Arctocephalus gazella) individuals. Despite seals and dogs having diverged around 44 million years ago, 33,324 out of 173,662 loci (19.2%) could be genotyped, of which 173 were polymorphic and clearly interpretable. Two SNPs were validated using KASP genotyping assays, with the resulting genotypes being 100% concordant with those obtained from the high-density array. Two loci were also confirmed through in silico visualisation after mapping them to the fur seal transcriptome. Polymorphic SNPs were distributed broadly throughout the dog genome and did not differ significantly in proximity to genes from either monomorphic SNPs or those that failed to cross-amplify in seals. However, the nearest genes to polymorphic SNPs were significantly enriched for functional annotations relating to energy metabolism, suggesting a possible bias towards conserved regions of the genome.High-density SNP arrays developed for humans and their companion species provide a rapid and convenient tool for generating SNP data in closely-related non-model organisms, but have not yet been widely applied to phylogenetically divergent taxa. Consequently, we used the CanineHD BeadChip to genotype 24 Antarctic fur seal ( Bos by around 23 million years, to obtain 149 polymorphic loci Single nucleotide Polymorphisms (SNPs) are increasingly popular tools for population genetic studies of natural populations, but can be difficult to develop in non-model organisms due to a paucity of genomic information FstAs with other markers such as microsatellites, a common problem with SNP discovery is that it can be prone to ascertainment bias. For example, both the size of the discovery panel of individuals and whether or not a SNP originates from a coding or non-coding region can influence minor allele frequencies, leading to downstream biases in population genetic estimates such as SNPs are increasingly being developed for use in marine mammals, where they have already provided insights into the population structure of bowhead and sperm whales Arctocephalus gazella), a sexually dimorphic pinniped that has been intensively studied for several decades at Bird Island, South Georgia. To improve genetic resolution for ongoing studies of reproductive success Canis lupus familiaris) genome was then exploited to map transcripts to specific chromosomes, allowing development of a genome-wide distributed panel of 104 polymorphic SNPs An alternative approach to SNP discovery, facilitated by emerging high-throughput sequencing technologies, is to develop a transcriptome, which can be interrogated bioinformatically to identify thousands of genetic markers. This was recently done for the Antarctic fur seal  during the austral summers of 2009/2010 and 2010/2011 at Bird Island, South Georgia  using protocols described in detail by Hoffman Tissue samples were collected by one of the authors (JF) as part of the Long Term Monitoring and Survey project of the British Antarctic Survey that has employed consistent sampling protocols since 1994. Tissues were obtained from adult males using standard protocols for remote biopsy sampling that have no known deleterious effects on the study animals. Sampling was authorised by the Senior Executive and the Environment Officers of the Government of South Georgia and the South Sandwich Islands, and samples were collected under Scientific Research Permits for the British Antarctic Survey field activities on South Georgia during the 2009/10, 2010/11 seasons. Tissue samples were collected and retained under permits issued by the Department for Environment, Food and Rural Affairs (license number AHZ/2024A/2005/1) and in accordance with the Convention on International Trade in Endangered Species of Wild Fauna and Flora (permit numbers 004/2011 and 464895/04). All procedures used were approved the British Antarctic Survey Ethics Committee (reference number PEA6), which includes members of Cambridge University.The samples were genotyped using the Illumina canine high density SNP chip which enables the simultaneous genotyping of 172,662 SNP markers identified from CanFam2.0, the second build of the dog genome reference sequence Automated allele calling was implemented using the software GenomeStudio 2010.1 . This program normalizes the intensity data for each of the loci and then assigns each sample a cluster position. The resulting genotype output is then provided together with two quality measures, the GenTrain and GenCall scores P-values were adjusted for the false discovery rate Identification of SNP markers in the seal samples was made on the basis of different genotype clusters observed within the Genome Studio software. Where two alleles were clearly observed, either in a heterozygous or homozygous state, the marker was categorized as a polymorphic SNP in seals. Where fluorescence intensity readings indicated the presence of a single allele across all samples , the marker was designated as monomorphic. This is based on the assumption that the observed data reflect amplification of a homologous sequence region in the seal genome that did not exhibit polymorphism at the nucleotide targeted by the canine assay. Genepop A. gazella) with 120 bp flanking sequence were mapped to the dog genome (Broad Institute release 67) and the Arctocephalus transcriptome A. gazella. A subsequent adjustment of the P-values was applied following Benjamini and Hochberg The full set of SNPs , which are based on fluorescently labeled allele-specific PCR primers. Assays were designed based on flanking regions in the dog genome . ConcordOut of a total of 173,662 loci on the CanineHD BeadChip, 33,324 (19.2%) were genotyped in a sample of 24 Antarctic fur seals . Of thesP<0.05, although only six remained significant following table-wide correction for the false discovery rate  and 4.1% of monomorphic SNPs (n = 1367) but only 1.0% of failed SNPs (n = 1467), significantly lower than for the previous two classes . When this analysis was repeated with the stringent requirement of at least 110 bp of sequence overlap, fewer than half as many mappings were obtained but the overall pattern was similar, BLAST hits being obtained for two polymorphic SNPs (1.0%), 659 monomorphic SNPs (2.0%) and 580 failed SNPs (0.41%).We first mapped all of the SNPs to the in silico the seven polymorphic SNPs revealing homology to the Arctocephalus transcriptome through visual inspection within the program Tablet Arctocephalus transcripts. Moreover, locus BICF2G630131208 had previously been independently called as a ‘high-quality SNP’ We next attempted to verify Two of the five KASP assays (BICF2G630131208 and BICF2G630510520) yielded identical genotypes to the Illumina array , therebyCanis lupis familiaris). A GFF file containing details of the working SNPs together with their locations relative to the dog genome is provided that allows these data to be viewed as an additional track within Ensembl . We therefore conducted an enrichment analysis to test for any differences in the functional annotations of these genes, based on a total of 5581 GO categories represented in the full dataset. Seven GO terms were nominally identified as being enriched in the genes nearest to polymorphic SNPs in comparison to monomorphic SNPs, based on an adjusted P-value threshold of 0.05  was significantly enriched in the genes nearest to polymorphic SNPs relative to both monomorphic and failed SNPs , with this inference being based upon the presence/absence of 26 different genes.We next explored the genomic distribution of SNPs in the dog  were found to be polymorphic Over 33,000 SNPs from the CanineHD array cross-amplified in the Antarctic fur seal, a number that although large represents only 19.2% of the total number of SNPs evaluated. This is around 10% lower than predicted by a linear regression of the percentage of SNPs amplified on the time to last common ancestor, based on data from 117 species genotyped on ovine, bovine and equine SNP50 bead chips Bos by around 23 million years, obtained a comparable 149 polymorphic loci Despite the overall cross-amplification rate being somewhat lower than expected, we were nonetheless able to identify 193 polymorphic SNPs, all but twenty of which showed clearly interpretable clustering patterns. This is roughly an order of magnitude fewer SNPs than obtained for bison using the CattleSNP50 BeadChip in vitro and in silico approaches for confirming or refuting the presence of SNPs identified in the Antarctic fur seal. In the first of these, the results observed for the two KASP assays that displayed 100% concordance with the high density array confirmed the presence of both SNPs in the fur seal genome. However, a third locus partially amplified and the two remaining KASP assays completely failed. Taken at face value, this would imply a conversion rate of somewhere between 40 and 60%, although we believe it would be premature to draw firm conclusions based on a sample size of only five loci tested. If anything, our study highlights the difficulty of in vitro SNP validation for target loci that are typically too small to reliably sequence and which may differ significantly in genotyping assay conversion success owing to associated differences in primer/probe target sites.Several recent studies have used high-density arrays developed in model species to cross-amplify SNPs in their wild counterparts Negative control samples (water) were not run on the canine SNP chip and spurious genotypes are sometimes generated by the Illumina Infinium chemistry in the absence of target template DNA. However, if we were witnessing spurious amplification that was not from seal DNA but from other constituents