Brassica napus L.). To identify resistance genes and genomic regions, genome-wide association studies (GWAS) of 585 diverse winter and spring canola accessions were performed using imputed whole-genome sequence (WGS) and transcriptome genotype-by-sequencing (GBSt). Blackleg disease phenotypes were collected across three years in six trials. GWAS were performed in several ways and their respective power was judged by the number of significant single nucleotide polymorphisms (SNP), the false discovery rate (FDR), and the percentage of SNP that validated in additional field trials in two subsequent years. WGS GWAS with 1,234,708 million SNP detected a larger number of significant SNP, achieved a lower FDR and a higher validation rate than GBSt with 64,072 SNP. A meta-analysis combining survival and average internal infection resulted in lower FDR but also lower validation rates. The meta-analysis GWAS identified 79 genomic regions (674 SNP) conferring potential resistance to L. maculans. While several GWAS signals localised in regions of known Rlm genes, fifty-three new potential resistance regions were detected. Seventeen regions had underlying genes with putative functions related to disease defence or stress response in Arabidopsis thaliana. This study provides insight into the genetic architecture and potential molecular mechanisms underlying canola L. maculans resistance.Blackleg disease causes yield losses in canola ( Brassica napus L., genome AACC, 2n = 38) is one of the most important sources of edible oil providing 14% of global production1. Strong canola growth demand requires critical increases in overall production and productivity, which can in part be achieved through overcoming biotic and abiotic stress. One of the major biotic stressors is blackleg disease, which makes it a high priority trait for canola breeding programs. It is caused by the fungal pathogen Lepthosphaeria maculans resulting in large yield losses, thereby becoming a major threat to world canola production. Most canola growing regions experience losses due to blackleg disease, with Canada and France having reported between 5 and 40% average yield losses annually3. Similar yield reduction is common in Australia, however, in some regions losses as severe as 90% have been observed6. Genetic resistance to blackleg can be either qualitative (race specific) or quantitative (non-race specific) and commercial cultivars tend to have qualitative resistance genes that, when effective, confer complete immunity against blackleg. However, quantitative resistance has the potential to only restrict the development of L. maculans formation throughout plant development stages10.Canola  using RFLP, SSR, AFLP, RAPD or DArT markers for blackleg disease resistance23. In addition, several environmentally stable QTL were identified and localized in A and C sub-genomes conferring resistance to L. maculans26. Moreover, assembling pangenomes has been applied to discover novel genes in various species28. A study by Hurgobin et al.29 pointed out that one of the causes for gene absence or presence in B. napus was homoeologous exchange and it contributed towards resistance gene diversity.Elucidating the genetic basis of blackleg resistance as well as identification and localization of resistance genes/genomic regions to 30. Raman et al.31 used SNP-chip based linkage maps and found a major L. maculans resistance locus on chromosome A07 that accounted for up to 69.2% of genetic variation and was mapped around the major locus Rlm4. While the genetic variance explained with bi-parental populations is often estimated to be high, these loci explain much less of the variation generally observed in more diverse germplasm. Fikere et al.32 demonstrated that a large proportion of genetic variation for blackleg disease resistance in diverse canola population remains unexplained by currently published QTL studies. Nevertheless, bi-parental QTL mapping experiments were an important first step towards increasing the understanding of blackleg disease architecture in canola.In the past decade, SNPs have become the genetic marker of choice over multi-allelic markers due to their abundance in the genome and high polymorphism rate33, maize34, soybean35, and flax37. In canola, several GWAS have been reported for agronomic and quality traits. Few GWAS have been reported on major canola diseases (Leptosphaeria maculans and Sclerotinia stem rot)39. Most recently, Raman et al.31 used a panel of 179 diverse canola lines genotyped with 18,804 SNPs to perform a GWAS that discovered a new L. maculans resistance gene Rlm12 on chromosome A01 as well as additional QTL.The development of advanced sequencing and SNP chip technologies, and the associated drop in genotyping prices, has enabled the use of thousands of SNP markers across the genome. The increased SNP density allows for the application of single SNP regressions or genome-wide association studies (GWAS) in more diverse germplasm. Genome-wide association studies have rapidly increased the discovery of new putative candidate genes for complex traits in a variety of crops such as rice41. This would increase the power to uncover pleiotropic loci affecting multiple traits. The increased power has been demonstrated in several studies in human and the meta-analyses have been applied widely in human and animal genetics as they avoid sharing of original genotypes and phenotypes44.We investigated two main ways to increase the power of GWAS using (1) meta-analysis of single trait GWAS to identify pleiotropic loci and (2) imputed whole-genome sequence (WGS) data. A meta-analysis GWAS combines single trait results from different traits or different environments45, IMPUTE46, BEAGLE47, FImpute48, and Eagle49 + Minimac50, and they vary in computational efficiency and imputation accuracies52. Imputation has been implemented in several crops, a study by Torkamaneh et al.53 achieved high accuracy when imputing from low-coverage genotyping-by-sequencing (GBS) to WGS in soybean. Similarly, Shi et al.54 examined the potential of exome sequence imputation in wheat. These studies emphasized the need to carefully evaluate imputation algorithms in new datasets as large differences in performance exist.The use of WGS variants substantially increases the number of markers and thus increases linkage disequilibrium (LD) between markers and causative mutations. Furthermore, the causative mutations may themselves be genotyped directly. This is proposed to lead to more effective pinpointing of genes or genomic regions linked with complex traits of interest. However, WGS for a large number of individuals is expensive. A more cost-effective approach is to impute WGS into individuals that have been genotyped with a lower density assay. A number of imputation software programs are available, such as MACHHere we report on an imputation analysis from transcriptome GBSt to WGS and GWAS in a relatively large population of spring and winter canola phenotyped across three growing seasons. We impute WGS into 585 canola lines genotyped with GBSt and analyse this dataset with single-trait GWAS with the aim of: (i) comparing the power of GWAS using GBSt and imputed WGS and (ii) performing meta-analysis of GWAS for two blackleg resistance traits  and identify genomic regions potentially harbouring new resistance genes associated with blackleg traits as well as compare its power to single-trait GWAS.In-field phenotypes for 585 diverse canola lines in field trials from 2015 to 2017 were adjusted for spatial variation and best linear unbiased estimates (BLUEs) were calculated per trial and per line  in three years  are shown in Supplementary Fig. We evaluated the imputation accuracy (correlation of observed and imputed genotypes) and concordance rate of WGS in 153 spring and winter type canola lines  threshold of > 0.1, which dropped the final SNP density to 1,234,708  phenotypes across the two sites in 2015 Figs. , and theAS Figs. . Adding -6 Table . In addi-6 Table . The metWe used the 2016 and 2017 datasets to validate and compare the different GWAS approaches. We implemented various validation strategies (VS) and quantified the most significant SNPs at rdcoded V; WGS GWAand Fig. .Figure 5−2 and P < 10−4 in VS4, but a lower validation rate for P < 10−3 and P < 10−5. This shows that meta-analysis improved the overall validation rate, but when specifically looking at highly significant SNP in both the discovery and validation GWAS, it validated a lower proportion of SNPs. Validation strategies 5 (VS5) and 6 (VS6) tested whether it is better to combine data per trait with a multi-location GWAS model (and fitting location as fixed) versus combining locations with a meta-analysis of single location GWAS, respectively. The validation rates observed clearly indicated that combining locations in a full model (VS5) is preferable to simply performing a meta-analysis of single location GWAS (VS6), which is to some extent expected. It is notable that for P < 10−3, the location full GWAS model (VS5) performed better than the blackleg meta-analysis (VS4).Validation strategy 1 (VS1) involved a single-trait GWAS–GBSt dosage discovery set during 2015 at Wickliffe and Green Lake sites with validation in single-trait GWAS–GBSt dosage in 2016 and 2017 trials for EME and AvInf. We found that a smaller number of significant SNPs validated in VS1 versus VS2, which validated the single-trait GWAS–WGS hardcoded discovery set in single-trait GWAS–hardcoded in the same datasets  calculated as:The number of significant SNPs  were detected on C02, and very few significant SNP were observed on C01 and C05  were located on 3685 kb of chromosome A02 followed by ChrA08, ChrC03, and ChrC06 Fig.  and S10.B. napus SNP effect annotations revealed that most significant SNP were intergenic, followed by intronic, synonymous, non-synonymous, stop gained and lost coding functions  putatively involved in resistance to L. maculans. The B. napus genes in the vicinity of these significant SNP were mapped with BLASTn against the A. thaliana genome, which revealed several genes with putative functions related to disease resistance.We have reported GWAS results for seedling emergence and blackleg traits  and we have validated and compared the results from different GWAS strategies across multiple locations and years. We conducted single-trait and multi-trait meta-analysis GWAS using WGS and GBSt in a diverse set of canola lines. The imputation to WGS was reasonably accurate and the imputation pipeline using Eagle + Minimac3 seemed to outperform FImpute. WGS-based GWAS were more powerful than using GBSt as indicated by the number of significant SNPs, the FDR, and the validation rates in additional years of field trial data. Meta-analysis identified more significant SNP than single trait GWAS with a similar FDR, but a smaller percentage of SNP were validated across years from the meta-analysis. GWAS using dosage were more powerful than hardcoded genotypes, where power was judged by the validation rates and FDR. Combining locations with multi-trait GWAS model increased validation rates substantially over combining single-trait GWAS results with a meta-analysis. The blackleg GWAS meta-analysis revealed 79 major genome regions  tend to have fewer errors than crop assemblies. Many crops are polyploid, often with significant homology between the sub-genomes in allopolyploids (e.g. canola) and this brings challenges to infer genotype dosage in autoploids. This adds complexity to inferring assemblies and to mapping reads to unique genome locations, which, in turn, leads to mapping errors and false positive SNPs. We have performed stringent quality control in our dataset in an attempt to reduce the number of erroneous SNPs and genotypes. Nevertheless, a large proportion of our imputed SNPs achieved only a low Minimac Rsq, which