Fine mapping and candidate gene analysis of the major QTL qSW-A03 for seed weight in Brassica napus

Fine mapping and candidate gene analysis of the major QTL qSW-A03 for seed weight in Brassica napus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fine mapping and candidate gene analysis of the major QTL qSW-A03 for seed weight in Brassica napus Jiangyu Meng, Dingxue Hu, Bin Wang, Yuelin Zhu, Chunyan Lu, Yan Deng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5271995/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Seed weight is a determining factor for improving rapeseed productivity. In the present study, a high-density genetic map was constructed via genome resequencing in an RIL population derived from a cross of two rapeseed varieties, ZS11 and DL704, with great differences in thousand seed weight (TSW). A total of 1,306 bins involving 1,261,526 SNPs were used to construct the bin map. On the basis of the genetic map, QTL mapping for seed weight was performed. In total, 15 QTLs associated with TSW were detected. A major and stable QTL, qSW-A03 , was mapped to a 2.8 cM interval on chromosome A03. Fine mapping delimited the qSW-A03 locus into a 59-kb region, and 11 genes within this region were predicted. By employing a combination of gene variation, gene expression difference and gene coexpression network analysis of seed weight, BnaA03G0362100ZS ( BnaDUF1666 ) was identified as a promising candidate gene. This study provides useful information for the genetic dissection of seed weight and promotes the molecular breeding of high-yield rapeseed. Brassica napus seed weight quantitative trait loci candidate genes DUF1666 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Brassica napus (AACC, 2n = 38), commonly known as rapeseed or canola, is the second-largest oilseed crop and contributes more than 13% of the stable supply of edible vegetable oil worldwide (Tan et al., 2024 ). Owing to the declining acreage and increasing demand for rapeseed production, improving yield has always been an important goal of rapeseed breeding (Jiao et al., 2021 ; Tan et al., 2024 ; Zhang et al., 2023a ). As one of the three direct components of rapeseed yield, seed weight is a determinant factor for improving rapeseed productivity (Wang et al., 2020 ). Therefore, dissecting the genetic basis of seed weight will deepen our understanding of seed development and accelerate the breeding process of high-yield varieties of rapeseed. In Brassica napus , the process of seed development is similar to that in Arabidopsis. A large cavity forms as endosperm cells proliferate at the early stage of seed development, and then the embryo begins to develop as the endosperm degenerates to provide nutrient substances for embryo development until a single layer of endosperm cells remains (Xiao et al., 2016 ). At maturity, rapeseed primarily consists of the embryo, which develops from the fertilized egg, and the seed coat, which develops from the integument. The fully developed embryo consists of four parts: the plumule, hypocotyl, radicle and cotyledon. The cotyledon occupies the main volume of mature seeds in rapeseed. The two large cotyledons of rapeseed are important storage organs that are rich in lipids and proteins (Dong et al., 2022 ). Because the integuments limit the space within which the embryo and endosperm develop, the seed coat determines the final size (capacity) and weight of mature seeds via cell expansion and/or proliferation (Li et al., 2019a ; Li et al., 2015 ; Li et al., 2019b ; Zhang et al., 2023b ; Zhang et al., 2021 ). Seed weight is a typical quantitative trait regulated by multiple genes. Quantitative trait locus (QTL) mapping and genome-wide association study (GWAS) are two effective means to uncover the complex genetic mechanism of seed weight in Brassica napus (Khan et al., 2021 ). In recent decades, at least 168 QTLs for seed weight- and seed shape-related traits, including seed length, seed width, length-width ratio, seed cross-section area, seed circumference, seed diameter and seed roundness, have been identified in various populations of Brassica napus (Dong et al., 2018 ; Raboanatahiry et al., 2018 ; Shi et al., 2019 ; Sun et al., 2018 ; Wang et al., 2020 ; Zhang et al., 2021 ). However, only two causal genes, BnaA9.CYP78A9 and BnaA9.ARF18 , have been cloned via QTL fine-mapping in rapeseed (Liu et al., 2015 ; Shi et al., 2019 ). In addition, the HECT E3 ligase gene BnaUPL3. C03 was shown to vary in the promoter region via GWAS, reducing its expression to increase LEC2 protein levels to regulate seed weight (Miller et al., 2019 ). These results suggest that there are abundant undiscovered gene resources that increase seed weight in rapeseed (Zhang et al., 2021 ). It is necessary to fine map QTLs and clone their causal genes for seed weight in Brassica napus . A genetic linkage map constructed through genotyping-by-sequencing (GBS) is a cost-effective, time-saving and powerful tool for mapping QTLs and for molecular breeding (Yepuri et al., 2022 ). It has been widely used for QTL mapping in different crop species, including clubroot resistance in Brassica oleracea (Lee et al., 2016 ), fiber traits in cotton (Fan et al., 2018 ; Pei et al., 2021 ), plant height and flowering time in Sorghum bicolor (Kong et al., 2018 ), and productivity and quality traits in peanut (Jadhav et al., 2021 ). In Brassica napus , GBS technology has also been used to construct high-density genetic maps for QTL mapping, such as seed quality traits (Gacek et al., 2021 ) and clubroot resistance (Yu et al., 2021 ). In this study, we constructed a genetic linkage map via GBS via a recombinant inbred line (RIL) population derived from the hybridization and propagation of rape DL704, which has relatively large seeds, and rape cultivar ZS11. The major QTL of seed weight, qSW-A03 , was identified and finely mapped to a 59-kb region. By combining gene structural variation analysis, gene transcription level analysis, and gene coexpression network analysis, candidate genes encoding a protein of unknown function were identified. These results provide meaningful information concerning the molecular regulatory mechanism of seed weight and the cultivation of high-yield varieties of rapeseed. Materials and Methods Plant materials, field experiments, and trait evaluation The mapping population consisted of 174 RILs derived from a cross between the cultivar ZS11 bred by the Institute of Oil Crops, Chinese Academy of Agricultural Sciences, and the synthetic rapeseed DL704 bred from distant hybridization with ZS9 ( B. napus ), B. oleracea and B. rape in the early breeding work of our laboratory. The RIL populations (F 2:4 , F 2:5 , F 2:6 REP1 and F 2:6 REP2) were generated from the F 2 population in 2018 via the single-seed descent (SSD) method. All the plant materials used were planted in the experimental field of Southwest University, Chongqing, China (29°33′N, 106°34′E). Every line was planted with a randomized complete block design. Each plot consisted of 24 plants, with 30 cm between rows and 25 cm within rows. The F 2:4 and F 2:5 populations were planted in 2020 and 2021, respectively. In 2022, the F 2:6 population was planted with two replications, F 2:6 REP1 and F 2:6 REP2. Field management followed standard agricultural practices. At anthesis, 3 or 4 plants with the same growth trend were selected randomly from each line for reproduction. At maturity, 50 well-developed siliques were harvested from the bottom of the main inflorescence of a single plant to determine the thousand-seed weight (TSW, g) from dried seeds. Five individual plants, with the same growth status and without disease and without selfing, were randomly selected from each line to detect TSW. The mean of three measurements of TSW was taken as the phenotypic value of the individual plants, and the mean TSW of 5 individual plants was taken as the final phenotypic value of each line. In addition, the TSWs of DL704 and ZS11 were measured from 2015 to 2022. Statistical analysis of phenotypic data Data collection, analysis and visualization were performed with WPS Office ( https://www.wps.cn ), SPSS 19.0 (Chicago, Illinois, USA), R software ( https://www.r-project.org/ ) and GraphPad Prism 8 ( https://www.graphpad.com/ ). For the RIL populations, the statistical values included the mean, maximum (Max), minimum (Min), standard deviation (SD), skewness (SK), kurtosis (K) and coefficient of variation (CV) with SPSS 19.0, and the phenotypic frequency distributions and correlations were analyzed with the rcorr function in the Hmis package ( https://www.rdocumentation.org/packages/Hmisc/versions/5.1-3 ) and the PerformanceAnalytics package ( https://www.rdocumentation.org/packages/PerformanceAnalytics/versions/1.5.3 ) in R software. Morphological and cellular analysis To observe the seed development characteristics, the flowering dates were marked with colorful wools on the bloomed flowers at the bottom of the main inflorescences of DL704 and ZS11. The sizes of the ovaries at 0 days after pollination (DAP) and the ovules at 7, 14, 21, 28, 35 and 42 DAP were observed. At each stage, 15–20 ovules from DL704 and ZS11 were measured. Pictures were taken under an OLYMPLUS MVX10 microscope. ImageJ ( https://imagej.net/ ) was used to count seed sizes at different DAPs. The WPS Office ( https://www.wps.cn/ ) and GraphPad Prism 8 ( https://www.graphpad.com/ ) were used to analyze differences in the projected areas of ovaries or seeds, and the ovaries or seeds of ZS11 were used as controls. At 42 DAP, the seeds were close to maturity. The seed coats of DL704 and ZS11 at 42 DAP were collected to analyze cytological differences. Cotyledons were sliced with a Leica CM1850 freezing microtome after being dissected from seeds of DL704 and ZS11. The whole seed coats and the slices of cotyledons were placed in a drop of clearing solution [30 mL H 2 O, 80 g chloral hydrate (C8383; Sigma‒Aldrich), and 10 mL 100% glycerol (G6279; Sigma‒Aldrich)] for 10 and 30 minutes, respectively (Fang et al., 2012 ). The cleared cells were photographed under an OLYMPLUS MVX10 microscope and Nikon ECLIPSE Ci. The mean cell area of at least 10 cells was determined on the basis of 10 individuals via ImageJ ( https://imagej.net/ ). The cell numbers in the region of the outer seed coat and the outer cotyledon were determined. Genetic map construction Genotyping of the RIL population was performed as described in a recently published study (Agyenim-Boateng et al., 2023 ). Eighty lines, including 40 lines with the highest and stable TSW values and 40 lines with the lowest and stable TSW values in the RIL population, were selected to construct a high-density bin-based genetic linkage map. Genomic DNA was extracted from fresh young leaves of ZS11, DL704, and lines in the RIL population via the CTAB method (Yang et al., 2022 ). DNA quality determination, library construction, and quality assessment were carried out by GENOSEQ (Wuhan, Hubei, China) following standard Illumina operating procedures. The concentration and quality of the extracted DNA were determined via a NanoDrop2000 Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). After the connector sequences, low-quality bases and reads less than 50 bp in the raw data were filtered by fastp 0.23.0 (Chen et al., 2018 ), the clean data were compared to the reference genome ZS11.v0 of B. napus (Song et al., 2020 ) with the MEM algorithm via BWA 0.7.15-r1140 (Li et al., 2013). Then, SAMtools 1.3.1 (Li et al., 2009 ) was used to compare and sort the results and remove PCR duplication. Next, the haplotype caller module in GATK 3.7 ( https://gatk.broadinstitute.org/hc/en-us ) was used to detect structural variation, including SNPs and InDels, and ANNOVAR 2016Feb1 (Wang et al., 2010 ) was used to annotate structural variation and predict the impact of structural variation on gene function. The variations were used to construct a genetic linkage map, which was filtered such that the sequencing depths of DL704 and ZS11 were less than 10 and that of their offspring was less than 2, the percentage of genotypic deletions in their offspring was less than 25%, the frequency of smaller alleles was less than 25%, and the relative heterozygosity of their offspring was less than 25%. The adjacent markers without exchange were merged into a bin marker, which was used to calculate the recombination rate and genetic distance to construct the bin map. The genotypes of ZS11 and DL704 were marked as A and B, respectively. The bin map was constructed with the Kosambi mapping function (Xie et al., 2010 ). The collinearity between the bin map and the reference genome was evaluated via the Pearson correlation coefficient. QTL analysis The QTL for TSW was mapped via the composite interval mapping (CIM) procedure of WinQTLCart 2.5 software with an LOD threshold of 3.0, which was selected on the basis of 1000 permutation tests at a 95% confidence level (Wang et al., 2020 ; Wang et al., 2012 ). The QTL nomenclature rule was as follows: q-trait abbreviation-chromosome number-population-QTL number (Tang et al., 2015 ). QTLs were defined as the same QTL when they overlapped in a confidence interval for the same trait identified in different environments and with the same direction of additive effect (Yang et al., 2022 ). QTLs identified in at least two generations or repeats were considered stable (Liu et al., 2017 ). The online tool Gbrowse Synteny ( http://cbi.hzau.edu.cn/bnapus/ ) was used for the homologous alignment analysis of QTL intervals and genes among different reference genomes in Brassica napus (Song et al., 2020 ). Fine mapping To confirm and narrow down the genetic region of QTL qSW-A03 , InDel markers were designed near or within the QTL qSW-A03 interval on the basis of the accession differences in the sequencing annotations (Table S12). The primer pairs were designed with Primer Premier 5 software ( http://www.premierbiosoft.com/primerdesign/ ) and synthesized by Tsingke Biotechnology Co., Ltd. (China). PCR was performed in a total volume of 10 µL, including 1 µL of genomic DNA (100 ng/µL), 5 µL of 2 × Taq Mix (ABclonal, Wuhan), 0.5 µL of forward primer (10 µmol/L), 0.5 µL of reverse primer (10 µmol/L) and 3 µL of ddH 2 O. The PCR amplification conditions were 94°C for 5 min, followed by 35 cycles (94°C for 30 s, annealing at 57°C for 30 s, and 72°C for 30 s), and a final extension at 72°C for 5 min. The PCR products were imaged with sliver staining after being separated on 10% polyacrylamide gels. qRT-PCR Samples were obtained from DL704 and ZS11 at six stages of seed development, including ovaries at 7 DAP. Each sample was collected from the bottom of the main inflorescence of three or four parents, mixed equally in nuclease-free centrifuge tubes, and then quickly frozen in liquid nitrogen. The samples were stored at -80°C. High-quality RNA was extracted with a SteadyPure Plant RNA Extraction Kit (Accurate Biotechnology Co., Ltd., Changsha, Hunan, China) and reverse-transcribed with All-In-One 5× RT MasterMix (Applied Biological Materials Inc., Vancouver, B. C., Canada). Quantitative PCR was performed with Universal SYBR qPCR Master Mix (Applied Biological Materials Inc., Vancouver, B. C., Canada) on a Bio-Rad CFX96TM Real-Time Detection System. The 2 −ΔΔCt method was used to calculate the relative expression levels of genes on the basis of four technical replicates per sample (Livak et al., 2001). BnaACTIN7 ( BnaC02G0037200ZS ) was used as the internal control for normalization. The sequences of primers used for qRT-PCR are shown in Table S13. Data collection, analysis and visualization were performed via the WPS Office ( https://www.wps.cn/ ) and GraphPad Prism 8 ( https://www.graphpad.com/ ). Coexpression analysis On the basis of the high efficacy of the gene coexpression network for controlling seed weight in Brassica napus constructed by our laboratory (Dong et al., 2022 ), the genes in the region of the major QTL qSW-A03 were added to candidate gene sets to analyze the coexpression relationship with the seed weight-harboring genes. The guide gene sets and the set of transcriptional profiles were the same as those in a previous study (Dong et al., 2022 ). The remaining genes in the candidate and guide gene sets were used for coexpression network analysis after the RNA-seq data were filtered by removing genes with low expression variances (< 1%) or maximum gene expression (RPKM < 1). The absolute value of the Pearson correlation coefficient (|R|) between genes was greater than 0.8. The subsequent coexpression network was constructed via the Markov cluster (MCL) algorithm with an inflation coefficient of 2. Small MCL clusters with < 25 nodes were merged into a larger cluster that was topologically adjacent to or surrounded by small clusters. Gene Ontology term analysis was performed via Metascape ( https://Metascape.org ) via orthologs of A. thaliana . The online tool gene index on BnIR ( https://yanglab.hzau.edu.cn/BnIR/gene_index ) was used for the homologous alignment of genes among different reference genomes of Brassica napus and Arabidopsis thaliana (Yang et al., 2023 ). Results Phenotypic analysis of TSW in parents and RIL populations For the parents, the mature seed size of DL704 was much greater than that of ZS11 (Fig. 1a-b), and the thousand seed weight (TSW) of DL704 (6.72 ± 0.44 g) was significantly ( P <0.001) greater than that of ZS11 (4.41 ± 0.51 g) from 2015 to 2022 (Fig. 1c). Descriptive statistics and phenotypic distributions and correlations of TSW for the RIL population from 2020-2022 are shown in Table 1 and Fig. 2. For the 174 lines in the RIL population across the three years, TSW exhibited broad and continuous variation with transgressive segregation, which was consistent with a normal distribution via skewness and kurtosis tests, and the variance values were relatively large, which indicated that the RIL population was conducive to QTL mapping of TSW, which had great potential for genetic improvement (Fig. 2a and Table 1). The correlation coefficients were greater than 0.60, with a significance level of P <0.001 among F 2:4 , F 2:5 , F 2:6 REP1 and F 2:6 REP2, indicating that loci regulating TSW can be transmitted to the offspring and expressed stably (Fig. 2a). TSW is a quantitative trait controlled by multiple genes. To map major loci, we selected 40 lines with the highest TSW values and 40 lines with the lowest TSW values by constructing a genetic map. For these 80 lines in the RIL population, there was no significant change in phenotypic characteristics, but differences in TSW displayed a bimodal distribution with higher kurtosis values, and the correlation coefficients were greater than 0.83 among F 2:4 , F 2:5 , F 2:6 REP1 and F 2:6 REP2 (Fig. 2b and Table 1). These data implied that these selected lines were more suitable for mapping stable and reliable QTLs. Morphological and cytological analysis To research the morphological and cytological variation