of the sample, we would not expect to observe a small subset of the same SNPs consistently amplifying across all 24 seal samples. Moreover, negative controls were included in the KASP assays and behaved as expected see . This isin silico visualisation. One of these loci had also previously been identified as being a ‘high-quality SNP’ during an independent round of marker discovery, indicating that this locus fulfils several stringent selection criteria. Finding such a match not only helps to confirm that the SNP in question is common to both dogs and seals, but it also implies that our transcriptome assembly is of reasonable quality. Unfortunately, our sample size of cross-amplified polymorphic SNPs was too small to allow larger numbers of SNPs to be similarly located. Nevertheless, our analysis suggests that transcriptome assemblies could potentially be of some value generally for SNP validation.A recently developed transcriptome Consistent with the canine array having been designed to provide even genome-wide coverage, we found no obvious spatial clustering within the canine genome of SNPs that were polymorphic in Antarctic fur seals. Moreover, in support of a recent study that cross-amplified rhesus macaque SNPs in seven old world monkey species Balancing selection is a powerful force that might also help to explain why certain polymorphisms are retained over long timescales while others are not in silico for SNPs, which in turn can be genotyped on whatever scale is required  that can be viewed as a track within Ensembl.(GFF)Click here for additional data file.Table S1Details of single-plex KASP assays (LGC Genomics) used to validate five putative fur seal SNPs.(XLSX)Click here for additional data file.Table S2Details of all 173,662 canine SNPs, characterised as polymorphic, monomorphic or failed in a sample of 24 Antarctic fur seals. Asterisks denote twenty loci that were polymorphic but which could not be scored reliably. Included are the flanking sequences of all SNPs together with their chromosomal coordinates in the dog (Canis lupis familaris) genome and the identity of any Arctocephalus gazella transcripts to which a given SNP mapped.(XLSX)Click here for additional data file.Table S3Genotypes generated for 173 clearly interpretable polymorphic SNPs in 24 Antarctic fur seals (see Results for details).(XLSX)Click here for additional data file.Table S4Polymorphism characteristics of 173 clearly interpretable polymorphic SNPs in 24 Antarctic fur seals (see Results for details). The GenTrain score takes into account the quality, shape and degree of separation of the genotype clusters, with higher values indicating improved clustering P-values for deviation from HWE are shown without correction for multiple statistical tests. Values significant at P<0.05 are highlighted in bold, while those remaining significant after controlling for the false discovery rate are underlined. Twenty loci that were polymorphic but which could not be reliably scored are not included.(XLSX)Click here for additional data file.Table S5Genotypes generated for five SNP loci using KASP assays in six dog and 24 Antarctic fur seal individuals.(XLSX)Click here for additional data file.Table S6Results of the enrichment analysis based on Gene Ontology (GO) codes of the nearest genes to each SNP . Only GO codes that were significantly enriched in comparisons involving polymorphic with monomorphic or polymorphic with failed SNPs are shown, following P-value adjustment for multiple tests.(XLSX)Click here for additional data file.


Runs of homozygosity (ROH) are contiguous lengths of homozygous genotypes that are present in an individual due to parents transmitting identical haplotypes to their offspring. The extent and frequency of ROHs may inform on the ancestry of an individual and its population. Here we use high density  bi-allelic SNPs in a range of cattle breed samples to correlate ROH with the pedigree-based inbreeding coefficients and to validate subsequent analyses using 54,001 SNP genotypes. This study provides a first testing of the inference drawn from ROH through comparison with estimates of inbreeding from calculations based on the detailed pedigree data available for several breeds.All animals genotyped on the HD panel displayed at least one ROH that was between 1–5 Mb in length with certain regions of the genome more likely to be involved in a ROH than others. Strong correlations  existed between the pedigree-based inbreeding coefficient and a statistic based on sum of ROH of length > 0.5 KB and suggests that in the absence of an animal’s pedigree data, the extent of a genome under ROH may be used to infer aspects of recent population history even from relatively few samples.Our findings suggest that ROH are frequent across all breeds but differing patterns of ROH length and burden illustrate variations in breed origins and recent management. Runs of homozygosity (ROH) are contiguous lengths of homozygous genotypes that are present in an animal due to parents transmitting identical haplotypes to their offspring. The extent and frequency of these may inform on the ancestry of an individual and its population. Particularly, consanguinity may be indicated from the presence of long ROH; the longer such segments are, the more likely that recent inbreeding occurred within a pedigree. HoweverThe distribution of shorter ROH may also inform on the presence of more ancient relatedness which is unaccounted for in an individual’s recorded pedigree due to the limitations in the recording process. In the The domestication of cattle, which occurred ~10,000 years ago, was a complex process, with evidence suggesting that it occurred in a minimum of two domestication events. HoweverRuns of homozygosity have been extensively studied in human populations and are an established method of distinguishing a population history of consanguinity, and with homozygosity mapping analysis showing a relationship with susceptibility to recessive diseases-3,5,6. Hcirca 50 k SNP density for identifying ROH; hereafter referred to as the reduced HD panel . In addition, three published genotype datasets at this density comprising of a total of 1166 animals from 42 different breeds  genotypes consisting of 777,972 bialleleic SNPs from the BovineHD BeadChip  were generated for 891 artificial insemination sires of multiple breeds. Breeds represented included Angus (n = 39), Belgian Blue (n = 38), Charolais (n = 117), Friesian (n = 98), Hereford (n = 40), Holstein (n = 262), Holstein-Friesian crosses (n = 111), Limousin (n = 128) and Simmental (n = 58). Additionally, the 48,734 SNPs common to both the HD and the Illumina BovineSNP50 Beadchip were retained in a reduced HD panel which was used to evaluate the lesser The impact of the following SNP edits on the number of SNPs remaining for each of the three density panels is summarised in Tablel) was calculated by a method similar to that proposed by Lencz et al.,[Runs of homozygosity (ROH) were defined in the populations of animals for each of the three SNP density panels using PLINK v1.07, which slides a window of 50 SNPs, in one SNP intervals, across the genome estimating homozygosity. No more than two SNPs with missing genotypes were allowed per window and up to one possible heterozygous genotype was permitted. To ensure low SNP density did not affect ROH length, the minimum required density of SNPs per kb differed between the two genotyping densities, as well as the maximum distance length between two consecutive homozygous SNPs in a run. To minimize the number of ROH that occur by chance in the HD panel, the minimum number of SNPs that constituted a ROH ,where nFor analysis of the HD panel genotypes, the minimum SNP density was 1 SNP every 50 kb to ensure low SNP density did not falsify ROH length, a minimum run length of 58 SNPs was needed to produce <5% randomly generated ROH and the maximum gap between two consecutive homozygous SNPs in a run was set at a 100 kb. In the analysis of the reduced HD panel and the Bovine SNP50 panel, the minimum SNP density was altered to 1 SNP every 120 kb and no restriction was placed on the minimum number of SNPs in a ROH and the maximum gap length between two consecutive homozygous SNPs in a run was kept at the default value of 1000 kb to account for the lesser genotype density.Runs of homozygosity were identified for each animal separately on the HD and reduced panel and for comparative purposes only, the maximum gap length in the HD panel for ROH identification was altered to 1000 kb, SNP density to 1 SNP every 120 kb and there was no restriction on the minimum number of SNPs that constituted a ROH, in order to limit bias. To establish that the reduced density also predicts the correct ROH length category for ROH, the extent that reduced panel ROH were correctly assigned to the HD ROH length category was plotted.Animals with overlapping ROH, and those ROH that were an allelic match, were also identified in the HD panel. The identification of overlapping regions was done by using the sliding window approach as mentioned above, and then for each SNP by calculating the proportion of homozygous windows in the population dataset that overlap that same position. The percentage of animals that had the region with the most overlapping ROH on each chromosome was plotted and the percentage of these overlapping ROH that were an allelic match ≥ 95 % were identified.The percentage population of each breed with ROH present at different ROH length categories was calculated, as well as the mean overall ROH sum per animal for each breed. The mean sum of ROH within each ROH length category was