Discovery of common loci and candidate genes for controlling salt-alkali tolerance and yield-related traits in Brassica napus L.

Abstract

Rapeseed (Brassica napus L.) is one of the most important oil crops in the world. Its yield is decided by multiple yield-related traits, which is also susceptible to environmental factors. Many yield-related quantitative trait loci (QTLs) have been reported in Brassica napus; however, no studies have been conducted to investigate both salt-alkali tolerance and yield-related traits simultaneously. Here, specific-locus amplified fragment sequencing (SLAF-seq) technologies were utilized to map the QTLs for salt-alkali tolerance and yield-related traits. A total of 65 QTLs were identified, including 30 QTLs for salt-alkali tolerance traits and 35 QTLs for yield-related traits, accounting for 7.61–27.84% of the total phenotypic variations. Among these QTLs, 18 unique QTLs controlling two to four traits were identified by meta-analysis. Six novel and unique QTLs were detected for salt-alkali tolerance traits. By comparing these unique QTLs for salt-alkali tolerance traits with those previously reported QTLs for yield-related traits, seven co-localized chromosomal regions were identified on A09 and A10. Combining QTL mapping with transcriptome of two parents under salt and alkaline stresses, thirteen genes were identified as the candidates controlling both salt-alkali tolerance and yield. These findings provide useful information for future breeding of high-yield cultivars resistant to alkaline and salt stresses.

Key Message

Common loci and candidate genes for controlling salt-alkali tolerance and yield-related traits were identified in Brassica napus combining QTL mapping with transcriptome under salt and alkaline stresses.

Introduction

Rapeseed (Brassica napus L.) is one of the most important oilseed crops in the world with the highest grain yield of rapeseed. With the rapidly growing demand for rapeseed oil, increasing the yield of oilseed crops is always a major breeding objective. In China, the self-sufficiency rate of vegetable oil is about 40%, which is far below the safety level of 60% (Wang 2007). Moreover, rapeseed planting areas have significantly decreased, and growth retardation and significant yield reductions of rapeseed have been observed with the increase of alkalinity and salinity of the rapeseed farmland (Machado and Serralheiro 2017). Therefore, it is urgent to improve environmental adaptation and yield of Brassica napus L.

Plant responses to salinity stress and agronomic traits such as seed yield and yield-associated traits (yield components and yield-related traits) are complicated quantitative traits that are controlled by numerous loci in Brassica napus (Paterson et al. 1988; Flowers 2004). Quantitative trait locus (QTL) mapping is one of the most common approaches for genetic analysis of quantitative traits. By traditional QTL mapping, QTLs for seed yield and yield-related traits have been identified in Brassica napus (Udall et al. 2006; Li et al. 2007; Mei et al. 2009; Cai et al. 2014). In contrast, QTLs associated with adaptation to salt stress are comparatively rare (Lang et al. 2017). Traditional genetic mapping methods need to create large-scale segregation populations and high-density genetic maps, which is time-consuming and costly. With the development of the next-generation sequencing (NGS) technology, a number of salt tolerance-related SNPs have been identified in B. napus using the 60k SNP array (Jian et al. 2014; Yong et al. 2015; Wei 2016; He et al. 2017; Hou et al. 2017; Wan et al. 2017; Zhang et al. 2017; Wassan et al. 2021; Zhang et al. 2022).

A high-density genetic linkage map is a valuable tool for QTL mapping. In B. napus, a number of genetic maps have been constructed based on restriction fragment length polymorphism (RFLP) markers (Ferreira et al. 1994; Foisset et al. 1996), amplified fragment length polymorphisms (AFLPs) (Cai et al. 2008; He et al. 2008), simple sequence repeats (SSRs) (Piquemal et al. 2005; Xu et al. 2010) or a small amount of single nucleotide polymorphism (SNP) markers (Raman et al. 2014). However, the density of the genetic maps is relatively low due to the small number of available molecular markers. Construction of high-density genetic map requires a large number of molecular markers. Due to the variability of single nucleotide DNA sequence, SNPs are currently the markers of choice for high-density genetic mapping construction (Ganal et al. 2009). A large number of SNPs throughout the genome can be obtained by restriction-site associated DNA tag sequencing (RADseq), whole-genome resequencing (WGRS), genotyping-by-sequencing (GBS), and specific-locus amplified fragment sequencing (SLAF-seq) for high-density genetic map construction (Baird et al. 2008; Poland et al. 2012; Sun et al. 2013; Bowers et al. 2016). The SLAF-seq technology has been used to construct high-density genetic maps in various plant species, such as rice (Song et al. 2018), soybean (Li et al. 2017), sunflower (Zhou et al. 2018), and cucumber (Zhu et al. 2016). In addition, QTLs have been detected based on the SLAF-seq method, such as quality-related QTLs in peanut (Hu et al. 2018) and soybean (Zhou et al. 2018), P efficiency-related QTLs of soybean (Zhang et al. 2016), seed weight QTLs in B. napus (Geng et al. 2016), flowering time QTLs in B. napus (Xu et al. 2021) and salt tolerance QTLs in soybean (Do et al. 2018). However, no studies have been reported the simultaneous identification of QTLs for traits related to salt-alkali tolerance and yield using the SLAF-Seq technology in Brassica napus. Moreover, only a small number of candidate genes for yield-related traits have been cloned in rapeseed. BnaC9.SMG7b, from the major QTL for SS, qSS.C9 (Zhang et al. 2012), functions as a positive regulator of seed number per silique in rapeseed by regulating the formation of functional female gametophytes (Li et al. 2015). The ribosome recycling factor (BnRRF) gene for seed weight has been identified based on whole-genome resequencing of 418 diverse rapeseed accessions (Hu et al. 2022). Some salt tolerance-related candidate genes have been identified by GWAS in B. napus, but the function of genes in B. napus in response to salt stress has not been elucidated.