in seeds, we measured the ovule sizes at different DAPs and further analyzed the differences in the sizes of the seed coats and cotyledons of the parents. During seed development, the seed size of DL704 was greater than that of ZS11 at different DAPs (Fig. 3a-g). Notably, the seed size (volume) increased the fastest in both ZS11 and DL704 before 14 DAP (Fig. 3h). After 14 DAP, the speed of seed growth began to slow in both ZS11 and DL704 until the seeds grew to their final size (Fig. 3h). Therefore, the time before 14 DAP was the key time for differences in seed size between ZS11 and DL704. We further examined the cell size and number of seed coats and cotyledons at 42 DAP when the seeds grew close to their final size. For the seed coat, the surface area of DL704 was significantly larger than that of ZS11 (Fig. 4a, c). Compared with those of ZS11, the cell size and number of the seed coat of DL704 also significantly increased by 33.07% and 19.58%, respectively (Fig. 4b, d, e). We extended our analysis to the cotyledons (Fig. 4f-j). The cotyledons of DL704 were also significantly larger than those of ZS11 (Fig. 4f, h). Compared with that in ZS11, the cell size of cotyledons in DL704 significantly increased by 104.14% (Fig. 4g, i), and the number of cotyledon cells in DL704 was 11.33% lower than that in ZS11 (Fig. 4j). These results suggest that the genes controlling large seeds predominantly promoted cell expansion but also influenced cell proliferation. Bin map construction We resequenced both parents with approximately 30× sequencing depth and 80 lines with approximately 5× sequencing depth on an Illumina HiSeq platform and produced clean data for SNPs and InDels to develop bin markers. The sequencing data statistics of the ZS11, DL704 and 80 lines were showed in Table S1. The GC contents of the parents and 80 lines ranged from 37.00% to 42.10%, with an average value of 39.63%. The comparison rates of the parents and 80 lines ranged from 90.47% to 98.67%, with an average value of 97.13%. The percentage of sequencing coverage of the parents was 87.41% and 78.40%, and that of the 80 lines ranged from 65.67% to 82.13%, with an average value of 71.94%. Both the data volume and the data quality can meet the subsequent analysis requirements. By aligning the clean reads with the reference genome sequence, we obtained 1,094,333 SNPs and 167,953 InDels (Table S2). After alignment between the two parents and 80 lines in the RIL population, a total of 1,261,526 markers on 19 chromosomes were used to construct the bin map, and the number of markers on each chromosome ranged from 6,562 to 158,876 (Table S3 and Table S4). The bin map covered 1,306 high-quality bins, which spanned 1588.128 cM, with an average distance of 1.648 cM between adjacent bin markers (Fig. 5, Table S3 and Table S4). The number of bins on each chromosome ranged from 12-119, and the marker interval ranged from 0.001-0.011 cM (Table S4). The recombination breakpoints were checked. The sources of the chromosome segments in each offspring were consistent with those of the parents (Fig. S1). In addition, the collinearity between the bin map and the reference genome was evaluated on the basis of the physical locations of the bin markers (Fig. S2, Table S5 and Table S6). The order of most bin markers in each linkage group was consistent with the physical map (Table S5). The Pearson correlation coefficient between the genetic and physical positions of all the chromosomes was greater than 0.86 (Table S6). The relationships between the genetic and physical maps of the 19 chromosomes were generally linear (Fig. S2). Thus, the bin map had a high level of collinearity with the physical map and sufficiently covered the reference genome. QTL mapping QTL mapping for TSW was performed on the basis of the bin map with the CIM strategy. A total of 15 QTLs for TSW were detected in the RIL populations (Table 2). They were distributed on four chromosomes, A03, C04, C07 and C09, explaining 7.09%-46.56% of the phenotypic variation. Among the 15 QTLs, 12 were detected across multiple populations or multiple years and overlapped in four intervals of three linkage groups or chromosomes (Fig. 6, Table 2). After integration, four stable QTLs were detected (Fig. 6, Table 3). Among them, qSW-A03 was detected in the F 2:4 , F 2:5 , F 2:6 REP1 and F 2:6 REP2 populations across 3 years with the highest LOD values, spanning a confidence interval of 2.8 cM from 53.5 to 56.3 cM on A03, aligning to the physical interval of 1.044Mb from 18.687 to 19.731 Mb on A03 of the reference genome ZS11.v0, and had the highest phenotypic variation, with 29.99%-46.56% (Table 3). Therefore, qSW-A03 was regarded as the major stable QTL accounting for the large seed size of DL704. Fine mapping of a major QTL, qSW-A03 To narrow the genomic region of qSW-A03 , we developed nine codominant InDel markers (Table S12) and mapped qSW-A03 to 279 kb from 19.111 to 19.390 Mb on A03 of the reference genome ZS11.v0 between two InDel markers, DA03-28 and DA03-50, in the F 2:4 , F 2:5 , F 2:6 REP1 and F 2:6 REP2 populations (Fig. 7b and Table S7). In the 279-kb region, five recombinants between the DA03-28 and DA03-50 intervals were screened and grouped into three genotypes, Class 1, Class 2 and Class 3, on the basis of the allelic composition and recombination breakpoints (Fig. 7c). The mean TSW value of each recombinant genotype was compared with that of the two parents to narrow qSW-A03 . The mean TSWs of both Class 1 and Class 2 were significantly greater than those of ZS11 and Class 3 and near to DL704. There were no other recombinant lines distinguished by the marker DA03-206, which was located between the markers DA03-202 and DA03-213 (Fig. 7c). On the basis of these results, qSW-A03 was narrowed down to a region between DA03-202 and DA03-213, corresponding to a 59-kb region from 19.197 to 19.256 Mb on A03 of the reference genome ZS11.v0 (Fig. 7c). Identification of candidate genes Eleven genes within the 59-kb region were identified, and their putative functions were predicted on the basis of the released rapeseed genome information (Fig. 7d and Table 4). None of the eleven genes have been previously reported to be involved in the regulation of seed weight. Therefore, qSW-A03 might be a novel QTL responsible for controlling seed weight. On the basis of the resequencing data of DL704 and ZS11, 144 exonic SNPs scattered in nine genes and seven exonic InDels in five genes were identified within the 59-kb region (Table S8 and Table S9). Among the exonic SNPs and InDels, 46 nonsynonymous SNP variations within eight genes resulted in amino acid variations, and all of the exonic InDels caused frameshift mutations. In addition, we identified 51 SNPs and 25 InDels located upstream or in the 5'UTRs of nine genes. In total, structural variations in all eleven genes exist between DL704 and ZS11. To further identify the candidate genes, we analyzed the transcription levels of the eleven genes in the parents via qRT-PCR (Fig. 8). Given that a large cavity forms at the early stage of seed development (Xiao et al. 2016), which is the time before 14 DAP, to account for the difference in seed size between ZS11 and DL704 (Fig. 3), ovules at 7 DAP were used to analyze the transcription levels of the eleven genes. The results revealed that the Cq values of two genes, BnaA03G0361600ZS and BnaA03G0361700ZS, were greater than 35. Therefore, these two genes were regarded as not expressed by either parent. Among the other nine genes, four genes, BnaA03G0361100ZS, BnaA03G0361300ZS, BnaA03G0361800ZS and BnaA03G0361900ZS, presented no significant difference in expression between the two parents. The remaining five genes, BnaA03G0361200ZS, BnaA03G0361400ZS, BnaA03G0361500ZS, BnaA03G0362000ZS and BnaA03G0362100ZS, were expressed differently between the two parents. Four of the five genes, BnaA03G0361200ZS, BnaA03G0361500ZS, BnaA03G0362000ZS and BnaA03G0362100ZS, were expressed significantly higher in DL704 than in ZS11, and one gene, BnaA03G0361400ZSZS, was expressed significantly lower in DL704 than in ZS11. According to the gene expression analysis, these five genes were regarded as candidate genes. The discovery of quantitative trait genes can combine gene coexpression analysis and QTL mapping (Cui et al., 2021; Dong et al., 2022; Ruprecht et al., 2017). Given this, we attempted to identify candidate genes by using the gene coexpression network of seed weight in B. napus (Dong et al., 2022). The eleven genes in the 59-kb region were added to the candidate gene set to analyze the coexpression relationships of the genes for seed weight. Among these eleven genes, only one gene, BnaA03G0362100ZS, was included in the new gene coexpression network for seed weight (Fig. 9, Table S10). BnaA03G0362100ZS encodes a protein of unknown function, DUF1666, which was named BnaDUF1666 . In the new gene coexpression network, BnaDUF1666 was coexpressed with 299 genes (Table S10). GO and KEGG analyses revealed that these 299 coexpressed genes were enriched in fatty acid biosynthesis and metabolism (FABM), seed maturation (SMa), and the cellular response to abscisic acid (ABA) stimulus (Fig. 9, Fig. S3 and Table S11). Among the 299 coexpressed genes, 28 genes were involved mainly in embryo and endosperm development ( NF-YA5 , NF-YB6, PKP-ALPHA ), seed filling ( SWEET15 , FAB1 , PKP-ALPHA ), and cell expansion and/or proliferation ( GIF1 , AGG3 , GW6, GRF5 ) (Cai et al., 2018; Chen et al., 2015; Das et al., 2019; Duan et al., 2015; He et al., 2017; Li et al., 2012; Li et al., 2016; Mu et al., 2013; Pidkowich et al., 2007; Shi et al., 2020; Sun et al., 2016; Yamamoto et al., 2009). These results provide insight into the dissection of BnaDUF1666 gene function and the underlying molecular mechanism involved in regulating seed weight. By combining gene structural variation analysis, gene transcription level analysis, and gene coexpression network analysis, the candidate gene BnaDUF1666 , which controlled rapeseed seed weight and size, was identified. Discussion Seed size or weight is a key trait that affects agricultural yield. QTL mapping is an efficient way to dissect complex quantitative traits in crops (Wang et al., 2020). In Brassica napus , a common and effective method to map QTLs for seed weight is the construction of a genetic linkage map via the use of molecular markers such as RFLP, SSR, SNP and InDel markers (Butruille et al., 1999; Ding et al., 2012; Dong et al., 2022; Fu et al., 2015; Geng et al., 2016; Li et al., 2014; Luo et al., 2017; Quijada et al., 2006; Shen et al., 2019; Shirakawa et al., 2009; Udall et al., 2006; Wang et al., 2020; Wang et al., 2016; Yang et al., 2012; Yang et al., 2017; Zhang et al., 2011; Zhao et al., 2016). These genetic linkage maps constructed with traditional molecular markers have provided much genetic information for QTL mapping in Brassica napus . However, the efficiency of fine mapping and map-based cloning is limited because of the low density of molecular markers and the polyploidy of B. napus . Therefore, it is necessary to construct more detailed and accurate genetic maps for effective QTL identification in B. napus . A genetic map based on GBS can scan and identify mutations at all the sites of the whole genome and does not require any previous marker information (Liu et al., 2023; Si et al., 2022), which makes it more accurate than previous genetic maps. In this study, a high-density genetic bin map was constructed with 1,306 high-quality bin markers comprising 1,094,333 SNPs and 167,953 InDels (Fig. 5, Table S3, Table S4 and Table S5). The bin map spanned 1588.128 cM, with an average distance of 1.648 cM between adjacent bin markers (Fig. 5, Table S3 and Table S4). Furthermore, there was good collinearity between the bin map and the reference genome, which indicates that the bin map had high accuracy and precision (Fig. S2 and Table S6). Therefore, the present bin map is more conducive to QTL detection for seed weight in rapeseed. To our knowledge, this bin map is the first bin map utilizing the GBS method for QTL mapping of seed weight in B. napus . In this study, we identified the major QTL qSW-A03 in the F 2:4 , F 2:5 , F 2:6 REP1 and F 2:6 REP2 populations across three years with a high-density genetic bin map (Fig. 6, Table 2 and Table 3). Moreover, qSW-A03 was identified with nine InDel markers in the RIL population, narrowing to a 59-kb physical region with recombinant lines (Fig. 7 and Table S7). These results showed that qSW-A03 can be stably inherited to regulate seed weight. To date, many QTLs and associated loci for seed weight have been identified via linkage and genome-wide association analyses in B. napus (Butruille et al., 1999; Ding et al., 2012; Dong et al., 2022; Fu et al., 2015; Geng et al., 2016; Khan et al., 2021; Li et al., 2014; Luo et al., 2017; Pal et al., 2021; Quijada et al., 2006; Raboanatahiry et al., 2018; Shen et al., 2019; Shirakawa et al., 2009; Udall et al., 2006; Wang et al., 2020; Wang et al., 2016; Xin et al., 2021; Yang et al., 2012; Yang et al., 2017; Zhang et al., 2023a; Zhang et al., 2011; Zhao et al., 2016). These QTLs or associated loci exist on all 19 chromosomes of B. napus according to their integration into the physical map of rapeseed. We aligned the QTLs previously detected on A03 to the reference genome ZS11.v0 and compared them with the QTL qSW-A03 identified in this study. The results revealed that qSW-A03 did not overlap with the previously reported QTLs. Moreover, none of the eleven genes in the QTL region have been reported to be involved in regulating seed weight/size in B. napus . Therefore, qSW-A03 is a novel QTL responsible for controlling seed weight in B. napus . Combining QTL mapping, gene expression differences and gene coexpression network analysis is a powerful strategy for effectively identifying candidate genes associated with a target trait (Cui et al., 2021; Derakhshani et al., 2020; Dong et al., 2022; Guo et al., 2019; Jiao et al., 2021; Lamb et al., 2006; Liu et al., 2023; Ruprecht et al., 2017; Shen et al., 2019; Wang et al., 2020; Xin et al., 2023; Zhang et al., 2021). By combining QTL mapping and gene expression difference analysis, some studies have identified candidate genes associated with seed weight in B. napus , such as a histidine kinase gene (BnaA03G37960D) underlying cqSW.A03-2 (Wang et al., 2020), two candidate genes (BnaC09G0551500ZS and BnaC09G0551700ZS) in qSW.C9 (Zhang et al., 2021). Furthermore, this strategy has also been applied to map-based cloning of two causal genes, BnaA9. CYP78A9 and ARF18 account for seed weight in B. napus (Liu et al., 2015; Shen et al., 2019; Shi et al., 2019). By combining QTL mapping and gene coexpression network analysis, Cui et al. (2021) first revealed novel QTGs involved in oil accumulation in rapeseed. This combination has also been successfully applied to discover candidate QTGs for determining seed weight in rapeseed (Dong et al., 2022). In the present study, we adopted this strategy, combining QTL mapping, gene expression differences, and gene coexpression network analysis, BnaDUF1666 (BnaA03G0362100ZS) was identified as the most promising candidate gene for seed weight regulation. Despite the unknown function of the candidate gene BnaDUF1666 according to the reference genome annotation, gene coexpression network analysis provides clues for the gene function and molecular mechanism of BnaDUF1666 in regulating seed weight. In this study, the 299 genes coexpressed with BnaDUF1666 were involved in seed and embryo development, carbohydrate transport, fatty acid biosynthesis and metabolism, seed maturation, and the ABA response (Fig. 9, Fig. S3, Table S10 and Table S11). These enriched terms implied that BnaDUF1666 may regulate seed weight. The cell size and number of seed coats led to a greater capacity of DL704 (Fig. 4a-e), which provided enough space for endosperm and embryo development. The larger cotyledons subsequently formed, which was predominantly promoted by cell expansion but also slightly limited by cell proliferation (Fig. 4f-j), increasing the capacity for lipids and proteins. Among these coexpressed genes, GIF1 , AGG3 , GW6 and GRF5 also regulate seed size through cell expansion and/or proliferation (Duan et al., 2015; He et al., 2017; Li et al., 2012; Shi et al., 2020; Sun et al., 2016). These results suggested that BnaDUF1666 may regulate seed size predominantly through cell expansion but also through proliferation in both the seed coat and cotyledon. In this study, BnaDUF1666 was differentially expressed between the parents at 7 DAP (Fig. 8). At the early stage of seed development, ABA negatively regulates endosperm proliferation by influencing the timing of endosperm cellularization (Cheng et al., 2014; Zhang et al., 2023b). These results suggest that BnaDUF1666 may determine the cell size and number of seed coats and regulate endosperm and embryo development during early seed development. Declarations Funding This program was financially supported in part by the National Key Research and Development Program of China (2022YFD1200400), the National Natural Science Foundation of China (32272060), the Science and Technology Innovation 2030 Major Project (2023ZD0404201), and the Natural Science Foundation of Chongqing (cstc2021jcyj-msxmX1198). Author contributions JM, DH, BW, YZ, CL, YD, and HC carried out the field experiments; JM, DH, BW and YZ participated in QTL fine mapping; JM, DH and BHW participated in data analysis; YH and QW designed and supervised the project. JM wrote the original draft. JM, BHW, YH and QW were involved in reviewing and editing the manuscript. All the authors read and contributed to the revision of the manuscript. Acknowledgements The authors would like to thank everyone who contributed their time and effort to this study. References Agyenim-Boateng KG, Zhang S, Gu R, Zhang S, Qi J, Azam M, Ma C, Li Y, Feng Y, Liu Y, Li J, Li B, Qiu L, Sun J (2023) Identification of quantitative trait loci and candidate genes for seed folate content in soybean. Theor Appl Genet 136:149 Butruille DV, Guries RP, Osborn TC (1999) Linkage analysis of molecular markers and quantitative trait loci in populations of inbred backcross lines of Brassica napus L. Genetics 153:949–964 Cai Y, Zhang W, Jin J, Yang X, You X, Yan H, Wang L, Chen J, Xu J, Chen W, Chen X, Ma J, Tang X, Kong F, Zhu X, Wang G, Jiang L, Terzaghi W, Wang C, Wan J (2018) OsPKpα1 encodes a plastidic pyruvate kinase that affects starch biosynthesis in the rice endosperm. J Integr Plant Biol 60:1097–1118 Chen LQ, Lin IW, Qu XQ, Sosso D, McFarlane HE, Londoño A, Samuels AL, Frommer WB (2015) A cascade of sequentially expressed sucrose transporters in the seed coat and endosperm provides nutrition for the Arabidopsis embryo. Plant Cell 27:607–619 Chen S, Zhou Y, Chen Y, Gu J (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884–i890 Cheng ZJ, Zhao XY, Shao XX, Wang F, Zhou C, Liu YG, Zhang Y, Zhang XS (2014) Abscisic acid regulates early seed development in Arabidopsis by ABI5-mediated transcription of SHORT HYPOCOTYL UNDER BLUE1 . Plant Cell 26:1053–1068 Cui Y, Zeng X, Xiong Q, Wei D, Liao J, Xu Y, Chen G, Zhou Y, Dong H, Wan H, Liu Z, Li J, Guo L, Jung C, He Y, Qian W (2021) Combining quantitative trait locus and co-expression analysis allowed identification of new candidates for oil accumulation in rapeseed. J Exp Bot 72:1649–1660 Das S, Parida SK, Agarwal P, Tyagi AK (2019) Transcription factor OsNF-YB9 regulates reproductive growth and development in rice. Planta 250:1849–1865 Derakhshani B, Jafary H, Maleki Zanjani B, Hasanpur K, Mishina K, Tanaka T, Kawahara Y, Oono Y (2020) Combined QTL mapping and RNA-Seq profiling reveals candidate genes associated with cadmium tolerance in barley. PLoS ONE 15:e0230820 Ding G, Zhao Z, Liao Y, Hu Y, Shi L, Long Y, Xu F (2012) Quantitative trait loci for seed yield and yield-related traits, and their responses to reduced phosphorus supply in Brassica napus . Ann Bot 109:747–759 Dong H, Tan C, Li Y, He Y, Wei S, Cui Y, Chen Y, Wei D, Fu Y, He Y, Wan H, Liu Z, Xiong Q, Lu K, Li J, Qian W (2018) Genome-wide association study reveals both overlapping and independent genetic loci to control seed weight and silique length in Brassica napus . Front Plant Sci 9:921 Dong HL, Yang L, Liu YL, Tian GF, Tang H, Xin SS, Cui YX, Xiong Q, Wan HF, Liu Z, Jung C, Qian W (2022) Detection of new candidate genes controlling seed weight by integrating gene coexpression analysis and QTL mapping in Brassica napus L. The Crop Journal Duan P, Ni S, Wang J, Zhang B, Xu R, Wang Y, Chen H, Zhu X, Li Y (2015) Regulation of OsGRF4 by OsmiR396 controls grain size and yield in rice. Nat Plants 2:15203 Fan L, Wang L, Wang X, Zhang H, Zhu Y, Guo J, Gao W, Geng H, Chen Q, Qu Y (2018) A high-density genetic map of extra-long staple cotton ( Gossypium barbadense ) constructed using genotyping-by-sequencing based single nucleotide polymorphic markers and identification of fiber traits-related QTL in a recombinant inbred line population. BMC Genomics 19:489 Fang W, Wang Z, Cui R, Li J, Li Y (2012) Maternal control of seed size by EOD3/CYP78A6 in Arabidopsis thaliana . Plant J 70:929–939 Fu Y, Wei D, Dong H, He Y, Cui Y, Mei J, Wan H, Li J, Snowdon R, Friedt W, Li X, Qian W (2015) Comparative quantitative trait loci for silique length and seed weight in Brassica napus . Sci Rep 5:14407 Gacek K, Bayer PE, Anderson R, Severn-Ellis AA, Wolko J, Lopatynska A, Matuszczak M, Bocianowski J, Edwards D, Batley J (2021) QTL genetic mapping study for traits affecting meal quality in winter oilseed rape ( Brassica napus L). Genes (Basel) 12:1235 Geng X, Jiang C, Yang J, Wang L, Wu X, Wei W (2016) Rapid identification of candidate genes for seed weight using the SLAF-Seq method in Brassica napus . PLoS ONE 11:e0147580 Guo T, Yang J, Li D, Sun K, Luo L, Xiao W, Wang J, Liu Y, Wang S, Wang H, Chen Z (2019) Integrating GWAS, QTL, mapping and RNA-seq to identify candidate genes for seed vigor in rice (Oryza sativa L). Mol Breed 39:87 He Z, Zeng J, Ren Y, Chen D, Li W, Gao F, Cao Y, Luo T, Yuan G, Wu X, Liang Y, Deng Q, Wang S, Zheng A, Zhu J, Liu H, Wang L, Li P, Li S (2017) OsGIF1 positively regulates the sizes of stems, leaves, and grains in rice. Front Plant Sci 8:1730 Jadhav MP, Gangurde SS, Hake AA, Yadawad A, Mahadevaiah SS, Pattanashetti SK, Gowda MVC, Shirasawa K, Varshney RK, Pandey MK, Bhat RS (2021) Genotyping-by-sequencing based genetic mapping identified major and consistent genomic regions for productivity and quality traits in Peanut . Front Plant Sci 12:668020 Jiao Y, Zhang K, Cai G, Yu K, Amoo O, Han S, Zhao X, Zhang H, Hu L, Wang B, Fan C, Zhou Y (2021) Fine mapping and candidate gene analysis of a major locus controlling ovule abortion and seed number per silique in Brassica napus L. Theor Appl Genet 134:2517–2530 Khan SU, Saeed S, Khan MHU, Fan C, Ahmar S, Arriagada O, Shahzad R, Branca F, Mora-Poblete F (2021) Advances and challenges for QTL analysis and GWAS in the plant-breeding of high-yielding: A focus on rapeseed. Biomolecules 11 Kong W, Kim C, Zhang D, Guo H, Tan X, Jin H, Zhou C, Shuang LS, Goff V, Sezen U, Pierce G, Compton R, Lemke C, Robertson J, Rainville L, Auckland S, Paterson AH (2018) Genotyping by sequencing of 393 Sorghum bicolor BTx623 x IS3620C recombinant inbred lines improves sensitivity and resolution of QTL detection. G3 (Bethesda) 8:2563–2572 Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313:1929–1935 Lee J, Izzah NK, Choi BS, Joh HJ, Lee SC, Perumal S, Seo J, Ahn K, Jo EJ, Choi GJ, Nou IS, Yu Y, Yang TJ (2016) Genotyping-by-sequencing map permits identification of clubroot resistance QTLs and revision of the reference genome assembly in cabbage ( Brassica oleracea L). DNA Res 23:29–41 Li H (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv: Genomics 00:1–3 Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079 Li N, Xu R, Li Y (2019a) Molecular networks of seed size control in plants. Annu Rev Plant Biol 70:435–463 Li N, Shi J, Wang X, Liu G, Wang H (2014) A combined linkage and regional association mapping validation and fine mapping of two major pleiotropic QTLs for seed weight and silique length in rapeseed ( Brassica napus L). BMC Plant Biol 14:114 Li N, Peng W, Shi J, Wang X, Liu G, Wang H (2015) The natural variation of seed weight is mainly controlled by maternal genotype in rapeseed ( Brassica napus L). PLoS ONE 10:e0125360 Li N, Song D, Peng W, Zhan J, Shi J, Wang X, Liu G, Wang H (2019b) Maternal control of seed weight in rapeseed ( Brassica napus L.): the causal link between the size of pod (mother, source) and seed (offspring, sink). Plant Biotechnol J 17:736–749 Li S, Liu Y, Zheng L, Chen L, Li N, Corke F, Lu Y, Fu X, Zhu Z, Bevan MW, Li Y (2012) The plant-specific G protein gamma subunit AGG3 influences organ size and shape in Arabidopsis thaliana . New Phytol 194:690–703 Li S, Gao F, Xie K, Zeng X, Cao Y, Zeng J, He Z, Ren Y, Li W, Deng Q, Wang S, Zheng A, Zhu J, Liu H, Wang L, Li P (2016) The OsmiR396c-OsGRF4-OsGIF1 regulatory module determines grain size and yield in rice. Plant Biotechnol J 14:2134–2146 Liu J, Hua W, Hu Z, Yang H, Zhang L, Li R, Deng L, Sun X, Wang X, Wang H (2015) Natural variation in ARF18 gene simultaneously affects seed weight and silique length in polyploid rapeseed. Proc Natl Acad Sci USA 112:E5123–5132 Liu Q, Wang Y, Fu Y, Du L, Zhang Y, Wang Q, Sun R, Ai N, Feng G, Li C (2023) Genetic dissection of lint percentage in short-season cotton using combined QTL mapping and RNA-seq. Theor Appl Genet Liu X, Teng Z, Wang J, Wu T, Zhang Z, Deng X, Fang X, Tan Z, Ali I, Liu D, Zhang J, Liu D, Liu F, Zhang Z (2017) Enriching an intraspecific genetic map and identifying QTL for fiber quality and yield component traits across multiple environments in upland cotton ( Gossypium hirsutum L). Mol Genet Genomics 292:1281–1306 Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 25:402–408 Luo Z, Wang M, Long Y, Huang Y, Shi L, Zhang C, Liu X, Fitt BDL, Xiang J, Mason AS, Snowdon RJ, Liu P, Meng J, Zou J (2017) Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example. Theor Appl Genet 130:1569–1585 Miller C, Wells R, McKenzie N, Trick M, Ball J, Fatihi A, Dubreucq B, Chardot T, Lepiniec L, Bevan MW (2019) Variation in expression of the HECT E3 ligase UPL3 modulates LEC2 levels, seed size, and crop yields in Brassica napus . Plant Cell 31:2370–2385 Mu J, Tan H, Hong S, Liang Y, Zuo J (2013) Arabidopsis transcription factor genes NF-YA1 , 5 , 6 , and 9 play redundant roles in male gametogenesis, embryogenesis, and seed development. Mol Plant 6:188–201 Pal L, Sandhu SK, Bhatia D, Sethi S (2021) Genome-wide association study for candidate genes controlling seed yield and its components in rapeseed ( Brassica napus subsp. napus ). Physiol Mol Biol Plants 27:1933–1951 Pei W, Song J, Wang W, Ma J, Jia B, Wu L, Wu M, Chen Q, Qin Q, Zhu H, Hu C, Lei H, Gao X, Hu H, Zhang Y, Zhang J, Yu J, Qu Y (2021) Quantitative trait locus analysis and identification of candidate genes for micronaire in an interspecific backcross inbred line population of Gossypium hirsutum x Gossypium barbadense . Front Plant Sci 12:763016 Pidkowich MS, Nguyen HT, Heilmann I, Ischebeck T, Shanklin J (2007) Modulating seed beta-ketoacyl-acyl carrier protein synthase II level converts the composition of a temperate seed oil to that of a palm-like tropical oil. Proc Natl Acad Sci USA 104:4742–4747 Quijada PA, Udall JA, Lambert B, Osborn TC (2006) Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed ( Brassica napus L.): 1. Identification of genomic regions from winter germplasm. Theor Appl Genet 113:549–561 Raboanatahiry N, Chao H, Dalin H, Pu S, Yan W, Yu L, Wang B, Li M (2018) QTL alignment for seed yield and yield related traits in Brassica napus . Front Plant Sci 9:1127 Ruprecht C, Vaid N, Proost S, Persson S, Mutwil M (2017) Beyond genomics: Studying evolution with gene oexpression networks. Trends Plant Sci 22:298–307 Shen W, Qin P, Yan M, Li B, Wu Z, Wen J, Yi B, Ma C, Shen J, Fu T, Tu J (2019) Fine mapping of a silique length- and seed weight-related gene in Brassica napus . Theor Appl Genet 132:2985–2996 Shi CL, Dong NQ, Guo T, Ye WW, Shan JX, Lin HX (2020) A quantitative trait locus GW6 controls rice grain size and yield through the gibberellin pathway. Plant J 103:1174–1188 Shi L, Song J, Guo C, Wang B, Guan Z, Yang P, Chen X, Zhang Q, King GJ, Wang J, Liu K (2019) A CACTA-like transposable element in the upstream region of BnaA9.CYP78A9 acts as an enhancer to increase silique length and seed weight in rapeseed. Plant J 98:524–539 Shirakawa M, Ueda H, Shimada T, Nishiyama C, Hara-Nishimura I (2009) Vacuolar SNAREs function in the formation of the leaf vascular network by regulating auxin distribution. Plant Cell Physiol 50:1319–1328 Si Z, Jin S, Chen J, Wang S, Fang L, Zhu X, Zhang T, Hu Y (2022) Construction of a high-density genetic map and identification of QTLs related to agronomic and physiological traits in an interspecific ( Gossypium hirsutum × Gossypium barbadense ) F 2 population. BMC Genomics 23:307 Song JM, Guan Z, Hu J, Guo C, Yang Z, Wang S, Liu D, Wang B, Lu S, Zhou R, Xie WZ, Cheng Y, Zhang Y, Liu K, Yang QY, Chen LL, Guo L (2020) Eight high-quality genomes reveal pan-genome architecture and ecotype differentiation of Brassica napus . Nat Plants 6:34–45 Sun LJ, Wang XD, Yu KJ, Li WJ, Peng Q, Chen F, Zhang W, Fu SX, Xiong DQ, Chu P, Guan RZ, Zhang JF (2018) Mapping of QTLs controlling seed weight and seed-shape traits in Brassica napus L. using a high-density SNP map. Euphytica 214:1 Sun P, Zhang W, Wang Y, He Q, Shu F, Liu H, Wang J, Wang J, Yuan L, Deng H (2016) OsGRF4 controls grain shape, panicle length and seed shattering in rice. J Integr Plant Biol 58:836–847 Tan Z, Han X, Dai C, Lu S, He H, Yao X, Chen P, Yang C, Zhao L, Yang QY, Zou J, Wen J, Hong D, Liu C, Ge X, Fan C, Yi B, Zhang C, Ma C, Liu K, Shen J, Tu J, Yang G, Fu T, Guo L, Zhao H (2024) Functional genomics of Brassica napus : Progresses, challenges, and perspectives. J Integr Plant Biol 66:484–509 Tang SY, Teng ZH, Zhai TF, Fang XM, Liu F, Liu DJ, Zhang J, Liu DX, Wang SF, Zhang K, Shao QS, Tan ZY, Paterson AH, Zhang ZS (2015) Construction of genetic map and QTL analysis of fiber quality traits for upland cotton ( Gossypium hirsutum L). Euphytica 201:195–213 Udall JA, Quijada PA, Lambert B, Osborn TC (2006) Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed ( Brassica napus L.): 2. Identification of alleles from unadapted germplasm. Theor Appl Genet 113:597–609 Wang H, Yan M, Xiong M, Wang P, Liu Y, Xin Q, Wan L, Yang G, Hong D (2020) Genetic dissection of thousand-seed weight and fine mapping of cqSW.A03-2 via linkage and association analysis in rapeseed ( Brassica napus L). Theor Appl Genet 133:1321–1335 Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38:e164 Wang S, Basten C, Zeng Z (2012) Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC. ( http://statgen.ncsu.edu/qtlcart/WQTLCart.htm ) Wang X, Chen L, Wang A, Wang H, Tian J, Zhao X, Chao H, Zhao Y, Zhao W, Xiang J, Gan J, Li M (2016) Quantitative trait loci analysis and genome-wide comparison for silique related traits in Brassica napus . BMC Plant Biol 16:71 Xiao YG, Sun QB, Kang XJ, Chen CB, Ni M (2016) SHORT HYPOCOTYL UNDER BLUE1 or HAIKU2 mixepression alters canola and Arabidopsis seed development. New Phytol 209:636–649 Xie W, Feng Q, Yu H, Huang X, Zhao Q, Xing Y, Yu S, Han B, Zhang Q (2010) Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing. Proc Natl Acad Sci USA 107:10578–10583 Xin S, Dong H, Cui Y, Liu Y, Tian G, Deng N, Wan H, Liu Z, Li X, Qian W (2023) Identification of a candidate QTG for seed number per silique by integrating QTL mapping and RNA-seq in Brassica napus L. Crop J 11:189–197 Xin S, Dong H, Yang L, Huang D, Zheng F, Cui Y, Wu S, Liao J, He Y, Wan H, Liu Z, Li X, Qian W (2021) Both overlapping and independent loci underlie seed number per pod and seed weight in Brassica napus by comparative quantitative trait loci analysis. Mol Breed 41:41 Yamamoto A, Kagaya Y, Toyoshima R, Kagaya M, Takeda S, Hattori T (2009) Arabidopsis NF-YB subunits LEC1 and LEC1-LIKE activate transcription by interacting with seed-specific ABRE-binding factors. Plant J 58:843–856 Yang P, Shu C, Chen L, Xu J, Wu J, Liu K (2012) Identification of a major QTL for silique length and seed weight in oilseed rape ( Brassica napus L). Theor Appl Genet 125:285–296 Yang P, Sun X, Liu X, Wang W, Hao Y, Chen L, Liu J, He H, Zhang T, Bao W, Tang Y, He X, Ji M, Guo K, Liu D, Teng Z, Liu D, Zhang J, Zhang Z (2022) Identification of candidate genes for lint percentage and fiber quality through QTL mapping and transcriptome analysis in an allotetraploid interspecific cotton CSSLs population. Front Plant Sci 13:882051 Yang Y, Shen Y, Li S, Ge X, Li Z (2017) High density linkage map construction and QTL detection for three silique-related traits in Orychophragmus violaceus derived Brassica napus population. Front Plant Sci 8:1512 Yang Z, Wang S, Wei L, Huang Y, Liu D, Jia Y, Luo C, Lin Y, Liang C, Hu Y, Dai C, Guo L, Zhou Y, Yang QY (2023) BnIR: A multi-omics database with various tools for Brassica napus research and breeding. Mol Plant 16:775–789 Yepuri V, Jalali S, Mudunuri V, Pothakani S, Kancharla N, Arockiasamy S (2022) Genotyping by sequencing-based linkage map construction and identification of quantitative trait loci for yield-related traits and oil content in Jatropha (Jatropha curcas L). Mol Biol Rep 49:4293–4306 Yu F, Zhang Y, Wang J, Chen Q, Karim MM, Gossen BD, Peng G (2021) Identification of two major QTLs in Brassica napus lines with introgressed clubroot resistance from turnip cultivar ECD01. Front Plant Sci 12:785989 Zhang C, Gong R, Zhong H, Dai C, Zhang R, Dong J, Li Y, Liu S, Hu J (2023a) Integrated multi-locus genome-wide association studies and transcriptome analysis for seed yield and yield-related traits in Brassica napus . Front Plant Sci 14:1153000 Zhang J, Zhang X, Liu X, Pai Q, Wang Y, Wu X (2023b) Molecular network for regulation of seed size in plants. Int J Mol Sci 24 Zhang L, Yang G, Liu P, Hong D, Li S, He Q (2011) Genetic and correlation analysis of silique-traits in Brassica napus L. by quantitative trait locus mapping. Theor Appl Genet 122:21–31 Zhang X, Huang Q, Wang P, Liu F, Luo M, Li X, Wang Z, Wan L, Yang G, Hong D (2021) A 24,482-bp deletion is associated with increased seed weight in Brassica napus L. Theor Appl Genet 134:2653–2669 Zhao W, Wang X, Wang H, Tian J, Li B, Chen L, Chao H, Long Y, Xiang J, Gan J, Liang W, Li M (2016) Genome-wide identification of QTL for seed yield and yield-related traits and construction of a high-density consensus map for QTL comparison in Brassica napus . Front Plant Sci 7:17 Tables Table 1 Phenotypic analysis of thousand seed weight in the RIL population Year Population Number Min Max Mean SD CV (%) SK K 2020 F 2:4 174 3.27 7.10 5.25 0.96 18.36 -0.22 -0.97 80 3.28 7.10 5.15 1.10 21.42 -0.02 -1.37 2021 F 2:5 174 3.09 8.01 5.29 0.89 16.87 0.27 -0.04 80 3.09 8.01 5.37 1.22 22.65 0.09 -1.26 2022 F 2:6 REP1 174 3.59 8.15 5.51 0.96 17.35 0.18 -0.73 80 3.59 8.15 5.51 1.15 20.80 0.16 -1.08 2022 F 2:6 REP2 174 3.25 8.28 5.48 0.98 17.87 0.19 -0.40 80 3.25 8.28 5.47 1.19 21.83 0.19 -0.89 Table 2 Detected QTLs for thousand seed weight by using the bin map Year RILs population QTL name Chromosome Position (cM) Marker LOD score Additive R 2 (%) Genetic interval (cM) Physical interval on ZS11.v0 (Mb) 2020 F 2:4 qSW-A03-F2:4 ★ A03 54.91 c03b059 10.44 0.70 31.57 53.1–56.5 18.687–19.731 qSW-C07-F2:4-3# C07 19.81 c17b025 5.27 0.40 12.15 16.4–21.3 34.329–36.221 qSW-C07-F2:4-1 C07 1.21 c17b003 3.73 0.36 8.96 0.8–1.8 5.012–9.050 qSW-C07-F2:4-2▲ C07 10.81 c17b011 6.70 0.46 14.88 6.5–12.6 26.654–29.282 2021 F 2:5 qSW-A03-F2:5 ★ A03 54.31 c03b058 15.95 0.95 46.56 53.4–58.0 18.687–27.468 qSW-C07-F2:5# C07 19.81 c17b025 4.65 0.38 8.79 18.2–22.2 34.751–37.426 2022 F 2:6 REP1 qSW-A03-F2:6REP1 ★ A03 54.91 c03b059 9.93 0.71 29.99 53.5–56.6 18.687–19.731 qSW-C07-F2:6REP1-2# C07 21.01 c17b027 4.25 0.37 9.61 20.4–25.4 35.805–42.536 qSW-C09-F2:6REP1* C09 84.31 c19b088 5.36 0.44 13.98 76.9–85.6 65.459–66.106 qSW-C04-F2:6REP1 C04 58.51 c14b039 3.49 -0.33 7.85 48.8–62.1 55.214–59.825 qSW-C07-F2:6REP1-1 C07 26.11 c17b035 3.03 0.32 7.09 25.4–28.8 42.539–45.080 2022 F 2:6 REP2 qSW-A03-F2:6REP2 ★ A03 54.31 c03b058 9.65 0.75 30.08 52.8–56.3 18.687–19.731 qSW-C07-F2:6REP2-2# C07 19.21 c17b024 5.04 0.43 12.05 16.8–22.7 34.329–37.426 qSW-C09-F2:6REP2* C09 84.31 c19b088 5.62 0.45 13.67 82.4–88.0 65.658–66.461 qSW-C07-F2:6REP2-1▲ C07 10.81 c17b011 4.08 0.41 10.02 5.5–14.5 26.654–30.738 Overlapped QTLs were marked with different symbols ★, #, * and ▲. Table 3 Overlapped QTLs for thousand seed weight in the RIL population Overlapped QTL Chromosome LOD score Additive R 2 (%) Genetic interval (cM) Physical interval on ZS11.v0 (Mb) RIL population qSW-A03 A03 9.65–15.96 0.70–0.95 29.99–46.56 53.5–56.3 18.687–19.731 F 2:4 , F 2:5 , F 2:6 REP1, F 2:6 REP2 qSW-C07-1 C07 4.25–5.27 0.37–0.43 8.79–12.15 20.4–21.3 35.805–36.221 F 2:4 , F 2:5 , F 2:6 REP1, F 2:6 REP2 qSW-C07-2 C07 4.08–6.70 0.41–0.46 10.02–14.88 6.5–12.6 26.654–29.282 F 2:4 , F 2:6 REP2 qSW-C09 C09 5.36–5.62 0.44–0.45 13.67–13.98 82.4–85.6 65.658–66.106 F 2:6 REP1, F 2:6 REP2 Table 4 Annotated genes in the 59-kb interval of the major QTL qSW-A03 Gene ID on ZS11.v0 Genomic position on ZS11.v0 ( bp) Function AtCode Darmor ID BnaA03G0361100ZS A03: 19194661..19199336 Transcription termination factor 2, Ttf2 AT3G20010 BnaA03g35550D BnaA03G0361200ZS A03: 19200647..19208780 T-complex protein 1 subunit alpha, CCT1 AT3G20050 BnaA03g35560D BnaA03G0361300ZS A03: 19227309..19228070 3-hydroxyacyl-CoA dehydrogenase AT3G15290 - BnaA03G0361400ZS A03: 19231696..19236950 Kinesin-like protein KIN12B, KIN12B AT3G20150 BnaA03g35600D BnaA03G0361500ZS A03: 19240341..19241408 Geranylgeranyl pyrophosphate synthase 10 AT3G20160 BnaA03g35610D BnaA03G0361600ZS A03: 19242420..19243850 