also calculated by summing all ROH per animal in each ROH length category and averaging this per breed population. The percentage of SNP involvement in ROH was also calculated by counting the amount of times a SNP appeared in a ROH in the population dataset.The measure of homozygosity per animal was calculated using the HD panel and reduced HD panel, by a method similar to that as proposed by McQuillan et al., (2008) except in the present study the centromeric region was also included in the calculation,ROH is the sum of ROH per animal above a certain criterion length and LAUTO is the total length of autosome covered by SNPs. LROH was calculated separately as the sum of all ROH >500 kb and the sum of all ROH >10,000 kb where SNP autosomal genome coverage was 2,510,611 kb in the HD panel and 2,500,265 kb in the reduced HD panel. Pedigree based inbreeding coefficients for all animals were calculated using the Meuwissen and Luo[in which LWe used a definition of ROH as tracts of homozygous genotypes that were >500 kb in length identified in a genome sliding window of 50 SNPs. No more than two missing genotypes and one possible heterozygous genotype were allowed in a window, and within our HD SNP panel data, all 867 animals tested, displayed at least one ROH that was between 1–5 Mb in length with almost all  of the population dataset also having at least one ROH between 5–10 Mb in length. Differences among breeds existed in their frequency in different ROH length categories  with on average, 12.39% of the chromosome consisting of a ROH. The number of ROH per chromosome tended to decrease with chromosome length  against the inbreeding coefficient based on pedigree recorded . Conservatively, we restricted our sample to the 230 animals that had complete generation equivalent values ≥6 of recorded pedigree ancestry. A clear linear relationship exists between the ROH and pedigree based estimates of inbreeding. The correlations between the pedigree inbreeding coefficient and individual sums of ROH were different from zero with values of r = 0.75 (P < 0.0001) for FROH and r = 0.71 (P < 0.0001) for FROH10000 using the HD panel and r = 0.73 (P < 0.0001) for FROH and r = 0.70 (P < 0.0001) for FROH10000 using the reduced HD panel. The intercept  of the regression of FROH on the pedigree inbreeding coefficient was greater than zero suggesting that the pedigree-based inbreeding coefficient may underestimate the levels of ancestral genomic relatedness that may exist. The intercept  of the regression of FROH10000 on the pedigree inbreeding coefficient was also greater than zero, but to a lesser extent when the 10 Mb ROH length limit is applied; consistent with longer ROH resulting from more recent inbreeding, such as that recorded in these pedigrees.Cattle offer an opportunity to assess ROH as an indicator of inbreeding by comparing pedigree-based estimates of inbreeding using the same individuals. FigureROH and 0.70 (P < 0.0001) using FROH10000 to those observed for the HD genotype panel.The most common SNP density platform for cattle is that from 54,000 SNP genotyping arrays (Illumina BovineSNP50 beadchip); an important question is whether this panel can reliably infer ROH that have been detected with the greater accuracy provided by the HD array. To facilitate this, a reduced genotype dataset comprising 48,734 SNPs that were common to both the HD and Bovine50 Beadchip genotype panel on the 867 genotyped sires was used for comparison purposes. The same ROH criteria were applied to each dataset, where no more than two missing genotypes and one possible heterozygote were permitted per window, no restriction was placed on the number of SNPs in a ROH in either density and the maximum gap between two consecutive SNPs was kept at the default value of 1000 kb to account for the lesser genotype density in the reduced HD panel and to limit bias for comparison of ROH. There were in total 157,600 ROH identified using the HD panel, whereas only 19,078 ROH were identified in the same individuals using the reduced HD panel. However, the vast majority of ROH that the reduced panel failed to recognise were between 0.5-1 Mb in length  which are products of ancient introductions from South Asia and are all hybrids to some extent, had much lower quantities of average ROH in comparison to the American zebu breeds analysed Figure. Within e island.Bos taurus breeds that is associated with the Bovine SNP50 genotyping chip, does not seem to invalidate the trends in ROH levels observed here, as ROH levels were in fact higher in those breeds with a higher number of polymorphic SNPs, as validated by Illumina  than the majority of chromosomes[Analyses of human ROH have previously established a correlation between extensive LD, locally low rates of recombination and high incidence of homozygous runs. Intensiomosomes and, intomosomes-31.http://genomes.sapac.edu.au/bovineqtl/index.html, ahttp://www.animalgenome.org/QTLdb/cattle.html,http://www.ncbi.nlm.nih.gov/). In particular, Chromosomes 9 and 5, which had the highest amount of long ROHs (>20 Mb), are well documented to contain QTL pertaining to milk fat yield and weight related traits respectively[The existence of QTL in ROH have been well documented in human studies,6,32. Heectively-39.ROH analyses quantifies a feature of genomic variation that may be used in inference of population history and to associate with important production and disease traits and perhaps signatures of selection. We show that ROH analysis in cattle provides a sufficient predictor of the pedigree inbreeding coefficient and the prevalent SNP50 genotyping array may be a sufficient tool to predict ROH in the genome. Patterns of ROH may be decomposed to highlight the effects of recent and more ancient ancestral relatedness and match known aspects of breed history in a sample set of wide provenance.The author(s) declares that there are no competing interests.DCP participated in the design of the study, carried out the ROH identification, the data analysis and drafted the manuscript. DPB participated in the design of the study, the data analysis and helped to draft the manuscript. SMP calculated all pedigree based inbreeding coefficients. DGB conceived of the study, and participated in its design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.Details of animals genotyped with the Bovine SNP50 density panel.Click here for fileThe number of runs of homozygosity (ROH), in the HD panel population, per chromosome (bars) and the mean per animal percentage coverage of the chromosome covered by ROH graph.Click here for fileThe maximum amount of overlapping runs of homozygosity (ROH) per chromosome and the percentage of overlapping ROHs that are a >95% allelic match graph.Click here for fileProportion of appearances of each SNP on chromosome 14 in a ROH across dairy, beef and British animals graph.Click here for fileLinkage disequilibrium plot using Haploview of the consensus overlap segment located on chromosome in 86% of the ROH.Click here for fileLinkage disequilibrium plot using Haploview of SNPs on chromosome 12 that did not appear in a ROH.Click here for fileAverage total sum of Runs of Homozygosity (ROH) per animal within breed vs the number of validated polymorphic loci in the Bovine SNP50 genotyping beadchip graph.Click here for file


Malus x domestica Borkh.) [Knowledge about single nucleotide polymorphism (SNP) markers is extremely important in the development of genotyping assays, allowing improvements in plant breeding through marker-assisted selection. With the emergence of next generation sequencing platforms, high-density SNP discovery in the genome of plant crops becomes more achievable. In this project, we carried out whole genome resequencing of two apple cultivars  using tWe sequenced four DNA samples (two from M13/91 and two from FH cultivars) on four lanes of the Illumina GA II platform (single-end sequencing), with the Illumina PhiX sample used as a control. Image analysis and base calling were performed using on-instrument real time analysis, after which the Off-Line Basecaller was used to convert per-cycle base call files (.bcl) into per read base call files (.qseq). The resulting short reads were aligned to the reference genome with the BWA software , with thAlignment of the M13/91 and FH reads to the reference genome resulted in a total of 37,757,897 and 60,400,252 reads mapped, respectively. Considering only reads with root-mean-square mapping quality greater than 20, and occurring at raw read depths (DP) greater than 6 and lower than 20, a total of 143,468 and 474,483 heterozygous putative SNPs were identified when comparing the reference genome with the M13/91 and FH cultivars, respectively. A total of 80,554 heterozygous putative SNPs are shared by both M13/91 and FH cultivars. When considering only homozygous putative SNPs, a total of 20,296 (M13/91) and 70,659 (FH) SNPs were identified. A search was also made between the M13/91 and FH cultivar genomes, resulting in a total of 2,631 SNPs which are homozygous in FH and heterozygous in M13/91, and 4,768 SNPs which are homozygous in M13/91 and heterozygous in FH. In order to determine whether the differences in SNP frequencies between these cultivars are due to differences in read coverage obtained from sequencing, we set up a cut-off value above which all SNP calls in both cultivars had the same coverage, and it showed that their SNP frequency is similar.We have used next generation sequencing data combined with high-density SNP detection methods to discover large numbers of putative SNPs in apple cultivars, which can be used in the development of genotyping assays.


