CCND2 gene, with the G allele associated with more backfat, while the A allele is associated with less backfat. We further phased the QTL region to discover a core haplotype of five SNPs associated with low backfat across three breeds. Linkage disequilibrium analysis using whole-genome sequence data revealed three candidate causal variants within intronic regions and downstream of the CCND2 gene, including the lead SNP. We evaluated the association of the lead SNP with the expression of the genes in the QTL region (including CCND2) in a large cohort of 100 crossbred samples, sequenced in four different tissues . Results show that the A allele increases the expression of CCND2 in an additive way in three out of four tissues. Our findings indicate that the causal variant for this QTL region is a regulatory variant within the third intron of the CCND2 gene affecting the expression of CCND2.Backfat is an important trait in pork production, and it has been included in the breeding objectives of genetic companies for decades. Although adipose tissue is a good energy storage, excessive fat results in reduced efficiency and economical losses. A large QTL for backfat thickness on chromosome 5 is still segregating in different commercial pig breeds. We fine mapped this QTL region using a genome-wide association analysis (GWAS) with 133,358 genotyped animals from five commercial populations  imputed to the porcine 660K SNP chip. The lead SNP was located at 5:66103958 (G/A) within the third intron of the  Backfat (BF) is an important trait in pork production included in the breeding objectives of genetic companies for decades . Pig comAdipose tissue has an important role as the storage of energy , howeverHence, to better understand quantitative traits such as backfat, further insight in the genetic basis of these traits is required. The development of commercial SNP chips has enabled genome-wide association studies (GWAS) to efficiently map regions throughout the genome affecting fat-related traits . While GFGF23 gene as likely causal given the association with phosphate homeostasis. In this study, we investigate the same QTL region on chromosome 5 with strong association with backfat across four commercial pig populations. Further, we aimed to fine map this QTL region to identify causative mutation(s) related to backfat and to better understand the biology of this trait. We concluded that the causative variant of this QTL region is a regulatory SNP in the third intron of the CCND2 gene.One region on chromosome 5 (66 Mb) has shown strong association with backfat in a wide range of populations. , Pietrain, Landrace, Large-White and Duroc , obtaine.k animal; μ is the overall mean; i; j of birth; k; l and N ; μ is the average of the pre-corrected phenotype; X is the genotype, coded as 0, 1, or 2 copies of one of the alleles of the k animal for the evaluated SNP; k is the residual polygenic effect, assuming u ∼N , 0, IA single-SNP GWAS was performed with the imputed GENOTYPED dataset for each population using the following linear animal model in GCTA software , 2014:y10 > 8.The proportion of phenotypic variance explained by a SNP was defined as To search for possible causal mutations at the chromosome 5 backfat locus, fine mapping analyses using high-throughput sequencing  was performed using following steps: (1) building and calculation of haplotype frequencies; (2) sequence data processing and evaluation of variants within the haplotype region; (3) evaluation of protein interactions and gene expression.From the GWAS results, the most significant SNP (lead SNP) was identified. Subsequently we performed a haplotype analysis up-and downstream of the lead SNP. For that, firstly the Beagle v5.1 software  was usedWhole-genome sequence (WGS) data was used to access the complete set of variants within the haplotype region (SSC5:66094630-66140348). For this purpose, 659 animals  were available . GenomicChromatin immunoprecipitation–coupled sequencing (ChIP-Seq) marked by trimethylated histone H3 lysine 4 (H3K4me3), mono-methylation histone H3 lysine 4 (H3K4me1) and acetylated histone 3 lysine 27(H3K27ac) and ATAC-Seq data were downloaded from the European Nucleotide Archive (ENA) with accession numbers PRJEB28147 , and PRJNA665194 . ChIP-Seq marks and ATAC-Seq data were aligned against the pig reference genome (Sscrofa 11.1) using BWA-mem v. 0.7.17 , pre-proFour different tissues: liver, spleen, lung and muscle were subjected to RNA extraction from 100 animals. The animals were F2 crosses resulting from a F1 sow (Landrace*LargeWhite) and a Synthetic boar line. The collected tissue was stored with RNAlater (Thermo Fisher Scientific) at -80°C until further use. RNA was extracted from the 400 samples using the QIAshredder homogenizer kit (Qiagen) and RNA extraction was performed with the Rneasy kit (Qiagen) following manufacture’s guidelines. Once all the samples passed the required quality control parameters, they were sequenced at 150 bp paired-end in an Illumina 6000 sequencing platform. DNA from 87 of these animals was also extracted from the spleen tissue and was subjected to high density genotyping with the Axiom™ Porcine Genotyping Array (Thermo Fisher Scientific) that queries 660K variant markers.https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). We mapped the RNA-seq data to the Sscrofa11.1 reference genome and Ensembl version 104 using STAR v2.7.8 (2pqα2) was estimated based on the allele frequencies (p and q) and the estimated allele substitution effect (α). The proportion of phenotypic variance explained by the SNP was defined as p-value < 0.05.For the bioinformatic analysis, RNA-Seq reads were trimmed using Trim Galore v0.3.7 , Pietrain (0.50 ± 0.01), Landrace (0.42 ± 0.01), Large White (0.52 ± 0.01) and Duroc (0.43 ± 0.01) .In this study, the use of large-scale genotype data allowed us to identify a A/G SNP  on SSC5 with a strong association with backfat across populations. This SNP showed significant association with backfat in four  out of the five evaluated pig populations . The assp-value) of 57.67, 43.29, 67.61 and 37.91 in the Synthetic, Pietrain, Landrace and Large-White population, respectively. The lead SNP, located at 66,103,958 bp on SSC5, explains up to 6% of the genetic variance in backfat (The most significant SNP (rs80985094) showed a -log10 ( backfat . Among t backfat .AAG) present in all populations (size ∼28 Kb). Assuming that recombination reduced the common interval surrounding a new variant reducing back fat, the region containing the causal mutation was expanded by two more SNPs (one SNP upstream and one SNP downstream) to search for possible functional variants. This increases the final region of interest to 46 kb to search for the causal variant . The frequencies of these core haplotypes are 57.70%, 35.87%, 22.67% and 16.85% for each population  (r2) between the variants in the core haplotype and the lead SNP (A) range from r2 = 0.03 and r2 = 0.96 (We phased the genotypes using Beagle 5.1  and selectively) . No commctively) . Hence, 2 = 0.96 . The addAAG) as defined in A haplotype-based association analysis was performed to compare the association with the lead SNP and the core haplotype  with high frequency across populations and in high LD with the lead SNP .CCND2) gene (ENSSSCG00000038694), while SSC5:66190273 is annotated in intergenic region upstream of the CCND2 gene.The LD between these variants and the lead SNP varies among the populations . The leaCCND2 and neighboring genes in a population of 100 crossbred animals in four different tissues . We studied the expression of the CCND2 gene is significantly associated with the lead SNP in liver, lung and spleen  and the C12orf4  gene show significant association with the lead SNP. The AKAP3 gene encodes a member of A-kinase anchoring proteins (AKAPs). This protein is highly expressed in spermatozoa may function as a regulator of motility, capacitation, and the acrosome reaction. The C12orf4 gene encodes a protein involved in mast cell degranulation.The results show that the expression of the d spleen . Also, tCCND2 gene. Therefore, to discover potentially regulatory elements, we used ChIP-seq and ATAC-Seq public datasets to investigate the region of interest. ChIP-Seq data was used to detect enhancers and promoters marked by H3K4me3, H3K4ac27 and H3K4me1. ATAC-Seq was used to detect open chromatin.The core haplotype region ranges from SSC5:66094630 to SSC5:66140348 and it covers the We have called broad peaks for marks on ATAC-Seq and ChIP-Seq . All marBackfat is one of the most important traits in pork production and is closely related to meat quality and efficiency-related traits in pigs. The composition of lipid in backfat is mainly triacylglycerol  which exThe lead SNP (rs80985094) explains up to 6% of the genetic variance and 3% of the phenotypic variance of backfat in the evaluated populations. However, the G allele of the lead SNP is fixed in the Duroc population, associated with increased backfat, explaining the absence of this QTL in Duroc. Durocs are known to have generally higher fat and intramuscular fat content compared to populations of Large White, Landrace, or Pietrain origin.FGF23 as a candidate gene in the same QTL region in different pig lines, these differences might be caused due the number of markers used in each study (660K vs 80K). However, given the proximity of the candidate causal SNP and the influence on the expression we argue that the CCND2 gene is the causal gene affecting backfat at this locus.Several studies have reported QTLs and candidate genes to possibly identify functional mutations in different loci associated with backfat in pigs . We havePietrain, Synthetic, and Landrace populations have high frequency for the A allele, which is associated with less backfat, while Large White has a high frequency for the G allele which is associated with more backfat. This result is in line with the selection in the Pietrain, Synthetic, and Landrace populations for leanness and feed efficiency compared to the Large-White population.CCND2 gene was found to be associated with adipose tissue development and differentiation   . Moreoveogenesis . In addiCCND2 gene, the core haplotype covers a region also overlaps with the LOC106507534 (a non-conding RNA). In human, the CCND2 gene region is located on chromosome 12 and LOC106507534 (pig annotation) is orthologous to a long non-coding RNA (lncRNA) named CCND2-AS1 (human annotation). This lncRNA is associated with regulation of Wnt/b-catenin signaling which directly regulates cell proliferation, cell polarity and cell fate determination during embryo development and tissue homeostasis  show a low frequency of the G allele. If the mutation from G to A occurred in a common ancestor of the white lines, selection will have acted against the G allele in these populations. Due to recombination, the size of the original haplotype on which the mutation occurred is reduced with each generation. Indeed, we still find a core haplotype surrounding the A variant which the four white lines have in common. Furthermore, we also find the original core haplotype with the wild type G allele GCGAG at low frequency in all four white populations. Indeed, this haplotype increases back fat in all four lines (We hypothesize that the lead SNP is the causal variant by affecting ur lines . This fuInterestingly, the A allele, associated with less backfat seems to be the ancestral allele in other mammals such as human, horse and cow, where the A allele is present. However, we did not find animals that have the A allele in Asian or European wild boars, indicating that for the pig lineage, the G allele is the ancestral allele. We did however find the A allele in several European local breeds including the Angler Sattelschwein, Bunte Bentheimer, and in Tamworth . The AngCCND2 gene as a strong candidate gene for backfat deposition in pigs supported by changes in expression and involvement in adipogenesis. However, the exact molecular mechanisms underlying this QTL region remain to be discovered.We report the CCND2 gene which is involved in adipogenesis during prenatal development and thereby influencing the expressed phenotype in the adult life. Moreover, we believe that the lead SNP is the causal mutation based on fine mapping using the WGS data and the significant eQTL result for the CCND2 gene. Our results will open up further study to validate the effect of the causal mutation and gene and to better understand its role on fat deposition and muscularity in pigs.In this study, we identified a QTL region on SSC5 with strong association with backfat across four pig populations. The lead SNP, rs80985094, explained up to 6% of genetic variance of backfat. Gene expression analysis shows that the SNP is associated with the expression of the


