Castanea crenata), using a genome-wide association study (GWAS). RNA-sequencing (RNA-seq) data were generated from large and small nut-bearing trees, using an Illumina HiSeq. 2000 system, and 3,271,142 SNPs were identified. A total of 21 putative SNPs were significantly associated with chestnut weight , based on further analyses. We also applied five machine learning (ML) algorithms, support vector machine (SVM), C5.0, k-nearest neighbour (k-NN), partial least squares (PLS), and random forest (RF), using the 21 SNPs to predict the nut weights of a second population. The average accuracy of the ML algorithms for the prediction of chestnut weights was greater than 68%. Taken together, we suggest that these SNPs have the potential to be used during marker-assisted selection to facilitate the breeding of large chestnut-bearing varieties.Nut weight is one of the most important traits that can affect a chestnut grower’s returns. Due to the long juvenile phase of chestnut trees, the selection of desired characteristics at early developmental stages represents a major challenge for chestnut breeding. In this study, we identified single nucleotide polymorphisms (SNPs) in transcriptomic regions, which were significantly associated with nut weight in chestnuts ( Moreover, it is the only nut known to contain vitamin C, with 40 mg of vitamin C per 100 g of raw product, which represents 65% of the United States Department of Agriculture (USDA) recommended daily uptake of vitamin C . Moreover, Castanea trees contribute to the maintenance of the ecosystem and soil fertility4.The chestnut is widely cultivated as a food crop in Asian and European countries, due to its high nutrient contents combined with its low fat content5. According to a report published by the Food and Agriculture Organization (FAO) in the United States, chestnut production has steadily increased in Asia through 2014, and chestnuts grown in Asia accounted for 89.6% (1.8 million tons) of the world chestnut production that year5. The Republic of Korea is the second largest chestnut producer5. According to the Korean export index, chestnuts represent the largest exported forest crop for Korea, and chestnut exports increased by approximately 23% in 2017 compared with 20166. In Korea, the cultivar Arima (C. crenata) and its hybrids, including C. crenata × C. mollissima, are widely cultivated in the fields7. Moreover, new Korean indigenous cultivars, such as Mipung8 and Jahong9, have been developed to improve production under various environmental conditions. The nut weight represents one of the most important traits that can determine the economic success of a grower1.Since 1997, global chestnut production has increased sharply, reaching its maximum level (approximately 2 million tons) in 200910. Representative molecular markers include restriction fragment length polymorphisms, randomly amplified polymorphic DNA, amplified fragment length polymorphisms, simple sequence repeats, inter-simple sequence repeats, and single nucleotide polymorphisms (SNPs)12. In general, most commercially desirable traits are quantitative traits associated with large numbers of genes. The rapid growth in next-generation sequencing technologies (NGS), combined with various statistical methods, has facilitated the use of genome-wide association studies (GWASs)13. In particular, genome-based breeding reduces the time required for various breeding schemes15. Furthermore, a transcriptome-based GWAS, which is a type of GWAS, uses transcriptome sequencing data to identify variants within the coding regions of the genome16. Transcriptome-based GWAS has several advantages compared with the traditional GWAS. First, transcriptome analysis can enhance the power of verifying associated genes by overcoming the potential allelic heterogeneity in SNP-based GWASs17, when a number of alleles act through one gene to influence a trait18. Second, the transcriptome-based method can reduce the interference from population heterogeneity by various arrangements of tagging different SNPs to the same causal variants19. Third, transcriptome-based analysis also practically reduces the burden of multiple testing for traditional GWASs. Furthermore, the gene is the functional unit in the genome with high consistency across populations, which is the major target used by most of the subsequent bioinformatics analyses16. Machine learning (ML) is the study of algorithms and statistical models that facilitate software applications to increase the accuracy of outcome predictions without explicit instructions. To predict phenotypes, large-scale genotypic information across the whole genome has been subjected to ML approaches. ML is the most effective method for predicting phenotypes based on genotypes and has been widely applied in various population studies21. For example, an ML model was constructed to predict rust resistance in wheat from a genomic selection22, and have also been applied to other genotype classifications23. Therefore, transcriptome-based GWAS combined with ML analysis might be one of the effective and operational methods to predict the phenotype, which is the result of interactions between the organism’s complement of genes.Conventional tree breeding (CTB) involves the planned interbreeding between closely related individuals to produce new cultivars that express desirable traits, such as enhanced productivity or being easier to harvest. Using CTB, the development of new cultivars that express beneficial traits can take a long time, making it difficult to meet the grower’s demands. Moreover, the long life cycle of trees, including chestnut trees, becomes a barrier to CTB compared with the breeding of annual crops. Recently, molecular markers have been identified and used for marker-assisted selectionk-nearest neighbour (k-NN), partial least squares (PLS), and C5.024, were used to predict nut weights with 21 SNPs. Through our association study and the ML approaches, we identified 21 SNPs associated with nut weights that were able to clearly discriminate between large and small nut-bearing populations. Hence, we suggest that the ML approach is an effective method for the prediction of nut weights. This study represents the first attempt to identify highly significant SNPs associated with nut weights in Korean chestnut trees.Our study aimed to predict the transcriptome-wide SNPs that are closely associated with nut weights. Five ML algorithms, including random forest (RF), support vector machines (SVM), Castanea mollissima) genome, and the resulting mapping rate was 84.4%, on average  indicated that the 42 individual chestnut trees could be classified into three clusters, large (n = 12), small (n = 8), and marginal (n = 22), using the first and second principal components, PC1 and PC2, respectively Fig. . Phyloge−5, Table A GWAS was performed to explore the association between nut weights and SNPs Fig. . Categork-NN and C5.0. The resulting prediction accuracy was 98.7%, on average  into a training dataset (29 cases) and a test dataset (13 cases) and then evaluated the model using five ML algorithms, including SVM, PLS, RF, age Fig. , indicatk-NN and RF outperformed the other algorithms for both the training and validation populations. The values of each area under the curve (AUC) from the quantitative regression mode in the association study were calculated for the 21 SNPs (FDR < 10−5). Overall, the results demonstrated that the ML-based predictive models performed well in the training dataset  was assessed to validate the performance of the ML models during the prediction of nut weights. The average prediction accuracy of the five ML models was 74.9% for the validation population Fig. . Among tset Fig. . In the set Fig. . The RF set Fig. . These r−5) were validated by the ML application, we re-evaluated these 21 SNPs and then performed genotyping PCR using a different population . To evaluate the 21 SNPs in the validation dataset, the flanking sequences of these SNPs were genotyped using Sanger sequencing and specific primers (Supplementary Tables pentatricopeptide repeat (PPR) gene, which plays an important role in determining the nut weights of other plants. The rate of the polymorphic status of this locus across 21 representative individuals primarily depended on the frequency of the minor allele in the large nut-bearing population (Supplementary Table Anaphase-promoting complex (APC) gene were primarily detected in the large nut-bearing groups  , using the same ML algorithms to predict nut weights according to genotypes. The overall performance yielded an average accuracy of 68% (ue) Fig. . For thiue) Fig. . The pre25. Desirable trait-based selective breeding is also an important process. Phenotypic and environmental factors, including plant growth and disease resistance, can be assessed at an early stage, such as during seed germination. However, traits related to yield are highly dependent on the plant life cycle. Perennial trees tend to remain in a juvenile phase for longer than other plants, requiring space and effort to maintain the tree until it enters the reproductive phase25. Therefore, improving the evaluation accuracy is more important than increasing the number of trees26. A transcriptome-based SNP approach can efficiently address these problems. Additionally, transcriptome-based GWAS represents a cost-effective and time-saving method for developing large-scale markers16, especially compared with the general GWAS or other conventional marker identification assays. Recently, transcriptome-based GWAS has been successfully applied to human diseases17.Many breeders have evaluated genetic and environmental variations to improve breeding efficiency27. Although the whole genome sequencing results for Chinese chestnut (C. mollissima) have recently been released, it is difficult to apply these data to Korean naturalised chestnuts, due to sequence variations within species29. Thus, we first conducted the transcriptome sequencing analyses on chestnut trees located in various areas of South Korea and reported the transcriptome-wide polymorphisms found in the coding regions associated with nut weight. An average of 84.4% of the coding genes found in C. crenata could be mapped onto C. mollisima, an average of 83.1% of which was uniquely mapped  transporters have been reported to regulate seed size through lipid accumulation during development40. In addition, DNA replication complex GINS and early flowering 4 (ELF4) genes are also involved in cell cycle regulation and flowering, respectively. These genes also may affect fruit size in large fruits. With this evidence, we suggest that the 21 SNPs identified in this study across 14 genes are highly likely to be associated with nut weight. Thus, functional biological studies of these 14 genes will provide a better understanding of the mechanisms of action underlying nut weight.Our study is the first study to utilise SNP