seems to indicate a reasonably large false positive SNP or genotyping error rate. Imputation accuracy could possibly be further improved with an improved B. napus genome assembly, a denser GBSt dataset coupled with greater fold coverage, a greater focus on sub-genome specific SNP, a larger WGS reference set, and more stringent quality control.The imputation of WGS into diverse reference population lines enabled an increase in the sample size for the GWAS. We have shown that the accuracy of imputation was > 0.71. This level of accuracy is similar to what has been previously achieved in other crops57. Indeed, we found that dosage genotypes detected more significant SNPs with a lower FDR and with higher validation rates than hardcoded genotypes indicating that they improved the GWAS power. The improved power is thought to come from the modelling of uncertainty in the imputed genotype. If there is a lot of uncertainty the genotype will be closer to the heterozygote genotype, thereby reducing their influence in the analysis and potentially leading to fewer false positive associations.We have mitigated the effect of SNP and genotype uncertainty in two ways. First, we have imposed a Minimac Rsqr threshold of 0.1, which corresponds to an approximate empirical imputation accuracy of 0.4. Using only SNP exceeding this threshold is helpful in reducing the false positive rate and reducing the multiple testing problem by only interrogating better imputed SNPs. Secondly, we have investigated using both hardcoded  and dosage genotypes in our GWAS analysis to evaluate its effect on power. Dosage imputed WGS genotypes were found to be better at pinpointing causal mutations in dairy cattleA. thaliana59; rice60 and in crop species in general, see a review by Edwards and Batley61. In our study, imputation to WGS accomplished two goals. Firstly, it increased the number of SNPs and therefore increased the linkage disequilibrium between markers to increase their association with causal loci. Secondly, WGS imputation augmented the dataset with intergenic markers, which can harbor regulatory variants. The increase in power has led to additional discoveries of regions involved in blackleg disease resistance. This initial resource of WGS individuals can be expanded and improved to make future GWAS more effective. Additionally, it remains to be tested whether WGS will improve genomic prediction.The largest increase in GWAS power was observed from using WGS imputed sequences rather than sequences obtained by GBSt alone. This phenomenon has been demonstrated in a number of species, including 42. An increase in power was observed previously from meta-analyses, whose aim is to discover loci that affect multiple traits64. Similarly, the advantages of large-scale meta-analysis for genetic mapping in plant were reviewed in several studies66 and suggested that meta-analysis provides prospects for the identification of genes relevant to trait improvement. In our case combining internal infection and survival from the 2015 trials in a meta-analysis substantially increased the number of significant SNPs and reduced the FDR. Validating the meta-analysis SNPs in the internal infection GWAS collected from the 2016 and 2017 trials revealed a higher validation rate for higher p-value levels or a lower validation rate for lower p-values compared to single trait GWAS  to the 2015 trials. Overall, this, along with the smaller samples size of later trials, would have depressed the validation rates. However, as all GWAS strategies were assessed on the same datasets the comparisons are expected to be fair. If the original phenotype and genotype data are available, it is preferable to fit an appropriate within trait full model with a fixed effect of location to combine data rather than meta-analysis. In our case, the full model was more successful at validation as p values became more stringent, indicating a lower rate of false positives. Future work could investigate a multi-trait GWAS to a multi-trait meta-analysis of single trait GWAS.Blackleg fungal populations are highly dynamic and multiple populations exist in the Victorian environment66 and rice67 , and using different lines from independent nursery, as shown in bread wheat68. Alternatively, a cross-validation approach could be used for validation69. Furthermore linkage mapping approaches70 in bi-parental populations have been used to validate significant markers identified in GWAS hits in canola and wheat.There are additional ways to validate SNPs identified in discovery sets, for example using partially known lines from the previous year datasets, as was shown in maizeL. maculans using primarily bi-parental populations and found many such regions are clustered on chromosome A07 and A10, see two reviews72. In addition, single-trait GWAS have, to date, identified > 334 putative loci for blackleg (L. maculans) disease resistance73.Previous studies have identified major genes and genomic regions conferring resistance to 74 determined in the same population that LD dropped to 0.2 at approximately 200 kb distance between SNPs, a level which has been confirmed in other studies75. This resulted in a total of 79 potential genomic regions across the A and C sub-genomes, of which 52 were new. While the remaining 27 of these putative regions were near previously published Rlm genes and quantitative QTL, further follow-up is required to confirm that our signals are indeed for known Rlm genes. Without a physical location for many published QTL it is often difficult to confirm whether our significant SNPs overlapped with previous linkage study results. When comparing cM from linkage groups and our physical positions our estimates of overlap were only approximations.We have arbitrarily defined significant genomic regions as separate if the distance between SNP was more than 200 kb. The size of region capturing potentially the same causative mutation is directly related to the amount of linkage disequilibrium (LD) in our population. Malmberg et al.31. On A02, all four regions overlapped with previous results and included the LepR1 resistance gene25. Not surprisingly, these regions included many of our most significant SNP, hence other studies have also found them. Chromosome A03 harboured eight potential regions of which only one had been previously identified25. Several of the new regions contained only one or two SNP, that meet the significance threshold, indicating that the signal in for these regions was less strong. Seven regions were found on A04 with one previously published25. One region on A05 confirmed a previous report25 and four were new. Our GWAS detected LepR4 and other previously published QTL76 as well as two new regions on A06. All seven regions on A07 overlapped with published resistance genes  as well as quantitative QTL23. Five regions were found on A08, of which one overlapped with Rlm5 and several QTL77, and four regions were new. One region of A09 overlapped with several QTL25 and three were novel. Two significant regions on A10 collocated with Rlm2, LepR3, BLMR1, BLMR2, and LepR276. Our observed signals in these two regions are likely associated to quantitative resistance rather than known major resistance genes, because Rlm2/LepR3 and LepR2 are defeated genes in the Australian context.Four regions were identified on A01 of which one is near the Rlm12 region and potentially other QTL23. No significant SNP were found on C02. Two and seven new regions were detected on C03 and C04, respectively, where one region on C04 was published25. C05 harboured one new region and nine new regions were found on C06 (one new region contained 80 significant SNP), of which one was previously discovered23. Rlm6, which was introgressed from B. juncea and is known to be present in Australian germplasm, was confirmed on C07 along with three new regions. Two regions were discovered on C0825, where one was new. Finally, C09 contained a new putative blackleg resistance region.A substantial number of regions potentially harbouring blackleg disease resistance were found on the C sub-genome. Chromosome C01 contained one new and one previously known region78. Intergenic variants may have potential roles in transcriptional or translational regulation. The annotation of regulatory regions is complex and requires techniques that provide information outside of genic regions. Some examples of such assays are MNase, ChIP sequencing and HiC that provide information on open and closed chromatin regions and DNA folding38.Assembly annotation is a crucial component to understanding and interpreting GWAS results. Many of our GWAS regions fell into the intergenic class, this is similar to findings in other species including mammals. It has also been observed in plants and algaeA. thaliana with previously known L. maculans resistant canola genes and found that a gene near one of our significant SNP and overlapping with Rlm249 had high homology with the AT1G01170.2 gene localized in A. thaliana Chr1-74105–74443, which is thought to have a role in stress responsive conditions. As Rlm2 is thought to be defeated in Australia, we speculate that our signal captured other resistance loci in the same region. Similarly, a gene near the locus for the B. juncea-introgressed Rlm577 blackleg resistant gene aligned to the AT1G10090.1 gene in Chr1 of A. thaliana. This gene is thought to be involved in stress responsive dehydration. The Victorian pre-breeding lines in our dataset do contain B. juncea introgressions. Moreover, we showed that candidate resistance genes were mapped in the vicinity of known R genes in B. napus genome and A. thaliana genes responsible for various stressors. This has demonstrated the importance of additional genomic resources in Brassica species such as TAIR (https://www.arabidopsis.org/) and Genoscope (https://www.genoscope.cns.fr/brassicanapus/) to enhance the discovery of new genes using related model plants. Several studies in other species confirm the benefits of BLAST to align putative candidate genes detected in GWAS analysis in wild wheat, tree species79 and canola31; B. juncea80; B. oleracea81. Overall, understanding the nature of genetic inheritance of a trait and genomic regions underlying association with blackleg traits will provide a new insight into the genetic architecture of the trait and accelerates the development of resistant cultivars.Due to the significant homology of genes, information can be shared across species. We further compared genomic regions detected in 32 (Supplementary Table 2 plots. The following phenotypes were recorded from the disease nursery: emergence count (number of plants emerged 6 weeks after sowing), blackleg traits such as adult plant survival rate and average internal infection of the stem at maturity. The agronomic trials were protected from blackleg disease twice, by using fungicide at sowing (impact-in-furrow) and a treatment at the 6–8 leaf stage with Prosaro 420 SC foliar fungicide. Thus, the blackleg management protocol was different in years 2016 and 2017, and these trials were only used for validation purposes. To determine the internal infection of the stem, we employed a modified method of the “Blackleg Canker Test” used in the Australian National Variety Trials . Briefly, in the 2015 trial, a total of 20 plants per row were sampled from a minimum of 7 randomly chosen positions . For the 2016 and 2017 trials, 5 consecutive plants in a middle row in each plot were sampled to generally survey for any level of blackleg in these fungicide protected trials. The sampled plants were then cut with secateurs at the crown and the cross section of the stem was examined. The area of the stem infected by L. maculans was recorded as a percentage of affected area of the total stem cross-section, as described in the Spring Blackleg Management Guide82. Plot values were used