Mangifera indica L.) is a tropical climacteric fruit that encompasses a myriad of metabolites mediating for its nutritive value, unique taste, flavor, and medicinal uses. Egypt is among the top mango producers worldwide, albeit little characterization has been made toward its fruits’ chemical composition. This study aims to assess metabolites difference via comparative profiling and fingerprinting of Egyptian mango in context to its cultivar (cv.) type and/or growth province. To achieve such goal, hyphenated chromatographic techniques (UPLC/MS) and UV spectroscopy were employed and coupled to multivariate data analysis for Egyptian mango fruits’ classification for the first time. UPLC/MS led to the detection of a total of 47 peaks identified based on their elution times and MS data, belonging to tannins as gallic acid esters, flavonoids, xanthones, phenolic acids and oxylipids. UV/Vis spectra of mango fruits showed similar absorption patterns mostly attributed to the phenolic metabolites, i.e., gallic acid derivatives and phenolic acids showing λmax at ca. 240 and 270 nm. Modeling of both UPLC/MS and UV data sets revealed that cv. effect predominated over geographical origin in fruits segregation. Awees (AS) cv. showed the richest phenolic content and in agreement for its recognition as a premium cv. of mango in Egypt. Results of total phenolic content (TPC) assay revealed that AS was the richest in TPC at 179.1 mg GAE/g extract, while Langara from Ismailia (LI) showed the strongest antioxidant effect at 0.41 mg TE/g extract. Partial least square modeling of UV fingerprint with antioxidant action annotated gallates as potential contributor to antioxidant effect though without identification of exact moieties based on UPLC/MS. The study is considered the first-time investigation of Egyptian mango to aid unravel phytoconstituents responsible for fruits benefits using a metabolomics approach.Mango ( Mangifera indica L., family Anacardiaceae) is a tropical tree native to the Indo-Burmese peninsula of Asia. However, it is cultivated currently in more than 87 tropical and subtropical countries all over the world with ca. 1000 varieties [Mango  and peel/skin, seeds, leaves, bark, roots, and flowers, have been phytochemically investigated . Mango fg method . Exampleg method ,8,9,10. Moreover, previous reports revealed well-documented anti-inflammatory, antioxidant, antimicrobial, gastro- and hepato-protective, immunomodulatory, and anti-hyperlipidemic effects for mango fruits ,11,12. PA multiplexed approach in food metabolomics based on analytical techniques, either indirect as LC/MS or direct, including nuclear magnetic resonance (NMR) and ultraviolet-visible spectrometry (UV/Vis), has been increasingly reported in food authentication and quality control ,17. PartHypericum perforatum (St. John’s Wort), Humulus lupulus L. (hop), and Crocus sativus (saffron) [With the large amount of data sets generated by these techniques, multivariate data analyses (MVA) either in unsupervised, i.e., principal component analysis (PCA), or supervised mode, i.e., orthogonal partial least-squares discriminant analysis (OPLS-DA), are used for the identification of potential markers for investigated samples classification and products fingerprinting and authentication. Such an approach has increasingly been applied for the quality control of functional foods in the context of determination of freshness, geographical and genotype, processing, and or adulteration detection. Examples include our previous reports in saffron) ,20,21. Isaffron) ,23.Escherichia coli strain and improvement in shelf lifetime of cow milk) activities [In the case of Egyptian mango fruits represented by its different major cvs., fewer reports have been made in the literature with regard to their phytochemical composition, except for a recent report on its aroma composed of low molecular weight volatile metabolites based on headspace solid phase microextraction (SPME) coupled with GC/MS , in additivities . The worA total of 17 Egyptian mango fruit samples represented by 11 cvs. Were collected at the fully ripened stage from three different provinces in Northern Egypt, including Sharqia Governorate (9 samples/cvs.), farmlands on the Cairo-Alexandria desert road (Nubaria) (4 samples), and Ismailia Governorate (4 samples). Nubaria fruit samples have been denoted to be cultivated in a sand soil type versus muddy type in the case of Sharqia and Ismailia Governorates, These cvs. included Hendy kalb el toor, Balady Arneba, Hendy Sinara, El Saady, Jolivia, Sokary, Sokary White, Naomy, and Langara from one origin. In addition, two samples from Founs and three samples from Zibdea and Awees were collected from different origins. Details including geographical origin, cvs. details and codes used in this study are presented in g for 30 min. Afterward, a 500 mg C18 cartridge previously conditioned with 100% MeOH and Milli-Q water was used to partially purify 1 mL aliquots that were twice eluted with 3 mL MeOH. The eluents were concentrated under a nitrogen stream, where the remaining residue was resuspended in 1 mL MeOH. Finally, 2 μL were subjected to analysis in triplicates (n = 3) from three different collected fruits analyzed under the same conditions using the UPLC system  coupled to a MicroTOF-Q hybrid quadrupole time-of-flight mass spectrometer  which is equipped with an Apollo II electrospray ion source in negative ion mode. In the range of m/z 100 to 1000, the eluted metabolites were detected. Regarding the instrument column, Water Acquity HSS T3, RP-18 was applied with the following character, including 150 × 1 mm, 1.8 µm particle size, and pore size of 100 A.Fruits were first manually pitted to remove seeds from fruit pulp. Samples from fruit pulp without the peel were then lyophilized using Benchtop Freeze Dryer, LYO60B-1PT, until complete dryness and finely powdered using Kenwood KHH326BK Multione Mixer 1000 W. Samples were extracted from mango fruit pulp following the protocol used previously by El-Hawary et al. . In brien = 4) of the 96-well quartz cell of the Gen 5 UV/Vis microplate reader . The absorption spectra were recorded in the range of 200–650 nm.Additionally, 200 µL of MeOH fruit extracts were pipetted into microplate wells .®, Redmond, WA, USA) for all samples, including their replicates, were mean-centered and pareto scaled for variables representing absorbance readings between 200 and 650 nm. The data set was then modeled, similar to UPLC/MS, using PCA and OPLS-DA models. The OPLS-DA models were calculated using the default seven-fold cross-validation method yielding acceptable R2X, R2Y, and Q2 with no negative values and values for both above 0.5. In addition, permutations tests and CV-ANOVA with a p-value less than 0.05 for significance were used to validate the results of the developed OPLS-DA models.Likewise, UV/Vis spectral data matrix exported using excel  using Folin–Ciocâlteu reagent following the protocol used by El-Hawary et al. [TPC was determined colorimetry in triplicates , where 175 µL of freshly prepared FRAP reagent  in 40 mM in HCl (10 Mm), acetate buffer , 20 mM FeCl3) were mixed in a 96-well microplate with 25 μL of mango methanol extract (prepared in FRAP assay was conducted in triplicates (p < 0.05) was applied for the classification and grouping of investigated mango cvs. based on TPC and antioxidant effect.Statistical analysis using one-way ANOVA with the aid of the Tukey method , in comparison with previous phytoconstituents reported in mango fruit  and sucrose (P1 and P3), appearing as the most abundant peaks in mango cvs. These results are in accordance with previous data revealing that citric acid and sucrose as the predominant acid and sugar in mangoes, respectively . Major sp-hydroxybenzoic, ferulic, sinapic, and dihydro-sinapic acids, respectively [Identification of glycosides was based on characteristic mass loss of either 132 amu (pentose), 162 amu (hexose), or both as manifested in peaks 7, 8, 11, 13, 16, and 17 belonging to glycosides of gallic, ectively ,28,29. Tectively .Additionally, several polyphenols such as gallotannins were identified, including methyl gallate (P12), methyl digallate ester/isomer P25 and P27), methyl trigallate ester P31), pentagalloyl glucose P26), hexagalloyl glucose (P28), and heptagalloyl glucose (P30), in accordance with the literature [, hexagal, methyl , pentagaO-pentoside, rhamnetin-O-hexoside, quercetin-O-hexoside, and rhamnetin, respectively and showing the typical sugar losses of hexoses (−162 amu) and pentoses (−132 amu) as observed in phenolic acids. Xanthones, a unique secondary metabolite class in mango, were represented by mangiferin/isomangiferin in P16 based on its deprotonated molecular ion at m/z 421 and production at m/z 281. In the later elution range of chromatogram typical for lyophilic classes elution type, several abundant peaks were detected assigned to oxylipids, i.e., fatty acids or glycerides, and to contribute to the rich fat content in mango fruits represented by P46 rt. 12.2 min  assigned for acylated glycerol I as the most abundant within that class.Moreover, flavonoid aglycones and glycosides were also detected in peaks P20, P32, P34, and P39, corresponding to quercetin-p-hydroxybenzoic acid, and catechins showing λmax at ca. 240 and 270 nm [viz. β-carotene [Based on the advantages of UV/Vis spectrometry, including its non-destructive, fast, and robust nature, samples were measured in parallel using the same extraction condition as explained in d 270 nm ,34, excecarotene , which mcarotene . HoweverConsidering the large number of analyzed Egyptian fruit specimens totaling 50 collected from 3 different origins, i.e., Sharqia, Ismailia, and Nubaria regions analyzed using UPLC/MS, samples were first subjected to MVA using the PCA model to assess for heterogeneity in metabolites in an untargeted manner. These samples represented 17 sample types represented by 3 biological replicates, with the PCA model score plot accounting for only 33% of the total variance as prescribed by PC1 (20.2%) and PC2 (13.0%).Classification of all samples stratified based on geographical origin A showed Applying the same PCA model using cultivar demarked samples B showed Examination of the loading plot revealed that citric acid derivatives  along with gallate derivatives  were more abundant in Balady, Hendy kalb el toor, and Sokary cvs. and accounting for its segregation. In contrast, sugars, i.e., sucrose, along with phenolic glycosides, i.e., ferulic acid, sinapic acid, and dehydro sinapic acid) were found more abundant in Awees and Langara cvs. C. Awees UV spectral data set of mango samples was further subjected to MVA as a direct fingerprinting tool to compare its classification potential to that of UPLC/MS, especially that phenolics likely to absorb in UV mediated for specimens’ segregation analyzed using UPLC/MS C. PCA mo2 0.676 values, and p < 0.05 and to confirm the unsupervised PCA model score result. Further, the S-line plot of the OPLS-DA model revealed that Langara cv. exhibited wavelength distinct UV absorbance at 260–270 nm corresponding to that of gallate moiety and in accordance with UPLC/MS results. Accordingly, UV analysis could complement UPLC/MS to serve as a quality control tool for the discrimination of Langara cv. among mango accessions , respectively. Samples