identification to determine the weights of the chestnuts grown by Korean chestnut trees. We systemically predicted 21 SNPs among 14 genes, which correlated strongly with nut weights in Korean chestnuts  predicted nut weights with high accuracy, both during training and during external validation , according to the manufacture’s protocol. Total RNA was then quantified using a Nano drop spectrophotometer (Thermo Scientific), and the quality was assessed using an RNA 6000 Nano assay kit (Agilent) and a Bioanalyzer 2100 (Agilent). NGS sequencing libraries were generated from 1 µg of total RNA, using the TruSeq RNA Sample Prep kit (Illumina) according to the manufacture’s protocol. In brief, the poly-A containing RNA molecules were purified using a poly-T oligo attached to magnetic beads. After purification, the total poly-A RNA was fragmented into small pieces, using divalent cations under elevated temperatures. The cleaved mRNA fragments were reverse transcribed into first strand cDNA, using random primers. Short fragments were purified using a QiaQuick PCR extraction kit and resolved with elution buffer for the end reparation and the addition of poly-A. Subsequently, the short fragments were connected with sequencing adapters. Each library was separated by an adjoining and distinct molecular identifier tag. The resulting cDNA libraries were then paired-end sequenced (2 × 101 bp) for use as individual samples with the HiSeq 2000 system (Illumina).41, and mapped to the reference genome of Chinese chestnut  from Hardwood Genomics Project (https://hardwoodgenomics.org)42, using bowtie2 v.2.2.343. To perform filtering on read lengths, reads with average quality scores that were too high (N nucleotide ratio higher than 10%) or too low (less than Q20) were removed. The scaffold genome sequence is publicly available in the NCBI GenBank repository . To optimise the small insertion and deletion artefacts, the reads were re-mapped to the reference with IndelRealigner, and the base-pair quality scores (QUAL) were calibrated using count covariates and the table recalibration functions in GATK v.3.544, as instructed in the best practices protocol. The variants for individual samples were called by UnifiedGenotyper, using the variant call format (GVCF) and the filters normalised quality score (NQS) ≥ 2 and mapping quality (MQ) ≥ 40 to obtain high-quality SNPs. Finally, the SNPs were annotated using SnpEff v.4.245, and the missing genotypes were imputed by Beagle v.4.146 based on the linkage dis-equilibrium (LD) score.The complete workflow of this study is delineated in Fig. In the training population, we used 42 specimens that were categorised into two groups  according to their nut weights  and the cetyltrimethylammonium bromide (CTAB) method, with minor modifications. PCR amplification was performed in a 20 µL volume using Taq DNA polymerase . The genotyping PCRs for this experiment used individual SNP primers, listed in Supplementary Table 49 in the PCA mode. The SNPs calculated using this method were further filtered to reduce the false positive predictions using the following filters: genotyping rate ≥ 90%, mapping quality ≥ 40, minor allele frequency (MAF) > 5% and Hardly-Weinberg equilibrium (HWE) < 0.001. Furthermore, the sub-populations (K) of the given samples were estimated through the number of clusters (K), which was obtained using STRUCTURE v.2.3.450, without any prior population information. The ad-hoc static ΔK  values were used to determine the uppermost hierarchical levels of the population structures present in a given population51. The range of clusters (K) was pre-defined from one to seven. The analysis was performed with 20 replicated runs, using 100,000 iterations after a burn-in period of 50,000 runs. The output of the STRUCTURE analysis was visualised using CLUMPAK52.To understand the population structure in a given dataset, the SNPs yielded from the variant calls were subjected to population stratification analysis using PLINK v.1.9p < 0.01.A GWAS was conducted using two modes, categorical association (case vs. control) and quantitative linear regression. The associations between genotype and phenotype for the two modes were calculated using PLINK v.1.9 in the association mode  was divided into a training dataset and a validation dataset at a 7:3 ratio for the prediction models. The best model was selected automatically by the caret R package53.Supervised ML was used to construct models to attain greater predictive power from the high-dimensional datasets. Here, we constructed models using significant SNPs (as features), which were selected from the transcriptome-based GWAS. The models were constructed from five learning models: SVM, et al.54. To calculate the ROC and the AUC, we used the plotROC R package55.To compare the prediction methods, we determined sensitivity, specificity, and accuracy, using the following equations: Sensitivity = [TP/(TP + FN)]; Specificity = [TN/(TN + FP)]; and Accuracy = [(TP + TN)/ (TP + FP + TN + FN)]; where TP was the number of true positives, TN was the number of true negatives, FP was the number of false positives, and FN was the number of false negatives. The performances of the prediction models were assessed using ROC curves, plotting the sensitivity as a function of (1-specificity) for different decision thresholds. Further, to quantitatively compare the ROC curves, we computed the AUC, and significant differences between two ROCs were assessed using a two-tailed Student’s t-test. These evaluation metrics were calculated as explained by Manavalan Supplementary Information


Staphylococcus aureus strains, including 29 vancomycin-intermediate S. aureus (VISA) strains, were inoculated in the absence or presence of vancomycin for 48 h. Growth curves and maximum growth rates revealed that strains with the same minimum inhibitory concentration (MIC) showed different levels of plasticity in response to vancomycin. A bivariate GWAS was performed to map single-nucleotide polymorphisms (SNPs) associated with growth plasticity. In total, 227 SNPs were identified from 14 time points, while 15 high-frequency SNPs were mapped to different annotated genes. The P-values and growth variations between the two cultures suggest that non-coding region (SNP 738836), ebh (SNP 1394043), drug transporter (SNP 264897), and pepV (SNP 1775112) play important roles in the growth plasticity of S. aureus. Our study provides an alternative strategy for dissecting the adaptive growth of S. aureus in vancomycin and highlights the feasibility of bivariate GWAS in bacterial phenotypic plasticity research.Phenotypic plasticity enables individuals to develop different phenotypes in a changing environment and promotes adaptive evolution. Genome-wide association study (GWAS) facilitates the study of the genetic basis of bacterial phenotypes, and provides a new opportunity for bacterial phenotypic plasticity research. To investigate the relationship between growth plasticity and genotype in bacteria, 41  Phenotypic plasticity reflects the adaptation of organisms and provides the ability of a given individual to develop different traits in different environments. Phenotypic plasticity was first applied to plants and quickly penetrated into various disciplines . How bacStaphylococcus aureus is an important human pathogen  has successfully indentified numbers of toxicity-related SNPs and highlights the potential of bacterial GWAS in clinical research . The growth data of 41 S. aureus strains, including 29 vancomycin-intermediate S. aureus (VISA) strains, in the absence or presence of vancomycin were obtained. The significant SNPs related to the growth plasticity of different strains were analyzed in a bivariate GWAS. These results not only provide an alternative strategy for dissecting VISA development, they also highlight the feasibility of bivariate GWAS for the analysis of phenotypic plasticity in bacteria.Genome-wide association study (GWAS) provides a new opportunity to explore the response of genes or chromosomal regions to different phenotypes . In the research . Recentlresearch , and a nresearch . Taking S. aureus strains were obtained by 60-day in vitro vancomycin treatment in our previous study  with elevated MICs were obtained and preserved at −80°. Before growth plasticity experiment, single colonies were transferred to BHI culture medium and shaken overnight at 37°C.All us study . Strainsus study . Each pa3 CFUs/ml. Growth curve experiments were carried out in 96-well plates  in 150-μl volumes in triplicate. Cell growth was monitored over 48 h by OD600 on a microplate reader . Fourteen data points were collected: 1, 2, 4, 6, 8, 10, 12, 16, 20, 24, 30, 36, 42, and 48 h. Data were plotted to a smooth curve. The MGR was determined as described previously (Overnight cultures were inoculated into fresh BHI medium with (2 μg/ml) or without vancomycin  at a concentration of 6 × 10eviously .S. aureus genomes were genotyped using an Illumina HiSeq 4000 instrument  at Allwegene ; S. aureus subsp. aureus NCTC 8325 (NC_007795.1) was used as the reference strain. Quality control of the raw sequencing data included the removal of low-quality and adapter reads, calculating the sequencing error rate, Q20 and Q30 statistics, and GC content analysis. The sequence data were then compared to S. aureus subsp. aureus NCTC 8325 genome using the BWA mapper v0.7.8  according to the manufacturer’s protocols. In total, 41 r v0.7.8 ; the inir v0.7.8  and repek. Given the certain subpopulation number of k, meanQ was calculated for each strain and for each subpopulation in the mapping population, inferring the posterior probability of subpopulation with each strain was belonging to. The Akaike information criterion (AIC) value was calculated for each k. The meanQ with best k value (minimum AIC) thus offered results of population structure.FastStructure software v1.0  was usedP-value to determine interesting SNPs at each time point using GEMMA software v0.97  was used to calculate the re v0.97 . Growth Like traditional LMM model, population structure and kinship were all considered to be implemented in multivariate LMM. GEMMA fit a mvLMM in the following form:n strains in mapping population constituted an n by 2 matrix of Y. X is a matrix full of 1s and XB simply represents the fixed effects other than SNP and the population structure. A is an 2 by 2 matrix of fixed effects of SNP, each row denoting effects of a particular genotype across two groups. V is a k by 2 matrix of population effects. U is an n by 2 matrix of unknown random effects and E is an n by 2 matrix of residual effects. S is an n by 2 design matrix for the two genotypes of a SNP and Q is a n by k design matrix of 1s and 0s from results of fastStructure. Kinship matrix K is reflected as variance of random effects U. Because of the property with bivariate phenotype and the accompanying bivariate random effect, U was assumed to follow