Prunus avium) and allotetraploid sour cherry (P. cerasus). This effort was led by RosBREED, a community initiative to enable marker-assisted breeding for rosaceous crops. Next-generation sequencing in diverse breeding germplasm provided 25 billion basepairs (Gb) of cherry DNA sequence from which were identified genome-wide SNPs for sweet cherry and for the two sour cherry subgenomes derived from sweet cherry (avium subgenome) and P. fruticosa (fruticosa subgenome). Anchoring to the peach genome sequence, recently released by the International Peach Genome Initiative, predicted relative physical locations of the 1.9 million putative SNPs detected, preliminarily filtered to 368,943 SNPs. Further filtering was guided by results of a 144-SNP subset examined with the Illumina GoldenGate® assay on 160 accessions. A 6K Infinium® II array was designed with SNPs evenly spaced genetically across the sweet and sour cherry genomes. SNPs were developed for each sour cherry subgenome by using minor allele frequency in the sour cherry detection panel to enrich for subgenome-specific SNPs followed by targeting to either subgenome according to alleles observed in sweet cherry. The array was evaluated using panels of sweet (n = 269) and sour (n = 330) cherry breeding germplasm. Approximately one third of array SNPs were informative for each crop. A total of 1825 polymorphic SNPs were verified in sweet cherry, 13% of these originally developed for sour cherry. Allele dosage was resolved for 2058 polymorphic SNPs in sour cherry, one third of these being originally developed for sweet cherry. This publicly available genomics resource represents a significant advance in cherry genome-scanning capability that will accelerate marker-locus-trait association discovery, genome structure investigation, and genetic diversity assessment in this diploid-tetraploid crop group.High-throughput genome scans are important tools for genetic studies and breeding applications. Here, a 6K SNP array for use with the Illumina Infinium® system was developed for diploid sweet cherry ( Prunus (Rosaceae), two cherry species, sweet (P. avium) and sour cherry (P. cerasus), are highly valued for their excellent quality fruit. These two species represent a natural diploid-tetraploid series with the tetraploid sour cherry (2n = 4x = 32) arising through natural hybridization between sweet cherry (2n = 2x = 16) and the wild tetraploid ground cherry (P. fruticosa) Within Prunus persica) reference genome sequence by the International Peach Genome Initiative High-throughput and low-cost next generation sequencing (NGS) is a powerful approach to identify single nucleotide polymorphisms (SNPs) which can be used as markers for the development of high-density genome scans. This approach has been successfully used to develop genome scan platforms for diploid crops such as apple Prunus species Prunus crops for which a high quality genome sequence is not available. For example, comparisons of available sweet cherry genetic maps with the high-density peach×almond “T×E” Prunus reference genetic map Extensive synteny conserved among the diploid Herein we use a cherry-peach comparative genomics strategy to develop a moderate-density cherry SNP array relevant for sweet and sour cherry breeding germplasm based on SNPs discovered using next generation sequencing platforms. This effort was led by RosBREED, a community initiative to enable marker-assisted breeding for rosaceous crops The workflow and design parameters described below are summarized in SOAPA SNP detection panel of 16 sweet and 8 sour cherry accessions was chosen for whole genome, low-coverage resequencing . The accSOAPsnp (http://soap.genomics.org.cn/soapsnp.html) as recommended by Li et al. SOAPsnp's “quality score” metric of the consensus genotype was greater than 30; (2) the sequencing depth at the putative SNP position was at least 8; and (3) the sequencing depth at the putative SNP position was no greater than 1254, corresponding to the average read depth of all SNPs plus three standard deviations. For each gene, the exonic, intronic, and intergenic space was defined using the Peach v1.0 ‘dhLovell’ genome annotation SNPs from resequenced accessions were detected using A set of 144 SNPs were chosen from among Stage 1 SNPs to validate the efficiency of SNP detection and adjust subsequent filtering parameters . The iniTo examine filtering parameters for associations with genotyping efficiency, validation panels of 79 sweet cherry  and 81 savium and fruticosa subgenomes  or polymorphic in both subgenomes  were removed (strategy described below). This filtering process yielded “Stage 2 SNPs”  format with custom scripts and their ADT scores calculated; only SNPs with a score ≥0.90 were retained. A/T and C/G transversions were removed from further consideration, thus retaining only SNPs of normalization bin “C” (for Infinium® II compatibility). SNPs with MAF <0.2 were discarded, as well as those not supported by between 10 and 30 reads and at least five detection panel accessions for sweet cherry or four accessions for sour cherry. Non-intragenic SNPs were removed for sweet cherry, i.e., only exonic and intronic SNPs were considered for the array. Non-intragenic SNPs were included in the final array for sour cherry, rather than intragenic, due to a mistake in filtering. SNPs not assigned to the first eight pseudomolecules representing peach's eight chromosomes were excluded. Non-uniquely anchored SNPs (those SNPs for which at least 90% of the 60 bp flanking sequences were anchored to more than one peach genome location) were also removed, and there could be no other detected SNP within 20 bp of the targeted SNP. For sour cherry, SNPs predicted to be between the  2 SNPs” .avium and fruticosa subgenomes , and therefore not expected to segregate, or SNPs segregating in both subgenomes , as neither were desirable for the final array. Sour cherry SNPs were examined for divergence from a 1∶1 ratio by Chi-squared analysis, with those significantly different (p<0.05) assumed to represent subgenome-specific SNPs . Finally, to determine whether such SNPs were polymorphic within the avium or fruticosa subgenome, sequence information from sweet cherry detection panel accessions was consulted: presence of the rare sour cherry allele in sweet cherry was assumed to indicate that the SNP identified polymorphism within the avium subgenome; absence of the allele in sweet cherry indicated a fruticosa subgenome SNP.Subgenome specificity of sour cherry SNPs was deduced by the following strategy. MAF within the sour cherry detection panel was used as a proxy for allele dosage. SNPs close to a 1∶1 ratio for two alternative SNP alleles were assumed to represent either SNPs between the Members of the international cherry genomics community requested inclusion of 487 SNPs in the final array. These “preferentially included SNPs” were: 304 sweet cherry RosCOS SNPs avium subgenome and half to the fruticosa subgenome. Genetic location in the cherry genome of each Stage 2 SNP was estimated by physically anchoring 80 genetically mapped markers , their estimated genetic location, the subgenome targeted for sour cherry, and the two sources of available SNPs . Preferentially included SNPs were automatically included, leaving 5513 SNPs to be chosen. For the ∼75% SNPs allocated to sweet cherry and ∼25% to sour cherry, SNPs were evenly spaced genetically across each crop's genome. For sour cherry, approximately half of the chosen SNPs were targeted to evenly genetically spanning the The RosBREED cherry 6K SNP array v1 was evaluated with panels of sweet cherry (n = 269) and sour cherry (n = 330) germplasm that included a diversity of cultivars, ancestors, founders, and progeny individuals forming a complex pedigree structure linking North American cultivated cherry germplasm for these crops  and S4. A total of 15.7 