u003c/em\u003e were from Jingu28, while \u003cem\u003eqGW2\u003c/em\u003e was from Ai88 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eHeading date\u003c/h2\u003e \u003cp\u003eSix QTL on Chr2, Chr5, Chr6 and Chr9 for HD were detected, with LOD values range from 3.75 to 21.64, accounting for 1.61\u0026ndash;37.81% of the phenotypic variation. Among them, \u003cem\u003eqHD2\u003c/em\u003e, \u003cem\u003eqHD9-1\u003c/em\u003e, \u003cem\u003eqHD9-2\u003c/em\u003e and \u003cem\u003eqHD9-3\u003c/em\u003e were four major QTL, with PVE of 22.48%, 37.81%, 11.60% and 10.21%, respectively. The additive effects of \u003cem\u003eqHD2\u003c/em\u003e, \u003cem\u003eqHD9-1\u003c/em\u003e and \u003cem\u003eqHD9-2\u003c/em\u003e were from Jingu28, while \u003cem\u003eqHD5\u003c/em\u003e, \u003cem\u003eqHD6\u003c/em\u003e and \u003cem\u003eqHD9-3\u003c/em\u003e were from Ai88 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eQTL clusters for multiple traits\u003c/h2\u003e \u003cp\u003eIn this study, 40 out of 46 QTL controlling different traits formed 13 co-located QTL clusters (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Of these, seven were double co-located QTL (\u003cem\u003eqPD1-2\u003c/em\u003e/\u003cem\u003eqFLW1-2\u003c/em\u003e, \u003cem\u003eqPL4-2\u003c/em\u003e/\u003cem\u003eqFLL4\u003c/em\u003e, \u003cem\u003eqFLL6\u003c/em\u003e/\u003cem\u003eqHD6\u003c/em\u003e, \u003cem\u003eqPL9-2\u003c/em\u003e/\u003cem\u003eqHD9-2\u003c/em\u003e, \u003cem\u003eqPD9\u003c/em\u003e/\u003cem\u003eqPW9-1\u003c/em\u003e, \u003cem\u003eqPL9-3\u003c/em\u003e/\u003cem\u003eqPW9-3\u003c/em\u003e and \u003cem\u003eqPH9-2\u003c/em\u003e/\u003cem\u003eqHD9-3\u003c/em\u003e) and two were triple co-located QTL (\u003cem\u003eqPH1\u003c/em\u003e/\u003cem\u003eqPD1-1\u003c/em\u003e/\u003cem\u003eqFLW1-1\u003c/em\u003e and \u003cem\u003eqPH2-1\u003c/em\u003e/\u003cem\u003eqTGW2\u003c/em\u003e/\u003cem\u003eqGL2\u003c/em\u003e). There were two quadruple co-located QTL clusters (\u003cem\u003eqPH2-2\u003c/em\u003e/\u003cem\u003eqPNL2\u003c/em\u003e/\u003cem\u003eqPD2\u003c/em\u003e/\u003cem\u003eqFLL2-1\u003c/em\u003e and \u003cem\u003eqPH9-1\u003c/em\u003e/\u003cem\u003eqPL9-1\u003c/em\u003e/\u003cem\u003eqFLL9\u003c/em\u003e/\u003cem\u003eqHD9-1\u003c/em\u003e), one quintuple co-located QTL cluster (\u003cem\u003eqPH2-3\u003c/em\u003e/\u003cem\u003eqFLL2-2\u003c/em\u003e/\u003cem\u003eqFLW2\u003c/em\u003e/\u003cem\u003eqGW2\u003c/em\u003e/\u003cem\u003eqHD2\u003c/em\u003e) and one septuple co-located QTL cluster (\u003cem\u003eqPH5-1\u003c/em\u003e/\u003cem\u003eqPH5-2\u003c/em\u003e/\u003cem\u003eqPNL5\u003c/em\u003e/\u003cem\u003eqFLW5-2\u003c/em\u003e/\u003cem\u003eqPW5\u003c/em\u003e/\u003cem\u003eqGWP5\u003c/em\u003e/\u003cem\u003eqHD5\u003c/em\u003e). Intriguingly, the quintuple co-located QTL cluster (\u003cem\u003eqPH2-3\u003c/em\u003e/\u003cem\u003eqFLL2-2\u003c/em\u003e/\u003cem\u003eqFLW2\u003c/em\u003e/\u003cem\u003eqGW2\u003c/em\u003e/\u003cem\u003eqHD2\u003c/em\u003e, Chr2: 46.59\u0026ndash;49.16 Mb) has large effects, explaining 8.17% (\u003cem\u003eqPH2-3\u003c/em\u003e), 35.21% (\u003cem\u003eqFLL2-2\u003c/em\u003e), 13.80% (\u003cem\u003eqFLW2\u003c/em\u003e), 6.39% (\u003cem\u003eqGW2\u003c/em\u003e) and 22.48% (\u003cem\u003eqHD2\u003c/em\u003e) of the phenotypic variation. Moreover, the septuple co-located QTL cluster (\u003cem\u003eqPH5-1\u003c/em\u003e/\u003cem\u003eqPH5-2\u003c/em\u003e/\u003cem\u003eqPNL5\u003c/em\u003e/\u003cem\u003eqFLW5-2\u003c/em\u003e/\u003cem\u003eqPW5\u003c/em\u003e/\u003cem\u003eqGWP5\u003c/em\u003e/\u003cem\u003eqHD5\u003c/em\u003e) contained two main effect QTL for PH, explaining 29.55% (\u003cem\u003eqPH5-1\u003c/em\u003e) and 27.25% (\u003cem\u003eqPH5-1\u003c/em\u003e) of the phenotypic variation. The quadruple co-located QTL cluster (\u003cem\u003eqPH9-1\u003c/em\u003e/\u003cem\u003eqPL9-1\u003c/em\u003e/\u003cem\u003eqFLL9\u003c/em\u003e/\u003cem\u003eqHD9-1\u003c/em\u003e) contained one main effect QTL for HD, with a PVE of 37.81% (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCandidate genes analysis\u003c/h2\u003e \u003cp\u003eFor heading date, there was a major QTL, \u003cem\u003eqHD9-1\u003c/em\u003e, with very large effect on chromosome 9, which was also included in the quadruple co-located QTL cluster (\u003cem\u003eqPH9-1\u003c/em\u003e/\u003cem\u003eqPL9-1\u003c/em\u003e/\u003cem\u003eqFLL9\u003c/em\u003e/\u003cem\u003eqHD9-1\u003c/em\u003e), suggesting that it might be a polytropic QTL. Moreover, the additive effect of \u003cem\u003eqHD9-1\u003c/em\u003e was from Jingu28. And then we analyzed the candidate interval (Chr9: 0.51\u0026ndash;1.84 Mb) of this locus and found that there was a candidate gene \u003cem\u003eSeita.9G020100\u003c/em\u003e (Chr9: 1,056,892-1,059,177 bp) with conserved CCT (constans, constans-like, and timing of chlorophyll A/B binding) motif, homologous with transcription factor \u003cem\u003eGhd7\u003c/em\u003e in rice. \u003cem\u003eGhd7\u003c/em\u003e encoding a CCT domain protein is a central regulator of growth, development, and stress responses, as well as a negative regulator of heading date in rice [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, we guessed that the gene \u003cem\u003eSeita.9G020100\u003c/em\u003e might be the casual gene