the multivariate distribution, with mean being a zero matrix, as shown in above equation. V0, Vn by n), and column covariance matrix VXB, SA, and QV are viewed as fixed effects, and U is viewed as random effects. In this manner, the Q + K model for GWAS of bivariate phenotype had been implemented, integrating both population structure effect (Q) and kinship effect (K). After model construction, GEMMA derived REMLE algorithm to solve Vg and Ve and other parameters in above equation. The threshold of GWAS was determined by 1000 permutation replicates. In each permutation, relationship between genotype and bivariate phenotype was reshuffled randomly. A minimum P-value from each permutation was selected out to construct an increasing vector of 1000 elements. By using 0.05 as the significance level, the 50th value was taken as the threshold for GWAS.Consider the bivariate phenotype from twogroups, P < 0.05 was considered statistically significant.All statistical analyses were performed with SPSS v12.0 . S. aureus in vancomycin  were collected from 41 S. aureus strains. These SNPs covered the S. aureus genome by one per 46 bp. This SNP density should be sufficient for the identification of genomic regions in a GWAS. The clean sequence data reported in this study have been deposited in the Genome Sequence Archive in BIG Data Center  encodes GMP synthetase. GMP synthase catalyzes the generation of GMP by de novo synthesis. The argS gene(SNP 603363) is involved in arginine-tRNA aminoacylation. The mnhA2 gene at position 615798 is related to the transmembrane transport of ions. Position 906471 of trpS is related to the synthesis of tryptophanyl-tRNA synthetase. SNP 965494, which was significant at time points 2, 3, 4, 5, 6, and 7, is located in atl gene, which is involved in peptidoglycan decomposition and the activation of amidase. The tyrA gene (SNP 1307807) encodes prephenate dehydrogenase. SNP 1394043 is located in ebh (SAOUHSC_01447), which edcodes extracellular matrix-binding protein. Position 1612045 is located in SAOUHSC_01705, which encodes enterotoxin family proteins. Position 1775112 (pepV) encodes the dipeptidase. AlsD (SNP 2290791) and gltB (SNP 2535773) are involved in acetoin and glutamate biosynthesis, respectively. The hsdM (SNP 1842166) encodes the type I restriction-modification system subunit M. GntR (SNP 2588313) and glcB (SNP 2621187) are related to gluconate transcription and glucose transmembrane transport, respectively.Position 264897 is located in SAOUHSC_00246, which is related to drug transporters. P-value and the frequency of occurrence (600 at 2 μg/ml (treated) from that at 0 μg/ml (untreated), and the strains were divided into two groups according to the genotype of each SNP  at 4 h, is located in a non-coding region between SAOUHSC_00755 and glxK (SAOUHSC_00756). Based on the comparison of the two genotypes, the growth plasticity was significantly different between genotypes T and C , with a –logP-value of 29.12073 at 4 h. Except for 1394043, four additional high-frequency SNPs  were also mapped to ebh gene (P = 0.0149), and genotype A showed better growth plasticity (P = 0.0494) (P = 0.3279) (pepV (SAOUHSC_01868), which encodes the dipeptidase. A schematic model of the lysine-signaling pathway mediated by SAOUHSC_01868 discovered in proteomic studies is shown in PepV regulates lysine synthesis, and lysine affected S. aureus growth in calf serum. These results indicate that non-coding region (SNP 738836), ebh (SNP 1394043), drug transporter (SNP 264897), and pepV (SNP 1775112) are important for the growth plasticity of S. aureus in vancomycin culture.Based on the highest –logcurrence , we focucurrence , and theeach SNP . Positio T and C . Positioebh gene . The groasticity . The gro 0.0494) . Regardi 0.3279) . PositioS. aureus. In total, 41 strains, including 29 VISA strains, were cultivated in the absence or presence of vancomycin. The growth plasticity was analyzed by a bivariate GWAS and several significant SNPs were identified. Our results suggest that four SNPs  may play important roles in the growth plasticity of S. aureus.Phenotypic changes include behavior, physiology, morphology, and growth, which can be found on individuals or across generations . PhenotyS. aureus is a major threat to public health .As the result of interactions between genotype and environment, the phenotypes of an individual can vary . The abic health . Previousitivity . The evoEscherichia coli enhanced tolerance to isobutanol , which may be crucial for the growth plasticity of S. aureus. The most significant SNP 738836 is located in a non-coding region. Although non-coding regions do not encode proteins, they still regulate gene expression in various biological processes of bacteria (glxK (SAOUHSC_00756). According to a previous report, SAOUHSC_00755 may be related to eDNA release, which promotes biofilm formation (S. aureus in vancomycin. The growth of SNP 1394043 was remarkably different (as determined by a t-test of the A and G genotypes). Ebh (SNP 1394043) contributes to cell membrane synthesis and envelope assembly pathways  encodes a putative transmembrane efflux pump protein, shows 79% similarity with norB (norB encodes multidrug resistance efflux pumps and is related to the ciprofloxacin resistance of MRSA (pepV) of SNP 1775112 is an important step in the synthesis of lysine and regulates synthesis downstream of lysine. This protein also catalyzes the conversion of N-succinyl-L,L-2,6-diaminopimelate to L,L-2,6-diaminopimelate and ultimately yields lysine and other intermediate products that indirectly affect peptidoglycan biosynthesis and cell growth (S. aureus.In our study, 227 significant SNPs related to the growth variation between cultures in the absence or presence of vancomycin were identified by a bivariate GWAS. As shown in bacteria . SNP 738ormation . During ormation . The relpathways . It has ecreases . In thisith norB . Previou of MRSA . Bacteri of MRSA . As showl growth . In summS. aureus. A bivariate GWAS was used to identify potential SNPs and genes responsible for growth plasticity. Our results provide an alternative strategy to dissect the adaptive growth of S. aureus in an antibiotic environment and highlight the feasibility of bivariate GWAS in the phenotypic plasticity research of bacteria.We chose vancomycin as an environmental stress factor to study the phenotypic plasticity of YJ and XH conceived and designed the experiments. MR, XZ, and JB performed the experiments. MY, XX, and YJ analyzed the data. MR and YJ wrote the manuscript. All authors reviewed the manuscript.The 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 development and utilization of genetic markers play a pivotal role in marker-assisted breeding of rice cultivars during pyramiding of valuable genes. Among molecular markers, SNPs have become the most promising due to their wide distribution within genomes and suitability for high -throughput automated genotyping. Although metadata of SNPs have been identified via next generation sequencing in rice, a large gap between the development of SNP markers and the application in breeding still exists. To promote the application of SNP markers based on the KASP (Kompetitive Allele-Specific PCR) method in rice breeding, a set of core SNP arrays was built via the screening of SNP databases and literature resources based on the KASP method.GBSSI, SSIIa, and Badh2 from the core SNP arrays for estimation of their grain quality performance. Eighteen rice lines, including Xiangwanxian 13, Basmati 370, Ruanhua A, and PR 33319–9–1-1-5-3-5-4-1, harbor all three favorable alleles. The core KASP arrays were also used for rice germplasm assessment, genetic diversity and population evaluation. Four hundred and eighty-one rice germplasms were divided into 3 groups: POP1, POP2 and POP3. POP1 and POP2 were indica rice subgroups consisting of 263 and 186 rice germplasms, respectively. POP3 was a japonica rice subgroup consisting of 32 rice germplasms. The average FST value for the three subgroups was 0.3501; the FST value of POP1 and POP3 was the largest (0.5482), while that of POP1 and POP2 was the smallest (0.0721). The results showed that the genetic distance between the japonica and indica rice subspecies was large, indicating that the core SNP markers were effective at discriminating the population structure of the germplasms. Finally, the core KASP arrays were used for association analysis with milled grain traits. A total of 31 KASP markers were significantly associated (P < 0.01) with ML and the LWR. Among the 31 markers, 13 were developed based on cloned genes or on identified loci related to yield traits. Notably, several KASP markers associated with grain quality were also found to be associated with brown planthopper resistance or green leafhopper resistance simultaneously.Five hundred and ninety six SNPs classified into eight subsets including quality control, indica-indica variation, highly polymorphic, functional genes, key genes targeting sites, gene cloned region, important trait associated and gap filling sites were chosen to design KASP primers and 565 out of them were successfully designed, and the assay design success rate was 94.8%. Finally, 467 out of the 565 successfully-designed SNPs can display diversity at the loci were used to develop a set of core SNP arrays. To evaluate the application value of the core SNP markers in rice breeding, 481 rice germplasms were genotyped with three functional KASP markers designed from the sequences of The core KASP arrays developed in our study were efficient and versatile for rice germplasm assessment, genetic diversity and population evaluation and are valuable for promoting SNP molecular breeding in rice. Our study demonstrated that useful assays combined with molecular breeding can be exploited for important economic trait improvement in rice breeding.The online version of this article (10.1186/s12284-019-0272-3) contains supplementary material, which is available to authorized users. Oryza sativa L.) is one of the most important crops and supplies food to approximately half of the human global population  and 93–11 (indica) , the Rice Diversity Project database (https://ricediversity.org/), the Rice Genome Annotation Project database (http://rice.plantbiology.msu.edu/), and the Rice SNP-Seek Database (http://snp-seek.irri.org). In 2015, SNPs and small-fragment InDels of 2859 rice genomes of good quality were obtained via the genome-wide resequencing of 3000 rice resources worldwide  in 1994, SNPs have been widely used in many fields, such as biology, agriculture, medicine, and biological evolution. Among molecular markers, SNPs have become the most promising due to their wide distribution within genomes and suitability for high-throughput automated genotyping. High-throughput sequencing technology has greatly advanced the discovery of SNP markers. A total of 408,898 candidate DNA