Skip to main content

Fine-mapping and transcriptome analysis of a candidate gene controlling plant height in Brassica napus L.



Brassica napus provides approximately 13–16% of global vegetable oil for human consumption and biodiesel production. Plant height (PH) is a key trait that affects plant architecture, seed yield and harvest index. However, the genetic mechanism of PH in B. napus is poorly understood.


A dwarf mutant df59 was isolated from a large-scale screening of an ethyl methanesulphonate-mutagenized rapeseed variety Ningyou 18. A genetic analysis showed that the dwarfism phenotype was controlled by one semi-dominant gene, which was mapped on C9 chromosome by quantitative trait loci sequencing analysis and designated as BnaDwf.C9. To fine-map BnaDwf.C9, two F2 populations were constructed from crosses between conventional rapeseed cultivars (Zhongshuang 11 and Holly) and df59. BnaDwf.C9 was fine-mapped to the region between single-nucleotide polymorphism (SNP) markers M14 and M4, corresponding to a 120.87-kb interval of the B. napus ‘Darmor-bzh’ genome. Within this interval, seven, eight and nine annotated or predicted genes were identified in “Darmor-bzh”, “Ningyou 7” and “Zhongshuang 11” reference genomes, respectively. In addition, a comparative transcriptome analysis was performed using stem tips from Ningyou 18 and df59 at the stem elongation stage. In total, 3995 differentially expressed genes (DEGs) were identified. Among them, 118 DEGs were clustered in plant hormone-related signal transduction pathways, including 81 DEGs were enriched in auxin signal transduction. Combining the results of fine-mapping and transcriptome analyses, BnaC09g20450D was considered a candidate gene for BnaDwf.C9, which contains a SNP that co-segregated in 4746 individuals. Finally, a PCR-based marker was developed based on the SNP in BnaC09g20450D.


The combination of quantitative trait loci sequencing, fine-mapping and genome-wide transcriptomic analysis revealed one candidate gene located within the confidence interval of 120.87-kb region. This study provides a new genetic resource for semi-dwarf breeding and new insights into understanding the genetic architecture of PH in B. napus.


Rapeseed (Brassica napus, AACC, 2n = 38) is not only an important oilseed crop worldwide, but also an emerging biofuel crop. Rapeseed oil is an ideal vegetable oil for human consumption, because it contains ~ 65% oleic, ~ 20% linoleic, ~ 9% linolenic and a very low level of stearic acid [1]. Vegetable oils are triglyceride sources for biodiesel production. In Europe, biodiesel has been produced mainly from rapeseed oil [2]. In addition, rape straw is an abundant lignocellulosic material for the production of liquid biofuel, particularly ethanol. In China, the cultivated area of rapeseed is ~ 67 million hectares, with an annual seed yield of ~ 4.5 million tons every year [3], and the collectable production of rape straw was 38.17 million tons in 2013 [4]. Plant height (PH) is a key trait that affects the plant architecture, seed yield, dry weight and harvest index [5]. Moreover, PH is the major contributor to lodging tolerance, a serious abiotic stress during rapeseed production. Lodging makes B. napus unsuitable for mechanical harvesting and causes dramatic decreases in yield and seed quality [6]. Therefore, it is important to understand the genetic bases of PH to breed new cultivars with an ideal plant architecture and to maximize B. napus’ economic benefits as an oil and bioenergy crop.

Plant growth and development consist of very precise and complicated procedures. The molecular mechanisms regulating PH in the model plant Arabidopsis thaliana and rice (Oryza sativa) have been well recorded, and most of the genes are involved in phytohormone-related pathways [7,8,9,10,11]. Gibberellic acid (GA) is an essential endogenous regulator of PH. For example, the rice ‘green revolution’ gene sd1 encodes GA20ox2, which is an oxidase enzyme involved in the biosynthesis of GA [12]. The wheat ‘green revolution’ gene Rht-B1/Rht-D1 encodes a DELLA protein that acts as a negative regulator in the GA-signaling pathway [13]. Auxins play pivotal functions in developmental processes, because they are involved in controlling virtually every aspect of plant biology [14]. Indole-3-acetic acid (IAA) is the key auxin in most plants, and it is mainly biosynthesized from tryptophan (Trp) through a Trp-dependent pathway [14, 15]. Loss-of-function mutations in IAA-related Trp-dependent biosynthetic genes (e.g., TAA1 or YUCCA) can seriously affect PH [16, 17]. In the auxin-signaling pathway, transport inhibitor resistant1/auxin-signaling F-box (TIR1/AFB), auxin/indole acetic acid proteins (Aux/IAAs) and auxin response factors (ARFs) are three key factors regulating auxin-induced gene expression [18, 19]. At low auxin levels, the transcriptional repressor Aux/IAA proteins and the corepressor TOPLESS interact with ARF proteins, resulting in auxin-induced gene repression. At high auxin levels, auxin interacts with TIR1, leading to the formation of TIR1/AFB-Aux/IAA-ARF complexes, then ubiquitin-ligase (E3) ubiquitinates AUX/IAAs, forming the ubiquitin 26S proteasome responsible for the degradation of AUX/IAAs [16, 18, 20]. Free ARF proteins can activate the transcription of auxin-response genes. Mutations that occur in the genes involved in auxin signaling may result in typical dwarf phenotypes, such as IAA7 in B. napus [21].

Brassica napus is a tetraploid species, which originated from a hybridization between B. rapa (AA, 2n = 20) and B. oleracea (CC, 2n = 18) around 7500 years ago [22]. In B. napus, many studies have focused on quantitative trait loci (QTLs) detection using linkage mapping or genome-wide association studies (GWASs), and hundreds of QTLs have been identified across all 19 chromosomes [23,24,25,26,27,28,29,30,31]. However, most QTLs explain a small percentage of the total phenotypic variance (PV) [23,24,25,26,27,28,29,30,31]. Only a few QTLs affecting PH have been fine-mapped and cloned in B. napus. Wang et al. obtained two dwarf mutants “Bndwf1” and “Bndwf1/dcl1”, and fine-mapped the associated QTLs on the A9 chromosome to a 152-kb interval and on the C5 chromosome to a 175-kb interval, respectively [32, 33]. Liu et al. mapped the semi-dominant gene ds-1 (BnaA06.RGA) on the A6 chromosome. It encodes a DELLA protein that functions as a repressor in GA signaling [34]. A single proline (P)-to-leucine (L) change was identified in the VHYNP motif of a DELLA protein in ds-1 that leads to a gain-of-function and caused a dwarf phenotype [34]. Subsequently, the DS-3 gene, which is syntenic to ds-1 with a similar gene function but a weaker effect on PH, was mapped on the C7 chromosome [35]. In the auxin-signaling pathway, only two genes that encode Aux/IAA proteins were identified and functionally validated in B. napus, BnaA3.IAA7 on the A3 chromosome and its homolog BnaC05.IAA7 on the C5 chromosome [21, 36]. The molecular mechanisms regulating the PH of B. napus remain elusive, and elucidating the mechanism of a new dwarf gene has important scientific significance and applicable value.

The rapeseed ideotype is a semi-dwarf stature with a plant height of ~ 120–140 cm, narrow branch angles (< 30°), and a middle-long silique length [21, 37]. A dwarf mutant of df59 with a PH of ~ 65 cm was obtained from ethyl methanesulphonate (EMS)-mutagenized Ningyou 18 (NY18). The df59 is an excellent germplasm resource for semi-dwarf breeding, with the average PH of F1 hybrid lines from crosses between NY18 and df59 being 126.75 ± 4.3 cm, which is in accordance with the ideotype criteria of B. napus [21, 37]. The aims of the present study were to: (1) fine-map the gene responsible for dwarf architecture in df59 using QTL sequencing (QTL-seq) and map-based cloning strategies; (2) elucidate the patterns of gene expression between NY18 and df59 using comparative transcriptomic analyses; and (3) develop a stable single-nucleotide polymorphism (SNP) marker tightly linked to the dwarf gene that could be used for marker-assisted selection. The present study provides a new gene source for the semi-dwarf breeding of new varieties, and the findings contribute to a better understanding of the molecular mechanisms underlying dwarfism.