Gb of sweet cherry and 9 Gb of sour cherry DNA reads were obtained from 308.6 million 80-base reads generated for the 16 sweet cherry and eight sour cherry accessions . ExcludiSNP performance via the GoldenGate® assay with a subset of SNPs screened over 160 sweet and sour cherry accessions depended on SNP source, crop screened, and various parameter scores . Sweet cavium subgenome and 1653 within the fruticosa subgenome. Partial Stage 2 filtering of the 487 preferentially included SNPs resulted in 413 suitable SNPs.Stage 2 filtering resulted in 31,945 SNPs suitable for the final array. Of these, 28,562 were derived from polymorphism within sweet cherry while the other 3383 were expected to be within sour cherry subgenomes, 1730 indicative of polymorphism within the avium and 764 fruticosa). After a small degree of loss due to technical issues in array manufacturing by Illumina Inc., 5696 SNPs were included on the final array, with 4214 (74%) targeting the sweet cherry genome (221 RosCOS) and 1482 (26%) targeting the sour cherry genome that consisted of 752 for the avium subgenome and 730 for fruticosa (avium subgenome were targeted with an average of 94 SNPs ranging from 66 (chromosome 5) to 164 (chromosome 1) SNPs, while the fruticosa chromosomes were targeted with an average of 91 SNPs and ranging from 73 (chromosome 4) to 161 (chromosome 1) SNPs (SNP choices for the 6K array consisted of 4408 sweet cherry SNPs (227 of those being RosCOS SNPs) and 1552 sour cherry SNPs (788 ruticosa . Sweet cruticosa . Sour ch 1) SNPs . Include 1) SNPs .Approximately a third of the SNPs on the RosBREED cherry 6K SNP array v1 were observed to be polymorphic in the sweet and sour cherry evaluation panels, and some of the SNPs developed for one crop were successful for the other. Informative SNPs were randomly distributed over the genome such that genetic variation was successfully sampled at medium density in any region of the genome .avium (29% of those SNPs) and only 15 fruticosa (2.9% of SNPs targeted to that subgenome). A large proportion of sweet cherry SNPs  were monomorphic in that crop while few failed .A total of 1825 SNPs were polymorphic across the sweet cherry evaluation panel of breeding germplasm, representing 32% of SNPs present on the array . Of the The 1825 SNPs polymorphic in the sweet cherry evaluation panel provided an average physical spacing of approximately 120 kb between SNPs across the sweet cherry genome, considering the peach genome as the proxy for cherry . Some gaavium subgenome SNPs and the rest fruticosa  to be polymorphic with their genotypes resolvable  – more truticosa . Anotherruticosa . Of thos% failed . Sweet cavium subgenome and 666 kb across the sour cherry fruticosa subgenome  and chromosome 7 for fruticosa (11.2 cM) (avium and fruticosa subgenomes resulted in average densities of one polymorphic SNP per 1.3 and 1.5 cM (T×E reference map), respectively, sweet cherry SNPs polymorphic in this other cherry crop improved genetic resolution for avium to one SNP per 0.29 cM, assuming all such SNPs were indeed within this subgenome  . Althougubgenome . Only twre >5 cM .avium SNPs , or the sour cherry fruticosa SNPs . Average heterozygosity in the 16 sweet cherry accessions of the detection panel for sweet cherry-targeted SNPs was 0.118 (s.d. 0.013) and was similar for all other non-seedling accessions of the evaluation panel . For sweet cherry accessions such as the MIM series and ‘Walpurgis’ with no known pedigree connections to detection panel accessions, heterozygosity was similar (results not shown). For all SNPs on the array, heterozygosity of sweet cherry accessions averaged 0.103 and ranged from 0.075 to 0.124.Half of the polymorphic SNPs in sweet cherry had a MAF >0.2, while a third were <0.10; in contrast, half of the polymorphic SNPs in sour cherry were >0.4 MAF and only 6% were <0.10 . Of the S locus on Prunus LG6 for the sweet cherry cross ‘New York 54’ × ‘Emperor Francis’, offspring representing the four possible non-recombinant haplotypes were identified  for which their SNP haplotypes were identical across the examined region; the 4S haplotype was also shared with the sweet cherry cultivar Emperor Francis  that was less than that of recent SNP detection and array development for peach and apple, two of cherry's rosaceous relatives. The recently developed International Peach SNP Consortium peach 9K SNP array v1 used 56 accessions that achieved 118× total genome coverage and for which 84.3% of included SNPs were informative when evaluated on diverse breeding germplasm fruticosa subgenome, it is suspected that the sour cherry avium subgenome will have significantly denser marker coverage than the fruticosa subgenome. However, it is probable that some of the sweet cherry SNPs that were polymorphic in sour cherry were within the fruticosa subgenome, given that 215 of the 236 sour cherry SNPs polymorphic in sweet cherry were avium-targeted.The proportion of polymorphic markers verified in the sour cherry germplasm was nearly double that found in the sweet cherry set. However, dosage could be resolved for only approximately half these SNP markers. Therefore, despite having twice the number of chromosomes, and using intergenic rather than intragenic SNPs, the final number of resolvable polymorphic sour cherry SNPs was just slightly more than that for sweet cherry, 2058 versus 1825. Given that only 314 of these markers were chosen to putatively target the Here, a comparative genomics approach relied heavily on the peach reference genome sequence for SNP detection  and for final choice of SNPs for the array (evenly spanning the “cherry” genome rather than random positions). Cherry and peach belong to the same genus, with the same chromosome number and apparent genetic co-linearity 2 = 0.96), suggesting that, using similar filtering parameters among these crops, false positives due to sequencing errors and failure due to undetected polymorphism in SNP-flanking sequences could be effectively avoided with a total sequencing depth of at least 133×. This prediction is an expected consequence of the pseudo-random genomic sampling that underlies shotgun sequencing approaches; detection of individual SNPs requires deep sequencing at individual bases and detection of SNPs genome-wide requires broad sequencing over the genome space. Therefore sufficiently deep sequencing is required to identify not only individual SNPs that are potentially of value for downstream assays but to also discover nearby SNP-flanking polymorphisms that can cause SNP assay failures.The high degree of observed monomorphism appears to be due to total sequencing depth of the SNP detection panel. Of the sweet cherry SNPs that did align to the peach genome and passed the various filters to be included on the array, the large proportion (58%) of monomorphism and low failure rate (5%) in sweet cherry accessions indicates that sequences were accurately aligned to unique locations in the peach genome but that detected SNPs were false positives. While a low failure rate (1%) for sour cherry SNPs in sour cherry accessions also indicates successful sequence alignment to the peach genome, the relatively low rate of monomorphism (13%) suggests that most of the originally detected sour cherry SNPs were true SNPs for that crop. However, if unresolved polymorphic sour cherry SNPs were actually monomorphic because their variation in genotype clustering (apparent polymorphism) was due to sequence variation in flanking sequences, then such an adjusted monomorphism rate for sour cherry (53%) would be similar to that of sweet cherry. For peach and apple, monomorphism and failure rates were lower, totaling 16% for peach avium subgenome. Focusing on sour cherry SNP detection instead, a 6K sour cherry array would be expected to provide polymorphic SNPs of ∼4300 for sour cherry  but only ∼900 for sweet