polymorphisms (SNPs and/or insertions/deletions (InDels) were identified via comparative analysis of the draft genome sequences of the rice cultivars Nipponbare  markers can also be used for SNP allelic genotyping  is a detection method that can type SNPs and InDels at specific sites. With respect to genotyping, KASP is based on terminal fluorescence reading, similar to the basis of TaqMan probe detection. Each method uses two-color fluorescence to detect individual samples of different genotypes at a single site; different fluorescent products reflect different DNA templates. For KASP tests, a SNP- or Indel-specific KASP assay mixture (specific primer mixture), KASP Master Mix  and DNA samples are mixed together for thermal cycling, after which the fluorescence is read by laser scanning at the end point. KASP-SNP markers are highly accurate, inexpensive and highly flexible, and they have a high conversion rate and a wide range of applications. Several functional SNPs and InDels have been converted into functional markers (FMs) via KASP assays, greatly improving the speed and efficiency of selection in crop breeding programs ) in the test range of rice accessions ranged mainly from 0.50 to 1.00 Fig. a. The geAccording to the physical position of the cloned gene or GWAS-identified locus, 244 markers were found to be related to important rice traits  Table . Yield-rWx, ALK and fgr can be used to improve eating quality, and Rf4 plays an important role in the molecular breeding of hybrid rice parents. Two dominant R genes, Pik and Pi2, were thought to provide broad-spectrum and high resistance to blast, so the pyramid of these two genes is valuable for breeding rice varieties that exhibit robust blast resistance. Two KASP-SNP markers, SK11.27979265 and SK06.10389729, were developed to distinguish between alleles of Pik and Pi2. GS3, a main QTL controlling grain weight and grain length, accounted for 80–90% of the variation in grain weight and grain length in a recombinant inbred line (RIL) population  of the populations could strongly explain the genetic distance between populations. The average FST value for the three subgroups was 0.3501; the FST value of POP1 and POP3 was the largest (0.5482), while that of POP1 and POP2 was the smallest (0.0721). The results showed that the genetic distance between the japonica and indica rice subspecies was large, indicating that the core SNP markers were effective at discriminating the population structure of the germplasms.Based on the genotyping results of 467 core SNP markers, the population structure of 481 rice germplasms was classified by Structure software. The population number K was set to 2–11, and each hypothetical K value was calculated five times. The ΔK of Evanno was maximal at K = 3. Therefore, the 481 rice germplasms were divided into 3 groups: POP1, POP2 and POP3 Fig. a. POP3 wPOP1 was further divided into pop1, pop2, pop3 and pop-mix subgroups, which include 92, 94, 52 and 25 rice germplasms, respectively Fig. . Based oGBSSI, SSIIa, and Badh2, were screened among the 481 rice germplasms. The results are shown in Fig. GBSSI , SSIIa , and Badh2  were identified, respectively . These markers provide important reference information for MAS breeding. To evaluate the validity of these trait-related markers, with milled grain properties as an example, a GWAS was conducted on 323 rice accessions via core SNP genotyping. We focused on the contribution of yield-related markers to grain traits and tried to identify new markers that affecting grain traits. Of the 244 markers, 18 were developed based on cloned genes related to grain shape or on reported GWAS-identified loci related to grain shape. Among these 18 markers, 9 were developed based on functional or polymorphic sites within the cloned genes 2 parameters and the physical distance of the LD paired sites were analyzed by Tassel 5.0. The resulting equation y = 0.075ln(x) + 0.558 reflects the relationship between the R2 values and the LD. When an R2 value decreases to half of the maximum value, the attenuation distance is approximately 100 kb, which is consistent with results in previous studies  with ML and the LWR  exists between the locus combinations of the core markers. The regression equations governing the RWR Table . Eightee2 value of the 13 markers contributing to their corresponding traits ranged from 0.0797 to 0.2688. SK05_27,742,506 is located within the cloned gene LTN1 sequence region and is significantly associated with four traits. The LTN1 gene encodes a protein containing a ubiquitin-binding domain and can regulate grain shape by affecting the absorption of Fe, K, Na, Ca and other metal elements. SK03_16,075,626 was significantly associated with ML, the LWR and MT, which is consistent with previous GWAS results in which this locus was significantly correlated with seed surface area. SK03_16,075,626 is located within the LOC_Os03g27990 sequence, which encodes the precursor of STRUBBELIG-RECEPTOR FAMILY 7, whose function is still unknown. SK05_5,396,755 was significantly associated with ML and the LWR and has been reported in previous GWAS results. This marker was located within the LOC_Os05g09550 sequence region, which encodes Der1-like family domain-containing proteins. SK05_5,537,852 and SK05_5,339,085 were developed based on cloned GW5 polymorphic loci. SK05_5,537,852 was significantly associated with ML and the LWR, while SK05_5,339,085 was significantly associated with MRW. SK03_16,733,441 is a FM of the GS3 gene and is significantly associated with ML in this study. SK03_872,082, which is located within LOC_Os03g02440 and encodes a WD repeat-containing protein 44, was developed in accordance with previous GWAS reports and was significantly associated with ML. SK06_27,438,624, SK06_27,481,339, SK06_27,498,819 and SK06_27,526,542 were all developed based on the cloned gene APO1, which affects grain traits by regulating the formation of rice vascular bundles. These four markers were significantly associated with the LWR; SK06_27,481,339 is located within the APO1 sequence interval. SK07_472,805 is located within the sequence of the cloned gene DEP, which encodes a transcription factor that consists of 267 amino acids, contains a MADS-box domain and plays an important role in the development of flower organs. SK08_21,582,840 is highly associated with ML and is located in COE1, which affects the growth of rice leaf veins and may indirectly affect grain development. Based on the above results, we detected cloned genes of GS3 and GW5, and the markers that were developed from the cloned genes related to grain shape or from reported GWAS loci related to grain shape were verified in the present study. Therefore, markers related to important rice traits within core SNP arrays can be used for allele screening to provide useful information to breeders.Among the 31 markers, 13 were developed based on cloned genes or on identified loci related to yield traits. The RGrh2, Grh4 and Grh6 method in studies of wild rice mutants revealed the specificity and sensitivity of the KASP assay . For maize, KBioscience successfully designed assays for 1250 SNPs that corresponded to 1536 SNPs from an Illumina GoldenGate chip (81.4%) , the Rice Diversity Project database (https://www.ricediversity.org/), the Rice Genome Annotation Project database (http://rice.plantbiology.msu.edu/), and the Rice SNP-Seek Database(http://snp-seek.irri.org), as well as reported genes or trait-related loci. Therefore, the core SNP array carries more genetic information and is more comprehensive than the SNP marker set. Among the eight groups of molecular markers in the core SNP array, two groups comprise cloned genes that are tightly linked or are target markers: one group is the FMs, and the other group consists of reliable SNP markers associated with traits reported in previous GWASs. These traits provide valuable information for MAS. In addition to these trait-related markers, in the present study, SNP markers that were related to quality control and indica-indica groupings and that were highly polymorphic were developed, which are valuable for analyzing the genetic background of materials.Compared with the reported SNP marker set, the core SNP markers in this study were derived from multiple rice SNP databases, e.g., the Gramene database . The average FST value between the two indica subgroups and the japonica subgroup was 0.489, which indicates that the indica population could be definitively distinguished from the japonica population; this value was similar to the FST value (0.517) of indica and japonica subspecies as determined via SNP markers ; this categorization seemed to differ from previously reported results in which only two groups, an indica group and a japonica group, were identified from collections , a high polymorphism information content (PIC) SNP dataset , the SNP array  method and quantified using a NanoDrop ND-1000 spectrophotometer . To increase the efficiency and reduce the genotyping costs, high-throughput DNA extraction method . For most of breeders in labs, 96-SNP array is the most commonly used because it is more economic, operable and use-friendly for various-sample-size genotyping. Actually KASP assays can also be manipulated on 384-plate format. The 384-plate format PCR system can be established as 5 μL reactions (2.5 μL of template DNA (25 ng), 2.5 μL of 2× Kaspar mix, and 0.07 μL of primer mix). The genotyping efficiency can be more greatly upgraded with the 384-plate format and the genotyping cost can be reduced at the same time due to less reagent usage per assay. The breeders can adjust the genotyping system according to specific needs and laboratory equipment conditions. Once the run was completed, analysis was carried out with StepOne Software V2. The primers of all the KASP arrays used in our study can be viewed in Additional file The SNP allele frequency and PIC were estimated for each locus using PowerMarker V3.25 determining whether a marker is associated with a trait and the R2 value (marker) indicating the fraction of the total variation explained by the marker were reported.The milled rice shape parameters of 323 out of 481 germplasms were investigated. Images of the more than 100 milled rice grains were captured via a CanoScan 5600F scanner  and the supplied software; the images were not enhanced. The length, width, length/width ratio (LWR), thickness, and 100 milled rice weight were measured precisely using SmartGrain software Additional file 2:Table S2. List of the rice germplasm used in this study. (XLSX 39 kb)Additional file 3:Table S3. Subset 1 of SNPs from Kurokawa et al. Additional file 6:Table S6. Subset 4 of SNPs. (XLSX 11 kb)Additional file 7:Table S7. Subset 5 of SNPs. (XLSX 14 kb)Additional file 8:Table S8. Subset 6 of SNPs. (XLSX 25 kb)Additional file 9:Table S9. Subset 7 of SNPs. (XLSX 12 kb)Additional file 10:Table S10. Subset 8 of SNPs from Zhao et al.


