Enterobacteriaceae family and Enterococcus genera, were selected and sequenced using the same protocol on the Illumina MiSeq platform in each individual centre. All generated sequencing data were analysed by one centre using BioNumerics (6.7.3) for (i) genotyping origin of replications and antimicrobial resistance genes, (ii) core-genome multi-locus sequence typing (cgMLST) for Escherichia coli and Klebsiella pneumoniae and whole-genome multi-locus sequencing typing (wgMLST) for all species. Additionally, a split k-mer analysis was performed to determine the number of SNPs between samples. A precision of 99.0% and an accuracy of 99.2% was achieved for genotyping. Based on cgMLST, a discrepant allele was called only in 2/27 and 3/15 comparisons between two genomes, for E. coli and K. pneumoniae, respectively. Based on wgMLST, the number of discrepant alleles ranged from 0 to 7 (average 1.6). For SNPs, this ranged from 0 to 11 SNPs (average 3.4). Furthermore, we demonstrate that using different de novo assemblers to analyse the same dataset introduces up to 150 SNPs, which surpasses most thresholds for bacterial outbreaks. This shows the importance of harmonization of data-processing surveillance of bacterial outbreaks. In summary, multi-centre WGS for bacterial surveillance is achievable, but only if protocols are harmonized.Whole-genome sequencing (WGS) is becoming the de facto standard for bacterial typing and outbreak surveillance of resistant bacterial pathogens. However, interoperability for WGS of bacterial outbreaks is poorly understood. We hypothesized that harmonization of WGS for outbreak surveillance is achievable through the use of identical protocols for both data generation and data analysis. A set of 30 bacterial isolates, comprising of various species belonging to the  The authors confirm that all supporting data, code and protocols have been provided within the article. All raw sequencing data were deposited at EBI-ENA under BioProject PRJEB40571.de novo assemblers for a single outbreak analysis will lead to bacterial isolates being misclassified as not related to the outbreak cluster. Additionally, we show that implementing WGS for regional or (inter)national surveillance of bacterial pathogens is feasible if identical laboratory procedures and data analysis workflows are used.Whole-genome sequencing (WGS) for typing bacterial outbreaks has surged in recent years. We performed an inter-laboratory ring-trial by sending out 30 bacterial isolates to assess the reproducibility of WGS. We demonstrated that the use of different Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter species) are of major interest as they are the leading cause of hospital-related infections and outbreaks. Furthermore, reports show that the number of infections by resistant micro-organisms have been on the rise in recent years. Infections by multi-drug-resistant (MDR) bacteria are associated with an increase in economic burden [The dissemination of antimicrobial resistance (AMR) has grown to an issue of worldwide proportions. Routine surveillance by molecular typing can aid in the fight against AMR, as outlined by the global action plan of the World Health Organization . ESKAPE c burden  and negac burden .To determine the spread of resistance and of resistant microbes, different molecular typing methods are being applied. Older, established typing methods for outbreak surveillance, such as pulsed field gel electrophoresis (PFGE), amplification fragment length polymorphism (AFLP), multi-locus sequencing typing (MLST) and multi-locus variable-number tandem repeat analysis (MLVA) are slowly being replaced by whole-genome sequencing (WGS). The introduction of WGS to the field of bacterial typing and spread of AMR has set a new standard for discriminatory power and accuracy, as it encompasses a comprehensive view of the bacterial core and accessory genome. This gives rise to the possibility to determine clonal relatedness in a more discriminatory fashion, and at the same time provide data on resistance genes, plasmids and virulence-potential, which would otherwise require a combination of other methods . CurrentApproaches like cgMLST and wgMLST determine the phylogeny among bacterial isolates based on differences in allelic profile in either the core genome or the entire genome, respectively. All coding sequences (CDS) or loci are identified using tools such as Prodigal . Then, ak: k-mer (kSNP [Inferring phylogeny based on SNPs can be performed by three different methods. (i) Alignment to a reference genome (Snippy ). (ii) (er (kSNP  or SKA [er (kSNP ).Staphylococcus aureus (MRSA) in 2013, in a neonatal intensive care unit. Next to standard assessment of epidemiological data and antibiograms, WGS was performed to resolve this putative outbreak [E. coli O157:H7, the Public Health Agency Canada evaluated WGS for outbreak detection [et al. suggested various clonal-cluster thresholds based on wgMLST or SNP analyses for a few common bacterial pathogens in outbreak situations [Currently, only a few studies have described clonal-cluster thresholds definitions using cgMLST, wgMLST or SNP-based methods. Generally, these studies determine the thresholds based on either (i) previous or ongoing bacterial outbreaks in hospitals and in the food production chain, or (ii) by means of follow-up on human carriers of these pathogens over time. Furthermore, most of these studies only describe single-clone outbreaks, which can hamper the interpretation when these thresholds are applied to different lineages of a specific species. Some clinically relevant lineages might be more clonal than others, and so require different thresholds. One of the first reports on the use of WGS for bacterial outbreak analysis were on methicillin-resistant outbreak . In thattuations .et al. recently determined clonal-cutoffs based on cgMLST and wgMLST for four extended-spectrum beta-lactamase-producing Enterobacteriaceae (ESBL-E): E. coli, K. pneumoniae, Citrobacter species and Enterobacter sp. [Kluytmans-Van Den Bergh cter sp. . In theiEnterococci. This One-Health approach could provide insights into the prevalence and spread of resistant bacteria between and within these separate domains. In the i-4–1-Health study, WGS data was generated in three independent locations, and thus inter-laboratory reproducibility needed to be assessed to allow the comparison of this data. To standardize the WGS results and interpretation, we made efforts to harmonize the WGS protocols, both for the wet-lab procedures and the bioinformatics analysis.The goal of the i-4-1-Health study is to assess the prevalence and spread of resistant bacteria among humans and animals in the Dutch-Belgian border . Across Here, we harmonized the inter-laboratory reproducibility of WGS for outbreak surveillance and genotyping of AMR and origin of replication (ORI) of plasmids for a selection of AMR bacteria frequently encountered in hospital-related infections and AMR surveillance within the i-4-1-Health project. As the implementation of WGS for routine outbreak surveillance is particularly dependent on standardized methodology, we evaluated the technical variation in phylogenetic comparison using a commercially available wgMLST tool in BioNumerics and an open-source reference-free SNP-based tool called SKA .E. coli; five K. pneumonia; four Citrobacter sp.; four Enterobacter sp.; two Klebsiella oxytoca; two Klebsiella aerogenes; two Enterococcus faecalis and two E. faecium. Six isolates  were collected previously [E. faecium and E. faecalis isolates were from a previous collection, stored at Antwerp University. The isolates were collected from perianal swabs of hospitalized patients (21) and clients in nursing homes (6), and from faeces from broilers (2) and weaned pigs (1) by selective culturing. The culturing methods are described elsewhere [In total 30 resistant bacterial isolates were selected based on their extended-spectrum beta-lactamase (ESBL) or carbapenemase activity, or based on ciprofloxacin or vancomycin resistance phenotype. The complete collection of isolates consisted of nine eviously  and kindeviously . The E. lsewhere . An overde novo assembly with an average coverage higher than 30 and less than 1000 contigs, as reported in BioNumerics (7.6.3). Samples not fulfilling acceptance criteria were re-sequenced. The accession numbers for the raw sequencing data are available in n=90) was performed in one institute.The DNA isolation and WGS procedure was performed as follows: DNA was extracted using the MasterPure DNA isolation kit (Lucigen) or MasterPure Gram Positive DNA purification kit (Lucigen). Sequencing libraries were prepared using NexteraXT (Illumina). Libraries were sequenced on the Illumina MiSeq platform in paired end 2×250 base-pair (bp) reads using the MiSeq V2 cartridge. Where possible, each set of isolates was subjected to WGS in a single run. Acceptance criteria for WGS were a novo assembly. From the raw reads and the de novo assembly, alleles were called for core-genome and whole-genome MLST (cgMLST/wgMLST). In BioNumerics, cgMLST schemes were only available for E. coli and K. pneumoniae consisting of 2513 and 634 fixed loci, respectively. Pairwise allelic distance was determined by counting the number of discrepant allele variants between two datasets, ignoring loci that were not present in both datasets. Resistance genes and origins of replication (ORI) were determined using blast [Raw sequencing reads were assembled using a custom pipeline in BioNumerics (7.6.3) employing SPAdes  (v3.7.0)g blast  and two g blast . AMR gende novo assembler to use for wgSNP analysis, we chose the assembler generating the least amount of pairwise SNPs (using SKA), among assemblies of the same isolate. To avoid complexity, only the E. coli dataset of this study was used. The following assemblers were used: (I) SPAdes (v3.14.0) [https://github.com/MUMC-MEDMIC/assemblercompare’ (v1.0). SKA [To determine the best v3.14.0) , (II) SKv3.14.0) , (III) Mv3.14.0)  (v1.2.9).0). SKA  was used.0). SKA  as the wStatistical analyses were done using scipy.stats module (V1.3.1)  and the de novo assembly is, ranged from 33712 bp (E. faecium) to 942715 bp (K. pneumoniae) and showed clear species dependence  alleles to 5493  compared to the median of the entire dataset (0.83). Further inspection of the Citrobacter sp. genome assemblies using blast webservice , showed low homology (~85% DNA identity score) to known Citrobacter sp. isolates available in the NCBI database .The number of wgMLST alleles called ranged from 1933 , and showed a lower number of alleles per kb , a skin commensal and previously described as a common contaminant of NGS datasets [One dataset of f 5.4 Mb . This dadatasets .E. cloacae-2 a sul1 gene, for E. coli-6 a tet(A) gene, for E. faecalis-1 an aac(6')-aph(2') gene, and for E. faecium-2 an aph(2'')-Ia gene. For Citrobacter sp.