in analyses and, for average internal infection, this was the mean value of multiple stems in each plot.We used data from 585 canola lines (391 winter and 194 spring types) grown at Wickliffe  and Green Lake , Victoria, Australia during 2015 growing season, previously described in Fikere et al.83, as described in84. The model was as follows:85.Phenotypes were spatially adjusted with autocorrelation error (i.e. AR1 x AR1) models for field condition variability to generate Best Linear Unbiased Estimates (BLUEs) using ASReml86. Briefly, mRNA was extracted from leaf tissue (Dynabeads: Life Technologies) and used for library preparation for RNA sequencing (SureSelect: Agilent Technologies). Circa 3 million reads were generated per sample using either an Illumina HiSeq3000 or a NextSeq500. The resulting sequence data was adaptor and quality trimmed before aligning to the Darmor-bzh whole genome reference87 using the Tophat2 algorithm88 and SNP were identified with SAMtools mpileup89.A total of 585 Spring and Winter type canola lines were genotyped using the protocol described in Malmberg et al.74 were re-analyzed. The sequencing protocols were fully described in86. Briefly, whole genome libraries were generated for all samples and sequenced on an Illumina HiSeq3000 aiming for 10 × read depth coverage per sample. The resulting sequence data was adaptor and quality trimmed before aligning to the Darmor-bzh whole genome reference87 using the BWA mem algorithm90, and SNP were identified with SAMtools mpileup89.Whole genome sequence data from 153 samples covering the diversity of the 585 GBSt lines described inWe implemented stringent quality control thresholds on 6 million WGS variants based on minor allele frequency (MAF > 0.1), missing rate per SNPs and missing rate per sample (< 50%), genotype read depth (DP > 5), heterozygosity rate per SNP (Hetero > 0.4) followed by imputation accuracy from Minimac R-square (Rsqr > 0.1). Finally, genotypes were checked for SNP duplications and any duplicates were removed keeping one of the set in the refined data. The final dataset was checked for their MAF > 0.01, giving a total of 153 lines and 1,234,708 million SNPs for subsequent analysis.48 and Eagle V2.349 followed by Minimac3 V2.0.150 in the set of 153 whole-genome sequenced lines via fivefold cross validation. Locations not present in the GBSt assay were masked in validation lines and imputed from the WGS data. The accuracy of imputation was assessed as the correlation and concordance of imputed and observed sequence genotypes. Additionally, we investigated whether it is beneficial to combine winter and spring lines for imputation or consider them separately. To do so, imputation was conducted within spring lines, within winter lines separately and using spring lines as a validation and winter lines as reference set and vice versa in a tenfold cross validation. After this evaluation, the Eagle2.3 Minimac3 pipeline was used for imputing the 585 lines from GBSt density to WGS using the entire set of 153 lines as the reference set. Minimac3 provides genotypes in full dosage format  and in hardcoded genotype format  for the imputed sequence variants. We investigated the effect of these formats on the power of GWAS.We assessed the imputation accuracy of two imputation pipelines: FImpute , g  to perform GWAS42. A k × 1 vector of the signed t-values of SNPi effects, i.e., beta/se, for the k traits; ti′ was the transpose ti (1 × k); and V−1 was the inverse of the k × k correlation matrix, where the correlations were calculated for all signed t-values per pair of traits. The k degrees of freedom. False discovery rates were calculated as The meta-analysis used the SNP effects from single-trait GWAS, as described in Bolormaa et al.VS1) single-GBSt-to-single-GBSt: single-trait GWAS 2015 in dosage GBSt in discovery set and single-trait GWAS in GBSt dosage validation in 2016 and 2017; (VS2) single-012-to-single-012: WGS single-trait GWAS 2015 in 012 (integer) in a discovery set and WGS single-trait GWAS in 012 (integer) validation in 2016 and 2017; (VS3) single-to-single: WGS single-trait dosage GWAS 2015 in discovery set and WGS single-trait dosage GWAS validation in 2016 and 2017; (VS4) meta-to-single: WGS meta-analysis GWAS of blackleg traits in 2015 and validation in WGS single trait GWAS in 2016 and 2017; (VS5) CombModel-to-single: WGS combined model sites per traits GWAS in 2015 and validation in WGS single trait GWAS in 2016 and 2017; (VS6) meta-AvInt-to-singleAvInt: WGS meta-analysis for AvInt GWAS in 2015 and validation in WGS single-trait AvInt GWAS in 2016 and 2017. Significance thresholds were chosen based on false discovery rates.GWAS were analysed in several different ways. Our aim was to validate which method resulted in the most power. A priori it is difficult to determine which method is more powerful. One way to judge the utility of an approach, aside from FDR, is how many of the significant SNP remain significant in a new dataset. In this study, we used emergence and internal infection values from the four agronomical field trials in two additional seasons (2016 and 2017) for validation. We performed the following validation strategies (VS): . Gene sequences within 30 kb up or downstream of significant SNPs detected in meta-analysis at the 10−5 p-value were included in the BLASTn analysis. BLAST matches to multiple loci with the same top identity metrics were removed. Prediction of functional variant annotation of an individual SNPs was performed using SnpEff92 using the B. napus Darmor-bzh genome annotation file v5 (gff3) and the whole genome reference sequence87. Details of the annotation procedures is provided in Malmberg et al.74. Finally, potential genes from the meta-analysis were used to define the regions of interest that contain potential candidate genes.In addition, to gain an understanding of the underlying gene functions, we used BLASTn  analysis against the Supplementary file 1.Supplementary file 2.Supplementary file 3.


The following results were obtained through a series of analyses, including additive effects and gene function annotation. Two significant SNPs were found in introns 12 and 13 of copine 4 (CPNE4) on chromosome 2. The wild-type and mutant individuals had significant differences in glycogen metabolism at two loci (p < 0.01 for both). Individuals carrying two mutations had increased muscle glycogen content. According to the gene annotation of chromosome 11, there is a significant INDEL in intron 6 of naked cuticle homolog 1 (NKD1). After the INDEL mutation, the glycogen content increased significantly. There was a significant difference between wild-type and mutant individuals (p < 0.01). These mutations likely affecting two genes (CPNE4 and NKD1) may affect glycogen storage in a pleiotropic manner. Gene annotation indicates that CPNE4 and NKD1 may affect the process of glucose metabolism. Our findings contribute to understanding the genetic regulation of muscle glycogen metabolism and provide theoretical support.Glucose metabolism plays an important role in many normal and pathological physiological processes in the body. The breakdown and synthesis of muscle glycogen provides ATP for muscle activities. A genome-wide association study for muscle glycogen was performed in 474 Jingxing yellow chickens to identify significant single nucleotide polymorphisms (SNPs) and insertions and deletions (INDELs) involved in muscle glycogen metabolism. A total of nine SNPs ( Muscle glycogen is the main source of glucose for muscle glycolysis. Glycolysis is the most primitive metabolic pathway for producing energy in living organisms. Study of muscle glycogen function and influencing factors will reveal the genetic mechanism of glucose metabolism and lead to its application in future molecular breeding research.Glycogen is the main form of glucose storage in the body, mainly in the form of liver and muscle glycogen. Glycogen is a branched polysaccharide found in the form of granules in the cytosol of the cytoplasm, and is connected by many glucosides. The blood glucose concentration is maintained at a relatively constant level through the synthesis and breakdown of liver glycogen. Numerous studies have suggested that the main function of muscle glycogen synthesis and breakdown is to provide the muscle with energy in the form of ATP. However, under some special physiological conditions, such as intense exercise, muscle glycogen plays an important role in maintaining blood sugar stability and regulating the balance of glucose metabolism . In the h2 = 0.43), as well as a very strong negative genetic correlation (rg) with pH (rg –0.97) of the final meat, indicating a common genetic control of GP and pH value [Glycolysis not only plays an important role in numerous pathological physiological processes in the body, but also has an impact on the flavor quality of meat. It also provides useful molecular markers for the breeding of high-quality broiler chicken breeds. Genetic research on poultry meat quality traits started late. Two studies conducted under the same experimental conditions and on the same broiler production line have shown that the pH value, color, and water retention capacity of meat have moderately high levels of heritability (from 0.35 to 0.57) . In addipH value . A studypH value . Whole-genome sequencing is a rapid and sensitive technology used to not only identify single nucleotide polymorphisms (SNPs) and candidate genes, but also shorten the cycle of molecular breeding ,13. PHKG, an open reading frame change generating a premature stop codon, which led to a 43% increase in GP and a 20% reduction in the moisture of pork [Research on multiple traits in multiple species using genome-wide association studies (GWAS) is being conducted ,15. The  of pork . The abo of pork . In poulGWAS is widely used to analyze the genetic structure of growth, reproduction, and meat quality traits of various poultry. There are no reports about association analyses of poultry glycogen traits, and the analysis data coverage of most other species using microarrays is limited. In this study, we performed GWAS analysis of common SNPs and INDELs by whole-genome sequencing of a chicken line to obtain candidate genes that significantly affect the glycolysis process. It is anticipated to apply the identified molecular markers for the molecular breeding of poultry.The work was approved by the Animal Management Committee of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences . Ethical approval for this experiment was obtained from the IAS-CAAS Animal Ethics Committee .The experimental animals were Jingxing yellow chicken. Experimental animals were selected for the laboratory-specific intramuscular fat selection line, which had been bred for 16 generations. A total of 474 chickens were selected for this trial, of which 217 were chickens in the intramuscular fat selection line and 256 chickens in the control line . All individuals were 98 days of age and allowed to eat and drink ad libitum. Blood samples were collected the day before slaughter by standard venipuncture into a tube with anticoagulant and stored at –20 °C for subsequent genomic DNA extraction. The breast muscle tissue samples from each chicken were stored at –80 °C for subsequent measurements of glycogen content.A sample of 0.1 g of pectoral muscle tissue was used for measurement. Glycogen is stable in concentrated alkali solutions. The tissue was placed into 300 µL concentrated alkali solution for 20 min before color development to destroy other components and preserve glycogen. Glycogen was then dehydrated with concentrated sulfuric acid to form an aldehyde derivative, which was reacted with anthrone to form a blue compound, which was colorimetrically quantified using a standard glucose solution treated in the same way, and the glycogen content