MLANA, SYT10, and CDC42EP5 that have been associated with coat color in mouse, and ADAMS3, DNAJC3, and PAG5 genes have been associated with fertility in cattle. Further analysis of the variants detected 688 candidate selective sweeps (ZHp Z-scores ≤ -4) across all three breeds, of which 223 regions were assigned as being putative selective sweeps (ZHp scores ≤-5). We also identified 96 regions with extremely low ZHp Z-scores (≤-6) in Afrikaner and Nguni. Genes such as KIT and MITF that have been associated with skin pigmentation in cattle and CACNA1C, which has been associated with biopolar disorder in human, were identified in these regions. This study provides the first analysis of sequence data to discover SNPs in indigenous South African cattle breeds. The information will play an important role in our efforts to understand the genetic history of our cattle and in designing appropriate breed improvement programmes.Single nucleotide polymorphism arrays have created new possibilities for performing genome-wide studies to detect genomic regions harboring sequence variants that affect complex traits. However, the majority of validated SNPs for which allele frequencies have been estimated are limited primarily to European breeds. The objective of this study was to perform SNP discovery in three South African indigenous breeds  using whole genome sequencing. DNA was extracted from blood and hair samples, quantified and prepared at 50 ng/μl concentration for sequencing at the Agricultural Research Council Biotechnology Platform using an Illumina HiSeq 2500. The fastq files were used to call the variants using the Genome Analysis Tool Kit. A total of 1,678,360 were identified as novel using Run 6 of 1000 Bull Genomes Project. Annotation of the identified variants classified them into functional categories. Within the coding regions, about 30% of the SNPs were non-synonymous substitutions that encode for alternate amino acids. The study of distribution of SNP across the genome identified regions showing notable differences in the densities of SNPs among the breeds and highlighted many regions of functional significance. Gene ontology terms identified genes such as  The development of next generation sequencing (NGS) technologies has enabled rapid and cost-effective generation of sequence data for SNP discovery in cattle . These dBos taurus), zebu cattle (Bos indicus), and shorthorn cattle (Bos taurus africanus) includes the Afrikaner, Nguni, Pedi, Mashona, and Tuli (South African indigenous cattle such as Afrikaner (AFR), Drakensberger (DRA), Nguni (NGI), Bonsmara, and Tuli have played a major role in traditional, social, and commercial history of the country . These bn cattle . As the n cattle . The zeband Tuli . Sanga cand Tuli . Their sand Tuli .Indigenous breed of SA have made major contributions to livestock production because of their ability to adapt and produce in different SA production systems . These bNGS technologies have identified a large number of SNPs and insertions-deletions (Indels), with many variants remaining to be detected, especially in cattle breeds that are phylogenetically distinct from the more extensively studied European breeds . More thst estimates between populations affects the weighting of principal components, which in turn, can affect inferences about admixture in populations , Drakensberger , and Nguni  were performed within breed to identify the least related individuals in these populations. Relationship coefficients between individuals were estimated using the method of The available pedigree data for each breed were obtained from the Agricultural Research Council (ARC) Integrated Registration and Genetic Information System (INTERGIS) database. Pedigree analysis of Afrikaner (® 2.0 Fluorometer (Thermo Fisher Scientific). All DNA samples were maintained at a concentration of 50 ng/μl in preparation for NGS sequencing at the ARC-Biotechnology Platform.Sampling of blood and hair was performed with the approval of the Animal Ethics Committee of the University of Pretoria (EC: S4285-15), according to guidelines for the proper handling of animals during sample collection. Genomic DNA was extracted from whole blood (200 μl/sample) using the Roche DNA extraction Kit  following the standard protocol of the manufacturer. The procedure included a proteinase K digestion followed by column purification for the extraction of high quality DNA. The extraction of DNA from hair roots was performed using an optimized Phenol-Chloroform protocol , that inEquimolar DNA pools were prepared for each breed using 170 ng of DNA per animal, and each DNA pool contained 30 animals per breed. Genomic libraries were prepared with the Truseq DNA sample preparation kit v2  using 1 μg of genomic DNA according to the manufacturer’s instructions. DNA was fragmented using a Covaris E220 sonicator (350 bp), end-repaired and A-tailed followed by the ligation of adapters  and 12 cycles of polymerase chain reaction (PCR) were performed. Quantities and the quality of usable material for each of the libraries were estimated by qPCR . The automated cBot Cluster Generation System  was used to generate clusters on the flow cell. Each DNA pool was then sequenced  on a single lane of the Illumina HiSeq 2500. The resulting images were analyzed with the Bcl2fastq v2.0 (Illumina) to generate the raw fastq files .Sequence reads were filtered for base quality and adapter trimming using Trimmomatic v0.33 . Reads wVariant discovery was performed within breed according to GATK Best Practices using the genomic variant call format (GVCF) workflow, using HaplotypeCaller . The worTo reduce the false discovery rate, hard filtering steps were conducted using the following criteria: Phred scaled polymorphism probability (QUAL) < 30.0, variant confidence normalized by depth (QD) < 2.0, mapping quality (MQ) < 40.0, strand bias (FS) > 60.0, HaplotypeScore > 13.0, MQRankSum < -12.5, and ReadPosRank-Sum < -8.0  v2.0 tool, Ensembl Build 87 . For all1 and when one or more genes were found in a window, the corresponding windows were ascribed the gene names.SNPs were examined to determine their distribution throughout the genome, identify regions enriched for novel and non-synonymous SNPs and identify genes associated with enriched regions. To identify genomic regions of exceptional SNP densities, we compared the distribution of four different categories of SNPs  in 1 Mb non-overlapping windows across the genome in each breed. The choice of window was based on a recent publication by nMAJ) and least abundant alleles (nMIN) and for each window in each breed pool, a pooled heterozygosity score is calculated as:Identification of selective sweeps was performed using the approach of nMAJ and ΣnMIN are the sums of nMAJ and nMIN for all SNPs in the window. Individual Hp values are then Z-transformed as follows:where ΣpμH and pσH are the mean and standard deviation for the Hp scores. To detect putative selective sweeps, a whole genome screen was performed to identify genomic regions with an excess of homozygosity (heterozygote deficiency) from the autosomes. SNPs were used to calculate Z-transformations of the pooled heterozygosity (ZHp) in each of the three breeds, and the number of sequence reads containing major and minor alleles were also counted. Subsequently, a 50% overlapping sliding window approach with 150 kb windows was used to compute ZHp in each of the windows, and plot the distribution of SNP counts within these windows. Windows with ZHp Z-scores of ≤-4 were retained as candidate selective sweep regions and regions with ZHp Z-scores of ≤-5 as putative selective sweeps. In addition, animal QTLdb was used to retrieve quantitative trait loci (QTL) information and visualize the QTL located within the putative selective sweep regions  of high quality paired-end reads using an Illumina HiSeq 2500 sequencer, of which 99% of the reads were mapped to the bovine reference genome (UMD 3.1). PCR duplicates were marked and reads were realigned around insertion and deletion events resulting in approximately 1.7 billion sequence reads (90.2%) across the three breeds, with an average coverage of 21.1-fold across the reference genome . The Ti/A total of 17.6 million variants were identified in the three studied breeds with the greatest number of variants in NGI and AFR and lowest in DRA . The detRun 6 of the 1000 Bull Genomes Project  was usedSNP annotation using VEP Ensembl gene annotation and dbSNP indicated that 62% of the SNPs were located in intergenic regions , 29% were located in genic regions including introns, splice sites, and exons. Fewer SNPs (9%) were located in upstream/downstream regions . Of the total number of Indels, 61% were located in intergenic regions, 28% in genic regions including introns, exons and splice sites, and 1% were located in untranlsted regions. In AFR, there were 433,495 (4.4%) SNPs located within 5 kb upstream of a transcription start site and 437,355 (4.4%) SNPs within 5 kb downstream of a transcription stop site; 3,974 (0.04%) SNPs were located in a 5′UTR and 18,999 (0.2%) in a 3′UTR. These totals were slightly different in other two breeds, and were slightly lower in NGI. A total of 20,508 SNPs across the three breeds were located in splice sites, and 498 SNPs were in splice/donor sites. A total of 109,067 non-synonymous single nucleotide polymorphisms (nsSNP) substitutions were observed. There were 868 SNPs predicted to cause premature stop codons and 75 to cause gains in coding sequence across the breeds.ATN1, C3AR1, and other genes) were found to be among top 1% missense variants in AFR but were not observed among the top 10% regions for either NGI or DRA.TRB genes  was found to be in the top 1% both for overall SNP density as well as missense SNPs density in AFR and DRA (but not in NGI). TRB genes have been associated with domestication in cattle, showing strong evolutionary pressure  in pigs  that has been associate with coat/hair pigmentation in mouse, is associated with meat tenderness in Nellore cattle  has been associated with network of genes that regulate milk yield in Holstein cattle (PAG5 gene in Chr 29 (corresponding region showing non-synonymous variants enrichment only in NGI) have been associated with pregnancy (formation of placenta) in cattle while R8I2 in Chr 15 have been associated with taste in rat, as well as sensory perception of smell in humans  scores to identify putative selective sweep regions. The ZHp Z-scores ranged from -10.26 to 2.18, from -5.27 to 1.37, and from -8.27 to 1.94 in AFR, DRA, and NGI, respectively. Thus, there appeared to be regions of excess homozygosity but not excess heterozygosity in the genomes of these animals. The genome-wide screening of the breeds revealed 113 distinct loci with ZHp Z-scores ≤-5, and 157 loci with ZHp Z-scores ≤-4 in AFR, 2 and 152, respectively in DRA, and 108 and 156, respectively in NGI. In total, 465 candidate selective sweeps with ZHp Z-scores ≤-4 were identified across the genomes of AFR, DRA, and NGI and 223 regions were identified as putative selective sweeps (ZHp Z-scores ≤ -5). The lowest number of putative selective sweeps was observed in DRA. The regions identified as candidate selective sweeps (ZHp Z-scores ≤-4) in DRA were located on 26 different chromosomes. We also identified 93 selective sweep regions with extremely low ZHp Z-scores (ZHp-scores ≤-6) as indicated in FAM101B) was identified in a sweep region with a ZHp Z-score of -9.05 in AFR on chromosome 19, and was also found in NGI with a ZHp Z-score of -8.2. This gene is involved in the regulation of the perinuclear actin network and nuclear shape through interaction with filamins, and plays an essential role in the formation of cartilaginous skeletal elements in human. There were also a few overlapping common genes that were identified across all the three breeds that could have been associated with breed formation in cattle. The KIT and MITF genes on chromosomes 6 and 12 respectively, have been associated with pigmentation in cattle, KDR on chromosome 6 is a tyrosine kinase receptor, ERBB4 on chromosome 2 is associated with a signaling pathway involved in the development and progression of melanocytes in human  major depression, (2) the development of brain cortex and formation of axons, (3) dietary habits, (4) associated with Herpes simplex encephalitis type 1, (5) induced by lymphocyte activation, (6) involved in Cystic Fibrosis, and (7) implicated in Down syndrome, respectively , adaptation, fertility , production (meat tenderness) and diseases , which represent some of the desired traits in livestock production. More validation of these associations are still needed, especially in farm animals. The global study of the 1000 Bulls Genomes Project has mined genes that influence complex genetic traits in cattle, opening the door for researchers to use the same approach to map high-value traits including those important for beef and milk production. We expect the current data would serve as an important resource, for a comprehensive analysis comparing genomic distribution and densities of SNPs on a global scale.It has been suggested that rare or low-frequency variants may explain a substantial proportion of the heritability of many complex diseases, most of which have previously not been fully captured in GWAS studies . The powSearching for genomic regions of reduced variability as signatures of strong positive selection can also help in identifying causal mutations controlling selected phenotypes . SelectiThe SNPs and Indels identified in this study will serve as useful genetic tools, and as candidates in searches for phenotype-altering DNA differences. Novel SNPs provide an insight into the genomic regions that are unique to each breed. Identification of nsSNPs provides the potential for the detection of genes and variants underlying variation in traits of economic importance in these breeds, in particular environmental adaptation. Genes located in genomic regions that are enriched for variation suggests their potential for selection due to effects on phenotypic characteristics. Identification of selective sweeps provides a broader insight into the events that happened during recent selection events and artificial selection processes that have shaped the livestock genome in SA indigenous cattle breeds. These results provide a framework for further genetic association and QTL fine-mapping studies in indigenous SA cattle.AZ designed the experiments, carried out the analysis, and drafted the manuscript. RS and JH assisted with structuring the methodology, data handling, and data management. AC and MM assisted with statistical analysis. AM, EVM-K, and JT structured scientific content. All authors provided editorial suggestions and revisions, read and approved the manuscript.The 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.


