Integrative omics analysis reveals the genetic basis of fatty acid composition in Brassica napus seeds

Integrative omics analysis reveals the genetic basis of fatty acid composition in Brassica napus seeds | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrative omics analysis reveals the genetic basis of fatty acid composition in Brassica napus seeds Yuting Zhang, Yunhao Liu, Zhanxiang Zong, Liang Guo, Wenhao Shen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5468888/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2025 Read the published version in Genome Biology → Version 1 posted 3 You are reading this latest preprint version Abstract Background The fatty acid content represents a crucial quality trait in Brassica napus or rapeseed. Improvements in fatty acid composition markedly enhance the quality of rapeseed oil. Results Here, we perform a genome-wide association study (GWAS) to identify quantitative trait locus (QTLs) associated with fatty acid content. We identify a total of seven stable QTLs, and find two loci, qFA.A08 and qFA.A09.1 , subjected to strong selection pressure. By transcriptome-wide association analysis (TWAS), we characterize 3,295 genes that are significantly correlated with the composition of at least one fatty acid. To elucidate the genetic underpinnings governing fatty acid composition, we then employ a combination of GWAS, TWAS, and dynamic transcriptomic analysis during seed development, along with the POCKET algorithm. We predict six candidate genes that are associated with fatty acid composition. Experimental validation reveals that four genes ( BnaA09.PYRD , BnaA08.PSK1 , BnaA08.SWI3 and BnaC02.LTP15 ) positively modulate oleic acid content while negatively impact erucic acid content. Comparative analysis of transcriptome profiles suggests that BnaA09.PYRD may influence fatty acid composition by regulating energy metabolism during seed development. Conclusions This study establishes a genetic framework for a better understanding of plant oil biosynthesis in addition to providing theoretical foundation and valuable genetic resources for enhancing fatty acid composition in rapeseed breeding. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Rapeseed ( Brassica napus L.) is an oilseed crop that is globally cultivated [ 1 , 2 ]. Triacylglycerols, the primary form of oil in rapeseed, are composed of a glycerol backbone and fatty acid (FA) chains [ 3 – 5 ]. FAs are categorized into saturated and unsaturated types based on the hydrocarbon chain’s degree of saturation [ 6 ]. Saturated FAs tend to accumulate on blood vessel walls and are less easily digested and absorbed by the human body [ 7 – 9 ], while unsaturated FAs are more beneficial to human health, helping reduce the risk of cardiovascular and cerebrovascular diseases [ 10 , 11 ]. In rapeseed, breeding efforts have focused on developing double-low (low erucic acid, low glucosinolate) and one-high (high oleic acid) varieties [ 12 , 13 ]. The FA biosynthetic pathway in rapeseed is a quantitative trait regulated by QTLs, and its FA composition is controlled by many genes with additive and epistatic effects [ 14 – 17 ]. Among the genes and loci for FA breed improvement, researchers identified two major effector loci with additive effects through linkage mapping, which are located on rapeseed chromosomes A08 and C03 [ 18 , 19 ]. The two homologs of FAE1 , BnaA08.FAE1 and BnaC03. FAE1 are key genes necessary for encoding the elongation of long-chain FAs and can control the synthesis of erucic acid in rapeseed [ 20 ]. Peng et al. silenced the FAD2 and FAE1 genes in rapeseed using RNAi technique and found a great increase in oleic acid and a decrease in erucic acid [ 21 ]. Wells et al. found that a significant increase in erucic acid content could be achieved by either repressing the expression of the FAD2 gene in rapeseed or by directly mutating the gene [ 22 , 23 ]. Additionally, it has been shown that the copy number of the FAD3 gene affects the content of linolenic acid in seeds [ 24 , 25 ]. Therefore, high linolenic acid breeding in rapeseed can be achieved by increasing the expression of FAD3 gene. In rapeseed germplasm resources, the genetic regulatory mechanisms affecting FA compositions have been largely clarified [ 26 , 27 ]. However, the proportions of FA composition in rapeseed are highly variable, and the construction of new genes affecting FA composition and their regulatory relationship networks requires further research and exploration. Traditional reverse genetics strategies and map-based cloning approaches are time-consuming and labor-intensive, which makes it difficult to comprehensively explore the key variants and genes affecting FAs [ 28 ]. With the continuous development of sequencing technology, genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) have been widely used in various fields of crop research [ 29 – 32 ]. In rapeseed, multi-omics data were used to analyze the genetic basis of seed oil content (SOC), seed coat content (SCC) and seed glucosinolate content (SGC) synthesis, resulting in the cloning of several key genes, such as Bna.PMT6 , Bna.CCRL , Bna.TT8 , Bna.GTR2 and Bna.TT4 [ 33 – 37 ]. These genes have provided abundant genetic resources for the genetic improvement of rapeseed quality traits. In this study, we comprehensively analyzed the genetic mechanism of FA composition in rapeseed, combining multi-omics data analysis, co-expression network construction, machine learning algorithms to predict and clone four new genes affecting FA composition. The results enriched the regulatory mechanisms affecting FA composition of rapeseed, as well as provided theoretical basis and genetic resources for rapeseed oil quality improvement. Results Fatty acid composition in rapeseed population The FAs in rapeseed include various saturated and unsaturated types. To investigate the genetic basis of FA composition in rapeseed, this study analyzed the content of seven FAs—palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), eicosapentaenoic acid (C20:1) and erucic acid (C22:1)—across 505 rapeseed accessions in six locations (Wuhan, Ezhou, Chengdu, Hefei, Kunming, and Lanzhou) over one to four years. The results revealed that unsaturated FAs had a significantly higher average content than saturated FAs in rapeseed, with C18:1 having the highest mean content (54.52%), followed by C18:2 at 16.59% (Fig. 1a). Pearson's correlation coefficient (PCC) analysis, following best linear unbiased prediction (BLUP) results, identified significant correlations among six of the FAs, excluding C18:3 (Fig. 1b; Additional file 1: Fig. S1). Notably, C22:1 showed a strong positive correlation with C20:1 (R 2 = 0.94), and strong negative correlation with C16:0, C18:0, C18:1 and C18:2 (R 2 = 0.76, 0.37, 0.97 and 0.67, respectively) (Additional file 1: Fig. S1). The high correlations between C18:1 and both C20:1 and C22:1 are likely due to the use of C18:1 as a substrate for carbon chain extension in the biosynthesis of C20:1 and C22:1. These findings confirm the reliability of the FA composition data and support the synthesis of multiple FA profiles for a comprehensive analysis of the regulatory network governing FA metabolism in rapeseed. In our previous study, 505 rapeseed accessions were grouped into three subpopulations: semi-winter1 (SW1), semi-winter 2 (SW2), and spring (SPR) [33]. FA composition analysis showed that SW1 and SPR had similar profiles, with higher levels of C16:0, C18:0, C18:1, and C18:2 compared to SW2, which had higher levels of C18:3, C20:1) and C22:1. Notably, C18:1 content was 40-45% higher in SW1 and SPR than in SW2, while C22:1 content was nearly absent (Additional file 1: Fig. S2). GWAS reveals key QTLs influencing fatty acid composition Seven co-localized QTLs were identified as controlling fatty acid composition Using GWAS with 10,620,048 variants, we analyze seven FAs and their ratios (C18:1/C18:2, C18:1/C18:3, C18:1/C20:1, C18:2/C18:3, C22:1/C18:1, C22:1/C18:2, and C22:1/C18:3) (Additional file 1: Fig. S3, 4; Additional file 2: Table S2), and a total of 169 QTLs were identified. After combining the co-localized QTLs, seven QTLs (named as qFA.A02 , qFA.A08 , qFA.A09.1 , qFA.A09.2 , qFA.C02 , qFA.C03.1 and qFA.C03.2 ) were consistently detected in GWAS across more than five FAs or six fatty acid ratios, which are distributed on chromosomes A02, A08, A09, C02 and C03, respectively (Fig. 1c, d; Additional file 1: Fig. S5; Additional file 2: Table S3). Candidate genes located within 100 kb of the lead SNPs in the seven stable QTLs were identified, yielding 1,412 potential GWAS candidate genes. These included key genes involved in FA synthesis, such as those encoding 3-ketoacyl-CoA synthase (KCS) enzymes ( Bna.FAE1 and Bna.KCS3 ) for FA elongation, Bna.LACS for long-chain FA synthesis, Bna.FAD3 for linolenic acid synthesis and Bna.LPAAT5 for triacylglycerol formation (Table 1). Interestingly, several flavonoid metabolism-related genes were also identified, including Bna.CCoAOMT , which is involved in lignin biosynthesis, and genes from transparent seed coat family ( Bna.TT6 and Bna.TT16 ), which influence seed coat color. These findings suggest a complex regulatory network in rapeseed seed development and underscore the genetic diversity underlying FA composition. Strong selection pressure of qFA.A09.1 and qFA.A08 during rapeseed domestication In the rapeseed breeding, seeds with low C22:1, high C18:1 and low glucosinolate content are key targets. Our study has identified two co-localized QTLs in the GWAS results for seed glucosinolate content (SGC) [35] and FAs [ -log 10 ( P qGSC.A08.1 ) = 6.53, -log 10 ( P qFA.A08 ) = 35.98, -log 10 ( P qGSC.A09.2 ) = 17.56, -log 10 ( P qFA.A09.1 ) = 10.99] (Fig. 1e), this suggests a co-selection for glucosinolate and FA traits. To further explore the selection of loci controlling FAs, ancestral alleles in the rapeseed genome were inferred using resequencing data from B. oleracea and B. rapa . Previous studies confirmed that qFA.A08 (which corresponds to qOC.A08.1 in our previous research) [33], including BnaA08.FAE1 , a key regulator of C22:1 synthesis in rapeseed, has undergone significant selection. Comparative analysis of the nucleotide diversity (π) between the ancient and derived haplotypes of the remaining six co-localized QTLs revealed that qFA.A09.1 experienced strong selection during domestication (Fig. 1f, g). These findings suggest that qFA.A08 and qFA.A09.1 were crucial for breeding high oleic and low erucic acid rapeseed. However, other QTLs associated with erucic and oleic acid content have remained underutilized in rapeseed breeding (Additional file 1: Fig. S6). Therefore, further exploration of QTLs and new genes involved in fatty acid composition is essential to enhance rapeseed improvement. TWAS revealed the molecular basis of seed fatty acid composition TWAS correlates gene expression with phenotype at the population level. To identify genes influencing the FA content, we conducted TWAS on seven FA compositions using expression data from two time points. A total of 3,295 genes were significantly associated with the FAs (FDR < 0.05), with 1,576 genes identified at 20 days after flowering (DAF) and 2,196 at 40 DAF (Fig. 2a). The highest number of significantly related genes was found in C18:1, followed closely by C20:1 and C22:1. C18:3 had almost no significant associations, while the other six FAs had 30 overlapping significant genes identified by TWAS (Fig. 2b). Analyzing the functions of the 3,295 genes significantly associated with FAs, we identified several genes related to lipid synthesis. These include ABC transporter family genes ( Bna.ABCG1 , Bna.ABCG7 , Bna.ABCG10 and Bna.ABCG25 ), acyl carrier proteins ( Bna.ACP ), acyl CoA thioesterase genes ( Bna.ACT ), FA desaturases ( Bna.FAD3 and Bna.FAD6 ), keto-CoA synthase and reductase genes ( Bna.KCS6 , Bna.KCS16 , Bna.FAE1 and Bna.KCR ), long-chain acyl-CoA synthetase gene ( Bna.LACS5 ), pyruvate kinase gene ( Bna.PK ) and lipid transfer proteins ( Bna.LTP , Bna.PPT1 , Bna.NTT1 and Bna.NTT2 ) (Fig. 2c, d; Additional file 1: Fig. S8a, b; Additional file 2: Table S8, 9). These findings confirm the accuracy of the TWAS approach in identifying candidate genes. GO enrichment analysis of the 477 overlapping significant genes at both time points revealed involvement in processes such as 3-hydroxyacetyl-coenzyme, dehydrogenase activity, salicylic acid-mediated signaling and cation transport (Additional file 1: Fig. S7a). At 20 DAF, the 1,576 significant genes were mainly enriched in pathways related to nucleotide biosynthesis, salicylic acid signaling, protein kinase regulation, translation initiation factors, and fatty acid acyl-coenzyme A binding (Additional file 1: Fig. S7b). At 40 DAF, the 2,196 significant genes were associated with nucleotide binding, glycolysis, energy metabolism, and other nucleotide-related processes (Additional file 1: Fig. S7c). These results suggest that fatty acid biosynthesis is closely linked with energy metabolism [44, 45]. Table 1 Seven co-localized QTLs and FA-related genes identified by genome-wide association analysis. QTL Position a Gene ID b Name Description Reference qFA.A02 24099591 BnaA02g33410D MYB96 Myb-related protein [38] qFA.A08 10151436 BnaA08g11130D FAE1 3-ketoacyl-CoA synthase 18 [20] BnaA08g11140D KCS17 3-ketoacyl-CoA synthase 17 [39] qFA.A09.1 2612389 BnaA09g04580D SHN3 Ethylene-responsive factor SHINE 3 [40] BnaA09g06090D MYB96 Myb-related protein [38] qFA.A09.2 31542153 BnaA09g46210D AAPT1 Ethanolaminephosphotransferase 1 [41] qFA.C02 44879480 BnaC02g42240D TT16 Transparent testa 16 [42] qFA.C03.1 55619752 BnaC03g65980D FAE1 3-ketoacyl-CoA synthase 18 [20] qFA.C03.2 56398549 BnaC03g66040D KCS17 3-ketoacyl-CoA synthase 17 [39] BnaC03g77180D SUD1 E3 ubiquitin ligase SUD1 [43] a Physical position of the lead SNP. b Gene ID associated with FAs in QTL. C18:1 and C22:1 are key targets in rapeseed breeding. We identified 881 and 1,939 significant genes by TWAS for C18:1 at 20 DAF and 40 DAF, respectively, with 402 overlapping genes (Fig. 2e; Additional file 2: Table S4, 5). These genes were primarily enriched in pathways related to nucleotide phosphorylation, hydroxyacyl-coenzyme A dehydrogenase activity, salicylic acid signaling, and nucleotide biosynthesis (Fig. 2f). For C22:1, 2,386 significantly genes were identified, with 364 genes overlapping between the two periods, which were involved in processes such as phosphotransferase activity, starch catabolism, and energy metabolism (Additional file 1: Fig. S8c, d; Additional file 2: Table S6, 7). The TWAS results for oleic and erucic acids showed a similar distribution pattern across the two time points, with a high correlation between gene associations for both FAs at 20 DAF and 40 DAF (Additional file 1: Fig. S9). Genes such as Bna.ACP , Bna.KCS , Bna.LTP , Bna.FATA , Bna.LACS5 and Bna.PPT1 were significantly associated with both FAs, showing positive correlations with C18:1 and negative correlations with C22:1 (Fig. 2c, d; Additional file 1: Fig. S8a, b; Additional file 2: Table S8, 9). Prediction of six key candidate genes affecting fatty acids To identify genes influencing FA composition, we analyzed the dynamic gene expression profiles of the rapeseed cultivar Zhongshuang11 (ZS11) from 2 DAF to 60 DAF, which resulted in the classification of eight expression modules (Fig. 3a). Enrichment analysis of significant genes from GWAS and TWAS for seed FA composition and SGC revealed that four modules—C1, C3, C6 and C8—were enriched with significant TWAS genes of FA and SGC (Fig. 3b). In order to further predict candidate genes affecting the FA composition in rapeseed, we prioritized genes within 100 kb upstream and downstream of seven FA-related QTLs using POCKET [33]. By integrating variation effect (VE), haplotype effect (HE), expression effect (EE) and gene function prediction, we identified BnaA09.PYRD, BnaA08.SWI3, BnaA09.PDR2 and BnaC03.SLOMO , as top candidates based on POCKET scoring (Fig. 4a; Additional file 1: Fig. S13a, 15a, 16a), Additionally, BnaA08.PSK1 , a TWAS-significant gene, located in the C1 or C6 module, exhibits similar expression patterns with several fatty acid synthesis-related genes (Fig. 3c, d). And BnaC02.LTP15 , found within the qFA.C02 locus and part of the C3 module (Additional file 1: Fig. S14a), shows expression patterns similar to genes involved in seed coat development and lipid synthesis, such as TT9 , LPAAT and DGD2 , indicating its possible involvement in lipid metabolism. Taken together, we hypothesize that BnaA09.PYRD , BnaA08.PSK1 , BnaA08.SWI3 , BnaC02.LTP15, BnaA09.PDR2 and BnaC03.SLOMO are key candidates influencing FA composition in rapeseed. BnaA09.PYRD and BnaA09.PDR2 were located within qFA.A09.1 locus (Fig. 4b; Additional file 1: Fig. S15b), while BnaA08.PSK1 and BnaA08.SWI3 showed the strongest GWAS signals from qFA.A08 locus (Additional file 1: Fig. S12a, 13b). Additionally, BnaC02.LTP15 was found within qFA.C02 locus (Additional file 1: Fig. S14b), and BnaC03.SLOMO within the qFA.C03.1 locus (Additional file 1: Fig. S16b). To assess the impact of SNP on protein function, we identified three nonsynonymous variants in both BnaA09.PYRD (BnvaA0902629811, C/T, P = 1.95 × 10 -12 ; BnvaA0902628936, G/C, P = 3.77 × 10 -7 ; BnvaA0902628958, T/C, P = 3.80 × 10 -7 ) and BnaA09.PDR2 (BnvaA0902561985, A/G, P = 6.66×10-9; BnvaA0902556267, G/C, P = 3.65×10-8; BnvaA0902561869, C/T, P = 2.02×10-5) genes (Fig. 4c; Additional file 1: Fig. S15c). In contrast, BnaC03.SLOMO has 14 nonsynonymous variants (Additional file 1: Fig. S16c), while BnaA08.PSK1 and BnaA08.SWI3 each have 11 nonsynonymous variants (Additional file 1: Fig. S12b, 13d). Notably, a frameshift mutation (BnvaA0810225954, ATC/A, P = 1.56 × 10 -21 ) was detected in exon of BnaA08.SWI3 , potentially causing a significant alteration in protein function (Additional file 1: Fig. S13c). To examine the association between haplotypes and fatty acid composition, we classified haplotypes based on candidate genes and their 2 kb promoter region variations, and using the lead SNP of qFA.A08 as a covariate to exclude the effect of FAE1 . The results showed that, with the qFA.A08 locus fixed, different haplotypes of BnaA09.PYRD , BnaC02.LTP15 , BnaA09.PDR2 and BnaC03.SLOMO had significant effects on C18:1 and C22:1. (Fig. 4d, e; Additional file 1: Fig. S14c-d, 15d-e, 16d-e). Previous analyses indicated that qFA.A08 and qFA.A09.1 were significantly selected during fatty acid domestication. Under the condition of consistent variation at the qFA.A08 locus, variations at the qFA.A09.1 locus resulted in significant differences in C18:1 and C22:1 (Additional file 1: Fig. S11). Further observation revealed that during the selection process, the expression level of the key gene BnaA09.PYRD at the qFA.A09.1 locus was increased. In derived varieties, high expression level of BnaA09.PYRD is often associated with higher C18:1 and lower C22:1, while the opposite trend is observed in ancient varieties (Fig. 4f-i). This suggests that the changes in fatty acid composition at the qFA.A09.1 locus are mediated through the regulation of BnaA09.PYRD gene expression. Transcriptional impact of candidate genes on fatty acid composition To assess the effect of the six candidate genes on FA composition, we analyzed the gene expression at 20 DAF and 40 DAF in the population, correlating them with C18:1 and C22:1. Four genes showed a positive correlation with C18:1 and a negative correlation with C22:1. BnaA08.SWI3 exhibited the strongest correlation, with R ² values of 0.53 and 0.55 for C18:1 at 20 DAF and 40 DAF, respectively ( P 20 DAF = 2.87×10 -46 ; P 40 DAF = 6.54×10 -49 ) (Additional file 1: Fig. S13e, f), and for C22:1 were 0.55 and 0.58 ( P 20 DAF = 3. 34 × 10 -48 ; P 40 DAF = 3.75 × 10 -52 ) (Additional file 1: Fig. S13g, h). BnaA09.PYRD , BnaC02.LPT15 , and BnaA09.PDR2 showed higher correlations at 20 DAF (Additional file 1: Fig. S10, 14e-h, 15f-i), while BnaA08.PSK1 and BnaC03.SLOMO were more significant at 40 DAF (Additional file 1: Fig. S12c-f, 16f-i). BnaPYRD , BnaPSK1 , BnaSWI3 and BnaLTP15 act as negative regulator of erucic acid To verify the functions of six genes, transgenic rapeseed plants were generated using cv. WH3411, characterized by low C18:1 and high C22:1, as the receptor material. BnaPYRD and BnaPSK1 mutants (CRISPR/Cas9) and overexpression lines, along with BnaSWI3 and BnaLTP15 mutant lines (CRISPR/Cas9), were successfully constructed (Additional file 1: Fig. S17-20). Compared with the wild type, C22:1 content in the seeds of BnaPYRD overexpression lines OE-19 and OE-5 decreased by 9.75% and 23.15%, respectively, while C18:1 content increased by 6.0% and 21.68% (Fig. 5a). In contrast, C22:1 content in the seeds of the quadruple mutant line pyrd-L35 and the triple mutant line pyrd-L1 increased by 11.08% and 6.52%, with a corresponding decrease in C18:1 content by 11.99% and 6.71% (Fig. 5a). The C22:1 content in the seeds of BnaPSK1 overexpression lines OE-12 and OE-14 decreased by 15.68% and 7.79%, respectively, with an increase in C18:1 content of 9.94% and 3.40% (Fig. 5b). Conversely, C22:1 content in the seeds of the double mutant line psk1-L5 and the triple mutant psk1-L26 increased by 14.82% and 14.01%, respectively, accompanied by a decrease in C18:1 content of 23.02% and 16.57 % (Fig. 5b). The BnaSWI3 quadruple mutant strains swi3-L3 , swi3-L10 , and double mutant strain swi3-L27 exhibited an increase in C22:1 content of 8.46% to 11.24% and a decrease in C18:1 content of 8.01% to 15.85% (Fig. 5c). In BnaLTP15 double mutant lines ltp15-L3 , ltp15-L11 and ltp15-L20 C22:1 content increased by 7.19% to 16.03%, and the C18:1 content decreased of 6.79% to 18.58% (Fig. 5d). In addition, the content of C20:1 in the seeds of these four gene mutant lines was reduced to varying degrees (Fig. 5a-d). Furthermore, the transgenic plants, both gain-of-function and loss-of-function mutants, showed no significant differences in overall growth compared with control plants (Additional file 1: Fig. S21). Collectively, our findings support the potential of these genes as promising targets for breeding programs aimed at improving seed oil quality in B. napus . BnaA09.PYRD may influence fatty acid composition by modulating energy metabolism during rapeseed development In rapeseed fatty acid breeding, qFA.A09.1 locus is under strong selection, making it crucial to identify genes affecting FA content within this segment. We predicted that BnaA09.PYRD might be a causal gene influencing FA composition. To further understand how BnaA09.PYRD modulates FA synthesis, transcriptome sequencing was conducted on seeds of WT and pyrd-L35 at 35 DAF. A total of 51,276 expressed genes (TPM > 1) and 5,398 differentially expressed genes (DEGs) (|log 2 (fold change) | > 1 and P adj < 0.05) were identified (Additional file 2: Table S10). These encompassed numerous genes associated with FA synthesis, including FA desaturase genes ( FAD8 , FATA1 and FATB ), 3-ketoacyl-CoA synthetase genes ( KCS1 , KCS4 and KCS5 ) and several long-chain acyl-CoA synthetase genes ( LACS5 , LACS7 and LACS8 ) (Fig. 5e). GO enrichment analysis indicated that these DEGs linked to lipid synthesis were mainly enriched in flavonoid biosynthesis pathways, galactolipid and phospholipid biosynthesis, FA elongation, wax biosynthesis and lignin mucin-associated pathways (Additional file 1: Fig. S22). The combined experimental results suggest that BnaA09.PYRD may modify FA compositions by modulating the conversion of C18:1 to C18:2 and the elongation of C18:1 to long-chain unsaturated FAs. To investigate the localization of BnaA09.PYRD protein in cells, GFP-fused expression vectors containing the gene were constructed and transformed into tobacco lower epidermal cells for visualization. Under confocal microscopy, BnaA09.PYRD localized in mitochondria (Fig. 5f). Moreover, primary metabolite analysis of the pyrd-L35 mutant revealed significant changes in the levels of several metabolites and amino acids in the tricarboxylic acid cycle (TCA) and the Calvin-Benson cycle (Fig. 5g). Based on these results, we hypothesize that BnaA09.PYRD regulates energy metabolism during rapeseed development, thereby affecting FA composition. The optimal haplotype of BnaA08.PSK1 remains underutilized in B. napus breeding. We categorized the accessions from the previous study into three breeding periods：‘Before 1980’, ‘1980-2000’ and ‘After 2000’ [46, 47]. Haplotypes for four functional genes were classified based on variants within the 2 kb promoter and gene region. The haplotype associated with the highest C18:1 and the lowest C22:1 levels was considered the optimal haplotype (Additional file 1: Fig. S23). The proportion of optimal haplotypes in BnaA08.SWI3 , BnaA09.PYRD , and BnaC02.LTP15 increased significantly over time. After 2000, the optimal haplotype for all three genes become the major haplotype (Fig 6a-c). In contrast, BnaA08.PSK1 haplotype exhibited a different distribution. The proportion of optimal haplotypes slightly increased from ‘Before 1980’ to ‘1980-2000’. Compared to ‘1980-2000’, the proportion of optimal haplotypes in the ‘After 2000’ remained nearly unchanged. The proportion of suboptimal haplotypes (Hap B) of BnaA08.PSK1 gradually increased becoming the major haplotype (Fig. 6d, e). In addition to temporal changes in haplotypes over time, we also categorized the accessions by geographical region. The optimal haplotypes of BnaA08.SWI3 and BnaA09.PYRD were predominant across regions, except in Eastern Europe (Additional file 1: Fig. S24a, b). The optimal haplotype of BnaC02.LTP15 was less frequent in North America and Oceania (Additional file 1: Fig. S24c). The optimal haplotype of BnaA08.PSK1 was not widely adopted in any region (Fig. 6f). These results suggest that BnaA08.SWI3 , BnaA09.PYRD and BnaC02.LTP15 have been actively utilized for improvement in B. napus breeding. However, the optimal haplotype of BnaA08.PSK1 remained underutilized. Discussion Mining fatty acid related QTLs in rapeseed FA metabolism in rapeseed is highly complex. To address this complexity, integrating bioinformatics with genetic tools and molecular breeding design can generate new genetic resources for FA improvement. Our study localized QTLs using GWAS, and the results align with previous research, showing that QTLs were primarily distributed on chromosomes A08, A09, C02 and C03 ( qFA.A08 , qFA.C09.1 , qFA.C02 , qFA.C03.1 , qFA.C03.2 , respectively) [ 48 , 49 ]. Notably, we identified two novel QTLs ( qFA.A02 and qFA.A09.2 ) that had not been previously reported (Fig. 1 c, d; Table 1 ). However, some key enzymes affecting FA metabolism, such as FAD2, FAD3 and SAD, were not detected in the GWAS, probably due to limitations in population diversity, which hindered the identification of relevant variants. Analysis of the seven fatty acid-related QTLs revealed that qFA.A08 and qFA.A09.1 were strongly selected during breeding. (Fig. 1 e-g). Although the genes affecting fatty acids at qFA.A09.1 have not been reported, it is hypothesized that selection of the BnaA09.PYRD gene at this locus may reduce erucic acid content and increase oleic acid content. In contrast, major variants in the other five QTLs were not significantly selected (Additional file 1: Fig. S6), suggesting that further exploration of QTLs and novel genes regulating FA composition is essential in rapeseed. Integrating key candidate genes affecting fatty acids through multi-omics approaches During GWAS based on 505 accessions of B. napus , LD decay was slow, and the QTL identified often had dozens of candidate genes [ 50 ]. There were always many false-positive loci in the results. A single GWAS cannot satisfy the construction of gene networks. To overcome these challenges, we integrated GWAS, TWAS and transcriptomic profiles from seed development to comprehensively construct a multi-omics gene network influencing FA composition in rapeseed. This network comprised seven independent QTLs and 3,295 TWAS-significant genes (Additional file 1: Fig. S25), including key lipid synthesis genes such as Bna.ACP, Bna.KCS , Bna.LTP , Bna.FATA , Bna.LACS5 and Bna.PPT1 (Additional file 2: Table S8, 9). This network plays a crucial role in regulating FA composition in rapeseed, contributing to a deeper understanding of the regulatory mechanisms governing FA metabolism. Mining the regulatory mechanism of candidate genes affecting fatty acid composition Our study successfully identified four genes influencing FA composition in rapeseed through functional validation (Fig. 5 a-d). Mutations in these genes can significantly enhance C22:1 content. Given C22:1’s substantial industrial value, increasing its content is essential to addressing the supply shortage of high-erucic-acid rapeseed varieties. Research on these genes ( BnaA08.SWI3 , BnaA09.PYRD , BnaC02.LTP15 , and BnaA08.PSK1 ) offers insights into the biosynthetic mechanisms of C22:1. One of these genes, PYRD , encodes a dehydrogenase involved in mitochondrial energy metabolism and the fourth step of pyrimidine biosynthesis [ 51 ]. Previous studies have shown that PYRD mutants display reduced pyrimidine metabolites, lower energy status, decreased photosynthetic capacity, and accumulation of reactive oxygen species. In this study, we hypothesized that the BnaA09.PYRD gene may affect FA composition by inhibiting FA elongation. In addition, seed transcriptome analysis indicated that BnaA09.PYRD may influence FA metabolism and flavonoid metabolism pathways, both of which are crucial for carbon allocation in rapeseed (Fig. 5 g). However, no current evidence suggests that BnaA09.PYRD interacts with key transcription factors (such as