U-box domain-containing protein 4-like, PUB4 AT3G20170 BnaA03g35620D BnaA03G0361700ZS A03: 19244019..19244557 Copper transport protein family AT3G20180 BnaC05g31900D BnaA03G0361800ZS A03: 19244539..19247146 Pollen receptor-like kinase 4, PRK4 AT3G20190 BnaA03g35630D BnaA03G0361900ZS A03: 19248058..19249588 Mitochondrial carrier protein, BTL1 AT3G20240 BnaA03g35640D BnaA03G0362000ZS A03: 19249704..19253745 Pumilio-family RNA binding repeat, APUM5 AT3G20250 BnaA03g35650D BnaA03G0362100ZS A03: 19254632..19256546 Protein of unknown function (DUF1666) AT3G20260 BnaA03g35660D Supplementary Files QTLqSWA03JiangyuMengSupplementaryfigures.docx QTLqSWA03JiangyuMengSupplementarytables.xlsx Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Major revisions 26 Nov, 2024 Reviewers agreed at journal 04 Nov, 2024 Reviewers invited by journal 04 Nov, 2024 Editor assigned by journal 16 Oct, 2024 First submitted to journal 15 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5271995","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374066168,"identity":"73d396a3-7a9f-47d5-b727-6b7ca2aed901","order_by":0,"name":"Jiangyu Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACNv7GhgMfftjYsTEzH3yQUFFDWAufxOGDD2f2pCXzs7clGzw4c4ywFjmGtGRjHrZDjDN7zphJPmxhJsJhDGfMJHh4DjAb3EhLq0hsYGPgb+9OwK+FucdMQsLiDp/BjeRjNxJ3yDBInDm7gbAtBjzPwLbcSDzDxmAgkUtIS46ZRALbYcYNN3LMChLbmInRkpZscACoBeR9BuK0gAK5ERrIEglnjvEQ9It8f2PD4T/QqPz4o6JGjr+9F78WDMBDmvJRMApGwSgYBVgBALGSTgdsjjyZAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0006-4855-6501","institution":"Southwest University","correspondingAuthor":true,"prefix":"","firstName":"Jiangyu","middleName":"","lastName":"Meng","suffix":""},{"id":374066169,"identity":"aa2e8721-f3b0-4487-a742-5b9a5bbd84ca","order_by":1,"name":"Dingxue Hu","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Dingxue","middleName":"","lastName":"Hu","suffix":""},{"id":374066170,"identity":"aead2aff-6e10-4092-8d20-c19dbd06526b","order_by":2,"name":"Bin Wang","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""},{"id":374066171,"identity":"df4ee8bb-9e20-4659-9034-fe78b82fca26","order_by":3,"name":"Yuelin Zhu","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Yuelin","middleName":"","lastName":"Zhu","suffix":""},{"id":374066172,"identity":"c7cb71ee-0009-4884-87d8-2793462a17a7","order_by":4,"name":"Chunyan Lu","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Lu","suffix":""},{"id":374066173,"identity":"b1a44a0c-36b5-4071-8c4d-9e83edc0375d","order_by":5,"name":"Yan Deng","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Deng","suffix":""},{"id":374066174,"identity":"98f29403-8d11-4454-b9ab-6b290e105dd0","order_by":6,"name":"Huiying Cai","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Huiying","middleName":"","lastName":"Cai","suffix":""},{"id":374066175,"identity":"00e926dc-ada0-4d70-a894-eff64398385b","order_by":7,"name":"Baohua Wang","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Baohua","middleName":"","lastName":"Wang","suffix":""},{"id":374066176,"identity":"0c2d05cd-c45b-4674-a5d6-0ac5d7dd41ad","order_by":8,"name":"Yajun He","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Yajun","middleName":"","lastName":"He","suffix":""},{"id":374066177,"identity":"e75e5ddf-3811-4016-975f-7e33b9d512a0","order_by":9,"name":"Wei Qian","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2024-10-16 02:28:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5271995/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5271995/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-025-04866-3","type":"published","date":"2025-03-17T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69264327,"identity":"8e62527d-385c-4223-b8d1-e42a62ae4e55","added_by":"auto","created_at":"2024-11-18 14:19:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":248887,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotype analysis of seeds in parents\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/951275438f3ecdb421c0d3d6.png"},{"id":69265415,"identity":"72bc8c04-d5b9-44ba-8e1b-fbdd9791b4d7","added_by":"auto","created_at":"2024-11-18 14:27:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121179,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic distributions and correlations of thousand seed weight in the RIL population\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/61cfb262c06afd2a52e1d958.png"},{"id":69264337,"identity":"6e4748da-e429-4fcc-b025-e9788b41e4d2","added_by":"auto","created_at":"2024-11-18 14:20:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":373222,"visible":true,"origin":"","legend":"\u003cp\u003eSeed development process of parents.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/24d66b8110ad30468e065d92.png"},{"id":69264332,"identity":"2a8c1429-cab2-4084-8307-45efa678b088","added_by":"auto","created_at":"2024-11-18 14:19:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":339638,"visible":true,"origin":"","legend":"\u003cp\u003eHistocytological analysis of seeds in parents\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/9579e8d4de95b6e10cffb9c9.png"},{"id":69264336,"identity":"fb41397e-b17b-4026-9c91-9f2a2f468c2b","added_by":"auto","created_at":"2024-11-18 14:19:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90043,"visible":true,"origin":"","legend":"\u003cp\u003eThe high-density bin genetic map\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/47e0b621b4ac99480a5b55a3.png"},{"id":69264335,"identity":"6a7e4d93-8645-46f3-87e0-d64aa50bf44c","added_by":"auto","created_at":"2024-11-18 14:19:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":174385,"visible":true,"origin":"","legend":"\u003cp\u003eDetected QTLs for thousand seed weight using the bin map\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/5dc5d38cf5be55938379cfbb.png"},{"id":69265416,"identity":"f24727e2-58d0-495f-a234-e4ed9e4ca575","added_by":"auto","created_at":"2024-11-18 14:27:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":111222,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and narrowing the interval of the major QTL \u003cem\u003eqSW-A03\u003c/em\u003e in the RIL population\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/a1015f190c97f03ba9d93f1b.png"},{"id":69264328,"identity":"09c9d9cd-1039-4617-ad34-1708442c10d2","added_by":"auto","created_at":"2024-11-18 14:19:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":65522,"visible":true,"origin":"","legend":"\u003cp\u003eExpression level differences of genes in the QTL \u003cem\u003eqSW-A03\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/a547ba1512c812e4062fab7b.png"},{"id":69265723,"identity":"06a8268e-0754-497e-9d8d-22a80b5273f5","added_by":"auto","created_at":"2024-11-18 14:35:55","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":779918,"visible":true,"origin":"","legend":"\u003cp\u003eGenes coexpressed with \u003cem\u003eBnaDUF1666\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/da21f19ad888fae93b9d6077.png"},{"id":79120658,"identity":"f3f85543-f410-4809-b088-9a6f7936eef8","added_by":"auto","created_at":"2025-03-24 16:10:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3537586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/11e08e34-7437-4294-a984-4ffb6f5226a6.pdf"},{"id":69264334,"identity":"dd9f9168-f172-40d8-bce2-f97e5ee2030c","added_by":"auto","created_at":"2024-11-18 14:19:55","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":651189,"visible":true,"origin":"","legend":"","description":"","filename":"QTLqSWA03JiangyuMengSupplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/c953fdbf37e0ad1aa24c7f55.docx"},{"id":69264330,"identity":"28c0abb8-9887-4ff8-992d-6b5e49dcb366","added_by":"auto","created_at":"2024-11-18 14:19:55","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":608957,"visible":true,"origin":"","legend":"","description":"","filename":"QTLqSWA03JiangyuMengSupplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5271995/v1/7f64d2d6c4f7e9e87e125c38.xlsx"}],"financialInterests":"","formattedTitle":"Fine mapping and candidate gene analysis of the major QTL qSW-A03 for seed weight in Brassica napus","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eBrassica napus\u003c/em\u003e (AACC, 2n\u0026thinsp;=\u0026thinsp;38), commonly known as rapeseed or canola, is the second-largest oilseed crop and contributes more than 13% of the stable supply of edible vegetable oil worldwide (Tan et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Owing to the declining acreage and increasing demand for rapeseed production, improving yield has always been an important goal of rapeseed breeding (Jiao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). As one of the three direct components of rapeseed yield, seed weight is a determinant factor for improving rapeseed productivity (Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, dissecting the genetic basis of seed weight will deepen our understanding of seed development and accelerate the breeding process of high-yield varieties of rapeseed.\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eBrassica napus\u003c/em\u003e, the process of seed development is similar to that in \u003cem\u003eArabidopsis.\u003c/em\u003e A large cavity forms as endosperm cells proliferate at the early stage of seed development, and then the embryo begins to develop as the endosperm degenerates to provide nutrient substances for embryo development until a single layer of endosperm cells remains (Xiao et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). At maturity, rapeseed primarily consists of the embryo, which develops from the fertilized egg, and the seed coat, which develops from the integument. The fully developed embryo consists of four parts: the plumule, hypocotyl, radicle and cotyledon. The cotyledon occupies the main volume of mature seeds in rapeseed. The two large cotyledons of rapeseed are important storage organs that are rich in lipids and proteins (Dong et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Because the integuments limit the space within which the embryo and endosperm develop, the seed coat determines the final size (capacity) and weight of mature seeds via cell expansion and/or proliferation (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeed weight is a typical quantitative trait regulated by multiple genes. Quantitative trait locus (QTL) mapping and genome-wide association study (GWAS) are two effective means to uncover the complex genetic mechanism of seed weight in \u003cem\u003eBrassica napus\u003c/em\u003e (Khan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In recent decades, at least 168 QTLs for seed weight- and seed shape-related traits, including seed length, seed width, length-width ratio, seed cross-section area, seed circumference, seed diameter and seed roundness, have been identified in various populations of \u003cem\u003eBrassica napus\u003c/em\u003e (Dong et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Raboanatahiry et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, only two causal genes, \u003cem\u003eBnaA9.CYP78A9\u003c/em\u003e and \u003cem\u003eBnaA9.ARF18\u003c/em\u003e, have been cloned via QTL fine-mapping in rapeseed (Liu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, the HECT E3 ligase gene \u003cem\u003eBnaUPL3. C03\u003c/em\u003e was shown to vary in the promoter region via GWAS, reducing its expression to increase LEC2 protein levels to regulate seed weight (Miller et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These results suggest that there are abundant undiscovered gene resources that increase seed weight in rapeseed (Zhang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is necessary to fine map QTLs and clone their causal genes for seed weight in \u003cem\u003eBrassica napus\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eA genetic linkage map constructed through genotyping-by-sequencing (GBS) is a cost-effective, time-saving and powerful tool for mapping QTLs and for molecular breeding (Yepuri et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has been widely used for QTL mapping in different crop species, including clubroot resistance in \u003cem\u003eBrassica oleracea\u003c/em\u003e (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), fiber traits in cotton (Fan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pei et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), plant height and flowering time in \u003cem\u003eSorghum bicolor\u003c/em\u003e (Kong et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and productivity and quality traits in peanut (Jadhav et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In \u003cem\u003eBrassica napus\u003c/em\u003e, GBS technology has also been used to construct high-density genetic maps for QTL mapping, such as seed quality traits (Gacek et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and clubroot resistance (Yu et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we constructed a genetic linkage map via GBS via a recombinant inbred line (RIL) population derived from the hybridization and propagation of rape DL704, which has relatively large seeds, and rape cultivar ZS11. The major QTL of seed weight, \u003cem\u003eqSW-A03\u003c/em\u003e, was identified and finely mapped to a 59-kb region. By combining gene structural variation analysis, gene transcription level analysis, and gene coexpression network analysis, candidate genes encoding a protein of unknown function were identified. These results provide meaningful information concerning the molecular regulatory mechanism of seed weight and the cultivation of high-yield varieties of rapeseed.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials, field experiments, and trait evaluation\u003c/h2\u003e \u003cp\u003eThe mapping population consisted of 174 RILs derived from a cross between the cultivar ZS11 bred by the Institute of Oil Crops, Chinese Academy of Agricultural Sciences, and the synthetic rapeseed DL704 bred from distant hybridization with ZS9 (\u003cem\u003eB. napus\u003c/em\u003e), \u003cem\u003eB. oleracea\u003c/em\u003e and \u003cem\u003eB. rape\u003c/em\u003e in the early breeding work of our laboratory. The RIL populations (F\u003csub\u003e2:4\u003c/sub\u003e, F\u003csub\u003e2:5\u003c/sub\u003e, F\u003csub\u003e2:6\u003c/sub\u003eREP1 and F\u003csub\u003e2:6\u003c/sub\u003eREP2) were generated from the F\u003csub\u003e2\u003c/sub\u003e population in 2018 via the single-seed descent (SSD) method.\u003c/p\u003e \u003cp\u003eAll the plant materials used were planted in the experimental field of Southwest University, Chongqing, China (29\u0026deg;33\u0026prime;N, 106\u0026deg;34\u0026prime;E). Every line was planted with a randomized complete block design. Each plot consisted of 24 plants, with 30 cm between rows and 25 cm within rows. The F\u003csub\u003e2:4\u003c/sub\u003e and F\u003csub\u003e2:5\u003c/sub\u003e populations were planted in 2020 and 2021, respectively. In 2022, the F\u003csub\u003e2:6\u003c/sub\u003e population was planted with two replications, F\u003csub\u003e2:6\u003c/sub\u003eREP1 and F\u003csub\u003e2:6\u003c/sub\u003eREP2. Field management followed standard agricultural practices. At anthesis, 3 or 4 plants with the same growth trend were selected randomly from each line for reproduction.\u003c/p\u003e \u003cp\u003eAt maturity, 50 well-developed siliques were harvested from the bottom of the main inflorescence of a single plant to determine the thousand-seed weight (TSW, g) from dried seeds. Five individual plants, with the same growth status and without disease and without selfing, were randomly selected from each line to detect TSW. The mean of three measurements of TSW was taken as the phenotypic value of the individual plants, and the mean TSW of 5 individual plants was taken as the final phenotypic value of each line. In addition, the TSWs of DL704 and ZS11 were measured from 2015 to 2022.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical analysis of phenotypic data\u003c/h3\u003e\n\u003cp\u003eData collection, analysis and visualization were performed with WPS Office (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wps.cn\u003c/span\u003e\u003cspan address=\"https://www.wps.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), SPSS 19.0 (Chicago, Illinois, USA), R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GraphPad Prism 8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.graphpad.com/\u003c/span\u003e\u003cspan address=\"https://www.graphpad.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For the RIL populations, the statistical values included the mean, maximum (Max), minimum (Min), standard deviation (SD), skewness (SK), kurtosis (K) and coefficient of variation (CV) with SPSS 19.0, and the phenotypic frequency distributions and correlations were analyzed with the rcorr function in the Hmis package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rdocumentation.org/packages/Hmisc/versions/5.1-3\u003c/span\u003e\u003cspan address=\"https://www.rdocumentation.org/packages/Hmisc/versions/5.1-3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the PerformanceAnalytics package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rdocumentation.org/packages/PerformanceAnalytics/versions/1.5.3\u003c/span\u003e\u003cspan address=\"https://www.rdocumentation.org/packages/PerformanceAnalytics/versions/1.5.3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in R software.\u003c/p\u003e\n\u003ch3\u003eMorphological and cellular analysis\u003c/h3\u003e\n\u003cp\u003eTo observe the seed development characteristics, the flowering dates were marked with colorful wools on the bloomed flowers at the bottom of the main inflorescences of DL704 and ZS11. The sizes of the ovaries at 0 days after pollination (DAP) and the ovules at 7, 14, 21, 28, 35 and 42 DAP were observed. At each stage, 15\u0026ndash;20 ovules from DL704 and ZS11 were measured. Pictures were taken under an OLYMPLUS MVX10 microscope. ImageJ (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imagej.net/\u003c/span\u003e\u003cspan address=\"https://imagej.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to count seed sizes at different DAPs. The WPS Office (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wps.cn/\u003c/span\u003e\u003cspan address=\"https://www.wps.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GraphPad Prism 8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.graphpad.com/\u003c/span\u003e\u003cspan address=\"https://www.graphpad.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to analyze differences in the projected areas of ovaries or seeds, and the ovaries or seeds of ZS11 were used as controls.