Skip to main content
Download PDF

Multi-omics analysis dissects the genetic architecture of seed coat content in Brassica napus

Genome Biology volume 23, Article number: 86 (2022) Cite this article

Abstract

Background

Brassica napus is an important vegetable oil source worldwide. Seed coat content is a complex quantitative trait that negatively correlates with the seed oil content in B. napus.

Results

Here we provide insights into the genetic basis of natural variation of seed coat content by transcriptome-wide association studies (TWAS) and genome-wide association studies (GWAS) using 382 B. napus accessions. By population transcriptomic analysis, we identify more than 700 genes and four gene modules that are significantly associated with seed coat content. We also characterize three reliable quantitative trait loci (QTLs) controlling seed coat content by GWAS. Combining TWAS and correlation networks of seed coat content-related gene modules, we find that BnaC07.CCR-LIKE (CCRL) and BnaTT8s play key roles in the determination of the trait by modulating lignin biosynthesis. By expression GWAS analysis, we identify a regulatory hotspot on chromosome A09, which is involved in controlling seed coat content through BnaC07.CCRL and BnaTT8s. We then predict the downstream genes regulated by BnaTT8s using multi-omics datasets. We further experimentally validate that BnaCCRL and BnaTT8 positively regulate seed coat content and lignin content. BnaCCRL represents a novel identified gene involved in seed coat development. Furthermore, we also predict the key genes regulating carbon allocation between phenylpropane compounds and oil during seed development in B. napus.

Conclusions

This study helps us to better understand the complex machinery of seed coat development and provides a genetic resource for genetic improvement of seed coat content in B. napus breeding.

Background

Brassica napus is one of the most important oil crops which produces approximately 13% of the edible oil worldwide (USDA ERS, 2020). The seeds of B. napus are mainly composed of two parts, embryo and seed coat [1]. Though oil and protein are concentrated in the embryo, seed coat plays crucial roles in many biological processes such as signal transmission, nutrition transportation, protection against biotic and abiotic stresses, and control of seed size [2, 3]. The seed coat is derived from the outer and inner integuments of the ovule [4]. Proanthocyanidins (PAs), an end products of flavonoid biosynthesis, are synthesized and accumulated in inner integument during the seed development [5]. The innermost cells of the outer integument form a palisade layer, which shows cell wall thickenings in the seed coats of B. napus [6, 7]. Seed coat content (SCC) refers to the ratio of seed coat mass to total seed weight and it is directly affected by the thickness of the seed coat, which is controlled by secondary cell wall formation, including the biosynthesis of cellulose, hemicellulose, and lignin [8, 9].

Previous studies have shown that seed oil content (SOC) is significantly negatively correlated with the SCC in B. napus [10, 11]. Several co-located QTLs were found to control SCC, SOC, and seed coat color [12]. More interestingly, malonyl-CoA is not only the substrate for fatty acid synthesis, but also a substrate for flavonoid synthesis [13]. In addition, malonyl-CoA is produced from acetyl-CoA, which is also involved in the synthesis of cell wall xyloglucan [14]. The seed coat color of B. napus is determined by the content of PAs (epicatechin and its derivatives) and insoluble PAs (In-PAs) [15,16,17]. It has been revealed that the of content PAs and polyphenol was higher in the seed coat of the black-seeded variety compared with the yellow-seeded variety [17]. Yellow-seeded rape has the advantages of thin seed coat, low seed coat and lignin content, high oil and protein content, and transparent oil [18,19,20]. Therefore, the development of yellow-seeded cultivars has been an important breeding goal for B. napus [21,22,23]. Nevertheless, the reason for the correlation between seed coat color and some traits of B. napus with yellow seeds such as lower SCC and higher SOC has not yet been fully elucidated. A full understanding of the metabolite synthesis in the seed coat which affects SCC would be beneficial for further improvement of SOC of B. napus [24,25,26].

Lignin, a complex natural polymer of monolignols, is one of the main components of secondary cell walls. Low lignin (acid detergent lignin) content is related to reduce of seed coat thickness [27, 28]. Both lignin and flavonoids are synthesized by the phenylpropane pathway, in which phenylalanine ammonia-lyase (PAL), trans-cinnamate 4-monooxygenase (C4H), and 4-coumarate-CoA ligase (4CL) catalyze phenylalanine to coumaroyl-CoA step by step and the lignin and flavonoids are finally produced in different branch pathways, respectively [29]. A close correlation between the lignin mainly located in seed coat and seed color was found in B. napus [30]. Some QTLs for lignin content and seed coat color were also found to co-locate in B. napus [31, 32]. Lignin and pigment synthesis share common precursors such as coumaroyl-CoA in the phenylpropane biosynthesis pathway may explain the reason for the coincidence of light seed color and low lignin content [33]. Therefore, lignin biosynthesis and flavonoid accumulation in the seed coat are likely to be regulated by the same key genes. More recent studies have reported that seed lignin content significantly positively correlates with SCC and negatively correlates with SOC and reduction of lignin content results in an increase of SOC [27, 34,35,36]. However, the genetic basis of SCC during the seed coat formation in B. napus remains unclear.

With the development of omics technologies, the use of omics data in resolving regulatory mechanisms of complex traits is becoming an important tool [37,38,39,40]. GWAS has been widely used in various crops such as rice, maize, cotton, and rapeseed, as it can locate the loci through the association of markers with traits [41,42,43,44,45,46,47,48,49,50,51,52]. Recently, expression genome-wide association analysis (eGWAS), co-expression module analysis and transcriptome-wide association studies (TWAS) based on population transcriptome data have been used to further reveal the regulatory relationship between gene and gene or phenotype [53,54,55,56,57,58,59,60,61]. In crops, several studies have been conducted to explore the trait-related regulatory genes and modules by the population transcriptome data with proven success [62, 63].

To dissect the genetic basis of SCC in B. napus, we attempted to identify genes, QTLs, and regulatory networks associated with SCC of B. napus using TWAS, GWAS, and gene module analysis in this study. We identified a great number of significant genes, loci, and four gene modules which are potentially involved in the regulation of SCC during seed coat formation in B. napus. The identification of SCC-related genes, loci, and regulatory networks would enrich the knowledge of genetic basis of SCC trait and accelerate the breeding B. napus varieties with low SCC and high SOC.

Results

Transcriptome-wide association studies illuminate the molecular basis of natural variation of SCC

We obtained SCC phenotypic data of 382 B. napus accessions and the results revealed a large variation in SCC ranging from 11.83 to 21.87% (Additional file 2: Table S1; Additional file 1: Fig. S1a). We found a significant negative correlation between SCC and SOC (square of Pearson coefficient = 0.18, P = 3.33×10−19. Additional file 1: Fig. S1b). To further determine the genes involved in the determination of SCC, TWAS were performed using transcriptomic data from the developing seeds of 257 accessions at 20 days after flowering (DAF) DAF and 253 accessions at 40 DAF collected from data in our previous studies (Additional file 2: Table S2) [63], because seeds oil biosynthesis starts from early developmental stage and seed oil is rapidly accumulated in late developmental stage [64]. There were 243 and 639 genes significantly associated with SCC at 20 DAF and 40 DAF, respectively (Fig. 1a, b; Additional file 2: Table S3, 4). Moreover, 103 significant genes were detected in both stages of seed development (Fig. 1c; Additional file 2: Table S5). A large proportion of TWAS-significant genes were shown to be related to phenylpropane and flavonoid biosynthesis pathways, such as PAL, 4CL, C4H, BANYULS (BAN), dihydroflavonol-4-reductase (DFR), flavanone 3-hydroxylase (F3H), and genes involved in TRANSPARENT TESTA (TT) family and MYB family genes (odds ratio = 724.85, P = 2.05×10−8) [65, 66]. TT family genes have been shown to participate in the PAs synthesis in Arabidopsis, we located multiple homologous genes of TT2, TT4, TT5, TT8, and TT12 in B. napus, indicating that the synthesis of PAs shows a close relationship with SCC, consistent with the previous results reported by Yan et al. [12] and Stein et al. [67]. In addition to PAL, C4H, and 4CL involved in the general phenylpropanoid pathway, it is worth to note that genes related to lignin biosynthesis, such as MYB family transcription factor genes MYB85, MYB63, MYB7 and MYB4 [68], as well as genes encoding catalytic enzymes such as pinoresinol reductase 1 (PRR1), caffeic acid O-methyltransferase 1 (OMT1), beta-glucosidase 46 (BGLU46) [69,70,71], and Cinnamoyl-CoA reductase-like (CCRL) [72,73,74] were also present in the TWAS-significant genes, indicating that SCC is affected by lignin biosynthesis (Additional file 2: Table S6, 7). The discovery of genes related to the secondary wall, such as MYB61 and xyloglucan endotransglucosylase (TCH4) also showed that the effect of secondary wall metabolism on SCC cannot be underestimated [75, 76]. These results indicate that there is a potential correlation between SCC and the phenylpropane biosynthesis and metabolic pathway or SOC, which is consistent with previous studies [4, 12].