for \u003cem\u003eqHD9-1\u003c/em\u003e and sequenced the promoter and gene sequence of \u003cem\u003eSeita.9G020100\u003c/em\u003e in Jingu28 and Ai88. Sequence alignment showed that there was a 277 bp deletion and one base (T) insertion on the promoter and a 5 bp (AATTA) deletion in the intron of Jingu 28, compared with Ai88 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Previous studies have shown that the insertion/deletion in the promoter may affect gene expression and thus lead to phenotypic changes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. And then, we guess that the 277 bp deletion in the promoter of \u003cem\u003eSeita.9G020100\u003c/em\u003e in Jingu28 changed gene expression and delayed flowering, thus increasing the heading date. Based on the 277 bp insertion/deletion, we designed an InDel marker \u003cem\u003eGhd7InDel\u003c/em\u003e. \u003cem\u003eGhd7InDel\u003c/em\u003e was used to amplify 257 foxtail millet accessions randomly selected from 916 foxtail millet accessions published by Jia \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and the result showed that 104 foxtail millet accessions have the 277 bp deletion (Allele\u003csup\u003eJingu28\u003c/sup\u003e) and 153 have not the 277 bp deletion (Allele\u003csup\u003eAi88\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, Table S5). The average heading time of foxtail millet accessions with Allele\u003csup\u003eJingu28\u003c/sup\u003e was significantly later than that with Allele\u003csup\u003eAi88\u003c/sup\u003e in 2023 in Taigu, Shanxi Province (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, Table S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor plant height, there were two major QTL \u003cem\u003eqPH5-1\u003c/em\u003e and \u003cem\u003eqPH5-2\u003c/em\u003e on chromosome 5, explaining 29.55% and 27.25% of the phenotypic variation, respectively, which were also included in the septuple co-located QTL cluster (\u003cem\u003eqPH5-1\u003c/em\u003e/\u003cem\u003eqPH5-2\u003c/em\u003e/\u003cem\u003eqPNL5\u003c/em\u003e/\u003cem\u003eqFLW5-2\u003c/em\u003e/\u003cem\u003eqPW5\u003c/em\u003e/\u003cem\u003eqGWP5\u003c/em\u003e/\u003cem\u003eqHD5\u003c/em\u003e). We analyzed the candidate genes of the QTL \u003cem\u003eqPH5-1\u003c/em\u003e (Chr5: 43.12\u0026ndash;43.79 Mb) and found a gibberellin biosynthesis related GA20 oxidase gene \u003cem\u003eSeita.5G404900\u003c/em\u003e, which is a homologue of the rice \u0026lsquo;green revolution\u0026rsquo; gene \u003cem\u003eOsSD1\u003c/em\u003e (\u003cem\u003eLOC_Os01g66100\u003c/em\u003e). Furthermore, sequence alignment of the \u003cem\u003eSeita.5G404900\u003c/em\u003e between Jingu28 and Ai88 showed that there was a SNP in the first exon, nine SNPs and a four-base insertion/deletion in the second intron and a one-base (G) insertion/deletion in the third exon (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The SNP in the first exon was a nonsense mutation that did not cause a change in amino acid sequence. The one-base deletion in the third exon of Ai88, compared with Jingu28, led to the frameshift mutation. Therefore, we hypothesized that the single-base deletion in the third exon affected the function of the gibberellin biosynthesis related GA20 oxidase gene \u003cem\u003eSeita.5G404900\u003c/em\u003e, thereby reducing the plant height of the Ai88. A semi-thermal asymmetric reverse PCR (STARP) marker \u003cem\u003eGA20oxSTARP-1\u003c/em\u003e was designed based on the single-base insertion/deletion in the third exon and used to amplify 257 foxtail millet accessions randomly selected from 916 foxtail millet accessions published by Jia \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The result showed that 254 foxtail millet accessions have not the single-base deletion (Allele\u003csup\u003eJingu28\u003c/sup\u003e) and three have the single-base deletion (Allele\u003csup\u003eAi88\u003c/sup\u003e), which were Ci348 (Baimi1), Ci736 (Zituigu) and Ci937 (C193, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, Table S5). The average plant height of foxtail millet accessions with Allele\u003csup\u003eJingu28\u003c/sup\u003e was significantly higher than that with Allele\u003csup\u003eAi88\u003c/sup\u003e in 2023 in Taigu, Shanxi Province (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, Table S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eComparison the QTL identified in this study with previous studies\u003c/h2\u003e \u003cp\u003eIn the present study, we used the F\u003csub\u003e2\u003c/sub\u003e mapping population and constructed a genetic map containing 213 SSR markers and two InDel markers, covering the whole genome with a genetic length of 1492.5 cM, with average distance of 6.94 cM between adjacent markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In total, we identified 46 QTL for 12 agronomic traits using the genotypes of F\u003csub\u003e2\u003c/sub\u003e population and phenotypes of F\u003csub\u003e2:3\u003c/sub\u003e families. The additive effect of 15 QTL were from Ai88, by contrast, 31 QTL were from Jingu28. Furthermore, 13 major effect QTL, namely \u003cem\u003eqPH5-1\u003c/em\u003e, \u003cem\u003eqPH5-2\u003c/em\u003e, \u003cem\u003eqFLL2-2\u003c/em\u003e, \u003cem\u003eqFLW1-1\u003c/em\u003e, \u003cem\u003eqFLW1-2\u003c/em\u003e, \u003cem\u003eqFLW2\u003c/em\u003e, \u003cem\u003eqFLW5-1\u003c/em\u003e, \u003cem\u003eqFLW5-2\u003c/em\u003e, \u003cem\u003eqPW5\u003c/em\u003e, \u003cem\u003eqHD2\u003c/em\u003e, \u003cem\u003eqHD9-1\u003c/em\u003e, \u003cem\u003eqHD9-2\u003c/em\u003e, and \u003cem\u003eqHD9-3\u003c/em\u003e, with a PVE of more than 10%, were detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor plant height, eight QTL (\u003cem\u003eqPH1\u003c/em\u003e, \u003cem\u003eqPH2-1\u003c/em\u003e, \u003cem\u003eqPH2-2\u003c/em\u003e, \u003cem\u003eqPH2-3\u003c/em\u003e, \u003cem\u003eqPH5-1\u003c/em\u003e, \u003cem\u003eqPH5-2\u003c/em\u003e, \u003cem\u003eqPH9-1\u003c/em\u003e and \u003cem\u003eqPH9-2\u003c/em\u003e) on chromosome 1, 2, 5 and 9 were detected. Among them, \u003cem\u003eqPH2-2\u003c/em\u003e might be a novel QTL. The \u003cem\u003eqPH1\u003c/em\u003e (19.87\u0026ndash;34.63 Mb) was overlapped with \u003cem\u003eqPH1-1\u003c/em\u003e (29,694,706\u0026thinsp;\u0026minus;\u0026thinsp;29,725,881 bp) and \u003cem\u003eqPH1-2\u003c/em\u003e (31,837,278\u0026thinsp;\u0026minus;\u0026thinsp;31,865,079 bp) identified by Wang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], \u003cem\u003eqPH1.1\u003c/em\u003e - \u003cem\u003eqPH1.3\u003c/em\u003e mapped by He \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the QTL on chromosome I (31,946,156\u0026thinsp;\u0026minus;\u0026thinsp;42,023,774 bp) mapped by Gao \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and \u003cem\u003eqph1\u003c/em\u003e (bin162, 32,124,053\u0026thinsp;\u0026minus;\u0026thinsp;32,154,417 bp) detected by Zhang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] under a long-day photoperiod. The \u003cem\u003eqPH2-1\u003c/em\u003e (8.10\u0026ndash;28.52 Mb) in this study was in accordance with \u003cem\u003eqPH2\u003c/em\u003e (26,040,234\u0026thinsp;\u0026minus;\u0026thinsp;28,913,785 bp) discovered by He \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The \u003cem\u003eqPH5-1\u003c/em\u003e (43.12\u0026ndash;43.79 Mb) was overlapped with \u003cem\u003eqph5\u003c/em\u003e (bin1188, 43,657,914\u0026thinsp;\u0026minus;\u0026thinsp;43,708,214 bp) under a long-day photoperiod and \u003cem\u003eqph5\u003c/em\u003e (bin1186, 43,191,338\u0026thinsp;\u0026minus;\u0026thinsp;43,553,311 bp) under a short-day photoperiod identified by Zhang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Zhu \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] performed bulk segregant analysis using an F\u003csub\u003e2\u003c/sub\u003e population crossed by a semi-dwarf line 263A and an elite high-stalk breeding variety Chuang 29 and led to the identification of a 16.2-Mb region (30.7\u0026ndash;32.6 Mb, 32.7\u0026ndash;38.5 Mb, 38.7\u0026ndash;41.2 Mb, and 41.3\u0026ndash;47.3 Mb) on chromosome 5 related to plant height and they found a gibberellin biosynthesis related GA20 oxidase gene (\u003cem\u003eSeita.5G404900\u003c/em\u003e), which had a single-base at the third exon, leading to the frameshift mutation at 263A. Interestingly, \u003cem\u003eqPH5-1\u003c/em\u003e (43.12\u0026ndash;43.79 Mb) and \u003cem\u003eqPH5-2\u003c/em\u003e (43.79\u0026ndash;45.75 Mb) in this study were overlapped with the interval on chromosome 5 published by Zhu \u003cem\u003eet al\u003c/em\u003e. (2022) and we also thought that \u003cem\u003eSeita.5G404900\u003c/em\u003e might be the candidate gene of the QTL \u003cem\u003eqPH5-1\u003c/em\u003e, because a one-base deletion existed in the third exon of Ai88, compared with Jingu28, leading to the frameshift mutation. Furthermore, \u003cem\u003eqPH5-2\u003c/em\u003e (43.79\u0026ndash;45.75 Mb) was overlapped with \u003cem\u003eqPH5-3\u003c/em\u003e (45,014,767\u0026thinsp;\u0026minus;\u0026thinsp;45,037,138 bp) identified by Wang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The \u003cem\u003eqPH9-1\u003c/em\u003e (0.21\u0026ndash;1.84 Mb) was in accordance with \u003cem\u003eqph9\u003c/em\u003e (bin1774, 1,559,957\u0026ndash;1,859,997 bp) under a long-day photoperiod mapped by Zhang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, \u003cem\u003eqPH9-2\u003c/em\u003e (37.30\u0026ndash;44.00 Mb) identified in our study was overlapped with \u003cem\u003eqPH9.2 - qPH9.5\u003c/em\u003e (\u003cem\u003eqPH9.2\u003c/em\u003e, 35,624,887\u0026thinsp;\u0026minus;\u0026thinsp;41,536,123 bp; \u003cem\u003eqPH9.3\u003c/em\u003e, 41,536,123\u0026thinsp;\u0026minus;\u0026thinsp;42,609,213 bp; \u003cem\u003eqPH9.4\u003c/em\u003e, 42,767,054\u0026thinsp;\u0026minus;\u0026thinsp;43,430,752 bp; \u003cem\u003eqPH9.5\u003c/em\u003e, 42,855,389\u0026thinsp;\u0026minus;\u0026thinsp;43,953,050 bp) discovered by He \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Interestingly, five QTL (\u003cem\u003eqPH2-1\u003c/em\u003e, \u003cem\u003eqPH2-3\u003c/em\u003e, \u003cem\u003eqPH5-2\u003c/em\u003e, \u003cem\u003eqPH9-1\u003c/em\u003e and \u003cem\u003eqPH9-2\u003c/em\u003e) were detected by Dai \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (Table S6).