\u003c/p\u003e \u003cp\u003eAt 42 DAP, the seeds were close to maturity. The seed coats of DL704 and ZS11 at 42 DAP were collected to analyze cytological differences. Cotyledons were sliced with a Leica CM1850 freezing microtome after being dissected from seeds of DL704 and ZS11. The whole seed coats and the slices of cotyledons were placed in a drop of clearing solution [30 mL H\u003csub\u003e2\u003c/sub\u003eO, 80 g chloral hydrate (C8383; Sigma‒Aldrich), and 10 mL 100% glycerol (G6279; Sigma‒Aldrich)] for 10 and 30 minutes, respectively (Fang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The cleared cells were photographed under an OLYMPLUS MVX10 microscope and Nikon ECLIPSE Ci. The mean cell area of at least 10 cells was determined on the basis of 10 individuals via ImageJ (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imagej.net/\u003c/span\u003e\u003cspan address=\"https://imagej.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The cell numbers in the region of the outer seed coat and the outer cotyledon were determined.\u003c/p\u003e\n\u003ch3\u003eGenetic map construction\u003c/h3\u003e\n\u003cp\u003eGenotyping of the RIL population was performed as described in a recently published study (Agyenim-Boateng et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Eighty lines, including 40 lines with the highest and stable TSW values and 40 lines with the lowest and stable TSW values in the RIL population, were selected to construct a high-density bin-based genetic linkage map. Genomic DNA was extracted from fresh young leaves of ZS11, DL704, and lines in the RIL population via the CTAB method (Yang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). DNA quality determination, library construction, and quality assessment were carried out by GENOSEQ (Wuhan, Hubei, China) following standard Illumina operating procedures. The concentration and quality of the extracted DNA were determined via a NanoDrop2000 Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). After the connector sequences, low-quality bases and reads less than 50 bp in the raw data were filtered by fastp 0.23.0 (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the clean data were compared to the reference genome ZS11.v0 of \u003cem\u003eB. napus\u003c/em\u003e (Song et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with the MEM algorithm via BWA 0.7.15-r1140 (Li et al., 2013). Then, SAMtools 1.3.1 (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) was used to compare and sort the results and remove PCR duplication. Next, the haplotype caller module in GATK 3.7 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gatk.broadinstitute.org/hc/en-us\u003c/span\u003e\u003cspan address=\"https://gatk.broadinstitute.org/hc/en-us\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to detect structural variation, including SNPs and InDels, and ANNOVAR 2016Feb1 (Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) was used to annotate structural variation and predict the impact of structural variation on gene function. The variations were used to construct a genetic linkage map, which was filtered such that the sequencing depths of DL704 and ZS11 were less than 10 and that of their offspring was less than 2, the percentage of genotypic deletions in their offspring was less than 25%, the frequency of smaller alleles was less than 25%, and the relative heterozygosity of their offspring was less than 25%. The adjacent markers without exchange were merged into a bin marker, which was used to calculate the recombination rate and genetic distance to construct the bin map. The genotypes of ZS11 and DL704 were marked as A and B, respectively. The bin map was constructed with the Kosambi mapping function (Xie et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The collinearity between the bin map and the reference genome was evaluated via the Pearson correlation coefficient.\u003c/p\u003e\n\u003ch3\u003eQTL analysis\u003c/h3\u003e\n\u003cp\u003eThe QTL for TSW was mapped via the composite interval mapping (CIM) procedure of WinQTLCart 2.5 software with an LOD threshold of 3.0, which was selected on the basis of 1000 permutation tests at a 95% confidence level (Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The QTL nomenclature rule was as follows: \u003cem\u003eq-trait abbreviation-chromosome number-population-QTL number\u003c/em\u003e (Tang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). QTLs were defined as the same QTL when they overlapped in a confidence interval for the same trait identified in different environments and with the same direction of additive effect (Yang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). QTLs identified in at least two generations or repeats were considered stable (Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The online tool Gbrowse Synteny (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cbi.hzau.edu.cn/bnapus/\u003c/span\u003e\u003cspan address=\"http://cbi.hzau.edu.cn/bnapus/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for the homologous alignment analysis of QTL intervals and genes among different reference genomes in \u003cem\u003eBrassica napus\u003c/em\u003e (Song et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFine mapping\u003c/h2\u003e \u003cp\u003eTo confirm and narrow down the genetic region of QTL \u003cem\u003eqSW-A03\u003c/em\u003e, InDel markers were designed near or within the QTL \u003cem\u003eqSW-A03\u003c/em\u003e interval on the basis of the accession differences in the sequencing annotations (Table S12). The primer pairs were designed with Primer Premier 5 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.premierbiosoft.com/primerdesign/\u003c/span\u003e\u003cspan address=\"http://www.premierbiosoft.com/primerdesign/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and synthesized by Tsingke Biotechnology Co., Ltd. (China). PCR was performed in a total volume of 10 \u0026micro;L, including 1 \u0026micro;L of genomic DNA (100 ng/\u0026micro;L), 5 \u0026micro;L of 2 \u0026times; Taq Mix (ABclonal, Wuhan), 0.5 \u0026micro;L of forward primer (10 \u0026micro;mol/L), 0.5 \u0026micro;L of reverse primer (10 \u0026micro;mol/L) and 3 \u0026micro;L of ddH\u003csub\u003e2\u003c/sub\u003eO. The PCR amplification conditions were 94\u0026deg;C for 5 min, followed by 35 cycles (94\u0026deg;C for 30 s, annealing at 57\u0026deg;C for 30 s, and 72\u0026deg;C for 30 s), and a final extension at 72\u0026deg;C for 5 min. The PCR products were imaged with sliver staining after being separated on 10% polyacrylamide gels.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eqRT-PCR\u003c/h3\u003e\n\u003cp\u003eSamples were obtained from DL704 and ZS11 at six stages of seed development, including ovaries at 7 DAP. Each sample was collected from the bottom of the main inflorescence of three or four parents, mixed equally in nuclease-free centrifuge tubes, and then quickly frozen in liquid nitrogen. The samples were stored at -80\u0026deg;C. High-quality RNA was extracted with a SteadyPure Plant RNA Extraction Kit (Accurate Biotechnology Co., Ltd., Changsha, Hunan, China) and reverse-transcribed with All-In-One 5\u0026times; RT MasterMix (Applied Biological Materials Inc., Vancouver, B. C., Canada). Quantitative PCR was performed with Universal SYBR qPCR Master Mix (Applied Biological Materials Inc., Vancouver, B. C., Canada) on a Bio-Rad CFX96TM Real-Time Detection System. The 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method was used to calculate the relative expression levels of genes on the basis of four technical replicates per sample (Livak et al., 2001). \u003cem\u003eBnaACTIN7\u003c/em\u003e (\u003cem\u003eBnaC02G0037200ZS\u003c/em\u003e) was used as the internal control for normalization. The sequences of primers used for qRT-PCR are shown in Table S13. Data collection, analysis and visualization were performed via the WPS Office (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wps.cn/\u003c/span\u003e\u003cspan address=\"https://www.wps.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GraphPad Prism 8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.graphpad.com/\u003c/span\u003e\u003cspan address=\"https://www.graphpad.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCoexpression analysis\u003c/h3\u003e\n\u003cp\u003eOn the basis of the high efficacy of the gene coexpression network for controlling seed weight in \u003cem\u003eBrassica napus\u003c/em\u003e constructed by our laboratory (Dong et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the genes in the region of the major QTL \u003cem\u003eqSW-A03\u003c/em\u003e were added to candidate gene sets to analyze the coexpression relationship with the seed weight-harboring genes. The guide gene sets and the set of transcriptional profiles were the same as those in a previous study (Dong et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The remaining genes in the candidate and guide gene sets were used for coexpression network analysis after the RNA-seq data were filtered by removing genes with low expression variances (\u0026lt;\u0026thinsp;1%) or maximum gene expression (RPKM\u0026thinsp;\u0026lt;\u0026thinsp;1). The absolute value of the Pearson correlation coefficient (|R|) between genes was greater than 0.8. The subsequent coexpression network was constructed via the Markov cluster (MCL) algorithm with an inflation coefficient of 2. Small MCL clusters with \u0026lt;\u0026thinsp;25 nodes were merged into a larger cluster that was topologically adjacent to or surrounded by small clusters. Gene Ontology term analysis was performed via Metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://Metascape.org\u003c/span\u003e\u003cspan address=\"https://Metascape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via orthologs of \u003cem\u003eA. thaliana\u003c/em\u003e. The online tool gene index on BnIR (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://yanglab.hzau.edu.cn/BnIR/gene_index\u003c/span\u003e\u003cspan address=\"https://yanglab.hzau.edu.cn/BnIR/gene_index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for the homologous alignment of genes among different reference genomes of \u003cem\u003eBrassica napus\u003c/em\u003e and \u003cem\u003eArabidopsis thaliana\u003c/em\u003e (Yang et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003ePhenotypic analysis of TSW in parents and RIL populations\u003c/h2\u003e\n\u003cp\u003eFor the parents, the mature seed size of DL704 was much greater than that of ZS11 (Fig. 1a-b), and the thousand seed weight (TSW) of DL704 (6.72 ± 0.44 g) was significantly (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) greater than that of ZS11 (4.41 ± 0.51 g) from 2015 to 2022 (Fig. 1c).\u003c/p\u003e\n\u003cp\u003eDescriptive statistics and phenotypic distributions and correlations of TSW for the RIL population from 2020-2022 are shown in Table\u0026nbsp;1 and Fig. 2. For the 174 lines in the RIL population across the three years, TSW exhibited broad and continuous variation with transgressive segregation, which was consistent with\u0026nbsp;a normal distribution via skewness and kurtosis tests, and the variance values were relatively large, which indicated that the RIL population was conducive to QTL mapping of TSW, which had great potential for genetic improvement (Fig. 2a and Table 1). The correlation coefficients were greater than 0.60, with a significance level of \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 among F\u003csub\u003e2:4\u003c/sub\u003e, F\u003csub\u003e2:5\u003c/sub\u003e, F\u003csub\u003e2:6\u003c/sub\u003eREP1 and F\u003csub\u003e2:6\u003c/sub\u003eREP2, indicating that loci regulating TSW can be transmitted to the offspring and expressed stably (Fig. 2a). TSW is a quantitative trait controlled by multiple genes. To map major loci, we selected 40 lines with the highest TSW values and 40 lines with the lowest TSW values by constructing a genetic map.\u0026nbsp;For these 80 lines in the RIL population, there was no significant change in phenotypic characteristics, but differences in TSW displayed a bimodal distribution with higher kurtosis values, and the correlation coefficients were greater than 0.83 among F\u003csub\u003e2:4\u003c/sub\u003e, F\u003csub\u003e2:5\u003c/sub\u003e, F\u003csub\u003e2:6\u003c/sub\u003eREP1 and F\u003csub\u003e2:6\u003c/sub\u003eREP2 (Fig. 2b and Table\u0026nbsp;1). These data implied that these selected lines were more suitable for mapping stable and reliable QTLs.\u003c/p\u003e\n\u003ch2\u003eMorphological and cytological analysis\u003c/h2\u003e\n\u003cp\u003eTo research the morphological and cytological variation in seeds, we measured the ovule sizes at different DAPs and further analyzed the differences in the sizes of the seed coats and cotyledons of the parents.\u003c/p\u003e\n\u003cp\u003eDuring seed development, the seed size of DL704 was greater than that of ZS11 at different DAPs (Fig. 3a-g). Notably, the seed size (volume) increased the fastest in both ZS11 and DL704 before 14 DAP (Fig. 3h). After 14 DAP, the speed of seed growth began to slow in both ZS11 and DL704 until the seeds grew to their final size (Fig. 3h). Therefore, the time before 14 DAP was the key time for differences in seed size between ZS11 and DL704.\u003c/p\u003e\n\u003cp\u003eWe further examined the cell size and number of seed coats and cotyledons at 42 DAP when the seeds grew close to their final size. For the seed coat, the surface area of DL704 was significantly larger than that of ZS11 (Fig. 4a, c). Compared with those of ZS11, the cell size and number of the seed coat of DL704 also significantly increased by 33.07% and 19.58%, respectively (Fig. 4b, d, e). We extended our analysis to the\u0026nbsp;cotyledons (Fig. 4f-j). The cotyledons of DL704 were also significantly larger than those of ZS11 (Fig. 4f, h). Compared with that in ZS11, the cell size of cotyledons in DL704 significantly increased by 104.14% (Fig. 4g, i), and the number of cotyledon cells in DL704 was 11.33% lower than that in ZS11 (Fig. 4j). These results suggest that the genes controlling large seeds predominantly promoted cell expansion but also influenced cell proliferation.\u003c/p\u003e\n\u003ch2\u003eBin map construction\u003c/h2\u003e\n\u003cp\u003eWe resequenced both parents with approximately 30×\u0026nbsp;sequencing depth and 80 lines with approximately 5×\u0026nbsp;sequencing depth on an Illumina HiSeq platform and produced clean data for SNPs and\u0026nbsp;InDels to develop bin markers.\u0026nbsp;The sequencing data statistics of the ZS11, DL704 and 80 lines were showed in Table S1. The GC contents of the parents and 80 lines ranged from 37.00% to 42.10%, with an average value of 39.63%. The comparison rates of the\u0026nbsp;parents\u0026nbsp;and 80 lines\u0026nbsp;ranged from 90.47% to 98.67%,\u0026nbsp;with an average value of 97.13%.\u0026nbsp;The percentage of\u0026nbsp;sequencing\u0026nbsp;coverage of the\u0026nbsp;parents was 87.41% and 78.40%, and that of the 80 lines ranged from 65.67% to 82.13%,\u0026nbsp;with an average value of 71.94%. Both the data volume and the data quality can meet the subsequent analysis requirements. By aligning the clean reads with the reference genome sequence,\u0026nbsp;we\u0026nbsp;obtained\u0026nbsp;1,094,333\u0026nbsp;SNPs and\u0026nbsp;167,953\u0026nbsp;InDels (Table S2). After alignment between the two parents and 80 lines in the RIL population, a total of\u0026nbsp;1,261,526 markers on 19 chromosomes\u0026nbsp;were used to construct the bin map, and the number of markers on each chromosome ranged from 6,562 to 158,876 (Table S3 and Table S4). The bin map covered 1,306 high-quality bins, which spanned\u0026nbsp;1588.128\u0026nbsp;cM, with an average distance of\u0026nbsp;1.648\u0026nbsp;cM between adjacent bin markers (Fig. 5, Table S3 and Table S4). The number of bins on each chromosome ranged from 12-119, and the marker interval ranged from 0.001-0.011 cM (Table S4). The recombination breakpoints were checked. The sources of the chromosome segments in each offspring were consistent with those of the parents (Fig. S1). In addition, the collinearity between the bin map and the reference genome was evaluated on the basis of the physical locations of the bin markers (Fig. S2, Table S5 and Table S6). The order of most bin markers in each linkage group was consistent with the physical map (Table S5). The Pearson correlation coefficient between the genetic and physical positions of all the chromosomes was greater than 0.86 (Table S6). The relationships between the genetic and physical maps of the 19 chromosomes were generally linear (Fig. S2). Thus, the bin map had a high level of collinearity with the physical map and sufficiently covered the reference genome.\u003c/p\u003e\n\u003ch2\u003eQTL mapping\u003c/h2\u003e\n\u003cp\u003eQTL mapping for TSW was performed on the basis of the bin map with the CIM strategy. A total of 15 QTLs for TSW were detected in the RIL populations (Table 2). They were distributed on four chromosomes, A03, C04, C07 and C09, explaining 7.09%-46.56% of the phenotypic variation. Among the 15 QTLs, 12 were detected across multiple populations or multiple years and overlapped in four intervals of three linkage groups or chromosomes (Fig. 6, Table 2). After integration, four stable QTLs were detected (Fig. 6, Table 3).\u0026nbsp;Among them,\u0026nbsp;\u003cem\u003eqSW-A03\u003c/em\u003e was detected in the F\u003csub\u003e2:4\u003c/sub\u003e, F\u003csub\u003e2:5\u003c/sub\u003e, F\u003csub\u003e2:6\u003c/sub\u003eREP1 and F\u003csub\u003e2:6\u003c/sub\u003eREP2 populations across 3 years with the highest LOD values, spanning a confidence interval of 2.8 cM from 53.5 to 56.3 cM on A03, aligning to the physical interval of 1.044Mb from 18.687 to 19.731 Mb on A03 of the reference genome ZS11.v0, and had the highest phenotypic variation, with 29.99%-46.56% (Table 3).\u0026nbsp;Therefore, \u003cem\u003eqSW-A03\u003c/em\u003e was\u0026nbsp;regarded as the major stable QTL\u0026nbsp;accounting for the large seed size of DL704.\u003c/p\u003e\n\u003ch2\u003eFine mapping of a major QTL, \u003cem\u003eqSW-A03\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eTo\u0026nbsp;narrow the genomic region of \u003cem\u003eqSW-A03\u003c/em\u003e, we developed nine codominant InDel markers (Table S12) and mapped \u003cem\u003eqSW-A03\u003c/em\u003e to 279 kb from 19.111 to 19.390 Mb on A03 of the reference genome ZS11.v0 between two InDel markers, DA03-28 and DA03-50, in the F\u003csub\u003e2:4\u003c/sub\u003e, F\u003csub\u003e2:5\u003c/sub\u003e, F\u003csub\u003e2:6\u003c/sub\u003eREP1 and F\u003csub\u003e2:6\u003c/sub\u003eREP2 populations (Fig. 7b and Table S7). In the 279-kb region, five recombinants between the\u0026nbsp;DA03-28 and DA03-50\u0026nbsp;intervals were screened and grouped into three genotypes, Class 1, Class 2 and Class 3, on the basis of the allelic composition and recombination breakpoints (Fig. 7c). The mean TSW value of each recombinant genotype was compared with that of the two parents to narrow\u0026nbsp;\u003cem\u003eqSW-A03\u003c/em\u003e.