Fig. 1
figure 1

Transcriptome-wide association studies of SCC. a, b Manhattan plot of TWAS results (FDR < 0.05) for SCC at 20 DAF (a) and 40 DAF (b). Each point represents a single gene tested. Genomic positions of genes are plotted on the X-axis and the log-transformed FDR values of association between gene expression and SCC are plotted on the Y-axis. The genes positively or negatively associated with the SCC are plotted above or under the black bold line, respectively. The golden dots indicate genes involved in the phenylpropanoid biosynthesis pathway and secondary cell wall development identified in Arabidopsis. The dashed gray horizontal lines represent the significance level. c Venn diagram showing shared genes between significant genes identified by TWAS at 20 DAF and 40 DAF. d GO enrichment analysis of overlapped significant genes identified by TWAS at 20 DAF and 40 DAF. The dot size and color indicate the gene number and the range of FDR values, respectively

Full size image

We further performed GO enrichment analysis of significant genes (FDR < 0.05) identified by TWAS at 20 DAF, 40 DAF, and the 103 overlapped significant genes in two stages, respectively. The results showed that these genes were mainly enriched in the biosynthetic pathways of secondary metabolites such as flavonoids and lignin. In addition, some genes were involved in stress-related and other processes (Fig. 1d; Additional file 1: Fig. S2; Additional file 2: Table S8-10). Taken together, these results indicate that the variation of SCC may be mainly determined by the secondary metabolites synthesized through the phenylpropane pathway.

Four gene modules are involved in controlling SCC in B. napus

To further reveal how multiple genes coordinate to regulate SCC, we analyzed the gene modules identified in two developmental stages of B. napus seeds to elucidate the putative regulatory mechanisms of SCC. At 20 DAF, only one significant component (the corresponding gene module was termed M65) was found to be significantly correlated with SCC (M65: LMM P = 1.52×10−11) (Fig. 2a). At 40 DAF, three components (the corresponding gene module was termed M139, M97, and M79) were significantly correlated with SCC (M139: LMM P = 6.09×10−13, M79: LMM P = 1.14×10−7, M97: LMM P = 4.1×10−4) (Fig. 2b). Except for M97, the other three modules were found to be significantly correlated with SOC in Tang’s study [63]. The genes in these modules are involved in transcriptional regulation, stress response, signal transduction, transmembrane transport, and photosynthesis. We performed correlation analysis between the modules and SCC and found that only M65 and M139 were significantly negatively correlated with SCC at both seed developmental stages (Fig. 2c, d).

Fig. 2
figure 2

SCC-related gene modules and co-expression networks in seeds. a Gene correlation network of SCC-related module (M65) at 20 DAF. Phenylpropanoid biosynthesis-related genes significantly associated to SCC detected by TWAS at 20 DAF are labeled in the module. b Gene correlation network of SCC-related modules (M97, M79, M139) at 40 DAF. Phenylpropanoid biosynthesis-related genes significantly associated to SCC detected by TWAS at 40 DAF are labeled in the module. c Correlation between the expression pattern (X-axis) of M65 and SCC (Y-axis). d Correlation between the expression pattern (X-axis) of M139 and SCC (Y-axis). e Correlation network built on the overlapped genes of the TWAS-significant genes, M65 and M139. The green dots represent phenylpropanoid biosynthesis-related transcription factors and red dots represent other phenylpropanoid biosynthesis-related genes, the blue dots represent the rest of genes. The size of a node is proportional to its degree (the number of correlated genes). Genes in a, b, and e are shown by dots and gene-gene correlations (FDR < 0.01) are indicated by gray lines

Full size image

To find the key regulators of seed coat development, the 68 genes overlapped among TWAS-significant genes, M65 at 20 DAF and M139 at 40 DAF were further analyzed. It was found that BnaC06g08390D (TT1), BnaA02g31540D (WRKY50), BnaA07g20440D (MYB63), BnaA05g01400D (CPC), DnaC09g24870D (CPC), BnaC03g25810D (CPC), BnaC09g24870D (TT8), and BnaA09g22810D (TT8) in the gene set are transcription factors associated with the phenylpropane biosynthesis and expression of the two TT8 homologous genes among them exhibited the highest association with SCC. In addition, we also identified BnaC07g42660D (CCRL), BnaC02g05070D (TT4), BnaA03g60670D (BAN), BnaC08g22640D (F3H), BnaC06g14510D (PAL2), BnaA02g03440D (GSTF12), and BnaA05g11950D (C4H), which are also phenylpropane biosynthesis pathway-related genes, among all genes a CCRL gene showed the highest association with SCC (Fig. 2e). TT8 is a well-known bHLH transcription factor regulating flavonoid synthesis [77] and CCRL encodes a NAD(P)-binding Rossmann-fold superfamily protein, which was predicted to participate in lignin synthesis as a cinnamoyl-CoA-reductase like protein [78, 79]. We therefore considered BnaTT8s and BnaC07.CCRL as the candidate key regulators of the SCC trait for further study.

qSCC.A09 infers a regulatory hotspot of SCC

GWAS were performed for SCC trait and 3 significant QTLs were identified, two of them were located on chromosome C05 named as qSCC.C05.1 and qSCC.C05.2, and one was on chromosome A09 named as qSCC.A09 (Fig. 3a). A series of genes related to seed coat development, such as mucilage-related 21 (MUCI21), PAL4, subunit of exocyst complex 8 (SEC8), and MYB52, were located in three QTL regions (Additional file 2: Table S11) and there were multiple TWAS-significant genes located in the three SCC-related QTL regions (Additional file 2: Table S12).

Fig. 3
figure 3

The regulatory hotspot on chromosome A09 affects SCC. a Manhattan plot of GWAS results for SCC. Three significant QTLs are denoted. b Manhattan plot of GWAS results for expression value of BnaA09.TT8, BnaC09.TT8 and BnaC07.CCRL. The SCC QTLs qSCC.A09 identified by GWAS are denoted. Significant eGWAS associations for TWAS-significant genes at 40 DAF are marked (gray lines); associations co-localized with qSCC.A09 are highlighted in green. BnaA09.TT8, BnaC09.TT8 and BnaC07.CCRL localization results are highlighted in black. c The log-transformed P values form GWAS plotted against those for variants from eGWAS of qTT8.A09 (around qSCC.A09 lead SNP 150 kb). d Flowchart of screening of putative phenylpropanoid and lignin synthesis-related genes regulated by BnaTT8s. Based on the eGWAS results of phenylpropanoid and lignin synthesis-related genes, whose eQTLs co-localized with qSCC.A09 were detected. eQTLs of some genes were not detected to be co-localized with qSCC.A09 using BnaTT8s as a covariate. Among these genes, differentially expressed genes when BnaTT8s were mutated were identified

Full size image

eGWAS is a powerful method to localize expression quantitative trait loci (eQTLs) that function upstream of target genes [53,54,55]. We subsequently performed eGWAS on BnaC07.CCRL and BnaTT8s to identify its eQTLs. Interestingly, we found three eQTLs qTT8.A09, qTT8.C09, and qCCRL.C07 identified by eGWAS co-located with qSCC.A09 (Fig. 3b, c; Additional file 2: Table S13). In addition, we performed eGWAS for 639 TWAS-significant genes at 40 DAF and found that over 500 TWAS significant genes associated with SOC had eQTLs localized in the qTT8.A09 regulatory hotspot (Additional file 1: Fig. S3; Additional file 2: Table S14). GO enrichment analysis of the 243 genes revealed a significant bias in many categories associated with phenylpropanoid metabolism, abiotic stresses, such as responses to UV, chemical, and wounding (Additional file 2: Table S15). Thus, these results suggest that the hotspot on chromosome A09 can regulate not only BnaC07.CCRL and BnaTT8s but also a series of TWAS-significant genes.

To further investigate whether the qSCC.A09 hotspot region could regulate the expression of lignin-related genes through transcription factor BnaTT8, a total of 255 B. napus homologous genes related to lignin biosynthesis were identified and eGWAS were performed for these genes (Additional file 2: Table S16). The eQTLs for 27 of these genes were co-localized in the qSCC.A09 hotspot region, indicating that they are potentially regulated by qSCC.A09. However, when adding the expression of BnaTT8 as a covariate to the eGWAS analysis, the eQTLs of 10 phenylpropanoid biosynthesis-related genes could not be detected, including BnaA03.C4H, BnaA05.PAL1, BnaA05.C4H, BnaA07.MYB63, BnaC03.MYB36, BnaC03.C4H, BnaC03.4CL, BnaC03.CCoAOMT, BnaC06.PAL2, and BnaC07.CCRL (Additional file 2: Table S17). Among them, the C4H and PAL homologs and CCRL, are differentially expressed in the BnaTT8 mutant compared with WT [80] (Fig. 3d). Furthermore, the expression levels of these six genes showed significant positive correlation with BnaTT8 expression (Additional file 1: Fig. S4, 5), indicating that the qSCC.A09 may regulate the expression of these six genes involved in the general phenylpropanoid pathway and lignin biosynthesis through BnaTT8.

Disruption of BnaTT8 or BnaCCRL confers lower SCC, lignin content, and higher SOC in B. napus

In order to clarify whether BnaA09.TT8 and BnaC09.TT8 affect SCC in B. napus, sgRNA1 and sgRNA2 targeting C-terminal conserved bHLH domain in the 7th exon were designed to generate BnaA09.TT8 and BnaC09.TT8 in B. napus cv. Westar using the CRISPR/Cas9 genome-editing system (Additional file 1: Fig. S6). A total of 43 T0-positive transgenic plants were generated and 12 T2 lines with homozygous mutations in both BnaA09.TT8 and BnaC09.TT8 identified by DNA sequencing of the PCR products of the target sites showed two different seed coat colors. Variegated seeded BnaTT8-sgRNA lines (L14, L17, and L21) and yellow seeded BnaTT8-sgRNA lines (L18, L24, and L30) were found to be BnaTT8 double homozygote mutants (T2) which were chosen for further study (Fig. 4a). The data from the assessment of off-target effects of CRISPR/Cas9 showed no mutations at the potential off-target sites (Additional file 2: Table S18). According to the genotyping analysis (Additional file 1: Fig. S6), the detected homozygous mutations at the target sites caused frame shifts and premature termination, which resulted in a truncated bHLH conserve domain (29 amino acids) of BnaA09.TT8 and a depletion of the whole bHLH conserve domain (43 amino acids) of BnaC09.TT8 in L14, L17, and L21 lines and non-functional BnaA09.TT8 and BnaC09.TT8 without the whole bHLH conserve domain in L18, L24, and L30 lines (Additional file 1: Fig. S7). These results suggest that the N-terminal 29 aa of the bHLH conserve domain of BnaA09.TT8 plays an important role in the BnaTT8 function and these yellow seeded lines (L18, L24 and L30) are BnaTT8 double knockout mutants. Oxidized PAs content differs between the BnaTT8 double knockout mutants and WT (Additional file 1: Fig. S13a-c).

