QTL Identification for Stem Fiber, Strength and Rot Resistance in a DH Population from an Alien Introgression of Brassica napus

Stem fiber, stem strength and stem-rot resistance are important agronomic traits in Brassica napus. To understand the molecular mechanism that controls the stem-related traits, we investigated the stem lignin (ADL), cellulose (Cel), hemicellulose (Hem) content, S/G monolignol ratio (SG), stem breaking force (BF), breaking strength (F) and Sclerotinia sclerotiorum resistance (SSR). Each trait was significantly positively or negatively correlated with more than three of the other six traits. QTL mapping for ADL, Cel, Hem, SG, BF, F and SSR were performed using a doubled haploid population derived from an intertribal B. napus introgression line ‘Y689′ crossed with B. napus cv. ‘Westar’. A total of 67 additive QTL were identified and integrated into 55 consensus QTL by meta-analysis. Among the 55 consensus QTL, 23 (41.8%) QTL were co-located and were integrated into 11 unique QTL. The QTL by environment (Q × E) interactions were analyzed and 22 combined QTL were identified. In addition, candidate genes within the QTL intervals were proposed based on the known function of Arabidopsis orthologs. These results provided valuable information for improving lodging resistance, S. sclerotiorum resistance and mechanized harvesting of B. napus.

In B. napus, seed fiber-related traits have been emphasized because of their effect on seed oil content and meal value. Some major QTL or candidate genes associated with seed acid detergent lignin (ADL) content have been identified [6,[18][19][20]. For stem fiber-related traits, Wei et al. [2] uncovered eight and nine significant SNPs involved in ADL content and the syringyl/guaiacyl (S/G) monolignol ratio, respectively. Moreover, the S/G monolignol ratio was significantly negatively correlated with lodging resistance and stem disease susceptibility. A recent study proved that decreasing the S/G ratio by knocking out of the

Phenotypic Performances of the Parents and DH Lines
At maturity stage, the detached stems of the two parents 'Y689' and 'Westar' as well as the control line 'Zhongyou 821' were inoculated with S. sclerotiorum. As a result, the level of resistance of 'Y689' to S. sclerotiorum was significant compared to 'Westar' and 'Zhongyou 821' (Figure 1). We then examined histochemical transverse sections of the second stem internodes of the parents stained with phloroglucinol reagent, the vascular bundle regions were stained dark red in 'Y689' but were light red in 'Westar' (Figure 2a), indicating the higher lignin quantity of 'Y689' than 'Westar'. The lignin compositions of the parents were also estimated by Mälue staining, with the Mäule reagent stains, G residues yellow and S residues red. Fresh hand-cut internode sections of the two parents were stained red in the vascular bundle regions (Figure 2b), indicating that these regions contained abundant S monomers. This result fit well with the measurement using the near-infrared spectroscopy (NIRS) method (Table 1).
Phenotypic data for ADL, Cel, Hem, SG, BF and F in the DH population were detected over four environments (14WH, 14ND, 15WH and 15ER), and SSR was detected over two environments (14WH and 15WH). The BF, F and SSR of the two parents differed significantly in all experimental environments, whereas for the ADL, Cel, Hem and SG, significant differences (p < 0.05) were only detected in two or three environments. Statistical data of the seven traits for the DH population and the parents are listed in Table 1. The DH lines exhibited broad variations among the seven traits. Frequency distributions of the phenotypic values of the seven traits in the DH lines are shown in Figure 3. Among these traits, Cel and Hem differed significantly between the environments of 14WH and the others. SG differed significantly between 15WH and the other environments. F was significantly different between 14ND and the other environments. Resistance to Sclerotinia sclerotiorum of the two parents 'Westar' and 'Y689′, and the control line 'Zhongyou 821′. The level of resistance of 'Y689′ to S. sclerotiorum was significant compared to 'Westar' and 'Zhongyou 821′. ** means the significance level between Y689 and the other two lines, p ≤ 0.01.  Resistance to Sclerotinia sclerotiorum of the two parents 'Westar' and 'Y689′, and the control line 'Zhongyou 821′. The level of resistance of 'Y689′ to S. sclerotiorum was significant compared to 'Westar' and 'Zhongyou 821′. ** means the significance level between Y689 and the other two lines, p ≤ 0.01.       (Table 1 and Supplementary Table S1). Within the seven traits, SG and F presented lower heritability, indicating that these two traits were easily influenced by environmental factors. The Pearson's correlation coefficients between the seven traits ranged from 0.00 to 0.79 (Supplementary Table S2). Each trait was significantly positively or negatively correlated with more than three of the other six traits. For example, Cel was determined to be significantly positively correlated with Hem (r = 0.43, p < 0.001) and SSR (r = 0.32, p < 0.001), and negatively correlated with ADL (r = −0.16, p < 0.05), SG (r = −0.16, p < 0.001), BF (r = −0.33, p < 0.001) and F (r = −0.39, p < 0.001). In these comparisons, BF was highly correlated with F (r = 0.79, p < 0.001), probably because F was calculated with the following formula: F = BF/(π × (D/2) 2 ), where D was the diameter of the stem. Moreover, SSR was significantly positively correlated with Cel (r = 0.32, p < 0.001) and significantly negatively correlated with BF (−0.32, p < 0.001) and F (−0.38, p < 0.001), indicating that stems with higher breaking strength/force and lower cellulose content were more resistant against the disease caused by Sclerotinia sclerotiorum.