Phenotypic variation and genetic analysis of plant height

At the seedling stage, df59 was already significantly shorter than NY18, with smaller leaves and shorter petioles (Fig. 1a, b). The internode length and PH were different between df59 and NY18 at the mature stage (Fig. 1c), while the petals and siliques of df59 were smaller than those of NY18 (Fig. 1d, e). At maturity, agronomic traits and seed yield-related traits were investigated in NY18, df59 and the F1 of their cross (Table 1). Among the 15 traits, 12 were significantly higher in NY18 than in df59 (Table 1), with the exception of the first effective branch number, pod number of main inflorescence and silique number per plant. The PH of the F1 was 126.75 ± 4.3 cm, which meets the ideotype requirement of ~ 120–140 cm and will be beneficial to high-density planting (Fig. 1f, Table 1). In addition, the seed yield per F1 plant was 28.4 ± 2.20 g, which was 84.02% of the NY18 yield (33.8 ± 2.51 g). The phenotypic values of seed oil content and seed fatty acid concentrations for the NY18, df59 and the F1 are provided in Additional file 1: Table S1. F1 and NY18 had similar C16:0, C18:0, C18:2, C18:3, C20:1 and oil content, but had different C18:1 and C22:1 content. Theoretically, if the planting density of the F1 increased to 1.5 times, the seed yield would increase ~ 26% per unit of area compared with the normal planting density of NY18.

Fig. 1

Phenotypic characterization of NY18, df59, and their F1 at different developmental stages. a Morphology of the NY18, df59 and their F1 at the seedling stage; b comparison of leaf phenotypes at the seedling stage; c comparison of internode lengths at the mature stage; d comparison of petal phenotypes at the flowering stage; e comparison of silique-related traits at the mature stage; f comparison of plant heights and plant architecture at the mature stage. Scale bars = 10 cm

Table 1 Phenotypic values of agronomic traits, seed yield-related traits and root-related traits for NY18, df59 and their F1

Root-related traits, including total root length, root surface area, root volume and number of root tips, were measured for NY18, df59 and their F1 at 10 days after germination (Additional file 2: Figure S1). The values of the four traits in df59 were significantly less than in NY18, and the F1 had approximate mean values of the two parents, which were in accordance with the PH value (Table 1).

The 165 individuals of the F2 population from a cross between NY18 and df59 (named NY–DF) observed contained 43 tall plants, 88 medium plants and 34 dwarf plants. A Chi-squared test indicated that the segregation pattern agreed with the Mendelian segregation ratio of 1:2:1 (χ2 = 1.719, P > 0.05). The mixed major-gene plus polygenes inheritance model in the software package SEA-G4F2 [38], was used to perform the genetic analysis of PH for the NY–DF F2 population along with the two parents and the F1. The results showed that PH is controlled by one major gene with additive-dominant effects. The major gene heritability was 80.04%, with an additive effect of 28.73 and a dominant effect of − 3.12 (Additional file 3: Table S2). The high heritability of the major gene indicated that PH in df59 was relatively stable and not greatly influenced by environment; therefore, it can be selected in the early generations of a breeding process.

QTL-seq of the NY–DF population

Two contrasting DNA pools were constructed from 16 extremely tall lines (T-pool) and 24 extremely dwarf lines (D-pool) in NY–DF F2 population. Illumina high-throughput sequencing generated a total of 92.986 Gb clean data for the two pools and two parental lines, with average Q20 ≥ 95.66% and Q30 ≥ 93.14%. When the obtained reads were matched to the B. napus “Darmor-bzh” reference genome, the results showed that the depths of sequencing coverage for T-pool, D-pool, NY18 and df59 were 35.85-fold, 31.27-fold, 16.79-fold and 17.84-fold, respectively.

Based on the genotyping, 4520 polymorphic SNPs with haplotype differences between the two parents were identified. The region (17.00–27.30 Mb) on chromosome C9 had an average SNP-index of 0.726 in the D-pool, with the highest being 1 (Fig. 2a), while the SNP-index in T-pool was 0.128, with the lowest being 0 (Fig. 2b). Then, the genome sequence of the parent NY18 was used as a reference to calculate the Δ(SNP-index) of the 4520 SNPs by combining the SNP-indices of the T- and D-pools. At a 95% significance level, the genomic region from 17.00 to 27.30 Mb had an average Δ(SNP-index) of 0.60 (Fig. 2c), suggesting that this region harbored a major QTL for PH, which was designated BnaDwf.C9.

Fig. 2

SNP-index and Δ(SNP-index) graphs from the QTL-seq analysis. SNP-indices of a D-pool and b T-pool; c Δ(SNP-index). The x-axes represent the B. napus chromosomes and the y-axes represent the SNP-index (a, b) or Δ(SNP-index) (c). The SNP-index and Δ(SNP-index) calculations are based on the descriptions in the “Methods”. The genomic region (17.00–27.30 Mb) on chromosome C9 had an average Δ(SNP-index) of 0.60 and is regarded as the candidate QTL, with a 95% significance level (P < 0.05)

Fine-mapping the BnaDwf.C9 locus

To fine-map the BnaDwf.C9 locus, two flanking penta-primer amplification refractory mutation system (PARMS) SNP markers, M1 (physical position of the B. napus ‘Darmor-bzh’ genome: 17,001,732) and M11 (27,299,112), were first designed to investigate recombinants in the F2 population (HO–DF) from a cross between Holly and df59 (Additional file 4: Table S3). The results showed that 81 recombinants were obtained among the 2356 F2 individuals, including 56 tall recombinants and 25 dwarf recombinants (Additional file 4: Table S3). Based on the re-sequencing information of NY18 and df59 at the target region, nine polymorphic SNP markers (M2–M10) between M1 and M11 were designed and used for genotyping the 81 recombinant individuals. Recombinant genotypes showed that M2 was located on the left side of BnaDwf.C9, while six SNP markers (M5–M10) were mapped on the right side (Fig. 3, Additional file 4: Table S3). In addition, M3 and M4 co-segregated with BnaDwf.C9 and were consistent with the PH phenotype. Eventually, the location of BnaDwf.C9 was narrowed down to the interval between M2 and M5 in a region of 770.72 kb in the ‘Darmor-bzh’ genome. No polymorphic SNP markers were able to narrow the interval in the HO–DF population.

Fig. 3

Map-based cloning and a candidate gene of BnaDwf.C9. First, the BnaDwf.C9 locus was fine-mapped to a 770-kb region using the HO–DF population containing 2356 F2 individuals. Then, the BnaDwf.C9 locus was further fine-mapped to the 120.87-kb region using the ZS–DF population containing 2210 F2 individuals. The region contains seven putative open reading frames, and only BnaC09g20450D had a SNP (C–T) that changed the proline at the 585th position to serine acid

For the further fine-mapping of BnaDwf.C9, a population of 2210 F2 individuals was constructed from a cross between Zhongshuang 11 (ZS11) and df59 (ZS–DF population). Using the methods described above, 34 recombinant individuals were detected between M2 and M5 in the ZS–DF population (Table 2). Subsequently, three new polymorphic SNP markers in this region (M12–M14) were designed, together with co-segregating SNP markers (M3 and M4) in the HO–DF population and used to screen the 34 recombinants (Additional file 5: Table S4), resulting in 6, 5, 4, 0 and 2 recombinants, respectively (Fig. 3, Table 2). These results suggested that the locus BnaDwf.C9 was fine-mapped to the region between M14 and M4, corresponding a 120.87 kb interval in the B. napus ‘Darmor-bzh’ genome (Fig. 3).