-2 and K. oxytoca-2, a false discovery of a blaTEM-116 was observed, as this genotype was not called in either of the other two datasets of these isolates. For four isolates, a discrepant genotype was called. These discrepancies were observed for K. aerogenes-2 (blaTEM), for E. cloacae-2 (aadA), and for K. oxytoca-1 (blaOXY and blaTEM). Twice, an unexpected ColpVC was found in a K. oxytoca-2 and K. pneumoniae-4 dataset, which were from two different centres, indicating either loss of this plasmid in the other dataset of this isolate, or contamination during DNA isolation or library preparation  and 615 (97.1%) core-genome alleles were called for E. coli and K. pneumoniae respectively . In total, 27 and 15 pairwise allelic distances were calculated among the nine E. coli and five K. pneumoniae isolates. In 25/27 (93%) and 12/15 (80%) comparisons, a perfect concordance of cgMLST profiles was observed. If no concordance in cgMLST profiles was observed, only one allele was differently called .To assess the baseline genetic variation of identical isolates when these isolates were sequenced in different sequencing institutes, we compared the cgMLST and wgMLST profiles among the isolates from the three participating institutes. Only for K. oxytoca (6), Citrobacter sp. (12), E. coli (27), K. pneumoniae (15), E. cloacae (12), K. aerogenes (6), E. faecalis (6) and E. faecium (6). Perfect concordance in wgMLST profiles was obtained in 26/90 (29%) comparisons (E. coli. For E. faecium-1 with the contamination of Cutibacterium had a perfect concordance of wgMLST profiles was observed (data not shown). This indicates the robustness of allele-based typing despite contamination with bacterial DNA from different species. For all species, an average allelic distance of 1.6 alleles (standard deviation 1.6) was observed.In total 90 pairwise comparisons were made for parisons . In 44/9Citrobacter sp., a highly diverse number of wgMLST alleles were called, ranging from 1933 to 4426. The genome size did not vary strongly . The normalized allele counts were lower for Citrobacter sp.  than in other species in this study . Therefore, the variation in the number of alleles in the wgMLST scheme for Citrobacter sp. cannot be determined in this study, as an incomplete set of alleles were called.For the four de novo assemblies. The best assembler was chosen based on the one that introduced the least number of pairwise SNPs in the datasets from the same isolates with the intra-assembler comparison. To reduce complexity, only the E. coli dataset was used. Secondly, the number of pairwise SNPs was determined for the entire dataset using the best suited assembler. Additionally, we also used the assembly-free method for determining SNPs, as in implemented by SKA.As mutations in the genome can also arise in intergenic regions (which are not taken into account in MLST-based methods), all assemblies of each isolate were screened using pairwise SNPs. First, the most optimal assembler for this task was chosen. For this, we determined the inter- and intra-assembler variation introduced on the number of pairwise SNP between two E. coli dataset. Therefore, only these two methods were used to analyse the complete dataset.The mean intra-assembly variation was 0.2 SNPs (assembly free), 2.7 SNPs (SKESA), 26.6 SNPs (SPAdes) and 77.8 SNPs (Megahit) . The meaK. pneumoniae, E. faecium, K. oxytoca and K. aerogenes was more than one pairwise SNP observed, with a maximum of five SNPs for K. oxytoca. Using the assembly-based approach zero SNPs were observed among assemblies in 10/90 (10%) comparisons  pairwise SNPs were observed between assemblies of the same isolates (but sequenced in different institutes). Overall, more pairwise SNPs were observed when assemblies were used for SNP analysis compared to screening raw reads for SNPs. The difference in number of k-mers between the assemblyfree and assembly-based methods ranged from −2.1–1.2% , indicating that a similar amount of k-mers were compared in both methods.Using the assembly-free approach, 63/90 (70%) and 21/90 (21%) comparisons show zero or one pairwise SNP, respectively . Only foparisons . Less thEnterobacteriaceae and Enterococci species to whole-genome sequencing. As a first step, we assessed various QC measures and observed a slight positive trend of the sequencing depth on the normalized number of alleles called in the sequencing depth range of 30 to 207-fold. It remains unclear what sequencing depth is needed to correctly reconstruct the maximum possible number of correct alleles in the genome. Kluytmans-Van Den Bergh et al. [Enterobacteriaceae if wgMLST is implemented compared to cgMLST. This would indicate that making more alleles available for comparison will improve the surveillance of outbreaks by cgMLST or wgMLST methods. Therefore, it is advisable to generate WGS data of sufficient depth to maximize the number of loci in the de novo assembly. On the other hand, deeper sequencing after a certain depth may not improve the phylogenetic signal any further, and does increase the run-time of subsequent de novo assembly.Using an inter-laboratory ring trial we evaluated the reproducibility of whole-genome sequencing for outbreak surveillance purposes. Participating institutes subjected the same set of 30 bacterial isolates of various h et al.  demonstrCitrobacter sp. may be explained by incomplete allele schemes, which do not contain the complete diversity of alleles. This indicates that the diversity of Citrobacter sp. genome assemblies present in public databases is incomplete, and our data may represent the discovery of a new antibiotic-resistant Citrobacter sp. in The Netherlands.Prokaryotes show a coding density of 1 CDS per 1 kb , howeveret al., which found a reproducibility rate of 99.97% [We next performed identification of AMR genes and plasmid ORIs. Overall, an excellent reproducibility was achieved, as a precision of 99% was obtained. Most discrepancies could be explained by the variation in the variant calling of a specific resistance gene. There was an unexplained absence of a resistance gene four times in 973 genotype calls. Although the DNA isolation method used here showed good results for the application of WGS , some lof 99.97% .et al. [E. coli and three K. pneumoniae, which were sequenced in duplicate [E. coli and K. pneumoniae on cgMLST, respectively. For wgMLST this was 29, 23, 8 and 14 alleles difference for E. coli, K. pneumoniae, Citrobacter sp. and Enterobacter sp., respectively. These clonal thresholds are higher by a safe margin than the variation between any of the replicates in our study presented here. Although variation on a genetic level was observed, the level of disparity remained below other thresholds commonly employed for hospital outbreak surveillance purposes [E. coli and K. pneumoniae based on cgMLST [It is of great importance that the genetic distance between technical duplicates does not surpass commonly used thresholds to classify isolates into clusters. In this study some variation among the wgMLST allelic profiles was observed, translating to an average of 0.49 discrepant allele per 1000 alleles. Kluytmans-Van Den Bergh et al.  reporteduplicate . This isn cgMLST . Therefode novo assemblies for WGS outbreak surveillance. Potential outbreaks could be missed due to the large number of SNPs detected, resulting in identical isolates not being flagged as clonally related. This would subsequently have implications for infection prevention and control. For the assembly-free method, we observed most replicates to have no SNPs between each other (70%), which is in line with the GenomeTrakr proficiency-test study, which found a similar fraction of datasets showed having no SNPs (73%) [In the wgSNP analysis, all methods except for the ‘assembly-free to assembly-free’ and ‘SKESA to SKESA’ showed pairwise comparisons with more than 20 SNPs. This indicates that using SPAdes or Megahit in combination with a SNP-based method is unsuitable for outbreak surveillance, as datasets from identical isolates have more SNPs than commonly used outbreak thresholds , indicatPs (73%) .k-mers that were compared for SNPs, as there was only a modest difference for the number of k-mers compared between the assembly-free and the assembly-based SNP analysis, which ranged from −2.1–1.2% difference in compared k-mers . Therefore, it is more likely that de novo assembly introduces phylogenetic noise in regions difficult to assemble, like regions such as mobile elements (transposons and plasmids). Previously described work employing SNP-based methodologies to infer phylogeny among bacterial isolates often mask regions in the genome that are sensitive for non-informative SNPs for phylogenetic reconstruction, such as mobile genetic elements (MGE). Masking of these regions requires specialized tools such as Gubbins [Variation in SNPs among isolates showed a lower number of SNPs based on the assembly-free method compared to the assembly-based method. It is unlikely that this is caused by different numbers of  Gubbins  that areEnterobacteriaceae and vancomycin-resistant Enterococcus were defined of primary interest, however other important nosocomial bacterial pathogens such as Pseudomonas sp., Staphylococci and Acinetobacter sp. were not included in the study. Furthermore, all three centres used the same protocols for the extraction and library preparation for sequencing on the Illumina MiSeq. Recommendations for future research would therefore be to determine if these harmonized wet-lab protocols and subsequent bioinformatic data processing are indeed required for the reconstruction of outbreak clusters.For this study, only three centres participated in this ring-trial, all part of the i-4-1-Health study group. Here, ESBL-producing and ciprofloxacin-resistant de novo assemblies by different assemblers. Finally, work presented here lays the foundation for routine proficiency testing in clinical microbiology laboratories.Overall, the work presented here demonstrated that whole-genome sequencing generates reproducible results when comparing results across laboratories that use identical wet-lab and dry-lab methodologies for WGS. Furthermore, to make multi-centre outbreak surveillance feasible in the future, we recommend that laboratories share raw sequencing reads, because systematic errors were introduced in the Click here for additional data file.