LEC1 , LEC2 and WRI1 ) or protein complexes to affect FA synthesis. Future research could leverage machine learning and deep learning models, along with chromatin accessibility data and eQTLs, to identify significant genomic variants and explore transcription factor sequences for conserved or specific coregulatory elements, with experimental validation [ 52 – 54 ]. Breeding applications of the optimal haplotypes in four functional genes Breeding goals for B. napus have shifted across periods. In the 1970s-1980s, double-low breeding was the primary objective. From the 1990s to the 2000s, the emphasis transitioned to achieving high yield and resistance [ 55 ]. Throughout the breeding process, the optimal haplotype of BnaA08.SWI3 in ‘Before 1980’ varieties emerged as predominant (Fig. 6 a). The proportion of optimal haplotypes has gradually increased with ongoing breeding efforts, demonstrating selection of the optimal BnaA08.SWI3 haplotype in double-low breeding. Conversely, the optimal haplotypes of BnaA09.PYRD , BnaC02.LTP15 , and BnaA08.PSK1 were underrepresented in ‘Before 1980’ varieties, suggesting that these optimal haplotypes were lack of selection during double-low breeding (Fig. 6 b-d). In contrast, the rapid increase in the proportion of optimal haplotypes in BnaA09.PYRD and BnaC02.LTP15 post-2000 indicates incidental selection during the breeding process aimed at high yield and high resistance. The optimal haplotypes of BnaA08.PSK1 did not show significant variation over time, which implies that the optimal haplotypes of BnaA08.PSK1 retain significant breeding potential. These findings offer valuable genetic resources for understanding the genetic basis of fatty acid composition and for breeding double-low rapeseed. Conclusions This study unravels the genetic regulatory network underlying fatty acid biosynthesis in rapeseed using an integrative multi-omics approach, and identifies four key genes that modulate the balance between oleic acid and erucic acid. These findings offer both a theoretical framework and valuable gene targets for the precise enhancement of oil quality. Beyond advancing our understanding of plant lipid metabolism, this work lays a solid foundation for molecular design breeding, facilitating the development of high-quality rapeseed cultivars for agricultural application. Methods Plant materials and trait determination The genetic transformation material used in this study was the rapeseed inbred line WH3411, characterized by high erucic acid content (34.9%) and high seed oil content (51.28%), it is one of the 505 rapeseed accessions included in the resequencing project with ID X1062 (Additional file 2: Table S1). This line was sourced from the Wuhan National Engineering Research Center for Rapeseed, China. The data used in the study included resequencing data of 505 rapeseed accessions, 309 seed transcriptome data at 20 days after flowering (DAF) and 274 seed transcriptome data at 40 DAF [33]. Additionally, time series transcriptome data were collected during ZS11 seed development (2 DAF to 60 DAF) [56]. For transgenic material subjected to transcriptome sequencing, 50 mg of developing seeds (35 DAF) were collected from both WT and bna.pyrd-L35 lines, with three biological replicates per genotype. The transcriptome data are available under the BioProject ID PRJNA1214085. FA phenotypes were collected from multiple locations over various years: Wuhan (2016-2019, 4 years), Ezhou (2017-2019, 3 years), Hefei (2017-2018, 2 years), Lanzhou (2018-2019, 2 years), Chengdu (2017) and Kunming (2018). Mature, open-pollinated seeds from the population were harvested and dried. FA composition was analyzed using a Foss NIRSystems 5000 near-infrared reflectance spectrometer, measuring palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), eicosenoic acid (C20:1) and erucic acid (C22:1). Six biological were identified for each germplasm for analysis. Variant identification and genotype imputation The rapeseed reference genome Darmor-bzh was obtained from Genoscope (http://www.genoscope.cns.fr/brassicanapus/) [57]. Sequence alignments were conducted using BWA software [58], and duplicate PCR fragments were removed using SAMTools markdup [59]. Variants from the 505 rapeseed accessions were identified using GATK [60]. SNPs and InDels with low mapping quality (MQ < 20) or shallow sequencing depth (DP < 50) were filtered out. Missing genotypes were imputed using the LD-KNN algorithm [61], resulting in final sequencing data with an accuracy greater than 99.7%. Genome-wide association analysis In the ALL, SWR and SWR1 subgroups, 10,620,048, 9,783,864, and 8,609,979 high-quality SNPs (with MAF > 0.05) were identified, respectively. The significance thresholds for association were calculated by GEC software [62] and were determined to be 7.96 × 10 -7 , 9.38 × 10 -7 and 1.69 × 10 -7 , for each subgroup, respectively. Association analyses of multiple FA compositions and their ratios were conducted using a mixed linear model implemented in GEMMA software [63, 64]. Transcriptome-wide association analysis The quality of transcriptome data was assessed using FastQC software [65], and filtered sequences were aligned to the reference genome using STAR software [66]. Sequence quantification was performed with Salmon [67], and normalization was conducted using Tximport software [68], and low-expression genes (with TPM < 1 in 95% of the accessions) in the population were removed in subsequent analyses. Association analysis was performed using the EMMAX mixed linear model [69], with an FDR-corrected P value ≤ 0.5 as the threshold for significance in TWAS. Significant QTLs selection analysis High-coverage resequencing data from B. oleracea and B. rapa were utilized to infer ancestral alleles [70]. The data were obtained from the NCBI database (with BioProject ID PRJNA312457), with 17 samples excluded due to low sequence quality. Sequences were aligned to the rapeseed An and Cn subgenomes using BWA software [58], with average alignment rates of 70.9% for the An subgenome and 81.1% for the Cn subgenome. Genotypes were extracted using SAMtools [59] and BCFtools [71] to prepare data for selection analysis. Delineation of gene expression modules during seed development The expression module analysis based on transcriptome data collected from 2 to 64 DAF during ZS11 seed development. Extreme values of average gene expression (TPM 10,000) were filtered out. Using the default parameters of the Mfuzz package [72] 50,025 genes were classified into eight expression modules. Transcriptome data analysis Libraries were sequenced using the Illumina HiSeq platform with paired-end reads. Quality control was conducted using FastQC [65], and low-quality sequences were filtered out using Trimmomatic [73]. Filtered data were aligned to the rapeseed reference genome using HISAT2 software [74], and gene expression values were quantified with featureCounts [75]. Differentially expressed genes (DEGs) were identified with DESeq2 [76], with significance thresholds set at Padj 1. Vector construction and plant transformation DNA from 7-day-old WH3411 seedlings was extracted using the CTAB method. The gDNA of BnaPYRD and BnaPSK1 was cloned and linked to pCAMBIA2306 with the 35S promoter. CRISPR targets were selected, and primers were designed using CRISPR-P v2.0 (http://cbi.hzau.edu.cn/CRISPR2/) (Additional file 2: Table S11). The sgRNA-Cas9 system was utilized for vector construction, with DNA sequence templates for CRISPR vectors sourced from pCBC-DT1T2. Purified PCR products were ligated into pKSE401 vector. The high-erucic-acid rapeseed germplasm WH3411 was transformed via Agrobacterium -mediated techniques using the hypocotyl and tissue culture system [77]. Subcellular Localization Subcellular Localization was conducted using cDNA synthesized from RNA extracted from the leaves of the WH3411 variety as a template, full-length CDS of rapeseed genes were amplified with gene-specific primers. The target fragments were then cloned into the GFP fusion vector PMDC83, using Bam HI and Kpn I restriction sites. After sequencing and verification, the constructs were transformed into Agrobacterium strain GV3101. Single colonies of Agrobacterium containing the PMDC83-GFP vector were expanded and collected. The bacterial pellets were resuspended in buffer solution (50 mM MES, pH 5.6; 5 mM Na3PO4; 1 mM acetosyringone) and infiltrated into tobacco leaves via syringe injection. Green fluorescence signals were observed under a fluorescence microscope (Olympus BX35) 2-5 days post-infiltration. The excitation wavelength for the GFP was set to 488 nm, and the emission filter wavelength was 500-530 nm. Fatty acid analysis FAs were extracted from mature seeds using the gas chromatography (GC) FA methyl ester method, following Lu et al [78]. Various FA species were measured with an Agilent 6890 GC. Declarations Ethical approval Not applicable. Competing interests The authors declare no competing interests. Peer review information Qingxin Song and Wenjing She were the primary editors of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. The peer-review history is available in the online version of this article. Funding Computations in this study were conducted on the bioinformatics computing platform of the National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University. This work is supported by the Biological Breeding-National Science and Technology Major Project (2023ZD04069), National Science Fund for Distinguished Young Scholars (32225037), National Natural Science Foundation of China (U2102217), Hubei Hongshan Laboratory Fund (2021HSZD004), Project X2662024ZKPY001 supported by the Fundamental Research Funds for the Central Universities and HZAU-AGIS Cooperation Fund (SZYJY2021004). Author Contribution H.Z., S.W. and L.G. designed and supervised this study. Y.Z. performed the bioinformatics analysis. Y.L. performed the related experiments. Y.Z. and Y.L. prepared the manuscript. H.Z., S.W. and L.G. revised the manuscript. All authors read and approved the final manuscript. Acknowledgement Computations in this study were conducted on the bioinformatics computing platform of the National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University. This work is supported by the Biological Breeding-National Science and Technology Major Project (2023ZD04069), National Science Fund for Distinguished Young Scholars (32225037)，National Natural Science Foundation of China (U2102217), Hubei Hongshan Laboratory Fund (2021HSZD004), Project X2662024ZKPY001 supported by the Fundamental Research Funds for the Central Universities and HZAU-AGIS Cooperation Fund (SZYJY2021004). Data Availability Resequencing data for 505 B. napus accessions are available at the Genome Sequence Archive (https://bigd.big.ac.cn/gsa/) under BioProject ID PRJCA002835 [79], with corresponding transcriptome data under BioProject ID PRJCA002836 [80]. PYRD-related transcriptome data can be accessed under BioProject ID PRJNA1214085 [81]. Time-series transcriptome data are deposited in NCBI under BioProject ID PRJNA722877 [82]. Additionally, resequencing data for B. oleracea and B. rapa are available from the NCBI database with BioProject ID PRJNA312457 [83]. The phenotypic data used in this study can be obtained from the website http://rgmi.hzau.edu.cn/phenotype and have also been deposited in Zenodo at https://zenodo.org/records/15048705 [84].All software and tools used in this study are publicly available as described in the Methods section. The code for POCKET can be accessed on GitHub at https://github.com/zhaouu/POCKET under the BSD 3-Clause license [85]. The customized scripts used in the present study are also available via Zenodo at https://doi.org/10.5281/zenodo.14842123 [86].Any additional information required to reanalyze the data reported in this paper can be provided upon request. References Friedt W, Tu J, Fu T. Academic and economic importance of Brassica napus rapeseed. 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Brassica napus raw sequence reads. NCBI. BioProject accession: PRJNA722877. https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA722877. 2021 Cheng F, Sun R, Hou X, Zheng H, Zhang F, Zhang Y, Liu B, Liang J, Zhuang M, Liu Y, Liu D, Wang X, Li P, Liu Y et al Genome resequencing of Brassica rapa and Brassica oleracea accessions. NCBI. BioProject accession: PRJNA312457. https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA312457. 2016 Zhang Y, Liu Y, Zong Z, Guo L, Shen W, Zhao H. The extensive data on the oil content and fatty acid composition of rapeseed populations from multiple regions and various time periods. Zenodo . https://zenodo.org/records/15048705. 2025 Zhao H. Prioritizing the candidate genes by incorporating information of knowledge-based gene sets, effects of variants, GWAS and TWAS. Github. https://github.com/zhaouu/POCKET. 2021 Zhang Y, Liu Y, Zong Z, Guo L, Shen W, Zhao H. Elucidation of the Genetic Basis of Seed Fatty Acids in Brassica napus through Integrative Omics Analysis. Zenodo . https://doi.org/10.5281/zenodo.14842123. 2025 Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.pdf Additional file 1. Supplementary figures Fig. S1 to Fig. S25. Additionalfile2.xlsx Additional file 2. Supplementary tables S1–S11. Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2025 Read the published version in Genome Biology → Version 1 posted Editorial decision: Accepted 25 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 24 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5468888","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433765463,"identity":"693a1e71-52a0-44a2-bb09-eeea897941d3","order_by":0,"name":"Yuting Zhang","email":"","orcid":"","institution":"Yazhouwan National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Zhang","suffix":""},{"id":433765464,"identity":"bfa530e7-582c-4f09-bbb9-e13ea60c4828","order_by":1,"name":"Yunhao Liu","email":"","orcid":"","institution":"Yazhouwan National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Yunhao","middleName":"","lastName":"Liu","suffix":""},{"id":433765465,"identity":"bd4761b7-05a7-42ab-86dd-ccc624c22b40","order_by":2,"name":"Zhanxiang Zong","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zhanxiang","middleName":"","lastName":"Zong","suffix":""},{"id":433765466,"identity":"dbea4e64-3574-47b4-b5d1-1041ba533ba6","order_by":3,"name":"Liang Guo","email":"","orcid":"","institution":"Yazhouwan National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Guo","suffix":""},{"id":433765467,"identity":"64207487-a48d-4ed6-bcde-cfc774a6ecd7","order_by":4,"name":"Wenhao Shen","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Shen","suffix":""},{"id":433765468,"identity":"e5458526-2a59-4eab-8872-fd92e070b8b2","order_by":5,"name":"Hu Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACCQYGZgYGGyDJ3MAMFTMgRksakGQkTcthIItYLfKzmx8+Lmw7H83fDtRS8McusYG9eZsEQ80dnFoY5xwzNp7Zdjt3xmGglhk8yYkNPMfKJBiOPcOphVkiwUyaF6ilAaSFR4I5sUEix0yCseEwTi1sEunfgFrO5c4HazGoT2yQf4NfCw/QTKCWA7kbwFoSDgNt4cGvRUIip9iY51xy7kaglsM8B44bt/GkFVskHMOtRX5G+sbHPGV2ufPOHz74mOdPtWw/++GNNz7U4NaCAg6AfQciEojTMApGwSgYBaMABwAA0bVOQve6mqkAAAAASUVORK5CYII=","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Hu","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-11-17 08:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5468888/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5468888/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13059-025-03558-x","type":"published","date":"2025-04-02T15:57:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79407265,"identity":"883afbef-1ac5-4568-8cd7-4c48a49d8656","added_by":"auto","created_at":"2025-03-28 04:36:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":319999,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-located loci from the fatty acid genome-wide association analysis and locus selected analysis. a \u003c/strong\u003eFA composition in 505 rapeseed accessions. Fatty acid phenotypic data (palmitic, stearic, oleic, linoleic, linolenic, eicosanoic and erucic acids) were collected from seeds of 505 accessions at six sites (Wuhan, Ezhou, Chengdu, Hefei, Kunming and Lanzhou) over one to four years. Data for each fatty acid were averaged across multiple years and locations for analysis. \u003cstrong\u003eb \u003c/strong\u003eCorrelation of phenotypic variation of FA composition. The Pearson correlation coefficient was calculated using multi-year, multi-location BLUP values for FAs in rapeseed. Light blue lines indicate positive correlation, while dark blue lines indicate negative correlation. The thickness of the lines indicates the strength of the correlations between FAs.\u003cstrong\u003e c \u003c/strong\u003eGenome-wide association analysis and co-localized QTLs for the composition of six FAs (C16:0, C18:0, C18:1, C18:2, C20:1 and C22:1). \u003cstrong\u003ed\u003c/strong\u003e Genome-wide association analysis and co-localized QTLs for seven FA ratios (C18:1/C18:2, C18:1/C18:3, C18:1/C20:1, C18:2/C18:3, C22:1/C18:1, C22:1/C18:2 and C22:1/C18:3). The \u003cem\u003ex\u003c/em\u003e-axis represents the rapeseed chromosomes, while the \u003cem\u003ey\u003c/em\u003e-axis shows the log\u003csub\u003e10\u003c/sub\u003e-transformed \u003cem\u003eP\u003c/em\u003e values obtained from GWAS. The dotted line marks the significance threshold. By integrate GWAS of different FA compositions and ratios, the co-localized QTLs \u003cem\u003eqFA.A02\u003c/em\u003e, \u003cem\u003eqFA.A08\u003c/em\u003e, \u003cem\u003eqFA.A09.1\u003c/em\u003e, \u003cem\u003eqFA.A09.2\u003c/em\u003e, \u003cem\u003eqFA.C02\u003c/em\u003e, \u003cem\u003eqFA.C03.1\u003c/em\u003e, and \u003cem\u003eqFA.C03.2\u003c/em\u003e were identified. \u003cstrong\u003ee \u003c/strong\u003eThe log\u003csub\u003e10\u003c/sub\u003e-transformed \u003cem\u003eP\u003c/em\u003e values form C18:1 GWAS plotted against those form seed glucosinolate content (SGC) GWAS.\u003cstrong\u003e f\u003c/strong\u003e Signal detection intensity for \u003cem\u003eqFA.A09.1\u003c/em\u003e in GWAS, showing nucleotide diversity (π) with LOWESS smoothed curves. \u003cstrong\u003eg \u003c/strong\u003eManhattan plot showing 2 Mb region upstream and downstream of the \u003cem\u003eqFA.A09.1\u003c/em\u003e. Colors indicate the LD (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) between the lead SNP and each variant in GWAS.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5468888/v1/597ad246706a753c0f7b11dd.png"},{"id":79407262,"identity":"a9bee45a-fca1-45c8-8165-e5bc53bc43bd","added_by":"auto","created_at":"2025-03-28 04:36:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptome wide association studies of fatty acids. a \u003c/strong\u003eVenn diagram shows TWAS significant genes for FAs at 20 and 40 days after flowering (DAF). \u003cstrong\u003eb\u003c/strong\u003e An upset plot displays TWAS significant gene sets associated with the FAs. The bar on the left represents the original number of the significant genes for seven FAs, while the bar above shows the number of intersecting genes. Circles and connecting lines below provide the phenotypic information for calculating the intersecting genes. \u003cstrong\u003ec\u003c/strong\u003e Manhattan plot of C18:1 at 20 DAF. \u003cstrong\u003ed\u003c/strong\u003e Manhattan plot of C18:1 at 40 DAF. Each point represents a gene, with those positively or negatively associated with C18:1 depicted above or below the thick black line, respectively. Gold dots indicate significant lipid metabolism genes previously identified in Arabidopsis. \u003cstrong\u003ee\u003c/strong\u003e Venn diagram shows the significant C18:1 associated genes from TWAS at 20 and 40 DAF. \u003cstrong\u003ef\u003c/strong\u003e GO enrichment analysis of significant overlapping C18:1 associated genes at 20 and 40 DAF. The \u003cem\u003ey\u003c/em\u003e-axis represents the names of the enriched pathways. The count value indicates the number of overlapped genes in each pathway, while the \u003cem\u003eFDR\u003c/em\u003e value reflects the significance of enrichment.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5468888/v1/4fad388bf059b326f9556d71.png"},{"id":79407256,"identity":"99d5c53d-4634-4b67-8da8-8e1d1bfcbc70","added_by":"auto","created_at":"2025-03-28 04:36:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":233478,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of candidate gene expression patterns during ZS11 seed development. a\u003c/strong\u003e Expression modules of genes across ZS11 seeds development stages from 2 to 64 DAF. \u003cstrong\u003eb\u003c/strong\u003e Bubble diagram of ZS11 development from 2 to 64 DAF, including modules and seed FA (SFA) and glucosinolate content (SGC). \u003cstrong\u003ec\u003c/strong\u003eExpression patterns of candidate genes and FA associated genes in module C1. \u003cstrong\u003ed\u003c/strong\u003eExpression patterns of candidate genes and FA associated genes in module C6.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5468888/v1/e8c18851a47851c0f942c94c.png"},{"id":79407261,"identity":"d9067eb3-a700-45bd-b23f-0d5c02e0d41b","added_by":"auto","created_at":"2025-03-28 04:36:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":172913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBnaA09.PYRD\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at the population level. a\u003c/strong\u003e List of top ten genes ranked within the QTL \u003cem\u003eqFA.A09.1\u003c/em\u003e. \u003cstrong\u003eb\u003c/strong\u003e Manhattan plot and gene information for the 100 kb genomic region upstream and downstream of the\u003cem\u003e qFA.A09.1\u003c/em\u003e locus. The dots indicate variants within the region, with the colour of each dot indicating the LD (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) between the variants and the lead SNP. The grey dashed line marks the position of the candidate gene’s position, and the gene structure in the locus is displayed at the bottom. \u003cstrong\u003ec\u003c/strong\u003e GWAS results around \u003cem\u003eBnaA09.PYRD\u003c/em\u003e, with nonsynonymous variants marked by gold dots. \u003cstrong\u003ed-e\u003c/strong\u003e Box plots of the effects of haplotypes on C18:1 and C22:1 constructed from the variants within the \u003cem\u003eBnaA09.PYRD\u003c/em\u003e and the upstream 2 kb region. (+) indicates the presence of effects at the \u003cem\u003eqFA.A08\u003c/em\u003e locus or gene, (-) indicates the absence of effects at the \u003cem\u003eqFA.A08 \u003c/em\u003elocus or gene (\u003cem\u003eBnaA09.PYRD\u003c/em\u003e). The center line of the box plot is the median C18:1 or C22:1 content in each haplotype, with the upper and lower edges of the box showing the respective quartiles. Dots represent the values of C18:1 or C22:1 content for each variety. The MLM module from EMMAX software was applied to calculate the effect of haplotypes. \u003cstrong\u003ef-i\u003c/strong\u003e Correlation analysis of gene expression values at 20 and 40 DAF with C18:1 and C22:1 content. Accessions were grouped by the genotypes of the lead SNP of \u003cem\u003eqFA.A09.1\u003c/em\u003e, colored by the two accession groups. The \u003cem\u003ex\u003c/em\u003e-axis indicates the normalized expression value, while the \u003cem\u003ey\u003c/em\u003e-axis represents phenotype value.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5468888/v1/a80faa2501ab9ca9c9387760.png"},{"id":79407263,"identity":"74ca511e-4415-431f-a96d-fae1da496fbb","added_by":"auto","created_at":"2025-03-28 04:36:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":248494,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional characterization of four genes in rapeseed.\u003c/strong\u003e (\u003cstrong\u003ea-d)\u003c/strong\u003e FAs composition of \u003cem\u003eBnaPYRD\u003c/em\u003e (\u003cstrong\u003ea\u003c/strong\u003e), \u003cem\u003eBnaPSK1\u003c/em\u003e (\u003cstrong\u003eb\u003c/strong\u003e), \u003cem\u003eBnaSWI3\u003c/em\u003e (\u003cstrong\u003ec\u003c/strong\u003e), and \u003cem\u003eBnaLTP15\u003c/em\u003e (\u003cstrong\u003ed\u003c/strong\u003e) in wild-type (WT) and mutant lines (n=3-6). * indicates \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** indicates \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 in Student's \u003cem\u003et\u003c/em\u003e-test. \u003cstrong\u003ee\u003c/strong\u003e Volcano plot of differential expression genes (DEGs) in \u003cem\u003ebna.pyrd\u003c/em\u003e mutants compared to WT. The \u003cem\u003ex\u003c/em\u003e-axis indicates the fold change (FC) of gene expression after \u003cem\u003elog2\u003c/em\u003e-transformation and the \u003cem\u003ey\u003c/em\u003e-axis indicates the adjusted \u003cem\u003eP\u003c/em\u003e value. The grey line indicates \u003cem\u003elog2\u003c/em\u003e(FC) = 1, and red points highlight DEGs related to lipid metabolism. \u003cstrong\u003ef \u003c/strong\u003eSubcellular localization of BnaA09.PYRD protein using BnaA09.PYRD-GFP in tobacco leaves. Green fluorescence indicates the target gene localization, red fluorescence indicates the mitochondrial marker, and the merged image shows co-localization.\u003cstrong\u003e g \u003c/strong\u003eA proposed working model for the function of \u003cem\u003eBnaA09.PYRD\u003c/em\u003e in the regulation of FA composition. Heatmaps illustrating the levels of carbon metabolites in WT and \u003cem\u003ebna.pyrd \u003c/em\u003emutants. Statistical significance was calculated using Student's \u003cem\u003et\u003c/em\u003e-tests, * indicates \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 and ** indicates \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01. Abbreviations include G6P (glucose-6-phosphate), RU5P (D-Ribulose-5-phosphate), DHAP (Dihydroxyacetone phosphate), PYR (pyruvate), ATP (Adenosine triphosphate), ADP (Adenosine diphosphate), NAD (Nicotinamide adenine dinucleotide), NADH (β- Nicotinamide adenine dinucleotide), Acetyl-CoA (Acetyl-coenzyme A), Malonyl-CoA (Malonyl-coenzyme A), ASP (Aspartic acid), PHEN (Phenylalanine), TYR (Threonine) and GLU (Glutamic acid).\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5468888/v1/c872c04800bcf70fa3d8015c.png"},{"id":79407273,"identity":"d4104e74-f72f-4a82-be4a-26966420888d","added_by":"auto","created_at":"2025-03-28 04:36:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":49045,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHaplotype frequency variation of four genes across breeding periods.\u003c/strong\u003e Proportion of \u003cem\u003eBnaA08.SWI3\u003c/em\u003e(\u003cstrong\u003ea\u003c/strong\u003e)\u003cem\u003e,\u003c/em\u003e \u003cem\u003eBnaA09.PYRD\u003c/em\u003e (\u003cstrong\u003eb\u003c/strong\u003e), \u003cem\u003eBnaC02.LTP15\u003c/em\u003e (\u003cstrong\u003ec\u003c/strong\u003e),\u003cem\u003e \u003c/em\u003eand \u003cem\u003eBnaA08.PSK1\u003c/em\u003e (\u003cstrong\u003ed\u003c/strong\u003e)\u003cem\u003e \u003c/em\u003ehaplotypes across breeding periods. Distinct colors indicate specific haplotypes. Dashed lines link optimal haplotypes for C18:1 and C22:1. Haplotype effects on C18:1 and C22:1 (\u003cstrong\u003ee\u003c/strong\u003e) and geographic distribution (\u003cstrong\u003ef)\u003c/strong\u003e across 418 accessions, derived from variants within\u003cem\u003e BnaA08.PSK1\u003c/em\u003e and its upstream 2 kb region.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5468888/v1/40e19fcafd811e744d2c5cc0.png"},{"id":80082257,"identity":"694ce0b4-2db9-495a-9747-333149b9ce92","added_by":"auto","created_at":"2025-04-07 16:08:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2744647,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5468888/v1/a21661bc-9807-4793-965a-3e54d8634289.pdf"},{"id":79407276,"identity":"cd88c407-e28c-4b51-a08c-c944d23798b5","added_by":"auto","created_at":"2025-03-28 04:36:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24595861,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1. Supplementary figures Fig. S1 to Fig. S25.\u003c/p\u003e","description":"","filename":"Additionalfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5468888/v1/d60e87d9de74e0c5efe374b3.pdf"},{"id":79407278,"identity":"bc6f3b83-a51c-4dab-8a20-59125245ccd9","added_by":"auto","created_at":"2025-03-28 04:36:38","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":923247,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2. Supplementary tables S1–S11.\u003c/p\u003e","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5468888/v1/7f6c2bd186522eaa237ed1a0.