Additive QTL Mapping and Meta-Analysis
With the method of CIM, a total of 67 significant additive QTL were detected across 2 years (Table 2 and Figure 4). These QTL were primarily distributed on chromosomes C09 (11 QTL), C06 (10 QTL), A07 (9 QTL) and C03 (7 QTL), singly explaining 1.92-27.18% of the phenotypic variation. Further, meta-analysis was used to integrate QTL across different environments (first round) and different traits (second round). In the first round of meta-analysis, 55 consensus QTL were detected, seven of which were major QTL. Notably, these major QTL were mainly clustered on Chromosomes A07 (112.5-120.35 cM) and C09 (116.79-123.5 cM) ( Table 2).
For ADL, 11 additive QTL were detected and account for 5.58-16.57% of the phenotypic variation. These QTL were then integrated into nine consensus QTL, with one QTL (cqADL.C09-3) considered to be a major QTL. For Cel, nine additive QTL were detected and accounted for 4.85-12.09% of the phenotypic variation. These QTL were integrated into eight consensus QTL, and no major QTL were detected. For Hem, five additive QTL were detected and accounted for 6.13-12.96% of the phenotypic variation. These QTL were integrated into three consensus QTL, with one QTL (cqHem.A07-1) considered to be a major QTL. For SG, eight additive QTL were detected and accounted for 4.40-27.18% of the phenotypic variation. These QTL were integrated into eight consensus QTL, with one QTL (cqSG.A07-1) found to be a major QTL. For BF and F, 7 and 16 additive QTL were identified, and accounted for 6.25-16.65% and 1.92-12.93% of the phenotypic variation, respectively. The QTL of cqBF.A07-2 was a major QTL for BF, which could be repeatedly detected in the environments of 14WH, 15ER and BLUE, with a mean PVE of 10.43%. The QTL of cqF.C06-2 was a major QTL for F, which could be repeatedly detected in 14WH and 15ER, with a mean PVE of 14.89%. For SSR, 11 additive QTL were detected and accounted for 4.91-17.11% of the phenotypic variation. These QTL were integrated into 10 consensus QTL, with two QTL (cqSSR.C09-1 and cqSSR.C09-2) considered to be major QTL. The QTL of cqSSR.C09-1 could be repeatedly detected in 15WH and BLUE, accounting for 12.64% and 14.26% of the phenotypic variation. The QTL of cqSSR.C09-2 could be detected in 14WH, with the PVE of 17.11%.
In the second round of meta-analysis, 23 of the 55 consensus QTL with overlapping regions were integrated into 11 unique QTL (Table 3). Of these, four unique QTL (uqA06-1, uqC01-1, uqC03-1 and uqC06-1) were pleiotropic for both SG and F, one unique QTL (uqC09-2) was pleiotropic for ADL, Cel and SSR. A high percentage (41.8%) of the consensus QTL detected in different traits could be integrated into unique QTL, which were in accordance with the result that these traits were significantly correlated with one another (Supplementary Table S2).