Cucumis melo L.) is an economically important horticultural crop with abundant morphological and genetic variability. Complex genetic variations exist even among melon varieties and remain unclear to date. Therefore, unraveling the genetic variability among the three different melon varieties, muskmelon (C. melo subsp. melo), makuwa (C. melo L. var. makuwa), and cantaloupes , could provide a basis for evolutionary research. In this study, we attempted a systematic approach with genotyping-by-sequencing (GBS)-derived single nucleotide polymorphisms (SNPs) to reveal the genetic structure and diversity, haplotype differences, and marker-based varieties differentiation. A total of 6406 GBS-derived SNPs were selected for the diversity analysis, in which the muskmelon varieties showed higher heterozygote SNPs. Linkage disequilibrium (LD) decay varied significantly among the three melon varieties, in which more rapid LD decay was observed in muskmelon (r2 = 0.25) varieties. The Bayesian phylogenetic tree provided the intraspecific relationships among the three melon varieties that formed, as expected, individual clusters exhibiting the greatest genetic distance based on the posterior probability. The haplotype analysis also supported the phylogeny result by generating three major networks for 48 haplotypes. Further investigation for varieties discrimination allowed us to detect a total of 52 SNP markers that discriminated muskmelon from makuwa varieties, of which two SNPs were converted into cleaved amplified polymorphic sequence markers for practical use. In addition to these markers, the genome-wide association study identified two SNPs located in the genes on chromosome 6, which were significantly associated with the phenotypic traits of melon seed. This study demonstrated that a systematic approach using GBS-derived SNPs could serve to efficiently classify and manage the melon varieties in the genebank.Melon ( Cucumis melo L. (Cucurbitaceae), is an economically valuable horticultural fruit crop that is highly important in Mediterranean and East Asian countries. The world production of melons was estimated to be about 27.3 million tons from 1.04 million ha [Cucumis specimens, Africa is believed to be the geographical origin of the melon [The melon, he melon , since whe melon  proposedC. melo is considered to consist of two subspecies, melo and agrestis [melo into ten groups and the subsp. agrestis into five groups, although these groups still display intermediate morphological features that are difficult to classify. Muskmelon is also known as C. melo, whereas the cantaloupe and makuwa are types of muskmelon, which causes concern with their differentiation. Oriental melon (C. melo L. var. makuwa), which is well-known as “chamoe” in Korea and was scientifically named after its origin village as the variety “makuwa”, is one of the most important annual diploid crops widely cultivated. It is primarily cultivated for its fruit in which fruit traits, such as fruit size, shape, skin color, and sugar content in the flesh, are highly variable [In general, agrestis . Howeveragrestis  divided variable . Even thC. melo species, which clearly supports subspecies separation [C. melo species from Africa, Asia, and Australia. In [Molecular-based phylogenetic studies have been reported for the efficient classification of paration ,5,10. Baparation  also repalia. In , the autalia. In ,13,14,15alia. In .The melon draft genome was recently released , which hTriticum species and subspecies that are very difficult to distinguish based on their morphological characteristics [Recently, genotyping-by-sequencing (GBS) approaches have been used as an efficient and affordable method for identifying a large number of genomic markers to differentiate species and subspecies ,25. In oeristics . Genotyperistics ,28. To reristics ,28. MoreC. melo have been recognized [C. melo”. Similarly, in the Korean genebank, all the melon accessions were commonly recorded as “C. melo”. Hence, it is impossible to differentiate seed accessions without standard passport descriptors. Moreover, all over the world, different melon varieties have been identified with the common name “melon”. However, in Korea, makuwa (C. melo L. var. makuwa) is generally called “chamoe” and treated as a different fruit from other melon species; people consume the fruit flesh and seeds of makuwa, which has smaller seeds than that of the melon. The seed size is an important characteristic to differentiate the makuwa from other melon varieties in terms of edibility. Hence, in the present study, we also attempted a GWAS to identify the SNPs associated with the phenotypic traits of melon seeds. The generated data provide new insights into the identification of candidate genomic regions that could be used to differentiate all three melon varieties in order to efficiently classify the melon accession resources in the genebank.According to the International Code of Nomenclature for Cultivated Plants (ICNCP), approximately 522 synonyms of cognized . TherefoCucumis melo L. cv. DHL92 v.3.5.1.To understand the genetic relationship between the three different melon varieties, 72 melon accessions consisting of muskmelon, makuwa, and cantaloupe were sequenced using GBS technology  Table S1C. melo subsp. melo) has higher heterozygote SNPs  reads were aligned to the reference genome. Among them, melon35 had the highest mapping rate (88%) and melon34 had the lowest rate (76.4%). Considering only the successfully mapped reads from the 72 melon accessions, SNPs were discovered by analyzing the single master alignment file and genotypes were named with GATK . A totalote SNPs . The numote SNPs  Table S3An admixture-based clustering implemented in the STRUCTURE software and the DAPC were performed to infer the genetic structure of a germplasm collection. The STRUCTURE analysis results  revealedfind.clusters function. Eight first PCs (53% of the variance conserved) of the PCA were retained, and three discriminant eigenvalues were confirmed by the cross-validation analysis. Clusters 1, 2, and 3 consisted of 18, 48, and six accessions, respectively. The distribution of the accessions in the three populations was fully matched with the classification of varieties as makuwa, muskmelon, and cantaloupe. Thus, each population was considered for genetic diversity analysis.Further, the DAPC was carried out to detect the possible number of clusters among the 72 accessions . The numTo quantify the genetic diversity of the three melon populations, Shannon’s diversity index (I) was employed using the GBS dataset. The I was 0.31 for pop1, 0.48 for pop2, and 0.47 for pop3 . The numThe genetic diversity among the 46 makuwa accessions was also assessed using diversity indices. The I was 0.11 for cultivar, and 0.10 for landrace varieties . The numC. melo germplasm collection; we decided to estimate the LD decays separately  for makuwa and cantaloupe was approximately 200 and 100 kb, respectively. For muskmelon, the LD decay distance was approximately 50 kb (r2 = 0.25). As the threshold value of the LD decay was very high for the makuwa (r2 < 0.5), we decided to estimate the LD decays between cultivar and landraces. The results reveal that the landraces decayed faster than the cultivar varieties  accession (melon56) was clustered together with the makuwa clade, and a dudaim melon accession (melon54) was located closer to the makuwa clade; this was similar to the results of the ADMIXTURE  and a misidentified accession (melon56) in the Bayesian phylogeny also showed a clear variation between makuwa and muskmelon accessions.The difference between muskmelon and makuwa varieties were found to be difficult to distinguish because of their genotypic relationship. Initially, a total of 6406 SNPs were filtered from the raw variants to discriminate the muskmelon, makuwa, and cantaloupe varieties. Furthermore, in a Pearson’s chi-squared test, a total of 52 SNPs specific to each variety were detected based on allele frequencies. The concatenated consensus SNP markers showed clear discrimination of makuwa, muskmelon, and cantaloupe varieties , where tBccI restriction enzyme, which was developed as CAPS_10. The PCR product (556 bp) was digested with the BccI enzyme, which produced an uncut allele (556 bp) pattern in muskmelon and cantaloupe, whereas a digested allele (152 and 404 bp) pattern was produced in makuwa  was digested with BsrI, which produced the opposite allele pattern of CAPS_10. Muskmelon and cantaloupe varieties produced a digested allele (115 and 470 bp), whereas makuwa produced an uncut allele (585 bp). Likewise, the muskmelon accession melon56 produced an uncut allele pattern similar to makuwa  on chromosomes 6, 8, and 9 were significantly associated with the TSW of melon seeds. However, SNPs located on chromosomes 8 and 9 were predicted to be a hypothetical protein and S-type anion channel SLAH2, splicing intron, respectively, whereas the SNPs S6_875904 and S6_5912593 were located in the genes annotated as protein ABIL 1 and titin homolog isoform x2, respectively. A set of 11 and 12 associated signals which spanned 275.25  and 369.07 kb , respectively, were also identified around these two SNP regions in the melon DHL92 V3.5.1 reference genome  on chromosomes 6, 8, and 9 were significantly associated with the TSW of melon seeds. However, SNPs located on chromosomes 8 and 9 were predicted to be a hypothetical protein and S-type anion channel SLAH2, splicing intron, respectively, whereas the SNPs S6_875904 and S6_5912593 were located in the genes annotated as protein ABIL 1 and titin homolog isoform x2, respectively. A set of 11 and 12 associated signals which spanned 275.25  and 369.07 kb , respectively, were also identified around these two SNP regions in the melon DHL92 V3.5.1 reference genome  have a greater range of heterozygous markers than the makuwa (C. melo L. var. makuwa) variety . The PhiPT value between accessions of muskmelon and makuwa was 0.549, whereas that between makuwa and cantaloupe was 0.301. Cantaloupe was found to be much closer to muskmelon than the makuwa varieties, with a PhiPT value of 0.065. The AMOVA revealed that based on pairwise PhiPT values, the genetic variability within clusters (54%) was greater than the variability among the clusters (46%)  values across all polymorphisms with MAF ≥ 0.05 . All Phirs (46%) . Pairwisrs (46%)  between rs (46%) . SimilarTo conclude the genome-wide LD, a high-density SNP array should be analyzed . The curr2 value and longer LD distance for the makuwa and cantaloupe suggested that these varieties may have undergone a severe bottleneck.In general, the LD declines more slowly in self-pollinated crops, where recombination is less effective than in cross-pollinating species ,51. HighTo describe the genetic structure of the melon populations, we performed haplotypes using integer neighbor-joining network analysis (iNJ). The iNJ network revealed three major networks , with a melo into ten groups, and the subsp. agrestis into five groups. However, some of these accessions displayed intermediate features and were difficult to classify. Early taxonomic work failed to separate the cultivated species from wild species, resulting in approximately 522 synonyms of C. melo species in the seed catalog [C. melo” and commonly identified as “melon”. Similarly, in the Korean genebank, all the melon accessions were recorded as “C. melo”, which makes them difficult to differentiate without standard passport data. Therefore, the present study aimed to test the accuracy of varieties discrimination for a total of 72 accessions, including makuwa (C. melo L. var. makuwa), muskmelon (C. melo subsp. melo), and cantaloupe  with 6406 genome-wide SNP markers. The Bayesian phylogeny tree clearly showed that makuwa and muskmelon accessions were clustered in an individual clade (C. melo subsp. melo) population, which was similar to the STRUCTURE and iNJ networks. In a recent study, a total of 23,931 GBS derived SNPs successfully classified 44 melon accessions into two well-defined clusters, which clearly distinguished between the subspecies agrestis and melo [Cantalupensis melon accession was placed closer to the subspecies melo, and a dudaim melon accession was placed between the subspecies, which coincides with the present study, where the variety of cantaloupes and a dudaim melon accession clustered, respectively. It is believed that cantaloupe melons originated from South Asia to Africa and spread to Europe [In general, the evolutionary relationship between species is revealed through phylogenetic analysis. In an earlier study, Pitrat  divided  catalog . Therefoal clade . The Bayand melo . Howevero Europe . Moreoveo Europe , are morSingle nucleotide polymorphisms are valuable markers for discovering species