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative omics analysis reveals the genetic basis of fatty acid composition in Brassica napus seeds","fulltext":[{"header":"Background","content":"\u003cp\u003eRapeseed (\u003cem\u003eBrassica napus\u003c/em\u003e L.) is an oilseed crop that is globally cultivated [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Triacylglycerols, the primary form of oil in rapeseed, are composed of a glycerol backbone and fatty acid (FA) chains [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. FAs are categorized into saturated and unsaturated types based on the hydrocarbon chain\u0026rsquo;s degree of saturation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Saturated FAs tend to accumulate on blood vessel walls and are less easily digested and absorbed by the human body [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], while unsaturated FAs are more beneficial to human health, helping reduce the risk of cardiovascular and cerebrovascular diseases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In rapeseed, breeding efforts have focused on developing double-low (low erucic acid, low glucosinolate) and one-high (high oleic acid) varieties [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe FA biosynthetic pathway in rapeseed is a quantitative trait regulated by QTLs, and its FA composition is controlled by many genes with additive and epistatic effects [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Among the genes and loci for FA breed improvement, researchers identified two major effector loci with additive effects through linkage mapping, which are located on rapeseed chromosomes A08 and C03 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The two homologs of \u003cem\u003eFAE1\u003c/em\u003e, \u003cem\u003eBnaA08.FAE1\u003c/em\u003e and \u003cem\u003eBnaC03. FAE1\u003c/em\u003e are key genes necessary for encoding the elongation of long-chain FAs and can control the synthesis of erucic acid in rapeseed [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Peng et al. silenced the \u003cem\u003eFAD2\u003c/em\u003e and \u003cem\u003eFAE1\u003c/em\u003e genes in rapeseed using RNAi technique and found a great increase in oleic acid and a decrease in erucic acid [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Wells et al. found that a significant increase in erucic acid content could be achieved by either repressing the expression of the \u003cem\u003eFAD2\u003c/em\u003e gene in rapeseed or by directly mutating the gene [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, it has been shown that the copy number of the \u003cem\u003eFAD3\u003c/em\u003e gene affects the content of linolenic acid in seeds [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, high linolenic acid breeding in rapeseed can be achieved by increasing the expression of \u003cem\u003eFAD3\u003c/em\u003e gene. In rapeseed germplasm resources, the genetic regulatory mechanisms affecting FA compositions have been largely clarified [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the proportions of FA composition in rapeseed are highly variable, and the construction of new genes affecting FA composition and their regulatory relationship networks requires further research and exploration.\u003c/p\u003e \u003cp\u003eTraditional reverse genetics strategies and map-based cloning approaches are time-consuming and labor-intensive, which makes it difficult to comprehensively explore the key variants and genes affecting FAs [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. With the continuous development of sequencing technology, genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) have been widely used in various fields of crop research [\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In rapeseed, multi-omics data were used to analyze the genetic basis of seed oil content (SOC), seed coat content (SCC) and seed glucosinolate content (SGC) synthesis, resulting in the cloning of several key genes, such as \u003cem\u003eBna.PMT6\u003c/em\u003e, \u003cem\u003eBna.CCRL\u003c/em\u003e, \u003cem\u003eBna.TT8\u003c/em\u003e, \u003cem\u003eBna.GTR2\u003c/em\u003e and \u003cem\u003eBna.TT4\u003c/em\u003e [\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These genes have provided abundant genetic resources for the genetic improvement of rapeseed quality traits. In this study, we comprehensively analyzed the genetic mechanism of FA composition in rapeseed, combining multi-omics data analysis, co-expression network construction, machine learning algorithms to predict and clone four new genes affecting FA composition. The results enriched the regulatory mechanisms affecting FA composition of rapeseed, as well as provided theoretical basis and genetic resources for rapeseed oil quality improvement.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eFatty acid composition in rapeseed population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe FAs in rapeseed include various saturated and unsaturated types. To investigate the genetic basis of FA\u0026nbsp;composition in rapeseed, this study analyzed the content of seven FAs\u0026mdash;palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), eicosapentaenoic acid (C20:1) and erucic acid (C22:1)\u0026mdash;across 505 rapeseed accessions in six locations (Wuhan, Ezhou, Chengdu, Hefei, Kunming, and Lanzhou) over one to four years. The results revealed that unsaturated FAs had a significantly higher average content than saturated FAs in rapeseed, with C18:1 having the highest mean content (54.52%), followed by C18:2 at 16.59% (Fig. 1a). Pearson\u0026apos;s correlation coefficient (PCC) analysis, following best linear unbiased prediction (BLUP) results, identified significant correlations among six of the FAs, excluding C18:3 (Fig. 1b; Additional file 1: Fig. S1). Notably, C22:1 showed a strong positive correlation with C20:1 (R\u003csup\u003e2\u003c/sup\u003e = 0.94), and strong negative correlation with C16:0, C18:0, C18:1 and C18:2 (R\u003csup\u003e2\u003c/sup\u003e = 0.76, 0.37, 0.97 and 0.67, respectively) (Additional file 1: Fig. S1). The high correlations between C18:1 and both C20:1 and C22:1 are likely due to the use of C18:1 as a substrate for carbon chain extension in the biosynthesis of C20:1 and C22:1. These findings confirm the reliability of the FA composition data and support the synthesis of multiple FA profiles for a comprehensive analysis of the regulatory network governing FA metabolism in rapeseed.\u003c/p\u003e\n\u003cp\u003eIn our previous study, 505 rapeseed accessions were grouped into three subpopulations: semi-winter1 (SW1), semi-winter 2 (SW2), and spring (SPR)\u0026nbsp;[33]. FA composition analysis showed that SW1 and SPR had similar profiles, with higher levels of C16:0, C18:0, C18:1, and C18:2 compared to SW2, which had higher levels of C18:3, C20:1) and C22:1. Notably, C18:1 content was 40-45% higher in SW1 and SPR than in SW2, while C22:1 content was nearly absent (Additional file 1: Fig. S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS reveals key QTLs influencing fatty acid composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSeven co-localized QTLs were identified as controlling fatty acid composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing GWAS with 10,620,048 variants, we analyze seven FAs and their ratios (C18:1/C18:2, C18:1/C18:3, C18:1/C20:1, C18:2/C18:3, C22:1/C18:1, C22:1/C18:2, and C22:1/C18:3) (Additional file 1: Fig. S3, 4; Additional file 2: Table S2), and a total of 169 QTLs were identified. After combining the co-localized QTLs, seven QTLs (named as \u003cem\u003eqFA.A02\u003c/em\u003e, \u003cem\u003eqFA.A08\u003c/em\u003e, \u003cem\u003eqFA.A09.1\u003c/em\u003e, \u003cem\u003eqFA.A09.2\u003c/em\u003e, \u003cem\u003eqFA.C02\u003c/em\u003e, \u003cem\u003eqFA.C03.1\u003c/em\u003e and \u003cem\u003eqFA.C03.2\u003c/em\u003e) were consistently detected in GWAS across more than five FAs or six fatty acid ratios, which are distributed on chromosomes A02, A08, A09, C02 and C03, respectively (Fig. 1c, d; Additional file 1: Fig. S5; Additional file 2: Table S3).\u003c/p\u003e\n\u003cp\u003eCandidate genes located within 100 kb of the lead SNPs in the seven stable QTLs were identified, yielding 1,412 potential GWAS candidate genes. These included key genes involved in FA synthesis, such as those encoding 3-ketoacyl-CoA synthase (KCS) enzymes (\u003cem\u003eBna.FAE1\u003c/em\u003e and \u003cem\u003eBna.KCS3\u003c/em\u003e) for FA elongation, \u003cem\u003eBna.LACS\u003c/em\u003e for long-chain FA synthesis, \u003cem\u003eBna.FAD3\u003c/em\u003e for linolenic acid synthesis and \u003cem\u003eBna.LPAAT5\u003c/em\u003e for triacylglycerol formation (Table 1). Interestingly, several flavonoid metabolism-related genes were also identified, including \u003cem\u003eBna.CCoAOMT\u003c/em\u003e, which is involved in lignin biosynthesis, and genes from transparent seed coat family (\u003cem\u003eBna.TT6\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Bna.TT16\u003c/em\u003e), which influence seed coat color. These findings suggest a complex regulatory network in rapeseed seed development and underscore the genetic diversity underlying FA composition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrong selection pressure of\u003cem\u003e\u0026nbsp;qFA.A09.1\u003c/em\u003e and \u003cem\u003eqFA.A08\u003c/em\u003e during rapeseed domestication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the rapeseed breeding, seeds with low C22:1, high C18:1 and low glucosinolate content\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eare key targets. Our study has identified two co-localized QTLs in the GWAS results for seed glucosinolate content (SGC) [35] and FAs [\u003cem\u003e-log\u003c/em\u003e\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003eP\u003csub\u003eqGSC.A08.1\u003c/sub\u003e\u003c/em\u003e)\u003cem\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 6.53, \u003cem\u003e-log\u003c/em\u003e\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003eP\u003csub\u003eqFA.A08\u003c/sub\u003e\u003c/em\u003e)\u003cem\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 35.98, \u003cem\u003e-log\u003c/em\u003e\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003eP\u003csub\u003eqGSC.A09.2\u003c/sub\u003e\u003c/em\u003e)\u003cem\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 17.56, \u003cem\u003e-log\u003c/em\u003e\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003eP\u003csub\u003eqFA.A09.1\u003c/sub\u003e\u003c/em\u003e)\u003cem\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 10.99] (Fig. 1e), this suggests a co-selection for glucosinolate and FA traits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further explore the selection of loci controlling FAs, ancestral alleles in the rapeseed genome were inferred using resequencing data from \u003cem\u003eB. oleracea\u003c/em\u003e and\u003cem\u003e\u0026nbsp;B. rapa\u003c/em\u003e. Previous studies confirmed that \u003cem\u003eqFA.A08\u0026nbsp;\u003c/em\u003e(which corresponds to \u003cem\u003eqOC.A08.1\u003c/em\u003e in our previous research) [33], including \u003cem\u003eBnaA08.FAE1\u003c/em\u003e, a key regulator of C22:1 synthesis in rapeseed, has undergone significant selection. Comparative analysis of the nucleotide diversity (\u0026pi;) between the ancient and derived haplotypes of the remaining six co-localized QTLs revealed that \u003cem\u003eqFA.A09.1\u003c/em\u003e experienced strong selection during domestication (Fig. 1f, g). These findings suggest that \u003cem\u003eqFA.A08\u003c/em\u003e and \u003cem\u003eqFA.A09.1\u003c/em\u003e were crucial for breeding high oleic and low erucic acid rapeseed. However, other QTLs associated with erucic and oleic acid content have\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eremained underutilized in rapeseed breeding (Additional file 1: Fig. S6). Therefore, further exploration of QTLs and new genes involved in fatty acid composition is essential to enhance rapeseed improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTWAS revealed the molecular basis of seed fatty acid composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTWAS correlates gene expression with phenotype at the population level. To identify genes influencing the FA content, we conducted TWAS on seven FA compositions using expression data from two time points. A total of 3,295 genes were significantly associated with the FAs (FDR \u0026lt; 0.05), with 1,576 genes identified at 20 days after flowering (DAF) and 2,196 at 40 DAF (Fig. 2a). The highest number of significantly related genes was found in C18:1, followed closely by C20:1 and C22:1. C18:3 had almost no significant associations, while the other six FAs had 30 overlapping significant genes identified by TWAS (Fig. 2b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalyzing the functions of the 3,295 genes significantly associated with FAs, we identified several genes related to lipid synthesis. These include ABC transporter family genes (\u003cem\u003eBna.ABCG1\u003c/em\u003e, \u003cem\u003eBna.ABCG7\u003c/em\u003e, \u003cem\u003eBna.ABCG10\u003c/em\u003e and \u003cem\u003eBna.ABCG25\u003c/em\u003e), acyl carrier proteins (\u003cem\u003eBna.ACP\u003c/em\u003e), acyl CoA thioesterase genes (\u003cem\u003eBna.ACT\u003c/em\u003e), FA desaturases (\u003cem\u003eBna.FAD3\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Bna.FAD6\u003c/em\u003e), keto-CoA synthase and reductase genes (\u003cem\u003eBna.KCS6\u003c/em\u003e, \u003cem\u003eBna.KCS16\u003c/em\u003e, \u003cem\u003eBna.FAE1\u003c/em\u003e and \u003cem\u003eBna.KCR\u003c/em\u003e), long-chain acyl-CoA synthetase gene (\u003cem\u003eBna.LACS5\u003c/em\u003e), pyruvate kinase gene (\u003cem\u003eBna.PK\u003c/em\u003e) and lipid transfer proteins (\u003cem\u003eBna.LTP\u003c/em\u003e, \u003cem\u003eBna.PPT1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Bna.NTT1\u003c/em\u003e and \u003cem\u003eBna.NTT2\u003c/em\u003e) (Fig. 2c, d; Additional file 1: Fig. S8a, b; Additional file 2: Table S8, 9). These findings confirm the accuracy of the TWAS approach in identifying candidate genes. GO enrichment analysis of the 477 overlapping significant genes at both time points revealed involvement in processes such as 3-hydroxyacetyl-coenzyme, dehydrogenase activity, salicylic acid-mediated signaling and cation transport (Additional file 1: Fig. S7a). At 20 DAF, the 1,576 significant genes were mainly enriched in pathways related to nucleotide biosynthesis, salicylic acid signaling, protein kinase regulation, translation initiation factors, and fatty acid acyl-coenzyme A binding (Additional file 1: Fig. S7b). At 40 DAF, the 2,196 significant genes were associated with nucleotide binding, glycolysis, energy metabolism, and other nucleotide-related processes (Additional file 1: Fig. S7c). These results suggest that fatty acid biosynthesis is closely linked with energy metabolism [44, 45].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Seven co-localized QTLs and FA-related genes identified by genome-wide association analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"862\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosition\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene ID\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eqFA.A02\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e24099591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBnaA02g33410D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eMYB96\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eMyb-related protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e[38]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eqFA.A08\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10151436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBnaA08g11130D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eFAE1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003e3-ketoacyl-CoA synthase 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e[20]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBnaA08g11140D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eKCS17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003e3-ketoacyl-CoA synthase 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e[39]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eqFA.A09.