from desert road farmlands (Nubaria) were positioned along PC1 to the left with negative score values, whereas samples from Ismailia and Sharqia were located in the right direction of PC1 and separated from each other along PC2, likely due to similar climatic conditions and soil type in Ismailia and Sharqia distant from desert type soil in the desert A. SharqiExamination of the corresponding loading plot in the PCA model showed that acyl glycerols in P46 and P47 were more abundant in mango fruits from desert regions, while sugar at P2 was more abundant in samples from Ismailia and Sharqia regions B.Fruits from Zibdea cv. were likewise obtained from different geographical regions and were of interest to identify whether geographical origin affects its metabolite profile. Modeling of Zibdea cv from different origins separately using PCA showed suitable separation of samples based on geographical origin, with PC1 and PC2 accounting for 40.3% and 15.1% of the total variance (55.4%), respectively. Samples from Ismailia were positioned along PC1 to the left with negative score values, while samples from Sharqia were located in the right direction of PC1. Samples from desert regions were midway aligned on PC1 but separated from other samples along PC2 C. ExaminPrevious reports showed that mango fruit pulp is rich in various phenolic constituents, including phenolic acids, flavonoids, and tannins . AdditioPolyphenols have been well documented to exhibit free radical scavenging and metal chelation properties, including reactive oxygen species (ROS), i.e., hydroxyl, hydrogen peroxides, and superoxide radicals ,39. DPPHIn the current study, TPC was determined and related to the antioxidant activity as determined using FRAP assay . Resultsp < 0.05 resulted in various groups reflecting significant differences between investigated mango cvs. , 2 < 30%), which indicated that other factors contributed to the antioxidant activity other than just total phenolics content, such as total carotenoids not monitored in this assay.The antioxidant results could be correlated to TPC and also were in agreement with previous literature . Regress2 = 0.54 and Q2 = 0.41), and as a prediction parameter explained 41%, the loading plot displaying a positive correlation with FRAP assay  model was deployed by assigning UV peak abundance as x-variables and the corresponding FRAP results as y-variables. The PLS score plot explained 54% of the total variance in Y (RAP assay .Investigation of variable importance in projection (VIP) enabled recognition of UV absorbance responsible for the antioxidant effects and the pinpointing of the relation between the x- and y-variables in the PLS model. The main potential UV max that had significant VIP scores included 238 and 270 nm, in addition to less contribution from that 466 nm. UV max at 270 corresponds to that of gallate moieties and is suggestive that they account for the most antioxidant effect in mango fruit pulp and much less contribution for carotenoids based on UV max at 466 nm. To identify exact gallate moieties or chemicals mediating antioxidant action in mango fruit, UPLC/MS peak abundance data set was further modeled against FRAP assay results using OPLS. A model with poor prediction power could only be retrieved and suggestive that UPLC/MS metabolite fingerprint failed to identify the exact phenolic mediating for antioxidant activity. Employment of other antioxidant models and inclusion of mango cvs. showing large differences in metabolite levels could aid in pinpointing active agents.A UPLC/MS- and UV-based metabolomics approach was conducted for the characterization and discrimination of various mango cvs. cultivated in three different geographical origins in Egypt for the first time to unravel phytoconstituents responsible for fruit health benefits. A total of 47 peaks were tentatively annotated in the Egyptian mango based on UPLC/MS platform for the first time. Asides, UV/Vis spectrometry, confirmed the richness of mango fruits in phenolic metabolites. Classification of all samples stratified based on geographical origin revealed overlap between the different fruit sample subsets and suggested that a larger contribution of cv.-related characteristics to the variability among fruit samples existed compared to geographical origin. Results revealed from modeling both Awees and Zibdea cvs. collected from different origins suggested that primary metabolites account more for discriminating geographical origin than phenolics, later class appearing more discriminatory among different cultivars. The same metabolomics platform can indeed be used to assess other conditions such as climate and agricultural practices on mango fruit metabolome. Moreover, the antioxidant results were found to be correlated with TPC, especially gallate moieties, which was confirmed by the PLS loading plot, whereas other metabolites were less likely to contribute to the antioxidant activity, i.e., carotenoids. Hence, further work is required to profile the mango samples in the positive ionization mode of the UPLC/MS analysis. In addition, isolation and structural elucidation of bioactive compounds from high-quality cv. should be targeted, followed by the application of quantitative NMR (qNMR) for standardization of its extracts in the future. Further, profiling of mango fruit seed and peel as waste byproducts should now follow to aid maximize their economic value based on detailed chemical profiling using the same approach as presented in this study.


Pinctada margaritifera is increasingly becoming a biological model to study this process due to its ability to produce a large range of colors. Previous breeding experiments demonstrated that color phenotypes were partly under genetic control, and while a few genes were found in comparative transcriptomics and epigenetic experiments, genetic variants associated with the phenotypes have not yet been investigated. Here, we used a pooled‐sequencing approach on 172 individuals to investigate color‐associated variants on three color phenotypes of economic interest for pearl farming, in three wild and one hatchery populations. While our results uncovered SNPs targeting pigment‐related genes already identified in previous studies, such as PBGD, tyrosinases, GST, or FECH, we also identified new color‐related genes occurring in the same pathways, like CYP4F8, CYP3A4, and CYP2R1. Moreover, we identified new genes involved in novel pathways unknown to be involved in shell coloration for P. margaritifera, like the carotenoid pathway, BCO1. These findings are essential to possibly implement future breeding programs focused on individual selection for specific color production in pearl oysters and improve the footprint of perliculture on the Polynesian lagoon by producing less but with a better quality.For hundreds of years, the color diversity of Mollusca shells has been a topic of interest for humanity. However, the genetic control underlying color expression is still poorly understood in mollusks. The pearl oyster  These F1 individuals originate from three multiparental families selected for their highly saturated color phenotypes . These F1 individuals were used to enhance the genetic signal of putative allelic variants linked with color phenotype, by reducing the genetic noise due to genetic diversity in the dataset. On the other hand, wild populations are used to avoid any family effect and false‐positive association due to a possibly reduced genetic diversity in F1. As such, a total of 510 samples were used for DNA extraction.For each location and each color phenotype, 50 individuals were selected by the visual appreciation of their color and phenotypically confirmed later by analyzing their inner shell color with Stenger,  . For each individual, a 5 mm2.2ImaginR, as described in Stenger et al. , in order to prevent dark shadows and light reflection on the shell surface, as described in Stenger et al. . Image pr et al. . Using t2.32O) was added to the digested samples, and the mixed solution was then centrifuged at 14,000 g for 15 min. DNA quality and quantity were assessed with a NanoDrop™ 1000 and agarose gels . Only high‐quality DNA extraction was added to the pools. Pools were constructed using equimolar proportions of each individual of each phenotype and each sampling location. Figure Samples were cleaned with DEPC before DNA extraction. Total DNA of 25 mg of mantle was extracted for each individual with a QIAamp DNA Mini Kit from QIAGEN® (Cat No./ID: 51306) following manufacturer recommendations, with an RNase A treatment step . An additional step was added after overnight digestion in order to remove mucopolysaccharides following the protocol in Sokolov : 50 μl o2.4In total, 172 individuals were pooled according to their color and their population of origin, resulting in 12 pools  used for each pool in order to adapt the SNP calling to the sample size. VCF Filter V.1.0.0  and ggplot2 V.3.2.0 , the information was decomposed with another model because we were interested in comparing the levels of the two factors simultaneously. As such, a general linear hypothesis (GLHT) and multiple comparisons for parametric models (MCP) with ANOVA and post hoc Tuckey was implemented in a two‐step strategy, separating color and site testing.The GLHT‐MCP was first used on the color results. The input model of the GLHT‐MCP was the result of the first GLM, and an ANOVA–Tukey test was performed for the MCP results. Results were stored in an R variable and the same process was used for the site variable and stored in another R object.To identify SNPs related to both color and geography to remove them from the results , we performed an additional test that encompasses all the results: Simultaneous comparison of the levels of each factor was done using a new simple GLHT on the first GLM with the specification of the linear hypotheses of both GLHT‐MCP performed on color (K1) and onsite (K2):glhtmodlinfct=rbindK1K2.p values. All analyses previously cited were performed with the R packages multcomp V. 1.4–10 .A Bonferroni correction method  was applied to all Wickham, . Paralle Calaway  recommencirclize . Significantly enriched GO categories were visualized in REVIGO . GOATOOLS . The obtained amino acid sequences of these alternative proteins (bearing the associated variants) were compared with P. margaritifera's protein sequences, and the corresponding frame in Expasy was selected. Protein modeling for the reference and alternative sequences were obtained for P. margaritifera's PBGD (porphobilinogen deaminase), shematrin, cytochrome P450 3A29, and Cadherin‐23 isoform X1  with other species referential sequences, (ii) I‐Tasser (https://zhanglab.ccmb.med.umich.edu/I‐TASSER/) was used for secondary structure prediction for both reference and alternative P. margaritifera's amino acid sequences and for finding crystal structure (PDB) or the ligand binding site, (iii) superimposition of the Protein Database files was performed on the website tool SuperPose (http://superpose.wishartlab.com) with default parameters.For all associated SNPs occurring in exonic regions of genes, complete exonic sequences were extracted using the 33.1Inner shell color analysis of the wild individuals revealed that 87.36%, 86.96%, and 98.3% of the sampled individuals were successfully assigned to the red, yellow, and green phenotypes, respectively. The remaining individuals had color patterns that did not allow proper phenotypic characterization . The darkness parameter of the yellow pool for the hatchery population is significantly different from Gambier (T‐test p = 0.011) but not from Tuamotu.Within a given color phenotype, the average hue value obtained with 3.2Between 875,890,480 and 978,499,888, raw sequences were obtained for the 12 pools  for the 12 pools preferentially groups the samples by geographical sites and not by color phenotypes  sequence (scaffold2460|size144317.10), a shematrin sequence (scaffold2460|size144317.17), a cytochrome P450 3A29 (CYP3A29) (scaffold2294|size169326.2), and a Cadherin‐23 isoform X1 (scaffold5945|size55440.1). The SNP impacting the PBGD sequence induced a change from a 307 threonine into a 307 isoleucine; the SNP impacting the shematrin sequence induced a change from a 340 aspartic acid to a 340 asparagine; the SNP impacting the CYP3A29 induced a change from a 140 leucine to a 140 phenylalanine and the SNP impacting the Cadherin induced a change from a 119 threonine into a 119 alanine.P. margaritifera genome's reference sequence and the alternative one revealed that both P. margaritifera sequences show a 31.58% similarity to the human PBGD  while the alignment of the secondary structure shows a lower similarity (97.72%)  Figure . The sup) Figure , indicat4P. margaritifera var. cumingii, and as such of its cultured pearls, a diversity that cannot be found in other Pinctada species  of the color‐associated SNPs indicated that the yellow hatchery AFD was similar to the other yellow AFD. In addition, levels of Pi diversity were found to be similar in all our samples, so that a possible inbreeding effect in the yellow hatchery sample is unlikely. Thus, the origin of the genetic variation and the outlier status of the yellow hatchery population cannot be identified at this stage but is to be noted.When considering both the geographical origin and the color of oysters, our results showed that geographical origin was the main driver of genetic differentiation among our samples. Indeed, samples were generally clustered by archipelago, except for the yellow individuals from the hatchery, which were outliers to all other samples. In wild populations, the yellow phenotype can present peculiarities regarding the positioning of the colored area of the inner shell: The colored area for the yellow individuals was indeed generally found at the bottom of the posterior section of the inner shell, while for the two other phenotypes, the colored spot was present as a large band in all posterior, connection, middle, and anterior sections of the inner shell , a pathway containing the gene porphobilinogen deaminase (PBGD). This GO category was already identified as specific to the red color in a previous study . PurH controls the synthesis of FAICAR molecule (5′‐Phosphoribosyl‐5‐formamido‐4‐imidazolecarboxamide) and can also use PurH as a substrate to produce IMP (Inosine 5′‐monophosphate), the final molecule in the de novo purine nucleotide pathway . CYP3A4 metabolizes the anthocyanidin and anthocyanin pigments . This gene is involved in the morphology of nacre lamellae in the shell of Finally, 38 different genes encoding putative copper/zinc superoxide dismutase (Cu/Zn SOD) were impacted by 47 yellow‐specific SNPs. Pigmentation pathways usually generate highly reactive molecules (porphyrins for example) that can be oxidized and have the potential to create reactive oxygen species (ROS), deleterious to the cell. Cu/Zn SOD are enzymes that catalyze the dismutation of the superoxide radical into either ordinary molecular oxygen or hydrogen peroxide. For example, Cu/Zn SOD expression is incredibly high in patients with vitiligo because of the melanogenesis pathway perturbation  stores iron in a soluble, nontoxic, readily available form and could be used in the heme biosynthesis pathway . The presence of these pigments in the shell of the pearl oysters will have to be confirmed through biochemistry since the RAMAN spectroscopy analysis used in previous studies on the shells of the Tahitian pearl oysters is not effective at identifying carotenoids. Also, new candidate genes were identified for the yellow phenotype  and for the green phenotype (cadherin‐23). Finally, we identified genes that are not directly involved in pigment synthesis but that are known to intervene in the control of potential cell damage due to the reactive nature of many pigments (oxidative stress and ROS degradation) like the Cu/Zn SOD genes.P. margaritifera in the lab, to provide a way to use crispR techniques and observe the color of nacre precipitation of these cells. RNAi techniques were tested but are very tedious to use on such a species, and very stressful for the animal. Currently, our only perspective is to use these SNPs and genotypes as many individuals as possible to validate the pattern of segregation we see here on a larger scale. A SNP Chip has been developed and includes all the SNPs identified in this study, to validate the association of these SNPs to color morphs. The chip is currently being tested in breeding programs with pearl farmers. Our results open new doors for the implementation of future breeding programs focused on individual genomic selection for specific color production in pearl oysters, and improve the footprint of perliculture on Polynesian lagoon by reducing the production volume while increasing the quality of the pearls.The actual functional role of the candidate SNPs in pigmentation pathways must be validated. Currently, it is not possible to cultivate mantle cell lineages of The authors have no conflicts of interest to disclose.Appendix S1Click here for additional data file.


Significance level was defined by a chromosome-wise False Discovery Rate of 5%. Significant genomic regions were identified in chromosomes 1, 3, 12 and 19 with the 170 SNP set, in chromosomes 7, 12 and 24 using the 507 SNP chip and only in chromosome 7 with the 50K SNP chip. Candidate genes located in these regions, using Oar_v4.0 as reference genome, were TIMP3, TLR5, LEPR and TLR9 (170 SNPs), SYNDIG1L and MGRN1 (507 SNP chip) and INO80, TLN2, TSHR and EIF2AK4 (50K SNP chip). These results validate genomic regions associated with FEC previously identified in Corriedale and other breeds and report new candidate regions for further investigation.Selection of genetically resistant animals is one alternative to reduce the negative impact of gastrointestinal nematodes (GIN) on sheep production. The aim of this study was to identify genomic regions associated with GIN resistance in Corriedale sheep by single-step genome-wide association studies (ssGWAS) using 170, 507 and 50K single nucleotide polymorphisms (SNPs). Analysis included 19,547 lambs with faecal egg counts (FEC) records, a pedigree file of 40,056 animals and 454, 711 and 383 genotypes from 170, 507 and 50K SNPs, respectively. Genomic estimated breeding values (GEBV) were obtained with single-step genomic BLUP methodology (ssGBLUP), using a univariate animal model, which included contemporary group, type of birth and age of dam as class fixed effects and age at FEC recording as covariate. The SNP effects as wells as  Haemonchus contortus and Trichostrongylus colubriformis [Parasitic infections caused by gastrointestinal nematodes (GIN) are the diseases with the greatest impact upon health and productivity for grazing ruminants, mainly sheep and goats, because they can cause weight loss, poorer growth performance and even death . In Urugriformis ,3. One sriformis ,5,6. In riformis , and lowriformis ,9.Identification of quantitative trait loci (QTL) associated with GIN resistance or susceptibility could improve the selection process and the understanding of biological processes related to host immune response . Genome-With single-step GBLUP (ssGBLUP), breeding values and marker effects can be estimated simultaneously, adjusting for population structure at the same time ,13. In aH. contortus, Teladorsagia circumcincta and T. colubriformis parasites. In another study, Berton et al. [In sheep, GWAS have been extensively used to detect SNPs associated with economically important traits. The first GWAS in sheep was done to understand horns genetic basis in wild Soay sheep  and sincn et al.  found gePeriasamy et al.  identifiThe aim of this study was to identify genomic regions associated with GIN parasite resistance in Corriedale sheep using 170, 507 and 50K SNPs, applying the ssGWAS approach, and explore the biological processes underlying the genetic resistance. e (FEC + 100). Descriptive statistics are presented in From 2000 to 2019, FEC records were collected in autumn from 19,547 Corriedale lambs  belonging to 29 extensive production farms . Corriedale is a dual-purpose breed (wool and meat production), originally developed in New Zealand from crossing Lincoln or Leicester rams with Merino females . FEC samGenealogical information from 40,056 animals was provided by the Rural Association of Uruguay and the Uruguayan Corriedale Breeders Society, who also supplied the FEC data recorded at the stud flocks. Genomic information from Corriedale animals with FEC phenotypes and complete pedigree information was available from three different sources, linked to different research initiatives. The three densities included here are: 170 SNPs  , a 507 SAt the time our analysis was undertaken, the distribution of genotyped animals was as follows. A total of 289 lambs of both sexes, belonging to one experimental unit, born between 2000 and 2011, and 46 sires and 119 dams, were genotyped with 170 SNP. In the case of the 507 SNP chip, genotypes of 565 lambs of both sexes, belonging to one experimental unit, born between 2000 and 2011, and 35 sires and 111 dams, were available. Finally, 265 lambs of both sexes, born between 2000 and 2019, belonging to two experimental units, as well as 64 sires and 54 dams were genotyped with the 50K SNP chip.All DNA samples were extracted, based on the protocol defined by Medrano et al. , from 15Genomic data quality control included the exclusion of markers located in sexual chromosomes, monomorphic SNPs and those with minor allele frequencies (MAF) < 0.05 and call rate < 90%; and animals with call rate < 90%. Numbers of SNPs and animals after quality control, used in this study, are presented in Only the 507 SNP chip has 91 SNPs in common with the 50K SNP chip, with no SNPs overlapping among the other chips. Even though there were subsets of animals genotyped with two or three SNP chips , the numAssociation analysis for FEC was performed for each SNP density  using ssGWAS methodology  with BLUy is the vector of LogFEC; b is the vector of fixed effects, which includes 467 contemporary groups , type of birth (two levels: unique or multiple), age of dam  and age at FEC recording as covariate, u is the vector of additive genetic effects, with u~N , where H is the matrix of pedigree and genomic information, and σ2u is the additive genetic variance; e is the vector of random residuals e~N  being I the identity matrix and σ2e the residual variance; and X and Z are the incidence matrices of fixed and additive genetic effects, respectively. The GEBVs for FEC were estimated based on described phenotypic, pedigree and genomic information, based on the following univariate animal model:G matrix was computed according to VanRaden [Z is the matrix that relates genotypes of each locus adjusted for allelic frequencies, D is the diagonal matrix of weights for SNP variances  and q is a normalization factor. GEBV mean accuracies, standard deviations, minimum and maximum are reported as The inverse of this matrix was constructed according to Aguilar et al. :(2)H−1=AVanRaden :(3)G=ZDZD = IEstablish the diagonal matrix of weights for SNP variances as an identity matrix: G matrix = ZDZ′qConstruct Estimate GEBVs for all animals included in the pedigree using ssGBLUPConvert GEBVs to SNP effects: i): Estimate each SNP weight Pi=PtOSTGSF90 .Ovis aries autosomic chromosomes are represented with its −log10 . A quantile-quantile (QQ) plot was used to evaluate if population structure was accurately accounted for in the model [10 significance values of each SNP [After obtaining the significance levels, Manhattan plots were generated with R software ), where he model ), since he model . This tyeach SNP . The most frequently used procedures to adjust for multiple testing are Bonferroni correction and False Discovery Rate (FDR) . The FDRTwo databases were used for gene identification based on the Oar_v4.0 ovine reference genome: iSheep database  and Genop-values < 0.05 were identified, seven of them significant with the 5% FDR per chromosome, which were located in chromosomes 1, 3, 12 and 19 , but these seven