relationships; however, it is highly challenging to validate a subset of melon accessions . VariousC. melo subsp. melo), whereas the melon56 accession belongs to the variety makuwa (C. melo L. var. makuwa), which requires more critical evaluation at the field level.Analysis revealed a total of 52 SNPs based on allele frequencies. The concatenated consensus SNPs clearly discriminated between makuwa and muskmelon accessions . SimilarBccI and BsrI), they produced distinct allele patterns between the muskmelon and makuwa populations  associated with seed development [Certainly, all over the world, seedless characteristics always improve the economic value of fruits. In Korea, people prepare makuwa (chamoe) to consume the fruit flesh, including the seeds, as the seeds are tiny compared with melon seeds. Hence, seed size is an important characteristic for differentiating the makuwa variety from muskmelon in terms of edibility. Various studies on genes or loci underlying agronomic traits have been reported in melon ,22,23. Ielopment  was deteBased on phenotypic traits, makuwa has a smaller seed weight than the muskmelon and cantaloupe accessions . HoweverThe SNP (S6_875904) located on the protein ABIL1 coding region was found to be missense, which makes the stop codon of the transcript, whereas the SNP (S6_5912593) located on the titin homolog isoform x2 was found to be synonymous. Genome-wide analysis of the maize genome reveals that synonymous mutations change tRNA adaptation, which affects the local translation rate . There iC. melo”, hereafter we would like to classify these melon accessions into the following three varieties: muskmelon (C. melo subsp. melo), makuwa (C. melo L. var. makuwa), and cantaloupe , which could be further classified into cultivar and landraces according to the germplasm introduction information. Seed phenotypic data such as 1000-seed weight (TSW), length (cm), and width (cm) were retrieved from the genebank management system (GMS) of the genebank for the association mapping analysis with each accession.For this study, a total of 72 melon accessions were obtained from the Korean genebank of the National Agrobiodiversity Center at the Rural Development Administration in South Korea . Based ow/v) agarose gel electrophoresis and quantified by spectrophotometry.For the GBS of the melon collection, 30 mg of freeze-dried leaf tissue was taken from the 72 accessions listed in ApeKI  at 75 °C for 3 h. Sequencing libraries for GBS were constructed according to previously described procedures [® Gel and polymerase chain reaction (PCR) Clean-up Kit . The purified samples were PCR amplified in a 50 µL reaction and the amplified products were evaluated for fragment sizes using BioAnalyzer 2100 . Illumina NextSeq500  was used to sequence the GBS libraries with a read length of 150 bp single-end reads.The extracted DNA was quantified and normalized to 12.5 ng/µL using Quant-iT PicoGreen dsDNA Assay Kit  with a Synergy HTX Multi-Mode Reader . The DNA was treated with the restriction enzyme ocedures . The DNACucumis melo L. cv. DHL92 v.3.5.1. reference genome using Bowtie2 [The sequence reads were preprocessed initially in three stages: demultiplexing, per-base quality control, and removal of adaptor using Stacks, FastQC, and Cutadapt software ,72,73. H Bowtie2 . The Gen Bowtie2 , was usefind.clusters” function in the R package adegenet, based on the Bayesian Information Criterion (BIC). In the DAPC analysis, a two-step procedure was followed in which the original data were transformed and submitted to a principal component analysis (PCA), and the principal components (PCs) were passed to a linear discriminant function analysis based on the groups identified in the K-means clustering. The PCs retained well with the population numbers, leading to accurate discriminant functions, resulting in perfect discrimination [optim.a.score” function was used to assesse the quality of discrimination that served as the best criteria to choose the optimal number of PCs in the DAPC [The discriminant analysis of principal components (DAPC) was used to assign the individual accessions to the population clusters . The DAPmination . Hence, the DAPC . The reshttp://software.genetics.ucla.edu/admixture/index.html, accessed on 12 April 2021). The admixture-linux-1.3.0 was run with default parameters in an unsupervised mode of K = 1 to 21. The cross-validation error for each K was computed with the -cv option (10 folds), which identified K = 2 as the most suitable modeling choice.To investigate the population structure, admixture analysis was performed on the 72 individuals using the ADMIXTURE tool  to investigate the molecular variation within and among the groups defined by the DAPC function. The AMOVA and the pairwise genetic differentiation (PhiPT) between and among melon varieties were calculated using GenAlEx software (6.5 version) with 999 permutations . Expecte2) with minor allele frequency (MAF) > 0.05. In addition, an LD threshold (r2) of 0.20 with a window size of 100 kb was used to calculate the correlation coefficient (r2) of alleles using the software PopLDdecay [http://popart.otago.ac.nz, accessed on 7 April 2020).To understand the genome-wide variability among the three varieties, pairwise estimates of linkage disequilibrium (LD) were measured by the squared correlation analysis of allele frequencies  was used to find the restriction enzyme sites within the SNP position. To detect SNPs retained in the melon varieties, amplification reactions were carried out using appropriate primers. The PCR product obtained from the amplification of specific SNP regions was digested with 1U of restriction enzyme . Digestion was performed at 37 °C (BccI) or 65 °C (BsrI) for 1 h, and the fragments were analyzed with 2.0% agarose gel electrophoresis.To validate the SNPs for variety discrimination, cleaved amplified polymorphic sequence (CAPS) markers were developed with the information of 52 SNPs. The web-based program dCAPS finder 2.0 .Genome-wide association analysis for phenotypic traits of seeds was performed with 6406 high-quality SNPs (MAF > 0.05). Here, GAPIT implemented a series of methods for GWAS and genomic selection for high statistical power, high prediction accuracy, and high computing speed . To perf package . The LD  package  togetherC. melo subsp. melo var. cantalupensis) varieties were closer to muskmelon (C. melo subsp. melo) than makuwa (C. melo L. var. makuwa). Bayesian phylogeny of the melon varieties showed a highly resolved phylogeny, and the developed SNP markers clearly discriminated the corresponding varieties more accurately. The SNP markers could be standardized easily with a very low cost and minimum equipment for quick operation in a genebank. In association mapping, two SNPs on chromosome 6 were significantly associated with the phenotypic traits of melon seeds. The SNP variations of protein ABIL1 and titin homolog isoform x2 could be used in molecular breeding to develop commercially improved cultivars/varieties. Overall, this study provides a systematic approach for the efficient classification of melon seed accessions using genome-wide information. Information on genomic variability between melon varieties will facilitate the efficient classification and utilization of these resources in the genebank.Highly informative SNP markers were developed in the present study through GBS analysis. The identified SNP markers provide a clear picture of the genomic relationships among the collection of the 72 melon accessions using a set of 6406 genome-wide SNPs. The DAPC and population structure seem to be defined mainly by their varieties. The cantaloupe (


Mycobacterium tuberculosis alignments representing local and global samples) showed SNPPar can detect homoplasic SNPs with very high specificity  and high sensitivity . SNPPar analysis of three empirically sampled datasets  produced results that were in concordance with previous studies, in terms of both individual homoplasies and evidence of convergence at the codon and gene levels. SNPPar analysis of a simulated alignment of ~64 000 genome-wide SNPs from 2000 M. tuberculosis genomes took ~23 min and ~2.6 GB of RAM to generate complete annotated results on a laptop. This analysis required ASR be conducted for only 1.25 % of SNPs, and the ASR step took ~23 s and 0.4 GB of RAM. SNPPar automates the detection and annotation of homoplasic SNPs efficiently and accurately from large SNP alignments. As demonstrated by the examples included here, this information can be readily used to explore the role of homoplasy in parallel and/or convergent evolution at the level of nucleotide, codon and/or gene.Homoplasic SNPs are considered important signatures of strong (positive) selective pressure, and hence of adaptive evolution for clinically relevant traits such as antibiotic resistance and virulence. Here we present a new tool, SNPPar, for efficient detection and analysis of homoplasic SNPs from large whole genome sequencing datasets (>1000 isolates and/or >100 000 SNPs). SNPPar takes as input an SNP alignment, tree and annotated reference genome, and uses a combination of simple monophyly tests and ancestral state reconstruction  to assign mutation events to branches and identify homoplasies. Mutations are annotated at the level of codon and gene, to facilitate analysis of convergent evolution. Testing on simulated data (120  The authors confirm all supporting data, code and protocols have been provided within the article, through supplementary data files or other online sources as indicated in the article.New content generated for this paper:https://github.com/d-j-e/SNPPar. The version described here is v1.0.SNPPar code is available from https://github.com/d-j-e/SNPPar_test. This repository includes all the simulated and real data sets used here.A GitHub repository containing the full protocol, ‘in-house’ code and data used to carry out the validation and performance testing is available at DNA sequences of bacterial pathogens are mutating all the time; most changes are deleterious or neutral, but sometimes a mutation leads to functional change that allows the pathogen to evade a potential threat. These random mutational changes (SNPs) are so very rarely beneficial that when they do arise in parallel in distantly related isolates (known as homoplasic SNPs) this indicates that the change may be positively selected because it confers an adaptive advantage to the bacteria. Finding homoplasic SNPs in large sets of bacterial genomes is challenging because current tools require substantial time and computational resources to run. Here we present SNPPar, a software program to efficiently and accurately automate the detection and annotation of homoplasic SNPs from large whole-genome sequence data sets. We use simulated data to demonstrate accuracy of the program, and re-analyse published datasets using SNPPar to illustrate how the results can be used to gain insights into the evolution of antibiotic resistance and other traits. We envisage SNPPar will help facilitate the undertaking of long-term, real-time surveillance of bacterial pathogens, and their adaptive evolutionary response to interventions and control measures such as new drugs or vaccines.Bacterial pathogen populations are under strong selection from antimicrobials and host immune defences, and there is increasing interest in utilizing whole genome sequencing (WGS) data to detect adaptive evolution in response to these strong selective pressures. Of particular interest in the field are repeated or convergent evolutionary patterns  in respoA key signature of adaptive evolution is the presence of homoplasies in the population . HomoplaHomoplasic SNPs can arise through different series of mutation events, classed as parallel, convergent or revertant evolution see . ParalleHomoplasic SNPs can be produced by spontaneous mutation (i.e. errors during chromosome duplication prior to cell division) but can also arise via recombination. Whilst recombination is a major driving force of evolution in many bacterial populations, in clonal pathogens that exhibit very little recombination, most new variation is acquired through spontaneous mutations. Genomics-based studies aiming to understand clonal evolution and positive selection typically seek to identify and remove homoplasic SNPs that result from recombination , which iA general approach to discovering homoplasic SNPs from whole genome alignments is to construct a phylogenetic tree from the alignment of all SNP sites, then probabilistically infer ancestral states for each SNP site at internal nodes of the tree using ancestral state reconstruction (ASR) . ExampleWGS has been widely adopted for the study of bacterial pathogens, and it is frequently applied to datasets numbering in tens to thousands