Fig. 4
figure 4

Functional characterization of BnaTT8 as a positive regulator of seed lignin biosynthesis in B. napus. a Seeds of CRISPR/Cas9-induced BnaA09.TT8 and BnaC09.TT8 homozygous double mutant lines (L14, L17, L21, L18, L24, and L30). Bar = 1 cm. b the seed coats of L21 and L24. Bar = 1 cm. Determination of thickness of seed coat (c), SCC (d), lignin content (e), and seed oil content (f) in the BnaTT8 double knockout mutant seeds and the control (WT). Values are means ± SD of three biological replicates (n = 6). Student’s t-test was used for statistical analysis between the BnaTT8-sgRNA lines and WT (*, P < 0.05)

Full size image

In addition, the thickness of seed coat of the BnaTT8 double knockout mutants (L18, L24, and L30) was reduced by 36.8% compared with that of WT, whereas no significant difference was found between the variegated seeded BnaTT8-sgRNA lines (L14, L17, and L21) and WT (Fig. 4b, c). It is worth noting that the SCC was reduced from 13.9% in WT to 8.29%, 7.68%, and 8.60% in the BnaTT8 double knockout mutants (L18, L24, and L30) and to 10.7%, 11.1%, and 10.9% in the variegated seeded BnaTT8-sgRNA lines (L14, L17, and L21) (Fig. 4d). The lower SCC was attributed to the thinner seed coat of the BnaTT8 double knockout mutants. The BnaTT8 knockout mutants (L18, L24m and L30) had, on average, 27.0% to 42.2% reduced lignin content and the variegated seeded BnaTT8-sgRNA lines (L14, L17 and L21) had, on average, 22.0% to 26.6% reduced lignin content in seed and seed coat compared with WT, respectively (Fig. 4e). Moreover, an approximately 4.90% to 6.03% increase in the SOC was found in the BnaTT8 double knockout mutants (L18, L24 and L30) and an approximately 2.50% to 2.63% increase in SOC was found in the variegated seeded BnaTT8-sgRNA lines (L14, L17, and L21) (Fig. 4f). The increase of SOC was found to be accompanied by an alteration in fatty acid composition in all the BnaTT8-sgRNA lines (Additional file 2: Table S19).

To further study whether the expression of lignin biosynthesis genes was impacted by BnaTT8s in B. napus, the expression levels of the genes involved in the general phenylpropanoid pathway and lignin biosynthesis pathway were quantitatively analyzed in developing seeds at 40 DAF. The expression of 4CL, hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase (HCT), ferulate 5-hydroxylase (F5H), and peroxidase (POD) genes was not significantly altered by disruption of BnaTT8 in B. napus (data not shown). The expression of PAL and C4H involved in the general phenylpropanoid pathway was down-regulated by 76.6% to 78.3% and 82.1% to 84.3% in the BnaTT8 double knockout mutants compared with WT, respectively. The disruption of BnaTT8 also significantly decreased the expression of CCR, caffeoyl-CoA O-methyltransferase (CCoAOMT), caffeic acid O-methyltransferase (COMT), and cinnamyl alcohol dehydrogenase (CAD) involved in the lignin biosynthesis pathway and DFR and BAN involved in the anthocyanin biosynthesis pathway compared with WT, suggesting that BnaTT8s may play a positive role in promoting the lignin biosynthesis as well as the anthocyanin biosynthesis through the general phenylpropanoid pathway (Additional file 1: Fig. S14).

CCRL gene has not been reported to be involved in seed coat formation in oilseeds. In Arabidopsis and B. napus. Although CCRL shares less than 40% protein sequence similarity with CCR (Additional file 1: Fig. S8) and is distant from BnaCCR1 and BnaCCR2 in phylogenetic tree (Additional file 1: Fig. S9), CCRL exhibited a reductase activity toward the p-coumaroyl-CoA, sinapoyl-CoA, or feruloyl-CoA like CCR (Additional file 1: Fig. S10). In order to study whether BnaCCRL affects SCC in B. napus, sgRNA1 and sgRNA2 targeting the conserved domain in the 3rd and 5th exon, respectively, were designed to generate BnaCCRL mutations in B. napus cv. Westar using the CRISPR/Cas9 genome-editing system (Additional file 1: Fig. S13). A total of 16 T0-positive transgenic plants were generated. Three T2 homozygous lines in which two homologs of BnaCCRL, BnaC07.CCRL and BnaA03.CCRL were mutated and one T2 homozygous lines in which three homologs of BnaCCRL, BnaC07.CCRL, BnaA03.CCRL, and BnaC05.CCRL were mutated (Fig. 5a; Additional file 1: Fig. S12, 11). The thickness of seed coat of BnaCCRL-sgRNA-L21 and BnaCCRL-sgRNA-L43 mutants was reduced by 10.1% to 13.1%, compared with that of WT (Fig. 5b, c). The SCC was reduced from 16.0% in WT to 13.0%-13.7% in the double mutants BnaCCRL-sgRNA lines (L35, L43, and L48) and 14.0% in the triple mutants BnaCCRL-sgRNA-L21 (Fig. 5d). Additionally, the BnaCCRL-sgRNA-L21 and BnaCCRL-sgRNA-L43 mutants had, on average, 11.8% to 17.1% reduced lignin content in seed coat and the BnaCCRL-sgRNA-L21 had, on average, 17.6% reduced lignin content in seed compared with WT, respectively (Fig. 5e). Moreover, an approximately 3.00% to 4.20% increase of SOC was found in the BnaCCRL-sgRNA lines (L35, L43, L48, and L21) with altered fatty acid composition (Fig. 5f; Additional file 2: Table S20).

Fig. 5
figure 5

Functional characterization of BnaCCRL as a positive regulator of seed lignin biosynthesis in B. napus. a Seeds of CRISPR/Cas9-induced BnaC07.CCRL and BnaA03.CCRL homozygous double mutant lines (L35, L43, L48) and BnaC07.CCRL, BnaA03.CCRL and BnaC05.CCRL homozygous triple mutant line L21. Bar = 1 cm. b the seed coats of L43 and L21. Bar = 1 cm. Determination of thickness of seed coat (c), SCC (d), lignin content (e), and seed oil content (f) in the BnaCCRL double or triple mutant seeds and the control (WT). Values are means ± SD of three biological replicates (n = 6). Student’s t-test was used for statistical analysis between the BnaCCRL lines and WT (*, P < 0.05)

Full size image

Taken together, these results demonstrate that the BnaTT8 or BnaCCRL mutation caused a significant reduction of SCC and increase of SOC. BnaTT8 may modulate SCC by elevating the lignin content through the general phenylpropanoid pathway, whereas BnaCCRL may control SCC directly by lignin synthesis to thicken seed coat, leading to a negative effect on SOC in B. napus.

Expression of TWAS significant gene showed opposite trends in SCC and SOC

Previous studies have shown that the SOC of B. napus showed a negative correlation with SCC [12, 27]. In this regard, we applied the 779 TWAS significant genes of SOC from previous studies [81] and the 692 TWAS significant genes of SCC obtained in this study to analyze the carbon source allocation between phenylpropanoid pathway and lipid accumulation (Fig. 6a; Additional file 2: Table S22). Among them, 275 TWAS significant genes were detected for both SCC trait and SOC trait (Fig. 6a; Additional file 2: Table S23). We further performed GO enrichment analysis (FDR<0.05) on these 275 overlapped significant genes. The results showed that these genes were mainly enriched in the metabolic pathways of oxidoreduction metabolism, transcription factor activity, flavonoid biosynthesis, and multiple amino acid transaminase activities (Additional file 1: Fig. S15). Subsequent correlation analysis of TWAS significant gene expression of SOC and SCC revealed the presence of many genes known to be associated with phenylpropanoid pathway and lipid metabolism whose expression showed opposite correlation with SOC and SCC. In particular, the degree of opposite correlation was more pronounced among some genes including TT8, CCRL, C4H, PAL, and 4CL (Fig. 6b; Additional file 1: Fig. S16). To further investigate the key role of these genes in the carbon allocation of B. napus, we found that gene expression of BnaC09.TT8 and BnaC07.CCRL were significantly positively correlated with SCC and negatively correlated with SOC, respectively (Additional file 1: Fig. S17-20).