was ultimately calculated. Blood collected from veins of the entire population was extracted using the standard phenol/chloroform extraction method. The quantity and quality of the DNA extracted from whole blood samples were determined using a Nanodrop ND-1000 Spectrophotometer  to measure their concentration and integrity, as well as by agarose gel electrophoresis to visually assess DNA integrity. Samples that passed the quality test were sent to the company for 10G whole-genome sequencing. A total of 474 adult females Jingxing yellow chickens (14 weeks of age) were selected to perform the GWAS. Data were first filled with the Beagle v5.0 software ,18 for ahttps://bigd.big.ac.cn/gsa/.After the examinations, paired-end libraries were generated for each eligible sample using standard procedures. The average insert size was 300–500 bp, and the average read length was PE150 bp (150 bp double-ended). All libraries were separately sequenced on a Novaseq 6000 sequencer  to an average raw read sequence coverage of ×10 for the natural populations. Accession data code is CRA002643 at http://bio-bwa.sourceforge.net), and then Picard-tools(version 1.119) (https://broadinstitute.github.io/picard/) and Sam-tools (version 1.9) (http://samtools.sourceforge.net/) were used to obtain the sorted BAM files. Before detecting mutations, the basic quality score recalibration (BQSR) had to be recalibrated. The BQSR involves two steps. In the first step, Base Recalibrator, the Picard tool and stool were used to obtain the classified BAM file for analysis. In the second step, Apply BQSR, the calibration table file obtained in the first step was used to readjust the basic quality value in the original BAM file; this new quality value was then re-exported to a new BAM file. GATK (version 4.0.2.1) selects variants and GATK variants for filtering [The filtered pure reads were compared with the reference genome (RefSeq GCF_000002315.4) using the MEM mode of the BWA software (version 0.7.12)  v0.94 software, which can perform genomic localization of traits based on population SNP or INDEL information using an efficient exact mixed model approach, was implemented with the valid individuals and SNPs or insertions and deletions (INDELs) for univariate analysis. The method makes exact GWAS computationally feasible for large sample sizes. Independent SNPs or INDELs were used to compute the centered relatedness matrix, and the significance software , and thehttps://www.r-project.org/). In this analysis, the first and second principal components were used as covariates, and the affinity matrix  was established by using unlinked SNPs or INDELs. Individual SNPs or INDELs were solved one by one. Additionally, the significance of the SNP or INDEL was determined by the likelihood ratio test (LRT). The genomic inflation factor was calculated with R  accessed in May 2019. The annotated genes that were the nearest or harboring significant SNPs were identified as candidate genes in which significant loci were located . Genes iThe maximum and minimum glycogen traits of the 474 individuals were 8.92 mg/g and 0.23 mg/g, respectively, and the average was 2.37 mg/g, with a coefficient of variation of 56%. In the GWAS analysis, population stratification effects may lead to false positive results . As our p-value correction was first performed using the PLINKv1.9 software [p-value. The D’ was set to 0.8, the maximum chain length was 500 Kb, and a total of 699,341 independent linkage disequilibrium (LD) blocks were obtained. In this study, for the Q-Q plot presented as software  for blocCPNE4 gene. The chromosome 3 rs13720604 has three genes in this region. The mutation of TTC7A mainly acts on the gastrointestinal tract. The TTC7A protein is part of a complex that localizes phosphatidylinositol 4-kinase (PI4K) to the plasma membranes. SOCS5 is a cytokine signaling inhibitor that acts on IGF-1 receptor signaling and EGF signaling. There are three SNPs on chromosome 4 that reached a significant level. GRIA3 is a glutamate receptor that acts as a ligand-gated ion channel in the central nervous system and plays an important role in excitatory synaptic transmission. MFAP3L protein may participate in the nuclear signaling of EGFR and MAPK1/ERK2 pathway. Chloride voltage-gated channel 3 (CLCN3) is a protein coding gene which is associated with cystic fibrosis. Among its related pathways are the activation of cAMP-dependent PKA and the transport of glucose and other sugars, bile salts and organic acids, metal ions, and amine compounds. There are two genes associated with the chromosome 11 locus. CYLD encodes a cytoplasmic protein with three conserved cytoskeletal-associated protein-glycine (CAP-GLY) domains that functions as a deubiquitinating enzyme. Gene Ontology (GO) annotations associated with the other gene on this locus, SNX20, include phosphatidylinositol binding.In the GWAS analysis of muscle glycogen, nine SNPs reached a significant level of potential association , annotatBased on the SNP sites typing results of the whole genome sequencing data, the average genotype frequency and phenotype of nine SNPs were calculated . The datThe additive effect of alleles is the average of the two homozygous dispersions. According to the cumulative effect, the glycogen content of the rs314624646, rs13720604, and rs740265511 mutations decreased, and the glycogen content of the remaining six SNPs increased. In stepwise conditional analysis, important SNP genotypes were added to the univariate model to elucidate independent signals, and significant differences were found between the nine SNP wild-type and mutant individuals .p < 1.32 × 10−6)  is among the activated target genes of NKD1. At 29.7 Kb upstream of chromosome 3, 27425548 is the FOSL2 gene, which, as a dimer with JUN, activates LIF transcription and CEBPB transcription in PGE2-activated osteoblasts. CEBPB plays a major role in adipogenesis and gluconeogenesis. There is no gene in the region of chromosome 1 7169549 plus or minus 100 Kb.In this study, for the Q-Q plot presented as  × 10−6) , which w × 10−6) . Genes sThe same method as that used for the calculation of SNPs in The same additive effect found that the three INDELs resulted in varying degrees of glycogen content. In stepwise conditional analysis, important INDEL types were added to the univariate model to identify independent signals, and significant differences were found between the three INDELs wild-type and mutant individuals .Muscle glycogen and glucose in the glucose metabolism cycle are the main compounds that maintain normal metabolism and function in skeletal muscle cells. Glycogen also affects meat quality through its GP . The Efficient Mixed-Model Association eXpedited (EMMAX) test and the Genome Association and Prediction Integrated Tool (GAPIT) both adopt the strategy of unchanged variance components in the fixed zero model to improve the operation speed. This is actually an approximate algorithm which is not as accurate as GEMMA. GEMMA can directly use the plink binary format data without complex data format conversion. It is more comprehensive and can perform single-label GWAS, multilabel GWAS and multitrait GWAS analysis.INDEL marker refers to the insertion or deletion of nucleotide fragments of different sizes at the same site in the genome between different individuals of the same species or between different individuals of different species . The disThis trial aims to identify relevant SNP or INDEL sites and genes that affect chicken muscle glycogen content. Although many studies have attempted to identify the genetic determinants of chicken meat traits, most of them focused on the use of chips to study the visual traits of the slaughtered animal, and to date, there has been no related research on glucose metabolism indicators, such as muscle glycogen.CPNE4, which belongs to the highly-conserved copine family. It encodes a calcium-dependent, phospholipid-binding protein which is significantly expressed in cancer tissues [CPNE4 affecting glycogen content. The two SNP mutations in chromosome 2 have a glycogen-increasing effect; the SNP is located on the intronic region of the gene, and may have an enhancing effect by regulating the promoter. However, there are fewer individuals with mutations, which may be due to the small size of the population sample, and a larger sample and further research are needed in the future. CPNE4 can be considered as a candidate gene that affects muscle glycogen of chicken muscle, and provides a reference for marker-assisted selection of Jingxing yellow chicken. However, the related mechanism needs further verification.According to the association analysis of data binding phenotypes of the 474 individuals using whole-genome sequencing, two SNP sites on chromosome 2 are on the intronic region of the gene  tissues . To dateGRIA3) gene. It has been reported that after astrocyte uptake of glutamate, the glutamate transporter initiates the glycolysis process [GRIA3 is involved in muscle glycogen synthesis and affects muscle glycogen content and changes in its content. The additive effect showed that the phenotype increased after mutation at this site; the mean values of the phenotypes of the wild-type and mutant individuals were significantly different. At 32.8 Kb upstream of the rs315075611 is the gene CLCN3, which encodes a member of the voltage-gated chloride channel (ClC) family. This ClC protein is required for lysophosphatidic acid (LPA)-activated Cl-current activity and fibroblast-myofibroblast differentiation. Indeed, the identification of receptors in lysophospholipid cells revealed that LPA is a transcellular PPAR gamma (PPARG) agonist [The site at 62.1 Kb upstream of the rs733544036 on chromosome 4 is located adjacent to the glutamate ionotropic receptor AMPA type subunit 3  has been reported [FOSL2, thereby influencing the expression of lipid metabolism genes, and ultimately, the glycogen content.Close to the chromosome three INDEL is the gene reported . We specA total of nine SNPs and three INDELs reached a significant level, and the SNP and INDEL genotypes were added to the univariate model to identify independent signals in a stepwise conditional analysis, which revealed nine SNPs and three INDELs wild type and mutations. Significant differences existed between individuals, and both site mutations and insertional deletions resulted in significant changes in glycogen content. Additive effects and other analyses showed that the loci identified by our analysis are valid and can be used for subsequent further verification, and also provide a relevant theoretical basis for related research.CPNE4 and NKD1 may affect the muscle glycogen metabolism process in chickens.In view of the described GWAS analysis, the relevant loci, and the identification of genes near each locus, we speculate that CPNE4 and NKD1, may be important candidate genes involved in the regulation of muscle glycogen content by influencing glucose metabolism. Our findings provide new insights into the genetic mechanism of muscle glycogen metabolism.This study comprised a GWAS analysis of SNP and INDEL associated with chicken glycogen traits, revealing genes associated with muscle glycogen content. The identified glycogen content-associated genes,