\u0026nbsp;The mean TSWs of both Class 1 and Class 2 were significantly greater than those of ZS11 and Class 3 and near to DL704. There were no other recombinant lines distinguished by the marker\u0026nbsp;DA03-206, which was located between\u0026nbsp;the markers DA03-202 and DA03-213 (Fig. 7c). On the basis of these results,\u0026nbsp;\u003cem\u003eqSW-A03\u003c/em\u003e was narrowed down to a region between DA03-202 and DA03-213, corresponding to a 59-kb region from 19.197\u0026nbsp;to\u0026nbsp;19.256 Mb on\u0026nbsp;A03 of the reference genome ZS11.v0 (Fig. 7c).\u003c/p\u003e\n\u003ch2\u003eIdentification of candidate genes\u003c/h2\u003e\n\u003cp\u003eEleven genes within the 59-kb region were identified, and their putative functions were predicted on the basis of the released rapeseed genome information (Fig. 7d and Table 4). None of the eleven genes have been previously reported to be involved in the regulation of seed weight. Therefore,\u0026nbsp;\u003cem\u003eqSW-A03\u003c/em\u003e might be a novel QTL responsible for controlling seed weight. On the basis of the resequencing data of DL704 and ZS11, 144 exonic SNPs scattered in nine genes and seven exonic InDels in five genes were identified within the 59-kb region (Table S8 and Table S9). Among the exonic SNPs and InDels, 46 nonsynonymous SNP variations within eight genes resulted in amino acid variations, and all of the exonic InDels caused frameshift mutations. In addition, we identified 51 SNPs and 25 InDels located upstream or in the 5'UTRs of nine genes. In total, structural variations in all eleven genes exist between DL704 and ZS11.\u003c/p\u003e\n\u003cp\u003eTo further identify the candidate genes, we\u0026nbsp;analyzed the transcription levels of the\u0026nbsp;eleven\u0026nbsp;genes in the parents via qRT-PCR (Fig. 8).\u0026nbsp;Given that a large cavity forms at the early stage of seed development (Xiao et al. 2016), which is the time before 14 DAP, to account for the difference in seed size between ZS11 and DL704 (Fig. 3), ovules at 7 DAP were used to analyze the transcription levels of the eleven genes. The results revealed that the Cq values of two genes, BnaA03G0361600ZS and BnaA03G0361700ZS, were greater than 35. Therefore, these two genes were regarded as not expressed by either parent. Among the other nine genes, four genes, BnaA03G0361100ZS, BnaA03G0361300ZS, BnaA03G0361800ZS and BnaA03G0361900ZS, presented no significant difference in expression between the two parents. The remaining five genes, BnaA03G0361200ZS, BnaA03G0361400ZS, BnaA03G0361500ZS, BnaA03G0362000ZS and BnaA03G0362100ZS, were expressed differently between the two parents. Four of the five genes, BnaA03G0361200ZS, BnaA03G0361500ZS, BnaA03G0362000ZS and BnaA03G0362100ZS, were expressed significantly higher in DL704 than in ZS11, and one gene, BnaA03G0361400ZSZS, was expressed significantly lower in DL704 than in ZS11. According to the gene expression analysis, these five genes were regarded as candidate genes.\u003c/p\u003e\n\u003cp\u003eThe discovery of quantitative trait genes can combine gene coexpression analysis and QTL mapping (Cui et al., 2021; Dong et al., 2022; Ruprecht et al., 2017). Given this, we attempted to identify candidate genes by using the gene\u0026nbsp;coexpression network of seed weight in \u003cem\u003eB. napus\u003c/em\u003e (Dong et al., 2022). The eleven genes in the 59-kb region were added to the candidate gene set to analyze the coexpression relationships of the genes for seed weight. Among these eleven genes, only one gene, BnaA03G0362100ZS, was included in the new\u0026nbsp;gene coexpression network\u0026nbsp;for seed weight\u0026nbsp;(Fig. 9, Table S10). BnaA03G0362100ZS encodes a protein of unknown function, DUF1666, which was named \u003cem\u003eBnaDUF1666\u003c/em\u003e. In the new gene coexpression network, \u003cem\u003eBnaDUF1666\u003c/em\u003e was coexpressed with 299 genes (Table S10). GO and KEGG analyses revealed that these 299 coexpressed genes were enriched in fatty acid biosynthesis and metabolism (FABM), seed maturation (SMa), and the cellular response to abscisic acid (ABA) stimulus\u0026nbsp;(Fig. 9, Fig. S3 and Table S11). Among the 299 coexpressed genes, 28 genes were involved mainly in embryo and endosperm development (\u003cem\u003eNF-YA5\u003c/em\u003e, \u003cem\u003eNF-YB6, PKP-ALPHA\u003c/em\u003e), seed filling (\u003cem\u003eSWEET15\u003c/em\u003e, \u003cem\u003eFAB1\u003c/em\u003e, \u003cem\u003ePKP-ALPHA\u003c/em\u003e), and cell expansion and/or proliferation (\u003cem\u003eGIF1\u003c/em\u003e, \u003cem\u003eAGG3\u003c/em\u003e, \u003cem\u003eGW6, GRF5\u003c/em\u003e)\u0026nbsp;(Cai et al., 2018; Chen et al., 2015; Das et al., 2019; Duan et al., 2015; He et al., 2017; Li et al., 2012; Li et al., 2016; Mu et al., 2013; Pidkowich et al., 2007; Shi et al., 2020; Sun et al., 2016; Yamamoto et al., 2009). These results provide insight into the dissection of \u003cem\u003eBnaDUF1666\u003c/em\u003e gene function and the underlying molecular mechanism involved in regulating seed weight.\u003c/p\u003e\n\u003cp\u003eBy combining gene structural variation analysis, gene transcription level analysis, and gene coexpression network analysis, the candidate gene \u003cem\u003eBnaDUF1666\u003c/em\u003e, which controlled rapeseed seed weight and size, was identified.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSeed size or weight is a key trait that affects agricultural yield. QTL mapping is an efficient way to dissect complex quantitative traits in crops\u0026nbsp;(Wang et al., 2020). In\u0026nbsp;\u003cem\u003eBrassica napus\u003c/em\u003e, a common and effective method to map QTLs for\u0026nbsp;seed weight is the construction of a genetic linkage map via the use of molecular markers such as RFLP, SSR, SNP and InDel markers (Butruille et al., 1999; Ding et al., 2012; Dong et al., 2022; Fu et al., 2015; Geng et al., 2016; Li et al., 2014; Luo et al., 2017; Quijada et al., 2006; Shen et al., 2019; Shirakawa et al., 2009; Udall et al., 2006; Wang et al., 2020; Wang et al., 2016; Yang et al., 2012; Yang et al., 2017; Zhang et al., 2011; Zhao et al., 2016). These genetic linkage maps constructed with traditional molecular markers have provided much genetic information for QTL mapping in \u003cem\u003eBrassica napus\u003c/em\u003e. However, the efficiency of fine mapping and map-based cloning is limited because of the low density of molecular markers and the polyploidy of \u003cem\u003eB. napus\u003c/em\u003e. Therefore, it is necessary to construct more detailed and accurate genetic maps for effective QTL identification in\u0026nbsp;\u003cem\u003eB. napus\u003c/em\u003e. A genetic map based on GBS can scan and identify mutations at all the sites of the whole genome and does not require any previous marker information\u0026nbsp;(Liu et al., 2023; Si et al., 2022), which makes it more accurate than previous genetic maps.\u0026nbsp;In this study,\u0026nbsp;a high-density genetic bin map was constructed with 1,306 high-quality bin markers comprising\u0026nbsp;1,094,333\u0026nbsp;SNPs and\u0026nbsp;167,953\u0026nbsp;InDels (Fig. 5, Table S3, Table S4 and Table S5). The bin map spanned\u0026nbsp;1588.128\u0026nbsp;cM, with an average distance of\u0026nbsp;1.648\u0026nbsp;cM between adjacent bin markers (Fig. 5, Table S3 and Table S4). Furthermore, there was good collinearity between the bin map and the reference genome, which indicates that the bin map had high accuracy and precision (Fig. S2 and Table S6). Therefore, the present bin map is more conducive to QTL detection for seed weight in rapeseed. To our knowledge, this bin map is the first bin map utilizing the GBS method for QTL mapping of seed weight in \u003cem\u003eB. napus\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, we identified the major QTL \u003cem\u003eqSW-A03\u003c/em\u003e in the F\u003csub\u003e2:4\u003c/sub\u003e, F\u003csub\u003e2:5\u003c/sub\u003e, F\u003csub\u003e2:6\u003c/sub\u003eREP1 and F\u003csub\u003e2:6\u003c/sub\u003eREP2 populations across three years with a\u0026nbsp;high-density genetic\u0026nbsp;bin map (Fig. 6, Table 2 and Table 3). Moreover,\u0026nbsp;\u003cem\u003eqSW-A03\u0026nbsp;\u003c/em\u003ewas identified with nine InDel markers in the RIL population, narrowing to a 59-kb physical region with recombinant lines (Fig. 7 and Table S7). These results showed that \u003cem\u003eqSW-A03\u003c/em\u003e can be stably inherited to regulate\u0026nbsp;seed weight. To date, many QTLs and associated loci for seed weight have been identified via linkage and genome-wide association analyses in \u003cem\u003eB. napus\u003c/em\u003e (Butruille et al., 1999; Ding et al., 2012; Dong et al., 2022; Fu et al., 2015; Geng et al., 2016; Khan et al., 2021; Li et al., 2014; Luo et al., 2017; Pal et al., 2021; Quijada et al., 2006; Raboanatahiry et al., 2018; Shen et al., 2019; Shirakawa et al., 2009; Udall et al., 2006; Wang et al., 2020; Wang et al., 2016; Xin et al., 2021; Yang et al., 2012; Yang et al., 2017; Zhang et al., 2023a; Zhang et al., 2011; Zhao et al., 2016). These QTLs or associated loci exist on all 19 chromosomes of \u003cem\u003eB. napus\u003c/em\u003e according to their integration into the physical map of rapeseed. We aligned the QTLs previously detected on A03 to the reference genome ZS11.v0 and compared them with the QTL \u003cem\u003eqSW-A03\u003c/em\u003e identified in this study. The results revealed that\u003cem\u003e\u0026nbsp;qSW-A03\u003c/em\u003e did not overlap with the previously reported QTLs. Moreover, none of the eleven genes in the QTL region have been reported to be involved in regulating seed weight/size in \u003cem\u003eB. napus\u003c/em\u003e. Therefore, \u003cem\u003eqSW-A03\u003c/em\u003e is a novel QTL responsible for controlling seed weight in \u003cem\u003eB. napus\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eCombining QTL mapping, gene expression differences and gene coexpression network analysis is a\u0026nbsp;powerful strategy for effectively identifying candidate genes associated with a target trait\u0026nbsp;(Cui et al., 2021; Derakhshani et al., 2020; Dong et al., 2022; Guo et al., 2019; Jiao et al., 2021; Lamb et al., 2006; Liu et al., 2023; Ruprecht et al., 2017; Shen et al., 2019; Wang et al., 2020; Xin et al., 2023; Zhang et al., 2021). By combining QTL mapping and gene expression difference analysis, some studies have identified candidate genes associated with seed weight in \u003cem\u003eB. napus\u003c/em\u003e, such as a histidine kinase gene (BnaA03G37960D) underlying \u003cem\u003ecqSW.A03-2\u003c/em\u003e (Wang et al., 2020), two candidate genes (BnaC09G0551500ZS and BnaC09G0551700ZS) in \u003cem\u003eqSW.C9\u0026nbsp;\u003c/em\u003e(Zhang et al., 2021). Furthermore, this strategy has also been applied to map-based cloning of two\u0026nbsp;causal\u0026nbsp;genes, \u003cem\u003eBnaA9. CYP78A9\u003c/em\u003e and \u003cem\u003eARF18\u003c/em\u003e account for seed weight in \u003cem\u003eB. napus\u003c/em\u003e (Liu et al., 2015; Shen et al., 2019; Shi et al., 2019). By combining QTL mapping and gene coexpression network analysis,\u0026nbsp;Cui et al. (2021)\u0026nbsp;first revealed novel QTGs involved in oil accumulation in rapeseed. This combination has also been successfully applied to discover candidate QTGs for determining seed weight in rapeseed\u0026nbsp;(Dong et al., 2022). In the present study, we adopted this strategy, combining QTL mapping, gene expression differences, and gene coexpression network analysis, \u003cem\u003eBnaDUF1666\u003c/em\u003e (BnaA03G0362100ZS) was identified as the most promising candidate gene for seed weight regulation.\u003c/p\u003e\n\u003cp\u003eDespite the unknown function of the candidate gene \u003cem\u003eBnaDUF1666\u003c/em\u003e according to the reference genome annotation, gene coexpression network analysis provides clues for the gene function and molecular mechanism of \u003cem\u003eBnaDUF1666\u0026nbsp;\u003c/em\u003ein regulating seed weight. In this study, the 299 genes coexpressed with \u003cem\u003eBnaDUF1666\u003c/em\u003e were involved in\u0026nbsp;seed and embryo development, carbohydrate transport, fatty acid biosynthesis and metabolism, seed maturation, and the\u0026nbsp;ABA response (Fig. 9, Fig. S3, Table S10 and Table S11). These enriched terms implied that \u003cem\u003eBnaDUF1666\u003c/em\u003e may regulate\u0026nbsp;seed weight. The cell size and number of seed coats led to a greater capacity of DL704 (Fig. 4a-e), which provided enough space for endosperm and embryo development. The larger cotyledons subsequently formed, which was predominantly promoted by cell expansion but also slightly limited by cell proliferation (Fig. 4f-j), increasing the capacity for lipids and proteins. Among these coexpressed genes, \u003cem\u003eGIF1\u003c/em\u003e, \u003cem\u003eAGG3\u003c/em\u003e, \u003cem\u003eGW6\u0026nbsp;\u003c/em\u003eand \u003cem\u003eGRF5\u003c/em\u003e also regulate seed size through cell expansion and/or proliferation\u0026nbsp;(Duan et al., 2015; He et al., 2017; Li et al., 2012; Shi et al., 2020; Sun et al., 2016). These results suggested that \u003cem\u003eBnaDUF1666\u003c/em\u003e may regulate seed size predominantly through cell expansion but also through proliferation in both the seed coat and cotyledon. In this study, \u003cem\u003eBnaDUF1666\u003c/em\u003e was differentially expressed between the parents at 7 DAP (Fig. 8). At the early stage of seed development, ABA negatively regulates endosperm proliferation by influencing the timing of endosperm cellularization\u0026nbsp;(Cheng et al., 2014; Zhang et al., 2023b). These results suggest that \u003cem\u003eBnaDUF1666\u003c/em\u003e may determine the cell size and number of seed coats and regulate endosperm and embryo development during early seed development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis program was financially supported in part by the National Key Research and Development Program of China (2022YFD1200400), the National Natural Science Foundation of China (32272060), the Science and Technology Innovation 2030 Major Project (2023ZD0404201), and the Natural Science Foundation of Chongqing (cstc2021jcyj-msxmX1198).\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eJM, DH, BW, YZ, CL, YD, and HC carried out the field experiments; JM, DH, BW and YZ participated in QTL fine mapping; JM, DH and BHW participated in data analysis; YH and QW designed and supervised the project. JM wrote the original draft. JM, BHW, YH and QW were involved in reviewing and editing the manuscript. All the authors read and contributed to the revision of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to thank everyone who contributed their time and effort to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgyenim-Boateng KG, Zhang S, Gu R, Zhang S, Qi J, Azam M, Ma C, Li Y, Feng Y, Liu Y, Li J, Li B, Qiu L, Sun J (2023) Identification of quantitative trait loci and candidate genes for seed folate content in soybean. Theor Appl Genet 136:149\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eButruille DV, Guries RP, Osborn TC (1999) Linkage analysis of molecular markers and quantitative trait loci in populations of inbred backcross lines of \u003cem\u003eBrassica napus\u003c/em\u003e L. Genetics 153:949\u0026ndash;964\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai Y, Zhang W, Jin J, Yang X, You X, Yan H, Wang L, Chen J, Xu J, Chen W, Chen X, Ma J, Tang X, Kong F, Zhu X, Wang G, Jiang L, Terzaghi W, Wang C, Wan J (2018) \u003cem\u003eOsPKpα1\u003c/em\u003e encodes a plastidic pyruvate kinase that affects starch biosynthesis in the rice endosperm. J Integr Plant Biol 60:1097\u0026ndash;1118\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen LQ, Lin IW, Qu XQ, Sosso D, McFarlane HE, Londo\u0026ntilde;o A, Samuels AL, Frommer WB (2015) A cascade of sequentially expressed sucrose transporters in the seed coat and endosperm provides nutrition for the \u003cem\u003eArabidopsis\u003c/em\u003e embryo. Plant Cell 27:607\u0026ndash;619\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Zhou Y, Chen Y, Gu J (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884\u0026ndash;i890\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng ZJ, Zhao XY, Shao XX, Wang F, Zhou C, Liu YG, Zhang Y, Zhang XS (2014) Abscisic acid regulates early seed development in \u003cem\u003eArabidopsis\u003c/em\u003e by ABI5-mediated transcription of \u003cem\u003eSHORT HYPOCOTYL UNDER BLUE1\u003c/em\u003e. Plant Cell 26:1053\u0026ndash;1068\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui Y, Zeng X, Xiong Q, Wei D, Liao J, Xu Y, Chen G, Zhou Y, Dong H, Wan H, Liu Z, Li J, Guo L, Jung C, He Y, Qian W (2021) Combining quantitative trait locus and co-expression analysis allowed identification of new candidates for oil accumulation in rapeseed. J Exp Bot 72:1649\u0026ndash;1660\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas S, Parida SK, Agarwal P, Tyagi AK (2019) Transcription factor OsNF-YB9 regulates reproductive growth and development in rice. Planta 250:1849\u0026ndash;1865\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDerakhshani B, Jafary H, Maleki Zanjani B, Hasanpur K, Mishina K, Tanaka T, Kawahara Y, Oono Y (2020) Combined QTL mapping and RNA-Seq profiling reveals candidate genes associated with cadmium tolerance in barley. PLoS ONE 15:e0230820\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing G, Zhao Z, Liao Y, Hu Y, Shi L, Long Y, Xu F (2012) Quantitative trait loci for seed yield and yield-related traits, and their responses to reduced phosphorus supply in \u003cem\u003eBrassica napus\u003c/em\u003e. Ann Bot 109:747\u0026ndash;759\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong H, Tan C, Li Y, He Y, Wei S, Cui Y, Chen Y, Wei D, Fu Y, He Y, Wan H, Liu Z, Xiong Q, Lu K, Li J, Qian W (2018) Genome-wide association study reveals both overlapping and independent genetic loci to control seed weight and silique length in \u003cem\u003eBrassica napus\u003c/em\u003e. Front Plant Sci 9:921\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong HL, Yang L, Liu YL, Tian GF, Tang H, Xin SS, Cui YX, Xiong Q, Wan HF, Liu Z, Jung C, Qian W (2022) Detection of new candidate genes controlling seed weight by integrating gene coexpression analysis and QTL mapping in \u003cem\u003eBrassica napus\u003c/em\u003e L. The Crop Journal\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan P, Ni S, Wang J, Zhang B, Xu R, Wang Y, Chen H, Zhu X, Li Y (2015) Regulation of \u003cem\u003eOsGRF4\u003c/em\u003e by OsmiR396 controls grain size and yield in rice. Nat Plants 2:15203\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan L, Wang L, Wang X, Zhang H, Zhu Y, Guo J, Gao W, Geng H, Chen Q, Qu Y (2018) A high-density genetic map of extra-long staple cotton (\u003cem\u003eGossypium barbadense\u003c/em\u003e) constructed using genotyping-by-sequencing based single nucleotide polymorphic markers and identification of fiber traits-related QTL in a recombinant inbred line population. BMC Genomics 19:489\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang W, Wang Z, Cui R, Li J, Li Y (2012) Maternal control of seed size by \u003cem\u003eEOD3/CYP78A6\u003c/em\u003e in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. Plant J 70:929\u0026ndash;939\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu Y, Wei D, Dong H, He Y, Cui Y, Mei J, Wan H, Li J, Snowdon R, Friedt W, Li X, Qian W (2015) Comparative quantitative trait loci for silique length and seed weight in \u003cem\u003eBrassica napus\u003c/em\u003e. Sci Rep 5:14407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGacek K, Bayer PE, Anderson R, Severn-Ellis AA, Wolko J, Lopatynska A, Matuszczak M, Bocianowski J, Edwards D, Batley J (2021) QTL genetic mapping study for traits affecting meal quality in winter oilseed rape (\u003cem\u003eBrassica napus\u003c/em\u003e L). Genes (Basel) 12:1235\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeng X, Jiang C, Yang J, Wang L, Wu X, Wei W (2016) Rapid identification of candidate genes for seed weight using the SLAF-Seq method in \u003cem\u003eBrassica napus\u003c/em\u003e. PLoS ONE 11:e0147580\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo T, Yang J, Li D, Sun K, Luo L, Xiao W, Wang J, Liu Y, Wang S, Wang H, Chen Z (2019) Integrating GWAS, QTL, mapping and RNA-seq to identify candidate genes for seed vigor in rice (Oryza sativa L). Mol Breed 39:87\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Z, Zeng J, Ren Y, Chen D, Li W, Gao F, Cao Y, Luo T, Yuan G, Wu X, Liang Y, Deng Q, Wang S, Zheng A, Zhu J, Liu H, Wang L, Li P, Li S (2017) \u003cem\u003eOsGIF1\u003c/em\u003e positively regulates the sizes of stems, leaves, and grains in rice. Front Plant Sci 8:1730\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJadhav MP, Gangurde SS, Hake AA, Yadawad A, Mahadevaiah SS, Pattanashetti SK, Gowda MVC, Shirasawa K, Varshney RK, Pandey MK, Bhat RS (2021) Genotyping-by-sequencing based genetic mapping identified major and consistent genomic regions for productivity and quality traits in \u003cem\u003ePeanut\u003c/em\u003e. Front Plant Sci 12:668020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiao Y, Zhang K, Cai G, Yu K, Amoo O, Han S, Zhao X, Zhang H, Hu L, Wang B, Fan C, Zhou Y (2021) Fine mapping and candidate gene analysis of a major locus controlling ovule abortion and seed number per silique in \u003cem\u003eBrassica napus\u003c/em\u003e L. Theor Appl Genet 134:2517\u0026ndash;2530\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan SU, Saeed S, Khan MHU, Fan C, Ahmar S, Arriagada O, Shahzad R, Branca F, Mora-Poblete F (2021) Advances and challenges for QTL analysis and GWAS in the plant-breeding of high-yielding: A focus on rapeseed. Biomolecules 11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong W, Kim C, Zhang D, Guo H, Tan X, Jin H, Zhou C, Shuang LS, Goff V, Sezen U, Pierce G, Compton R, Lemke C, Robertson J, Rainville L, Auckland S, Paterson AH (2018) Genotyping by sequencing of 393 \u003cem\u003eSorghum bicolor\u003c/em\u003e BTx623 x IS3620C recombinant inbred lines improves sensitivity and resolution of QTL detection. G3 (Bethesda) 8:2563\u0026ndash;2572\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313:1929\u0026ndash;1935\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J, Izzah NK, Choi BS, Joh HJ, Lee SC, Perumal S, Seo J, Ahn K, Jo EJ, Choi GJ, Nou IS, Yu Y, Yang TJ (2016) Genotyping-by-sequencing map permits identification of clubroot resistance QTLs and revision of the reference genome assembly in cabbage (\u003cem\u003eBrassica oleracea\u003c/em\u003e L). DNA Res 23:29\u0026ndash;41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv: Genomics 00:1\u0026ndash;3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078\u0026ndash;2079\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi N, Xu R, Li Y (2019a) Molecular networks of seed size control in plants. Annu Rev Plant Biol 70:435\u0026ndash;463\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi N, Shi J, Wang X, Liu G, Wang H (2014) A combined linkage and regional association mapping validation and fine mapping of two major pleiotropic QTLs for seed weight and silique length in rapeseed (\u003cem\u003eBrassica napus\u003c/em\u003e L). BMC Plant Biol 14:114\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi N, Peng W, Shi J, Wang X, Liu G, Wang H (2015) The natural variation of seed weight is mainly controlled by maternal genotype in rapeseed (\u003cem\u003eBrassica napus\u003c/em\u003e L). PLoS ONE 10:e0125360\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi N, Song D, Peng W, Zhan J, Shi J, Wang X, Liu G, Wang H (2019b) Maternal control of seed weight in rapeseed (\u003cem\u003eBrassica napus\u003c/em\u003e L.): the causal link between the size of pod (mother, source) and seed (offspring, sink). Plant Biotechnol J 17:736\u0026ndash;749\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Liu Y, Zheng L, Chen L, Li N, Corke F, Lu Y, Fu X, Zhu Z, Bevan MW, Li Y (2012) The plant-specific G protein gamma subunit AGG3 influences organ size and shape in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. New Phytol 194:690\u0026ndash;703\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Gao F, Xie K, Zeng X, Cao Y, Zeng J, He Z, Ren Y, Li W, Deng Q, Wang S, Zheng A, Zhu J, Liu H, Wang L, Li P (2016) The OsmiR396c-OsGRF4-OsGIF1 regulatory module determines grain size and yield in rice. Plant Biotechnol J 14:2134\u0026ndash;2146\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Hua W, Hu Z, Yang H, Zhang L, Li R, Deng L, Sun X, Wang X, Wang H (2015) Natural variation in \u003cem\u003eARF18\u003c/em\u003e gene simultaneously affects seed weight and silique length in polyploid rapeseed. Proc Natl Acad Sci USA 112:E5123\u0026ndash;5132\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Q, Wang Y, Fu Y, Du L, Zhang Y, Wang Q, Sun R, Ai N, Feng G, Li C (2023) Genetic dissection of lint percentage in short-season cotton using combined QTL mapping and RNA-seq. Theor Appl Genet\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Teng Z, Wang J, Wu T, Zhang Z, Deng X, Fang X, Tan Z, Ali I, Liu D, Zhang J, Liu D, Liu F, Zhang Z (2017) Enriching an intraspecific genetic map and identifying QTL for fiber quality and yield component traits across multiple environments in upland cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L). Mol Genet Genomics 292:1281\u0026ndash;1306\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLivak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 25:402\u0026ndash;408\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo Z, Wang M, Long Y, Huang Y, Shi L, Zhang C, Liu X, Fitt BDL, Xiang J, Mason AS, Snowdon RJ, Liu P, Meng J, Zou J (2017) Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example. Theor Appl Genet 130:1569\u0026ndash;1585\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller C, Wells R, McKenzie N, Trick M, Ball J, Fatihi A, Dubreucq B, Chardot T, Lepiniec L, Bevan MW (2019) Variation in expression of the HECT E3 ligase UPL3 modulates LEC2 levels, seed size, and crop yields in \u003cem\u003eBrassica napus\u003c/em\u003e. Plant Cell 31:2370\u0026ndash;2385\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMu J, Tan H, Hong S, Liang Y, Zuo J (2013) \u003cem\u003eArabidopsis\u003c/em\u003e transcription factor genes \u003cem\u003eNF-YA1\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, and \u003cem\u003e9\u003c/em\u003e play redundant roles in male gametogenesis, embryogenesis, and seed development. \u003cem\u003eMol Plant\u003c/em\u003e 6:188\u0026ndash;201\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePal L, Sandhu SK, Bhatia D, Sethi S (2021) Genome-wide association study for candidate genes controlling seed yield and its components in rapeseed (\u003cem\u003eBrassica napus subsp. napus\u003c/em\u003e). Physiol Mol Biol Plants 27:1933\u0026ndash;1951\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePei W, Song J, Wang W, Ma J, Jia B, Wu L, Wu M, Chen Q, Qin Q, Zhu H, Hu C, Lei H, Gao X, Hu H, Zhang Y, Zhang J, Yu J, Qu Y (2021) Quantitative trait locus analysis and identification of candidate genes for micronaire in an interspecific backcross inbred line population of \u003cem\u003eGossypium hirsutum\u003c/em\u003e x \u003cem\u003eGossypium barbadense\u003c/em\u003e. Front Plant Sci 12:763016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePidkowich MS, Nguyen HT, Heilmann I, Ischebeck T, Shanklin J (2007) Modulating seed beta-ketoacyl-acyl carrier protein synthase II level converts the composition of a temperate seed oil to that of a palm-like tropical oil. Proc Natl Acad Sci USA 104:4742\u0026ndash;4747\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuijada PA, Udall JA, Lambert B, Osborn TC (2006) Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (\u003cem\u003eBrassica napus\u003c/em\u003e L.): 1. Identification of genomic regions from winter germplasm. Theor Appl Genet 113:549\u0026ndash;561\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaboanatahiry N, Chao H, Dalin H, Pu S, Yan W, Yu L, Wang B, Li M (2018) QTL alignment for seed yield and yield related traits in \u003cem\u003eBrassica napus\u003c/em\u003e. Front Plant Sci 9:1127\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuprecht C, Vaid N, Proost S, Persson S, Mutwil M (2017) Beyond genomics: Studying evolution with gene oexpression networks. Trends Plant Sci 22:298\u0026ndash;307\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen W, Qin P, Yan M, Li B, Wu Z, Wen J, Yi B, Ma C, Shen J, Fu T, Tu J (2019) Fine mapping of a silique length- and seed weight-related gene in \u003cem\u003eBrassica napus\u003c/em\u003e. Theor Appl Genet 132:2985\u0026ndash;2996\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi CL, Dong NQ, Guo T, Ye WW, Shan JX, Lin HX (2020) A quantitative trait locus \u003cem\u003eGW6\u003c/em\u003e controls rice grain size and yield through the gibberellin pathway. Plant J 103:1174\u0026ndash;1188\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi L, Song J, Guo C, Wang B, Guan Z, Yang P, Chen X, Zhang Q, King GJ, Wang J, Liu K (2019) A CACTA-like transposable element in the upstream region of \u003cem\u003eBnaA9.CYP78A9\u003c/em\u003e acts as an enhancer to increase silique length and seed weight in rapeseed. Plant J 98:524\u0026ndash;539\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirakawa M, Ueda H, Shimada T, Nishiyama C, Hara-Nishimura I (2009) Vacuolar SNAREs function in the formation of the leaf vascular network by regulating auxin distribution. Plant Cell Physiol 50:1319\u0026ndash;1328\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSi Z, Jin S, Chen J, Wang S, Fang L, Zhu X, Zhang T, Hu Y (2022) Construction of a high-density genetic map and identification of QTLs related to agronomic and physiological traits in an interspecific (\u003cem\u003eGossypium hirsutum\u003c/em\u003e \u0026times; \u003cem\u003eGossypium barbadense\u003c/em\u003e) F\u003csub\u003e2\u003c/sub\u003e population. BMC Genomics 23:307\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong JM, Guan Z, Hu J, Guo C, Yang Z, Wang S, Liu D, Wang B, Lu S, Zhou R, Xie WZ, Cheng Y, Zhang Y, Liu K, Yang QY, Chen LL, Guo L (2020) Eight high-quality genomes reveal pan-genome architecture and ecotype differentiation of \u003cem\u003eBrassica napus\u003c/em\u003e. Nat Plants 6:34\u0026ndash;45\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun LJ, Wang XD, Yu KJ, Li WJ, Peng Q, Chen F, Zhang W, Fu SX, Xiong DQ, Chu P, Guan RZ, Zhang JF (2018) Mapping of QTLs controlling seed weight and seed-shape traits in \u003cem\u003eBrassica napus\u003c/em\u003e L. using a high-density SNP map. Euphytica 214:1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun P, Zhang W, Wang Y, He Q, Shu F, Liu H, Wang J, Wang J, Yuan L, Deng H (2016) \u003cem\u003eOsGRF4\u003c/em\u003e controls grain shape, panicle length and seed shattering in rice. J Integr Plant Biol 58:836\u0026ndash;847\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan Z, Han X, Dai C, Lu S, He H, Yao X, Chen P, Yang C, Zhao L, Yang QY, Zou J, Wen J, Hong D, Liu C, Ge X, Fan C, Yi B, Zhang C, Ma C, Liu K, Shen J, Tu J, Yang G, Fu T, Guo L, Zhao H (2024) Functional genomics of \u003cem\u003eBrassica napus\u003c/em\u003e: Progresses, challenges, and perspectives. J Integr Plant Biol 66:484\u0026ndash;509\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang SY, Teng ZH, Zhai TF, Fang XM, Liu F, Liu DJ, Zhang J, Liu DX, Wang SF, Zhang K, Shao QS, Tan ZY, Paterson AH, Zhang ZS (2015) Construction of genetic map and QTL analysis of fiber quality traits for upland cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L). Euphytica 201:195\u0026ndash;213\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUdall JA, Quijada PA, Lambert B, Osborn TC (2006) Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (\u003cem\u003eBrassica napus\u003c/em\u003e L.): 2. Identification of alleles from unadapted germplasm. Theor Appl Genet 113:597\u0026ndash;609\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Yan M, Xiong M, Wang P, Liu Y, Xin Q, Wan L, Yang G, Hong D (2020) Genetic dissection of thousand-seed weight and fine mapping of \u003cem\u003ecqSW.A03-2\u003c/em\u003e via linkage and association analysis in rapeseed (\u003cem\u003eBrassica napus\u003c/em\u003e L). Theor Appl Genet 133:1321\u0026ndash;1335\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38:e164\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Basten C, Zeng Z (2012) Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://statgen.ncsu.edu/qtlcart/WQTLCart.htm\u003c/span\u003e\u003cspan address=\"http://statgen.ncsu.edu/qtlcart/WQTLCart.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Chen L, Wang A, Wang H, Tian J, Zhao X, Chao H, Zhao Y, Zhao W, Xiang J, Gan J, Li M (2016) Quantitative trait loci analysis and genome-wide comparison for silique related traits in \u003cem\u003eBrassica napus\u003c/em\u003e. BMC Plant Biol 16:71\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao YG, Sun QB, Kang XJ, Chen CB, Ni M (2016) \u003cem\u003eSHORT HYPOCOTYL UNDER BLUE1\u003c/em\u003e or \u003cem\u003eHAIKU2\u003c/em\u003e mixepression alters canola and \u003cem\u003eArabidopsis\u003c/em\u003e seed development. New Phytol 209:636\u0026ndash;649\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie W, Feng Q, Yu H, Huang X, Zhao Q, Xing Y, Yu S, Han B, Zhang Q (2010) Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing. Proc Natl Acad Sci USA 107:10578\u0026ndash;10583\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXin S, Dong H, Cui Y, Liu Y, Tian G, Deng N, Wan H, Liu Z, Li X, Qian W (2023) Identification of a candidate QTG for seed number per silique by integrating QTL mapping and RNA-seq in \u003cem\u003eBrassica napus\u003c/em\u003e L. Crop J 11:189\u0026ndash;197\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXin S, Dong H, Yang L, Huang D, Zheng F, Cui Y, Wu S, Liao J, He Y, Wan H, Liu Z, Li X, Qian W (2021) Both overlapping and independent loci underlie seed number per pod and seed weight in \u003cem\u003eBrassica napus\u003c/em\u003e by comparative quantitative trait loci analysis. Mol Breed 41:41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamamoto A, Kagaya Y, Toyoshima R, Kagaya M, Takeda S, Hattori T (2009) \u003cem\u003eArabidopsis\u003c/em\u003e NF-YB subunits LEC1 and LEC1-LIKE activate transcription by interacting with seed-specific ABRE-binding factors. Plant J 58:843\u0026ndash;856\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang P, Shu C, Chen L, Xu J, Wu J, Liu K (2012) Identification of a major QTL for silique length and seed weight in oilseed rape (\u003cem\u003eBrassica napus\u003c/em\u003e L). Theor Appl Genet 125:285\u0026ndash;296\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang P, Sun X, Liu X, Wang W, Hao Y, Chen L, Liu J, He H, Zhang T, Bao W, Tang Y, He X, Ji M, Guo K, Liu D, Teng Z, Liu D, Zhang J, Zhang Z (2022) Identification of candidate genes for lint percentage and fiber quality through QTL mapping and transcriptome analysis in an allotetraploid interspecific cotton CSSLs population. Front Plant Sci 13:882051\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Shen Y, Li S, Ge X, Li Z (2017) High density linkage map construction and QTL detection for three silique-related traits in \u003cem\u003eOrychophragmus violaceus\u003c/em\u003e derived \u003cem\u003eBrassica napus\u003c/em\u003e population. Front Plant Sci 8:1512\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Z, Wang S, Wei L, Huang Y, Liu D, Jia Y, Luo C, Lin Y, Liang C, Hu Y, Dai C, Guo L, Zhou Y, Yang QY (2023) BnIR: A multi-omics database with various tools for \u003cem\u003eBrassica napus\u003c/em\u003e research and breeding. Mol Plant 16:775\u0026ndash;789\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYepuri V, Jalali S, Mudunuri V, Pothakani S, Kancharla N, Arockiasamy S (2022) Genotyping by sequencing-based linkage map construction and identification of quantitative trait loci for yield-related traits and oil content in Jatropha (Jatropha curcas L). Mol Biol Rep 49:4293\u0026ndash;4306\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu F, Zhang Y, Wang J, Chen Q, Karim MM, Gossen BD, Peng G (2021) Identification of two major QTLs in \u003cem\u003eBrassica napus\u003c/em\u003e lines with introgressed clubroot resistance from turnip cultivar ECD01. Front Plant Sci 12:785989\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C, Gong R, Zhong H, Dai C, Zhang R, Dong J, Li Y, Liu S, Hu J (2023a) Integrated multi-locus genome-wide association studies and transcriptome analysis for seed yield and yield-related traits in \u003cem\u003eBrassica napus\u003c/em\u003e. Front Plant Sci 14:1153000\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Zhang X, Liu X, Pai Q, Wang Y, Wu X (2023b) Molecular network for regulation of seed size in plants. Int J Mol Sci 24\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Yang G, Liu P, Hong D, Li S, He Q (2011) Genetic and correlation analysis of silique-traits in \u003cem\u003eBrassica napus\u003c/em\u003e L. by quantitative trait locus mapping. Theor Appl Genet 122:21\u0026ndash;31\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Huang Q, Wang P, Liu F, Luo M, Li X, Wang Z, Wan L, Yang G, Hong D (2021) A 24,482-bp deletion is associated with increased seed weight in \u003cem\u003eBrassica napus\u003c/em\u003e L. Theor Appl Genet 134:2653\u0026ndash;2669\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao W, Wang X, Wang H, Tian J, Li B, Chen L, Chao H, Long Y, Xiang J, Gan J, Liang W, Li M (2016) Genome-wide identification of QTL for seed yield and yield-related traits and construction of a high-density consensus map for QTL comparison in \u003cem\u003eBrassica napus\u003c/em\u003e. Front Plant Sci 7:17\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 554px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 Phenotypic analysis of thousand seed weight in the RIL population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCV (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e7.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e18.