\u003c/p\u003e \u003cp\u003eFor panicle neck length, two QTL (\u003cem\u003eqPNL2\u003c/em\u003e and \u003cem\u003eqPNL5\u003c/em\u003e) were detected in our study. In fact, we thought three QTL for PNL should be detected, because one QTL near the end of chromosome 2 reached the LOD threshold of 3.6, however, the WinQTLCart software didn\u0026rsquo;t detect the locus for having not enough molecular marker. The \u003cem\u003eqPNL5\u003c/em\u003e (40.92\u0026ndash;43.79 Mb) was in accordance with \u003cem\u003eqnl5\u003c/em\u003e (bin1188, 43,657,914\u0026thinsp;\u0026minus;\u0026thinsp;43,708,214 bp) under a long-day photoperiod by Zhang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and \u003cem\u003eqPNL2\u003c/em\u003e might be a novel QTL (Table S6).\u003c/p\u003e \u003cp\u003eIn the present study, seven QTL (\u003cem\u003eqPL3\u003c/em\u003e, \u003cem\u003eqPL4-1\u003c/em\u003e, \u003cem\u003eqPL4-2\u003c/em\u003e, \u003cem\u003eqPL5\u003c/em\u003e, \u003cem\u003eqPL9-1\u003c/em\u003e, \u003cem\u003eqPL9-2\u003c/em\u003e and \u003cem\u003eqPL9-3\u003c/em\u003e) on Chr3, Chr4, Chr5 and Chr9 were found to be related to PL and only the additive effect of \u003cem\u003eqPL9-3\u003c/em\u003e was from Ai88. Like panicle neck length, one QTL for PL near the end of chromosome 2 reached the LOD threshold of 3.6, however, the WinQTLCart software also didn\u0026rsquo;t detect the locus for having not enough molecular marker. Therefore, eight QTL for PL should be detected. The \u003cem\u003eqPL4-1\u003c/em\u003e (6.83\u0026ndash;17.35 Mb), \u003cem\u003eqPL4-2\u003c/em\u003e (17.35\u0026ndash;32.75 Mb), \u003cem\u003eqPL5\u003c/em\u003e (3.49\u0026ndash;5.41 Mb) and \u003cem\u003eqPL9-1\u003c/em\u003e (0.51\u0026ndash;1.84 Mb) in our study were overlapped with \u003cem\u003eqpl4-1\u003c/em\u003e (bin837, 7,479,756\u0026ndash;7,510,803 bp), \u003cem\u003eqpl4-2\u003c/em\u003e (bin870, 28,284,656\u0026thinsp;\u0026minus;\u0026thinsp;29,738,603 bp), \u003cem\u003eqpl5-1\u003c/em\u003e (bin987, 4,176,711\u0026ndash;4,313,654 bp) and \u003cem\u003eqpl9\u003c/em\u003e (bin1771, 758,712\u0026ndash;1,360,456 bp) mapped by Zhang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], respectively. The \u003cem\u003eqPL3\u003c/em\u003e and \u003cem\u003eqPL9-3\u003c/em\u003e were also identified by Jia \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, \u003cem\u003eqPL3\u003c/em\u003e, \u003cem\u003eqPL4-1\u003c/em\u003e, \u003cem\u003eqPL4-2\u003c/em\u003e and \u003cem\u003eqPL5\u003c/em\u003e have been detected by Zhi \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and \u003cem\u003eqPL3\u003c/em\u003e, \u003cem\u003eqPL4-2\u003c/em\u003e and \u003cem\u003eqPL9-3\u003c/em\u003e have been mapped by Dai \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The \u003cem\u003eqPL9-2\u003c/em\u003e (1.84\u0026ndash;3.90 Mb) might be a novel QTL (Table S6).\u003c/p\u003e \u003cp\u003eThere were four QTL (\u003cem\u003eqPD1-1\u003c/em\u003e, \u003cem\u003eqPD1-2\u003c/em\u003e, \u003cem\u003eqPD2\u003c/em\u003e and \u003cem\u003eqPD9\u003c/em\u003e) located on Chr1, Chr2 and Chr9 affecting PD in this study and \u003cem\u003eqPD1-2\u003c/em\u003e might be a novel QTL. The \u003cem\u003eqPD2\u003c/em\u003e (32.04\u0026ndash;41.26 Mb) and \u003cem\u003eqPD9\u003c/em\u003e (2.88\u0026ndash;9.70 Mb) were overlapped with \u003cem\u003eqpd2\u003c/em\u003e (bin392, 31,510,348\u0026thinsp;\u0026minus;\u0026thinsp;32,450,713 bp) and \u003cem\u003eqpd9-1\u003c/em\u003e (bin1796, 3,617,446\u0026ndash;3,678,459 bp) detected by Zhang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], respectively. The \u003cem\u003eqPD2\u003c/em\u003e was also identified by Zhi \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Three QTL (\u003cem\u003eqPD1-1\u003c/em\u003e, \u003cem\u003eqPD2\u003c/em\u003e and \u003cem\u003eqPD9\u003c/em\u003e) were mapped by Dai \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (Table S6).\u003c/p\u003e \u003cp\u003eFor flag leaf length, five QTL (\u003cem\u003eqFLL2-1\u003c/em\u003e, \u003cem\u003eqFLL2-2\u003c/em\u003e, \u003cem\u003eqFLL4\u003c/em\u003e, \u003cem\u003eqFLL6\u003c/em\u003e and \u003cem\u003eqFLL9\u003c/em\u003e) with a PVE from 0.52 to 35.21% were detected and \u003cem\u003eqFLL2-1\u003c/em\u003e and \u003cem\u003eqFLL6\u003c/em\u003e might be two novel QTL. Three QTL (\u003cem\u003eqFLL2-2\u003c/em\u003e, \u003cem\u003eqFLL4\u003c/em\u003e and \u003cem\u003eqFLL9\u003c/em\u003e) were detected by Zhang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, \u003cem\u003eqFLL2-2\u003c/em\u003e was also identified by Jia \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For flag leaf width, five major QTL in the present study were located on Chr1, Chr2 and Chr5 and four QTL (\u003cem\u003eqFLW1-1\u003c/em\u003e, \u003cem\u003eqFLW2\u003c/em\u003e, \u003cem\u003eqFLW5-1\u003c/em\u003e and \u003cem\u003eqFLW5-2\u003c/em\u003e) among them might be novel QTL. The \u003cem\u003eqFLW1-2\u003c/em\u003e was detected by Jia \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] (Table S6).