In this study, we constructed a high-density genetic map of B. napus using the SLAF-seq technology. QTLs associated with salt-alkali tolerance and yield-related traits were identified. Combining QTL mapping and RNA-seq technologies, several potential candidate genes controlling both salt-alkali tolerance and yield have been identified. This study will lay a good foundation for understanding the genetic basis of rapeseed breeding for yield and salt-alkali tolerance traits.

Materials And Methods

Plant material and population construction 

B. napus line 2205 (salt-tolerant) and line 1423 (salt-sensitive) were used as the parental lines to develop segregating populations. A cross was made between 2205 and 1423 to create F1. An F_7:8 population of 82 recombinant inbred lines (RILs) derived from the cross between 2205 and 1423 was used to construct the high-density genetic map and phenotypic evaluation. Two parents and their 82 individuals were planted in the greenhouse (16/8 h, 25/20^◦C). Two-week old leaf tissues of the two parents and RIL individuals were collected and frozen in liquid nitrogen before SLAF-seq analysis.    

Phenotypic determination and statistical analysis

The alkaline and salt tolerance traits were evaluated at the seedling stage using a hydroponic system (Tocquin et al. 2003), with slight modification using large hydroponic containers (24 × 18 × 7 cm, L × W × H). In brief, thirty healthy seeds from each of these 82 RIL lines and the parents were germinated in a plastic container with 0.25x modified Hoagland solution for six days. Ten similar seedlings per line were selected and individually transferred to the hydroponic system (0.25× for the first week, 0.5× for the second week) and grown for two weeks in a growth chamber (16/8 h, 25/20 °C). The seedlings were then treated with 200 mM NaCl for ten days and 75 mM NaHCO₃ for seven days in the four-five leaf stage, which were used to measure the morphological and physiological indexes, including salt tolerance rate (STR), root length (RL), shoot fresh weight (SFW), shoot dry weight (SDW), root fresh weight (RFW) and chlorophyll content (SPAD). The grade standard of salt tolerance (STR) was determined as described previously (Ma et al. 2009). Shoots of individual seedlings were oven-dried at 105 °C for 30 min, and then dried further at 80◦C for 10 days to measure the SDW. The chlorophyll content (SPAD) was measured on a SPAD-502 plus (Konica Minolta, Japan). The units used were centimeters for length and grams for weight. For the yield-related traits, the 82 RILs were planted in the experimental field of Northwest Agriculture and Forestry University in Yangling, Shaanxi, China, in the winters of 2018, 2019 and 2020 and harvested in the springs of 2019, 2020 and 2021. During the maturity period, five plants per line were selected for yield-related trait determination in the springs of 2019, 2020 and 2021, respectively, including plant height (PH), length of main inflorescence (LMI), seeds per silique (SS), seed yield per plant (SYP), and number of siliques per plant (NSP). All phenotypic traits were evaluated using IBM SPSS version 20.0. Correlation coefficient and heatmap were obtained using the CloudTUTU platform (www.cloudtutu.com).

SLAF library construction and high-throughput sequencing

SLAF-seq was used to genotype 82 RIL individuals and their parents, as previously described with minor modifications (Sun et al. 2013). The B. napus reference genome, which has a size of 1.2 Gb (download link: http://www.genoscope.cns.fr/ brassica napus/data/), was used to perform a SLAF pilot experiment. The DNA of the RIL individuals and their parents was extracted from young leaf samples according to a modified CTAB method (Doyle 1990). First, the genomic DNA from each sample was digested at 37 °C with HaeIII and Hpy166II. Subsequently, fragments ranging from 364 to 414 base pairs (with indexes and adaptors) in size were gel-purified and diluted for pair-end sequencing (each end 125 bp) using an Illumina HiSeq 2500 system (Illumina, Inc; San Diego, CA, USA). 