of isolates. WGS is also increasingly used for routine public health surveillance of tuberculosis  and foodHere we present SNPPar, a Python program that seeks to speed up the process of homoplasic SNP discovery and analysis in large bacterial WGS data sets (>1000 isolates and/or >100 000 SNPs). It calls on TreeTime for efficient ASR, and then processes the output to provide a wealth of additional data on mutation events and coding effects to facilitate the detailed assessment of evidence for adaptive evolution in large bacterial SNP data sets.https://github.com/d-j-e/SNPPar. Required inputs are a phylogenetic tree (Newick format), annotated reference genome (GenBank format) and details of SNPs for analysis . The workflow for SNPPar is outlined in SNPPar is written in Python 3, utilizing the ETE3  package First, SNPPar sorts SNP sites into different pathways for analysis see .Bi-allelic SNPs that are singletons  are trivially identified as monophyletic and assigned as a single mutation event arising on the relevant terminal branch see .Bi-allelic SNPs for which the minor allele is present in two or more input sequences (i.e. non-singleton) are assessed against the input tree to determine whether they are monophyletic , or paraphyletic and thus passed to ASR to infer mutation events on the tree see . See theTri-allelic and tetra-allelic SNPs are passed directly to ASR to infer mutation events on the tree.SNPPar has three different sorting options available, which use all or part of the above pathways. ‘Complex’ sorting employs all of the above steps. ‘Simple’ sorting employs step (1) only and sends all but those SNPs identified as singletons to ASR. ‘Intermediate’ sorting (the default) employs most of the above steps, except any non-singleton biallelic SNP with one or more missing allele calls  is sent to ASR without assessing monophyly against the tree, because dealing with such missing data can be time-consuming (see below). For the largest empirical dataset used in this study , the resulting SNP set designated for ASR included only a fraction of sites in the input SNP set . Hence, this sorting stage can considerably reduce processing time compared to conducting ASR on all sites. Initial testing, further confirmed by the validation testing below, led to the conclusion that intermediate sorting for datasets with missing calls was much quicker than the initial implementation, complex sorting, albeit with a small hit to peak memory use (see Results). Therefore, intermediate sorting was set as the default behaviour for SNPPar, but the other options are still available because (a) simple sorting can be used if users are concerned about the accuracy of the internal monophyly test and prefer to send all non-singleton SNPs to TreeTime, or (b) complex sorting can be used if memory is limited.ASR is performed for SNP sites identified during sorting, in order to infer mutation events on the tree using an ML approach. By default, SNPPar performs the ASR step by concatenating alleles from the SNP sites selected for analysis into a single FASTA-format alignment, then passing this together with the input tree to TreeTime’s ‘ancestral’ command . TreeTime’s default behaviour is to ignore sites with any missing base-calls, and hence SNPPar uses the TreeTime option to enforce reporting of all sites. Two output files are captured for processing by SNPPar: the sequences reconstructed for each internal node in the tree (‘node sequences’), and a copy of the input tree with the inferred mutation events annotated on each branch (‘event tree’), from which SNPPar extracts a list of all mutation events per SNP site. Optionally, FastML can be selected as an alternative ML algorithm for ASR, but this is much slower, and we found no difference in accuracy (see Results).All mutation events, whether inferred via internal sorting or ASR, are collated into a master event list that records the SNP coordinate in the reference genome, the precise base substitution  and branch for each mutation event. These events are then annotated with their functional effects, with reference to the protein-coding gene  features annotated in the input reference genome and taking into account the inferred codon at each node not just the SNP site (see Supplementary Information for details). The final step in SNPPar is to identify, count and report unique homoplasic SNPs found in the full mutation event list. SNPPar can also identify the type of homoplasy for each homoplasic SNP , either in the main analysis run or as a post-processing step applied to a previously generated mutation event list and tree. SNPs called at the same position are compared to classify them as parallel, convergent, revertant or divergent. Comparisons are done pairwise, beginning with those that share both the same ancestral and derived calls, which are assigned as parallel. Where two events share the same ancestral call but a different derived call, they are assigned as divergent; where the derived call is the same but the ancestral call is different, this is assigned as convergent. Revertants are defined as cases where mutations (x→y) and (y→x) are identified at the same site, and one of the corresponding nodes is a descendant of the other, in which case the descendant is labelled as revertant.The reference genome used to call SNPs should be provided in GenBank format. It must be annotated, as this is essential to infer the function of the SNPs. SNPPar assumes the reference sequence is circular (for the purpose of reporting relative position of intergenic SNPs), and ignores any CDS that are split across the origin. The input tree must be rooted, bifurcating and in Newick format, with branch lengths expressed in substitutions per site. SNPs can be input in two ways: (i) a multi-FASTA concatenated alignment of SNP alleles (FASTA headers being sample names that match exactly the tip names in the input tree) plus a list indicating the coordinates of SNPs relative to the reference genome ; or (ii) a comma-separated SNP allele table, with SNPs in rows  and samples in columns (column headings being sample names that match exactly the tip names of the input tree).http://tree.bio.ed.ac.uk/software/figtree/) or iTOL [Primary outputs are an annotated list of all mutation events and their coding effects, an annotated list of just those mutation events found at homoplasic SNP positions, and two tree files in which nodes are labelled with identifiers and mutation event counts . One tree file is in NEXUS format, compatible with visualization in FigTree .All development and testing was done on a 2017 Apple MacBook Air  using Python v3.7.2, ETE3 v3.1.1 and TreeTime v0.6.3. Results using FastML v3.11 for ASR on simulated data are included for comparison purposes. The full protocol, scripts and data used to carry out the validation and performance testing are available on GitHub (Mycobacterium tuberculosis (Mtb) SNP alignment data [Mtb lineage 2 isolates from Ho Chi Minh City; HCMC L2); (2) single lineage, globally distributed ; and (3) species-wide, globally distributed . Empirical parameters (i.e. number of SNPs and GTR model parameter estimates) were extracted from 10 randomly sampled subsets of various sizes from each population . For each subset, (i) the number of SNPs was recorded and used later to correct the number of SNPs generated by simulation; and (ii) a reference tree and GTR model parameter estimates  were obtained by selecting the tree with the highest likelihood from a random sample of five trees estimated from the SNP alignment using RAxML v8.2.12 using a GTR +G model and ascertainment bias correction. SeqGen v1.3.4 [In order to test the accuracy of SNPPar when performing homoplasic SNP detection, we simulated 120 sets of sequences based on substitution model parameters and phylogenetic trees estimated from real ent data . Note thent data . The datn v1.3.4  was usedSNPPar was run on each simulated data set using either simple or intermediate sorting, with homoplasic SNP classification turned on.  The list of homoplasic SNPs output by SNPPar was compared to the list of those expected based on the simulation. The starting tree was used as input rather than inferring new trees from each simulated alignment, because this would test the accuracy of the tree-building stage as much as the ability of SNPPar to find homoplasic SNPs. Any SNP expected to be called as homoplasic that was reported as such by SNPPar was scored as a true-positive; any other homoplasic SNP reported was considered a false-positive; any expected homoplasic SNP not reported by SNPPar was considered a false-negative. For homoplasic SNP calls, we also considered whether the type was classified correctly . As only one case of convergence was expected , only parallel and revertant type calls were considered.SNPPar’s total run time and maximum memory usage was recorded for each replicate run. The results were analysed in R v3.5.1 using RStudio v1.1.383, with independent ANOVA tests to examine the effects of isolate count, SNP count or total alignment length  on total run time and memory.Elizabethkingia anophelis outbreak in Wisconsin, USA [Burkholderia dolosa isolates amongst cystic fibrosis patients receiving treatment at a hospital in Boston, USA, in the 1990s [B. dolosa has three chromosomes, each reference chromosome and its associated SNPs were analysed separately using SNPPar . The published tree for B. dolosa had branch lengths in units of substitutions (i.e. SNP counts), and hence these were scaled to units of substitutions per site by dividing each by the chromosome sequence length prior to use as input for SNPPar.Testing of the performance of SNPPar in real-world analysis scenarios was demonstrated using two previously published bacterial WGS data sets in which convergent SNPs have been examined. One was from a 2015–16 sin, USA   and extracted the SNPs as previously above. We then constructed the phylogeny using IQTREE 2  using a Mtb data simulated for three different population structures , subsampled at four different sizes, each with 10 replicates . The two key outcomes assessed were the correct detection of homoplasic SNPs, and the correct identification of the type of homoplasy for each reported homoplasic SNP. The effects of sample characteristics  on the two key outcomes was also explored.We assessed he accuracy of SNPPar using Accuracy results (using the default option of intermediate sorting) are summarized in n=107 runs) or one (12 runs) false negative; the single exception was one run on a dataset of the smallest sample size from the Global L2 population, in which two false negative calls were made. As a general trend, the more complex the population, the more likely a homoplasy would be missed , a mean of 13.7 homoplasic SNPs were called, of which a mean of 8.4 % were classified incorrectly; in the largest samples (n=2000), a mean of 425.5 homoplasic SNPs were called of which a mean of 0.84 % were classified incorrectly. The most common error was a reversion being incorrectly classified as a parallel event, followed by a parallel event being incorrectly classified as a reversion ; both cases were caused by the necessity to make arbitrary ancestral allele calls at the root node, which is unresolvable with any method.The true-positive homoplasic SNPs were assessed to see whether the type  was correctly reported by SNPPar. Overall, most types were correctly identified, and accuracy was improved with sample size . In the smallest sample size , but this was due solely to differences in arbitrary assignments at the root node .Resource usage  for SNPPar analyses with empirical and simulated data is shown in n=419/430 sites correctly called as homoplasic, vs n=429/430 for SNPPar or HomoplasyFinder; see Supplementary Information), suggesting that SNPPar’s sorting approach provides benefits over TreeTime analysis of the full alignment in terms of recall accuracy as well as memory use.Comparing the performance of SNPPar to TreeTime with regard to the ASR step, the whole alignment of a simulated data set of Global L124 with 2000 isolates took ~22 min for TreeTime to analyse all ~64 000 SNPs using >24 GB of peak memory; SNPPar using the default intermediate sorting sent only ~800 SNPs  to TreeTime for ASR, which took ~23 s with a peak memory use of ~0.4 GB of RAM. Interestingly, TreeTime alone had a lower sensitivity than SNPPar in this test . For example, the largest simulated data set  took on average ~25 min and used ~2.7 GB of memory with TreeTime for ASR (using intermediate sorting) and ~3 h and ~1.6 GB with FastML for ASR. FastML by itself, given the same whole alignment of Global L124 simulated data and computing resources as TreeTime above, failed to complete the analysis after 7 days. SNPPar using FastML also failed  during the ASR