\u003c/p\u003e \u003cp\u003eIn our study, five QTL (\u003cem\u003eqPW5\u003c/em\u003e, \u003cem\u003eqPW7\u003c/em\u003e, \u003cem\u003eqPW9-1\u003c/em\u003e, \u003cem\u003eqPW9-2\u003c/em\u003e and \u003cem\u003eqPW9-3\u003c/em\u003e) controlling panicle weight were detected, of which \u003cem\u003eqPW9-2\u003c/em\u003e and \u003cem\u003eqPW9-3\u003c/em\u003e might be two novel QTL. The \u003cem\u003eqPW5\u003c/em\u003e, \u003cem\u003eqPW7\u003c/em\u003e and \u003cem\u003eqPW9-1\u003c/em\u003e were identified by Zhang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], Wang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], Liu \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], Zhi \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Dai \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For grain weight per panicle, thousand grain weight, grain length and grain width, only one QTL for each trait was detected in the present study, which was \u003cem\u003eqGWP5\u003c/em\u003e, \u003cem\u003eqTGW2, qGL2\u003c/em\u003e and \u003cem\u003eqGW2\u003c/em\u003e, respectively. The \u003cem\u003eqGWP5\u003c/em\u003e and \u003cem\u003eqTGW2\u003c/em\u003e were overlapped with \u003cem\u003eqGW5\u003c/em\u003e (41,302,002\u0026ndash;41,350,538 bp) identified by Wang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and \u003cem\u003eqTGW2.1\u003c/em\u003e (28,215,787\u0026thinsp;\u0026minus;\u0026thinsp;28,932,236 bp) mapped by Zhi \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], respectively. The \u003cem\u003eqGL2\u003c/em\u003e and \u003cem\u003eqGW2\u003c/em\u003e were two novel QTL (Table S6).\u003c/p\u003e \u003cp\u003eSix QTL (\u003cem\u003eqHD2\u003c/em\u003e, \u003cem\u003eqHD5\u003c/em\u003e, \u003cem\u003eqHD6\u003c/em\u003e, \u003cem\u003eqHD9-1\u003c/em\u003e, \u003cem\u003eqHD9-2\u003c/em\u003e and \u003cem\u003eqHD9-3\u003c/em\u003e) on Chr2, Chr5, Chr6 and Chr9 for HD were detected. Three QTL, \u003cem\u003eqHD2\u003c/em\u003e, \u003cem\u003eqHD9-1\u003c/em\u003e and \u003cem\u003eqHD9-3\u003c/em\u003e were identified by Jia \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and \u003cem\u003eqHD5\u003c/em\u003e, \u003cem\u003eqHD6\u003c/em\u003e and \u003cem\u003eqHD9-2\u003c/em\u003e might be novel QTL (Table S6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eCo-located QTL clusters for different traits\u003c/h2\u003e \u003cp\u003eIt is a widespread phenomenon that many QTL controlling different traits were co-located in the same intervals of the genome. For example, Zhi \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] identified 34 co-located QTL clusters controlling agronomic traits related to panicle architecture and grain yield. In the present study, 13 co-located QTL clusters distributed on Chr1, Chr2, Chr4, Chr5, Chr6 and Chr9 were formed, including seven double co-located QTL (\u003cem\u003eqPD1-2\u003c/em\u003e/\u003cem\u003eqFLW1-2\u003c/em\u003e, \u003cem\u003eqPL4-2\u003c/em\u003e/\u003cem\u003eqFLL4\u003c/em\u003e, \u003cem\u003eqFLL6\u003c/em\u003e/\u003cem\u003eqHD6\u003c/em\u003e, \u003cem\u003eqPL9-2\u003c/em\u003e/\u003cem\u003eqHD9-2\u003c/em\u003e, \u003cem\u003eqPD9\u003c/em\u003e/\u003cem\u003eqPW9-1\u003c/em\u003e, \u003cem\u003eqPL9-3\u003c/em\u003e/\u003cem\u003eqPW9-3\u003c/em\u003e and \u003cem\u003eqPH9-2\u003c/em\u003e/\u003cem\u003eqHD9-3\u003c/em\u003e), two triple co-located QTL (\u003cem\u003eqPH1\u003c/em\u003e/\u003cem\u003eqPD1-1\u003c/em\u003e/\u003cem\u003eqFLW1-1\u003c/em\u003e and \u003cem\u003eqPH2-1\u003c/em\u003e/\u003cem\u003eqTGW2\u003c/em\u003e/\u003cem\u003eqGL2\u003c/em\u003e), two quadruple co-located QTL clusters (\u003cem\u003eqPH2-2\u003c/em\u003e/\u003cem\u003eqPNL2\u003c/em\u003e/\u003cem\u003eqPD2\u003c/em\u003e/\u003cem\u003eqFLL2-1\u003c/em\u003e and \u003cem\u003eqPH9-1\u003c/em\u003e/\u003cem\u003eqPL9-1\u003c/em\u003e/\u003cem\u003eqFLL9\u003c/em\u003e/\u003cem\u003eqHD9-1\u003c/em\u003e), one quintuple co-located QTL cluster (\u003cem\u003eqPH2-3\u003c/em\u003e/\u003cem\u003eqFLL2-2\u003c/em\u003e/\u003cem\u003eqFLW2\u003c/em\u003e/\u003cem\u003eqGW2\u003c/em\u003e/\u003cem\u003eqHD2\u003c/em\u003e) and one septuple co-located QTL cluster (\u003cem\u003eqPH5-1\u003c/em\u003e/\u003cem\u003eqPH5-2\u003c/em\u003e/\u003cem\u003eqPNL5\u003c/em\u003e/\u003cem\u003eqFLW5-2\u003c/em\u003e/\u003cem\u003eqPW5\u003c/em\u003e/\u003cem\u003eqGWP5\u003c/em\u003e/\u003cem\u003eqHD5\u003c/em\u003e, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). In fact, the quintuple co-located QTL cluster (\u003cem\u003eqPH2-3\u003c/em\u003e/\u003cem\u003eqFLL2-2\u003c/em\u003e/\u003cem\u003eqFLW2\u003c/em\u003e/\u003cem\u003eqGW2\u003c/em\u003e/\u003cem\u003eqHD2\u003c/em\u003e) should be one septuple co-located QTL cluster containing QTL for PNL and PL, because one locus controlling PNL and PL near the end of chromosome 2 reached the LOD threshold of 3.6, however, the WinQTLCart software didn\u0026rsquo;t detect the QTL for having not enough molecular marker (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Co-location of QTL for different traits in this study was consistent with significant correlations between these traits. Co-located QTL may be conferred by pleiotropic genes that play important roles in the network of agronomic and yield development of foxtail millet, or by closely-linked alleles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eCandidate genes located at some QTL\u003c/h2\u003e \u003cp\u003eFlowering time (also known as heading date in cereals) is a critical determinant of regional adaptability of plants, and thus crop yields. In this study, six QTL (\u003cem\u003eqHD2\u003c/em\u003e, \u003cem\u003eqHD5\u003c/em\u003e, \u003cem\u003eqHD6\u003c/em\u003e, \u003cem\u003eqHD9-1\u003c/em\u003e, \u003cem\u003eqHD9-2\u003c/em\u003e and \u003cem\u003eqHD9-3\u003c/em\u003e) on