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2612389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBnaA09g04580D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eSHN3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eEthylene-responsive factor SHINE 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e[40]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBnaA09g06090D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eMYB96\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eMyb-related protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e[38]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eqFA.A09.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e31542153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBnaA09g46210D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eAAPT1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eEthanolaminephosphotransferase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e[41]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eqFA.C02\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e44879480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBnaC02g42240D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eTT16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eTransparent testa 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e[42]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eqFA.C03.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e55619752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBnaC03g65980D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eFAE1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003e3-ketoacyl-CoA synthase 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e[20]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eqFA.C03.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e56398549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBnaC03g66040D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eKCS17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003e3-ketoacyl-CoA synthase 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e[39]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBnaC03g77180D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eSUD1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eE3 ubiquitin ligase SUD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e[43]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Physical position of the lead SNP.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eGene ID associated with FAs in QTL.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC18:1 and C22:1 are key targets in rapeseed breeding. We identified 881 and 1,939 significant genes by TWAS for C18:1 at 20 DAF and 40 DAF, respectively, with 402 overlapping genes (Fig. 2e; Additional file 2: Table S4, 5). These genes were primarily enriched in pathways related to nucleotide phosphorylation, hydroxyacyl-coenzyme A dehydrogenase activity, salicylic acid signaling, and nucleotide biosynthesis (Fig. 2f). For C22:1, 2,386 significantly genes were identified, with 364 genes overlapping between the two periods, which were involved in processes such as phosphotransferase activity, starch catabolism, and energy metabolism (Additional file 1: Fig. S8c, d; Additional file 2: Table S6, 7). The TWAS results for oleic and erucic acids showed a similar distribution pattern across the two time points, with a high correlation between gene associations for both FAs at 20 DAF and 40 DAF (Additional file 1: Fig. S9). Genes such as \u003cem\u003eBna.ACP\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Bna.KCS\u003c/em\u003e, \u003cem\u003eBna.LTP\u003c/em\u003e, \u003cem\u003eBna.FATA\u003c/em\u003e, \u003cem\u003eBna.LACS5\u0026nbsp;\u003c/em\u003eand \u003cem\u003eBna.PPT1\u003c/em\u003e were significantly associated with both FAs, showing positive correlations with C18:1 and negative correlations with C22:1 (Fig. 2c, d; Additional file 1: Fig. S8a, b; Additional file 2: Table S8, 9).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of six key candidate genes affecting fatty acids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify genes influencing FA composition, we analyzed the dynamic gene expression profiles of the rapeseed cultivar Zhongshuang11 (ZS11) from 2 DAF to 60 DAF, which resulted in the classification of eight expression modules (Fig. 3a). Enrichment analysis of significant genes from GWAS and TWAS for seed FA composition and SGC revealed that four modules\u0026mdash;C1, C3, C6 and C8\u0026mdash;were enriched with significant TWAS genes of FA and SGC (Fig. 3b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn order to further predict candidate genes affecting the FA composition in rapeseed, we prioritized genes within 100 kb upstream and downstream of seven FA-related QTLs using POCKET\u0026nbsp;[33]. By integrating variation effect (VE), haplotype effect (HE), expression effect (EE) and gene function prediction, we identified \u003cem\u003eBnaA09.PYRD, BnaA08.SWI3, BnaA09.PDR2\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;BnaC03.SLOMO\u003c/em\u003e, as top candidates based on POCKET scoring (Fig. 4a; Additional file 1: Fig. S13a, 15a, 16a), Additionally, \u003cem\u003eBnaA08.PSK1\u003c/em\u003e, a TWAS-significant gene, located in the C1 or C6 module, exhibits similar expression patterns with several fatty acid synthesis-related genes (Fig. 3c, d). And \u003cem\u003eBnaC02.LTP15\u003c/em\u003e, found within the\u003cem\u003e\u0026nbsp;qFA.C02\u003c/em\u003e locus\u0026nbsp;and part of the C3 module (Additional file 1: Fig. S14a), shows expression patterns similar to genes involved in seed coat development and lipid synthesis, such as \u003cem\u003eTT9\u003c/em\u003e, \u003cem\u003eLPAAT\u003c/em\u003e and \u003cem\u003eDGD2\u003c/em\u003e, indicating its possible involvement in lipid metabolism. Taken together, we hypothesize that \u003cem\u003eBnaA09.PYRD\u003c/em\u003e, \u003cem\u003eBnaA08.PSK1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;BnaA08.SWI3\u003c/em\u003e, \u003cem\u003eBnaC02.LTP15, BnaA09.PDR2\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;BnaC03.SLOMO\u0026nbsp;\u003c/em\u003eare key candidates influencing FA composition in rapeseed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBnaA09.PYRD\u003c/em\u003e and \u003cem\u003eBnaA09.PDR2\u0026nbsp;\u003c/em\u003ewere located within\u003cem\u003e\u0026nbsp;qFA.A09.1\u0026nbsp;\u003c/em\u003elocus\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Fig. 4b; Additional file 1: Fig. S15b), while \u003cem\u003eBnaA08.PSK1\u003c/em\u003e and \u003cem\u003eBnaA08.SWI3\u0026nbsp;\u003c/em\u003eshowed the strongest GWAS signals from\u003cem\u003e\u0026nbsp;qFA.A08\u003c/em\u003e locus (Additional file 1: Fig. S12a, 13b). Additionally, \u003cem\u003eBnaC02.LTP15\u0026nbsp;\u003c/em\u003ewas found\u003cem\u003e\u0026nbsp;\u003c/em\u003ewithin \u003cem\u003eqFA.C02\u0026nbsp;\u003c/em\u003elocus\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Additional file 1: Fig. S14b), and \u003cem\u003eBnaC03.SLOMO\u0026nbsp;\u003c/em\u003ewithin the \u003cem\u003eqFA.C03.1\u0026nbsp;\u003c/em\u003elocus\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Additional file 1: Fig. S16b).\u003cem\u003e\u0026nbsp;\u003c/em\u003eTo assess the impact of SNP on protein function, we identified three nonsynonymous variants in both \u003cem\u003eBnaA09.PYRD\u0026nbsp;\u003c/em\u003e(BnvaA0902629811, C/T, P = 1.95 \u0026times; 10\u003csup\u003e-12\u003c/sup\u003e; BnvaA0902628936, G/C, P = 3.77 \u0026times; 10\u003csup\u003e-7\u003c/sup\u003e; BnvaA0902628958, T/C, P = 3.80 \u0026times; 10\u003csup\u003e-7\u003c/sup\u003e) and \u003cem\u003eBnaA09.PDR2\u0026nbsp;\u003c/em\u003e(BnvaA0902561985, A/G, P = 6.66\u0026times;10-9; BnvaA0902556267, G/C, P = 3.65\u0026times;10-8; BnvaA0902561869, C/T, P = 2.02\u0026times;10-5) genes (Fig. 4c; Additional file 1: Fig. S15c). In contrast, \u003cem\u003eBnaC03.SLOMO\u003c/em\u003e has 14 nonsynonymous variants (Additional file 1: Fig. S16c), while\u003cem\u003e\u0026nbsp;BnaA08.PSK1\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;BnaA08.SWI3\u003c/em\u003e each have 11 nonsynonymous variants (Additional file 1: Fig. S12b, 13d). Notably, a frameshift mutation (BnvaA0810225954, ATC/A, P = 1.56 \u0026times; 10\u003csup\u003e-21\u003c/sup\u003e) was detected in exon of \u003cem\u003eBnaA08.SWI3\u003c/em\u003e, potentially causing a significant alteration in protein function (Additional file 1: Fig. S13c).\u003c/p\u003e\n\u003cp\u003eTo examine the association between haplotypes and fatty acid composition, we classified haplotypes based on candidate genes and their 2 kb promoter region variations, and using the lead SNP of \u003cem\u003eqFA.A08\u003c/em\u003e as a covariate to exclude the effect of \u003cem\u003eFAE1\u003c/em\u003e. The results showed that, with the \u003cem\u003eqFA.A08\u003c/em\u003e locus fixed, different haplotypes of \u003cem\u003eBnaA09.PYRD\u003c/em\u003e, \u003cem\u003eBnaC02.LTP15\u003c/em\u003e, \u003cem\u003eBnaA09.PDR2\u003c/em\u003e and \u003cem\u003eBnaC03.SLOMO\u003c/em\u003e had significant effects on C18:1 and C22:1. (Fig. 4d, e; Additional file 1: Fig. S14c-d, 15d-e, 16d-e). Previous analyses indicated that \u003cem\u003eqFA.A08\u003c/em\u003e and \u003cem\u003eqFA.A09.1\u003c/em\u003e were significantly selected during fatty acid domestication. Under the condition of consistent variation at the \u003cem\u003eqFA.A08\u003c/em\u003e locus, variations at the \u003cem\u003eqFA.A09.1\u003c/em\u003e locus resulted in significant differences in C18:1 and C22:1 (Additional file 1: Fig. S11). Further observation revealed that during the selection process, the expression level of the key gene \u003cem\u003eBnaA09.PYRD\u003c/em\u003e at the \u003cem\u003eqFA.A09.1\u003c/em\u003e locus was increased. In derived varieties, high expression level of \u003cem\u003eBnaA09.PYRD\u003c/em\u003e is often associated with higher C18:1 and lower C22:1, while the opposite trend is observed in ancient varieties (Fig. 4f-i). This suggests that the changes in fatty acid composition at the \u003cem\u003eqFA.A09.1\u003c/em\u003e locus are mediated through the regulation of \u003cem\u003eBnaA09.PYRD\u003c/em\u003e gene expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptional impact of candidate genes on fatty acid composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the effect of the six candidate genes on FA composition, we analyzed the gene expression at 20 DAF and 40 DAF in the population, correlating them with C18:1 and C22:1. Four genes showed a positive correlation with C18:1 and a negative correlation with C22:1. \u003cem\u003eBnaA08.SWI3\u0026nbsp;\u003c/em\u003eexhibited the strongest correlation, with \u003cem\u003eR\u003c/em\u003e\u0026sup2; values of 0.53 and 0.55 for C18:1 at 20 DAF and 40 DAF, respectively (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e20 DAF\u0026nbsp;\u003c/sub\u003e= 2.87\u0026times;10\u003csup\u003e-46\u003c/sup\u003e; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e40 DAF\u003c/sub\u003e = 6.54\u0026times;10\u003csup\u003e-49\u003c/sup\u003e) (Additional file 1: Fig. S13e, f), and for C22:1 were 0.55 and 0.58 (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e20 DAF\u003c/sub\u003e = 3. 34 \u0026times; 10\u003csup\u003e-48\u003c/sup\u003e; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e40 DAF\u003c/sub\u003e = 3.75 \u0026times; 10\u003csup\u003e-52\u003c/sup\u003e) (Additional file 1: Fig. S13g, h).\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eBnaA09.PYRD\u003c/em\u003e, \u003cem\u003eBnaC02.LPT15\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;BnaA09.PDR2\u0026nbsp;\u003c/em\u003eshowed higher correlations at 20 DAF (Additional file 1: Fig. S10, 14e-h, 15f-i), while \u003cem\u003eBnaA08.PSK1\u003c/em\u003e and \u003cem\u003eBnaC03.SLOMO\u003c/em\u003e were more significant at 40 DAF (Additional file 1: Fig. S12c-f, 16f-i).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBnaPYRD\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e, \u003cem\u003eBnaPSK1\u003c/em\u003e, \u003cem\u003eBnaSWI3\u003c/em\u003e and \u003cem\u003eBnaLTP15\u003c/em\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eact as negative regulator of erucic acid\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo verify the functions of six genes, transgenic rapeseed plants were generated using cv. WH3411, characterized by low C18:1 and high C22:1, as the receptor material. \u003cem\u003eBnaPYRD\u003c/em\u003e and \u003cem\u003eBnaPSK1\u003c/em\u003e mutants (CRISPR/Cas9) and overexpression lines, along with \u003cem\u003eBnaSWI3\u003c/em\u003e and \u003cem\u003eBnaLTP15\u003c/em\u003e mutant lines (CRISPR/Cas9), were successfully constructed (Additional file 1: Fig. S17-20).\u0026nbsp;Compared with the wild type, C22:1 content in the seeds of \u003cem\u003eBnaPYRD\u003c/em\u003e overexpression lines OE-19 and OE-5 decreased by 9.75% and 23.15%, respectively, while C18:1 content increased by 6.0% and 21.68% (Fig. 5a).\u0026nbsp;In contrast,\u0026nbsp;C22:1 content in the seeds of the quadruple mutant line \u003cem\u003epyrd-L35\u003c/em\u003e and the triple mutant line \u003cem\u003epyrd-L1\u003c/em\u003e increased by 11.08% and 6.52%, with a corresponding decrease in C18:1 content by 11.99% and 6.71% (Fig. 5a).\u0026nbsp;The C22:1 content in the seeds of \u003cem\u003eBnaPSK1\u003c/em\u003e overexpression lines OE-12 and OE-14 decreased by 15.68% and 7.79%, respectively, with an increase in C18:1 content of 9.94% and 3.40% (Fig. 5b).\u0026nbsp;Conversely, C22:1 content in the seeds of the double mutant line \u003cem\u003epsk1-L5\u003c/em\u003e and the triple mutant \u003cem\u003epsk1-L26\u003c/em\u003e increased by 14.82% and 14.01%, respectively, accompanied by a decrease in C18:1 content of 23.02% and 16.57 % (Fig. 5b).\u0026nbsp;The \u003cem\u003eBnaSWI3\u0026nbsp;\u003c/em\u003equadruple mutant strains \u003cem\u003eswi3-L3\u003c/em\u003e, \u003cem\u003eswi3-L10\u003c/em\u003e, and double mutant strain \u003cem\u003eswi3-L27\u003c/em\u003e exhibited an increase in C22:1 content of 8.46% to 11.24% and a decrease in C18:1 content of 8.01% to 15.85% (Fig. 5c).\u0026nbsp;In \u003cem\u003eBnaLTP15\u003c/em\u003e double mutant lines \u003cem\u003eltp15-L3\u003c/em\u003e, \u003cem\u003eltp15-L11\u003c/em\u003e and \u003cem\u003eltp15-L20\u003c/em\u003e C22:1 content increased by 7.19% to 16.03%, and the C18:1 content decreased of 6.79% to 18.58% (Fig. 5d).\u0026nbsp;In addition, the content of C20:1 in the seeds of these four gene mutant lines was reduced to varying degrees (Fig. 5a-d).