SNPs observed p-values deviate from the expected ones, suggesting an association between them and logFEC , and another SNP is in OAR1 in the LEPR gene (leptin receptor). Another three SNPs in OAR12 are located within the TLR5 gene (toll-like receptor 5) and two other SNPs in OAR19 are within the TLR9 gene region (toll-like receptor 9).In OAR3, one significant SNP is within the p-values < 0.05, and from these, four SNPs were significant with the 5% FDR per chromosome. These SNPs are located in chromosomes 7, 12 and 24 . An association between these four SNPs and logFEC is suggested by the deviation of observed p-values from the expected ones (2 and 24  and wereted ones .MGRN1 gene (mahogunin ring finger 1) and another SNP in OAR7 is close to the SYNDIG1L gene (synapse differentiation inducing 1-like).In OAR24, one significant SNP is within the p-values < 0.05, and from these, five SNPs were significant with a 5% FDR per chromosome, all of them located in chromosome 7 , but these five SNPs observed p-values deviate from the expected ones, which suggests an association between these SNPs and logFEC (mosome 7 . The QQ d logFEC .INO80 gene (INO80 complex ATPase subunit), TLN2 gene , TSHR gene (thyroid stimulating hormone receptor) and EIF2AK4 gene (eukaryotic translation initiation factor 2 α kinase 4).In OAR7, four significant SNPs are within the SYNDIG1L, MGRN1, INO80 and TLN2 were not mapped in the UniProt database.After identifying the candidate genes, proteins and gene ontology (GO), terms were assigned . In addiGIN infections have a significant impact on health and productivity in sheep production systems . With thIn the current study, QTL associated with logFEC were explored using low density (170 and 507 SNPs) and medium density coverage (50K SNPs), with a minimum SNP overlapping only between the 507 SNP and 50K SNP chips.By using the ssGWAS methodology, large phenotypic and pedigree databases of genotyped and non-genotyped animals could have contributed to a more reliable QTL detection . In addimetallopeptidase 3 inhibitor gene (TIMP3), which codes for a group of inhibitor proteins that control extracellular matrix metalloproteases enzymatic activity (MMPs). These MMPs regulate degradation of all the extracellular matrix components and participate in the processing of bioactive molecules such as cytokines, chemokines, growth factors and their receptors [T. circumcincta parasite, it was found that the last ones presented elevated levels of MMP-7 and TIMP-1 transcripts, which could be associated with a greater tissular damage in the abomasal mucosa of animals that still have not developed immunity [metallopeptidase 3 inhibitor exerts in MMPs regulation involved in tissular damage triggered by a parasitic infection.The 170 SNPs considered here were selected in a study conducted by Periasamy et al.  due to teceptors , so theyeceptors . Previoueceptors . In an eimmunity . Furtherimmunity . In agreimmunity , a signiimmunity  suggesteTLR5, LEPR and TLR9). TLR5 and TLR9 are genes that code for toll-like receptors (TLR), a family of transmembrane proteins present in epithelial cells that recognize molecular patterns associated with pathogens in the gastrointestinal mucosa and activate the innate immune system [H. contortus infection has been associated with the activation of genes that code for this type of receptors [H. contortus and T. colubriformis, higher expressions of TLR2, TLR4, TLR8, TLR9 and TLR10 genes were found in the abomasal mucosa of resistant animals, which could be an indication of an early inflammatory response to infection [H. contortus artificial infections, a higher expression of the TLR2 gene was observed in abomasal tissue of Morada Nova-resistant lambs [TLR10 gene (TLR10_292 SNP), while in the current study five SNPs were found but in the TLR5 and TLR9 genes instead, also in Corriedale sheep but under natural infection. This validates the relationship found in previous studies between these receptors and the immune response triggered in the host in the face of a parasitic infection.In addition, another six significant SNPs were identified with the 170 SNPs . These SNPs are located within three candidate genes . Leptin is a protein that is produced in the adipose tissue, in response to feed intake and energy balance regulation [H. contortus could be associated with serum leptin levels. The significant association found in the current study between logFEC and LEPR_260 in the Corriedale breed validates previous studies conducted in other breeds of leptin’s involvement with the immune response in the presence of a parasitic infection.The last significant SNP found with the 170 SNPs was the LEPR_260 that is located within the gulation . Since lgulation . Leptin gulation  and thisgulation . A previgulation  suggestemahogunin ring finger 1 gene (MGRN1) and the s45225.1 SNP is at 11,375 bases distance from the synapse differentiation inducing 1-like gene (SYNDIG1L). This gene codes for an integral membrane protein, which in previous studies was reported as associated with traits related to teat number in pigs [MGRN1 gene codes for the E3 ubiquitin ligase that participates in metallic ions union. In a GWAS for resistance to GIN in Tunisian sheep, no association was found between this gene with logFEC, but instead it was related to carcass bone weight [Regarding the 507 SNP chip, in the current study significant associations were found between the logFEC and four SNPs in chromosomes 7, 12 and 24, but only two of them were within or at short distance of genes (s45225.1 and s68231.1). This last SNP is within the  in pigs  and to te weight . Other se weight  and bulle weight .INO80, TLN2, TSHR and EIF2AK4, respectively). INO80 gene codes for a subunit of the INO80 chromatin remodeling complex. This complex participates in the regulation of transcription, DNA damage repair and DNA replication, processes needed for cell integrity [talin 2 gene (TLN2) codes for a cytoskeleton protein that participates in cellular adhesion, and it was found to be associated with Alzheimer´s disease [eukaryotic translation initiation factor 2 α kinase 4 gene (EIF2AK4) codes for a member of a family of kinases that regulates protein synthesis, and mutations in this gene seem to cause pulmonary veno-occlusive disease [TSHR gene codes for the thyroid stimulating hormone receptor that controls the thyroid cellular metabolism. Cun et al. [Finally, with the 50K SNP chip significant associations were found between logFEC and five SNPs in chromosome 7, but only four of them  were located within previously reported genes . The TIMP3, TLR5, TLR9, LEPR, TSHR and EIF2AK4 genes are related to cellular processes (GO: 0009987), response to stimulus (GO:0050896) and biological regulation (GO:0065007), and from these only three are listed as related to immune system processes . From these three genes, two are related to the innate immune response  and the TSHR gene is related to lymphocyte activation (GO:0046649) and specifically to B cell differentiation (GO:0030183).The gene ontology analysis with UniProt database showed the biological processes related to six genes found in the current study; the other four were not mapped in this database , confirming previous findings in other breeds. On the other hand, some non-reported candidate genes were found in association with logFEC, which could be valuable for future studies . In the current study, genomic regions associated with GIN resistance, as well as candidate genes, were identified. Some of these candidate genes are related to the immune system (


Both genes have been associated with hypoxia responses or altitude adaptation in humans. For the Xi dogs, the top ranked region on CFA25 contained the Transient Receptor Potential Cation Channel Subfamily C Member 4 (TRPC4) gene. This calcium channel is important for optimal muscle performance during exercise. The outstanding signals in the Mountain dogs were on CFA5 with 213 significant SNPs that spanned genes involved in cardiac development, sight and generation of biochemical energy. These findings support the use of the combined index approach for identifying novel regions of genome diversity in dogs. As with other methods, the results do not prove causal links between these regions and phenotypes, but they may assist in focusing future studies that seek to identify functional pathways that contribute to breed diversity.During breed development, domestic dogs have undergone genetic bottlenecks and sustained selective pressures, as a result distinctive genomic diversity occurs to varying degrees within and between breed groups. This diversity can be identified using standard methods or combinations of these methods. This study explored the application of a combined selection index, composite selection signals (CSS), derived from multiple methods to an existing genotype dataset from three breed groups developed in distinct regions of Asia: Qinghai-Tibet plateau dogs , Xi dogs (with superior running ability) and Mountain hounds (used for hunting ability). The CSS analysis confirmed top ranked genomic regions on CFA10 and CFA21 in Qinghai-Tibet plateau dogs, CFA1 in Xi dogs and CFA5 in Mountain hounds. CSS analysis identified additional significant genomic regions in each group, defined by a total of 1,397, 1,475 and 1,675 significant SNPs in the Qinghai-Tibetan Plateau dogs, Xi dogs and Mountain hounds, respectively. Chitinase 3 Like 1  The first domestication events created genetic bottlenecks that have since been exacerbated by further selective pressures in the creation of specific breeds. Around 500 distinct breeds are currently recognized by breed clubs, such as the American Kennel Club (https://www.akc.org/)3, Australian National Kennel Council 4,5 or The Kennel Club 7. Each breed is classified by a standard, which includes morphological criteria, behavioural traits and coat color9. These guidelines were applied during the development of modern pure-breed dogs, consequently phenotypic and genetic heterogeneity has been substantially reduced within breeds while maintaining diversity across breeds.The development of modern dog breeds from ancestral populations is a good model for understanding domestication and genetic diversificationFst, XP-EHH and di15. Akey et al.12 demonstrated that, combined with high density SNP markers, the Fst statistic was a powerful tool to scan the canine genome for selection signatures, and developed a modified pairwise statistic (di) based on Fst to detect locus specific deviation between breeds 10. The other widely used statistical test to identify regions of interest is Cross Population EHH (XP-EHH)17. Using long-range haplotype information, XP-EHH measures whether a selected allele has risen in frequency in one population but not in a second reference population16. The use of a single methodology may be enhanced by combining signals across different tests20. One such composite method, named composite selection signals (CSS), has been developed in domesticate species (cattle and sheep) for application to genomic data where a detailed individual phenotypic or population history is not available22. Application of the CSS method to cattle breeds and across species (cattle and sheep) have detected signals throughout the genome with a high degree of sensitivity.Previous studies of genomic diversity in dogs have used summary statistics to measure locus specific divergence in allele frequencies, for example 11.The aim of this study was to investigate the utility of the CSS method for exploring diversity in three groups of geographically distinct Asian dogs: Qinghai-Tibetan Plateau dogs, Shandong and Shaanxi Xi dogs, and Sichuan Mountain hounds 11: Qinghai-Tibetan Plateau dogs , Xi dogs and Mountain hounds. Dogs that reside in the high Qinghai-Tibetan Plateau have adapted to life at high altitudes. The other two dog groups are geographically distinct and are generally described as having superior running and hunting ability, respectively, but morphological and physiological traits in the Xi and Mountain hound breed groups are