Salvelinus alpinus). Novel single nucleotide polymorphisms (SNPs) were identified in charr from the Fraser, Nauyuk and Tree River aquaculture strains, which originated from northern Canada and fish from Iceland using high coverage sequencing, reduced representation sequencing and RNA-seq datasets. The array was designed to capture genome-wide variation from a diverse suite of Arctic charr populations. Cross validation of SNPs from various sources and comparison with previously published Arctic charr SNP data provided a set of candidate SNPs that generalize across populations. Further candidate SNPs were identified based on minor allele frequency, association with RNA transcripts, even spacing across intergenic regions and association with the sex determining (sdY) gene. The performance of the 86,503 SNP array was assessed by genotyping Fraser, Nauyuk and Tree River strain individuals, as well as wild Icelandic Arctic charr. Overall, 63,060 of the SNPs were polymorphic within at least one group and 36.8% were unique to one of the four groups, suggesting that the array design allows for characterization of both within and across population genetic diversity. The concordance between sdY markers and known phenotypic sex indicated that the array can accurately determine the sex of individuals based on genotype alone. The Salp87k genotyping array provides researchers and breeders the opportunity to analyze genetic variation in Arctic charr at a more detailed level than previously possible.We have generated a high-density, high-throughput genotyping array for characterizing genome-wide variation in Arctic charr ( Salvelinus alpinus) has a Holarctic distribution spanning marine and freshwater ecosystems and is one of the most morphologically and ecologically diverse vertebrates [Arctic charr  that might provide insight on the adaptive divergence of salmonid species.Studies of the genetic architecture of traits and the discovery of quantitative trait loci (QTL) in Arctic charr have been limited by the relatively small numbers of available genetic markers –12. Low Oncorhynchus mykiss) and Atlantic salmon  [Cyprinus carpio) [Crassostrea gigas and Ostrea edulis) [Ictalurus furcatus and I. punctatus) [Further insights into the genetics and evolution of Arctic charr require a high-throughput, high-density genotyping array so that fish can be genotyped for a large number of markers in a cost-effective manner. High-density SNP genotyping assays 6K to 285K) for other salmonids such as rainbow trout  –22 have o salar) –28 and t5K for oto salar) ,30. Geno carpio)  and some edulis)  and bluenctatus) .vgll3) that controls variation in age at maturity [The creation of SNP arrays for aquaculture species follow previous developments in terrestrial livestock (such as poultry and cattle) and data from these arrays are now being successfully applied in genomic selection programs that improve the performance of aquaculture populations for important traits such as disease resistance –40. Withmaturity –26,41–43maturity  could alOur aims were to: (1) Expand the number of SNPs identified in Arctic charr; (2) Determine the position of SNPs within the genome identified through different molecular approaches for comparative analysis; (3) Design a SNP genotyping array that captures the diversity of Arctic charr by incorporating SNPs identified in a diverse suite of populations and (4) Design an array that contains SNPs located in functional genes and coverage of intergenic regions through the inclusion of markers that are evenly spaced throughout the genome. Putative SNPs were identified in fish from the three major Canadian aquaculture strains  that were founded from populations in northern Canada  as well Animals were reared and sampled in compliance with the animal utilization protocols (AUP) #3174 and #2431, which were approved by the University of Guelph Animal Care Committee.et al. 2011 [The fish used for SNP discovery and testing of the array originated from aquaculture strains in Canada and natural populations in Iceland . The Naual. 2011  for detaet al. 2017 [Candidate SNPs for the array were detected using a variety of sequencing methodologies. First, genotype-by-sequencing (GBS)  was perfal. 2017  for detade novo SNP identification [After sequencing, raw fastq files were filtered for quality control in Trimmomatic using default parameters  (Versionfication  (Versionwww.ncbi.nlm.nih.gov/sra) BioProject accession number #SRP026259 and BioSample accession numbers #SAMN06165956 and #SAMN06165957) [The GBS dataset was processed with Stacks twice, the first time using a process_radtags trim parameter (-t) of 85 and the second time using a trim parameter of 40 (-t 40). This dual approach was used because a trim parameter of 85 caused stacks to eliminate any reads shorter than 85 bp in length. Previous analysis of GBS data in the production of the first generation Arctic charr SNP linkage map (NCBI sequence read archive (6165957)  identifi6165957)  were ideSNPs were filtered in different ways depending on the source. Those derived from families were analyzed manually to remove SNPs that met one of the following criteria in all families: 1. >50% of progeny with missing genotypes; 2. detection of erroneous genotypes ; and 3. significant segregation distortion  . Markerset al. 2018 [et al. 2018 [Second, 11 families  were RAal. 2018 ). SNPs tet al. 2018 [Third, eight hybrid fish derived from crosses between the Tree River and Nauyuk strains were each sequenced on one lane of an Illumina HiSeq2500  . A Burroal. 2018 ) were rede novo assembly that was performed using mRNA sequence libraries from 18 individuals. Briefly transcriptome assemblies were constructed in the Velvet-Oases software package using eight different k-mer lengths  [Fourth, SNPs were identified from a previous transcriptomic analysis of Fraser strain Arctic charr . These S 81, 89) ,51. Cont 81, 89)  was usedWe first selected SNPs that had been detected by more than one sequencing platform . These were considered as cross validated if SNPs in the two datasets were found at the same base pair position in the Arctic charr draft genome   and if tsdY (GenBank accession: JF826022.1), using Burrows-Wheeler Aligner (BWA) [We next included markers that could be used to determine the genotypic sex of individuals. The eight libraries from the high coverage sequencing SNP data were compared to a partial transcript for the Arctic charr sex-determining gene, er (BWA) . SAMtooler (BWA) .et al. [We next focused on SNPs identified in the eight Nauyuk x Tree River hybrid individuals subjected to high coverage sequencing that had not been selected through cross validation. These SNPs were placed in a MySQL database and filtered based on the following initial parameters: depth (5 ≤ DP ≤ 45), quality score (QUAL ≥ 20), genotype quality (GQ ≥ 20) and mapping quality (MQ ≥ 30). In order to identify which SNPs fell within transcripts, Blastn was used to compare 101bp probes (SNP at bp 51) to the transcriptome from Christensen et al.  and SNPsIn silico analysis produced a probability of conversion to a reliable assay for each SNP (p-convert score). This returned a set of 80,786 SNPs (77.7%) from the initial submission with a ‘recommended’ or ‘neutral’ designation. To fill the remaining spots on the array, 13,912 additional intergenic common SNPs from the high coverage sequencing dataset were added and the revised set of candidate markers was resubmitted. After resubmission to Affymetrix for array tiling, the Salp87k array design with 86,503 SNPs was finalized  . We alsoTo investigate the ability of the array to characterize the genetic diversity of divergent populations, SNP variation in a test set of 482 individuals including fish from the four groups (three aquaculture strains and wild fish from Iceland) was evaluated . Three o™ Analysis Suite User Manual version 3.1). Recommended SNPs for each group were obtained and compared to one another to assess the number of assays that were polymorphic (and therefore informative) within the different groups. Finally, we validated the ability of the sdY associated markers to identify sex by comparing genotypes to phenotypic sex based on visual examination of the gonads in 446 of the test fish.Aliquots of DNA were sent to the Clinical Genomics Centre at Mt. Sinai Hospital, Toronto, Canada and genotyped as per the manufacturer’s instructions. Genotypic data were imported into the Axiom Analysis Suite (Version 3.1.51) and filtered following the manufacturer’s ‘best practices workflow’ . Genotypic data for the four groups were generated in separate Axiom Analysis Suite sessions, following the manufacturer’s ‘best practices workflow’. A recommended SNP was one whose genotype data met all quality control thresholds  . We deteIn addition to the 17,846 high priority markers, the initial candidate marker set included SNPs from the high coverage sequencing data , the sex associated markers (6) and non-cross validated markers from the GBS and RNA-seq datasets  for a total of 103,932 markers. The high coverage sequencing dataset yielded the largest set of SNPs in the initial submission but was constructed using sequence data from only eight individuals. This meant that there was relatively low-resolution allele frequency information available to inform decisions about which markers to include. Care was taken to assess the genomic location of the SNPs from these eight individuals and to select SNPs that represented as many genes as possible and also provide even coverage of intergenic regions. We aimed to directly represent as many genes as possible on the array so that future analyses utilizing the array, such as genome-wide association studies, could accurately identify potential causative genes associated with important SNPs. The lack of validation of most of the SNPs from the eight individuals means that we cannot rule out the possibility that the observed polymorphisms could be the result of sequencing error or other non-biological causes. Thus, these were considered putative in nature prior to validation through assessment of array performance.The different data sources utilized in SNP discovery were complimentary, providing detail on marker frequency and segregation in populations  or high depth of coverage and genomic context  . SNPs frFor each of the four groups , more than 62,000 SNPs were recommended for use by the Axiom Analysis Suite and identified as either monomorphic or polymorphic . It is iFor each group, between 14,000 and 46,000 