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e7.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e1.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e21.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-1.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e8.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e16.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e8.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e1.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e22.65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-1.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:6\u003c/sub\u003eREP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e8.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e17.35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e8.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e1.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e20.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-1.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:6\u003c/sub\u003eREP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e8.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e17.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e8.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e1.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e21.83\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"919\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 919px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2 Detected QTLs for thousand seed weight by using the bin map\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRILs population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQTL name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromosome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosition (cM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLOD score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic interval (cM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical interval on ZS11.v0 (Mb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-A03-F2:4\u003c/em\u003e\u003cem\u003e★\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e54.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec03b059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e10.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e31.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e53.1\u0026ndash;56.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e18.687\u0026ndash;19.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C07-F2:4-3#\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e19.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec17b025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e12.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e16.4\u0026ndash;21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e34.329\u0026ndash;36.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C07-F2:4-1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec17b003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e8.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.8\u0026ndash;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5.012\u0026ndash;9.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C07-F2:4-2▲\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e10.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec17b011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e6.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e14.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e6.5\u0026ndash;12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e26.654\u0026ndash;29.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-A03-F2:5\u003c/em\u003e\u003cem\u003e★\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e54.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec03b058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e15.95\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.95\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e46.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e53.4\u0026ndash;58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e18.687\u0026ndash;27.468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C07-F2:5#\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e19.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec17b025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4.65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e8.79\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e18.2\u0026ndash;22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e34.751\u0026ndash;37.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:6\u003c/sub\u003eREP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-A03-F2:6REP1\u003c/em\u003e\u003cem\u003e★\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e54.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec03b059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e9.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.71\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e29.99\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e53.5\u0026ndash;56.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e18.687\u0026ndash;19.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C07-F2:6REP1-2#\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e21.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec17b027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e9.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e20.4\u0026ndash;25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e35.805\u0026ndash;42.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C09-F2:6REP1*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e84.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec19b088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e13.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e76.9\u0026ndash;85.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e65.459\u0026ndash;66.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C04-F2:6REP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e58.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec14b039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e7.85\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e48.8\u0026ndash;62.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e55.214\u0026ndash;59.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C07-F2:6REP1-1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e26.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec17b035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e7.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e25.4\u0026ndash;28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e42.539\u0026ndash;45.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:6\u003c/sub\u003eREP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-A03-F2:6REP2\u003c/em\u003e\u003cem\u003e★\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e54.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec03b058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e9.65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e30.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e52.8\u0026ndash;56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e18.687\u0026ndash;19.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C07-F2:6REP2-2#\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e19.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec17b024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.43\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e12.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e16.8\u0026ndash;22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e34.329\u0026ndash;37.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C09-F2:6REP2*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e84.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec19b088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e13.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e82.4\u0026ndash;88.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e65.658\u0026ndash;66.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C07-F2:6REP2-1▲\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e10.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ec17b011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e10.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5.5\u0026ndash;14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e26.654\u0026ndash;30.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 919px;\"\u003e\n \u003cp\u003eOverlapped QTLs were marked with different symbols\u0026nbsp;★, #, * and ▲.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"885\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 885px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3 Overlapped QTLs for thousand seed weight in the RIL population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverlapped QTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromosome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLOD score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic interval (cM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical interval on ZS11.v0 (Mb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRIL population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-A03\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eA03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e9.65\u0026ndash;15.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.70\u0026ndash;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e29.99\u0026ndash;46.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e53.5\u0026ndash;56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e18.687\u0026ndash;19.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:4\u003c/sub\u003e, F\u003csub\u003e2:5\u003c/sub\u003e, F\u003csub\u003e2:6\u003c/sub\u003eREP1, F\u003csub\u003e2:6\u003c/sub\u003eREP2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C07-1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.25\u0026ndash;5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.37\u0026ndash;0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e8.79\u0026ndash;12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e20.4\u0026ndash;21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e35.805\u0026ndash;36.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:4\u003c/sub\u003e, F\u003csub\u003e2:5\u003c/sub\u003e, F\u003csub\u003e2:6\u003c/sub\u003eREP1, F\u003csub\u003e2:6\u003c/sub\u003eREP2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C07-2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.08\u0026ndash;6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.41\u0026ndash;0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e10.02\u0026ndash;14.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e6.5\u0026ndash;12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e26.654\u0026ndash;29.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:4\u003c/sub\u003e, F\u003csub\u003e2:6\u003c/sub\u003eREP2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSW-C09\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eC09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.36\u0026ndash;5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.44\u0026ndash;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e13.67\u0026ndash;13.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e82.4\u0026ndash;85.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e65.658\u0026ndash;66.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eF\u003csub\u003e2:6\u003c/sub\u003eREP1, F\u003csub\u003e2:6\u003c/sub\u003eREP2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"883\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 883px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4 Annotated genes in the 59-kb interval of the major QTL \u003cem\u003eqSW-A03\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene ID on ZS11.v0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenomic position on ZS11.v0\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003ebp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAtCode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDarmor ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBnaA03G0361100ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eA03: 19194661..19199336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003eTranscription termination factor 2, Ttf2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAT3G20010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBnaA03g35550D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBnaA03G0361200ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eA03: 19200647..19208780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003eT-complex protein 1 subunit alpha, CCT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAT3G20050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBnaA03g35560D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBnaA03G0361300ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eA03: 19227309..19228070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e3-hydroxyacyl-CoA dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAT3G15290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBnaA03G0361400ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eA03: 19231696..19236950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003eKinesin-like protein KIN12B, KIN12B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAT3G20150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBnaA03g35600D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBnaA03G0361500ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eA03: 19240341..19241408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003eGeranylgeranyl pyrophosphate synthase 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAT3G20160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBnaA03g35610D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBnaA03G0361600ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eA03: 19242420..19243850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003eU-box domain-containing protein 4-like, PUB4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAT3G20170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBnaA03g35620D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBnaA03G0361700ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eA03: 19244019..19244557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003eCopper transport protein family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAT3G20180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBnaC05g31900D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBnaA03G0361800ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eA03: 19244539..19247146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003ePollen receptor-like kinase 4, PRK4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAT3G20190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBnaA03g35630D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBnaA03G0361900ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eA03: 19248058..19249588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003eMitochondrial carrier protein, BTL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAT3G20240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBnaA03g35640D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBnaA03G0362000ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eA03: 19249704..19253745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003ePumilio-family RNA binding repeat, APUM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAT3G20250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBnaA03g35650D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBnaA03G0362100ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eA03: 19254632..19256546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003eProtein of unknown function (DUF1666)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAT3G20260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBnaA03g35660D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Brassica napus, seed weight, quantitative trait loci, candidate genes, DUF1666","lastPublishedDoi":"10.21203/rs.3.rs-5271995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5271995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSeed weight is a determining factor for improving rapeseed productivity. In the present study, a high-density genetic map was constructed via genome resequencing in an RIL population derived from a cross of two rapeseed varieties, ZS11 and DL704, with great differences in thousand seed weight (TSW). A total of 1,306 bins involving 1,261,526 SNPs were used to construct the bin map. On the basis of the genetic map, QTL mapping for seed weight was performed. In total, 15 QTLs associated with TSW were detected. A major and stable QTL, \u003cem\u003eqSW-A03\u003c/em\u003e, was mapped to a 2.8 cM interval on chromosome A03. Fine mapping delimited the \u003cem\u003eqSW-A03\u003c/em\u003elocus into a 59-kb region, and 11 genes within this region were predicted. By employing a combination of gene variation, gene expression difference and gene coexpression network analysis of seed weight, BnaA03G0362100ZS (\u003cem\u003eBnaDUF1666\u003c/em\u003e) was identified as a promising candidate gene. This study provides useful information for the genetic dissection of seed weight and promotes the molecular breeding of high-yield rapeseed.\u003c/p\u003e","manuscriptTitle":"Fine mapping and candidate gene analysis of the major QTL qSW-A03 for seed weight in Brassica napus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-18 14:19:45","doi":"10.21203/rs.3.rs-5271995/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-11-26T10:57:48+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-11-05T02:12:17+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-04T22:07:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-16T06:20:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2024-10-15T22:24:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"30cf9639-e316-4416-ac47-d810d5404fa5","owner":[],"postedDate":"November 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:06:26+00:00","versionOfRecord":{"articleIdentity":"rs-5271995","link":"https://doi.org/10.1007/s00122-025-04866-3","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2025-03-17 15:57:50","publishedOnDateReadable":"March 17th, 2025"},"versionCreatedAt":"2024-11-18 14:19:45","video":"","vorDoi":"10.1007/s00122-025-04866-3","vorDoiUrl":"https://doi.org/10.1007/s00122-025-04866-3","workflowStages":[]},"version":"v1","identity":"rs-5271995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5271995","identity":"rs-5271995","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}