not specifically defined.Three groups of dogs were investigated individually as the targets of analysis, similar to Yang et al.Living at high-altitude in the Himalayan Mountains resulted in physiological adaptations that allowed dogs to perform in low oxygen conditions. Hequ Tibetan Mastiffs and Tibetan Mastiffs are ancient breeds of large dogs, native to the northeastern part of the Qinghai-Tibetan Plateau of China and Linzhi, located in the southeastern part of the Tibet Autonomous Region (TAR) of China. They adapted to a high-altitude environment over a relatively short period of time. A genome-wide scan using these dogs as the target group and a 1 Mb sliding window size identified a total of 1,397 significant SNPs Table . The topWhen genomic regions identified in the Qinghai-Tibetan Plateau dog population were examined, they contained a total of 220 annotated genes (ROS CFam v1.0). This list of genes was subjected to gene set analysis. The findings in each individual gene ontology category  or pathway analysis that satisfied cut-off criteria (Benjamini < 0.05 or FDR < 0.05), are listed in Supplementary Table Shandong Xi dogs and Shaanxi Xi dogs are considered two of the ancient dog breeds in China. The long-haired Xi dog and short-haired Xi dog were once widely distributed in Hebei, Shandong, Shaanxi, Mongolia and other places. A genome-wide CSS scan for the Xi dogs identified a total of 1,475 significant SNPs Table . These r23. Indeed, the efficient operation of cation channels at neuromuscular junctions is critical for skeletal muscle contraction during running26.The 23 significant genomic regions contained 242 annotated genes. The full lists of GO term clusters and enrichment analysis is provided in Supplementary Tables Liangshan and Qingchuan Mountain hounds are Chinese native breeds mainly distributed in a landlocked mountainous region of Western Sichuan province. They were traditionally used for hunting. These dogs were compared to a group of geographically distinct breeds. A genome-wide CSS scan using the Mountain hounds as the target group identified a total of 1,675 significant SNPs Table . The topIn the Mountain hound population, the 17 significant genomic regions contained 198 annotated genes. The list of GO term annotations and pathway descriptions is provided in Supplementary Table Similar to the previous sections, to present the relevant grouped annotation networks, ClueGO v2.2.8/CluePedia v1.5.8 was employed. These groups are shown in Fig. Fst, ΔDAF/SAF, and XP-EHH) this analysis confirmed regions described in similar studies and identified novel distinctive regions in each of these breed groups. Similar results were generated when the SAF method was substituted for DAF and is most useful in circumstances where ancestral allele determination is not possible  play an important role in regulating external and internal adaptation to hypoxic stress31, while inflammatory pathways are characterized by signaling through the effector molecule, Nuclear Factor kappa B , was also found in the CFA10 region, while the key regulator of HIF pathways, P4HB (prolyl 4-hydroxylase beta polypeptide)31, was within a significant region on CFA9.In other canine studies, selection signals have been detected using single methods or by analysis using two methods independently to verify findings. When our results were compared to the study used as the source of genotype data, two regions in common were found on chromosome 10 and 21, both in the top 0.1% of CSS signals in our study. The CFA10 region contains the gene for the HIF regulatory protein OR52A1 originally named as a possible candidate. The olfactory receptor gene family is the largest gene family in the genome and is particularly rich in dogs. However, this can be confounding when prioritizing gene candidates. We note that embedded within the olfactory receptor genes in this region is the dog hemoglobin beta chain gene (HBB) and suggest that this is the likely candidate for these signals, similar to Fan et al.34. HBB has been previously recognized in other species as a gene under selection in response to altitude adaptation36. Surprisingly, the HBB gene is only provisionally annotated in the dog genome assembly as LOC609402, and this may have resulted in this gene being overlooked as a candidate in some studies.The common region identified on CFA21 in both studies is rich in olfactory receptor genes with (DNAH9) was named by Yang et al.11, and Kinesin Family Member 1C (KIF1C) and Enolase 3 (ENO3) were added to the potential candidates in this location by Li et al.35. One other gene at this location is Myocardin (MYOCD) which have never been mentioned in the context of dogs, but we note is associated with altitude adaptation in humans37. We also identified genes with functions related to altitude adaptation in a region on CFA28. For example, Arachidonate Lipoxygenase gene family member 5 (ALOX5) which has a role in oxidoreductase activity, oxygen and iron binding, is potentially a novel candidate for these dogs38. Two other genes, Kinesin Family Member 6 (KIF6) and ERCC Excision Repair 6 (ERCC6), were also previously reported as candidate genes35.The significant region identified here on CFA5 from the analysis of Qinghai-Tibetan Plateau dogs contains a number of genes that may contribute to altitude adaptation. Dynein Axonemal Heavy Chain 9 (CHI3L1) and Leucine Rich Repeat Containing G Protein-Coupled Receptor 6 (LGR6) genes. CHI3L1 has been identified in altitude induced pulmonary oedema and the response of lung tissue to inflammation in humans40. LGR6 is important for proliferation of lung epithelial cell progenitors to maintain tissue homeostasis and normal lung function41.Chromosome 7 had the highest CSS value in our study but has not been picked up by previous studies on Tibetan Mastiffs. This region includes the Chitinase 3 Like 1 .35 and included a region on CFA9 with the potential candidate sodium-coupled neutral amino acid transporter 10 (SLC38A10). Both studies also identified a region on CFA30 where the calcium ion release channel Ryanodine Receptor 3 (RY3R) gene is located, and CFA25 containing the Doublecortin Like Kinase 1 (DCLK1) gene.Other regions identified corresponded to those found in Tibetan Mastiffs by Li et alOTULIN, a deubiquitinase that affects signaling via Mammalian Target of Rapamycin (mTOR) and Nuclear Factor kappaB , which has been found to influence the response to hypoxia and angiogenesis, and Polycystin-1 (PKD1) which has been shown to influence lung development and inflammatory responses. The Rieske Fe-S domain containing (RFESD) gene was found in a significant region on CFA3. This gene encodes a molecule with iron and oxygen binding functions.Uniquely identified regions included a region on CFA34 containing (TRPC4) gene, which codes for a protein that, together with TRPC1 and stromal-interacting molecule-1 (STIM1), is an important regulator of a process known as store-operated calcium entry (SOCE)44. This process is critical for optimal skeletal muscle function during exercise when there are repetitive sarcolemma membrane depolarizations. SOCE has been implicated in fatigue resistance and in the development and maintenance of muscle mass. Another of the CSS identified selected regions on CFA1 overlapped with that described by Yang et al.11 and contained NOL8 and IARS, but the region they highlighted on CFA9 containing multiple keratin genes was not significant in our study.Running speed and strength are dependent on musculoskeletal and neuromuscular physiological processes involving balance, coordination, neuromuscular function and muscle development (myogenesis). The regions identified in the analysis of Xi dogs included genes that are central to these processes, most notably those involved in cerebellar function, excitation–contraction coupling in muscle and muscle development. The top ranked region on CFA25 contained the Transient Receptor Potential Cation Channel Subfamily C Member 4 25.Other genes in CSS top ranked selected regions were associated with myofiber development and function. Notable amongst the top annotations of genes within the identified selected regions were those associated with acetylcholine receptor function. Acetylcholine is the neurotransmitter found at the neuromuscular junction and initiates muscle contraction, a key point for continuous skeletal muscle function during exercise in dogs.11 and contained genes involved in eye development and sight (RBP7 and NPHP4), cardiac development (UBE4B) and regulation of metabolism (H6PD). An additional gene in this region, Spastic Paraplegia 7 (SPG7), affects mitochondrial function. Variants in SFG7 in humans are known to affect muscle ATP production, eye movement and Purkinje neurons of the cerebellum, critical for motor coordination and balance46.The analysis of Mountain hounds highlighted multiple signals on CFA5 and CFA20, followed by CFA24. The top ranked region on CFA5 (62.08–65.6 Mb) corresponded to a region identified by Yang et al47. However, the number of adjacent SNPs included in a window is a variable parameter which requires optimization50. The ideal window size will differ depending on SNP array density, the pattern of linkage disequilibrium (LD) throughout the genome, the statistical methods applied and the genetic architecture of trait variation51. A comparison of two window sizes during analyses in this study showed that the size of the window had some influence on the final results, as previously noted52. However, LD (0.1 < r2 < 0.2) extends up to 0.3 Mb11 in the Chinese dog breeds, so the smaller 200 Kb window size may lose SNP information, whereas 1 Mb window size was considered more robust for this analysis and the addition of the smoothing steps within analysis windows has the capacity to incorporate the power of LD and reduce false positive signals due to single variant effects. Hence, a 200 kb window may add specificity to the analysis, but at the expense of sensitivity.Numerous windows-based scan methods have been utilized for detecting signatures of selection in dogs and other species.11 showed that the CSS method using the parameters specified here focused the significant regions in each analysis by reducing the number by approximately 50% while simultaneously increasing the number of potential candidate genes for each of the regions. It is important to note that these methods do not differentiate the nature of the variation which may arise from regions of selection, genetic drift or carrier haplotype mechanisms that may be affected by defining characteristics of the dogs under study53. However, some functional relevance of the candidate genes within the significant genomic regions was supported by in silico functional analysis. The impact of the identified gene networks and pathways on the respective breeds awaits further analysis, but interpretation would be improved if traits were quantified. Ultimately no causal link can be established without detailed functional studies. Such studies will benefit from the current application of the CSS method to defining breed differences in these dogs.In this study we have applied the composite index method, CSS, to evaluate genomic regions that differentiate dogs from three breed groups. The CSS method has been shown to be a sensitive method for detecting selection signals in cattle and sheep, and here we have demonstrated its utility in canine sub-populations. Using an index approach improved sensitivity and robustness of identifying signals. A comparison to the relevant study by Yang et al11. The three geographically distinct dog groups: Qinghai-Tibet plateau dogs (n = 34), consisting of 15 samples of Tibetan Mastiffs, 7 samples of Hequ Tibetan Mastiffs and 12 samples of Linzhi dogs; Xi dogs (n = 24), consisting of 12 samples of Shandong Xi dogs and 12 samples of Shaanxi Xi dogs and merged as a target group with fast running speed (60 km/h); Mountain hounds (n = 24); consisting of 12 samples of Liangshan dogs and 12 samples of Qingchuan hounds, classified based on exceptional hunting ability. The fourth