cherry. Considering that SNP polymorphism was desired for both crops, both hypothetical scenarios are inferior to the actual dual-crop SNP detection strategy employed that achieved polymorphism of 1825 SNPs in sweet cherry and 2058 SNPs in sour cherry. However, the 2058 SNPs polymorphic in sour cherry are likely to be skewed somewhat toward the avium subgenome because most were developed for sweet cherry (P. avium). A better strategy for the dual-crop array would have been to strongly bias the choice of sour cherry SNPs to the fruticosa subgenome; in hindsight, targeting all sour cherry SNPs to the fruticosa subgenome would have better balanced subgenome coverage.The above considerations suggest some alternative array development strategies. The number of polymorphic SNPs in the cherry array could have been doubled by focusing available resources for SNP detection on one crop or the other (sweet or sour cherry) to double the sequencing depth for that crop. Due to a shared genomic background, such a single cherry crop array would have been useful for the other crop to a certain extent. Following simple extrapolations from our observed cross-species results  and 5, aavium subgenome of sour cherry is believed to represent a subset of P. avium species diversity that pre-dates modern cultivated sweet cherry avium-subgenome sour cherry SNPs (215) were polymorphic for sweet cherry and 33% of sweet cherry SNPs (1380) were polymorphic for sour cherry. Therefore, ∼30% of SNPs on the array are expected to be polymorphic for any diverse set of sweet cherry germplasm, including wild populations. A similar transferability rate may extend to wild P. cerasus populations for the 1482 sour cherry SNPs. Transferability of SNP polymorphism between the distinct P. avium and P. fruticosa species  is estimated to be only a tenth of the transferability within P. avium given that 10% of the sour cherry SNPs polymorphic for sweet cherry accessions were originally developed for fruticosa. The ∼200 RosCOS SNPs performed exceptionally well because of their previous validation in cherry germplasm Prunus genus The RosBREED cherry 6K SNP array v1 appears to be informative beyond just cherry breeding and cultivated germplasm. The S locus  and between the two crops (4S). The 1′S, 4S, and 13′S alleles of sour cherry are known to be avium-derived as these S alleles are present in sweet cherry germplasm 4S allele of the sweet cherry parent ‘Emperor Francis’ was observed to be identical to the 4S haplotype present in both sour cherry parents. In contrast, the 35S, 36aS, and 36bS alleles are considered to be fruticosa-derived as they have not been observed in sweet cherry germplasm S-allele haplotypes. However, as the S locus region is known to be subject to recombinational suppression and as a result, a site for the accumulation of polymorphism, this level of subgenome-balanced polymorphism should not be extrapolated genome-wide.Close examination of haplotype segregation for a 16-SNP region at the S locus  indicatehttp://www.rosaceae.org).The cherry SNP array described here will foster genetics studies in the Rosaceae and help bridge the gap between genomics and breeding in cherry because breeding germplasm was the basis of detected SNPs and SNP choices of the final array. The RosBREED cherry 6K SNP array v1 is commercially available from Illumina and we expect that it will be used worldwide for genetic studies in cherry and related species. The SNP markers included in the cherry 6K Illumina arrays are available for download in Excel format and viewable in GBrowse at the Genome Database for Rosaceae Click here for additional data file.Table S2Sour cherry validation panel of 81 sour cherry accessions, used for validation of detected SNPs.(DOCX)Click here for additional data file.Table S3Sweet cherry evaluation panel of 269 sweet cherry accessions, used in the Infinium® II assay for SNP array evaluation.(DOCX)Click here for additional data file.Table S4Sour cherry evaluation panel of 330 sour cherry accessions, used for SNP array evaluation.(DOCX)Click here for additional data file.Table S5All SNPs of the RosBREED cherry 6K SNP array v1. Information includes physical and genetic positions, targeted genome, and performance outcomes for sweet cherry (n = 269) and sour cherry (n = 330) evaluation panels.(XLSX)Click here for additional data file.Table S6Prunus reference T×E genetic map Markers of the (XLSX)Click here for additional data file.Table S7Markers of the sweet cherry genetic map (XLSX)Click here for additional data file.


Salmonella enterica serovar Typhimurium (or simply Typhimurium) is the most common serovar in both human infections and farm animals in Australia and many other countries. Typhimurium is a broad host range serovar but has also evolved into host-adapted variants (i.e. isolated from a particular host such as pigeons). Six Typhimurium strains of different phage types (defined by patterns of susceptibility to lysis by a set of bacteriophages) were analysed using Illumina high-throughput genome sequencing.200 insertion and 15 pseudogenes due to early termination. Four of these inactivated or deleted genes may be virulence related. Nine prophage or prophage remnants were identified in the six strains. Gifsy-1, Gifsy-2 and the sopE2 and sspH2 phage remnants were present in all six genomes while Fels-1, Fels-2, ST64B, ST104 and CP4-57 were variably present. Four strains carried the 90-kb plasmid pSLT which contains several known virulence genes. However, two strains were found to lack the plasmid. In addition, one strain had a novel plasmid similar to Typhi strain CT18 plasmid pHCM2.Variations between strains were mainly due to single nucleotide polymorphisms (SNPs) with an average of 611 SNPs per strain, ranging from 391 SNPs to 922 SNPs. There were seven insertions/deletions (indels) involving whole or partial gene deletions, four inactivation events due to ISThe genome data suggest that variations between strains were mainly due to accumulation of SNPs, some of which resulted in gene inactivation. Unique genetic elements that were common between host-adapted phage types were not found. This study advanced our understanding on the evolution and adaptation of Typhimurium at genomic level. Salmonella enterica serovar Typhimurium is one of the leading causes of Salmonella-related gastroenteritis in humans. The Anderson phage typing scheme [g scheme , in whicg scheme . Typhimug scheme . Host adg scheme .In Australia, three phage types were found to be predominantly isolated from human infections as well as in animals based on surveillance data from 1996 to 2011 . DT135 hWe previously addressed the origins and relationships of the common phage types in Australia using single nucleotide polymorphisms (SNPs) as molecular markers . SNPs diGenome variations have been observed in studies comparing whole genome sequences of Typhimurium strains LT2 (DT4), SL1344 (DT44), NCTC13348 (DT104), and D23480 (unknown phage type), with strain specific pseudogenes and SNPs found within each genome . Mobile Six strains were selected to represent the spectrum of Typhimurium diversity Table  based onDNA libraries were prepared with insert size of 500 bp and were sequenced using the Illumina Genome Analyzer (Illumina). We used 2 × 50 bp paired end sequencing. Contigs were assembled using the Short Oligonucleotide Analysis package (SOAP) (version 1.04) . SOAPdenAccession numbers for the genome sequences obtained in this study were AROB00000000-AROG00000000.Mapping of reads against the Typhimurium LT2 genome was performed using the Burrows-Wheeler alignment (BWA) tool (version 0.5.8) . The outde novo assembly was done using quality trimmed reads. This may have reduced the number of SNPs. de novo assembly eliminated the problem with reads that may be mapped to spurious positions (mostly repeats or homologous regions) with