Fig. 6
figure 6

Correlation analysis between TWAS significant gene expression and SOC trait or SCC trait. a Venn diagram showing shared genes between significant genes identified by TWAS for SOC and SCC. b Correlation analysis between TWAS significant gene expression and SOC or SCC at 40 DAF. Each dot represents a gene. c Regulatory network of key genes for carbon source allocation in B. napus

Full size image

To investigate the gene regulatory relationships during carbon source allocation in B. napus, we constructed a co-expression network using TWAS-SCC and TWAS-SOC significant genes (correlation > 0.6). Genes involved in the significant association between SOC and SCC were found to be closely linked, and genes affecting both SCC and SOC played a balancing role in the carbon source allocation process (Fig. 6c). These results suggest that overlapping regulatory networks for the synthesis of phenylpropanoid metabolites and fatty acids in the seed coat may exist. We have discovered a great number of genes involved in the carbon source allocation between phenylpropanoid pathway and oil synthesis pathway, which lay a basis for understanding that SCC reduction can contribute to increase of SOC by regulating the flow of carbon source into seed oil.

Discussion

SCC, a quantitative trait formed during seed development, has been reported to be associated with seed color and lignin content in B. napus [11, 17, 22, 34, 35, 82]. Since the 1860s, yellow seed of oilseed rape with the low SCC and high SOC has attracted a great deal of attention from breeders. Numerous studies have revealed that SCC negatively correlates with the SOC [10,11,12, 36]. The present work revealed a great number of significant genes and genomic variations correlated with SCC using the genomic and transcriptomic data of developing seeds of a large natural B. napus population generated in our previous study [63]. The identified SCC-related QTLs and genes will help elucidate the regulatory mechanisms of the SCC trait in B. napus.

Different from GWAS, which identify SNPs and structural variations in the regions correlated with a phenotype, TWAS is a method to directly identify genes significantly associated with a phenotype due to the changes in gene expression levels. Our TWAS results revealed that there were 243 and 639 genes significantly associated with SCC at 20 DAF and 40 DAF, respectively (Fig. 1a, b). Most of the 103 overlapped genes between 20 DAF and 40 DAF were enriched in the secondary metabolite metabolic and biosynthetic pathway, especially in phenylpropane and flavonoid metabolic and biosynthetic pathway (Fig. 1c, d; Additional file 2: Table S5). Flavonoid and lignin biosynthesis are two branch pathways involved in phenylpropane biosynthesis pathway, sharing common precursors such as coumaroyl-CoA. PAs determined the seed coat color are synthesized by flavonoid pathway, involving the structural genes encoding catalytic enzymes such as TT4/CHS, TT5/CHI, TT6/F3H, TT7/F3'H, TT3/DFR, TT18/LDOX, BAN/ANR, and laccase (LAC) and the regulator genes encoding MYB, TFIIIA-like, WRKY-like, MADS-box-like, bHLH, MATE efflux, bZIP, C2H2, and HD-ZIP IV transcription factors [83,84,85,86,87]. In another branch pathway, the structural genes HCT, C3H/C4H, CCR, F5H, COMT, CCoAOMT, CAD, LAC, and POD are responsible for lignin biosynthesis [88, 89] that affects the seed coat thickness. Lots of flavonoid and lignin-related genes were identified from the TWAS results for the SCC trait (Fig. 1a). Among these genes, BnaC07.CCRL encoding a cinnamoyl-CoA-reductase-like protein is involved in lignin biosynthesis [78] and BnaTT8s encoding bHLH transcription factors are well-known regulators of flavonoid biosynthesis controlling seed coat color and seed fatty acid content in Arabidopsis and B. napus [80, 90]. According to the gene module analysis, the expression correlation analysis and the eGWAS results, BnaC07.CCRL and BnaTT8s, are predicted to be key regulators of SCC (Fig. 2a, b, e; Fig. 3a–c). The lower SCC was found in the seeds of BnaTT8 knockout mutants and BnaCCRL knockout mutants generated by CRISPR/Cas9 gene editing technology, respectively (Fig. 4d; Fig. 5d), suggesting the accuracy of TWAS and module analyses for SCC trait.

More interestingly, qSCC.A09 identified by GWAS were found to function upstream of BnaC07.CCRL and BnaTT8s using eGWAS. Moreover, to further study how BnaTT8 affects SCC, eGWAS was performed using BnaTT8 as a covariant and differentially expressed genes in the BnaTT8 mutant [80] were also analyzed for identification of the downstream genes. Thus, six genes including BnaC07.CCRL and some homologs of PAL and C4H were predicted to be regulated by BnaTT8 (Fig. 3d). Gene expression of PAL and C4H involved in the general phenylpropanoid pathway was significantly downregulated when BnaTT8 genes were knockout (Additional file 1: Fig. S14), consistent with the eGWAS results (Fig. 3d). Additionally, it was found that not only the expression levels of DFR and BAN involved in flavonoid biosynthesis pathway but also that of CCoAOMT, CCR, COMT, and CAD involved in lignin biosynthesis pathway were downregulated in the BnaTT8 knockout mutants (Additional file 1: Fig. S14), suggesting BnaTT8s are also an important regulator of the lignin biosynthesis, as well as flavonoid biosynthesis. Although BnaTT8s shared a similar expression pattern with the lignin biosynthetic genes, no TT8 binding motif was found in the promoter region of these lignin synthesis genes. Taken together, our results indicate that BnaTT8 is a master regulator of SCC, which may indirectly regulate lignin content through the general phenylpropane pathway when flavonoid was synthesized by the direct regulation of BnaTT8 in B. napus.

Based on GWAS for SCC trait, we identified three QTLs on chromosomes A09 and C05, including qSCC.C05.1, qSCC.C05.2, and qSCC.A09 (Fig. 3a; Additional file 2: Table S11). Moreover, qSCC.A09 was co-located with the eQTL results from eGWAS for BnaC07.CCRL, BnaTT8s, and some other TWAS-significant genes (Fig. 3b, c; Additional file 2: Table S13), suggesting qSCC.A09 is a regulatory hotspot of SCC. Interestingly, all these three QTLs have also been mapped by GWAS for SOC trait or lignin content by previous studies [12, 27, 34], suggesting a strong correlation among SCC, SOC, and lignin content. In addition, M65, M139, M97, and M79 significantly associated with SCC at different stages of the seed development were generated by the gene module analysis of TWAS results (Fig. 2a–d), among them M65, M139, and M79 have also been identified to be associated with SOC trait [63]. These results indicate that there is a close correlation between seed coat formation and seed oil accumulation, showing an overlapped gene expression regulatory network.

The MBW complex formed by TRANSPARENT TESTA 2 (TT2), TT8, and TRANSPARENT TESTA GLABRA 1 (TTG1) is one of the most important regulatory complex for flavonoid biosynthesis, which can directly activate the expression of BAN (BANYULS) and DFR (Dihydroflavonol-4-reductase) and control anthocyanin and proanthocyanidins synthesis. BnaTT2 or BnaTT8 mutation can disrupt the function of the ternary complex, leading to yellow seed coats and high SOC [80, 91]. However, it remains unclear that how BnaTT2 or BnaTT8 regulate the SOC. The editing sites of BnaTT8 genes in this study (Additional file 1: Fig. S6) are different from that in the previous study [80]. Due to the different editing sites, we obtained the BnaTT8 mutants with different seed coat colors, including variegated seeded BnaTT8-sgRNA lines (L14, L17, and L21) and yellow seeded BnaTT8-sgRNA lines (L18, L24, and L30) (Fig. 4a). Our results revealed that BnaTT8s can affect SOC through regulation of SCC.

Moreover, we also found that the expression of significantly associated genes identified by TWAS, including TT8, CCRL, C4H, PAL, 4CL, LPAAT, GLTP, and many other genes related to phenylpropane metabolism and lipid accumulation, showed an opposite correlation between SCC and SOC (Fig. 6b; Additional file 1: Fig. S16). The BnaTT8 knockout double mutants generated here also showed a typical yellow seeded traits with the high SOC (Fig. 4a, f), which was consistent with the results [80]. We also found that the disruption of BnaCCRL also caused the significantly increased SOC (Fig. 5f). Our results indicate BnaTT8 and BnaCCRL may indirectly affect SOC by positive regulation of SCC through promoting the lignin biosynthesis. From the viewpoint of secondary metabolism, when the general phenylpropanoid pathway was turned down, more carbon source, malonyl-CoA, will flow into oil biosynthesis [92]. Under the condition of fixed seed size and limited carbon source, the thickening of the secondary wall affected by lignin synthesis and the accumulation of flavonoids in the seed coat not only compress the specific proportion of embryo, but also compete with the biosynthesis of fatty acids for the limited carbon source in the seed, thereby affecting seed oil accumulation.

Conclusions

Taken together, this study provides insights into the dissection of the genetic basis of SCC by TWAS and GWAS in B. napus. Over seven hundred genes, four gene modules, and three QTLs were identified to be significantly associated with SCC from the multi-omics data. It was revealed that some significantly associated genes identified by TWAS for SCC and SOC traits may play a key role in the allocation of carbon sources in B. napus seeds. The identified SCC-related candidate genes and loci will promote the studies on the mechanism of seed coat formation and provide valuable information for the breeding of B. napus with low SCC and high SOC.