QTL by Environment Interaction Mapping
Based on the phenotypic and genotypic data, QEI mapping was performed to verify the significantly additive QTL and to detect some probable interaction between additive QTL and the environments. As a result, a total of 22 combined QTL associated with ADL, Cel, Hem, SG, BF, F and SSR were identified, using LOD thresholds of 4.98, 5.07, 4.97, 5.04, 5.00, 4.91 and 3.84, respectively (Table 4). Of these, 15 QTL (68.2%) were also detected by CIM, including five major QTL (IqADL.C09-1, IqHem.A07-1, IqBF.A07-1, IqF.C06-1 and IqSSR.C09-1). With these combined QTL, IqF.C06-1 associated with F presented the strongest QEI effect, with PVE (A) and PVE (A by E) values of 9.61 and 14.15, respectively. Two combined QTL (C07-1 and IqCel.C07-2) associated with Cel were not corresponding major consensus QTL, but exhibited a very strong QEI effect, which could explain 24.29% and 18.45% of the phenotypic variations. Furthermore, values of PVE (A) and PVE (A by E) were compared to evaluate the additive and QEI effect of each combined QTL. Consequently, the PVE (A by E) values in all five combined QTL associated with Hem and BF were larger than PVE (A), probably because Hem and BF were less influenced by environmental factors.

Discussion
The stem-related traits of lignin, cellulose and hemicellulose can enhance the mechanical strength of the plant body, which is conducive to lodging resistance, convenient for mechanized harvesting, and can form barriers to pathogen infection. Moreover, the stem related traits have direct or indirect effects on yield because the plants with rigid stem can reduce lodging and enhance S. sclerotiorum resistance. In some reports, lodging has been shown to result in a yield reduction of as much as 46% [36] and S. sclerotiorum could cause 0-80% yield loss in B. napus [33]. Therefore, it is very important to focus on the stem traits in contemporary B. napus breeding.
However, the genetic mechanism and the inner relationship of these traits have yet to be clarified in B. napus. Previously, we created an alien ingression line by intertribal hybridization between B. napus cv. 'Zhongyou821 and Capsella bursa-pastoris (L.) Medic (2n = 4x = 32), and named it as 'Y689 . This ingression line showed wooden stems, tight branch angles, and high resistance to S. sclerotiorum. These characters should result from the introgression of the genetic element from C. bursa-pastoris, which likely resulted in the longer growth period of 'Y689 than the recipient cultivar [37]. To better clarify the genetic basis of these traits, we constructed a DH population derived from the cross of 'Y689 with B. napus cv. 'Wester', which was susceptible to S. sclerotiorum. In the present study, we analyzed the relationship between the fiber-related traits (lignin, cellulose, hemicellulose and S/G), stem strength (breaking force and strength) and the resistance to S. sclerotiorum. We found that the breaking strength (F) was significantly positively correlated with ADL, and significantly negatively correlated with SG and stem disease susceptibility (Supplementary Table S2), indicating that increasing the ADL content, especially the G proportion, could enhance the stem strength and disease resistance. G monolignol played a vital role in stem strength, which supported the result that decreasing the S/G ratio could improve the S. sclerotiorum resistance in B. napus and increase stem strength [2,5]. Therefore, we could increase the ADL content and alter ADL composition to improve stem strength, lodging and S. sclerotiorum resistance in future breeding programs.
Accurate characterization of the phenotype influenced the results of QTL mapping. For a long time, lack of accurate determination of lignocellulose has limited the progress of genetic analysis in B. napus. As a quick assay for lignocellulosic component and property analysis, NIRS has been broadly applied in rice [38], Miscanthus samples [39] and B. napus [18]. In this study, we applied NIRS to determine the content of lignin, cellulose, hemicellulose and the S/G monolignol ratio. In addition, the breaking force and stem diameter were measured to determine the stem strength. However, the stem diameter sometimes varied from different positions upon the stem. Moreover, stem strength was also affected by the positions of knots. In this study, we investigated the phenotyping data across four environments for stem fiber and strength-related traits, and two environments for S. sclerotiorum resistance. The BLUP value was calculated across all environments, which could enhance the accuracy of mapping results as it could give the lowest variance of the estimate linear estimators, and the errors do not need to be normal.