\u0026nbsp;Furthermore, the transgenic plants, both gain-of-function and loss-of-function mutants, showed no significant differences in overall growth compared with control plants\u0026nbsp;(Additional file 1: Fig. S21). Collectively, our findings support the potential of these genes as promising targets for breeding programs aimed at improving seed oil quality in \u003cem\u003eB. napus\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBnaA09.PYRD\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;may influence fatty acid composition by modulating energy metabolism during rapeseed development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn rapeseed fatty acid breeding, \u003cem\u003eqFA.A09.1\u0026nbsp;\u003c/em\u003elocus\u003cem\u003e\u0026nbsp;\u003c/em\u003eis under strong selection, making it crucial to identify genes affecting FA content within this segment. We predicted that \u003cem\u003eBnaA09.PYRD\u003c/em\u003e might be a causal gene influencing FA composition. To further understand how \u003cem\u003eBnaA09.PYRD\u003c/em\u003e modulates FA synthesis, transcriptome sequencing was conducted on seeds of WT and \u003cem\u003epyrd-L35\u003c/em\u003e at 35 DAF. A total of 51,276 expressed genes (TPM \u0026gt; 1) and 5,398 differentially expressed genes (DEGs) (|log\u003csub\u003e2\u003c/sub\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(fold change) | \u0026gt; 1 and \u003cem\u003eP\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u0026lt; 0.05) were identified (Additional file 2: Table S10). These encompassed numerous genes associated with FA synthesis, including FA desaturase genes (\u003cem\u003eFAD8\u003c/em\u003e,\u003cem\u003e\u0026nbsp;FATA1\u003c/em\u003e and\u003cem\u003e\u0026nbsp;FATB\u003c/em\u003e), 3-ketoacyl-CoA synthetase genes (\u003cem\u003eKCS1\u003c/em\u003e, \u003cem\u003eKCS4\u003c/em\u003e and \u003cem\u003eKCS5\u003c/em\u003e) and several long-chain acyl-CoA synthetase genes (\u003cem\u003eLACS5\u003c/em\u003e, \u003cem\u003eLACS7\u003c/em\u003e and \u003cem\u003eLACS8\u003c/em\u003e) (Fig. 5e). GO enrichment analysis indicated that these DEGs linked to lipid synthesis were mainly enriched in flavonoid biosynthesis pathways, galactolipid and phospholipid biosynthesis, FA elongation, wax biosynthesis and lignin mucin-associated pathways (Additional file 1: Fig. S22). The combined experimental results suggest that \u003cem\u003eBnaA09.PYRD\u003c/em\u003e may modify FA compositions by modulating the conversion of C18:1 to C18:2 and the elongation of C18:1 to long-chain unsaturated FAs.\u003c/p\u003e\n\u003cp\u003eTo investigate the localization of BnaA09.PYRD protein in cells, GFP-fused expression vectors containing the gene were constructed and transformed into tobacco lower epidermal cells for visualization. Under confocal microscopy, BnaA09.PYRD localized in mitochondria (Fig. 5f). Moreover, primary metabolite analysis of the \u003cem\u003epyrd-L35\u003c/em\u003e mutant revealed significant changes in the levels of several metabolites and amino acids in the tricarboxylic acid cycle (TCA) and the Calvin-Benson cycle (Fig. 5g). Based on these results, we hypothesize that \u003cem\u003eBnaA09.PYRD\u003c/em\u003e regulates energy metabolism during rapeseed development, thereby affecting FA composition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe optimal haplotype of \u003cem\u003eBnaA08.PSK1\u0026nbsp;\u003c/em\u003eremains underutilized in \u003cem\u003eB. napus\u0026nbsp;\u003c/em\u003ebreeding.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe categorized the accessions from the previous study into three breeding periods：\u0026lsquo;Before 1980\u0026rsquo;, \u0026lsquo;1980-2000\u0026rsquo; and \u0026lsquo;After 2000\u0026rsquo; [46, 47]. Haplotypes for four functional genes were classified based on variants within the 2 kb promoter and gene region. The haplotype associated with the highest C18:1 and the lowest C22:1 levels was considered the optimal haplotype (Additional file 1: Fig. S23). The proportion of optimal haplotypes in \u003cem\u003eBnaA08.SWI3\u003c/em\u003e, \u003cem\u003eBnaA09.PYRD\u003c/em\u003e, and \u003cem\u003eBnaC02.LTP15\u003c/em\u003e increased significantly over time. After 2000, the optimal haplotype for all three genes become the major haplotype (Fig 6a-c). In contrast, \u003cem\u003eBnaA08.PSK1\u003c/em\u003e haplotype exhibited a different distribution. The proportion of optimal haplotypes slightly increased from \u0026lsquo;Before 1980\u0026rsquo; to \u0026lsquo;1980-2000\u0026rsquo;. Compared to \u0026lsquo;1980-2000\u0026rsquo;, the proportion of optimal haplotypes in the \u0026lsquo;After 2000\u0026rsquo; remained nearly unchanged. The proportion of suboptimal haplotypes (Hap B) of \u003cem\u003eBnaA08.PSK1\u003c/em\u003e gradually increased becoming the major haplotype (Fig. 6d, e).\u003c/p\u003e\n\u003cp\u003eIn addition to temporal changes in haplotypes over time, we also categorized the accessions by geographical region. The optimal haplotypes of \u003cem\u003eBnaA08.SWI3\u003c/em\u003e and \u003cem\u003eBnaA09.PYRD\u003c/em\u003e were predominant across regions, except in Eastern Europe (Additional file 1: Fig. S24a, b). The optimal haplotype of \u003cem\u003eBnaC02.LTP15\u003c/em\u003e was less frequent in North America and Oceania (Additional file 1: Fig. S24c). The optimal haplotype of \u003cem\u003eBnaA08.PSK1\u003c/em\u003e was not widely adopted in any region (Fig. 6f). These results suggest that \u003cem\u003eBnaA08.SWI3\u003c/em\u003e, \u003cem\u003eBnaA09.PYRD\u003c/em\u003e and \u003cem\u003eBnaC02.LTP15\u003c/em\u003e have been actively utilized for improvement in \u003cem\u003eB. napus\u003c/em\u003e breeding. However, the optimal haplotype of \u003cem\u003eBnaA08.PSK1\u0026nbsp;\u003c/em\u003eremained\u003cem\u003e\u0026nbsp;\u003c/em\u003eunderutilized.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMining fatty acid related QTLs in rapeseed\u003c/h2\u003e \u003cp\u003eFA metabolism in rapeseed is highly complex. To address this complexity, integrating bioinformatics with genetic tools and molecular breeding design can generate new genetic resources for FA improvement. Our study localized QTLs using GWAS, and the results align with previous research, showing that QTLs were primarily distributed on chromosomes A08, A09, C02 and C03 (\u003cem\u003eqFA.A08\u003c/em\u003e, \u003cem\u003eqFA.C09.1\u003c/em\u003e, \u003cem\u003eqFA.C02\u003c/em\u003e, \u003cem\u003eqFA.C03.1\u003c/em\u003e, \u003cem\u003eqFA.C03.2\u003c/em\u003e, respectively) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Notably, we identified two novel QTLs (\u003cem\u003eqFA.A02\u003c/em\u003e and \u003cem\u003eqFA.A09.2\u003c/em\u003e) that had not been previously reported (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, d; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, some key enzymes affecting FA metabolism, such as FAD2, FAD3 and SAD, were not detected in the GWAS, probably due to limitations in population diversity, which hindered the identification of relevant variants. Analysis of the seven fatty acid-related QTLs revealed that \u003cem\u003eqFA.A08\u003c/em\u003e and \u003cem\u003eqFA.A09.1\u003c/em\u003e were strongly selected during breeding. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee-g). Although the genes affecting fatty acids at \u003cem\u003eqFA.A09.1\u003c/em\u003e have not been reported, it is hypothesized that selection of the \u003cem\u003eBnaA09.PYRD\u003c/em\u003e gene at this locus may reduce erucic acid content and increase oleic acid content. In contrast, major variants in the other five QTLs were not significantly selected (Additional file 1: Fig. S6), suggesting that further exploration of QTLs and novel genes regulating FA composition is essential in rapeseed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIntegrating key candidate genes affecting fatty acids through multi-omics approaches\u003c/h2\u003e \u003cp\u003eDuring GWAS based on 505 accessions of \u003cem\u003eB. napus\u003c/em\u003e, LD decay was slow, and the QTL identified often had dozens of candidate genes [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. There were always many false-positive loci in the results. A single GWAS cannot satisfy the construction of gene networks. To overcome these challenges, we integrated GWAS, TWAS and transcriptomic profiles from seed development to comprehensively construct a multi-omics gene network influencing FA composition in rapeseed. This network comprised seven independent QTLs and 3,295 TWAS-significant genes (Additional file 1: Fig. S25), including key lipid synthesis genes such as \u003cem\u003eBna.ACP, Bna.KCS\u003c/em\u003e, \u003cem\u003eBna.LTP\u003c/em\u003e, \u003cem\u003eBna.FATA\u003c/em\u003e, \u003cem\u003eBna.LACS5\u003c/em\u003e and \u003cem\u003eBna.PPT1\u003c/em\u003e (Additional file 2: Table S8, 9). This network plays a crucial role in regulating FA composition in rapeseed, contributing to a deeper understanding of the regulatory mechanisms governing FA metabolism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMining the regulatory mechanism of candidate genes affecting fatty acid composition\u003c/h2\u003e \u003cp\u003eOur study successfully identified four genes influencing FA composition in rapeseed through functional validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-d). Mutations in these genes can significantly enhance C22:1 content. Given C22:1\u0026rsquo;s substantial industrial value, increasing its content is essential to addressing the supply shortage of high-erucic-acid rapeseed varieties. Research on these genes (\u003cem\u003eBnaA08.SWI3\u003c/em\u003e, \u003cem\u003eBnaA09.PYRD\u003c/em\u003e, \u003cem\u003eBnaC02.LTP15\u003c/em\u003e, and \u003cem\u003eBnaA08.PSK1\u003c/em\u003e) offers insights into the biosynthetic mechanisms of C22:1. One of these genes, \u003cem\u003ePYRD\u003c/em\u003e, encodes a dehydrogenase involved in mitochondrial energy metabolism and the fourth step of pyrimidine biosynthesis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Previous studies have shown that \u003cem\u003ePYRD\u003c/em\u003e mutants display reduced pyrimidine metabolites, lower energy status, decreased photosynthetic capacity, and accumulation of reactive oxygen species. In this study, we hypothesized that the \u003cem\u003eBnaA09.PYRD\u003c/em\u003e gene may affect FA composition by inhibiting FA elongation. In addition, seed transcriptome analysis indicated that \u003cem\u003eBnaA09.PYRD\u003c/em\u003e may influence FA metabolism and flavonoid metabolism pathways, both of which are crucial for carbon allocation in rapeseed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg). However, no current evidence suggests that \u003cem\u003eBnaA09.PYRD\u003c/em\u003e interacts with key transcription factors (such as \u003cem\u003eLEC1\u003c/em\u003e, \u003cem\u003eLEC2\u003c/em\u003e and \u003cem\u003eWRI1\u003c/em\u003e) or protein complexes to affect FA synthesis. Future research could leverage machine learning and deep learning models, along with chromatin accessibility data and eQTLs, to identify significant genomic variants and explore transcription factor sequences for conserved or specific coregulatory elements, with experimental validation [\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eBreeding applications of the optimal haplotypes in four functional genes\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eBreeding goals for \u003cem\u003eB. napus\u003c/em\u003e have shifted across periods. In the 1970s-1980s, double-low breeding was the primary objective. From the 1990s to the 2000s, the emphasis transitioned to achieving high yield and resistance [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Throughout the breeding process, the optimal haplotype of \u003cem\u003eBnaA08.SWI3\u003c/em\u003e in \u0026lsquo;Before 1980\u0026rsquo; varieties emerged as predominant (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The proportion of optimal haplotypes has gradually increased with ongoing breeding efforts, demonstrating selection of the optimal \u003cem\u003eBnaA08.SWI3\u003c/em\u003e haplotype in double-low breeding. Conversely, the optimal haplotypes of \u003cem\u003eBnaA09.PYRD\u003c/em\u003e, \u003cem\u003eBnaC02.LTP15\u003c/em\u003e, and \u003cem\u003eBnaA08.PSK1\u003c/em\u003e were underrepresented in \u0026lsquo;Before 1980\u0026rsquo; varieties, suggesting that these optimal haplotypes were lack of selection during double-low breeding (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb-d). In contrast, the rapid increase in the proportion of optimal haplotypes in \u003cem\u003eBnaA09.PYRD\u003c/em\u003e and \u003cem\u003eBnaC02.LTP15\u003c/em\u003e post-2000 indicates incidental selection during the breeding process aimed at high yield and high resistance. The optimal haplotypes of \u003cem\u003eBnaA08.PSK1\u003c/em\u003e did not show significant variation over time, which implies that the optimal haplotypes of \u003cem\u003eBnaA08.PSK1\u003c/em\u003e retain significant breeding potential. These findings offer valuable genetic resources for understanding the genetic basis of fatty acid composition and for breeding double-low rapeseed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study unravels the genetic regulatory network underlying fatty acid biosynthesis in rapeseed using an integrative multi-omics approach, and identifies four key genes that modulate the balance between oleic acid and erucic acid. These findings offer both a theoretical framework and valuable gene targets for the precise enhancement of oil quality. Beyond advancing our understanding of plant lipid metabolism, this work lays a solid foundation for molecular design breeding, facilitating the development of high-quality rapeseed cultivars for agricultural application.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePlant materials and trait determination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genetic transformation material used in this study was the rapeseed inbred line WH3411, characterized by high erucic acid content (34.9%) and high seed oil content (51.28%), it is one of the 505 rapeseed accessions included in the resequencing project with ID X1062 (Additional file 2: Table S1). This line was sourced from the Wuhan National Engineering Research Center for Rapeseed, China. The data used in the study included resequencing data of 505 rapeseed accessions, 309 seed transcriptome data at 20 days after flowering (DAF) and 274 seed transcriptome data at 40 DAF [33]. Additionally, time series transcriptome data were collected during ZS11 seed development (2 DAF to 60 DAF) [56]. For transgenic material subjected to transcriptome sequencing, 50 mg of developing seeds (35 DAF) were collected from both WT and \u003cem\u003ebna.pyrd-L35\u003c/em\u003e lines, with three biological replicates per genotype. The transcriptome data are available under the BioProject ID\u0026nbsp;PRJNA1214085.