SLAF-seq data analysis and genotyping

The SLAF-seq data grouping and genotyping were performed using procedures described by Sun et al. (2013). Briefly, low-quality reads (quality score <20e) were filtered out and all SLAF pair-end reads were clustered based on sequence similarity by BLAT. Sequences with over 90% similarity were grouped into one SLAF locus. Minor allele frequency (MAF) evaluation was used to define alleles in each SLAF. Groups containing more than four tags were filtered out as repetitive SLAFs, and the SLAFs with 2-4 tags were identified as polymorphic SLAFs. High-quality SLAF markers for genetic mapping were filtered by the following criteria: (i) average sequence depths should be > 4-fold in each progeny and > 20-fold in the parents, (ii) markers with more than 10% missing data were filtered, and (iii) the chis-square test was performed to examine the segregation distortion.

High-density genetic map construction and QTL Mapping   

Based on the genotyping data of 82 RILs, marker loci were partitioned primarily into chromosomes using a modified logarithm of odds (MLOD) score values of >3 as the cutoff value. To ensure efficient construction of the high-density and high-quality map, the HighMap strategy was used to order the SLAF markers and correct genotyping errors within the linkage groups (Liu et al. 2014). The genetic map was constructed by applying a multipoint method of maximum likelihood (Van Ooijen 2011) and genotyping errors were corrected with the SMOOTH algorithm (Van Os et al. 2005). A k-nearest neighbor algorithm was applied to impute missing genotypes (Huang et al. 2012). Finally, genetic map distances were estimated using the Kosambi mapping function (Kosambi 1943). QTLs were identified based on LOD scores at the significance level of 0.05 determined through 1,000 permutation tests using the CIM method with the WinQTLCart version 2.5 (Basten et al. 1997). As a result, a LOD score of 2.5 was used as the threshold to identify QTLs and these QTLs were termed identified QTLs (Burns et al. 2003). The 2-LOD intervals surrounding the QTL peak determined the QTL CIs. The QTLs with overlapping CIs for different traits were further integrated into one unique QTL by QTL meta-analysis using BioMercator 4.2 software (Goffinet and Gerber 2000), (Arcade et al. 2004). Referring to the nomenclature previously described (Udall et al. 2006), identified QTL were designated with an initial letter “q” followed by the abbreviation of the trait, the year or treatment, and the corresponding chromosome (e.g., qPH19-C3, qSTRs-C6, qSTRa-A3). Unique QTLs were designated with the initial letters “uq-" followed by the abbreviation of the trait and the corresponding chromosome (e.g., uqPH-C3). The linkage genetic map and QTLs were visualized using MapChart 2.2 (Voorrips 2002).

QTL comparison for all traits of present and previous QTLs

In order to further verify the reliability of the QTLs, QTL mapping studies co-localized with the present study were collected for alkaline and salt tolerance [9-18], yield-related traits including PH, LMI, SS, SYP and NSP (Udall et al. 2006; Jiaqin et al. 2009; Basunanda et al. 2010; Shi et al. 2011; Wang et al. 2016b; Zhao et al. 2016; Luo et al. 2017; Ye et al. 2017; Shen et al. 2018; Yang et al. 2018; Li et al. 2020; Hu et al. 2022) and flowering time (FT) (Long et al. 2007; Wei et al. 2014; Xu et al. 2015; Wang et al. 2016a; Wei et al. 2017; Li et al. 2018a; Li et al. 2018b; Jian et al. 2019; Wu et al. 2019; Xu et al. 2021) conducted on B. napus. QTL from previous studies and the present study were compared to the physical genomic regions of B. napus, “Darmor-bzh” (http://www.genoscope.cns.fr/brassicanapus/data/). If only one flanking marker can be aligned to the reference genome, we take a uniform area of 1 cM to delimit all QTLs.     

RNA extraction, RNA‑seq and data analysis

Total RNA was isolated from roots of line 2205 (salt-tolerant) and line 1423 (salt-sensitive) under salt treatments (3h and 24h after the treatments, ST) and control (0 h, CK) using TRizol (Invitrogen, Carlsbad, CA, United States) and purified using a Qiagen RNeasy kit (Qiagen, Germany). Each sample had three biological replicates. A total of 18 RNA samples were used to construct the cDNA library. Sequencing of each cDNA library was carried out on the Illumina HiSeq 6000 system. We used BWA to map clean reads to the B. napus genome (Darmor-bzh). Differential expression analysis of two samples was performed using the DESeq2 R package (Wang et al. 2009). Genes were considered to be differentially expressed genes (DEGs) with a |log2FC| ≥1 and an adjusted p-value <0.01. Venn diagram and gene-expression heat map were drawn using OmicShare tools (http://www.omicshare.com/tools). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to perform enrichment analysis of DEGs. GO and KEGG enrichment were performed using TBtools V1.076 software (Chen et al. 2020).

Quantitative real-time PCR analysis

Gene expressions were measured by RT-qPCR using the SYBR Green Master Mix (TaKaRa, Japan). The expressions of genes were calculated by the 2^-△△Ct method according to the method described by Vandesompele et al., (2002) (Vandesompele et al. 2002) and the expression of actin2 was used as an internal control. Each assay was repeated three times independently, and the statistical significance was evaluated by Student’s t test (significance, P < 0.05). Primers used for RT-qPCR are listed in Table S9.