step for two of the largest real SNP alignments  which completed in ~25 and~3 min, respectively, using SNPPar with TreeTime and intermediate sorting).E. anophelis [B. dolosa [Mtb dataset used for simulation , were analysed with SNPPar using default parameters (details in Methods). The data sets, commands and outputs are available on GitHub (https://github.com/d-j-e/SNPPar_test).Two data sets from previously published papers reporting homoplasy analysis in phelis  and B.dolosa  along wiE. anophelis is an opportunistic pathogen that rarely causes outbreaks, and the genome data from an unusually large outbreak in the US provide a rare opportunity to explore positive selection in this organism [E. anophelis outbreak genomes took <3 s, and identified the same homoplasic SNP that was reported in the original study (which used FastML) [wzc capsular export gene (A2T74_09840), resulting in a nonsense mutation (converting the glutamate codon ‘GAA’ to stop codon ‘TAA’) and thus truncating the protein as previously reported [wzc, and another sugar transporter similar to wza). SNPPar identified the same numbers of mutation events in these 27 genes. Five protein-altering SNPs (involving six independent mutation events) were correctly found in wzc, including the two for the parallel nonsense SNP at codon 469 (see organism . SNPPar  FastML)  , which took a total of ~11 s. Twenty SNP sites with more than one mutation event were previously reported in this data set [gyrA , consistent with the original study in which this gene was noted as under positive selection [rpl4 (BDAG_02781), which is involved in both pathogenicity and macrolide resistance [rpl4, consistent with the original report which identified it as under positive selection, with four independent mutations affecting codon 70 [rpl4 arose in parallel a number of times (four and eight respectively), but never in the same isolate. Overall, SNPPar identified five parallel non-synonymous SNPs that arose three or more times independently in the population, which we take to indicate a strong signal of positive selection: one gyrA-83 SNP, one bla1-172 SNP, the two rpl4 SNPs, and an SNP in codon 275 of the glycosyltransferase gene wbaD (BDAG_02317). Notably an additional divergent SNP also occurred at the same site in wbaD codon 275 , SNPPar took 51 min (using 2.6 GB) and identified a total of 2879 homoplasic mutation events affecting 461 genes. We used the output to calculate the rate of homoplasic protein-altering mutations per gene, normalized to the total number of synonymous mutation events per gene, as a measure of adaptive selection (see rpsL [katG [pncA [rpoB [embB [sseC2) has been implicated in down-regulation of genes involved in latency in Mtb [katG and rpsL, across two and three codons respectively, to 18 in pncA, across 18 different codons; see katG, which is known for its isoniazid-resistance-conferring mutations [For the tion see . The toption see  include see rpsL , katG [3sL [katG , pncA [3tG [pncA , rpoB [4cA [rpoB  and embBoB [embB ). The oty in Mtb . The fivutations , most ofPerformance testing of SNPPar based on simulated data demonstrated that it has very high sensitivity to detect homoplasic mutation events  and also has high specificity . The number of false-negative calls did increase slightly with increasing sample complexity see , and henExamination of the computational efficiency of SNPPar demonstrated a linear relationship between execution time and total alignment length for simulated data, when using the default TreeTime for ASR see . Real daSNPPar analysis made it relatively easy to reproduce the selection analyses reported previously on empirical data; one command, run using standard input files of tree and SNP alignment, took a few seconds on a laptop, producing details of all SNPs annotated with their coding effects and position in the tree. The richness of the output makes it straightforward to not only identify homoplasic mutation events, but also to explore convergent evolution events in detail, including by summarizing the data at the levels of SNP site, codon and gene see  and visuMtb [There are some caveats with using SNPPar. First, results from SNPPar are only as good as the input data that are provided. Prior quality control of the SNP alignment is important, not only to obtain high-confidence SNP allele calls by removing those found in repeat regions or other regions with ambiguous or low-quality calls (e.g. the (P)PE genes found in Mtb ), but alMtb  or ClonaMtb ) prior tMtb , and henWe have demonstrated that SNPPar works efficiently on large genome-wide SNP sets; it is highly accurate in detecting true positive homoplasic SNPs relative to an input tree with no false positive calls. SNPPar located homoplasic SNPs efficiently and accurately with both the simulated and real data sets tested here. Currently, SNPPar is the only tool that automates the detection and annotation of homoplasic SNPs efficiently from large SNP alignments. As further demonstrated by the real examples, this information can be used to explore the role of homoplasy in parallel and/or convergent evolution at the codon and gene level, in addition to identification of homoplasic SNPs per se. Whilst SNPPar was designed and tested specifically for the detection of homoplasic SNPs in bacteria, it could potentially be used for investigating homoplasic SNPs in other haploid organisms and viruses.Click here for additional data file.


However, only fragmented knowledge is available concerning the underlying genes and processes that foster heterosis. Although cotton is among the highly valued crops, its improvement programs that involve the exploitation of heterosis are still limited in terms of significant accomplishments to make it broadly applicable in different agro-ecological zones. Here, F1 hybrids were derived from mating a diverse Upland Cotton germplasm with commercially valuable cultivars in the Line × Tester fashion and evaluated across multiple environments for 10 measurable traits. These traits were dissected into five different heterosis types and specific combining ability (SCA). Subsequent genome-wide predictions along-with association analyses uncovered a set of 298 highly significant key single nucleotide polymorphisms (SNPs)/Quantitative Trait Nucleotides (QTNs) and 271 heterotic Quantitative Trait Nucleotides (hQTNs) related to agronomic and fiber quality traits. The integration of a genome wide association study with RNA-sequence analysis yielded 275 candidate genes in the vicinity of key SNPs/QTNs. Fiber micronaire (MIC) and lint percentage (LP) had the maximum number of associated genes, i.e., each with 45 related to QTNs/hQTNs. A total of 54 putative candidate genes were identified in association with HETEROSIS of quoted traits. The novel players in the heterosis mechanism highlighted in this study may prove to be scientifically and biologically important for cotton biologists, and for those breeders engaged in cotton fiber and yield improvement programs.For about a century, plant breeding has widely exploited the heterosis phenomenon–often considered as hybrid vigor–to increase agricultural productivity. The ensuing F The phenomenon of biological progeny outperforming either of their parents is defined as heterosis . The conHighly conceptual quantitative genetic models attributed to heterosis, known as dominance , over doGossypium hirsutum is responsible for approximately 95% of cotton production worldwide  mapping have emerged as extensively utilized, powerful techniques for the genetic dissection of complex mechanisms via high density molecular markers . Further1 individuals of upland cotton; these were then evaluated phenotypically in 10 environments over 2 years. The current study aimed at the (i) detection of SNP loci associated with trait performance of F1 hybrids and of Quantitative Trait Nucleotides (QTNs) related to the heterosis of these traits and (ii) the identification and validation of potential candidate genes, especially heterotic Quantitative Trait Nucleotide (hQTNs), related to the investigated traits.Accordingly, this study was planned and executed in which GWAS was used to detect allelic variation in the genome of cotton and identify candidate SNPs strongly associated with economic quantitative traits. Restriction-site associated DNA (RAD) sequencing was applied to 1136 FA set of 284 diverse Upland Cotton accessions, collected from gene bank of ICR, CAAS, Anyang, Henan, along with four highly ranked and renowned commercial cultivars. This collection is from a range of different regions of China. A major portion, 83.3% (240), was collected from various conventional cotton-growing areas, including the Yangtze River region, the Yellow River Valley, and Northern and Southern areas of China. The other 16.67% (48) consisted of introductions from different geographical areas of United States, Ivory Coast, Australia, Russia, Turkmenistan, Uganda, Kenya, Burundi, Chad, Sudan, and Vietnam (11 different countries), as shown in 1 hybrid populations. The ensuing F1 hybrids were divided into four groups  according to their respective parents.Line × Tester mating design was proposed for the current study to obtain different variables related to heterosis and combining abilities, for the purpose of utilizing them as variables in association studies. For this purpose, 284 female parents (lines) were crossed with four testers as males, namely 7886, A971Bt, 4133Bt, SGK9708 coded, respectively as tester A, C, D, and E to produce the F1 hybrids along with their respective parents were subjected to a field evaluation for plant phenotyping. Field plantations of the experimental material were established during the crop seasons of 2012 and 2013 at 12 locations in cotton-growing belts of China, these mainly spanning parts of the Yangtze River region  and the Yellow River region . These locations were selected on the basis of significant differences in agro-ecological features including climate, amount of precipitation, temperature, soil fertility, growing period, and cultural practices.Each set of FField experiments were carried out that used a triplicated randomized complete blocked design (RCBD) at all 12 locations. A single row of genotypes was 8.0 m, row × row distance was maintained at 0.8 m the plant × plant distance was kept at 0.3 m in the Yellow River region and at 0.5 m in the Yangtze River region. The distance between replications was 1.0 m. Local cotton growing practices were followed for sowing; i.e., direct sowing of delinted seeds/seeds with lint or transplanting of seedlings using the method predominantly used in that given region. All recommended agronomic practices–fertilizer application, seed treatment, seed rate, sowing methods, thinning, cultural practices, irrigation, insect pest control and weed management–were followed in similar manner to establish and maintain a good crop stand on all 12 experimental locations.1 populations, as well as their parents, planted at different locations as experimental units during the two different years.Data collection for all traits under study was carried out for all experimental units, by following the unanimous standard Descriptors for Data collection used for Cotton Germplasm, which was developed based on guidelines issued by the International Plant Genetic Resources Institute (IPGR). Ten individual guarded plants were randomly selected and tagged for data collection related to agronomic traits as well as quality-related characters. When the crop had about 70% open bolls, 30 bolls from each tagged individual plant (middle branches) per plot were harvested and examined for agronomic and fiber quality traits. The ginning of collected seed cotton samples was done using a roller-gin. About 150 g of ginned and clean lint samples were taken and sent to the Laboratory of Quality and Safety Risk Assessment for Cotton Products, Anyang, Henan, China, to examine fiber quality-related traits. Fiber quality analysis was carried out using high-volume instrument (HVI). Ten phenotypic traits–boll weight (BW), lint percentage (LP), fiber fineness or micronaire (MIC), fiber strength (FS), fiber length (FL), fiber elongation (FE), fiber uniformity (FU), fiber uniformity index (FUI) number of bolls per plant (BN) and plant height (PH)–were recorded  from 284TaqI , at 65°C for 10 min. Bar-coded adapters were ligated to the digested DNA fragments with T4 DNA ligase , during 1 h incubation at 22°C. The samples were then heated at 65°C for 20 min, after which 24 samples were pooled. The DNA fragments (400–600 bp) were purified from 2% agarose gel electrophoresis with the help of the QIA-quick Gel Extraction kit . Adapter-ligated DNA fragments were further amplified by polymerase chain reaction (PCR), using the Phusion-High-fidelity DNA-polymerase . Next, these amplified fragments were separated via agarose gel electrophoresis, and the ensuing DNA fragments (400–600 bp) were purified with the QIA-quick PCR Purification kit . Finally, the purified libraries were quantified on a 2100 Bioanalyzer  and each library sequenced by the Hi-Seq 2000 system . The raw reads were then aligned with the G. hirsutum L. TM-1 reference genome (v.1.1)1, using the “mem -t 8” parameter of the BWA program (PRJNA353524).Young fresh leaves were collected from each genotype and immediately frozen and then stored at −80°C. Genomic DNA was extracted following the CTAB method  albeit w program . The GAT program . The gen1 crosses were subjected to univariate analysis for determining variability among the studied traits  recorded from 284 Fd traits . The relformulas :Heterobeltosis was calculated this way:Mid-parent heterosis (MP) was calculated this way:Competitive heterosis (K) over local cultivar(s)/Check(s) (CK) was calculated as:In the current study, two standard heterosis measures, K3 and K4, were calculated by using two commercial Chinese cotton cultivars, i.e., Rui za 816 and Eza mian 10 hao (Tai D5), respectively.The heterosis index (HI) was calculated as follows:The specific combining ability (SCA) variance was calculated by using Line × Tester variance analysis .To explore different genetic factors presumably associated with heterosis in cotton, GWAS was performed by considering familial relatedness as well as population structure , utilizi2, using Bayesian-based estimation. The data obtained from the four sets of F1 genotypes along with parents were considered for calculations, based on principle that the 284 female parents and 4 male parents must be homozygous; otherwise, they were removed. Moreover, only those female and male parents having a different genotype  were considered for analysis, to ensure a heterozygous F1 genotype. The expected F1 genotype was calculated, by focusing on the genotype of respective male and female parent and heterozygous SNPs in either of the parents have been scored as missing.The paired-end reads of each individual were identified by its barcode and aligned against the reference genome, using the BWA v.0.7.12 . The pro1 sets: F1_A, F1_C, F1_D and F1_E, were finalized for the GWAS analysis using high-quality SNPs.Single nucleotide polymorphisms that met the following criteria were removed: (1) Length (distance) between two adjacent SNP loci was less than 5 bp. (2) SNPs with call rates lower than 70%  in the wp = 1.0 × 10–5 was used throughout. For the visualization of results, Manhattan and quantile–quantile plots were constructed in R using the package “qqman.” The peak SNPs with the highest p-value as well as their detection across multiple environments, were considered as key SNPs. For further confirmation, the favorable allelic variations of the key SNPs were identified for each trait variable . Box plots for the relative phenotypic values were drawn in R software. The HAPLOVIEW 4.2 software (G. hirsutum L. reference genome (v.1.1)  .3, to propose annotated putative candidate genes for each locus. For the transcriptome-based predictions, the gene expression database (TM-1) (Gene ontology (GO) analysis was performed using the cotton functional genomics databasee (TM-1)  was used1s and parents. Upper and lower ends as shown in the vertical projections are assumed to represent highest to lowest data points . The At sub-genome housed a greater number of SNPs associated with the fiber quality-related traits, while the Dt sub-genome harbored more SNPs for the agronomic traits. The At08 chromosome had the most SNPs , whereas the At04 chromosome had the least   . All thefor F1_E . On chroome Dt04 .10 (p) ≥ 4) with the 10 studied traits of the cotton parents and 4 F1 sets  > 4) key SNPs/QTNs were identified, based on the highest p-value, presence in multiple environments and function i.e., boxplots and haploblock analysis  were identified for further scrutiny  of 298 QTNs on the basis of genes’ annotation with reference to TM-1 genome of hirsutum . Overallscrutiny . Based oscrutiny . These gscrutiny . The GO ectively . Of 64 pAmaranthus cruentus, Amaranthus hypochondriacus) (Lycopersicon esculentum) (Oryza sativa)  , maize (driacus) , tomato ulentum) , and ric sativa) . The num1 sets, which formed at earlier stages of the cotton breeding period and were not affected by geographical influences of China.Due to the scarcity of available genetic divergence in the founder parents of Cotton World stock, global climatic changes are continuously posing threats to Upland Cotton crops with respect to progress in breeding and their survival. Thus, it is imperative that we explore potential genetic diversity that might have been eroded from cultivated cotton collections. Population structure within the collection of accessions is considered crucial for explaining heterogeneity. Chinese cotton production as well as cotton breeding programs are largely based on the introduction of germplasms since long . HoweverG. hirsutum accessions used as parents exhibited reasonable amounts of phenotypic and genotypic diversity. It offers a highly efficient way to mine heterosis-related loci by high-resolution GWAS in plants. Through GWAS, the relationships among significantly associated hQTNs to fiber quality as well as other agronomic traits, and the annotation of putative genes containing these hQTNs, were examined here in depth.In the last two decades, GWAS has been extensively utilized by researchers to map different quantitative traits in plants and this achievement is considered a complex milestone . It is t1 sets is another noteworthy feature of this study. In this way, the loci controlling heterosis of different traits could be separated from those concerned with trait performance in earlier studies. We distinguished 19 highly stable hQTNs for LP, BW, FL, FS and MIC traits based on their detection from five heterosis types and/or four F1 sets across multiple differing environments. These stable heterotic loci could be used in the future to assist in Upland Cotton breeding via MAS applications. The remaining significant hQTNs that were found related to other traits could also prove useful in cotton breeding programs. Moreover, a reasonable number of identified SNPs from the F1 sets and trait phenotypes overlapped with those detected from the heterosis types. These findings revealed that both heterosis and trait performance were not independently controlled by different loci, which agrees with a recent study on upland cotton (The identification of hQTNs using five different types of heterosis, trait performance, SCA and four Fd cotton . Conversd cotton  reportedGh_D08G0894, which encodes an ethylene-responsive transcription factor detected earlier in Arabidopsis (Gh_D02G1201 and Gh_A10G1233, showed an association with FE-related hQTNs. Both Gh_D02G1201 and Gh_A10G1233 encode FAR-RED elongated proteins which are involved in light responses and FE development (Gh_A11G1858, which displayed an association with the hQTN related to FL, encodes a serine-threonine kinase SAPK1 protein that may play a role in the signal transduction of a hyperosmotic response markedly influencing the fiber development process (Gh_A01G0481 and Gh_A11G0475; the former encodes a cytochrome P450 protein which may have a role in the maturation or aging of tissues (Gh_A03G0366, which encodes a scarecrow-like protein that acts as transcription factor and which regulates the development of vegetative and reproductive plant parts (Gh_A05G0285, a gene earlier detected in cotton for coding nucleotide binding protein responsible for fiber development (Gh_A03G1505, Gh_A03G0332, Gh_A02G0495, and Gh_D06G1287; these reportedly encode in cotton the production of calcium-dependent proteins related respectively to the kinase family (Gh_A01G1348 gene known for encoding axial regulator proteins controlling the biomass vigor of hybrid cotton (Boll weight was identified in relation with bidopsis  and latebidopsis . Ethylenbidopsis ; its crubidopsis . Two canelopment  along wielopment . Gh_A11G process . FU was  tissues , while t tissues . The FUInt parts . The LP-elopment . The MICe family , histonee family , phosphae family , and cyte family . Finallyd cotton .1 progenies’ performance in hybrid breeding. An earlier study on rice revealed the power of SNP-directed yield estimation of F1 hybrids. In the current study, 298 QTNs were uncovered in association with fiber quality as well as agronomic traits. A set of 271 hQTNs were detected with 19 highly stable heterotic loci in relation with LP, BW, FL, FS, and MIC based on their detection from five evaluated types of heterosis and/or four F1 hybrid sets across a wide spectrum of environments. These discovered hQTNs and putative candidate genes related to HETEROSIS of quoted traits could be used further deliberately in marker-assisted breeding of forthcoming cotton hybrid breeding programs. Once the genotype-based predictions achieve relatively high levels of accuracy, the labor and time costs of hybrid breeding are greatly reduced. The reported information derived in this study is of practical and scientific significance for both cotton breeders and biologists engaged in elucidating the heterosis mechanism of fiber as it could assist in successful accomplishments in both domains.The current study is based on the concept of genomic hybrid breeding, previously utilized in rice , which eThe data related to RAD-sequencing have been submitted to NCBI under reference No. PRJNA353524. Other used data are provided in the form of XD and JS: conceived and designed the research. JS, QW, HQ, JL, HL, JiY, ZM, and DX: managed the project. ZS, ZP, XG, MShI, MFN, MSaI, and HA: designed and performed molecular experiments in lab along with molecular data analysis. YJ, SH, JS, HQ, HL, DX, JuY, JZ, ZL, ZC, XiZ, XuZ, AH, XY, GZ, LL, HZ, BP, and LW: prepared samples and performed phenotyping in Anyang, Henan, Xinxiang, Wuhan, Jingzhou, Baoding, Changde, Shandong etc. ZS, SA, and MShI: analyzed and interpreted data and prepared figures and tables. ZS, MShI, and XD: drafted and processed the manuscript and all authors helped throughout this process and take active part in critical revisions and improvements in important intellectual contents. All authors read the manuscript critically and approved the final version of manuscript for publication. All authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.It is declared that, authors, JL, JY, ZC, and GZ were employed by “Zhongmian Seed Technologies Co., Ltd., Zhengzhou, China,” the author, HL employed by “Jing Hua Seed Industry Technologies Inc., Jingzhou, China,” the author, DX employed by “Guoxin Rural Technical Service Association, Hebei, China,” the authors, JZ as well as LL were employed by “Zhongli Company of Shandong, Shandong, China,” and the author AH employed by “Sanyi Seed Industry of Changde in Hunan Inc., Changde, China.” The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The quality of pedigree and SNPs and the inbreeding coefficients (F) and effective population size (Ne) were evaluated. The correlations between inbreeding based on the runs of homozygosity (FROH) and pedigree (FPED) were also calculated. The average FPED for all animals was 3.44%, while the FROH varied from 10.81% for a minimum size of 1 Mbp to 3.98% for a minimum size of 16 Mbp. The average minor allele frequency was 0.28 ± 0.11. The observed and expected within breed heterozygosities were 0.38 ± 0.13 and 0.37 ± 0.12, respectively. The Ne, obtained using both the data sources, reached values around 50 animals. Moderate correlation coefficients (0.49–0.54) were observed between FPED and FROH. It is necessary to make decisions that stabilize the inbreeding rate in the long-term using optimal contribution selection based on the available SNP data.Herein, the genetic diversity of the local Přeštice Black-Pied pig breed was assessed by the simultaneous analysis of the pedigree and single nucleotide polymorphism (SNP) data. The information about sire line, dam, date of birth, sex, breeding line, and herd for 1971 individuals was considered in the pedigree analysis. The SNP analysis ( The Přeštice Black-Pied (PBP) is a Czech local pig breed . At the The biological diversity of the local pig breeds plays an important role in food security and nutrition. Nevertheless, it is seriously endangered by the continuous loss of genetic diversity . PlentifAt present, two basic sources of data about population can be obtained. The pedigree data still represents the base for analyses of genetic diversity. In general, they have been collected over ten generations. However, the results obtained from pedigree data alone could misrepresent the extent of genetic diversity. This disadvantage