\u003c/p\u003e\n\u003cp\u003eFA phenotypes were collected from multiple locations\u0026nbsp;over various years:\u0026nbsp;Wuhan (2016-2019, 4 years), Ezhou (2017-2019, 3 years), Hefei (2017-2018, 2 years), Lanzhou (2018-2019, 2 years), Chengdu (2017) and Kunming (2018).\u0026nbsp;Mature, open-pollinated seeds from the population were harvested and dried.\u0026nbsp;FA composition was analyzed using a Foss NIRSystems 5000 near-infrared reflectance spectrometer, measuring palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), eicosenoic acid (C20:1) and erucic acid (C22:1). Six biological were identified for each germplasm for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariant identification and genotype imputation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rapeseed reference genome Darmor-bzh was obtained from Genoscope (http://www.genoscope.cns.fr/brassicanapus/) [57]. Sequence alignments were conducted using BWA software [58], and duplicate PCR fragments were removed using SAMTools markdup [59]. Variants from the 505 rapeseed accessions were identified using GATK [60]. SNPs and InDels with low mapping quality (MQ \u0026lt; 20) or shallow sequencing depth (DP \u0026lt; 50) were filtered out. Missing genotypes were imputed using the LD-KNN algorithm [61], resulting in final sequencing data with an accuracy greater than 99.7%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide association analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the ALL, SWR and SWR1 subgroups, 10,620,048, 9,783,864, and 8,609,979 high-quality SNPs (with MAF \u0026gt; 0.05) were identified, respectively. The significance thresholds for association were calculated by GEC software\u0026nbsp;[62]\u0026nbsp;and were determined to be 7.96 \u0026times; 10\u003csup\u003e-7\u003c/sup\u003e, 9.38 \u0026times; 10\u003csup\u003e-7\u003c/sup\u003e and 1.69 \u0026times; 10\u003csup\u003e-7\u003c/sup\u003e, for each subgroup, respectively. Association analyses of multiple FA compositions and their ratios were conducted using a mixed linear model implemented in GEMMA software\u0026nbsp;[63, 64].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome-wide association analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quality of transcriptome data was assessed using FastQC software [65], and filtered sequences were aligned to the reference genome using STAR software\u0026nbsp;[66].\u0026nbsp;Sequence quantification was performed with Salmon\u0026nbsp;[67],\u0026nbsp;and normalization was conducted using Tximport software\u0026nbsp;[68], and low-expression genes (with TPM \u0026lt; 1 in 95% of the accessions) in the population were removed in subsequent analyses.\u0026nbsp;Association analysis was\u0026nbsp;performed using the EMMAX mixed linear model\u0026nbsp;[69], with an FDR-corrected \u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue \u0026le; 0.5 as the threshold for significance in TWAS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificant QTLs selection analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-coverage\u0026nbsp;resequencing data from \u003cem\u003eB. oleracea\u003c/em\u003e and \u003cem\u003eB. rapa\u003c/em\u003e were utilized to infer ancestral alleles\u0026nbsp;[70]. The data were obtained from the NCBI database (with BioProject ID PRJNA312457), with 17 samples excluded due to low sequence quality. Sequences were aligned to the rapeseed \u003cem\u003eAn\u003c/em\u003e and \u003cem\u003eCn\u003c/em\u003e subgenomes using BWA software\u0026nbsp;[58],\u0026nbsp;with average alignment rates of 70.9% for the \u003cem\u003eAn\u003c/em\u003e subgenome and 81.1% for the \u003cem\u003eCn\u003c/em\u003e subgenome.\u0026nbsp;Genotypes were extracted using SAMtools\u0026nbsp;[59]\u0026nbsp;and BCFtools\u0026nbsp;[71]\u0026nbsp;to prepare data for selection analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDelineation of gene expression modules during seed development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression module analysis based on transcriptome data collected from 2 to 64 DAF during ZS11 seed development. Extreme values of average gene expression (TPM \u0026lt; 1 or TPM \u0026gt; 10,000) were filtered out. Using the default parameters of the Mfuzz package [72] 50,025 genes were classified into eight expression modules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLibraries were sequenced using the Illumina HiSeq platform with paired-end reads. Quality control was conducted using FastQC\u0026nbsp;[65],\u0026nbsp;and low-quality sequences were filtered out using Trimmomatic\u0026nbsp;[73]. Filtered data were aligned to the rapeseed reference genome using HISAT2 software\u0026nbsp;[74],\u0026nbsp;and\u0026nbsp;gene expression values were quantified with featureCounts\u0026nbsp;[75].\u0026nbsp;Differentially expressed genes (DEGs) were identified with DESeq2 [76], with significance thresholds set at \u003cem\u003ePadj\u003c/em\u003e \u0026lt; 0.05 and |log\u003csub\u003e2\u003c/sub\u003e (fold change)| \u0026gt; 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVector construction and plant transformation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA from 7-day-old WH3411 seedlings was extracted using the CTAB method. The gDNA of \u003cem\u003eBnaPYRD\u003c/em\u003e and \u003cem\u003eBnaPSK1\u003c/em\u003e was cloned and linked to pCAMBIA2306 with the 35S promoter. CRISPR targets were selected, and primers were designed using CRISPR-P v2.0 (http://cbi.hzau.edu.cn/CRISPR2/) (Additional file 2: Table S11). The sgRNA-Cas9 system was utilized for vector construction, with DNA sequence templates for CRISPR vectors sourced from pCBC-DT1T2. Purified PCR products were ligated into \u003cem\u003epKSE401\u0026nbsp;\u003c/em\u003evector. The high-erucic-acid rapeseed germplasm WH3411 was transformed via \u003cem\u003eAgrobacterium\u003c/em\u003e-mediated techniques using the hypocotyl and tissue culture system [77].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubcellular Localization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubcellular Localization was conducted using cDNA synthesized from RNA extracted from the leaves of the WH3411 variety as a template, full-length CDS of rapeseed genes were amplified with gene-specific primers. The target fragments were then cloned into the GFP fusion vector PMDC83, using \u003cem\u003eBam\u003c/em\u003eHI and \u003cem\u003eKpn\u003c/em\u003eI restriction sites. After sequencing and verification, the constructs were transformed into \u003cem\u003eAgrobacterium\u003c/em\u003e strain GV3101. Single colonies of \u003cem\u003eAgrobacterium\u003c/em\u003e containing the PMDC83-GFP vector were expanded and collected. The bacterial pellets were resuspended in buffer solution (50 mM MES, pH 5.6; 5 mM Na3PO4; 1 mM acetosyringone) and infiltrated into tobacco leaves via syringe injection. Green fluorescence signals were observed under a fluorescence microscope (Olympus BX35) 2-5 days post-infiltration. The excitation wavelength for the GFP was set to 488 nm, and the emission filter wavelength was 500-530 nm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFatty acid analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFAs were extracted from mature seeds using the gas chromatography (GC) FA methyl ester method, following Lu et al [78]. Various FA species were measured with an Agilent 6890 GC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003ePeer review information\u003c/h2\u003e\n\u003cp\u003eQingxin Song and Wenjing She were the primary editors of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. The peer-review history is available in the online version of this article.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eComputations in this study were conducted on the bioinformatics computing platform of the National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University. This work is supported by the Biological Breeding-National Science and Technology Major Project (2023ZD04069), National Science Fund for Distinguished Young Scholars (32225037), National Natural Science Foundation of China (U2102217), Hubei Hongshan Laboratory Fund (2021HSZD004), Project X2662024ZKPY001 supported by the Fundamental Research Funds for the Central Universities and HZAU-AGIS Cooperation Fund (SZYJY2021004).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eH.Z., S.W. and L.G. designed and supervised this study. Y.Z. performed the bioinformatics analysis. Y.L. performed the related experiments. Y.Z. and Y.L. prepared the manuscript. H.Z., S.W. and L.G. revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eComputations in this study were conducted on the bioinformatics computing platform of the National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University. This work is supported by the Biological Breeding-National Science and Technology Major Project (2023ZD04069), National Science Fund for Distinguished Young Scholars (32225037)，National Natural Science Foundation of China (U2102217), Hubei Hongshan Laboratory Fund (2021HSZD004), Project X2662024ZKPY001 supported by the Fundamental Research Funds for the Central Universities and HZAU-AGIS Cooperation Fund (SZYJY2021004).\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eResequencing data for 505 B. napus accessions are available at the Genome Sequence Archive (https://bigd.big.ac.cn/gsa/) under BioProject ID PRJCA002835 [79], with corresponding transcriptome data under BioProject ID PRJCA002836 [80]. PYRD-related transcriptome data can be accessed under BioProject ID PRJNA1214085 [81]. Time-series transcriptome data are deposited in NCBI under BioProject ID PRJNA722877 [82]. Additionally, resequencing data for B. oleracea and B. rapa are available from the NCBI database with BioProject ID PRJNA312457 [83]. The phenotypic data used in this study can be obtained from the website http://rgmi.hzau.edu.cn/phenotype and have also been deposited in Zenodo at https://zenodo.org/records/15048705 [84].All software and tools used in this study are publicly available as described in the Methods section. The code for POCKET can be accessed on GitHub at https://github.com/zhaouu/POCKET under the BSD 3-Clause license [85]. The customized scripts used in the present study are also available via Zenodo at https://doi.org/10.5281/zenodo.14842123 [86].Any additional information required to reanalyze the data reported in this paper can be provided upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFriedt W, Tu J, Fu T. Academic and economic importance of \u003cem\u003eBrassica napus\u003c/em\u003e rapeseed. 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Elucidation of the Genetic Basis of Seed Fatty Acids in \u003cem\u003eBrassica napus\u003c/em\u003e through Integrative Omics Analysis. \u003cem\u003eZenodo\u003c/em\u003e. https://doi.org/10.5281/zenodo.14842123. 2025\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genome-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gbio","sideBox":"Learn more about [Genome Biology](https://genomebiology.biomedcentral.com/)","snPcode":"13059","submissionUrl":"https://submission.springernature.com/new-submission/13059/3","title":"Genome Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5468888/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5468888/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe fatty acid content represents a crucial quality trait in \u003cem\u003eBrassica napus\u003c/em\u003e or rapeseed. Improvements in fatty acid composition markedly enhance the quality of rapeseed oil.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHere, we perform a genome-wide association study (GWAS) to identify quantitative trait locus (QTLs) associated with fatty acid content. We identify a total of seven stable QTLs, and find two loci, \u003cem\u003eqFA.A08\u003c/em\u003e and \u003cem\u003eqFA.A09.1\u003c/em\u003e, subjected to strong selection pressure. By transcriptome-wide association analysis (TWAS), we characterize 3,295 genes that are significantly correlated with the composition of at least one fatty acid. To elucidate the genetic underpinnings governing fatty acid composition, we then employ a combination of GWAS, TWAS, and dynamic transcriptomic analysis during seed development, along with the POCKET algorithm. We predict six candidate genes that are associated with fatty acid composition. Experimental validation reveals that four genes (\u003cem\u003eBnaA09.PYRD\u003c/em\u003e, \u003cem\u003eBnaA08.PSK1\u003c/em\u003e, \u003cem\u003eBnaA08.SWI3\u003c/em\u003e and \u003cem\u003eBnaC02.LTP15\u003c/em\u003e) positively modulate oleic acid content while negatively impact erucic acid content. Comparative analysis of transcriptome profiles suggests that \u003cem\u003eBnaA09.PYRD\u003c/em\u003e may influence fatty acid composition by regulating energy metabolism during seed development.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study establishes a genetic framework for a better understanding of plant oil biosynthesis in addition to providing theoretical foundation and valuable genetic resources for enhancing fatty acid composition in rapeseed breeding.\u003c/p\u003e","manuscriptTitle":"Integrative omics analysis reveals the genetic basis of fatty acid composition in Brassica napus seeds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-28 04:36:25","doi":"10.21203/rs.3.rs-5468888/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-03-25T12:45:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T09:27:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genome Biology","date":"2025-03-24T16:13:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genome-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gbio","sideBox":"Learn more about [Genome Biology](https://genomebiology.biomedcentral.com/)","snPcode":"13059","submissionUrl":"https://submission.springernature.com/new-submission/13059/3","title":"Genome Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32d45144-cdf0-4d32-967f-e247d124fda1","owner":[],"postedDate":"March 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-07T16:04:23+00:00","versionOfRecord":{"articleIdentity":"rs-5468888","link":"https://doi.org/10.1186/s13059-025-03558-x","journal":{"identity":"genome-biology","isVorOnly":false,"title":"Genome Biology"},"publishedOn":"2025-04-02 15:57:38","publishedOnDateReadable":"April 2nd, 2025"},"versionCreatedAt":"2025-03-28 04:36:25","video":"","vorDoi":"10.1186/s13059-025-03558-x","vorDoiUrl":"https://doi.org/10.1186/s13059-025-03558-x","workflowStages":[]},"version":"v1","identity":"rs-5468888","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5468888","identity":"rs-5468888","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}