group is the reference groups and contains 85 samples including non-target dogs and additional samples from other Asian breeds , was applied to assign haplotype phase from unphased genotype data. BEAGLE provides a population-based analysis with fast process time and high accuracy for medium to large sample sizes57. BEAGLE works by sampling haplotypes using a Hidden Markov model and is well suited to non-human datasets58. Prior to phasing, genotypic data was split by chromosome and converted to VCF format using PLINK v1.954. The VCF files were used as input to BEAGLE using default settings . The resulting haplotype information was then used in XP-EHH calculations.The SNP array data yielded unphased genotypes, making it difficult to observe whether different alleles were on the same haplotype. Hence, the BEAGLE 5.0 software59 package was used for computing XP-EHH scores. The EHH values were estimated by comparing samples from two groups via a target and a reference group. A positive value indicates that selection was likely to have occurred in the target group, while a negative score indicated selection happened in the reference group.The XP-EHH test was used to find alleles with an increase in frequency to the point of fixation or near-fixation in the populations under investigation. The SELSCANFst) has been widely used to measure genetic differentiation in positive selection and directly displays the variance in allele frequency between two populations61. The Fst values were determined as the mean values of allele frequency (method-of-moments) between the groups. The basis of the formula is as follows62: (HT–HS)/HT. Here HT is a total counting of heterozygosity in populations and HS is based on the average of heterozygosity across subpopulations.The fixation index  and derived allele frequency (DAF). Both can be used to detect high frequency alleles in the target group and are sensitive for distinguishing selected allelestarget—SAF reference. The use of DAF required assignment of ancestral alleles and their frequencies. In the present study, the major alleles in wolves were assigned as ancestral alleles, following the approach used previously63 and in comparison with data from Alaskan Huskies as in a prior study15. The major alleles (common variant) from this dataset were assigned as ancestral alleles. A total of 166,579 SNPs were assigned for analysis. The derived allele frequency difference was computed according to the following formula: ∆DAF = DAF target—DAF reference. The normal distribution of derived allele frequency was estimated and the ∆DAF values were transferred to Z scores .The selected allele frequency is based on the observed major allele frequency for the subpopulation, hence ∆SAF = SAF 22 and depends on rank-based p-values. Briefly, the CSS statistic is calculated from univariate measures  as follows: univariate statistics are first converted to fractional ranks between 0 to 1 by 1/(n + 1) to n/(n + 1), where n is the number of SNPs. Next, fractional ranks are converted to Z-values using the inverse normal cumulative distribution function (CDF). Thirdly, the mean Z-score is taken from all statistical tests at each SNP position and converted to a p-value using the normal N distribution, where m is the number of univariate statistics. The logarithm of the p-value  is equivalent to the CSS statistic.This index was developed by Randhawa and colleagues50. The windows were shifted on SNP positions along the genome in either a 200 Kb or 1 Mb sliding window. Significance threshold was set based on the calculated mean of CSS values of all the SNPs within a window and corresponding to top 0.1% or top 0.5%. Regions generated from the analysis were defined by the first and last significant SNP in the corresponding window. Overlapping windows were merged into a single region bound by the first and last significant SNP after merging. The average CSS value was the mean of smoothed CSS values of all significant SNPs in the region.A sliding window method was applied to reduce background noise and enhance robustness of detected signals from genomic regionshttps://ggvs-rstudio.vip.sydney.edu.au/auth-sign-in).Genome annotation was performed based on genomic regions derived from CSS analysis. Annotated genes from the Ensembl gene annotation database (CanFam3.1) were retrieved by matching chromosome and position. All processes were performed using R software (http://david.abcc.ncifcrf.gov/)65,66 via Kyoto Encyclopedia of Genes and Genomes  and Gene Ontology  knowledge base resources. The Benjamini corrected p-value ≤ 0.05 and FDR ≤ 0.05 were used to indicate a statistically significant difference.Genes from all significant genomic regions were used for functional enrichment analysis. The Database for Annotation, Visualisation and Integrated Discovery (DAVID) v6.8 was used for analyzing functional classification, gene ontology, interconnected pathways, and understanding high-level functions and biological systems from large-scale molecular datasets , molecular function (MF), cellular component (CC) categories, and one pathway analysis (KEGG) were constructed to grouped functional categories. The selection criteria applied the hypergeometric test  and the significance was set as 0.05.Additional analysis used the Cytoscape software (Version 3.8.2)Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.Supplementary Information 12.Supplementary Information 13.Supplementary Information 14.


Staphylococcus aureus (MRSA) provides high-resolution typing, facilitating surveillance and outbreak investigations. The aim of this study was to evaluate the genomic variation rate in MRSA, by comparing commonly used core genome multilocus sequencing (cgMLST) against single nucleotide polymorphism (SNP) analyses. WGS was performed on 95 MRSA isolates, collected from 20 carriers during years 2003–2019. To assess variation and methodological-related differences, two different cgMLST schemes were obtained using Ridom SeqSphere+ and the cloud-based 1928 platform. In addition, two SNP methods, 1928 platform and Northern Arizona SNP Pipeline (NASP) were used. The cgMLST using Ridom SeqSphere+ and 1928 showed a median of 5.0 and 2.0 allele variants/year, respectively. In the SNP analysis, performed with two reference genomes COL and Newman, 1928 showed a median of 13 and 24 SNPs (including presumed recombination) and 3.8 respectively 4.0 SNPs (without recombination) per individual/year. Accordantly, NASP showed a median of 5.5 and 5.8 SNPs per individual/year. In conclusion, an estimated genomic variation rate of 2.0–5.8 genetic events per year (without recombination), is suggested as a general guideline to be used at clinical laboratories for surveillance and outbreak investigations independently of analysis approach used.Whole genome sequencing (WGS) of methicillin-resistant  Staphylococcus aureus is a commensal that can be found in the human microbiota of the nasal mucosa in 20–40% of the population1. However, this bacterium is also associated with infections in humans, ranging from mild superficial skin and soft tissue infections, to severe conditions such as infective endocarditis, bacteraemia, osteomyelitis and septic arthritis. In Sweden 3112 cases of new MRSA episodes were reported in 2020, a decrease of 19% compared to 2019 before the Covid-19 pandemic2. Previously hospital-acquired MRSA (HA-MRSA) was mainly a healthcare-related problem, today, community-acquired MRSA (CA-MRSA) appears to be the main route of transmission. CA-MRSA lineages have over time been observed in healthcare-related outbreaks, therefore the distinction between the two groups has become blurred4. Since the year of 2000, MRSA is included in the Public Health Agency of Sweden’s (PHAS) monitoring program as a notifiable disease where contact tracing is mandatory regarding every case according to the Communicable Diseases Act. For every new MRSA case, the local infection control unit decides to whether or not eradicate the bacteria. MRSA carriers are then followed with sampling once a year. For surveillance, basic molecular typing that divides isolates into clonal complexes (CC) based on multilocus sequence typing (MLST) as well as Staphylococcus aureus protein A (spa) typing is generally used6. To obtain more detailed information, typing of MRSA by whole genome sequencing (WGS) is nowadays widely used to get a more extensive surveillance, e.g. in an outbreak situation8.9.WGS provides access to complete microbial genomes, and its discriminatory power enables to perform large-scale genomic epidemiological studies on a national level10. There are several widely used approaches for depicting the relatedness of isolates using genome data, such as core genome multilocus sequence typing (cgMLST) or single nucleotide polymorphism (SNP) analyses. S. aureus cgMLST enables a standardized, discriminatory and reproducible method using the same defined core genome for the differentiation of MRSA and produces a definitive nomenclature to be shared between different stakeholders if the same cgMLST scheme is used11. SNP-based analysis, on the other hand, often provides an even higher discriminatory power, but comparison between laboratories is more difficult due to the variability in settings and use of reference genomes. The threshold settings and the variety of SNP filters needs to be the same if comparison between laboratories is to be accomplished13. S. aureus evolves primarily through replication-induced point mutations, which accumulate SNPs over time. There are few reports in this area, mainly focusing on short-term studies about small outbreaks in care facilities or regarding evolution within a specific MLST or CC. According to these studies, the estimated mutation rate for MRSA varied between 2 and 10 SNPs in a single genome per year16. However, the mutation rate can be defined by single nucleotide alterations in a SNP analysis but it can also be a genetic variation as a single SNP, several SNPs or allelic variations in cgMLST and hereby referred as the genomic variation rate in MRSA carriers11.The use of WGS facilitates identification of emerging lineages within the community and/or responsible for hospital outbreaks due to the continuous alteration of the genome that occurs through replication errors, acquisition of genetic material from other bacteria by horizontal gene transfer of mobile genetic elements and recombination eventsIn the present study, the aim was to evaluate the genomic variation rate in MRSA using isolates from long-term carriers by comparing two commonly used analysis approaches i.e. cgMLST and SNP as guidelines for clinical laboratories in surveillance and outbreak investigations.All MRSA isolates (n = 95) were successfully whole genome sequenced and subsequently analysed. Four samples, two from patient 5, one from patient 9 and one from patient 12 were excluded due to change of sequence type (ST) and complex type (CT), showing an obvious acquisition of a different MRSA strain during the follow up as shown in Table Eleven different MLST-types were identified among the carriers where the majority belonged to ST88 (7 patients/29 samples), ST22 (2/9), ST6 1/4), ST1 (1/2), ST1340 (1/6), ST59 (1/2), ST45 (2/9), ST5210 (1/5), ST72 (2/10), ST5 (1/9) and ST80 (1/4). Eight different CC were found among the patients, CC1 (1 patient/2 samples), CC5 (3/19), CC8 (2/10), CC22 (3/14), CC45 (2/9), CC59 (1/2), CC80 (1/4) and CC88 (7/29). The cgMLST analysis run by Ridom SeqSphere+ further divided the samples into 17 different CT groups as shown in Table /4, ST1  allele variants/year per individual while the analysis by 1928 displayed a median of 2.0 (range 0.5–6.8) allele variants/year. The summary of the estimated genomic variation rate for each patient and method are shown in Table The 1928 SNP analysis, excluding recombination events, showed a median of 4.0 (range 0.75–8.7) and 3.8 (range 0.8–8.8) SNP changes per individual/year whilst the analysis, including recombination events, showed a median of 23.6 (range 8.4–47.0) and 13.0 (range 3.6–22.3) using the COL- and Newman