Methods

Plant materials and traits measurements

A total of 382 B. napus accessions re-sequenced in previous studies [63] were collected from spring, winter, and semi-winter accessions and cultivated under natural conditions on the experimental farm of the Huazhong Agricultural University, Wuhan, China in 2016–2017. The T0 and T1 transgenic and WT plants were grown in a greenhouse (16/8 h of light/dark at 22 °C) in 2016 and 2017, respectively. The three selected homozygous BnaTT8 knockout mutants (T2) without T-DNA were grown on the experimental farm of Huazhong Agriculture University, Wuhan, China in 2018~2019. All the field experiments followed a randomized complete block design with three replications. The seed coat was separated from the embryo with tweezers under a microscope. The seed weight and the seed coat weight were weighed with an electronic balance of ten thousandths. The SCC was calculated as the ratio of the seed coat weight to the seed weight. The oil content and protein content of dried seeds were determined by a Foss NIR Systems 5000 near-infrared reflectance spectroscope using the parameters described by Gan et al. [93]. The FA composition in mature seeds was determined using gas chromatography (GC) system with a Model 6890 GC analyzer (Agilent Technologies, Inc., Wilmington, DE) as described previously [94]. Yield-related traits, including plant height, length of main inflorescence branch, number of siliques on the main inflorescence, number of first branch, branch height, number of siliques per plant, length per silique, number of seeds per silique, 1000-seed weight, and seed yield per plant, were measured as described previously [95].

Variation calling, genotype filling, and annotation

We applied the results of the genotype imputation of the previous article [63]. The genome sequences of B. napus were downloaded from GENOSCOPE (http://www.genoscope.cns.fr/brassicanapus/) [96]. BWA [97] software was used to align the reads to the reference genome and the PCR duplicates were removed with SAMTools [98]. GATK v3.6 [99] was used to identify the existed sequencing variations in the 382 varieties and merge the files. SNPs and InDels with lower mapping quality (MQ <20) or reads with lower sequencing depth (DP < 50) were filtered. After obtaining the genotype, the missing genotype was estimated by the LD-KNN algorithm [100].

Genome-wide association studies and expression genome-wide association study

A total of 8,593,156 SNPs (MAF > 0.05) were used for genome-wide association studies (GWAS) in the whole population. The significance threshold of association was calculated by Genetic Type I error calculator (GEC) software [101], the calculated significance threshold is 7.96×10−6. We used the GEMMA [102] software package to perform correlation analysis on multiple traits in Wuhan using a mixed linear model [103]. eGWAS was performed using gene expression as phenotypic data for GWAS [54].

Transcriptome-wide association Studies, GO enrichment analysis, and independent component analysis (ICA) for expression data

The transcriptome data and the ICA gene modules were obtained from previous studies [63], and we filtered out the genes according to the 95th percentile of log2-transformed expression values, and low-expression genes in the population were removed in subsequent analyses. Linear analysis and mixed linear model of EMMAX [104] were used for correlation analysis. FDR corrected p value ≤ 0.05 was used as the significance threshold. Proteins in B. napus were aligned to all Arabidopsis proteins by BLASTP [105], and the most significant proteins in Arabidopsis based on a probability threshold of 10−5 were selected for enrichment analysis.

Construction of the CRISPR/Cas9 vector and plant transformation

The CRISPR/Cas9 genome-editing system was utilized for gene editing of BnaTT8 and BnaCCRL in this study, respectively [106]. The primer sequences for the sgRNA vector construction were listed in (Additional file 2: Table S21). Agrobacterium tumefaciens-mediated hypocotyl transformation in B. napus was conducted as previously described [107]. The B. napus cv. Westar was used as the transformation receptor.

Quantitative RT-PCR

To analyze the expression level of the genes involved in phenylpropane metabolic and lignin biosynthesis pathway, quantitative RT-PCR was performed. Total RNA was isolated from the developing seeds at 4 weeks after flowering using the TRIzol® Reagent (Invitrogen). RNA samples were reverse-transcribed using M-MLV reverse transcriptase (Promega) and oligo (dT) 15 primer, according to the manufacturer’s instructions. The expression level of the B. napus β-actin gene (AF111812) was used as a loading control. The primer sequences for quantitative RT-PCR analyses were listed in (Additional file 2: Table S21).

Total lignin assay

Seed coats and seeds were collected from the homozygous BnaTT8-sgRNA lines (L14, L17, L21, L18, L24, and L30), the homozygous BnaCCRL-sgRNA lines (L43 and L21), and the B. napus cv. Westar (WT), respectively. Total lignin content was measured in the seed coats and whole seeds using a two-step acid hydrolysis method as previously described [108]. Acid-insoluble and acid-soluble are two types of lignin. The acid-insoluble lignin (AIL) was calculated gravimetrically after correction for ash, and the acid-soluble lignin (ASL) was measured by UV spectroscopy. The following formula was used: for acid-insoluble lignin determination, the 0.5-g sample was recorded as W1. The weight of the crucible and dry residue was recorded to the nearest 0.1 mg (W2). The weight of the crucibles and ash were recorded to the nearest 0.1 mg (W3). All experiments were carried out in triplicate.

$$\mathrm{AIL}\ \left(\%\right)=\left(\mathrm{W}2+\mathrm{W}3\right)\times 100/\mathrm{W}1\%.$$

For acid-insoluble lignin determination, where A is the absorption value, D is the dilution ratio of the sample, and K (the absorptivity constant) = 110 L/g/cm.

$$\mathrm{ASL}\ \left(\%\right)=\left(\mathrm{A}\times \mathrm{D}\times \mathrm{V}/1000\times \mathrm{K}\times \mathrm{W}1\right)\ 100\%.$$

Total lignin content (%) = AIL (%) + ASL (%)

Vanillin and DMACA staining and quantification of insoluble PCs

Flavan-3,4-diols and flavan-4-ols as monomers of PAs in detached developing seed coats were detected by vanillin. The seed coats were incubated in a solution of 0.5% (w/v) vanillin in 5 N HCl for 15 min at room temperature [109]. PAs and flavon-3-ols in detached developing seed embryos were detected by DMACA. The seed embryos were stained with 2% DMACA dissolved in a 6 N HCl/95% ethanol mixture (1:1, v/v) for 30 min at room temperature and then were washed for 15 min with water [110]. Insoluble procyanidin oligomers were measured using the hot butanol-HCl method described by Auger et al. [111]. 50 entire seeds were ground with 9 mL acetone/water/TFA mixture (80:20:0.05, v/v/v) for 5 min and then sonicated for 15 min at 4 °C. The pellets were collected after centrifugation (18,000g, 10 min at 4 °C). After added approximately 15 mL of butanol-HCl (95:5, v/v) and 500 μL of 2% ferricammonium sulfate (w/v) in 2 N HCl, the pellets were heated at 95 °C for 3 h. Absorbance of pink-red cyanidin released after cleavage of PAs was recorded at a wavelength of 550 nm.

Protein purification and enzymatic assay of BnaCCRL

The full-length cDNA of BnaCCRL was cloned and inserted into pET-28b-Sumo vector at the BamHI/XhoI cloning sites using ClonExpress® II One Step Cloning Kit. The primer sequences used for cloning were listed in (Additional file 2: Table S21). pET-28b-Sumo-BnaCCRL was then transformed into the E.coli BL21 (DE3) cells. Protein expression was induced by addition of 0.2 mM IPTG (isopropyl-b-D-thiogalactoside) at 16 °C. The pallets were collected by centrifugation at 4000 rpm for 20 min. Cells were resuspended in lysis buffer (150 mM NaCl, 25 mM Tris-HCl pH8.0) and lysed by a cell homogenizer. The lysate was centrifuged at 14,000 rpm for 1 h to remove cellular debris. The supernatant was loaded onto a Ni-NTA superflow affinity column, and the resin was washed by wash buffer (150 mM NaCl, 25 mM Tris-HCl pH8.0). The protein was eluted in fractions containing 25 mM Tris-HCl pH8.0 and 300 mM imidazole. The concentration of purified protein was measured with a protein assay kit (Bio-Rad). The purified proteins were separated by 12% SDS-PAGE, stained with Coomassie Brilliant Blue, and immunoblotted using His-tag antibodies (1:3000 dilution; A-5588, Sigma).

The activities of BnaCCRL were analyzed by the reduction of p-coumaroyl-CoA, sinapoyl-CoA, or feruloyl-CoA in the presence of NADPH based on the functions in the biosynthesis pathway. Total 500 μl standard mixture for CCR activity analysis contains 82 μM substrates, 160 μM NADPH, and 100 mM phosphate buffer at pH6.2 with 15 μg purified proteins. The reaction was underdone in 500 μl standard with 82 μM p-coumaroyl-CoA, sinapoyl-CoA, or feruloyl-CoA, respectively. Absorbance was recorded at a wavelength of 366 nm [112]. After the reaction was initiated, the absorption values were monitored every minute. Enzymatic activity was calculated using molar absorption coefficients of substrates and products at 366 nm provided by Stöekigt and Zenk [113].

Availability of data and materials

Genome sequencing and RNA-seq data were generated in our previous work (62). Data generated in this study are submitted as Additional files with this manuscript. Other data are also available from the corresponding authors upon request.

References

  1. Sl L. Simple analysis of physical property of rapeseed. China Oils and Fats. 2005;30:17–20.

    Google Scholar 

  2. Ingram GC. Dying to live: cell elimination as a developmental strategy in angiosperm seeds. J Exp Bot. 2016;68:785–96.

    Google Scholar 

  3. Radchuk V, Borisjuk L. Physical, metabolic and developmental functions of the seed coat. Front Plant Sci. 2014;5:510.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wan L, Xia Q, Qiu X, Selvaraj G. Early stages of seed development in Brassica napus: a seed coat-specific cysteine proteinase associated with programmed cell death of the inner integument. Plant J. 2002;30:1–10.

    Article  CAS  PubMed  Google Scholar 

  5. Moïse JA, Han S, Gudynaitę-Savitch L, Johnson DA, Miki BLA. Seed coats: Structure, development, composition, and biotechnology. In Vitro Cellular Developmental Biology - Plant. 2005;41:620–44.