Results

Phenotypic variation and correlation analysis

We measure six salt tolerance traits (STT), four alkaline tolerance traits (ATT) and five yield-related traits from the RIL lines and the two parents. All traits performed significant differences between line 1423 and line 2205 in all environments. Compared with other traits, PH showed the lowest coefficient of variance (CV), while SYP exhibited the highest CV (Table1). All traits exhibited continuous normal or near-normal distributions (Fig. S1). Correlation analysis among all traits was carried out. STR was usually described as an effective salt stress indicator. For STT, STR was significantly negatively correlated with all physiological and morphological indicators. For ATT, STR was only significantly negatively correlated with SPAD and RFW. SPAD was significantly positively correlated with RFW and RL (Fig. 1). A correlation was observed between salt tolerance traits and alkaline tolerance traits. For instance, aSPAD was significantly positively correlated with sRL, sRFW, sSFW, and sSDW. These results suggest that alkaline and salt tolerance in B. napus might be linked to these traits and different. Seed yield (SYP) showed significant positive correlations with all yield-associated traits across different years, and especially for NSP and SS with a correlation coefficient of 0.80 and 0.62, respectively. Interestingly, SYP was significantly positively correlated with sSPAD. However, sSPAD was significantly positively correlated with all salt-related traits. Taken together, these results demonstrate that there is a complex association between seed yield and other traits, either directly or indirectly, including salt and alkaline tolerance traits and yield-related traits.    

Construction of the linkage map based on SLAF-Seq  

In total, 33.35 Gb of raw reads consisting of 166.75 Mb paired-end reads were generated from Illumina sequencing of the SLAF libraries. The average percentage of Q30 bases (bases with a quality score of 30, indicating a 1% chance of an error and thus a 99% confidence level) was 94.83%, and the GC content was 36.43%. A total of 73.82 and 78.45 million reads were generated for the salt-tolerant parent 2205 and the salt-sensitive parent 1423, respectively, while 2.03 million reads were obtained for the 82 RILs. A total of 2,532,319 SNPs were detected, among which, 1,516,733 polymorphic SNPs were successfully sorted into eight segregation patterns (ab × cd, ef × eg, ab × cc, cc × ab, hk × hk, lm × ll, nn × np, and aa × bb), and 673,113 SNPs that belong to the aa × bb segregation pattern were used in linkage analysis. After a three-step filtering process (see Methods), 5,250 SNPs were used for the genetic map construction. In total, 4,159 SNP markers were grouped into 19 linkage groups (LGs) compared with the B. napus reference genome (Fig. S2). The total genetic distance was 1736.59 cM with a mean marker distance of 0.42 cM between adjacent markers. The lengths of LGs ranged from 46.14 cM (LG8) to 136.79 cM (LG9). The number of markers in each LG ranged from 84 to 327, with an average of 219 markers per LG (Table S1).

QTLs for salt-alkali tolerance and yield-related traits 

A total of 65 QTLs were detected for salt-alkali tolerance and yield-related traits with a total phenotypic variance explained (PVE) of 7.61–27.84% and a LOD of 2.72–8.40 (Table S2 and Fig. 2). Notably, most QTLs for salt-alkali tolerance traits and each of QTL for SS and NSP showed a PVE of more than 10%. The QTLs for yield-related traits were mainly distributed on chromosomes A01, A09, C08 and C09 (4-6 each), while the QTLs for salt-alkali tolerance traits mapped on A09, A10 and C04. By meta-analysis, 42 of 65 identified QTLs were integrated into 18 unique QTLs controlling two to four traits (Table S3). Six unique QTLs were identified for salt-alkali tolerance traits, of which four formed two adjacent QTL clusters with a genetic distance of 1cM on A10, and the other two controlling SDWs, SFWs and RFWs were located on A09 and C04, respectively, with an average PVE of 15%. Seven unique QTLs were identified for seed yield and yield-related traits. For example, QTL uqA5 and uqC6 for SYP and LMI, uqC9-2 for SYP and NSP, uqA9-2 for SYP, NSP, PH and LMI and uqC3-2 for LMI and PH were identified with positive additive effects, which was in accordance with the significant positive correlations among these traits. Interestingly, five unique QTLs were detected for both salt-alkali tolerance and yield-related traits. For instance, QTL qSYP19-A6-1 overlapped with QTL qRFWa-A6 on A06. QTL uqC8 controlling PH, LMI and RFW and uqC9-1 controlling SS and RL showed consistent additive effects.  