references, respectively. The considered percentage of the genome compared to the reference ranged from 85.4 to 92.4% for the COL reference and 82.6% to 91.3% for the Newman reference. The corresponding results for the NASP pipeline were 5.8 (range 2.2–12.5) and 5.5 (range 1.2–12.3) changes per individual/year  and 4.1 SNP/year 1928 WOR). The ST1 (n = 2) and ST1340 (n = 6) showed the lowest and highest estimated genomic variation rate of 1.5 and 10.8 alleles/year and 2.5 and 12.5 SNP/year (NASP) whereas the rate for 1928 (WOR) was 1.0 and 8.7 SNP/year, respectively. The estimated genomic variation rate for each type was calculated by adding the genomic variation rate for each of the STs divided with the total number of years among the patients within the same ST as shown in Table 8 WOR. Thspa typing as the golden standard for surveillance and outbreak investigations15.For correct interpretation of temporal divergence of bacterial lineages of SNPs and allele differences, knowledge about the genomic variation rate of MRSA is critical. Molecular methods such as typing of the bacteria are used to confirm if a new case of MRSA belongs to a known clone or if it is unique. Surveillance of MRSA is essential in order to identify modes and routes and to prevent or limit further transmission. Nowadays many laboratories perform WGS, a technique that more commonly replace The continuous development of high-throughput measurement techniques as WGS produces large numbers of data sets therefore access to bioinformatics knowledge is the key in rapidly computing and interpreting the information obtained from the analysis. Hence, more user-friendly bioinformatics platforms need to be developed to allow easier interpretation, management and exploration of data for personnel who do not have bioinformatics expertise.17. In this study the estimated genomic variation rate was found to be within a range of 2.0–5.0 alleles/year with the cgMLST analysis, 3.8–5.8 SNP/year in the SNP-analysis (excluding recombination) and 13 and 23.6 SNP/year (including recombination) depending on the method and the reference used. Diversification of MRSA strains occurred in three patients during the follow-up . ST88 has been found in many countries including Sweden and is the predominant clone in Sub-Saharan Africa23. In our collection, ST88 showed an estimated genomic variation rate that fits into the estimated genomic variation rate for the whole collection, which is not that surprising due to it is a quite large proportion of the collection. The estimated genomic variation rate however, varied between the other STs. In the present study, spa typing was performed with SeqSphere+ using WGS data, however, the use of WGS did not succeed recognising one of the spa types. Therefore, spa typing with Sanger sequencing was performed for the isolates where the spa type had not succeeded and additionally in the cases where only the spa type differed (same ST and CT) in isolates from the same patient. Sanger sequencing verified this change of spa types to be correct and the isolate that failed was found to be t458. The spa type t458 surprisingly contained only one repeat, which indicates fail in the data analysis and not the WGS, since short read WGS is known for having difficulties to analyse spa types with many similar repeats24. In our laboratory, we use WGS for routine typing and surveillance of MRSA. Here we also included the spa type due to many laboratories still use spa typing as standard method for typing of MRSA even though it lacks of discrimination power in outbreak investigations27.The majority of samples belonged to the group of CA-MRSA, which are known for being more virulent and transmissible than HA-MRSA causing infection even in healthy individuals28. This could explain why the cgMLST analysis performed with 1928 showed a lower estimated genomic variation rate compared to SeqSphere+ since they are based on two different references, i.e. Newman and COL. The core genome from the Newman’s reference is only based on 1704 genes compared to the COL reference genome that is 1861 genes resulting in a difference of 157 genes, which could support the variation of random genetic variations. In contrast to the cgMLST results based on different core genomes, the SNP-based analyses performed with NASP and 1928 actually showed comparable results, independently of the reference genome used. Otherwise, it is usually more difficult to compare results from SNPs analyses when using different references since anything that does not match the reference will be excluded from the results11. Therefore, the choice of reference genome in a SNP analysis is known to be of high importance and needs to be very closely related to the isolates if high resolution is the goal, as in outbreak investigations.The selection of a reference genome with a well-defined core genome can be a source of bias if it is not closely related to the analysed sequences and can affect the subsequent analyses such as the detection of genetic events20. One important aspect to consider about NASP is that it does not actively consider recombinant sites. To take recombination events into account the data requires further analyses, which was not done since few SNPs between person-isolates were observed.A few limitations need to be considered in our study. The genomic variation rate per ST was calculated using a small collection of samples. However, our results indicate a variation of genomic variation rate between different STs, therefore, it is important to investigate a larger sample collection since different STs dominate in different parts of the world. In addition, in this study, the mutation rate was studied within-host and not between carriers, which often is the case during an outbreak investigation. Another limitation is that in the clinical laboratory, only one random colony is chosen to analyse further and all the molecular results are thus based on this colony. This might have led to a selection of the more dominant strain during a certain period of time although several strains might have been present in the patient’s flora. Therefore, this could have affected the presence of “new” strains in some patients. Another possibility is that the patient did acquire a new strain through infection from another person or that the carrier possesses a mixed MRSA-population that is  more predominantIn conclusion, the result of this study suggest that an estimated genomic variation rate of 2.0–5.8  per year can be used as a guideline in clinical laboratories for surveillance and outbreak investigations, regardless of the analysis method used i.e. cgMLST or SNP if excluding recombination events. However, it should be taken into account that the estimated genomic variation rate may vary in different MLST sequence types but also the choice of reference genome and typing method used due to the overall variability and suitability they possess.In the present study only subcultured bacterial isolates that were previously collected and retrieved, as part of the Swedish monitoring program, was stored. No tissue material or other biological material from patients was stored. No patient information was collected. Thus, the requirement for patient consent was waived and therefore ethical approval was not required.Ninety-five MRSA isolates originating from twenty long-term carriers were collected from 2003 to 2019 by clinical routine and as part of the national monitoring program. One isolate per patient and per annual sampling  and species identity was verified by using Matrix Assisted Laser Desorption/Ionization Time of Flight (MALDI-TOF) using database Örebro20181107 / DB 7864 + SR taxonomy . For subsequent WGS, all isolates were incubated overnight on Mueller-Hinton Agar II  at 36 °C. The isolates were then suspended in 200 µl 0.85% NaCl, pre-heated and shaken during one hour at 37 °C with lysozyme 72.000 U (Sigma L6876-1G) and Lysostaphin 135 U . Genomic DNA was purified using the magLEAD 12gC, MagDEA Dx kit , following the manufacturer’s instructions. DNA was quantified using a Qubit fluorometer . Purity 260/280 and 260/230 was measured using a NanoDrop 1000 (Thermofisher) and the fragmentation of the samples was determined using the DNA integrity number (DIN) by using a TapeStation 4200 system .LibPrep Edition , using AMPure XP beads . The normalization and pooling were performed manually, based on the size of each fragment determined using TapeStation and the DNA concentration was measured with Qubit. Sequencing was performed using an Illumina MiSeq Platform  with MiSeq Reagent Kit v3, 600 cycle kit (Illumina) according to the manufacturer's instructions.Sequencing libraries were constructed using the Nextera XT DNA library preparation kit . Amplification of the tagmented DNA was performed using index primers, and the amplified products were purified with the ACSIA NGSS. aureus reference genomes, COL (NC_002951.2)30 and Newman (NC_009641)31, were used for alignment of the samples in each method. The chosen references in this study are based on the default settings for each cgMLST approach, which are used in the clinical routine for NGS analysis at the microbiology laboratory at Örebro University hospital.The genome sequencing data of all isolates (n = 95) from the 20 long-term MRSA carriers were processed by the software Ridom SeqSphere+ 5.0.0 , the cloud-based 1928 platform , and the Northern Arizona SNP Pipeline (NASP) version 1.0.1 for the cgMLST profiling and SNP analysis to evaluate the genomic variation rate 10.The reads were de novo assembled using Velvet  integrated in Ridom SeqSphere+k-mer based allele calling method. The core genome scheme is based on 1704 genes built on the Newman reference genome31. More than 30-fold average coverage over the whole genome in combination with ≥ 95% of good cgMLST targets are considered acceptable sequence quality. The 1928 platform’s cgMLST method uses a custom developed allele-calling algorithm based on an alignment-free k-mer approach. Phylogenetic trees were constructed using 1928 UPGMA-tree algorithm and distance matrix calculations were made by calculating all pairwise allelic differences between samples, missing genes were pairwise ignored36.For the 1928 platform, core gene data for all sequenced bacteria were extracted by a 38 for read alignment and FreeBayes (v1.3.2)39 for variant calling. The same publicly available reference genome for S. aureus were used as in the NASP analysis, COL and Newman. To account for putative recombinant regions, SNPs within a window of 25 base pairs were filtered out and excluded from the analysis. The window size was determined based on SNP density estimation over the genome. Distance matrices were computed from pairwise comparisons both including and excluding putative recombinant regions 40 to identify SNPs within the core genome. SNPs were called by aligning raw sequence reads with BWA (v 0.7.5a) and subsequent use of GATK (v 4.2.0.0) requiring at least tenfold sequencing depth and > 90% unambiguous base calls for all positions. Subsequently, the resulting SNP matrix files were used to obtain pairwise comparisons and create distance matrices.The sequencing data was aligned to the COL and Newman reference genome using NASP v1.0.1spa typing. However, not all the spa types for all the samples were successfully identified using WGS-data, therefore, spa typing for the isolates where the spa type had not been reported and additionally in the cases where the spa type differed was performed according to Berglund et al.41. The missing spa types were assigned using the software Ridom Staph 42.The analysis performed on the MRSA isolates by SeqSphere+ includes The average, mean, standard deviation, median and range, were performed with Statistical software SPSS version 25 to calculate the distance between samples for each patient and for the different analysis. The pairwise relatedness of cgMLST and SNP were calculated according to the allelic and SNP differences between samples, respectively.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.