Chr2, Chr5, Chr6 and Chr9 for HD were detected. Among them, \u003cem\u003eqHD2\u003c/em\u003e, \u003cem\u003eqHD9-1\u003c/em\u003e, \u003cem\u003eqHD9-2\u003c/em\u003e and \u003cem\u003eqHD9-3\u003c/em\u003e were four major QTL, with PVE of 22.48%, 37.81%, 11.60% and 10.21%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For \u003cem\u003eqHD9-1\u003c/em\u003e, the gene \u003cem\u003eSeita.9G020100\u003c/em\u003e (Chr9: 1,056,892-1,059,177 bp) with conserved CCT (constans, constans-like, and timing of chlorophyll A/B binding) motif might be the candidate gene for it, which was homologous with transcription factor \u003cem\u003eGhd7\u003c/em\u003e in rice and \u003cem\u003eCONSTANS-like 1\u003c/em\u003e (\u003cem\u003eCOL1\u003c/em\u003e, \u003cem\u003eAT5G15850\u003c/em\u003e) in \u003cem\u003eArabidopsis\u003c/em\u003e. In rice, \u003cem\u003eGhd7\u003c/em\u003e encoding a CCT domain protein is a negative regulator of heading date and the down-regulation of \u003cem\u003eGhd7\u003c/em\u003e can promote early flowering [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Furthermore, we guess that the 277 bp deletion in the promoter of \u003cem\u003eSeita.9G020100\u003c/em\u003e in Jingu28 changed gene expression and increased the heading date. However, it needs to be further verified by some molecular biology experiments, for instance, expression analysis, promoter activity analysis, gene overexpression or knockout and so on. Moreover, we found that the average heading time of foxtail millet accessions with Allele\u003csup\u003eJingu28\u003c/sup\u003e was significantly later than that with Allele\u003csup\u003eAi88\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), showing that the 277 bp insertion/deletion might be an important functional site and the InDel marker \u003cem\u003eGhd7InDel\u003c/em\u003e could be used for marker-assisted selection (MAS) in foxtail millet. In rice, \u003cem\u003eGhd7\u003c/em\u003e is also a central regulator of growth, development and stress responses and the interaction between \u003cem\u003eHd1\u003c/em\u003e and \u003cem\u003eGhd7\u003c/em\u003e is important for controlling yield traits in rice [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Interestingly, the \u003cem\u003eqHD9-1\u003c/em\u003e in our study was contained in the quadruple co-located QTL cluster (\u003cem\u003eqPH9-1\u003c/em\u003e/\u003cem\u003eqPL9-1\u003c/em\u003e/\u003cem\u003eqFLL9\u003c/em\u003e/\u003cem\u003eqHD9-1\u003c/em\u003e), indicating that \u003cem\u003eSeita.9G020100\u003c/em\u003e might affect multiple traits in foxtail millet.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eqHD2\u003c/em\u003e, one gene \u003cem\u003eSeita.2G444300\u003c/em\u003e was existed in the candidate interval, which is the homologous gene of \u003cem\u003ePSEUDO-RESPONSE REGULATOR 37\u003c/em\u003e (\u003cem\u003ePRR37\u003c/em\u003e) in rice and \u003cem\u003ePRR7\u003c/em\u003e in \u003cem\u003eArabidopsis\u003c/em\u003e. \u003cem\u003ePRR37\u003c/em\u003e encodes a CCT domain-containing protein and was a core rice gene controlling photoperiod sensitivity. Under long days conditions, \u003cem\u003ePRR37\u003c/em\u003e suppressed flowering and increased plant height, the number of spikelets per panicle, and yield [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. \u003cem\u003ePRR7\u003c/em\u003e, a central component of the \u003cem\u003eArabidopsis\u003c/em\u003e clock, was directly involved in the repression of master regulators of plant growth, light signaling and stress responses [\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In foxtail millet, it was confirmed that overexpression of \u003cem\u003eSeita.2G444300\u003c/em\u003e (\u003cem\u003eSiPRR37\u003c/em\u003e) delayed the heading date and increased plant height. Therefore, we guess that \u003cem\u003eSeita.2G444300\u003c/em\u003e (\u003cem\u003eSiPRR37\u003c/em\u003e) might be responsible for the \u003cem\u003eqHD2\u003c/em\u003e or \u003cem\u003eqPH2-3\u003c/em\u003e/\u003cem\u003eqFLL2-2\u003c/em\u003e/\u003cem\u003eqFLW2/\u003c/em\u003eq\u003cem\u003eGW2\u003c/em\u003e/\u003cem\u003eqHD2\u003c/em\u003e.\u003c/p\u003e \u003cp\u003ePlant height is an important trait that determines tradeoffs between competition and the distribution of resource, which is crucial for yield potential. Therefore, understanding the genetic basis of plant height in foxtail millet would help to design foxtail millet cultivars with ideal plant architecture and high grain yield potential. In the present study, eight QTL (\u003cem\u003eqPH1\u003c/em\u003e, \u003cem\u003eqPH2-1\u003c/em\u003e, \u003cem\u003eqPH2-2\u003c/em\u003e, \u003cem\u003eqPH2-3\u003c/em\u003e, \u003cem\u003eqPH5-1\u003c/em\u003e, \u003cem\u003eqPH5-2\u003c/em\u003e, \u003cem\u003eqPH9-1\u003c/em\u003e and \u003cem\u003eqPH9-2\u003c/em\u003e) were detected. For \u003cem\u003eqPH1\u003c/em\u003e, its dwarf allele was from Ai88. The LOD value of \u003cem\u003eqPH1\u003c/em\u003e was 6.19, explaining 3.71% of the phenotypic variations. It localized to a 14.76-Mb interval flanked by markers \u003cem\u003eSICAAS1020\u003c/em\u003e and \u003cem\u003eb243\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The candidate genes of the mapping interval of \u003cem\u003eqPH1\u003c/em\u003e were