Table 2 Thirty-four recombinants and their genotypes detected in ZS–DF F2 population

Genome-wide transcriptomic analyses of NY18 and df59

High-throughput RNA sequencing (RNA-seq) generated 20.32‒26.83 million raw reads for each sample. After trimming the low-quality sequences, 16.29–22.37 million clean reads were aligned to the B. napus ‘Darmor-bzh’ genome (Additional file 6: Table S5). A total of 3995 differentially expressed genes (DEGs) were identified between NY18 and df59 stem tip transcriptomes using the threshold false discovery rate < 0.005 and at least a 2.0-fold expression changed. In total, 1266 genes were significantly upregulated and 2729 genes were significantly down-regulated in df59 compared with NY18 (Additional file 7: Table S6).

To understand gene functions associated with the dwarf phenotype in df59, a Gene ontology (GO) enrichment analysis of the DEGs was performed. For both up- and down-regulated genes, the most three significantly enriched GO terms in the ‘biological process’, ‘cellular component’ and ‘molecular function’ groups were the same (Additional file 8: Table S7). For example, “metabolic process”, “cellular process” and “single-organism process” were the three most enriched GO terms of the “biological process” group.

To discern DEG functions, we also conducted a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The 1266 up- and 2729 down-regulated genes were enriched in 92 and 114 pathways, respectively (Additional file 9: Table S8). The top 20 enriched pathways for all of the up- and down-regulated DEGs are shown in Fig. 4. The five most significantly enriched pathways were carbohydrate metabolism, signal transduction, biosynthesis of other secondary metabolites, amino acid metabolism, and global and overview (Fig. 4). As the PHs in A. thaliana and rice were mainly regulated by plant hormones [7,8,9,10,11], we focused on the DEGs involved in signal transduction pathway (138 genes). Among them, 118 DEGs were clustered in plant hormone-related signal transduction pathways. In total, 81 DEGs were enriched in auxin signal transduction, accounting for 68.6% of the 118 DEGs (Additional file 10: Table S9). The remaining 37 DEGs were enriched in brassinosteroid (10 genes), abscisic acid (seven genes), GA (five genes), ethylene (five genes), jasmonic acid (four genes), salicylic acid (four genes) and cytokinin (two genes) signaling pathways (Additional file 10: Table S9). This result strongly indicated that the mutation leading to the dwarf phenotype of df59 may occur in an important gene in the auxin signal transduction. The KEGG pathway enrichment analysis provided an important clue for identifying candidate genes.

Fig. 4

KEGG pathway categories of differentially expressed genes between NY18 and df59 at the stem elongation stage

Identification of a candidate gene

Candidate genes underlying the 17.42–17.54 Mb of BnaDwf.C9 were analyzed based on the B. napus “Darmor-bzh” reference genome [22]. The segment harboring the BnaDwf.C9 locus contained seven annotated or predicted genes (Table 3). An RNA-seq analysis demonstrated that none of the expression levels among the seven genes significantly differed between NY18 and df59 at the stem elongation stage (Additional file 11: Table S10). Interestingly, BnaC09g20480D, BnaC09g20490D and BnaC09g20500D were not expressed at all in either NY18 or df59. Three other genes, BnaC09g20440D, BnaC09g20460D and BnaC09g20470D, showed no sequence differences between NY18 and df59 in the open reading frames (ORFs) based on the re-sequencing results. To confirm this result, the ORFs of the three genes in NY18 and df59 were amplified. DNA sequencing also revealed that the ORFs in NY18 and df59 were absolutely accordant. Therefore, BnaC09g20440D, BnaC09g20460D and BnaC09g20470D were not candidate genes as there were neither sequence differences in the ORFs nor expression differences between NY18 and df59.

Table 3 Genes on the mapped 120.87 kb interval of “Darmor-bzh” reference genome and their annotation

In addition, “Ningyou 7” [39] and “ZS11” [40] reference genomes were used for the candidate gene analysis within the 120.87-kb interval harboring BnaDwf.C9. A new gene (chrC09g002459) in the “Ningyou 7” genome that was not in “Darmor-bzh” was identified (Additional file 11: Table S10). However, the gene was not expressed in either NY18 or df59. In the “ZS11” genome, BnaC09G0251200ZS was a new gene with no expression, while BnaC09G0251600ZS was a new predicted gene with a sequence length of only 240 bp, which showed neither a sequence nor expression difference between NY18 and df59 (Additional file 11: Table S10). Therefore, these genes were excluded as candidate genes for BnaDwf.C9.

BnaC09g20450D, named chrC09g002455 in “Ningyou 7” and divided into BnaC09G0251300ZS and BnaC09G0251400ZS in “ZS11”, is homologous to AT2G01190 in Arabidopsis. AT2G01190 belongs to the octicosapeptide/Phox/Bem1p family of proteins and encodes a protein of unknown function (Table 3). The PB1 domain (aa 69–167) is an important functional domain in BnaC09g20450D (Additional file 12: Figure S2). In plants, PB1-mediates interactions of ARF and Aux/IAA to modulate auxin-regulated gene transcription [41, 42]. The KEGG pathway enrichment analysis showed that most of the DEGs related to signal transduction were enriched in the auxin signal transduction pathway. The RNA-seq analysis revealed that BnaC09g20450D had similar transcript levels between NY18 and df59 (Additional file 11: Table S10), while the re-sequencing analysis revealed a SNP in NY18 and df59 (Fig. 3). Subsequently, we amplified and sequenced the promoter region (2.0-kb upstream of the start codon) and the ORF of BnaC09g20450D from NY18 and df59. No polymorphism was identified in the promoter sequences. The ORF is 1887-bp in length, encoding a protein of 628 amino acids, and a single nucleotide substitution (C to T) was identified in the second exon, which changed the proline at the 585th position to serine acid (P585S) (Fig. 3). However, the SNP did not occur in PB1 domain or any other domain (Additional file 12: Figure S2). The SNP in BnaC09g20450D was also developed as PARMS SNP marker M3, which co-segregated with BnaDwf.C9 in 2536 individuals of the HO–DF population and in 2210 individuals of the ZS–DF population (Fig. 3). Therefore, we speculated that BnaC09g20450D was the most likely candidate gene of BnaDwf.C9.

Development of a PCR-based SNP marker specific for BnaDwf.C9

The SNP in the candidate gene BnaC09g20450D was targeted to develop a molecular marker. A SNP marker named BnaPHC9-SNP containing four primers (BnaM3pcr-F/BnaM3pcr-R/BnaM3pcr-Fc/BnaM3pcr-Rt) was designed based on the 400-bp flanking sequence of the SNP. BnaPHC9-SNP was first used to amplify NY18, df59 and their F1, which produced a 351-bp fragment in NY18, a 179-bp fragment in df59, and both 351-bp and 179-bp fragments in the F1. Subsequently, 21 (tall PH), 21 (dwarfism PH) and 22 (medium PH) individuals were randomly selected from the ZS–DF F2 population to test the SNP marker amplification. The results of the agarose gel electrophoresis showed that only a 179-bp PCR fragment was present in individuals with a dwarf PH (Fig. 5a), only a 351-bp fragment was amplified in individuals with a tall PH (Fig. 5b), and both 179-bp and 351-bp fragments were produced in individuals with a medium PH (Fig. 5c). These results suggested that BnaPHC9-SNP could produce specific PCR amplicons from different alleles of the SNP and that it can be used for the rapid identification of the BnaDwf.C9 locus, which confers the dwarfing trait, in breeding programs.