Brassica napus L.) accessions. However, breeding of new B. napus cultivars tolerance to Pb stress has been restricted by limited knowledge on molecular mechanisms involved in Pb tolerance. This work was carried out to identify genetic loci related to Pb tolerance during seedling establishment in rapeseed.Lead (Pb) pollution in soil has become one of the major environmental threats to plant growth and human health. Safe utilization of Pb contaminated soil by phytoremediation require Pb-tolerant rapeseed  under 0 or 100 mg/L Pb stress condition, shown an extensive variation in 472 worldwide-collected rapeseed accessions. Based on the criterion of relative RL > 80%, six Pb-tolerant genotypes were selected. Four quantitative trait loci (QTLs) associated with Pb tolerance were identified by Genome-wide association study. The expression level of nine promising candidate genes, including B. napus. The findings can provide valuable genetic resources for the breeding of Pb-tolerant B. napus cultivars and understanding of Pb tolerance mechanism in Brassica species.To our knowledge, this is the first study on Pb-tolerant germplasms and genomic loci in  Lead (Pb) pollution in soil, from anthropogenic activities such as burning of fossil fuels, mining, discharge of untreated industrial wastes and effluents, and unreasonable disposal of lead batteries, has become a worldwide environmental issue , 2. Pb iBrassica napus L.), an ideal plant for phytoremediation, is an important source of edible vegetable oil, vegetable, animal fodder, green manure and biodiesel [B. napus germplasm has been investigated [Rapeseed , AtPSAE1 (Photosystem I reaction center subunit IV A) and several ABC (ATP-binding cassette) transporter genes  have been identified as being involved in tolerance to Pb stress [HvCBT1 (CaM binding transporter) in barley, AtCNGC1 (cyclic nucleotide-gated ion channel) in Arabidopsis and NtCBP4 in tobacco, as one of the nonselective entry pathways used by Pb [B. napus [Unlike in other plants, few data is available on molecular mechanisms involved in Pb tolerance in rapeseed. In Arabidopsis b stress –27. Preved by Pb –31. For ed by Pb –39. GWASB. napus , 40–43.B. napus. The findings can provide valuable genetic resources for breeding of Pb-tolerant cultivars and understanding of the molecular mechanisms responding to Pb stress in Brassica species.The objectives of this study were screening elite germplasms tolerance to Pb stress at seedling establishment stage among 472 worldwide-collected rapeseed accessions and identification of QTLs and candidate genes related to Pb tolerance by GWAS for the first time in B. napus genotypes, the radicle lengths (RL) of 472 accessions grown under 0 or 100 mg/L Pb stress condition for seven days were compared. Although the RL varied significantly among all the accessions under both normal and Pb stress conditions (with a range from 31.15 to 130.50 mm (mm), and 8.67 to 80.60 mm, respectively), the RL of all accessions under Pb stress condition were shorter than that under normal condition  was employed to evaluate the tolerance to Pb stress of eviously , 45. We To select stable Pb-tolerant genotypes for potentially used in phytoremediation or new cultivar breeding, we performed correlation analyses, and found that the RRLs of three replications were significantly correlated with each other with a correlation coefficient value over 0.85 Fig. b. Based p) from Q + K model provided the best fit with the expected distribution , principal component analysis (P), kinship (K), Q + K and P + K models were tested. As shown in quantile-quantile plots (Q-Q) plot, the distribution of observed −log10(p) > 4.3) and three moderately associated SNPs (3.5 < −log10(p) < 4.3) located on chromosome A09, C03 and C04 were detected   contained six SNPs, with a peak SNP Bn-scaff_16614_1-p721297 which gave a 5.61% contribution to the phenotypic variance  analyses indicated that these nine associated signals were located in four QTLs. QTL Pb-A09, Pb-C03–1, Pb-C03–2 and Pb-C04, respectively) were annotated by nucleic acid basic local alignment search tool (BLASTN) with A. thaliana genome and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The top 20 enriched metabolic pathways were shown in Additional file BnaA09g14510D, BnaA09g14520D and BnaA09g14540D, enriched in glutathione metabolism pathway, and three genes, BnaC03g68440D, BnaC03g68450D, and BnaC03g68460D, enriched in the biosynthesis pathway of pantothenate and CoA, as well as in the biosynthesis degradation pathways of valine, leucine and isoleucine were selected for further analyses , AtTBR (Trichome birefringence) and AtHIPP01  respectively, were also selected for further analyses. All these nine candidate genes may contribute to Pb tolerance in B. napus by regulating glutathione metabolism, cell wall development, ubiquitination and amino acid metabolism, respectively  assay. We observed that the expression level of BnaC03g68440D, BnaC03g68450D and BnaC03g68460D located in QTL Pb-C03–2 were enriched in the same pathways. We found that BnaC03g68440D and BnaC03g68450D were significantly induced by Pb stress in III-229  contributed to HMs tolerance mainly by playing important roles in the cellular antioxidant defense mechanisms and serving as non-enzymatic carriers for intracellular transport , 56. We eviously . Therefoediation .Pb-A03–1, BnaC03g02630D is homologous with AtUBP13  gene, was also identified in QTL Pb-A03–1  contributes to the synthesis and deposition of secondary wall cellulose, and helps to maintain the esterification of pectin , 64. It of TBR ATG06700 gePb-C03–2, BnaC03g68440D, BnaC03g68450D, and BnaC03g68460D, enriched in the biosynthesis pathway of pantothenate and CoA, as well as in the biosynthesis degradation pathways of valine, leucine and isoleucine , which catalyzes both the last anabolic step and the first catabolic step of branched-chain-amino-acids  metabolism, can mediate HMs tolerance in plant –71. In QArabidopsis, the AtHIPP20, AtHIPP22, AtHIPP26 and AtHIPP27 genes were involved in Cd detoxification [BnaC04g16200D, the homolog of AtHIPP01, was significantly up-regulated in Pb-tolerant genotype III-229 and down-regulated in Pb-sensitive genotype 6024–1 under Pb stress , have important functions in plant responses to both biotic and abiotic stresses , 73. In fication , 75. We B. napus .B. napus. We found that Pb tolerance shown an extensive variation in 472 worldwide-collected rapeseed accessions. Based on the criterion of relative RL > 80%, six Pb-tolerant genotypes were selected. Four QTLs associated with Pb tolerance were identified by GWAS. Nine promising candidate genes, including GSTUs, BCATs, UBP13, TBR and HIPP01, located in these four QTL regions were selected. The expression level of these nine genes were significantly higher or induced by Pb in Pb-tolerant accessions in comparison to Pb-sensitive accessions. These findings can provide valuable genetic resources for the breeding of Pb-tolerant B. napus cultivar and understanding of Pb tolerance mechanism in Brassica species.To our knowledge, this is the first study on Pb-tolerant germplasms and genomic loci in B. napus accessions  with less temperature fluctuation (±0.3 °C).For decreasing experiment error, the 472 germplasms were split into several sets (about 40 accessions per set). In each set, seeds of these accessions were sterilized with 70% ethyl alcohol for 5 min, then rinsed at least three times with distilled water. Per treatment, fifty seeds of each accession were sown in petri dishes with four layers of filter paper soaked in 20 ml deionized water supplemented with 0 or 100 mg/L Pb. Seeds were kept in dark for two days at 23 °C with a relative humidity of 60–70%, then in 16 h light/8 h dark photoperiod with a light intensity of 300 μmol mThe RL of seven-day-old seedlings were measured with a ruler. The RRL were calculated based on the RL under control (RL_CK) and Pb stress (RL_Pb) condition with the formula RRL (%) = (RL_Pb / RL_CK) × 100. The RRL_Means of each accession were calculated by RRL values of three replications. Accessions with higher RRL were genotypes more tolerant to Pb stress. The distributions of RL and RRL were plotted using ggplot package in R software .B. napus genome sequences [B. napus genome sequences were considered. Furthermore, blast matches to multiple loci with the same E-value were excluded. At finally, a total of 19,945 high-quality SNP markers were used for following analysis.Genotypic data of Single-nucleotide polymorphism (SNP) had been implemented with 60 K Brassica Infinium® SNP array in previous reports , 77. In equences . Only thThe principal component analysis (P), population structure (Q) and kinship (K) matrix of the GWAS population were estimated by GCTA tool, STRUCTURE 2.3.4, and TASSEL 4.0, respectively as described in our previous studies , 77. TASp) = 4.3 . Besides, to avoid ignoring the effects of minor loci, SNPs passing the threshold of 3.5 were also selected for subsequent analysis as described previously [Q-Q plots and Manhattan plots were constructed using qqman package in R software . To redueviously . The dif2) > 0.4 between the peak SNPs and surrounding SNPs using the LDheatmap package in R software. To further identify the candidate genes for Pb tolerance, the flanking SNPs outside and adjacent to the blocks were considered as the candidate regions’ boundaries. The local manhattan plots of QTLs were drawn using qqman package in R software. Genes located in the candidate regions were obtained from the reference genome of the B. napus “Darmor-Bzh” (http://www.genoscope.cns.fr/brassicanapus/) [http://www.genome.jp/) [P value. Pathways with qvalue less than 0.05 are significantly enriched. For identifying more candidate genes, all genes located in the candidate regions were also annotated by performing BLASTN in A. thaliana genomic database (https://www.arabidopsis.org/) [The associated QTLs were defined across regions of SNPs with LD value (ranapus/) . Pathwayome.jp/) . Rich fais.org/) .BnACTIN7 using a △CT method . The tukey test was employed for differentiation analysis on relative gene expression level between accessions and treatments.The expression level of candidate genes in two Pb-tolerant (HANNA and III-229) and two Pb-sensitive (6024–1 and EH3143) genotypes were evaluated by quantitative real time PCR (qRT-PCR). Total RNA was extracted from seven-day-old radicles grown under normal (0 mg/L) or Pb stress (100 mg/L) condition using TransZol kit (Trans Gene Biotech). A total amount of 500 ng RNA was used to synthesize first strand cDNA using HiScript® II Q Select RT SuperMix for qPCR (+gDNA wiper) kit (Vazyme Biotech). The gene copy specific primers of candidate genes were designed using Primer Premier 5  under control (CK) and Pb stress (Pb) condition.Additional file 2: Figure S2. The quantile–quantile plot (QQ-plot) of different models for RRL.Additional file 3: Figure S3. Association mapping for RRL on chromosome A09 and C04. (A) Association mapping for RRL in the QTL Pb-A09 . (B) Association mapping for RRL in the QTL Pb-C04  associated with RRL. The red, pink, blue and green plots represent the association signals for RRL_Means , RRL1 (RRL in replication 1), RRL2 (RRL in replication 2) and RRL3 (RRL in replication 3), respectively. The blue and red horizontal line indicate the threshold of significantly associated SNPs at −log10  = 4.3 and threshold of moderately associated SNPs at 3.5 ≤ −log10 (p) ≤ 4.3, respectively as in Fig. r2 > 0.4).Additional file 4: Figure S4. The top 20 enriched pathways of genes in the associated regions.Additional file 5: Table S1. The phenotypic variation for RRLs in this natural rapeseed population. SE, Standard Error; CV, Coefficient of variation; RRL1, RRL2, RRL3 represent the relative RRL in replication 1, 2 and 3 respectively. RRL_Means was the average value of three RRLs; ** indicate significant correlation at the 1% level.Additional file 6: Table S2. The details Pb-tolerant genotypes screeneded from 472 global-coolected rapeseeds. RL_CK and RL_Pb represent the radicle length (RL) under control and Pb stress (100 mg/L), respectively; RRL represent the relative radicle length.Additional file 7: Table S3. The information for top 20 enriched pathways for genes in the associated regions.Additional file 8: Table S4. List of 472 rapeseed accessions used for association study.Additional file 9: Table S5. The primer sequences of qRT – PCR.