Comparative analysis of salt-alkali tolerance and yield-related traits of present and previous QTLs 

Among the 18 QTLs for salt tolerance traits, ten salt-related QTLs contained 245 significantly associated SNPs identified previously, of which four were detected for the same traits (Table S4). Specifically, the QTLs qSDWs-C3 and uqC4 for SDW, qRLs-C9 for RL, and uqA10-3 for SFW included nine, one and two SNPs, respectively, indicating their high reliability. In a previous study, some QTLs were detected using the F2 of the same parents, among which five QTLs (qSH12-a, qRDW19-c, qEC8-c, qSOD14-b and qLDW14-b) and one QTL qSH4-b had overlapping CIs with the QTLs qRFWs-C8 and qRLs-C9 in the present study, respectively, indicating the complexity of salt tolerance traits. In addition, nine alkaline-related QTLs contained 163 significantly salt-associated SNPs identified previously, of which qRLa-A10-1 and qRLa-A10-2 for RL possessed five SNPs for the same traits. For yield-related traits, ten of 35 QTLs were colocalized with QTLs identified in the previous study (Table S5). One QTLs for SYP and NSP were identical to qSYP21-A9 and qNSP20-A1 in the present study, respectively. qSS20-A1, qSS20-C1 and qSS20-C9-1 (present study) were identical to 18 QTLs for SS. qLMI19-C6 (present study) was identical to three QTLs identified previously. For PH, four QTLs (qPH19-C8-1, qPH20-C8, qPH19-C4 and qPH19-C8-2) were identical to significantly PH-associated InDels identified by GWAS. 

QTLs for salt-alkali tolerance traits co-localized with QTLs for yield-related traits 

As shown in Table S3, six unique QTLs for salt-alkali tolerance traits were located on A09, A10 and C04, especially A10. To explore the connection between salt-alkali tolerance traits and yield-related traits, these six unique QTLs plus qRLa-A10-1 and qRLa-A10-2 identified in the current study were compared with the QTLs for yield-related traits previously identified. Since flowering time is significantly associated with plant height, it was added as a yield-related trait. We found that seven QTLs, composed of three identified QTLs and four unique QTLs, overlapped with the QTLs for the six yield-related traits previously identified except for uqC4 (Table 2). qRLa-A10-1 and qRLa-A10-2 for RL overlapped with one SNP for SYP, one SNP for SS, one SNP for LMI, one QTL for LMI and one QTL for FT identified previously. uqA10-1 and uqA10-2 for SPAD and RFW overlapped with one QTL for SYP, one QTL for LMI, two QTLs for PH, one SNP for PH and twelve QTLs for FT identified previously. qRFWs-A9 and uqA10-3 for RFW, SDW and SFW overlapped with one QTL for SS, four QTLs for PH on A09, and one SNP for LMI, ten SNPs for FT and eleven QTLs for FT on A10 identified previously, respectively. Moreover, uqA10-4 for STR and SDW overlapped with five QTLs for FT identified previously. Taken together, seven QTLs that co-control salt-alkali tolerance traits and yield-related traits were identified and named as cQTLs, suggesting that A09 and A10 are important loci for controlling both salt-alkali tolerance traits and yield-related traits.  

Identification of differentially expressed genes by transcriptome sequencing

To investigate the potential molecular mechanism underlying the differences in salt tolerance between lines 2205 and 1423 and to explore salt-related candidate genes, RNA-seq analysis was performed using RNA extracted from the roots of the 1423 and 2205 plants under control conditions (mock treated) and salt treatments. We first tested the root length of line 1423 and line 2205 in response to the 200 mM NaCl treatment. Under salt stress, the root length of line 2205 is always higher than that of line 1423 and significant difference was observed at 3h and 24h between line 2205 and line 1423 (Fig. S3). Based on the results, we used the root tissues treated with NaCl for 3h and 24h in the RNA-seq experiment. After trimming off the adapter sequences and removing the low-quality reads, we obtained 484,217,730 and 467,967,990 clean reads for line 2205 and line 1423, respectively, with an average read length of 90 bp and a Q20 percentage (percentage of sequences with sequencing error rates lower than 1%) over 97%. In total, 433,216,368 (89.47 % of the clean reads) and 424,400,341(90.69% of the clean reads) reads were mapped to the B. napus reference genome for line 2205 and line 1423, respectively, and 93,990 transcripts were identified after comparison. Sequencing and assembly statistics are summarized in Table S6. Both Pearson’s correlation and PCA showed a high correlation among the replicas, with the exception of sample T-3h1 (round markers in Fig. S4), which was identified as an outlier and excluded from downstream differentially expressed genes (DEGs) analysis (Fig. S4 and Table S7).    