    Article  Google Scholar 

  6. Figueiredo Duarte D, Köhler C. Signalling events regulating seed coat development. Biochem Soc Trans. 2014;42:358–63.

    Article  CAS  PubMed  Google Scholar 

  7. Vaughan JG, Whitehouse JM. Seed structure and the taxonomy of the Cruciferae. Bot J Linn Soc. 1971;64:383–409.

    Article  Google Scholar 

  8. Jiang Y, Deyholos MK. Transcriptome analysis of secondary-wall-enriched seed coat tissues of canola (Brassica napus L.). Plant Cell Rep. 2010;29:327–42.

    Article  CAS  PubMed  Google Scholar 

  9. Cosgrove DJ, Jarvis MC. Comparative structure and biomechanics of plant primary and secondary cell walls. Front Plant Sci. 2012;3:204.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Wang R, Li J, Chen L, Tang Z, Zhang X: Genetic correlation analysis for main characters in yellow-seeded rapeseed lines (Brassica napus L.). Chin J Oil Crop Sci 2003, 25:8-11.

  11. My J, Li Jn F, Fy ZZ, Xk Z, Lz L. QTL Analysis of the Oil Content and the Hull Content in Brassica napus L. Agricultural Sci China. 2007;6:414–21.

    Article  Google Scholar 

  12. Yan XY, Li JN, Fu FY, Jin MY, Chen L, Liu LZ. Co-location of seed oil content, seed hull content and seed coat color QTL in three different environments in Brassica napus L. Euphytica. 2009;170:355–64.

    Article  CAS  Google Scholar 

  13. Falcone Ferreyra ML, Rius S, Casati P. Flavonoids: biosynthesis, biological functions, and biotechnological applications. Front Plant Sci. 2012;3:222.

  14. Rennie EA, Scheller HV. Xylan biosynthesis. Curr Opin Biotechnol. 2014;26:100–7.

    Article  CAS  PubMed  Google Scholar 

  15. Marles MAS, Gruber MY. Histochemical characterisation of unextractable seed coat pigments and quantification of extractable lignin in the Brassicaceae. J Sci Food Agric. 2004;84:251–62.

    Article  CAS  Google Scholar 

  16. Akhov L, Ashe P, Tan Y, Datla R, Selvaraj G. Proanthocyanidin biosynthesis in the seed coat of yellow-seeded, canola quality Brassica napus YN01-429 is constrained at the committed step catalyzed by dihydroflavonol 4-reductase. Botany. 2009;87:616–25.

    Article  CAS  Google Scholar 

  17. Qu C, Fu F, Lu K, Zhang K, Wang R, Xu X, et al. Differential accumulation of phenolic compounds and expression of related genes in black- and yellow-seeded Brassica napus. J Exp Bot. 2013;64:2885–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chen BY, Heneen WK. Inheritance of seed colour in Brassica campestris L. and breeding for yellow-seeded B. napus L. Euphytica. 1992;59:157–63.

    Article  Google Scholar 

  19. Tang Z, Li J, Zhang X, Chen L, Wang R: Genetic variation of yellow-seeded rapeseed lines (Brassica napus L.) fromdifferent genetic sources. Plant Breeding 1997, 116:411—414.

  20. Meng J, Shi S, Gan L, Li Z, Qu X. The production of yellow-seeded Brassica napus (AACC) through crossing interspecific hybrids of B. campestris (AA) and B. carinata (BBCC) with B. napus. Euphytica. 1998;103:329–33.

    Article  Google Scholar 

  21. Wang J, Xian X, Xu X, Qu C, Lu K, Li J, et al. Genome-Wide Association Mapping of Seed Coat Color in Brassica napus. J Agric Food Chem. 2017;65:5229–37.

    Article  CAS  PubMed  Google Scholar 

  22. Wang F, He J, Shi J, Zheng T, Xu F, Wu G, Liu R, Liu aS: Embryonal Control of Yellow Seed Coat Locus ECY1 Is Related to Alanine and Phenylalanine Metabolism in the Seed Embryo of Brassica napus. G3 (Bethesda) 2016, 6:1073-1081.

  23. Hong M, Hu K, Tian T, Li X, Chen L, Zhang Y, et al. Transcriptomic Analysis of Seed Coats in Yellow-Seeded Brassica napus Reveals Novel Genes That Influence Proanthocyanidin Biosynthesis. Front Plant Sci. 2017;8:1674.

  24. Lian J, Lu X, Yin N, Ma L, Lu J, Liu X, et al. Silencing of BnTT1 family genes affects seed flavonoid biosynthesis and alters seed fatty acid composition in Brassica napus. Plant Sci. 2017;254:32–47.

    Article  CAS  PubMed  Google Scholar 

  25. Chen M, Wang Z, Zhu Y, Li Z, Hussain N, Xuan L, et al. The Effect of TRANSPARENT TESTA2 on Seed Fatty Acid Biosynthesis and Tolerance to Environmental Stresses during Young Seedling Establishment in Arabidopsis. Plant Physiol. 2012;160:1023–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chen M, Zhang B, Li C, Kulaveerasingam H, Chew FT, Yu H. TRANSPARENT TESTA GLABRA1 Regulates the Accumulation of Seed Storage Reserves in Arabidopsis. Plant Physiol. 2015;169:391–402.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Behnke N, Suprianto E, Möllers C. A major QTL on chromosome C05 significantly reduces acid detergent lignin (ADL) content and increases seed oil and protein content in oilseed rape (Brassica napus L.). Theor Appl Genet. 2018;131:2477–92.

    Article  CAS  PubMed  Google Scholar 

  28. Wittkop B, Snowdon RJ, Friedt W. New NIRS calibrations for fiber fractions reveal broad genetic variation in Brassica napus seed quality. J Agric Food Chem. 2012;60:2248–56.

    Article  CAS  PubMed  Google Scholar 

  29. Fraser CM, Chapple C. The Phenylpropanoid Pathway in Arabidopsis. Arabidopsis Book. 2011;9:e0152.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wittkop B, Snowdon RJ, Friedt W. Status and perspectives of breeding for enhanced yield and quality of oilseed crops for Europe. Euphytica. 2009;170:131–40.

    Article  Google Scholar 

  31. Badani AG, Gustafson JP, Snowdon RJ, Wittkop B, Lipsa FD, Baetzel R, et al. Colocalization of a partially dominant gene for yellow seed colour with a major QTL influencing acid detergent fibre (ADF) content in different crosses of oilseed rape (Brassica napus). Genome. 2006;49:1499–509.

    Article  CAS  PubMed  Google Scholar 

  32. Liu L, Stein A, Wittkop B, Sarvari P, Li J, Yan X, et al. A knockout mutation in the lignin biosynthesis gene CCR1 explains a major QTL for acid detergent lignin content in Brassica napus seeds. Theor Appl Genet. 2012;124:1573–86.

    Article  CAS  PubMed  Google Scholar 

  33. Whetten RW, MacKay JJ, Sederoff RR. RECENT ADVANCES IN UNDERSTANDING LIGNIN BIOSYNTHESIS. Annu Rev Plant Physiol Plant Mol Biol. 1998;49:585–609.

    Article  CAS  PubMed  Google Scholar 

  34. Wang J, Jian H, Wei L, Qu C, Xu X, Lu K, et al. Genome-Wide Analysis of Seed Acid Detergent Lignin (ADL) and Hull Content in Rapeseed (Brassica napus L.). PLoS One. 2015;10:e0145045.

  35. Dimov Z, Suprianto E, Hermann F, Möllers C. Genetic variation for seed hull and fibre content in a collection of European winter oilseed rape material (Brassica napus L.) and development of NIRS calibrations. Plant Breeding. 2012;131:361–8.

    Article  Google Scholar 

  36. Yu B, Boyle K, Zhang W, Robinson SJ, Higgins E, Ehman L, et al. Multi-trait and multi-environment QTL analysis reveals the impact of seed colour on seed composition traits in Brassica napus. Molecular Breeding. 2016;36:111.

  37. Wu Y, Zeng J, Zhang F, Zhu Z, Qi T, Zheng Z, et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun. 2018;9:918.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Zhang FT, Zhu ZH, Tong XR, Zhu ZX, Qi T, Zhu J. Mixed Linear Model Approaches of Association Mapping for Complex Traits Based on Omics Variants. Sci Rep. 2015;5:10298.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yan KK, Zhao H, Pang H. A comparison of graph- and kernel-based –omics data integration algorithms for classifying complex traits. BMC Bioinformatics. 2017;18:539.

  40. Akiyama M. Multi-omics study for interpretation of genome-wide association study. J Hum Genet. 2020;66:3–10.

    Article  PubMed  Google Scholar 

  41. Wu J, Feng F, Lian X, Teng X, Wei H, Yu H, et al. Genome-wide Association Study (GWAS) of mesocotyl elongation based on re-sequencing approach in rice. BMC Plant Biol. 2015;15:218.

  42. Spindel JE, Begum H, Akdemir D, Collard B, Redoña E, Jannink JL, et al. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity. 2016;116:395–408.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang M, Yan J, Zhao J, Song W, Zhang X, Xiao Y, et al. Genome-wide association study (GWAS) of resistance to head smut in maize. Plant Sci. 2012;196:125–31.