Fig. 5

Electrophoretic profiles obtained for individuals with different plant heights from the developed SNP marker. Z: Zhongshuang 11; D: df59; F1: the F1 of a cross between Zhongshuang 11 and df59; M: DNA marker. The SNP marker can amplify a specific PCR products in df59 and dwarf individuals (179-bp fragment), b Zhongshuang 11 and tall individuals (351-bp fragment), and c F1 and middle individuals (both 351-bp and 179-bp fragments). PCR products were analyzed by electrophoresis in 2.5% agarose gel


df59 is an elite genetic resource for semi-dwarf breeding

In the 1960s, the introduction of dwarfing traits into wheat and rice, combined with the application of improved cultivation methods, led to spectacular increases in grain yields, the so-called “green revolution” [12, 13]. B. napus is the third most important oilseed crop, providing 13–16% of vegetable oil globally [43], and rapeseed oil has a strong potential for use in biodiesel production [44]. In China, up to 70% of the total rapeseed cultivation areas were planted with hybrid rapeseed, because they normally produce a 25% greater seed yield and have greater yield stability [37]. However, the utilization of heterosis in rapeseed also led to the PH increasing significantly, resulting in an increased risk of lodging and a decrease in the harvest index. Owing to the lack of excellent dwarf B. napus germplasm resources, there is no high-yielding semi-dwarf variety widely planted, as there is for wheat and rice. df59 is a dwarf mutant obtained from a large-scale screening of EMS-mutagenized NY18. The PH of F1 individuals of a cross between df59 and NY18 was 126.75 ± 4.3 cm (Table 1), and the seed yield was 84% that of NY18, suggesting that the F1 are suitable for high-density planting and mechanized harvest [37]. The average harvest index in B. napus of 0.28 [45, 46], was much lower than those of wheat and rice (0.4–0.6) [45]. The harvest index of F1 individuals was 0.41, which was significantly greater than that of NY18 (0.34), indicating that the F1 more efficiently utilized water and soil resources (Table 1). In addition, the PH of ZS11 was 162.6 ± 5.5 cm, while the F1 PH of a ZS11 × df59 cross was 114.4 ± 3.9 cm (data not shown), suggesting that allele in df59 showed a dwarfing effect in multiple genetic backgrounds. In summary, df59 is an outstanding male parent for breeding new semi-dwarf hybrid varieties that can be densely planted and machine harvested.

BnaDwf.C9 is a new locus associated with plant height in B. napus

In B. napus, hundreds of QTLs associated with PH have been identified and located on all 19 chromosomes [23,24,25,26,27,28,29,30,31]. Several QTLs on the A3, A6, A9, C5 and C7 chromosomes have been fine-mapped or cloned [21, 32,33,34,35,36]. However, only a few QTLs with minor PVs were obtained on the C9 chromosome in previous studies. For example, Shi et al. detected a minor QTL with a PV range of 3.2–4.3% [23]; Ding et al. identified a environment-specific QTL with a PV of 10.9% [25]; and Wang et al. detected four QTLs at the mature stage, with a PV range of 3.69–9.87% [26]. Using a population containing 520 diverse rapeseed accessions, Sun et al. identified 68 loci, which were distributed over the chromosomes, except for the C8 and C9 chromosomes, significantly associated with PH using a GWAS [29]. Luo et al. and Li et al. also performed GWASs for PH, and no locus significantly associated with PH was identified on the C9 chromosome [27, 28]. Thus, it appeared that there was no major QTL controlling PH on the C9 chromosome in B. napus. In the present study, BnaDwf.C9 was fine-mapped to a 120.87-kb region on the C9 chromosome (Fig. 3). Although it difficult to directly compare BnaDwf.C9 with those reported QTLs owing to the lack of common markers, we still believe that BnaDwf.C9 is a potentially new locus, because it showed an obvious effect on PH.

The candidate gene may regulate plant height of df59 through the auxin-signaling pathway

The plant hormone auxin plays an essential role in most aspects of growth and developmental processes [18, 19, 47]. In most plants, auxin regulates the transcription of auxin-responsive genes through the well-established TIR1/AFB-Aux/IAA-ARF pathway [18, 19]. The Aux/IAAs contain four functional domains [18, 19]: Domain I is a repressor domain that contains a leucine repeat EAR motif [48]; Domain II is an internal domain that contains the degron motif GWPPV; Domains III and IV share high homology and contain C-terminal regions that form a PB1 protein–protein interaction domain [49]. In B. napus, a G-to-A mutation in the GWPPV motif of domain II causes multiple phenotypic alterations, including a reduction in PH and branch angles [21]. Among the seven genes underlying the mapped interval of BnaDwf.C9 (Table 3, Additional file 11: Table S10), only BnaC09g20450D has a SNP (C-to-T mutation) between NY18 and df59. BnaC09g20450D also contains a PB1 domain (aa 69–167) (Additional file 12: Figure S2); however, the P585S substitution did not occurred in the PB1 domain or any other domain. The PB1 domain can facilitate the formation of ARF-ARF, ARF-Aux/IAA, and Aux/IAA-Aux/IAA homo- and hetero-oligomers, because the ARF and Aux/IAA proteins contain similar PB1 domains [49]. We speculate that the P585S substitution in BnaC09g20450D disrupts the normal function of the PB1 domain in an unknown way and then affects auxin signaling resulting in the dwarf stature of df59. The RNA-seq analysis also showed 81 DEGs in the auxin-signaling pathway between NY18 and df59 (Additional file 10: Table S9). Combining the results of fine-mapping and RNA-seq, we hypothesized that the candidate gene of BnaDwf.C9 regulated PH through the auxin-signaling pathway, but the molecular mechanism remains unknown and requires further study.

The developed SNP marker can facilitate the application of BnaDwf.C9 in breeding

Traditional breeding, which largely depends on the breeder’s experience and subjective judgment, is a relatively resource- and time-consuming process. Marker-assisted breeding is an effective and accurate approach to perform target-trait selection in breeding, because traits can be examined at any developmental stage, providing results without incurring environmental impacts [50, 51]. In the present study, we determined that the dwarf B. napus mutant df59 has important potential application value in breeding. However, it is important to determine how to efficiently and extensively use the dwarfing gene in semi-dwarf breeding. To address this question, a molecular marker BnaPHC9-SNP was developed for BnaDwf.C9 based on a SNP that co-segregated in two populations, containing 4746 individuals in total (Table 2, Additional file 4: Table S3). BnaPHC9-SNP was a dominant, allele-specific functional marker, which could amplify specific PCR products in df59 (179-bp fragment), NY18 (351-bp fragment) and their F1 (both 351-bp and 179-bp fragments). The marker was subsequently confirmed using individuals with different PHs in the ZS–DF F2 population (Fig. 5), suggesting that BnaPHC9-SNP can be used to select the target PH without morphological characterization, which would speed up the breeding process. To date, a variety of high-throughput SNP genotyping methods has been developed, including the TaqMan system [52] and Kompetitive allele-specific PCR. However, for most breeders, the equipment needed in these methods is prohibitory and more expensive than using normal primers. The SNP marker developed in this study requires no fluorescence-tagged probes or real-time PCR instruments, and PCR products can be correctly detected using agarose gel electrophoresis. Therefore, BnaPHC9-SNP can be widely used in marker-assisted selection of BnaDwf.C9 and speed up the breeding process.


Brassica napus provides not only edible vegetable oil for human consumption, but also a triglyceride source for biofuel and lubricant production. In the present study, we isolated the dwarf mutant df59 from an EMS-mutagenized NY18. The main agronomic traits in NY18, df59 and their F1 showed that df59 is an elite genetic resource for semi-dwarf breeding. Subsequently, the combination of QTL-seq and fine-mapping revealed a candidate gene located within an interval of 120.87 kb on the C9 chromosome. The transcriptome analysis suggested that the PH of df59 was most likely influenced by a gene involved in auxin signal transduction. In addition, a comprehensive analysis revealed that BnaC09g20450D was the most likely candidate gene. Then, a molecular marker was developed based on the SNP in BnaC09g20450D. These results enrich our knowledge of the genetic architecture underlying PH in B. napus and also provide valuable resources for semi-dwarf breeding.