In this study, we performed QTL mapping based on the high-density linkage map and phenotypic data of the seven traits. This linkage map was also used for B. napus branch angle and plant architecture analysis in our previous publications [37,40], and many stable QTL were identified, indicating the reliability of this linkage map. Thus far, genetic analysis of stem fiber related traits in B. napus is limited to one study using GWAS and transcriptomic analyses [2]. To our knowledge, the present study is the first QTL mapping study for stem fiber and strength-related traits was performed based on a bi-parent population in B. napus. We compared the physical position of the two results, and found that only one QTL cqBF.A07-3 (A07:19,528-19,664 kb) associated with BF herein was close to the reported significant SNPs (A07: 20,909-20,962 kb). Other QTL regulating stem fiber and strength identified in this study were novel (Supplementary Table S4). For SSR, Li et al. [41] reviewed the reported QTL and found two conserved QTL regions (A09: 22.5-26.5 Mb and C06: 29.5 to 35.4 Mb). We compared this result and the recent studies [42][43][44] with our result, and found the QTL identified in our result were novel (Supplementary Table S4). The detection of these new loci was largely attributable to the introduction of new gene sources for germplasm innovation via distant hybridizations.
By two-round meta-analysis, all the identified QTL were integrated into consensus QTL and further unique QTL. Many consensus QTL were co-located, and could be integrated into unique QTL, indicating that these QTL had pleiotropic effects or multiple genes that were tightly linked. Fine mapping will be necessary to determine the pleiotropic effects of a single QTL or a tight linkage of two QTL in the same region. These results were supported by the correlation analysis that significant correlations existed between the traits of stem fiber, stem strength and disease resistance (Supplementary Table S2).
QEI mapping was conducted to confirm the consensus QTL and estimate QEI effect. In this study, a total of 22 combined QTL were identified by QEI mapping, and some of the QTL presented a strong QEI effect. Of these, 15 QTL could be detected both by singleenvironment analysis and QEI mapping, which might be better ones for fine mapping and molecular breeding. For most of the combined QTL, the value of PVE (A by E) was lower than PVE (A), indicating the low QEI effect for most of the QTL. For some combined QTL, such as IqCel.C07-2, IqSG.A07-1 and IqF.C06-2, the value of PVE (A by E) was higher than PVE (A), suggesting the strong QEI effect that was detected.
Candidate genes for each trait were screened based on GO annotations. Some noteworthy candidate genes were identified in overlapping regions of fiber-related (ADL, Cel, Hem and SG) QTL. For instance, BnaC09g44570 corresponding to AT1G30230, encoded a guanine nucleotide exchange factor that played an important role in translation elongation. It was reported that silencing this gene could cause a dwarf phenotype with 38% and 20% reduction in total lignin and crystalline cellulose, respectively. This loss-of-function mutant also had a lower S/G lignin monomer ratio relative to wild type plants [45]. In addition, cqHem.A07-1 and cqBF.A07-2 were major co-located QTL associated with Hem and BF, respectively. In this region, BnaA07g26180 corresponding to AT1G02640 was identified as candidate gene, encoding a protein similar to a beta-xylosidase. It was reported that overexpressing this gene could increase enzymatic saccharification efficiency in cultured Arabidopsis wood cells [46].
The DH lines and the two parents were grown using a randomized complete block design with two replicates. Each plot consisted of 10 plants, with 30 cm between rows and 20 cm within rows. The field management followed the common agricultural practices.