analyzed and we found that \u003cem\u003eSeita.1G242300\u003c/em\u003e is an orthologue gene to rice \u003cem\u003eLOC_Os02g41954\u003c/em\u003e and \u003cem\u003eArabidopsis AT4G21200\u003c/em\u003e. \u003cem\u003eLOC_Os02g41954\u003c/em\u003e and \u003cem\u003eAT4G21200\u003c/em\u003e encode gibberellin 2-beta-dioxygenase 7 (ATGA2ox7) and gibberellin 2-oxidase 8 (ATGA2ox8), respectively. \u003cem\u003eArabidopsis thaliana\u003c/em\u003e gibberellin 2-oxidase 8 (AtGA2ox8) catalyzes the 2β-hydroxylation of the C\u003csub\u003e20\u003c/sub\u003e-GA precursors GA\u003csub\u003e12\u003c/sub\u003e and GA\u003csub\u003e53\u003c/sub\u003e. Increased expression of either ATGA2ox7 or ATGA2ox8 caused a dwarf phenotype in both \u003cem\u003eArabidopsis\u003c/em\u003e and tobacco, while loss of function ATGA2ox7 ATGA2ox8 double mutants had higher levels of active GAs and displayed phenotypes associated with excess GAs [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eqPH5-1\u003c/em\u003e, it explained 29.55% of the phenotypic variation. We found a gibberellin biosynthesis related GA20 oxidase gene \u003cem\u003eSeita.5G404900\u003c/em\u003e existed in the candidate interval of \u003cem\u003eqPH5-1\u003c/em\u003e, which is a homologue of the rice \u0026lsquo;green revolution\u0026rsquo; gene \u003cem\u003eOsSD1\u003c/em\u003e (\u003cem\u003eLOC_Os01g66100\u003c/em\u003e). In rice, the semi-dwarf mutant \u003cem\u003esd1\u003c/em\u003e is the result of a deficiency of active GAs in the elongating stem caused by the loss of function of \u003cem\u003eSD1\u003c/em\u003e [\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In the present study, we found that there was a one-base (G) deletion in the third exon of \u003cem\u003eSeita.5G404900\u003c/em\u003e in Ai88 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), leading to the frameshift mutation, which might affect the function of the gibberellin biosynthesis related GA20 oxidase gene \u003cem\u003eSeita.5G404900\u003c/em\u003e, thereby reducing the plant height of the Ai88. In addition, a semi-thermal asymmetric reverse PCR (STARP) marker \u003cem\u003eGA20oxSTARP-1\u003c/em\u003e was designed based on the single-base insertion/deletion in the third exon and used to allelic variation analysis. The result showed that there were only three foxtail millet accessions with the single-base deletion (Allele\u003csup\u003eAi88\u003c/sup\u003e) in the 257 foxtail millet accessions randomly selected from 916 foxtail millet accessions published by Jia \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which were Ci348 (Baimi1), Ci736 (Zituigu) and Ci937 (C193, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, Table S5), respectively, indicating the single-base deletion was a rare variation. Moreover, the average plant height of foxtail millet accessions with Allele\u003csup\u003eAi88\u003c/sup\u003e was significantly lower than that with Allele\u003csup\u003eJingu28\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), indicating this variation and the marker \u003cem\u003eGA20oxSTARP-1\u003c/em\u003e could be used for molecular breeding of plant height in foxtail millet.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, a genetic linkage map with 213 published SSR markers and two InDel markers was constructed. QTL mapping identified 46 QTL for 12 agronomic traits. The 277 bp insertion/deletion on the promoter of \u003cem\u003eSeita.9G020100\u003c/em\u003e and the one-base (G) insertion/deletion in the third exon of \u003cem\u003eSeita.5G404900\u003c/em\u003e might be candidate functional sites for major QTL \u003cem\u003eqHD9-1\u003c/em\u003e and \u003cem\u003eqPH5-1\u003c/em\u003e, respectively.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eQTL\u003c/strong\u003e: Quantitative trait locus/ Quantitative trait loci\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSSR\u003c/strong\u003e: Simple repeat sequence\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInDel\u003c/strong\u003e: Insertion-deletion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRIL\u003c/strong\u003e: Recombinant inbred line\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS\u003c/strong\u003e: Genome wide association analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMTA\u003c/strong\u003e: Marker-trait association\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSV\u003c/strong\u003e: Structural variation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRTM\u003c/strong\u003e: Restricted two-stage multi-locus multi-allele\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMLM\u003c/strong\u003e: Mixed linear model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCTAB\u003c/strong\u003e: Cetyltrimethyl ammonium bromide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCIM\u003c/strong\u003e: Composite interval mapping\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLOD\u003c/strong\u003e: Logarithm of odds\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCR\u003c/strong\u003e: Polymerase chain reaction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ecM\u003c/strong\u003e: Centimorgan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMb\u003c/strong\u003e: Mega base pairs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTARP\u003c/strong\u003e: Semi-thermal