Plant materials

The dwarf mutant df59 was isolated from EMS-mutagenized lines of NY18. NY18 is a variety cultivated by Jiangsu Academy of Agricultural Sciences, China. Mature seeds of NY18 were mutagenized with 1.0% EMS solution (W/V, Sigma-Aldrich) at pH 7.0 phosphate buffer for 12 h at 25 °C, according to the descriptions of Li et al. with modifications [53]. The mutagenized seeds (M1 generation) were rinsed with water for 4 h and sown in the field. Individual plants were bagged at the flowering stage. Self-pollination seeds of approximately 10,000 individual plants were harvested. Each M2 seeds were then sown into independent line. Dwarf lines were bagged to harvest seeds for evaluation in the M3 generation.

The NY–DF F2 population containing 165 individual lines derived from a cross between NY18 and df59 was used for genetic inheritance and QTL-seq. Two conventional rapeseed cultivars, Holly and ZS11, were used as parental lines to develop segregating populations for fine-mapping of the QTLs associated with dwarf architecture. Holly is a Canadian spring variety, while ZS11 is an elite Chinese semi-winter rapeseed cultivar. The HO–DF F2 population derived from a cross between Holly and df59 contained 2536 lines, while the ZS–DF F2 population derived from a cross between ZS11 and df59 contained 2210 lines.

Trait measurement

NY–DF, HO–DF and ZS–DF populations, as well as their parents and the F1 were all planted in the field of Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu Province, China. No specific permissions were required for the field trials. The field experiments were conducted in accordance with Wang et al. with 20 plants per row and 40 cm between the rows [54]. The PH values for all the materials were measured at the mature stage.

At maturity, five open-pollinated plants each of NY18, df59 and their F1 growing in the middle of the plot were selected. Agronomic traits and seed yield-related traits were measured in accordance with the description of Zhao et al. [55] and Wang et al. [56], including PH, biomass yield, seed yield, thousand seed weight, first effective branch height, first effective branch number, internode length, harvest index, length of main inflorescence, pod number of main inflorescence and silique number per plant. The harvest index was calculated as the ratio of seed yield to biomass yield [45]. In total, 10 well-developed siliques, which were randomly selected from the first branch adjacent to the main inflorescence, were used to determine the silique-related traits of NY18, df59 and their F1, including silique length, silique breadth, seed number per silique and silique volume [56]. The seed oil content was measured by nuclear magnetic resonance using standard methods and fatty acid compositions were determined by near infrared reflectance spectroscopy in accordance with Chen et al. [57].

To investigate root traits at the seeding stage, four plump seeds each of NY18, df59 and the F1 were planted in a seed germination pouch (CYG-19LB; Pheno Trait Technology, Co., Ltd, Beijing, China) at 25 °C, and three repetitions were conducted. After 10 days, total root length (cm), root surface area (cm2), root volume (cm3) and number of root tips for each seeding were measured using LA-S Root Analysis software (Wanshen Ltd, Hangzhou, China).

Statistical and QTL-seq analyses

The mixed major-gene plus polygenes inheritance model of the software package SEA-G4F2 was used to identify the inheritance of PH in the NY–DF F2 population [38].

From the 165 lines of the NY–DF F2 population, 16 extremely tall lines and 24 extremely dwarf lines were selected. Genomic DNA was extracted from young leaves using a Plant Genomic DNA Kit (Tiangen, Beijing, China), and T-pool and D-pool were constructed by mixing equal ratios of appropriate individual DNAs. Sequencing libraries of the two bulks and two parents were constructed, and sequence data were generated using the Illumina HiSeq™ PE150 (Illumina, Inc; San Diego, CA, USA) platform. Both data sequencing and data analyses were performed by Novogene Bioinformatics Technology Co. Ltd. (Beijing, China). The raw data were filtered through a series of quality controls which resulted in the removal of reads with ≥ 10% unidentified nucleotides, > 50% bases having phred quality < 5 or > 10 nt aligned to the adapter. The clean reads of each sample were aligned against the B. napus “Darmor-bzh” reference genome [22] using BWA software [58], and the SAMtools command “rmdup” was used to remove multiple read pairs [59]. Variant calling was performed for all the samples using the Unified Genotyper function in GATK software [60]. SNPs were determined using the Variant Filtration parameter in GATK. Using NY18 as the reference parent, the above two bulks SNP-indices were calculated as the proportion of reads containing SNPs not found in NY18. The Δ(SNP-index) was calculated using the formula: Δ(SNP-index) = SNP-index (D-pool) − SNP-index (T-pool). A QTL was considered a candidate associated with PH if the Δ(SNP-index) was significantly different (P < 0.05).

Fine-mapping of the QTL for plant height

The major QTL for PH was identified based on QTL-seq and named BnaDwf.C9, and SNPs underlying the confidence interval of BnaDwf.C9 were obtained. Total DNAs of HO–DF and ZS–DF F2 individuals were extracted from fresh leaves using a modified cetyl-trimethylammonium bromide method [61]. PARMS was used to screen recombinant plants for fine-mapping among HO–DF and ZS–DF F2 populations. The principle of PARMS SNP genotyping is similar to that of Kompetitive allele-specific PCR assays [62]. The master mix for PARMS markers was purchased from Gentides Biotech Co., Ltd. (Wuhan, China). Detailed information for conducting the qPCR-based PARMS assay was previously published by Zhang et al. [62] and Liu et al. [63].

PARMS SNP markers flanking the confidence interval of BnaDwf.C9 were screened from the two parents, the F1, and 10 individuals each of the extremely tall and dwarf lines of the HO–DF population. Then two polymorphic markers (M1 and M11) were used to screen the 2536 individuals of the HO–DF F2 population (Fig. 3). Combined with phenotypes, plants containing recombinants between the two markers were selected. Furthermore, the recombinant plants were analyzed with newly developed polymorphic PARMS SNP markers (M2–M10), and BnaDwf.C9 was finally mapped to an interval between M2 and M5 (Fig. 3).

For the further fine-mapping of BnaDwf.C9, M2 and M5 were used to screen the 2210 individuals of the ZS–DF F2 population. Using the same method as described above, plants containing recombinants between M2 and M5 were selected. Three new polymorphic PARMS SNP markers (M13–M15) together with co-segregating SNP markers (M3 and M4), were used to analyze the selected recombinant plants (Fig. 3, Additional file 5: Table S4). Finally, we narrowed down BnaDwf.C9 to a genomic region between the markers M14 and M4.

RNA library construction and sequencing

Equivalent amounts of stem tips from NY18 and df59 at the stem elongation stage were collected for RNA extraction, and two biological replicates were performed. Total RNA was extracted using a MiniBEST Plant RNA Extraction Kit (TaKaRa, Dalian, China). RNA-seq and data analysis were carried out by Novogene Bioinformatics Technology Co. Ltd. In brief, the RNA concentration was measured using a Qubit® RNA Assay Kit and a Qubit® 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA). The RNA integrity was assessed using an RNA Nano 6000 Assay Kit and the Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). Each of the two sequencing libraries for NY18 and df59 were constructed using an NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer’s instructions, and the library quality was assessed on the Agilent Bioanalyzer 2100 system. The four libraries were sequenced on an Illumina HiSeq 2000 platform, and 100 bp paired-end reads were generated. These methods were described by Yu et al. with modifications [64].