could be addressed by the inclusion of population data from the SNP analyses. Single nucleotide polymorphism (SNP) analysis  has contributed to a considerable refinement of the assessment of genetic diversity, especially in endangered animal populations. In PBP breed, assessment of genetic diversity based on microsatellites analysis  and pediThe objective of the present study was to assess the genetic diversity of PBP pigs using the pedigree and SNP data.The PBP breed belongs to the primitive indigenous breeds reared in semi-intensive or extensive production systems. This breed has been kept in situ in closed population for more than 25 years. It is characterized by a smaller body frame and mottled black and white coloration. The average weight of mature live boars and sows is 260–280 kg and 215–235 kg, respectively. Despite the fact that individuals are bred in herds with other commercial breeds, they do not have a strong selection pressure. In animals selected for breeding, the basic meat production and reproductive traits  in 2020 was considered the reference population for pedigree analysis of the PBP breed. Basic characteristics of the population are summarized in AnT) has been split into the “new” inbreeding coefficient (FAnN) and the “old” inbreeding coefficient (FAnO). The FAnT was calculated as inbreeding coefficient of ancestors up to five generations back. In addition to the calculation of the inbreeding coefficient of an individual, the influence of inbreeding was also evaluated by the sires , and dams . Expected inbreeding coefficient was computed as the co-ancestry of the breeding animals assuming random mating [Inbreeding coefficient (F) of an individual was calculated using Wright’s coefficients and Meuwissen and Luo’s algorithm ,11. The m mating . The co-m mating .Pedigree completeness index (PCI) was calculated to express the quality of the pedigree using the algorithm devised by MacCluer et al., which summarized the proportion of known ancestors in each ascending generation . It quank is the dam or sire line of an animal, ai is the proportion of known ancestors in generation i, and d is number of generations considered in the calculation of the pedigree completeness. For description of methods used to calculate maximum generations traced back, average complete equivalent generations, number of founders and number of ancestors explaining appropriate proportion of gene pool [Andene pool .e). The Ne was calculated based on standard equation:e-Ln and used the slope from logarithmic regression performed on year of birth of the PBP pigs in the pedigree analysis [e-Ecg. Ne-Coan was computed using the additive genetic relationship against the inbreeding coefficient as described by Falconer and Mackay [Different methods were used to compute the effective population size  born between 2005 and 2020. Bristles, frozen blood samples, and used insemination doses were the sources of DNA. All DNA analyses were performed at the laboratory of genetics of the Czech-Moravian Breeders’ Corporation. DNA was isolated using the magnetic bead method and the silicate column method for bristles and insemination doses, respectively. DNA from the blood samples was isolated using the GeneAll Exgene DNA micro isolation kit . Selected DNA isolates were applied to PorcineSNP60 v2 BeadChips  using the Illumina HD Infinium technology protocol. A total of 61 565 SNPs were identified in the selected DNA isolates. The average call rate was 0.99, 0.97, and 0.94 for the bristles, blood samples, and insemination doses, respectively. The animals with call rate > 0.90 were used in the subsequent analyses.p-value of Hardy–Weinberg equilibrium > 0.0001, and minor allele frequency (MAF) > 0.05 were included in the SNP analyses. After the quality control analysis of all SNPs, the data from 174 animals containing 39 779 SNPs were used for further analysis. Runs of homozygosity (ROH) segments were expressed by a specific minimum number of continuous homozygous SNP markers with a maximum spacing of 1 Mb and a minimum density of one SNP marker per 100 kb. The minimum number of SNPs in ROH (n = 39) was determined based on the approach proposed in the study by Lencz et al. to avoid the detection of false-positive ROH segments (α = 0.05) [Only the SNPs mapped on autosomes were considered in this study. The PLINK v. 1.9 software was used for quality control and subsequent analyses . The SNP = 0.05) . As show = 0.05) ,27,28,29ROH) class by dividing the sum of the ROH segments length (kb) in a particular class by the total length of the autosomal genome (kb) covered by the SNPs and expressed in percentage. The inbreeding coefficient, Fhat1 is the variance-standardized relationship minus one. The inbreeding coefficient, Fhat2 is approximately equal to the estimates . The inbreeding coefficient Fhat3 is based on the correlation between uniting gametes. All the inbreeding coefficients were calculated using PLINK 1.9 software [e was calculated separately for K1, K2, K3, K4, and K8, which represent 1, 2, 4, and 8 centiMorgans per mega base, respectively. The overall degree of population stratification was assessed by calculating Nei’s genetic distances (Da) between animals and the pair-wise Wright’s index at the group level using the StAMPP R package [The inbreeding coefficients, calculated based on the SNP data, were calculated by four different methods. The genomic inbreeding coefficient was calculated for each ROH  observed in the animals from the completely evaluated pedigree was 3.44% on average. Although this value is high, more in-depth analysis is required for a closed population of a genetic resource. Average inbreeding coefficient calculated from the pedigree data for animals born in 2020 was 8.10 ± 1.50%, ranking from 5.50% to 13.00%  is an important parameter and is expressed as the difference of the measured inbreeding between two consecutive generations. This value increased at a rate of +1.58% per generation in average. As expected, the inbreeding rate, as calculated by balancing the founder’s contribution, was positive, and was 0.43% per generation.The overall negative trend of FAnN vs. FAnO in animals born in the last three years was 1:2.44. The total inbreeding observed in mothers had a slightly higher share in an individual’s inbreeding than fathers . However, in a more detailed investigation, we observed differences during the acquisition of inbreeding.e was derived based on multiple parameters depending on various conditions of population under consideration. Different methods also take into account different time windows of pedigree data in calculations and therefore respond differently to rapid changes in population size with different sensitivity. The calculated Ne by five methods is compared in The No) and expected (He) within breed heterozygosity was 0.38 ± 0.13 and 0.37 ± 0.12, respectively.The MAF distributed over each autosome are summarized in ROH for all considered ROH classes. The F value based on ROH decreased with the increase in the minimum length of ROH. The highest average value for FROH was observed in class 1 , while the lowest average value for FROH was in class 16 .The inbreeding coefficients (F), calculated based on the SNP data, were analyzed by different methods. The average value of Fhat1 was −0.026 ± 0.06 and varied from −0.131 to 0.109. The inbreeding coefficient expressed as Fhat2 showed a lower average value compared to that of Fhat1 (−0.032 ± 0.05 and ranged from −0.116 to 0.104). The correlation coefficient between uniting gametes, Fhat3 showed an average value of −0.028 ± 0.046 and varied between −0.086 and 0.107). Sows showed lower Fhat1 values compared to boars . In contrast, an opposite trend was observed for Fhat2 .p < 0.001) except for the correlation between FPED and Fhat1. FPED had a moderate correlation with inbreeding coefficients based on ROH and varied from 0.491 (with FROH in ROH class 16) to 0.542 (with FROH in ROH class 8). The correlation observed between FPED and Fhat2 was also moderate (0.508). However, no relationship was observed between FPED Fhat1.The correlation coefficients between inbreeding coefficients, as determined based on the pedigree and SNP data, are shown in e) calculated using different constant recombination rates is displayed in e up to 35 past generations. In particular, for 1 cM per Mb, the Ne value has decreased slowly and could be observed from 8 to 35 past generations. Then, a short period of three generations without any change in Ne was observed, followed by a relatively rapid decrease of Ne between the fifth to fourth generation. Similar trends were observed for other constant recombination rates with relatively stable historical period from 9 or 13 to 35 past generations, followed by a rapid decrease in Ne.The genetic structure all of the individuals in which the SNP data was derived from the Nei’s genetic distance matrix is presented in Based on the established performance testing and recording of the local PBP breed, the results from the quality of the pedigree are similar to our previous study on the PBP breed . NeverthThe GI of the PBP breed  presented in our study are higher than these values published in the literature. The slightly higher value of F was observed in a previous study for Guadyerbas pig breed maintained in small isolated herds [PED in our previous study [The average Fed herds . They reed herds . We obtaus study . Howeverus study . A studyus study . In the e before making conclusions regarding the genetic diversity of populations [e value of the PBP breed calculated by various methods  ranged from 40 to 80 individuals during the years of study  or Ne-ln, which uses shortest time window. Some authors made suggestions for minimal value of Ne. Welsh et al. stated 50 individuals as the minimum value of Ne [e value be maintained between 31 and 250 animals for good population fitness [It is generally recommended to perform a detailed analysis of the observed Nulations . In our tudy see . Similarue of Ne . Meuwiss fitness . Tang et fitness .Over the last two decades, enormous progress has been made in using of the SNP data for assessment of genetic diversity. At present, high-density SNPs can be used to analyze the past and current population demography . The MAFPED) and SNP data were different. It should also be mentioned that the animals in which SNP analysis was preformed represent a wide range of boars and sows born during 2005 to 2020. Even though, all of them are parents of animals used in next breeding, they were not equal to the number of animals in reference populations (only 58% were born in last four years). Schiavo et al. reported a similar trend; they analyzed three commercial and four autochthonous pig breeds raised in Italy for calculation of inbreeding coefficient values and correlation coefficients among the evaluated types of inbreeding [PED and FROH did not vary significantly depending on the changes in ROH length. In contrast, few authors reported a negligible correlation between FROH and FPED [In general, observed and expected heterozygosity of the PBP population are comparable to other studies performed on wide range of pig breeds and they are on the lower range of values published in previous literature ,45,46. Tbreeding . In accoand FPED . Zanellaand FPED . It is aand FPED .e is slightly negative, especially over the last three years. Reduction of the number of effective animals recorded in the analyzed breed population has been confirmed both by pedigree and LD analysis.From the perspective of current and past effective population size, the population analyzed in our study required an additional attention. In our study, the overall trend in NThe present study reports a detailed analysis of the local PBP pig breed based on all available data. From the results, it is evident that the PBP pig population showed a high degree of inbreeding. Consequently, this results in low value of effective population size. It is necessary to make decisions that stabilize the rate of inbreeding in the long-term by using optimal contribution selection (OCS). The specific mating management, which considers OCS, whereby animals with higher genetic diversity will be preferred for breeding, will be applied in the PBP population in cooperation with breeders’ association. Despite a high volume of pedigree data being recorded, the results of the SNP data analysis should be considered for decision-making in animal conservation programs. However, the final decision-making depends on the understanding of the breeders. For some local populations with very limited pedigree information, the evaluation of genomic information is one of the ways to identify inbred individuals and to quantify the degree of increase in homozygosity due to mating of related animals, respectively.