asymmetric reverse PCR\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChr\u003c/strong\u003e: Chromosome\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePVE\u003c/strong\u003e: Phenotypic variation explained\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e: Single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGA\u003c/strong\u003e: Gibberellic acid\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its additional flies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Special Plan for Scientific and Technological Innovation Talent Team of Shanxi Province (202204051002036), Shanxi Agricultural University Doctoral Research Initiation Project (2021BQ23, 2021BQ24), Award Scientific Program for Excellent Doctors in Shanxi Province (SXBYKY2021070, SXBYKY2021059), and Natural Science Foundation of Shanxi Province (202203021222169, 20210302123382).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLLG, GHY and JGW designed the experiments. LLG, GHY, QXZ and HL performed experiments. SYW, JHF, TGW, LJY, YQZ, YXM and LC collected the phenotype. LLG and GHY wrote the manuscript. XRL, SQD, XQC and XYY helped revise the manuscript. XMD provided the 257 foxtail millet accessions. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollege of Agronomy, Shanxi Agricultural University, Taigu, China\u003c/p\u003e\n\u003cp\u003eInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYang X, Wan Z, Perry L, Lu H, Wang Q, Zhao C, Li J, Xie F, Yu J, Cui T et al. 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Loss-of-function of a Rice Gibberellin Biosynthetic Gene, GA20 oxidase (GA20ox-2), Led to the Rice \u0026lsquo;Green Revolution\u0026rsquo;\u003cem\u003e. \u003c/em\u003eBreeding Science. 2002;52(2):143-150.\u003c/li\u003e\n\u003cli\u003eSpielmeyer W, Ellis MH, Chandler PM. \u003cem\u003eSemidwarf\u003c/em\u003e (\u003cem\u003esd-1\u003c/em\u003e), \u0026quot;green revolution\u0026quot; rice, contains a defective gibberellin 20-oxidase gene\u003cem\u003e. \u003c/em\u003eProceedings of the National Academy of Sciences of the United States of America. 2002;99(13):9043-9048.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Foxtail millet, Genetic linkage map, QTL mapping, Agronomic traits, Candidate gene","lastPublishedDoi":"10.21203/rs.3.rs-5061888/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5061888/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFoxtail millet (\u003cem\u003eSetaria italica\u003c/em\u003e) is one of the most ancient cultivated cereal crops and is ideal for the functional genomics of the Panicoideae crops. In the present study, we generated an F\u003csub\u003e2\u003c/sub\u003e population derived from a cross between an elite foxtail millet variety Jingu28 and a backbone line Ai88 and constructed a genetic linkage map with 213 published SSR markers and two InDel markers. Quantitative trait locus (QTL) mapping identified 46 QTL for 12 agronomic traits, including 13 major effect QTL. Meanwhile, 40 QTL controlling different traits formed 13 co-located QTL clusters. Moreover, one putative candidate gene \u003cem\u003eSeita.9G020100\u003c/em\u003e for \u003cem\u003eqHD9-1\u003c/em\u003e with conserved CCT (constans, constans-like, and timing of chlorophyll A/B binding) motif and a gibberellin biosynthesis related GA20 oxidase gene \u003cem\u003eSeita.5G404900 \u003c/em\u003efor \u003cem\u003eqPH5-1 \u003c/em\u003ewere identified based on homologous gene comparison. The 277 bp insertion/deletion on the promoter of \u003cem\u003eSeita.9G020100\u003c/em\u003e and the one-base (G) insertion/deletion in the third exon of \u003cem\u003eSeita.5G404900\u003c/em\u003e might be candidate functional sites. Furthermore, two markers (\u003cem\u003eGhd7InDel \u003c/em\u003eand \u003cem\u003eGA20oxSTARP-1\u003c/em\u003e) were developed based on these two variation sites, respectively. These results will help to elucidate the genetic basis of important agronomic traits in foxtail millet and be useful for marker-assisted selection of varieties with ideal plant architecture and high yield potential.\u003c/p\u003e","manuscriptTitle":"Construction of a genetic linkage map and QTL mapping of the agronomic traits in Foxtail millet (Setaria italica)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 10:05:22","doi":"10.21203/rs.3.rs-5061888/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-18T19:54:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-13T11:58:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-13T11:57:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-09-10T05:27:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7108100d-ebce-4058-a2eb-fa0c2f31cf9b","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-24T16:00:11+00:00","versionOfRecord":{"articleIdentity":"rs-5061888","link":"https://doi.org/10.1186/s12864-024-11169-2","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-02-17 15:57:14","publishedOnDateReadable":"February 17th, 2025"},"versionCreatedAt":"2024-11-19 10:05:22","video":"","vorDoi":"10.1186/s12864-024-11169-2","vorDoiUrl":"https://doi.org/10.1186/s12864-024-11169-2","workflowStages":[]},"version":"v1","identity":"rs-5061888","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5061888","identity":"rs-5061888","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}