The aim of the present study was to evaluate the genetic variability within casein alpha S1 (CSN1S1), casein alpha S2 (CSN1S2), beta-casein (CSN2), kappa-casein (CSN3), and progestagen-associated endometrial protein (PAEP) genes. The study was conducted on 1900 Polish Black and White Holstein-Friesian dairy cows that were genotyped using customized EuroGenomics microarrays. Out of investigated 24 genetic variants within four genes coding milk protein, 16 expressed genetic diversity. The fact that many of the investigated Single Nucleotide Polymorphisms (SNPs) were monomorphic may suggest that in the past the reproduction program favored one of these genotypes. Populations of high-yielding cows become more and more even and similar in terms of genotype, which may have an adverse effect in future breeding, therefore, the variability in genetic variants of genes coding milk protein should be monitored.The aim of the present study was to evaluate the genotype and allele frequencies of 24 polymorphisms in casein alpha S1 (CSN1S1), casein alpha S2 (CSN1S2), beta-casein (CSN2), kappa-casein (CSN3), and progestagen-associated endometrial protein (PAEP) genes. The study included 1900 Polish Black and White Holstein-Friesian dairy cows that were subjected to genotyping via microarrays. A total of 24 SNPs (Single Nucleotide Polymorphisms) within tested genes were investigated. Two CSN1S1 SNPs were monomorphic, while allele CSN1S1_3*G in CSN1S1_3 SNP dominated with a frequency of 99.39%. Out of seven CSN2 SNPs, four were polymorphic; however, only for CSN2_3 all three genotypes were detected. Only three out of nine SNPs within CSN3 were monomorphic. Three PAEP SNPs were also found to be polymorphic with heterozygotes being most frequent. Hardy–Weinberg equilibrium (HWE) was observed for eight variants. It was shown that only CSN3_6 was not in HWE. The fact that many of investigated SNPs were monomorphic may suggest that in the past the reproduction program favored one of these genotypes. SNPs that are included in commercially available microarrays should be monitored in relation to changes in their frequencies. If a SNP has turned monomorphic, maybe it should be considered for removal from the microarray. All over the world dairy products made from cow’s milk are a crucial part of human diet. The demand for milk products steadily increases, especially due to the fact that milk is a known source of micro and macronutrients, such as lipids, proteins, vitamins, and minerals . The composition of cow’s milk is well known, with 80% being water, 3% to 4% fat, 3.5% proteins, approximately 5% lactose, and 1.2% minerals. It is worth mentioning that milk proteins have a high biological value as they contain several essential amino acids. Caseins make up approximately 80% of all milk proteins in cow’s milk ,7. Apart from caseins, cow’s milk is composed in 20% of whey proteins, one of which is β-lactoglobulin (βLG) coded by the PAEP gene . PAEP is localized on bovine chromosome 11 . LiteratCurrently, the genomic assessment of cattle breeding value that is carried out in many countries, preceded by genotyping of animals with the use of Illumina microarrays, allows obtaining additional information on the variability of SNP located in genes, e.g., determining milk protein variants. Studying the results coming from microarrays allows tracking changes in SNP frequencies in a given population  as well The aim of the present study was to evaluate the genotype and allele frequencies of 24 single nucleotide polymorphisms in casein alpha S1, casein alpha S2, beta-casein, kappa-casein, and progestagen-associated endometrial protein genes in the population of Polish Black and White Holstein-Friesian dairy cows. In accordance with Resolution No. 13/2016 of the National Ethics Committee for Animal Experiments (Poland) of 17 June 2016, the consent of the Ethics Committee is not required for the collection of animal material for genotyping. The study was carried out on a total of 1900 Polish Black and White Holstein-Friesian dairy cows that were born between 2014 and 2016 and calved between 2016 and 2018. Animals were used in a conventional milking system. All studied cows came from 3 high-yielding herds, with the average milk yield per lactation at the level of 12,321.29 kg, mean fat, protein, lactose, and solids contents at the level of 3.65%, 3.34%, 4.87%, and 12.69%, respectively. The genetic material for the study was collected during a routine estimation of breeding value. The genotyping using microarrays was outsourced to the Polish Federation of Cattle Breeders and Dairy Farmers . As a part of Euro Genomics, cooperative PFCB&DF uses customized EuroGenomics arrays. For the purpose of the present study, the following arrays were used: Eurogenomics v3_POL; Eurogenomics v4_POL; Eurogenomics v5_POL; Eurogenomics v6_POL; Eurogenomics v8b_POL; Eurogenomics MD_POL with Infinium HD Illumina protocol. The different panels had very little overlap and due to the limited number of genotypes available, the imputation was not successful, thus, only 6765 genome wide SNPs with a call rate of >0.95 across all animals and minor allele frequency MAF > 0.001 were used. Biopsy samples (ear punch) were collected with the use of AllFlex Tissue Sampling Unit  in the period from 2015 to 2019. Samples were kept in room temperatures and imminently transferred to the laboratory by postal services (2–3 days). In the laboratory, samples were coded and frozen at −20 °C degrees. After unfreezing, samples were transferred to 96 well plates, after which the lysis buffer and proteinase K were added. This mix was then incubated at 56 °C and mixed (600 RPM) in a thermomixer overnight. Before the next step, samples were centrifuged (60 s × 3200 g) to remove undigested parts. Next, lysate was transferred to deep-well plates and DNA extraction was processed with the use of Thermo KingFisher™ Cell and Tissue DNA Kit  according to the manufacturer’s protocol in the KingFishre Duo DNA system . DNA concentration was measured according to Illumina protocol (Infinium HD) by the fluorimetric method with the use of the Quant-iT PicoGreen™ dsDNA Assay Kit  in the Fluoroscan™ Microplate Fluorometer  system. Samples were then normalized to 70 ng/ul by dilution with a use of TE buffer . Normalized samples were processed according to Illumina HD protocol. Beadchips were immediately scanned on the Illumina iScan system, and scans were analyzed using GenomeStudio Software V2011.1 version 1.9.4 . All samples from one type of array were clusterized again together before export with the use of cluster file. Only data with call rates over 0.95 were chosen to subsequent statistical analysis. Eurogenomics arrays’ content (probe list and sequences) are confidential with underlying restrictions for publication. For the purpose of the present study, authors have chosen SNPs that were present on all array versions. Farmers have authorized the authors to access a portion of the SNP data.2 test with 1 degree of freedom and α = 0.05).The genotypic data regarding SNP in milk protein genes was provided by PFCB&DF following farmers’ consent. SNP data for the genes tested in the present study are presented in It is worth mentioning that in populations with a high number of heterozygous animals in which genetic diversity is still present, it is possible to influence the change in production levels by favouring different alleles while selecting animals for breeding. Since all alleles are present in the population, breeders may design selection programs and select bulls in relation to favorable alleles, so that new and favorable variants would appear in their herds with higher frequency. Therefore, it is necessary to investigate allelic frequencies of different commercially important genes. In the present study, 24 SNPs located in milk protein genes were investigated . Out of It is worth noticing that c.222G>T SNP is also included into the makeup of widely recognized CSN1S2 alleles: allele A (contains guanine) and D (thymine). Investigation of SNP within casein alpha-S2 gene (CSN1S2) that resulted in guanine to thymine substitution showed the frequency of c.222TT genotype  to be almost at the level of 100%, and the T allele was present in 99.95% of all tested cases . Only feSeven SNPs within beta-casein CSN2) gene were investigated; however, only five were polymorphic. Furthermore, for CSN2_3 (c.350A>C), only three genotypes were detected  . Six out gene werNine SNP in CSN3 were investigated in the present study, and six of them were polymorphic . Frequencies of each respective SNP are presented in The study also evaluated frequencies of four PAEP SNPs, indicating that one was monomorphic (PAEP_1). For other three SNPs, most frequent were heterozygotes  . Compari2 was not applicable. This may suggest that in the studied population one of the genotypes was favored during reproduction programs in the past. Other authors also described results of χ2 test used to verify any deviation from H-W equilibrium, for instance, Ardicli et al. [2 was at the level of 0.42, and p-value at 0.52), similarly with CSN1S2 genotypes. Furthermore, Hristov et al. [2 test in order to evaluate the validity of H-W equilibrium. They investigated polymorphism in CSN1S1 in Bulgarian Rhodopean Cattle (BRC) and noted that while χ2 (0.26) and p-value (0.88) suggest that observed and expected frequencies did not vary significantly and the population was in H-W equilibrium, the relatively low frequency of CC genotypes (0.023) might suggest that in the past animals with BB and/or BC genotypes were preferred and used during reproduction in BRC breed. The studied population was tested whether it was in Hardy–Weinberg (H-W) equilibrium in relation to investigated SNPs . Since ti et al.  who invev et al.  subjecte2 applicable. Observed frequencies for CSN2_3 did not differ from those expected , which suggests that there was no significant difference from H-W prediction (p < 0.05) confirming that the population was not in H-W equilibrium [Only for one CSN2 SNP, all three genotypes were detected rendering H-W equilibrium χediction . Sebastiilibrium . Such re2 at the level of 6.69 and p-value at 0.01 was out of Hardy–Weinberg equilibrium. Another study that described a population with significantly similar observed and expected genotype frequencies and conformed validity of H-W equilibrium was conducted by Hristov et al. [2 = 0.00, p < 0.01), which might suggest that in the past selection during reproduction, that favored one of the genotypes, occurred. Out of nine CSN3 SNPs, four were tested whether they were in accordance with H-W model, for others the test was not applicable due to the monomorphisms of the population or the lack of presence of all genotypes . Out of v et al. . In the v et al. , however2 = 1.04; α = 0.01). Similarly, population of BRC cattle investigated by Hristov et al. [2 = 0.13, p-value = 0.94). The study investigated four SNPs in the PAEP gene, out of which one was monomorphic, while three polymorphic SNPs were proven to be in H-W equilibrium . Barbosav et al.  was in aIt is worth noting that the Hardy–Weinberg model is often used in literature as the base model for evolution. Therefore, if any population is proven to be out of Hardy–Weinberg equilibrium, it may suggest that some kind of evolutionary process may affect the population. Deviation from H-W equilibrium may be also an indicator that matings in the herd were planned favoring one of the genotypes .Milk proteins are important from an economical point-of-view as the polymorphism between different protein genes and milk and cheese properties have been wildly described. The information on these genotypes is important for breeders when they want to improve their herds. However, years of selection may have caused significant changes in the frequencies of different genotypes. The fact that all investigated SNPs for CSN1S1 and CSN1S2 and many SNPs in CSN2 and CSN3 genes were monomorphic may suggest that in the past the reproduction program favored one of these genotypes. Therefore, it is worth monitoring the frequencies of different SNPs, especially if they are included in commercially available microarrays. If a monomorphic SNP is included in microarray, it generates unnecessary costs in production, and it also takes up a place that a different SNP, also beneficial for farmers, can take.


A divergent selection experiment on intramuscular fat (IMF) content was carried out during nine generations in rabbits. The IMF content was successfully improved through generations. Besides, selection for IMF content generated a correlated response on its composition. Association analyses were performed to understand the genetic background of IMF composition using two rabbit lines divergently selected for IMF content. Several genomic regions and genes were identified, revealing the polygenic nature of the intramuscular fatty acid composition in rabbits.SCD, PLIN2, and ERLIN1. The main genomic regions associated with the fatty acids were not previously associated with IMF content in rabbits. Nonetheless, MTMR2 is the only gene that was associated with both the IMF content and composition in rabbits. Our study highlighted the polygenic nature of the fatty acids in rabbits and elucidated its genetic background. Intramuscular fat (IMF) content and its composition affect the quality of meat. Selection for IMF generated a correlated response on its fatty acid composition. The increase of IMF content is associated with an increase of its saturated (SFA) and monounsaturated (MUFA) fatty acids, and consequently a decrease of polyunsaturated fatty acids (PUFA). We carried out a genome wide association study (GWAS) for IMF composition on two rabbit lines divergently selected for IMF content, using a Bayes B procedure. Association analyses were performed using 475 individuals and 90,235 Single Nucleotide Polymorphisms (SNPs). The main objectives were to identify genomic regions associated with the IMF composition and to generate a list of candidate genes. Genomic regions associated with the intramuscular fatty acid composition were spread across different rabbit chromosomes (OCU). An important region at 34.0–37.9 Mb on OCU1 was associated with C14:0, C16:0, SFA, and C18:2n6, explaining 3.5%, 11.2%, 11.3%, and 3.2% of the genomic variance, respectively. Another relevant genomic region was found to be associated at 46.0–48.9 Mb on OCU18, explaining up to 8% of the genomic variance of MUFA/SFA. The associated regions harbor several genes related to lipid metabolism, such as  Intramuscular fat (IMF) content and its fatty acid composition are key traits influencing meat quality . The incThe high heritability of IMF and its substantial variability allowed its improvement through selection in cattle , chickenSCD), Leptin receptor (LEPR), Fatty Acid Synthase (FASN), and Fatty Acid Elongase (ELOVL). These studies underlined the complexity and the polygenic nature of the fatty acids in pigs [Several association analyses were performed in order to unravel the genetic background of the IMF fatty acid composition. Previous Genome-Wide Association Studies (GWAS) reported genetic markers and genes associated with fatty acid composition in pigs ,12,13 an in pigs ,12,13 an in pigs ,15,16. T in pigs . IMF con in pigs . In thisThe present study was carried out on 478 individuals from the 9th generation of two lines divergently selected for IMF (239 rabbits each line). The base population had 13 sires and 83 does . High  . FurtherThe studied traits were the main individual fatty acids C14:0, C16:0, C18:0, C16:1n7, C18:1n9, C18:2n6, C18:3n3, C20:4n6, the groups SFA, MUFA, PUFA, and the ratios PUFA/SFA, MUFA/SFA, n-6/n-3. Other minor individual fatty acids such as C12:0, C15:0, C17:0, C20:0, C18:1n7, C20:1n11, C22:1n9, C18:3n6, C20:2n6, C20:3n6, C20:3n3, C20:5n3, C22:4n6, C22:5n3, and C22:6n3 were not studied individually, however, they were considered to estimate SFA, MUFA, and PUFA groups. The studied fatty acids accounted for more than 93% of total fatty acids.R2 > 0.75 was used as quality parameter for imputed genotypes. After quality control, 475 animals  and 90,235 SNPs remained in the dataset.Rabbits were genotyped using the Affymetrix Axiom OrcunSNP Array . The Single Nucleotide Polymorphisms (SNP) array contained 199,692 molecular markers. Quality control was performed using Axiom Analysis Suite v.4.0.3 of Thermo Fisher Scientific . SNPs with a call rate greater than 0.95, a minor allele frequency greater than 0.05, and a known chromosome position were retained for the association analyses. In addition, individuals with a missing genotype frequency greater than 0.05 were excluded from the dataset. The missing genotypes were imputed by Beagle software v.4.1  using 96y is the phenotypes vector. X is the incidence matrix for fixed effects and b is the vector of the fixed effects of month (five levels), sex (two levels), and parity order (three levels), with bounded flat prior density. zj is the genotypes vector for a SNP at locus j . jα is the random substitution effect for SNP j, distributed as jδ is a random 0/1 variable (jδ = 1 represents the presence of SNP j with probability 1−π and jδ = 0 represents the absence of SNP j with probability π). e is the residual term, distributed as The association analyses were performed using Bayesian procedures . A Multied as in ,22 from ed as in . δj is ad traits . Marginaj. Bayes factors (BF) were estimated to evaluate the statistical relevance of the association between SNPs and traits. Bayes factors were calculated as follows :BFj=p^j1The genomic windows exceeding 1.0% of the genomic variance of the trait were considered to be associated with the trait. In addition, the genomic windows exceeding 0.5% of the genomic variance of the trait and having SNPs with a BF greater than 10 were also considered as associated with the trait. These thresholds of 1% and 0.5% represent 20 and 10 times the expected percentage of the genomic variance explained by each genomic window, respectively. SNPs with a BF greater than 10 were considered as associated with the trait . The assOryctolagus cuniculus as the reference genome [Genes were retrieved from the extended regions. Candidate genes were obtained from the Ensembl Genes 98 database using the e genome . The bioe genome . The gene genome .In addition, we examined the LD in the most relevant extended regions to visualize the relationships between the SNPs. LD analyses were performed using plink software  and LDhep-values (EMP1) were provided for each SNP. EMP1 was estimated as K/N, where N is the total number of permutations and K is the number of times the SNP comes out associated in the simulated data. SNPs with EMP1 values close to 10−5 were deemed true positives [Results should be validated in order to overcome the problem of multiple testing. The phenotypes were resampled 100,000 times to remove any possible relationship between the phenotypes and genotypes, using the following covariates: sex, month, parity order, and the three first components of the principal component analysis of the genotypes (explaining 36% of the variance) in order to avoid population structures. Permutation testing was performed on the new data corresponding to the null hypothesis. Empirical ositives . Permutaositives .GWAS within line were performed to compare the SNPs effects. Confidence intervals of the SNPs effects (effect ± 2SD) were calculated in both lines. SNPs with overlapping confidence intervals were considered as true positive associations. In contrast, SNPs with non-overlapping confidence intervals were discarded, since we assumed that these associations were caused by sampling effect and genetic drift.All experimental procedures were approved by the Universitat Politècnica de València. Research Ethics Committee, according to Council Directives 98/58/EC and 2010/63/EU (reference number 2017/VSC/PEA/00212).The association analyses were performed using the 475 individuals and 90,235 SNPs remaining in the dataset after quality