RNA-seq data analysis

Raw RNA-seq reads were processed to remove reads containing the adapter, reads containing ploy-Ns and low-quality reads. Paired-end clean reads were aligned to the B. napus “Darmor-bzh” reference genome [22] using TopHat version 2.0.6 [65]. The read numbers mapped to each gene were counted using HTSeq version 0.6.1 [66], and reads per kilobase of exon per million reads of each gene were calculated. Differential expression analyses of NY18 and df59 (two biological replicates per sample) were performed using the DESeq R package (1.10.1). Genes with a false discovery rate < 0.005 and log2(fold change) ≥ 1 were declared DEGs.

The GO annotation of DEGs was performed using the GOseq R package [67], and GO terms with corrected P < 0.05 were considered to be significantly enriched terms. In addition, DEGs were submitted to the KEGG ( website, and KEGG enrichment pathways of DEGs were determined using KOBAS online analysis database [68].

Development of an allele-specific marker for traditional PCR amplification

Based on the results of the fine-mapped BnaDwf.C9, the PARMS SNP marker M3 co-segregated with the BnaDwf.C9 gene associated with PH. To confirm this SNP, the coding region of the gene that contained the SNP was amplified and sequenced from NY18 and df59. A region, including 200 bp both upstream and downstream of the SNP, was considered the target region. A SNP marker containing four primers and named BnaPHC9-SNP was designed for the allele-specific amplification of the SNP. The primers were a forward locus primer (BnaM3pcr-F: 5′-GAGAAATACTCCGCAACCTACG-3′), reverse locus primer (BnaM3pcr-R: 5′-ATGTTCCGAAACCAACCAGAG-3′), allele primer 1 (BnaM3pcr-Fc: 5′-TATGAATATGTGGAAAATGAGC-3′) and allele primer 2 (BnaM3pcr-Rt: 5′-GCGTGTAGTATACCTGCTTGGA-3′). BnaM3pcr-F began amplifying from 157-bp upstream of the SNP, while BnaM3pcr-R began amplifying from 193-bp downstream of the SNP. BnaM3pcr-Fc and BnaM3pcr-Rt were upstream and downstream, respectively, with 3′-terminal bases of C and A (according to the allele of NY18 or df59), respectively. A mismatch base was introduced at the third-to-last base of the primer BnaM3pcr-Fc. All the primer oligonucleotides were synthesized by Tsingke Biological Technology Co., Ltd. (Wuhan, China).

The PCR mixture (including dNTPs, Taq buffer and Taq enzyme) was purchased from Gentides Biotech Co., Ltd. The PCR reagent mixture (20 μL total volume) contained: 2 × PCR MIX: 10 μL, BnaM3pcr-F: 0.8 μL, BnaM3pcr-R: 0.8 μL, BnaM3pcr-Fc: 0.8 μL, BnaM3pcr-Rt: 0.8 μL, DNA: 1 μL (50 ng/μL), ddH2O: 5.8 μL. The PCR assay was conducted as described by Zhang et al. with modifications [62], as follows: denaturation at 94 °C for 15 min, followed by 10 cycles of 94 °C for 20 s and 65 °C (− 0.8 °C per cycle) for 1 min, followed by 30 cycles of 94 °C for 20 s and 57 °C for 1 min, and a final extension at 72 °C for 5 min. PCR products were analyzed by electrophoresis in 2.5% agarose gel for 40 min at 100 V in TAE buffer (40 mM Tris–acetate, 1 mM EDTA, pH 8.0).

Availability of data and materials

The raw sequence data have been deposited in the NCBI ( Sequence Read Archive (SRA) under Accession numbers SRR10915207, SRR10915208, SRR10915209 and SRR10915210. All other relevant data during this study are included in the manuscript and additional files.



Auxin response factor


Auxin/indole acetic acid protein

B. napus :

Brassica napus


Differentially expressed gene


Dwarf pool


Ethyl methanesulphonate


Gibberellic acid


Gene ontology


Genome-wide association Study


Kyoto Encyclopedia of Genes and Genomes


Indole-3-acetic acid


Ningyou 18


Open reading frames


Plant height


Phenotypic variance


Quantitative trait locus


Single-nucleotide polymorphism


Transport inhibitor resistant1/auxin signaling F-box


Tall pool


Zhongshuang 11


  1. 1.

    Weiss EA. Oilseed crops. London: Blackwell Publishing Limited; 2000.

    Google Scholar 

  2. 2.

    Pullen J, Saeed K. Investigation of the factors affecting the progress of base-catalyzed transesterification of rapeseed oil to biodiesel FAME. Fuel Process Technol. 2015;130:127–35.

    CAS  Article  Google Scholar 

  3. 3.

    Fan C, Tian J, Hu Z, Wang Y, Lv H, Ge Y, Wei X, Deng X, Zhang L, Yang W. Advances of oilseed rape breeding (in Chinese with an English abstract). J Plant Genet Resour. 2018;19(3):447–54.

    Google Scholar 

  4. 4.

    Zhang B, Ma Y, Geng W, Cui J, Mu K, Hu L. Assessment of rape straw resources for biomass energy production in China (in Chinese with an English abstract). Renew Energy Resour. 2017;35(1):126–34.

    Google Scholar 

  5. 5.

    Islam N, Evans EJ. Influence of lodging and nitrogen rate on the yield and yield attributes of oilseed rape (Brassica napus L.). Theor Appl Genet. 1994;88(5):530–4.

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Khan S, Anwar S, Kuai J, Noman A, Shahid M, Din M, Ali A, Zhou G. Alteration in yield and oil quality traits of winter rapeseed by lodging at different planting density and nitrogen rates. Sci Rep. 2018;8(1):634.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    Zhou F, Lin Q, Zhu L, Ren Y, Zhou K, Shabek N, Wu F, Mao H, Dong W, Gan L, et al. D14-SCFD3-dependent degradation of D53 regulates strigolactone signalling. Nature. 2013;504:406–10.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Jiang L, Liu X, Xiong G, Liu H, Chen F, Wang L, Meng X, Liu G, Yu H, Yuan Y, et al. DWARF 53 acts as a repressor of strigolactone signalling in rice. Nature. 2013;504:401–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Sun T. Gibberellin metabolism, perception and signaling pathways in Arabidopsis. The Arabidopsis Book. 2008;6:e0103.

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Sazuka T, Kamiya N, Nishimura T, Ohmae K, Sato Y, Imamura K, Nagato Y, Koshiba T, Nagamura Y, Ashikari M, et al. A rice tryptophan deficient dwarf mutant, tdd1, contains a reduced level of indole acetic acid and develops abnormal flowers and organless embryos. Plant J. 2009;60(2):227–41.

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Wang Y, Li J. Molecular basis of plant architecture. Annu Rev Plant Biol. 2008;59:253–79.

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Sasaki A, Ashikari M, Ueguchi-Tanaka M, Itoh H, Nishimura A, Swapan D, Ishiyama K, Saito T, Kobayashi M, Khush GS, et al. Green revolution: a mutant gibberellin-synthesis gene in rice. Nature. 2002;416(6882):701–2.

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Peng J, Richards DE, Hartley NM, Murphy GP, Devos KM, Flintham JE, Beales J, Fish LJ, Worland AJ, Pelica F, et al. ‘Green revolution’ genes encode mutant gibberellin response modulators. Nature. 1999;400(6741):256–61.

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Lehmann T, Hoffmann M, Hentrich M, Pollmann S. Indole-3-acetamide-dependent auxin biosynthesis: a widely distributed way of indole-3-acetic acid production? Eur J Cell Biol. 2010;89(12):895–905.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Petrasek J, Hoyerova K, Motyka V, Hejatko J, Dobrev P, Kaminek M, Vankova R. Auxins and cytokinins in plant development 2018. Int J Mol Sci. 2019;20(4):909.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  16. 16.