    Article  CAS  PubMed  Google Scholar 

  44. Schaefer RJ, Michno JM, Jeffers J, Hoekenga O, Dilkes B, Baxter I, et al. Integrating Coexpression Networks with GWAS to Prioritize Causal Genes in Maize. Plant Cell. 2018;30:2922–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wu X, Li Y, Shi Y, Song Y, Zhang D, Li C, et al. Joint-linkage mapping and GWAS reveal extensive genetic loci that regulate male inflorescence size in maize. Plant Biotechnol J. 2016;14:1551–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Su J, Pang C, Wei H, Li L, Liang B, Wang C, et al. Identification of favorable SNP alleles and candidate genes for traits related to early maturity via GWAS in upland cotton. BMC Genomics. 2016;17:687.

  47. He Y, Wu D, Wei D, Fu Y, Cui Y, Dong H, Tan C, Qian W: GWAS, QTL mapping and gene expression analyses in Brassica napus reveal genetic control of branching morphogenesis. Sci Rep. 2017;7:15971.

  48. Wei L, Jian H, Lu K, Filardo F, Yin N, Liu L, et al. Genome-wide association analysis and differential expression analysis of resistance to Sclerotinia stem rot in Brassica napus. Plant Biotechnol J. 2016;14:1368–80.

    Article  CAS  PubMed  Google Scholar 

  49. Liu R, Gong J, Xiao X, Zhang Z, Li J, Liu A, et al. GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers. Front Plant Sci. 2018;9:1067.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Wan H, Chen L, Guo J, Li Q, Wen J, Yi B, et al. Genome-Wide Association Study Reveals the Genetic Architecture Underlying Salt Tolerance-Related Traits in Rapeseed (Brassica napus L.). Front Plant Sci. 2017;8:593.

  51. Huang X, Zhao Y, Wei X, Li C, Wang A, Zhao Q, et al. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet. 2011;44:32–9.

    Article  CAS  PubMed  Google Scholar 

  52. Harper AL, Trick M, Higgins J, Fraser F, Clissold L, Wells R, et al. Associative transcriptomics of traits in the polyploid crop species Brassica napus. Nat Biotechnol. 2012;30:798–802.

    Article  CAS  PubMed  Google Scholar 

  53. Ballester M, Ramayo-Caldas Y, Revilla M, Corominas J, Castelló A, Estellé J, et al. Integration of liver gene co-expression networks and eGWAs analyses highlighted candidate regulators implicated in lipid metabolism in pigs. Sci Rep. 2017;7:46539.

  54. Zou F, Chai HS, Younkin CS, Allen M, Crook J, Pankratz VS, et al. Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants. PLoS Genet. 2012;8:e1002707.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Grigoryev DN, Cheranova DI, Chaudhary S, Heruth DP, Zhang LQ, Ye SQ. Identification of new biomarkers for Acute Respiratory Distress Syndrome by expression-based genome-wide association study. BMC Pulm Med. 2015;15:95.

  56. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48:245–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wainberg M, Sinnott-Armstrong N, Mancuso N, Barbeira AN, Knowles DA, Golan D, et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet. 2019;51:592–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Gusev A, Mancuso N, Won H, Kousi M, Finucane HK, Reshef Y, Song L, Safi A, Schizophrenia Working Group of the Psychiatric Genomics C, McCarroll S, et al: Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet. 2018;50:538-548.

  59. Wu L, Shi W, Long J, Guo X, Michailidou K, Beesley J, et al. A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer. Nat Genet. 2018;50:968–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kremling KAG, Diepenbrock CH, Gore MA, Buckler ES, Bandillo NB. Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays. G3 (Bethesda). 2019;9:3023–33.

    Article  CAS  Google Scholar 

  61. Tan Z, Xie Z, Dai L, Zhang Y, Zhao H, Tang S, et al. Genome-and transcriptome-wide association studies reveal the genetic basis and the breeding history of seed glucosinolate content in Brassica napus. Plant Biotechnol J. 2022;20:211-225.

  62. Li Z, Wang P, You C, Yu J, Zhang X, Yan F, et al. Combined GWAS and eQTL analysis uncovers a genetic regulatory network orchestrating the initiation of secondary cell wall development in cotton. New Phytol. 2020;226:1738–52.

    Article  CAS  PubMed  Google Scholar 

  63. Tang S, Zhao H, Lu S, Yu L, Zhang G, Zhang Y, et al. Genome- and transcriptome-wide association studies provide insights into the genetic basis of natural variation of seed oil content in Brassica napus. Mol Plant. 2021;14:470-487.

  64. Lu S, Liu H, Jin C, Li Q, Guo L. An efficient and comprehensive plant glycerolipids analysis approach based on high-performance liquid chromatography–quadrupole time-of-flight mass spectrometer. Plant direct. 2019;3:e00183.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Xu W, Dubos C, Lepiniec L. Transcriptional control of flavonoid biosynthesis by MYB-bHLH-WDR complexes. Trends Plant Sci. 2015;20:176–85.

    Article  CAS  PubMed  Google Scholar 

  66. Appelhagen I, Thiedig K, Nordholt N, Schmidt N, Huep G, Sagasser M, et al. Update on transparent testa mutants from Arabidopsis thaliana: characterisation of new alleles from an isogenic collection. Planta. 2014;240:955–70.

    Article  CAS  PubMed  Google Scholar 

  67. Stein A, Wittkop B, Liu L, Obermeier C, Friedt W, Snowdon RJ, et al. Dissection of a major QTL for seed colour and fibre content inBrassica napusreveals colocalization with candidate genes for phenylpropanoid biosynthesis and flavonoid deposition. Plant Breeding. 2013;132:382–9.

    Article  CAS  Google Scholar 

  68. Miyamoto T, Tobimatsu Y, Umezawa T. MYB-mediated regulation of lignin biosynthesis in grasses. Curr Plant Biol. 2020;24:100174.

    Article  Google Scholar 

  69. Nakatsubo T, Mizutani M, Suzuki S, Hattori T, Umezawa T. Characterization of Arabidopsis thaliana pinoresinol reductase, a new type of enzyme involved in lignan biosynthesis. J Biol Chem. 2008;283:15550–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Goujon T, Sibout R, Pollet B, Maba B, Nussaume L, Bechtold N, et al. A new Arabidopsis thaliana mutant deficient in the expression of O-methyltransferase impacts lignins and sinapoyl esters. Plant Mol Biol. 2003;51:973–89.

    Article  CAS  PubMed  Google Scholar 

  71. Escamilla-Trevino LL, Chen W, Card ML, Shih MC, Cheng CL, Poulton JE. Arabidopsis thaliana beta-Glucosidases BGLU45 and BGLU46 hydrolyse monolignol glucosides. Phytochemistry. 2006;67:1651–60.

    Article  CAS  PubMed  Google Scholar 

  72. Kawasaki T, Koita H, Nakatsubo T, Hasegawa K, Wakabayashi K, Takahashi H, et al. Cinnamoyl-CoA reductase, a key enzyme in lignin biosynthesis, is an effector of small GTPase Rac in defense signaling in rice. Proc Natl Acad Sci U S A. 2006;103:230.

    Article  CAS  PubMed  Google Scholar 

  73. Poke FS, Vaillancourt RE, Elliott RC, Reid JB. Sequence variation in two lignin biosynthesis genes, cinnamoyl CoA reductase (CCR) and cinnamyl alcohol dehydrogenase 2 (CAD2). Mol Breed. 2003;12:107–18.

    Article  CAS  Google Scholar 

  74. Chabannes M, Barakate A, Lapierre C, Marita JM, Ralph J, Pean M, et al. Strong decrease in lignin content without significant alteration of plant development is induced by simultaneous down-regulation of cinnamoyl CoA reductase (CCR) and cinnamyl alcohol dehydrogenase (CAD) in tobacco plants. Plant J. 2001;28:257–70.

    Article  CAS  PubMed  Google Scholar 

  75. Penfield S, Meissner RC, Shoue DA, Carpita NC, Bevan MW. MYB61 Is Required for Mucilage Deposition and Extrusion in the Arabidopsis Seed Coat. Plant Cell. 2001;13:2777–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Xu W, Purugganan MM, Polisensky DH, Antosiewicz DM, Fry SC, Braam J. Arabidopsis TCH4, regulated by hormones and the environment, encodes a xyloglucan endotransglycosylase. Plant Cell. 1995;7:1555–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Baudry A, Caboche M, Lepiniec L. TT8 controls its own expression in a feedback regulation involving TTG1 and homologous MYB and bHLH factors, allowing a strong and cell-specific accumulation of flavonoids in Arabidopsis thaliana. Plant J. 2006;46:768–79.

    Article  CAS  PubMed  Google Scholar 

  78. Rogers LA, Dubos C, Cullis IF, Surman C, Poole M, Willment J, et al. Light, the circadian clock, and sugar perception in the control of lignin biosynthesis. J Exp Bot. 2005;56:1651–63.

    Article  CAS  PubMed  Google Scholar 

  79. Kim JI, Dolan WL, Anderson NA, Chapple C. Indole Glucosinolate Biosynthesis Limits Phenylpropanoid Accumulation in Arabidopsis thaliana. Plant Cell. 2015;27:1529–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Zhai Y, Yu K, Cai S, Hu L, Amoo O, Xu L, et al. Targeted mutagenesis of BnTT8 homologs controls yellow seed coat development for effective oil production in Brassica napus L. Plant Biotechnol J. 2020;18:1153–68.

    Article  CAS  PubMed  Google Scholar 

  81. Tang S, Zhao H, Lu S, Yu L, Zhang G, Zhang Y, et al. Genome- and transcriptome-wide association studies provide insights into the genetic basis of natural variation of seed oil content in Brassica napus. Mol Plant. 2021;14:470–87.