Histochemical Staining
According to the protocol established by [50], the histochemical localization of the accumulated lignin was stained with phloroglucinol reagent. Fresh hand-cut sections from the 'Y689' and 'Westar' plants were incubated for 10 min in phloroglucinol solution (1% in 70% ethanol), the phloroglucinol was removed and treated with 18% HCl for 5 min, then photographed under a light microscope.
Mäule staining was performed to detect the lignin composition. Sections were treated for 5 min with KMnO 4 , rinsed with water, treated with 10% HCl for 5 min, rinsed again with water and mounted in concentrated NH 4 OH for examination [51].

Phenotypic Evaluations and Statistical Analysis
At maturity, six representative plants from the center of each plot were selected for measurements of ADL, Cel, Hem, SG, BF and F. A length of 30 cm fresh stems were detected to measure the breaking force (BF) and the diameter (D), using an electronic Vernier caliper. The breaking strength (F) was calculated with the formula: F = BF/ (π × (D/2) 2 ) [2].
Stem samples were then dried in a ventilated oven (65 • C) for about one week until the weight was constant. Based on the Near-infrared (NIR) models developed by Southwest University, Chongqing, China [2], the ADL, Cel, G and S monolignins were measured using NIR (FOSS, NIRS 5000) with the WinISI software.
At maturity, three representative plants from the center of each plot were detached to evaluate Sclerotinia sclerotiorum resistance. The S. sclerotiorum isolate was maintained and cultured on potato dextrose agar (PDA) medium in the dark at 22 • C and 6-mmdiameter mycelia agar plugs punched from the growing margin of a 3-day-old culture of S. sclerotiorum were used as inoculums [52]. Two 6-mm-diameter mycelia agar plugs were then placed above the 30 cm fresh stems with a 10-cm interval. Lesion sizes were measured after 3 days of inoculation with the infection temperature of 22 • C [53].
The data for each plant were averaged to represent the phenotype of a plot, and data from replicated plots were averaged to represent the phenotype of a DH line. Broad-sense heritability (h 2 ) and correlations among all traits were analyzed using SAS GLM and CORR packages, respectively [37,40].

QTL Mapping
The population and linkage map utilized in this study were also constructed for B. napus branch angle [40] and plant architecture [37] analyses in our previous publications. Briefly, the DH population was genotyped by B. napus 60 K SNP array, and 3073 available SNP markers were screened for linkage map construction. This map covered a length of 2242.14 cM and had an average marker interval of 0.73 cM [40].
Based on the genotyping and phenotyping data, QTL analysis was conducted using QTL IciMapping V4.1 [54,55] with the inclusive composite interval mapping (ICIM) method. The ICIM-BIP and ICIM-MET functionalities in this software were used for each experiment independently and for QTL-environment interaction analysis, respectively. The thresholds of the logarithm of the odds (LOD) scores for evaluating the statistical significance of QTL effects were determined using 1000 permutations at 95% confidence level.
QTL identified by the ICIM-BIP functionality were named as additive QTL. Through a two-round strategy of QTL meta-analysis [56], the additive QTL were integrated into consensus QTL for the same trait, and then the consensus QTL were integrated into unique QTL for different traits, using the software BioMercator V4.2 [57]. QTL identified by the ICIM-MET functionality were named as combined QTL.

Candidate Gene Mining
The genetic linkage map could be aligned to the B. napus reference genome by BLAST. Based on the collinearity relationship between the genetic linkage map and the reference genome, genes within QTL regions were BLAST to the A. thaliana genome. These genes were further annotated with gene ontology (GO) terms, and genes belonging to related GO terms were regarded as potential candidate genes. For ADL and SG, the GO term was GO: 0009808 (lignin metabolic process). For Cel, the GO term was GO: 0030243 (cellulose metabolic process). For Hem, the GO term was GO: 0010410 (hemicellulose metabolic process). For BF and F, the GO term was GO: 0009505 (plant-type cell wall). For SSR, those encoding proteins containing NBS-LRR domains published from [35] were regarded as the most plausible candidate genes.

Conclusions
The stem lignin, cellulose, hemicellulose, syringyl/guaiacyl monolignol ratio, breaking force, breaking strength and S. sclerotiorum resistance were investigated in one DH population consisting of 208 lines in B. napus. We found that the breaking strength was significantly positively correlated with stem lignin content and S. sclerotiorum resistance, and significantly negatively correlated with the syringyl/guaiacyl monolignol ratio. QTL mapping was performed for these seven traits, and a total of 67 additive QTL were identified. These QTL were integrated into 55 consensus QTL, 23 of which were then integrated into 11 unique QTL by meta-analysis. To confirm the QTL reliability and assess the QTL by environment interaction (QEI) effect, QEI mapping was conducted and 22 combined QTL were identified. In addition, candidate genes within the QTL intervals were proposed based on the known function of Arabidopsis orthologs. One gene, BnaC09g44570, located in overlapping regions of fiber-related (ADL, Cel, Hem and SG) QTL, likely played an important role for fiber biosynthesis. These results provided valuable information for improving lodging resistance, S. sclerotiorum resistance and mechanized harvesting of B. napus.

Supplementary Materials:
The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/plants11030373/s1, Table S1: The broad-sense heritabilities of ADL, Cel, Hem, SG, BF, F, and SSR in this study, Table S2: Correlations between ADL, Cel, Hem, SG, BF, F, and SSR, based on the BLUE data, Table S3: List of the candidate genes in this study, Table S4: Comparative analysis of the QTL identified in the present and previous studies based on the B. napus genome sequences, Table S5: Summary of the environments where the parents and DH population were grown.