    Zhao Y, Christensen SK, Fankhauser C, Cashman JR, Cohen JD, Weigel D, Chory J. A role for flavin monooxygenase-like enzymes in auxin biosynthesis. Science. 2001;291(5502):306–9.

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Tao Y, Ferrer J, Ljung K, Pojer F, Hong F, Long J, Li L, Moreno JE, Bowman ME, Ivans LJ, et al. Rapid synthesis of auxin via a new tryptophan-dependent pathway is required for shade avoidance in plants. Cell. 2008;133(1):164–76.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Calderon-Villalobos LI, Tan X, Zheng N, Estelle M. Auxin perception-structural insights. Csh Perspect Biol. 2010;2(7):5546.

    Google Scholar 

  19. 19.

    Lavy M, Estelle M. Mechanisms of auxin signaling. Development. 2016;143(18):3226–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Szemenyei H, Hannon M, Long J. TOPLESS mediates auxin-dependent transcriptional repression during Arabidopsis embryogenesis. Science. 2008;319(5868):1384–6.

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Li H, Li J, Song J, Zhao B, Guo C, Wang B, Zhang Q, Wang J, King G, Liu K. An auxin signaling gene BnaA3.IAA7 contributes to improved plant architecture and yield heterosis in rapeseed. New Phytol. 2019;222(2):837–51.

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Chalhoub B, Denoeud F, Liu S, Parkin IA, Tang H, Wang X, Chiquet J, Belcram H, Tong C, Samans B, et al. Early allopolyploid evolution in the post-Neolithic Brassica napus oilseed genome. Science. 2014;345(6199):950–3.

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Shi J, Li R, Qiu D, Jiang C, Long Y, Morgan C, Bancroft I, Zhao J, Meng J. Unraveling the complex trait of crop yield with quantitative trait loci mapping in Brassica napus. Genetics. 2009;182(3):851–61.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Basunanda P, Radoev M, Ecke W, Friedt W, Becker HC, Snowdon RJ. Comparative mapping of quantitative trait loci involved in heterosis for seedling and yield traits in oilseed rape (Brassica napus L.). Theor Appl Genet. 2010;120(2):271–81.

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Ding G, Zhao Z, Liao Y, Hu Y, Shi L, Long Y, Xu F. Quantitative trait loci for seed yield and yield-related traits, and their responses to reduced phosphorus supply in Brassica napus. Ann Bot. 2012;109(4):747–59.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Wang X, Wang H, Long Y, Liu L, Zhao Y, Tian J, Zhao W, Li B, Chen L, Chao H, et al. Dynamic and comparative QTL analysis for plant height in different developmental stages of Brassica napus L. Theor Appl Genet. 2015;128(6):1175–92.

    PubMed  Article  Google Scholar 

  27. 27.

    Luo X, Ma C, Yue Y, Hu K, Li Y, Duan Z, Wu M, Tu J, Shen J, Yi B, et al. Unravelling the complex trait of harvest index in rapeseed (Brassica napus L.) with association mapping. BMC Genomics. 2015;16(1):379.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. 28.

    Li F, Chen B, Xu K, Gao G, Yan G, Qiao J, Li J, Li H, Li L, Xiao X, et al. A genome-wide association study of plant height and primary branch number in rapeseed (Brassica napus). Plant Sci. 2016;242:169–77.

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Sun C, Wang B, Yan L, Hu K, Liu S, Zhou Y, Guan C, Zhang Z, Li J, Zhang J, et al. Genome-wide association study provides insight into the genetic control of plant height in rapeseed (Brassica napus L.). Front Plant Sci. 2016;7:1102.

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Zheng M, Peng C, Liu H, Tang M, Yang H, Li X, Liu J, Sun X, Wang X, Xu J, et al. Genome-wide association study reveals candidate genes for control of plant height, branch initiation height and branch number in rapeseed (Brassica napus L.). Front Plant Sci. 2017;8:1246.

    PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Shen Y, Xiang Y, Xu E, Ge X, Li Z. Major co-localized QTL for plant height, branch initiation height, stem diameter, and flowering time in an alien introgression derived Brassica napus DH population. Front Plant Sci. 2018;9:390.

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Wang Y, He J, Yang L, Wang Y, Chen W, Wan S, Chu P, Guan R. Fine mapping of a major locus controlling plant height using a high-density single-nucleotide polymorphism map in Brassica napus. Theor Appl Genet. 2016;129(8):1479–91.

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Wang Y, Chen W, Chu P, Wan S, Yang M, Wang M, Guan R. Mapping a major QTL responsible for dwarf architecture in Brassica napus using a single-nucleotide polymorphism marker approach. BMC Plant Biol. 2016;16(1):178.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Liu C, Wang J, Huang T, Wang F, Yuan F, Cheng X, Zhang Y, Shi S, Wu J, Liu K. A missense mutation in the VHYNP motif of a DELLA protein causes a semi-dwarf mutant phenotype in Brassica napus. Theor Appl Genet. 2010;121(2):249–58.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Zhao B, Li H, Li J, Wang B, Dai C, Wang J, Liu K. Brassica napus DS-3, encoding a DELLA protein, negatively regulates stem elongation through gibberellin signaling pathway. Theor Appl Genet. 2017;130(4):727–41.

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Zheng M, Hu M, Yang H, Tang M, Zhang L, Liu H, Li X, Liu J, Sun X, Fan S, et al. Three BnaIAA7 homologs are involved in auxin/brassinosteroid-mediated plant morphogenesis in rapeseed (Brassica napus L.). Plant Cell Rep. 2019;38(8):883–97.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Fu T, Zou Y. Progress and future development of hybrid rapeseed in China. Eng Sci. 2013;11(5):13–8.

    Google Scholar 

  38. 38.

    Cao X, Liu B, Zhang Y. SEA: a software package of segregation analysis of quantitative traits in plants (in Chinese with an English abstract). J Nanjing Agric University. 2013;36(6):1–6.

    Google Scholar 

  39. 39.

    Zou J, Mao L, Qiu J, Wang M, Jia L, Wu D, He Z, Chen M, Shen Y, Shen E, et al. Genome-wide selection footprints and deleterious variations in young Asian allotetraploid rapeseed. Plant Biotechnol J. 2019;17(10):1998–2010.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Song J, Guan Z, Hu J, Guo C, Yang Z, Wang S, Liu D, Wang B, Lu S, Zhou R, et al. Eight high-quality genomes reveal pan-genome architecture and ecotype differentiation of Brassica napus. Nat Plants. 2020.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Korasick DA, Westfall CS, Lee SG, Nanao MH, Dumas R, Hagen G, Guilfoyle TJ, Jez JM, Strader LC. Molecular basis for AUXIN RESPONSE FACTOR protein interaction and the control of auxin response repression. P Natl Acad Sci USA. 2014;111(14):5427–32.

    CAS  Article  Google Scholar 

  42. 42.

    Nanao MH, Vinos-Poyo T, Brunoud G, Thévenon E, Mazzoleni M, Mast D, Lainé S, Wang S, Hagen G, Li H, et al. Structural basis for oligomerization of auxin transcriptional regulators. Nat Commun. 2014;5(1):3617.

    PubMed  Article  CAS  Google Scholar 

  43. 43.

    Wang B, Wu Z, Li Z, Zhang Q, Hu J, Xiao Y, Cai D, Wu J, King GJ, Li H, et al. Dissection of the genetic architecture of three seed-quality traits and consequences for breeding in Brassica napus. Plant Biotechnol J. 2018;16(7):1336–48.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Uttamaprakrom W, Reubroycharoen P, Vitidsant T, Charusiri W. Catalytic degradation of rapeseed (Brassica napus) oil to a biofuel using MgO: an optimization and kinetic study. Journal of the Japan Institute of Energy. 2017;96(6):190–8.