Global expression analysis showed that 15,274 DEGs named as S_SR were found in line 1423 and 12,845 DEGs named as T_SR were found in line 2205 after 3h and 24 h salt stresses (Fig. 3a, b). In total, 19,311 DEGs were identified under salt stress and named as "salt-inducible genes", among which, 6,466 and 4,037 genes showed unique differentially expression in lines 1423 and 2205, respectively. The NaCl treatment led to a less transcriptomic change in line 2205 compared with that in line 1423, indicating that line1423 is more sensitive to salt stress than line 2205 at the transcriptional level. A total of 12,815 DEGs were regulated at 0 h, 3 h and 24 h between lines 1423 and 2205 (Fig. 3c). To further screen salt tolerant genes, 2,909 genes that exhibited unique differential expression between lines 1423 and 2205 under normal conditions were removed, and the remaining 10,423 DEGs were named as V_SR. Among these three groups (S_SR, T_SR and V_SR), a total of 24,852 DEGs were identified and 4,882 DEGs were detected in all three groups, while 4,783 and 2,809 DEGs were only detected in S_SR and T_SR, respectively (Fig. 3d). The expression levels of eight randomly selected genes were measured by using RT-qPCR, which showed consistent results with RNA-seq, suggesting that the RNA-seq data were reliable (Fig. S5). 

Functional classification of differentially expressed genes 

In order to explore the salt tolerance mechanism of the two parents, three groups of the differentially expressed genes (DEGs), S_SR_ unique (4783), T_SR_ unique (2809) and STV_SR (4882), were functionally annotated using gene ontologies (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Fig. 4). GO enrichment analysis revealed that both T_SR_ unique (2809) and STV_SR (4882) DEGs were uniquely involved in secondary metabolic process and response to light stimulus (Fig. 4b, c), while the S_SR_ unique (4783) DEGs were uniquely involved in response to nitrogen compound, response to jasmonic acid and response to fatty acid (Fig. 4a). Moreover, DEGs identified in S_SR_ unique (4783) were enriched with significantly higher GO terms compared with those in T_SR_ unique (2809) and STV_SR (4882). KEGG enrichment analysis also provided similar enrichment results to the GO enrichment. Analysis of the KEGG pathways showed that metabolism pathways, especially for phenylpropanoid biosynthesis, flavonoid biosynthesis and carbohydrate metabolism were uniquely enriched among the T_SR_ unique (2809) and STV_SR (4882) DEGs (Fig. 4e, f), while plant hormone signal transduction, glutathione metabolism, glycosaminoglycan binding proteins and amino acid metabolism were uniquely enriched among the S_SR_ unique (4783) DEGs (Fig. 4d). These results indicated that the two parents had different molecular mechanisms in response to salt stress, and line 1423 was more sensitive to salt stress and elicited more responses in the body. The similar enrichment results of the two groups (T_SR_ unique and STV_SR) also showed that most of the key salt tolerance related genes came from line 2205. 

Candidate genes for controlling both salt-alkali tolerance and yield 

To further reveal candidate genes controlling both salt-alkali tolerance and yield, these 24,852 DEGs were integrated with seven cQTLs and 1081 genes were found in these QTLs regions. A total of 390 DEGs response to salt stress were co-localized with the cQTLs (Fig. 5a). Since the cQTLs contained both alkaline and salt tolerance traits, we also detected the DEGs using the transcriptome data of the two parents under alkaline stress (unpublished). A total of 400 DEGs were located with the cQTLs in response to alkaline stress (Fig. 5b). Of these, 99 and 162 DEGs were not only "salt-inducible genes" (S_SR/T_SR), but also differentially expressed between lines 1423 and 2205 (V_SR), respectively. To further narrow down the number of candidate genes, the expression profiles of these 99 and 162 DEGs were performed (Fig. S6). The genes that were continuously induced in 1423 or 2205 and differentially expressed between the two parents were selected for further analysis, and, 28 (28S) and 75 (75A) genes were obtained respectively (Fig. 5c). The heat maps of the relative expression profiles are shown in Fig. 5d and 5e. A total of 100 genes were induced by salt or alkaline stress, of which three genes were in response to both salt and alkaline stress (Fig. 5c). Among these 100 genes, 85 were homologous to 83 Arabidopsis genes. GO and KEGG analyses were performed for these 83 Arabidopsis genes, and the result revealed that 64 DEGs were annotated to GO terms and 31 DEGs were mapped to biological pathways, especially the metabolism pathways (Table S8). Based on the functional annotation, 13 genes were screened as the candidate genes (Table 3), of which, four were involved in response to salt, cold and heat abiotic stresses and six genes were annotated with regulation of cell growth, pollen tube growth and gibberellin biosynthetic process. Notably, three genes (BnaA10g16340D, BnaA10g20120D and BnaA10g25000D) were annotated with both abiotic stress and plant organ development, which would serve as the most promising candidates.