    Article  CAS  PubMed  Google Scholar 

  82. Qu C, Zhao H, Fu F, Wang Z, Zhang K, Zhou Y, et al. Genome-Wide Survey of Flavonoid Biosynthesis Genes and Gene Expression Analysis between Black- and Yellow-Seeded Brassica napus. Front Plant Sci. 2016;7:1755.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Marles MAS, Ray H, Gruber MY. New perspectives on proanthocyanidin biochemistry and molecular regulation. Phytochemistry. 2003;64:367–83.

    Article  CAS  PubMed  Google Scholar 

  84. Baudry A, Heim MA, Dubreucq B, Caboche M, Weisshaar B, Lepiniec L. TT2, TT8, and TTG1 synergistically specify the expression of BANYULS and proanthocyanidin biosynthesis in Arabidopsis thaliana. Plant J. 2004;39:366–80.

    Article  CAS  PubMed  Google Scholar 

  85. Wang XC, Wu J, Guan ML, Zhao CH, Geng P, Zhao Q. Arabidopsis MYB4 plays dual roles in flavonoid biosynthesis. Plant J. 2019;101:637–52.

    Article  PubMed  Google Scholar 

  86. Stracke R, Ishihara H, Huep G, Barsch A, Mehrtens F, Niehaus K, et al. Differential regulation of closely related R2R3-MYB transcription factors controls flavonol accumulation in different parts of the Arabidopsis thaliana seedling. Plant J. 2007;50:660–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Marinova K, Pourcel L, Weder B, Schwarz M, Barron D, Routaboul JM, et al. The Arabidopsis MATE transporter TT12 acts as a vacuolar flavonoid/H+ -antiporter active in proanthocyanidin-accumulating cells of the seed coat. Plant Cell. 2007;19:2023–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Boerjan W, Ralph J, Baucher M. Lignin biosynthesis. Annu Rev Plant Biol. 2003;54:519–46.

    Article  CAS  PubMed  Google Scholar 

  89. Vanholme R, Demedts B, Morreel K, Ralph J, Boerjan W. Lignin biosynthesis and structure. Plant Physiol. 2010;153:895–905.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Chen M, Xuan L, Wang Z, Zhou L, Li Z, Du X, et al. TRANSPARENT TESTA8 Inhibits Seed Fatty Acid Accumulation by Targeting Several Seed Development Regulators in Arabidopsis. Plant Physiol. 2014;165:905–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Xie T, Chen X, Guo T, Rong H, Chen Z, Sun Q, et al. Targeted knockout of BnTT2 homologues for yellow-seeded Brassica napus with reduced flavonoids and improved fatty acid composition. J Agric Food Chem. 2020;68:5676–90.

    Article  CAS  PubMed  Google Scholar 

  92. Bates PD, Stymne S, Ohlrogge J. Biochemical pathways in seed oil synthesis. Curr Opin Plant Biol. 2013;16:358–64.

    Article  CAS  PubMed  Google Scholar 

  93. GAN L, SUN X-l, JIN L, WANG G-q, XU J-w, WEI Z-l, FU T-d: Establishment of Math Models of NIRS Analysis for Oil and Protein Contents in Seed of Brassica napus. Scientia Agricultura Sinica 2003, 36:1609-1613.

  94. Yang Q, Fan C, Guo Z, Qin J, Wu J, Li Q, et al. Identification of FAD2 and FAD3 genes in Brassica napus genome and development of allele-specific markers for high oleic and low linolenic acid contents. Theor Appl Genet. 2012;125:715–29.

    Article  CAS  PubMed  Google Scholar 

  95. Cai G, Yang Q, Chen H, Yang Q, Zhang C, Fan C, et al. Genetic dissection of plant architecture and yield-related traits in Brassica napus. Sci Rep. 2016;6:21625.

  96. Chalhoub B, France D, Shengyi L, I. A. P. Parkin, H. Tang, X. Wang, J. Chiquet, H. Belcram, C. Tong, B. Samans, et al: Early allopolyploid evolution in the post-Neolithic Brassica napus oilseed genome. Science 2014, 345:950-953.

  97. Li H: Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:13033997 2013, 00:1–3.

  98. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. Genome Project Data Processing S: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9.

    Article  PubMed  PubMed Central  Google Scholar 

  99. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Chen W, Gao Y, Xie W, Gong L, Lu K, Wang W, et al. Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet. 2014;46:714–21.

    Article  CAS  PubMed  Google Scholar 

  101. Li M-X, Yeung JMY, Cherny SS, Sham PC. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum Genet. 2011;131:747–56.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44:821–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. GOLDSTEIN H: Multilevel mixed linear model analysis using iterative generalized least squares. Biometrika 1986, 73:43-56.

  104. Kang HM, Sul JH, Service SK, Zaitlen NA. Kong S-y, Freimer NB, Sabatti C, Eskin E: Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42:348–54.

    Article  PubMed  Google Scholar 

  105. Jacob A, Lancaster J, Buhler J, Harris B, Chamberlain RD. Mercury BLASTP: Accelerating Protein Sequence Alignment. ACM Trans Reconfigurable Technol Syst. 2008;1:9.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Xie K, Minkenberg B, Yang Y. Boosting CRISPR/Cas9 multiplex editing capability with the endogenous tRNA-processing system. PNAS. 2015;112:3570–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Dai C, Li Y, Li L, Du Z, Lin S, Tian X, et al. An efficient Agrobacterium-mediated transformation method using hypocotyl as explants for Brassica napus. Mol Breed. 2020;40:96.

  108. Wu Z, Zhang M, Wang L, Tu Y, Zhang J, Xie G, et al. Biomass digestibility is predominantly affected by three factors of wall polymer features distinctive in wheat accessions and rice mutants. Biotechnol Biofuels. 2013;6:183.

  109. Lu Y, Liu X, Liu S, Yue Y, Guan C, Liu Z. A simple and rapid procedure for identification of seed coat colour at the early developmental stage of Brassica juncea and Brassica napus seeds. Plant Breed. 2012;131:176–9.

    Article  Google Scholar 

  110. Li X, Westcott N, Links M, Gruber MY. Seed Coat Phenolics and the Developing Silique Transcriptome of Brassica carinata. J Agric Food Chem. 2010;58:10918–28.

    Article  CAS  PubMed  Google Scholar 

  111. Auger B, Marnet N, Gautier V, Maia-Grondard A, Leprince F, Renard M, et al. A detailed survey of seed coat flavonoids in developing seeds of Brassica napus L. J Agric Food Chem. 2010;58:6246–56.

    Article  CAS  PubMed  Google Scholar 

  112. Lüderitz T, Grisebach H. Enzymic Synthesis of Lignin Precursors Comparison of Cinnamoyl-CoA Reductase and Cinnamyl Alcohol: NADP+ Dehydrogenase from Spruce (Picea abies L.) and Soybean (Glycine max L.). Eur J Biochem. 1981;119:115–24.

    Article  PubMed  Google Scholar 

  113. Stöekigt J, Zenk M. Chemical syntheses and properties of hydroxycinnamoyl-coenzyme A derivatives. Z Naturforsch C. 1975;30:352–8.

    Article  Google Scholar 

Download references

Acknowledgements

We thank the National Key Laboratory of Crop Genetic Improvement at Huazhong Agricultural University for providing the Bioinformatics Computing Platform.

Review history

The review history is available as Additional file 3.

Peer review information

Wenjing She was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Funding

This study was supported by the National Natural Science Foundation of China (31871658), Hubei Hongshan Laboratory (2021HSZD004), and the 111 Project Crop genomics and Molecular Breeding (B20051).

Author information

Author notes

  1. Yuting Zhang, Hui Zhang and Hu Zhao contributed equally to this work.

Authors and Affiliations

  1. National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China

    Yuting Zhang, Hui Zhang, Hu Zhao, Yefan Xia, Xiangbo Zheng, Ruyi Fan, Zengdong Tan, Chenhua Duan, Yansong Fu, Long Li, Jiang Ye, Shan Tang, Honghong Hu, Weibo Xie, Xuan Yao & Liang Guo

  2. Hubei Hongshan Laboratory, Wuhan, China

    Yuting Zhang, Hui Zhang, Hu Zhao, Yefan Xia, Xiangbo Zheng, Ruyi Fan, Zengdong Tan, Chenhua Duan, Long Li, Jiang Ye, Shan Tang, Honghong Hu, Weibo Xie, Xuan Yao & Liang Guo

  3. Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China

    Weibo Xie

Authors
  1. Yuting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yefan Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiangbo Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ruyi Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zengdong Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chenhua Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yansong Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Long Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jiang Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Shan Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Honghong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Weibo Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Xuan Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Liang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.G. and Y.X. conceived and supervised this study. H.Z., Y.X., R.F., J.Y., X.Z., C.D., L.L., F.Y., and S.T. performed the experiments. Y.Z., H.Z., and Z.T. performed the bioinformatics analysis. Y.Z., H.Z., and Y.X. wrote the manuscript. H.H., W.X., X.Y., and L.G. discussed the study and revised the manuscript. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xuan Yao or Liang Guo.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

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:

Fig. S1 to Fig. S20.

Additional file 2:

Table S1 to Table S23.

Additional file 3.

Review history.

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 http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) 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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Zhang, H., Zhao, H. et al. Multi-omics analysis dissects the genetic architecture of seed coat content in Brassica napus. Genome Biol 23, 86 (2022). https://0-doi-org.brum.beds.ac.uk/10.1186/s13059-022-02647-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s13059-022-02647-5

Keywords

Genome Biology

ISSN: 1474-760X

Contact us