    Article  Google Scholar 

  45. 45.

    Chao H, Raboanatahiry N, Wang X, Zhao W, Chen L, Guo L, Li B, Hou D, Pu S, Zhang L, et al. Genetic dissection of harvest index and related traits through genome-wide quantitative trait locus mapping in Brassica napus L. Breeding Sci. 2019;69(1):104–16.

    CAS  Article  Google Scholar 

  46. 46.

    Lu K, Xiao Z, Jian H, Peng L, Qu C, Fu M, He B, Tie L, Liang Y, Xu X, et al. A combination of genome-wide association and transcriptome analysis reveals candidate genes controlling harvest index-related traits in Brassica napus. Sci Rep-UK. 2016;6(1):36452.

    CAS  Article  Google Scholar 

  47. 47.

    Salehin M, Bagchi R, Estelle M. SCFTIR1/AFB-based auxin perception: mechanism and role in plant growth and development. Plant Cell. 2015;27(1):9–19.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Kagale S, Rozwadowski K. EAR motif-mediated transcriptional repression in plants: an underlying mechanism for epigenetic regulation of gene expression. Epigenetics-US. 2011;6(2):141–6.

    CAS  Article  Google Scholar 

  49. 49.

    Guilfoyle TJ. The PB1 domain in auxin response factor and Aux/IAA proteins: a versatile protein interaction module in the auxin response. Plant Cell. 2015;27(1):33–43.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Zhao X, Ma W, Gale KR, Lei Z, He Z, Sun Q, Xia X. Identification of SNPs and development of functional markers for LMW-GS genes at Glu-D3 and Glu-B3 loci in bread wheat (Triticum aestivum L.). Mol Breeding. 2007;20(3):223–31.

    CAS  Article  Google Scholar 

  51. 51.

    Jiang G. Molecular marker-assisted breeding: a plant breeder’s review. Advances in plant breeding strategies: breeding, biotechnology and molecular tools. Switzerland: Springer; 2015.

    Google Scholar 

  52. 52.

    De la Vega FM, Lazaruk KD, Rhodes MD, Wenz MH. Assessment of two flexible and compatible SNP genotyping platforms: TaqMan SNP genotyping assays and the SNPlex genotyping system. Mutat Res. 2005;573:111–35.

    PubMed  Article  CAS  Google Scholar 

  53. 53.

    Li H, Li J, Zhao B, Wang J, Yi L, Liu C, Wu J, King G, Liu K. Generation and characterization of tribenuron-methyl herbicide-resistant rapeseed (Brassica napus) for hybrid seed production using chemically induced male sterility. Theor Appl Genet. 2015;128(1):107–18.

    PubMed  Article  CAS  Google Scholar 

  54. 54.

    Wang X, Yu K, Li H, Peng Q, Chen F, Zhang W, Chen S, Hu M, Zhang J. High-density SNP map construction and QTL identification for the apetalous character in Brassica napus L. Front Plant Sci. 2015;6:1164.

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Zhao W, Wang X, Wang H, Tian J, Li B, Chen L, Chao H, Long Y, Xiang J, Gan J, et al. 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. 2016;7:17.

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Wang X, Chen L, Wang A, Wang H, Tian J, Zhao X, Chao H, Zhao Y, Zhao W, Xiang J, et al. Quantitative trait loci analysis and genome-wide comparison for silique related traits in Brassica napus. BMC Plant Biol. 2016;16(1):71.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Chen F, Zhang W, Yu K, Sun L, Gao J, Zhou X, Peng Q, Fu S, Hu M, Long W, et al. Unconditional and conditional QTL analyses of seed fatty acid composition in Brassica napus L. BMC Plant Biol. 2018;18(1):49.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  58. 58.

    Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  60. 60.

    McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Porebski S, Bailey LG, Baum BR. Modification of a CTAB DNA extraction protocol for plants containing high polysaccharide and polyphenol components. Plant Mol Biol Rep. 1997;15(1):8–15.

    CAS  Article  Google Scholar 

  62. 62.

    Zhang B, Zhao N, Liu Y, Jia L, Fu Y, He X, Liu K, Xu Z, Bao B. Novel molecular markers for high-throughput sex characterization of Cynoglossus semilaevis. Aquaculture. 2019;513:734331.

    CAS  Article  Google Scholar 

  63. 63.

    Lu J, Hou J, Ouyang Y, Luo H, Zhao J, Mao C, Han M, Wang L, Xiao J, Yang Y, et al. A direct PCR-based SNP marker-assisted selection system (D-MAS) for different crops. Mol Breeding. 2020;40(9):1–10.

    Google Scholar 

  64. 64.

    Yu K, Wang X, Chen F, Chen S, Peng Q, Li H, Zhang W, Hu M, Chu P, Zhang J, et al. Genome-wide transcriptomic analysis uncovers the molecular basis underlying early flowering and apetalous characteristic in Brassica napus L. Sci Rep-UK. 2016;6(1):30576.

    CAS  Article  Google Scholar 

  65. 65.

    Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–11.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Anders S, Pyl PT, Huber W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–9.

    CAS  PubMed  Article  Google Scholar 

  67. 67.

    Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11(2):14.

    Article  CAS  Google Scholar 

  68. 68.

    Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, Kong L, Gao G, Li C, Wei L. KOBAS 20: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011;39:316–22.

    Article  CAS  Google Scholar 

Download references


Not applicable.


The work was supported by National Natural Science Foundation of China (31971973, 31801402), the Earmarked Fund for China Agriculture Research System (CARS-12), Jiangsu Agricultural Science and Technology Innovation Fund (CX(19)3053), China Postdoctoral Science Foundation (2018M630231) and Jiangsu Collaborative Innovation Center for Modern Crop Production.

Author information




XW and MZ designed the research and wrote the manuscript. HL and LZ carried out the statistical analysis and QTL-seq analysis. FC and WZ performed the fine-mapping of candidate gene. MP participated in the field experiment. SF and MH developed the molecular markers. HW, JZ and WH led and coordinated the overall study. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jiefu Zhang or Wei Hua.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors consent for publication.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1: Table S1.

The phenotypic values of seed oil content and seed fatty acid concentrations for the NY18, df59 and the F1.

Additional file 2: Figure S1.

Root related traits of NY18, df59 and their F1 at 10 days after germination.

Additional file 3: Table S2.

Genetic parameters estimated in one major gene with additive-dominant model in the NY–DF F2 population.

Additional file 4: Table S3.

Eighty-one recombinants and their genotypes detected in HO–DF F2 population.

Additional file 5: Table S4.

PARMS SNP markers used for fine-mapping of the BnaDwf.C9 locus in ZS–DF F2 population.

Additional file 6: Table S5.

Summary of transcriptome sequencing data.

Additional file 7: Table S6.

The differentially expressed genes between NY18 and df59.

Additional file 8: Table S7.

GO terms for the differentially expressed genes between NY18 and df59.

Additional file 9: Table S8.

KEGG pathways for the differentially expressed genes between NY18 and df59.

Additional file 10: Table S9.

The 118 differentially expressed genes clustered in plant hormone-related signal transduction pathways.

Additional file 11:

Genes on the mapped 120.87 kb interval of “Darmor-bzh”, “Ningyou 7” and “Zhongshuang 11” reference genomes and their expression pattern.

Additional file 12: Figure S2.

The conserved domains for the candidate gene BnaC09g20450D.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Zheng, M., Liu, H. et al. Fine-mapping and transcriptome analysis of a candidate gene controlling plant height in Brassica napus L.. Biotechnol Biofuels 13, 42 (2020).

Download citation


  • Brassica napus L.
  • Plant height
  • Quantitative trait loci sequencing
  • Fine-mapping
  • Transcriptome analysis
  • Candidate gene
  • Molecular marker