Discussion

Rapeseed varieties with high stress resistance and high yield are highly desirable in the rapeseed oil industry. To make full use of rapeseed germplasm resources, it is extremely important to assess its genetic diversity and to detect QTLs associated with stress tolerance and yield-related traits. A high-density genetic map is essential for QTL mapping and identification of functional genes. The SLAF-seq strategy has been successfully applied to high-density genetic map construction and QTL mapping for many plants, including B. napus (Geng et al. 2016; Zhang et al. 2016; Do et al. 2018; Hu et al. 2018; Xu et al. 2021). In this study, by SLAF-seq, we constructed a high-density genetic map with 4,159 SNP markers using the RIL population derived from a cross between lines 2205 and 1423 (Fig. S2). The total genetic distance was 1736.59 cM with a mean SNP marker distance of 0.42 cM between adjacent markers (Table S1). In a previous study, a genetic map using the same parents has been generated based on SSR and AFLP markers using the F₂ populations (Lang et al. 2017). However, the map only includes 532 polymorphic markers and spans 1341.1 cM with an average marker interval of 2.52 cM, demonstrating that SLAF-seq is an efficient approach for acquiring abundant polymorphic markers to rapidly construct higher resolution genetic maps.

To our knowledge, a few studies have been reported on QTL mapping of salt-alkali tolerant related genes used linkage mapping in B. napus. Here, we identified QTLs for both salt and alkaline tolerance traits in B. napus using a high-density genetic map constructed by the SLAF-Seq technology and found that QTLs for salt tolerance and alkaline tolerance traits were very different. Most QTLs under salt stress were distributed on A09, A10 and C04, while QTLs under alkaline stress were only on A10. Although no overlapping regions were detected for the same trait under salt and alkaline stress, the QTLs for both were mainly distributed on A10, and the QTLs for RFW on A10 were relatively close (Table S2), suggesting that the two parents (lines 1423 and 2205) in our study have different mechanisms in response to salt and alkali stresses. Besides, the QTL (qSTRa-A10) for STRa and the QTL (qSDWs-A10-2) for SDWs with opposite additive effects were detected to overlap with each other on A10, which was consistent with the significant negative correlation between STRa and SDWs. Compared with the salt tolerant related QTLs from Lang et al. 2017, six co-associated QTLs were observed which were mainly distributed on C08. However, no QTL was detected for the same trait (Table S4), which may be due to differences of population types and environments for phenotypic investigation, suggesting that the population type has a great influence on QTL mapping and the salt tolerance traits are also greatly influenced by the environment. Plant response to salt stress is a complex physiological process, which causes changes in many related traits, such as fresh weight of plant and dry weight of plant (Yeo 1998). In this study, STR was significantly correlated with RL, RFW, SFW and SDW, suggesting that these indicators can reflect the salt tolerance of plants to some extent. QTLs qSDWs-C3 and uqC4 possessed nine significant SNPs for SDW, and uqA10-3 possessed one significant SNPs for SFW identified previously. Similarly, six significant SNPs for RL were co-located with QTLs qRLa-A10-1 and qRLs-C9 (Table S4). These results revealed that these QTLs detected in the present study were stable and valid. We detected three uqQTLs (uqA9-1, uqA10-3 and uqC4) controlling RFW, SFW and SDW simultaneously (Table S3). Notably, 17 SDW-related significant SNPs were co-located with QTL uqA9-1, but they were far from uqA9-1 which is located at 3.76–4.99 Mb (Table S3), indicating that uqA9-1 is a reliable and novel salt-related QTL associated with RFW, SFW and SDW. Interestingly, multiple QTLs for salt-alkali traits were identified on chromosome 10, especially, QTLs uqA10-1, uqA10-2 and uqA10-4 which were adjacent to each other with a PVE of more than 10%. The QTL uqA10-4 was found to control both alkaline and salt tolerance traits. Moreover, these three QTLs contained seven salt-related significant SNPs for different traits identified previously, which further verified the reliability of the QTLs in the present study. These results suggest that the A10 chromosome is an important locus controlling alkaline and salt tolerance traits simultaneously.

Contrary to the alkaline and salt tolerance traits, QTL mapping of yield-related traits has been frequently reported in B. napus. In this study, 35 QTLs were obtained for yield-related traits, of which ten QTLs have been repeatedly reported by previous studies (Table S5). qSS20-A1 and qLMI19-C6 were repeatedly identified 3–15 times in at least two different experiments (Table S5), and six other identified QTLs (qPH19-C4, qPH19-C8-2, qSS20-C1, qSS20-C9-1, qSYP21-A9 and qNSP20-A1) were repeatedly detected only one time, all of which exhibit a PVE of more than 10% expect qPH19-C4. These results indicate that these QTLs are stable under different genetic backgrounds and may be useful in the molecular marker-assisted selection of breeding. It is worth noting that five QTLs (qSYP21-A9, qSS20-C1, qSS20-C9-1, qPH19-C8-2, qLMI19-C6) were repeatedly detected in previous studies, but they belonged to the pleiotropic QTL controlling two to three traits which exhibit a PVE of more than 15% in this study (Table S3). These QTLs will be highlighted as new pleiotropic QTLs. Besides these nine QTLs, the remaining QTLs identified in our study should be novel QTLs. Notably, QTL (qPH21-C5) for PH on C05 was detected for the first time.