polymorphic markers were identified. The highest number of polymorphic markers was observed in the Nauyuk strain , while the lowest number of SNPs was seen in the Icelandic fish , despite the larger number of fish genotyped. This pattern was expected, as a large number of the SNPs included in the design of the array were identified from the Nauyuk and Tree River strains as the result of high coverage sequencing. The discovery and selection of SNPs for inclusion on the array could have been improved and the utility of the array maximized by analyzing all of the population samples with the high coverage sequencing method. However, it appears that the array is still able to characterize variation within Fraser and Icelandic fish, albeit to a lesser extent. Reduced representation sequencing, although yielding fewer markers in the Fraser and Icelandic fish, did provide more representative estimates of minor allele frequency and other metrics of SNP efficacy than the high coverage sequencing dataset.A total of 63,060 polymorphic markers were observed (72.9% of the markers on the array) across the four populations of Arctic charr in the test set , S3 FileThe percentage of SNPs shared between groups appeared to be a function of geographic separation rather than phylogeographic grouping . The twoThe array is likely to be of value for the study of cultured and wild populations of Arctic charr in Canada and Iceland. Results of the test set showcase the ability of the array to characterize genetic variation in the three major Canadian aquaculture strains for use in selective breeding programs. Moreover, since these strains were founded relatively recently (1974–1988), it is possible that the array could be effective at characterizing genetic variation in wild Canadian populations. However, array performance in wild fish would need to be tested given that the aquaculture strains were created with small numbers of founders ,45 and tOf the 86,503 markers on the array, 84,920 (98.2%) were successfully positioned to a single location on the new Arctic charr reference genome assembly  contained one or more array SNPs, while 44.3%  were not represented by any SNPs on the array. The 55.7% of unplaced contigs represented by one or more SNP on the genotyping array comprise 91.6% of the sequence data within the unplaced contigs  indicating that the smallest unplaced contigs were not well represented .The number of polymorphisms observed in the genome’s 15,216 unplaced contigs was sparser than within the 39 chromosomes. The percentage of contigs that had one or more polymorphic loci varied among strains . Thus, genetic diversity across these unplaced regions was not as well represented as across the chromosomes. This is likely in part due to the small size of these contigs relative to the chromosomes  [Of the 42,439 gene entries, 22,433 genes had one or more array SNP present between their start and end positions. This indicates that 52.8% of the genes in the genome were directly represented by a SNP on the array, with between 15% and 47% of these genes possessing a polymorphic SNP among the four test groups . This resdY gene accurately predicted sex for all 463 individuals with known phenotypic sex  ,33. ThisS1 File(TSV)Click here for additional data file.S2 FileBase pairs given indicate the location of the SNP, and the INFO column contains the 71mer sequence (SNP at base pair position 36) utilized in the array design.(VCF)Click here for additional data file.S3 File(DOCX)Click here for additional data file.


Whole genome sequencing (WGS) is a reliable tool for studying tuberculosis (TB) transmission. WGS data are usually processed by custom-built analysis pipelines with little standardisation between them.To compare the impact of variability of several WGS analysis pipelines used internationally to detect epidemiologically linked TB cases.Mycobacterium tuberculosis complex (MTBC) strains from 2016 were included. Epidemiological information obtained from municipal health services was available for all mycobacterial interspersed repeat unit-variable number of tandem repeat (MIRU-VNTR) clustered cases. WGS data was analysed using five different pipelines: one core genome multilocus sequence typing (cgMLST) approach and four single nucleotide polymorphism (SNP)-based pipelines developed in Oxford, United Kingdom; Borstel, Germany; Bilthoven, the Netherlands and Copenhagen, Denmark. WGS clusters were defined using a maximum pairwise distance of 12 SNPs/alleles.From the Netherlands, 535 The cgMLST approach and Oxford pipeline clustered all epidemiologically linked cases, however, in the other three SNP-based pipelines one epidemiological link was missed due to insufficient coverage. In general, the genetic distances varied between pipelines, reflecting different clustering rates: the cgMLST approach clustered 92 cases, followed by 84, 83, 83 and 82 cases in the SNP-based pipelines from Copenhagen, Oxford, Borstel and Bilthoven respectively.Concordance in ruling out epidemiological links was high between pipelines, which is an important step in the international validation of WGS data analysis. To increase accuracy in identifying TB transmission clusters, standardisation of crucial WGS criteria and creation of a reference database of representative MTBC sequences would be advisable. Mycobacterium tuberculosis complex (MTBC) isolates have been developed, such as IS6110 restriction fragment length polymorphism typing [Since the early 1990s, several DNA typing methods for m typing  and spolm typing . Althougm typing ,5, as wem typing . Multiplm typing -16.M. tuberculosis H37Rv genome has been widely used as a reference genome and mutations compared to the H37Rv genome are reported as single nucleotide polymorphisms (SNPs) or insertions and deletions. Nevertheless, the analysis of WGS data remains subject to variability due to the presence of repetitive regions in the genome that cannot be accurately analysed using the most widely applied WGS techniques. Therefore, genomic regions with repetitive sequences are generally excluded from data analysis. Currently, there is no international standardisation in the analysis of WGS data, e.g. for the exact genomic regions excluded, the applied software and parameters, or the quality and quantity required for sequence data.Due to the highly conserved genome of MTBC strains, it is possible to analyse WGS data from any MTBC strain by comparison to a common reference genome. The M. tuberculosis isolates was introduced to rule in a possible epidemiological link between TB cases [Initially, a maximum distance of 12 SNPs between TB cases . HoweverTB cases . A pilotTB cases .Core genome multilocus sequence typing (cgMLST) has been suggested as an alternative for genotyping of MTBC strains from WGS data, using a set of 2,891 genes that can be reliably recovered from the WGS data for any MTBC strain ,20. SimiIn this study, we compare the analysis of WGS data of 535 culture-positive MTBC isolates from the Netherlands; data analysis was performed at four different European institutes: National Institute for Public Health and the Environment , Oxford University , Research Center Borstel  and Statens Serum Institut , using four distinct SNP-based analysis pipelines and a cgMLST gene-by-gene approach. Results of the five individual WGS pipelines were compared regarding their ability to rule out an epidemiological link between TB cases.Supplementary Table S1 for 24-loci MIRU-VNTR classification and Supplementary Table S2 for sequence quality). DNA used for sequencing was isolated from positive Mycobacteria Growth Indicator Tubes and purified with the QIAamp DNA mini kit method . Libraries were prepared using the Nextera XT DNA Library Prep Kit and run on the Illumina HiSeq 2500 sequencer that generated 2 x 125bp paired-end reads. In order to achieve a mean coverage depth of ≥ 80 reads, a minimum sample yield of 350 Mbp was required for sequenced samples considering the 4.4 Mbp genome size of M. tuberculosis.In total, 535 routinely collected culture-positive MTBC isolates from the Netherlands in 2016 were subjected to MIRU-VNTR typing  and WGS M. tuberculosis isolates. In the Netherlands, 75% of TB cases are foreign-born [M. tuberculosis are represented in this set of isolates.Of 535 cases, 134 were clustered with another case in 2016 by MIRU-VNTR typing  and epidemiological investigation was performed by MHSs for these cases . The MHSign-born  and all The 535 strains with RIVM sample numbering were first coded into unique sample numbers for each institute. Sequence data for the strains, generated at the RIVM, were then shared in fastq format with the Research Center Borstel and Oxford University; SSI downloaded reference mapped reads in Bam format from the European Nucleotide Archive (accession number PRJEB25592). WGS data for the 535 TB cases were analysed using a cgMLST-based gene-by-gene approach with the commercially available software SeqSphere+ and four different in-house SNP-based analysis pipelines from the Oxford University, Research Center Borstel, SSI and RIVM. Of these, the SNP pipeline from Borstel has been published under the name MTBseq .Each institute ran the sequence data using their respective in-house developed WGS analysis pipeline(s) and were blinded to the results of cluster investigations, i.e. whether patients were epidemiologically linked according to the investigations by the MHSs. A summary of settings applied in each SNP-based pipeline can be seen in The distance matrices from each participating institute were sent to the RIVM. Using R, the matrices from each institute were decoded into the RIVM sample numbering, transformed to distance matrices in the long format and then merged together so that the results of the distinct pipelines could be compared more efficiently. Following this, a subset was created for isolates that were clustered by WGS for each pipeline; a pairwise genetic distance of 12 SNPs/alleles was used as threshold for clustering of cases by WGS ,20. We iSupplementary Material S1.Fastq.gz files were mapped unpaired against the H37Rv reference genome (GenBank accession: AL123456.3) using Bowtie2 in Breseq version 0.28.1 . Sequenchttps://github.com/oxfordmmm/CompassCompact. The docker image is available via: https://cloud.docker.com/u/oxfordmmm/repository/docker/oxfordmmm/compasscompact.Sequence read data were mapped to the H37Rv reference genome (GenBank accession: NC_000962.2) using Stampy version 1.0.17  . SNPs weM. tuberculosis H37Rv genome (GenBank accession: NC_000962.3) with the alignment program BWA [https://github.com/ngs-fzb/MTBseq_source [Reads were aligned to the gram BWA  and mappgram BWA  and Samtgram BWA  for re-cgram BWA , the remq_source .WGS data in the form of BAM alignments created by the MTBseq pipeline were imported into the SeqSphere+ version 5.1.0 software  and all genes defined as loci in the specified MLST scheme were extracted and the sequence submitted to the nomenclature server (cgMLST.org) for translation into allele numbers. The cgMLST scheme used contains 2,891 core genes and was defined using the MLST Target Definer tool of the SeqSphere+ program and a set of 45 strains covering the full known diversity of the MTBC . Repetithttps://github.com/micronorman/IRLM-SnpPipeline.Usually, reads are mapped to the H37Rv reference genome (GenBank accession: NC_000962.3) using the BWA mapping program  and refiM. tuberculosis, followed by 2.4% M. bovis (n = 13), 0.2% M. caprae (n = 1), and 0.2% M. orygis (n = 1). As part of the original study [M. tuberculosis isolates were assigned lineages using PhyResSe [Supplementary Table S3).In total, 527 of 535 TB cases had complete data from the Netherlands Tuberculosis Register, of which 40.8% (215/527) were in the age category 25–44 years (median: 35 years), 60% (316/527) were men and 78.4% (413/527) were first generation migrants. Ninety-seven percent (n = 520) of the isolates were al study , the 520PhyResSe ; 127 isoSupplementary Table S4, Supplementary Table S2).A small proportion of the 535 datasets was excluded from the analysis in the individual pipelines due to poor sequence data quality: (i) 13 from the SSI pipeline, (ii) 11 from the Research Center Borstel pipeline (MTBseq), (iii) 10 from the RIVM pipeline, (iv) seven from the cgMLST approach, and (v) six from the Oxford University pipeline. The 10 samples excluded from the RIVM, Research Center Borstel, and SSI pipelines were not included in any further analysis  pipeline and 82 in the RIVM pipeline . In all Of the 535 isolates analysed, 134 were clustered with another isolate by MIRU-VNTR, and epidemiological cluster investigations were performed by MHSs for these MIRU-VNTR clustered cases. Results of cluster investigations showed that epidemiological links were identified for 41/134 MIRU-VNTR clustered cases and for the remaining 93 cases, no epidemiological links within the Netherlands could be identified. All pipelines exhibited short pairwise genetic distances between isolates of the 41 epidemiologically linked cases .All 41 epidemiologically linked cases were clustered by the Oxford University SNP pipeline and the cgMLST approach using a threshold of ≤ 12 SNPs/alleles. However, one of these linked isolates had a relatively low mean coverage depth (13x), which was handled differently in each pipeline depending on the minimum mean sample coverage depth accepted for inclusion in the data analysis. As the RIVM, Research Center Borstel (MTBseq) and SSI pipeline applied a minimum mean sample coverage depth of 20x, 30x and 20x, respectively , this isAmong the 93 patient isolates clustered by MIRU-VNTR, for which no epidemiological links were identified by MHSs, the cgMLST approach clustered 39 cases. The remaining pipelines all clustered the same 34 non-epidemiologically linked cases, except for one non-epidemiological link between two cases that was not analysed by the Research Center Borstel (MTBseq) pipeline due to low coverage . The meaM. tuberculosis lineages were represented in the dataset used in this study [M. tuberculosis genome (including the currently excluded repetitive regions) [A representative set of 535 routine MTBC samples from the Netherlands was used to compare five different WGS data analysis pipelines. The five pipelines yielded highly comparable results, with only two epidemiologically linked cases being missed using the previously proposed threshold of 12 SNPs/alleles ,20. All is study ,25, meanis study -37. In tregions)  to be anregions) ,39-42.All pipelines clustered additional cases for which no epidemiological links were identified by epidemiological investigations. Interestingly, all pipelines detected a few isolates that were clustered by WGS only (but not by MIRU-VNTR). As epidemiological data was only collected for patients clustered by MIRU-VNTR, epidemiological linkage information was not available for the cases clustered by WGS only. These cases could, however, represent actual transmission chains that were missed by MIRU-VNTR due to one or two repeat number changes during transmission from patient to patient. On occasion, MHSs find an epidemiological link between cases, e.g. a mother and daughter, that could not be genotypically confirmed as they do not have identical MIRU-VNTR patterns. These might represent the few patient isolates from our study that were found to be clustered by WGS only. The few discrepant results between pipelines in the clustering of isolates by WGS only were most likely due to the presence of mixed bacterial populations, which were handled differently depending on the settings applied by each pipeline. For example, in the two pairs clustered by WGS only in the cgMLST approach, Oxford University pipeline and Research Center Borstel (MTBseq) pipeline, one of the isolates contained two clonal populations with varying MIRU-VNTR patters in 4/24 loci. The SNPs that caused the genetic differences between these pairs were present at lower allele frequencies. Isolates containing mixed populations or contaminating DNA are likely under-reported due to the detection of mixed infections is not widely implemented in analysis pipelines. Therefore, more detailed investigation into this issue would be warranted to help guide the international standardisation of WGS analysis.The genetic distances between strains of MIRU-VNTR clustered cases were in general lowest in the cgMLST approach and highest in the RIVM SNP pipeline – and consequently most cases were clustered by the cgMLST approach and fewest cases by the RIVM pipeline. While the focus of the current study was to investigate whether distinct pipelines were able to identify the epidemiologically linked cases, we did observe an unexpected result for the highly unrelated strains that were not clustered by WGS or MIRU-VNTR. The cgMLST approach, for obvious reasons, showed relatively lower genetic distances especially for highly unrelated strains. However, among the SNP-based pipelines, the less stringent RIVM pipeline  sometimes showed lower genetic distances between highly unrelated strains compared to the more stringent Oxford University SNP pipeline. A possible explanation for this could be that the RIVM pipeline excludes all SNPs within 12bp apart, while this rule is not applied in the Oxford University pipeline. These differences do not have an impact on the identification of epidemiologically linked cases, but could be studied in more detail to better understand the characteristics of the different pipelines.The same isolates clustered by WGS in the RIVM SNP pipeline were also clustered in the four other pipelines, however, more non-epidemiologically linked cases were clustered in the other four pipelines. It is possible that these cases represent actual transmission missed by MHSs, as cluster investigation by interviews also misses a proportion of epidemiological links . On the Several differences were observed between WGS pipelines with respect to the applied parameters, which can explain the few discrepant results between pipelines in the exclusion of sequence data from data analysis and the clustering of cases by WGS. These pipeline differences can be divided into four levels: (i) alignment, (ii) quality metrics, (iii) SNP calling, and (iv) distance calculation. First, the different pipelines applied distinct programs for alignment against the H37Rv reference genome. Second, pipelines applied varied quality criteria with respect to the minimum coverage depth  and breadth, which led to differences in the number of datasets excluded from data analysis as shown in this study. However, this does not influence the pairwise genetic distances calculated for each included isolate. Third, on the SNP calling level different criteria were applied that partly explain the differences in pairwise genetic distances reported by each pipeline. One major difference lies in the genetic regions excluded during data analysis, which consisted of excluding repetitive regions only, excluding drug resistance associated genes in addition, and/or excluding SNPs within 12bp in addition. Furthermore, differences were observed in the minimum coverage depths applied to support a SNP , minimum allele frequencies , and the software used for SNP calling. Finally, pipelines behave differently in how positions not meeting respective thresholds are treated when calculating the genetic distance. One option is to treat these positions as the reference sequence, another option is to exclude these regions from the whole database in case they are missing in at least one isolate and a third option is to capture the respective sequence information, either by using a consensus approach or by complementing the respective positions with data from aligned reads.It is striking and encouraging that despite differences in the pipelines, for identification of highly epidemiologically related strains the performance was generally similar. The absolute number of SNPs called between more distant isolates showed more variability but was not the focus of this comparison. We believe that differences in the SNP calling parameters  and how to deal with positions with missing or low quality sequence data when calculating the genetic distance mainly explain the differences observed between pipelines. Better understanding of the individual pipeline characteristics and limitations is needed in order to accurately interpret and compare results between the pipelines and to set standards for the implementation and reporting of WGS data analysis.Although different approaches were taken to analyse WGS data, all pipelines were able to clearly distinguish epidemiologically linked cases from highly unrelated cases. Standardisation on crucial criteria of WGS data analysis and reporting on an international level will allow more efficient investigations of cross-border transmission and will help establish protocols allowing inter-laboratory comparison of results. To allow routine identification and monitoring of the spread of specific clusters, a database of strains  or a dep