control. Allele frequencies differ among lines due to the random genetic drift and selection. As the animals came from a divergent selection experiment for IMF, genes with large causal variants will show extreme frequencies for alternate alleles between lines due to selection. We expect that high-IMF line will show genes with high frequencies given a reference allele, while the same genes will have lower frequencies in low-IMF line. Therefore, these genes will show intermediate frequencies when both lines are used. We did not consider the line as a fixed effect to take advantage from these extreme differences caused by selection and detect the genes with large causal variants. We also performed GWAS with the fixed effect of line in the model. The same associated genomic regions were obtained with lower percentages of the genomic variances of the traits. Divergent selection experiments can improve the detection power, according to simulation studies, especially for large causal variants and their nearby SNPs ,32. ThisThe genomic windows that exceeded the thresholds levels were spread across several chromosomes. All rabbit chromosomes (OCU), except OCU7, OCU11, and OCU20. The associated regions with SFA, MUFA, PUFA, and the ratios were displayed in ACER2) and perilipin 2 (PLIN2). ACER2 promotes the hydrolysis of ceramides, the generation of sphingosine, and consequently the biosynthesis of sphingolipids [PLIN2 covers the intracellular lipid droplets and plays a major role in their stabilization [PLIN2 was considered as a useful marker for lean growth and its expression was positively associated with IMF content [On OCU1, a large genomic region at 34.0–37.9 Mb was associated with C14:0, C16:0, C18:0, and SFA, explaining 3.54%, 11.2%, 0.87%, and 11.3% of the genomic variances, respectively . The samgolipids ,34. Besilization . In pigs content ,37. In r content . Hence, MTMR2 gene involved in lipid metabolic processes according to DAVID database. MTMR2 was also associated with the IMF content in rabbits, the genomic region at 120.6–121.9 Mb on OCU1 accounted for 2.03% of the genomic variance of IMF content [MTMR2 gene was located at a signature of selection for IMF in pigs [On the same chromosome (OCU1), the genomic window at 121.0–121.9 Mb was associated with C14:0, C18:0, C18:1n9, and MUFA. This region harbors the  content . Besides in pigs .FGF1 and NR3C1. FGF1 was also considered as a candidate gene for IMF in chickens [NR3C1 gene in lipid metabolism in pigs [ENSOCUG00000000157 novel gene related to lipid biosynthesis. The novel gene is also known as ST3GAL1 in humans, pigs, and mice. The validated SNPs at this region showed high values of the BF  (The genomic region at 25.0–26.9 Mb on OCU3 accounted for 6.05% of the genomic variance of linoleic acid (C18:2n6). Two candidate genes were retrieved, chickens , while a in pigs . On the r C14:0) . Moreover C14:0) . The associated genomic region at 7.2–7.9 Mb on OCU5 did not map to genes related to lipid metabolism, however, it encompasses the highest BF. The validated SNP at position 7.43 Mb on OCU5 was associated with C16:0 (BF = 459), C16:1n7 (BF = 77), PUFA (BF = 193), and the ratio PUFA/SFA (BF = 390) . LITAF and SNX29). LITAF regulates the response to lipopolysaccharide according to gene oncology (GO: 0032496), whereas SNX29 affects phosphatidylinositol (family of lipids) binding (GO: 0035091). SNX29 was also a candidate gene for IMF in pigs [SMG1 and GDE1) according to DAVID database. Three different regions on OCU6 were associated with the fatty acid composition. The first region at the start of the chromosome was associated with C16:0, PUFA, and PUFA/SFA. However, it did not map to putative candidate genes related to lipid metabolism. The second genomic window at 5–5.9 Mb was associated with C18:2n6, and included two genes related to lipid metabolism , while PLEKHA5 controls lipid binding . On the other hand, PLCZ1 regulates lipid metabolic and catabolic processes . On the same chromosome, the genomic region at 53–53.8 Mb was associated with MUFA/SFA. This region harbors DGKH gene involved in triglycerides degradation [On OCU8, the genomic region at 21.0–21.9 Mb was associated with C18:0, C16:1n7, C18:1n9, MUFA, and PUFA. This region harbors radation . The genPPARG gene involved in adipocyte differentiation [PPARG was also associated with fat deposition in sheep [PPARG and IMF content in Korean cattle [NPC1 and OSBPL1 genes. NPC1 is associated with lipid transport [OSBPL1 is involved in both lipid transport (GO: 0006869) and binding (GO: 0008289).The genomic region at 12.0–12.5 Mb on OCU9 accounted for 1.05% of the genomic variance of C16:1n7. This region harbors ntiation . PPARG win sheep . In pigsin sheep . Moreoven cattle . On the ransport , while OITGB8 gene related to lipid metabolic process. This gene was also located at a signature of selection for IMF content in pigs [On OCU10, the genomic region at 5–5.6 Mb was associated with the ratio MUFA/SFA. This region harbors the  in pigs .CRP, DCAF8, FCER1A, PIGM, and ATP1A2). The second region at 126.0–126.9 Mb was associated with C18:0, C18:1n9, and MUFA, harboring HEYL, MFSD2A, and PPT1 genes. MFSD2A regulates the docosahexaenoic acid (C22:6n3) transport [Two genomic regions on OCU13 were found to be associated with the fatty acids. The first region at 33–34.9 Mb accounted for 3.42% of the genomic variance of linoleic acid (C18:2n6). This region includes five genes related to lipid metabolism according to DAVID database . Stearic (C18:0) and oleic (C18:1n9) acids were associated with the genomic window at 82–82.9 Mb on OCU14. This region harbors the tabolism . ADIPOQ n cattle . Moreoven cattle . On the LRAT and TLR2). On the same chromosome, the genomic region at 70–71.9 Mb accounted for 3.59% of the genomic variance of linoleic acid (C18:2n6), harboring ANXA3 and GK2 genes related to lipid metabolism.The genomic region at 10–10.9 Mb on OCU15 was associated with C16:0, SFA, C18:2n6, PUFA, and the ratio PUFA/SFA. Two genes related to lipid metabolism mapped to this region .Two regions on OCU16 were associated with the fatty acids. The first region at 62.0–62.9 Mb was associated with C18:2n6 and MUFA/SFA, harboring ALDH1A2, LIPC, and MYOE1). LIPC regulates lipid metabolic process (GO: 0006629), whereas MYO1E controls lipid binding (GO: 0035091). On OCU17, the region at 9–10.9 Mb explained 3.87% of the genomic variance of C18:2n6, however, it did not contain candidate genes. Besides, the region at 12–13.9 Mb was associated with C16:1n7, MUFA, C18:2n6, and PUFA. This genomic region harbors three genes related to lipid metabolism (SAMD8 gene related to ceramides biosynthetic process (GO: 0046513). Besides, a large genomic region at 46–48.9 Mb on OCU18 was associated with the majority of the traits, explaining 7.9% of the genomic variance of the ratio MUFA/SFA. This region harbors the following genes: GOT1, ERLIN1, ENSOCUG00000001375, and ENSOCUG00000014801. ERLIN1 promotes lipid binding; it encodes for proteins that bind to cholesterol and interacts with the sterol regulatory element binding protein (SREBP). This latter stimulates the cholesterol and fatty acid synthesis in liver [ERLIN1 was associated with lipid metabolism in cattle [ENSOCUG00000001375 and ENSOCUG00000014801 are known in pigs and humans as Stearoyl-CoA Desaturase (SCD). SCD gene is responsible for the biosynthesis of MUFA from SFA. A strong association between SCD and the fatty acid composition was reported in pigs [SCD and PPARG expressions [The genomic region at 11–11.9 Mb on OCU18 was associated with the ratio MUFA/SFA, harboring in liver ,54. Moren cattle . The nov in pigs , cattle  in pigs , and goa in pigs . In addiressions . The latADAP2 gene related to phosphatidylinositol bisphosphate binding (GO: 1902936). The genomic region at 21.0–21.9 Mb on OCU19 was associated with linoleic acid (C18:2n6) and disclosed the Many other genes were identified in the associated genomic regions; however, their functional annotations did not show a direct relationship with lipids metabolism. The current study identified several genomic regions associated with the fatty acid composition of IMF in rabbits, showing its polygenic nature, since the associated genomic regions did not explain large percentages of the genomic variances. We did not detect genomic regions with mayor effects like those found in pigs .2 = 1) for both lines, while correlations between SNPs were greater for high-IMF line (Linkage disequilibrium (LD) was assessed throughout the most relevant extended regions . Different LD patterns were detected . The extIMF line . Thus, tMTMR2 is the only gene that was associated with both IMF content and its composition. In addition, ACER2 and PLIN2 genes were associated with the fatty acid composition and were also detected at a selection signature for IMF content [Taken together, the present study underlined different genomic regions associated with the fatty acid composition of IMF in rabbits. The results were corroborated having high values of Bayes factors . The pre content .SCD, PLIN2, ERLIN1, and PPARG. The main genomic regions in which we found genes related to lipid metabolism were not detected in our previous experiment for IMF. MTMR2 is the only gene that was associated with both the IMF content and its composition. To our knowledge, this is the first GWAS for fatty acid composition in rabbits. Our study highlighted the polygenic nature of the fatty acid composition as no large percentages of the genomic variances were explained by the associated genomic regions. Further analyses throughout the associated regions would be needed in order to validate their importance.Association analyses for the fatty acid composition were carried out on two rabbit lines divergently selected for IMF content. Several associated regions were detected. The most relevant regions were found at 34–37.9 Mb on OCU1, at 149.0–149.9 Mb on OCU3, and at 46.0–48.9 Mb on OCU18. The associated regions disclose several genes related to lipid metabolism such as