This study is the first to simultaneously map stress resistance and yield-related traits using a high-density genetic map in rapeseed. Correlation analysis revealed that there were certain correlations between salt-alkali tolerance and yield-related traits, which was also reflected in the genome positions and effects of the detected QTL. For example, uqA6, uqC1, uqC3-1 and uqC8 were identified to pleiotropically control both salt-alkali tolerance and yield-related traits (Table S3), which may be used as key QTLs for high stress resistance and yield-related traits. As mentioned above, the loci on the A09, A10 and C04 chromosomes were key loci for salt-alkali tolerance traits, especially A10 in our study. These key loci did not co-localize with QTLs for yield-related traits in this study, but seven QTLs co-localized with QTLs for yield-related traits have been reported previously, especially plant height and flowering time (Table 2). Such co-localization of salt-alkali tolerance and yield-related traits have not been reported in previous studies. To further explore the candidate genes controlling both salt-alkali tolerance and yield, thirteen candidate genes were identified by combining QTL mapping and RNA-seq analysis (Table 3). Among them, four genes (BnaA09g07980D, BnaA10g14870D, BnaA10g19960D and BnaA10g25660D) were annotated to response to salt, cold and heat stresses, but none was reported to function under salt stress except BnaA10g19960D which is homologous to RAP2.6L of A. thaliana. RAP2.6L overexpression enhances plant resistance to salt and drought (Krishnaswamy et al. 2011). Six genes (BnaA09g09160D, BnaA09g08360D, BnaA09g08940D, BnaA09g08950D, BnaA10g25700D and BnaA10g26380D) were related with cell growth, pollen tube growth and gibberellin biosynthetic process and homologous to PILS5, CALS5, NAC036, BUP and CTR1 of A. thaliana. The mutant cals5 exhibits severely reduced silique length and seed yield (Dong et al. 2005). BnaA09g08940D and BnaA09g08950D were homologous to AT2G17040 (ANAC036) of A. thaliana. Overexpression of ANAC036 resulted in a semidwarf phenotype in Arabidopsis (Kato et al. 2010). Pollen development is a major factor that affects the yield of crops, and the pollen of the bup mutant is severely reduced (Hoedemaekers et al. 2015).The ctr1-1 mutant performs a delayed flowering (Achard et al. 2007) and is more tolerant to salt and osmotic stress compared with the wild type during germination and post-germination development, especially when the stress level is high (Wang et al. 2007). Interestingly, three potential candidates (BnaA10g16340D, BnaA10g20120D and BnaA10g25000D), homologous to CHR17, SWEET15/SAG29 and DREB2 of A. thaliana, were identified that may regulate simultaneously salt resistance and yield of plants. Overexpression of StDREB2 enhances drought stress tolerance in cotton and SlDREB2 mediates salt stress tolerance in tomato and Arabidopsis (Hichri et al. 2016; El-Esawi and Alayafi 2019). In addition, the expression of DREB2 changes during pollen germination and pollen tube growth, and DREB2, as a downstream regulator of ANAC019, functions in flower development under drought stress in Arabidopsis (Wang et al. 2008; Sukiran et al. 2019). Although chr17 single mutants not exhibit developmental defects, the chr11-1 chr17-1 mutant plants are smaller with a much earlier flowering time, and the flowers fail to open, and are completely sterile compared with the wild-type (Li et al. 2012). Notably, SWEET15/SAG29 exhibits opposing phenotypes in stress resistance and yield. The sweet15 mutant exhibits retarded embryo development and reduced seed weight, while the overexpression of SAG29 exhibits accelerated senescence and the plants are hypersensitive to salt stress (Seo et al. 2011). Functional exploration of all the mentioned candidate genes has not been reported in B. napus. Further studies are needed to reveal the roles of these genes, which will be valuable for future breeding of high-yield cultivars resistant to salinity stress.

Conclusion

In conclusion, we identified 65 QTLs for salt-alkali tolerance and yield-related traits by SLAF-seq. A total of 18 unique QTLs controlling two to four traits were detected by meta-analysis, among which, six novel and unique QTLs were identified for salt-alkali tolerance traits. Compared these unique QTLs with QTLs for the six yield-related traits previously identified, seven co-localized chromosomal regions were identified on A09 and A10. An integrated analysis of QTL mapping and RNA-seq identified several promising candidates controlling both salt-alkali tolerance and yield in these regions, which will lay a solid foundation for breeding of high-yield cultivars resistant to salt-alkali stress.

Declarations

Funding

The research was supported by the Key research and development projects of Yangling Seed-industry Innovation Center (Ylzy-yc2021-01).

Competing interests 

The authors declare that they have no competing interests.

Author Contributions

ZH was responsible for designing and supervising this study; YZ performed data analysis and manuscript drafting. YZ, QZ, HW, ST and HC worked on phenotypic survey. YS, BA and AX provided assistance for data analysis and phenotypic survey. All authors have read and approved the content of the manuscript.

Data Availability

The datasets generated during the current study are available in the Sequence Read Archive (SRA) database of National Center for Biotechnology Information (NCBI) under accession PRJNA847790 and accession PRJNA847444.

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