mismatches being called SNPs. For SOAPdenovo assembly, reads were trimmed after the first base falling below Q7. The read was only excluded if the length of reads was 17 bases after the trimming. For BWA mapping, no filtering of reads was performed.A cutoff of 20 reads covering the SNP site was used initially to remove SNP sites with low coverage. We also used SOAPdenovo to assemble reads into contigs and then compared with the LT2 genome to identify SNPs. SNPs identified by both methods were compared. These common SNPs were manually inspected using SAMtools (version 0.1.7)  and its SAMtools were used to manually confirm all SNPs for our initial analysis of one genome. We found a consistent pattern where SNPs were in fact sequencing errors when the region was covered only by ends of reads which is known to have poorer quality. For SNP sites with heterogeneous reads , the majority of the SNPs were genuine if the SNP was supported by ≥70% of the reads. A small proportion of SNP calls were genuine for those falling between 30% and 70%. None of the SNPs was genuine if less than 30% of reads supported the SNP. In case we removed genuine SNPs of lower than 20X coverage, we inspected SNP sites between >10 and <20 reads coverage and rescued genuine SNPs and added to the final set of SNPs. These genuine SNPs with lower than 20X coverage generally had 100% support for a SNP. Non-genuine SNPs were typically located at the ends of the reads and visual inspection identified them with relatively low subjectivity.http://www.sanger.ac.uk) was included for comparison. SNPs were then used to generate a maximum parsimony (MP) tree using the PAUP package [S. enterica serovar Enteritidis strain PT4 (NCTC13349) (Accession No.: AM933172) and S. enterica serovar Choleraesuis strain SC-B67 (NC_006905) (Accession No.: NC_006905) were used as outgroups.Another custom script was used to determine whether SNPs were synonymous (sSNP) or non synonymous (nsSNP). The validated SNPs were also used for comparison to other Typhimurium genome sequences D23580 (Accession No.: FN424405) , 14028 S package  to illusInsertions and deletions (indels) were identified using the mapping data from BWA . The disIdentification of new IS insertions were done using a similar method. Paired end reads with one end mapping to an existing IS location in the reference genome while the other end mapping to a distance location. The insertion point was determined visually based on the typical pattern of abrupt end of reads mapping as no overlapping reads can be found at the insertion point. We did not determine the precise location of the insertion using reads that contain part of unique sequence and part of IS sequence.Using progressiveMauve , some coS. enterica serovars Enteritidis and Choleraesuis strains were used as an outgroup. Outgroup genomes were aligned using progressiveMauve to the LT2 reference genome. A custom script was used to extract the nucleotide for each outgroup genome at the corresponding SNP containing locations.Maximum parsimony was done using PAUP  with heuet al.[http://www.sanger.ac.uk/resources/downloads/bacteria/salmonella.html). All except DT99 selected in this study are broad host range phage types.Two isolates were selected from SNP cluster I from our previous study , includiet al.. L847  generated per genome was ~9,200,000 and the coverage depth on average for all genomes was ~75X, with the lowest coverage of 63X Table . Coveragde novo assembly. We first used BWA [We used two approaches to identify SNPs: mapping to the reference genome and used BWA  to map rThe SNPs were classified into four categories: non-synonymous (nsSNP), synonymous (sSNP), intergenic (IG) and single base indels. IG SNPs on average accounted for approximately 17.5% of the total number of SNPs in each strain. The average percentage of sSNP for each strain was 36.2% with L904 (DT9) having the lowest ratio of sSNPs (32.5%) while the nsSNPs ranged from 42.1% to 46.8% with an average of 43.9%. Single base indels accounted for only 2% of the SNPs. L796 (DT99) had the highest number of indels (32 indels) representing 3.5% of SNPs, while L927 (DT12a) had the lowest with eight indels which accounted for 1.5% of all SNPs. The ratio between sSNPs and nsSNPs ranged from 0.70 to 0.89.The SNP data from the six isolates were compared with those from seven publicly available Typhimurium genomes: LT2, SL1344, D23580, 14028 S, T000240 and DT104 and an incomplete DT2 genome. A maximum parsimony (MP) tree  I, GC II and GC III. Strain L927 (DT12a) was found to have diverged the earliest since it was closest to the outgroup and shared most recent common ancestor with the three clusters which were grouped together and supported by 97 SNPs. GC II and GC III were grouped together and supported by 64 SNPs. GC I contained three isolates L945 (DT108), T000240 and LT2 and was supported by 56 SNPs. GC II contained four publicly available genomes DT2, 14028 S, SL1344, D23580, and three NGS strains; L852 (DT135a), L904 (DT9) and L796 (DT99). GC II was well separated from GC III and was supported by 203 SNPs. GC III contained two isolates, DT104 and L847 (DT197) and was supported by 32 SNPs. The strain specific SNPs for L796 (DT99), L847 (DT197), L852 (DT135a), L904 (DT9), L927 (DT12a) and L945 (DT108) were 378, 274, 96, 202, 197 and 194, respectively. Amongst the publicly available genomes, DT2 had a large number of strain specific SNPs (683 SNPs) while the strain specific SNPs for the remaining six genomes  ranged from 71 to 151.In this study, the HI was greater than 0 which suggests that some SNPs had conflicting phylogenetic signals. The SNPs were mapped onto the internal and external branches in the MP tree. There were 33 SNPs present in multiple internal branches, indicating reverse/parallel changes which were likely to be resulted from recombination between lineages. Of these, 28 were IG SNPs. For the external branches, the distribution of SNPs was used as an indicator of recombination within a lineage. It has been previously suggested that recombination event constitutes the presence of three or more substitution events within a 1 kb region . There wS. enterica is known to carry three insertion sequences, IS1, IS3 and IS200[1 is rarely detected in Typhimurium [3 was previously found in a high proportion of Typhimurium isolates included in Salmonella reference collection A (SARA) [3 sequence was found at the same location in the six strains sequenced. IS200 was the only IS commonly found in the genome strains.Insertion sequences (IS) play an important role in bacterial evolution as transposition can potentially inactivate a gene . S. enteand IS200. IS1 is himurium  and was A (SARA) . However200 locations found among the 13 Typhimurium genomes, with 27 located in IG regions  occurred only in a single strain as shown in Figure IS200 was seven ranging from four to 10 copies. IS200_1 and IS200_2 were likely to have been gained by the most recent common ancestor of GC I, GC II and GC III since L927 (DT12a) does not contain either IS. Additionally, IS200_2 was likely to have been lost by L904 (DT9) and L852 (DT135a). Similarly, IS200_24 and IS200_25 were likely to have been gained by the most recent common ancestor of GC II as all except one strain contained IS200_24 and all contained IS200_25 ) and brnQ (DT104) occurred in a single strain. The insertions at dacB, icdA and rnd were shared amongst two or more strains. The disruption of rnd appeared to have occurred early during the divergence of GC II as all strains belonging to this cluster had the IS200 insertions. The insertions of IS200 into dacB and icdA occurred three and two times respectively. It appeared to be independent for both cases as the gene disruptions were present in strains from different clusters  may be related to antimicrobial resistance. of AccA . The IS2dacB was found in strains L945 (DT108) and L847 (DT197). This gene encodes a penicillin-binding protein 4 (PBP4) which functions as a trap target for β-lactams [dacB-coded PBP4 triggers overproduction of β-lactamase AmpC and the specific activation of the CreBC (BlrAB) two-component regulator leading to a high level of β-lactam resistance [Another inactivated gene -lactams . Interessistance .Typhimurium strains harbor many prophages -40. The sopE2 and sspH2 phage remnants were present in all six genomes. They contain virulence genes commonly associated with type III effector proteins that are injected by the bacterium through type III secretion [Prophages Gifsy-1, Gifsy-2 and the ecretion , and thunanH and sodCIII[abiU of unknown function [artAB which codes for a putative toxin. It is often present in epidemic multiple drug resistant DT104 strains [Fels-1 and Fels-2 were only found in strains, L847 (DT197) and L904 (DT9), respectively. The sparse presence of these prophages was not surprising as Fels-1 and Fels-2 are commonly absent in Typhimurium strains . Fels-1 d sodCIII while Fefunction . ST104 p strains . InteresST64B codes a virulence factor similar to Ssek NleB type III secreted effector proteins . This prE. coli strains suggesting a co-evolution between the two species [Prophage CP4-57 controls phage excision during the biofilm development stage which in turn enhances the motility in the host and increases biofilm dispersal while reducing growth . This pr species . This pr species ,48.sopE, and when disrupted reduces invasiveness [The P2-like phage SopEΦ was notably absent in all six strains sequenced in this study. SopEΦ contains the virulence gene siveness . This prsiveness  and in tAltogether, the results suggest that prophages may not be maintained in all Typhimurium genomes. On the other hand, other studies have shown that prophages can be easily transferred between strains, particularly if the prophages are integrated at a tRNA site, like the case of ST104 and ST64B . Thus, pgipA) was found in L927 (DT12a) and L847 (DT197) and the indel in STM0291 was found in L852 (DT135a) and SL1344. Three of the seven deletions encompassed a whole gene while the remaining four deletions were partial deletion of a gene leading to truncations of 15% to 37% of the gene. Truncations of more than 20% were treated as pseudogenes [Regions of > 500 bp that are present in the reference genome LT2 but absent in the NGS strains are identified as large indels. Seven large indels were found in the six NGS strains with sizes ranging from 558 bp to 1,992 bp Table . Indel eudogenes . Thus, agipA is required for survival in Peyer’s patches [gipA mutants have been shown to be attenuated to some extent after oral infection in mice, but displayed the same level of virulence as the wild type if inoculated intraperitoneally. safA (STM0299) is part of the saf fimbrial operon. safA mutants are attenuated in a pig model, but not in calves or chickens [saf operon is not needed for virulence in mice [sopA is used to alter host cell physiology and promote bacterial survival in host tissues [sopA mutant has reduced Salmonella-induced polymorphonuclear leukocytes transepithelial migration [Interestingly, the genes deleted or truncated appeared to have a role in virulence.  patches . gipA muchickens , and the in mice ,55. sopA tissues . sopA muigration .gipA deletion, sapA and sopA truncation. However, this phage type has increased in frequency in Australia in recent years, which argues against the importance of these genes for virulence in humans, although the increased detection of this phage type may be due to increased ability to colonise food animals, leading to increased exposure in humans. L927 (DT12a), also one of the most frequent phage types, carries gipA deletion and contains a truncated STM4534, a transcriptional regulator which regulates the phosphotransferase system and possibly other systems [Loss of these genes is expected to be disadvantageous to each corresponding strain, and may explain the differences in the ecology of several phage types. L847 (DT197) carries  systems .Apart from gene truncations due to partial deletion of a gene as described above, we identified 15 genes terminated earlier when compared to strain LT2, due to a stop codon  had the most number of strain specific pseudogenes followed by strains L927 (DT12a), L847 (DT197), DT2 and L904 (DT9) with seven, three, three, two and one pseudogenes, respectively. It is interesting to note that L796 (DT99) had a higher number of disrupted genes as well as the highest number of SNPs. This strain also had a 27% shorter DNA polymerase II encoded by napF and blc, if active, are involved in energy conversion and metabolic pathways. napF encodes a predicted 3Fe-4S iron sulfur protein [NapF mutant causes a growth defect under anaerobic conditions on glycerol/nitrate medium but is not essential for the activity of periplasmic nitrate reductase [E. coli, blc promoter is expressed during stationary growth phase which is controlled by rpoS sigma factor, directing the expression of genes necessary for adaptation to low nutrients condition. Therefore, both napF and blc are important for conserving energy.Several pseudogenes for example,  protein . NapF mueductase . TherefosthB, may be involved in host-restriction. SthB, if active, codes for a fimbrial usher protein. Deletion of sthABCDE operon results in reduced caecal colonisation in mice [sthB mutants in chicken hosts have reduced colonisation [Only one pseudogene,  in mice . Furthernisation . This psspv , the pef (plasmid-encoded fimbriae) region, rck (resistance to complement killing), a homolog of dsbA (disulfide bond isomerase) and a homolog of the AraC family of transcriptional regulators [Most Typhimurium strains including LT2 carry a 90-kb virulence plasmid, pSLT . It contgulators -65. The Strains L945 (DT108) and L927 (DT12a) contained reads covering only 2.6% and 0.8% of the pSLT plasmid suggesting that both of these strains did not have pSLT. It is likely that L945 (DT108) has lost the plasmid as all other strains from GC I contained the plasmid. In contrast, it is not known whether L927 (DT12a) has lost the plasmid or the plasmid was only gained after the divergence from the L927 (DT12a) lineage.repA was identified in one of the contigs suggesting that a novel plasmid was present in L945 (DT108).L945 (DT108) contained additional contigs that were not able to be aligned with LT2 chromosomal sequence or pSLT plasmid sequence. These contigs were then searched against GenBank using BLASTn. Eighty eight contigs, ranging from 104 bp to 5,980 bp, from L945 (DT108) had the closest match, with 65% DNA sequence identity, to the cryptic plasmid pHCM2 of 106 kb belonging to Typhi strain CT18. Sequence homologous to +/H+ antiporter; an aminotransferase; glycogen synthesis protein glgX[Phage types of DT99 and DT2 are commonly associated with pigeons  and the tein glgX and glycrpoS mutant LT2 strain has reduced virulence in mouse models [fimH has been shown to improve the bacterial adhesion of serovar Enteritidis to chicken leukocytes [ycjF were found in both L796 (DT99) and DT2, although the SNP locations differed between the two, at codons 301 and 324 for L796 (DT99) and codon 181 for DT2. ycjF codes for a hypothetical protein and its homolog in E. coli is essential for virulence in vivo in a mouse septicaemia model [Studies have shown that adaptation could be resulted from changes as small as one SNP, which can result in either increased or reduced virulence in animal models ,69. For ukocytes . There wia model . Other tComparative genome analysis of DT99 and DT2 revealed few genetic features that are specific to these two host adapted phage types. Therefore, multiple factors are likely to have contributed to the adaptation to pigeons.Six diverse Typhimurium strains based on our previous SNP typing study were investigated at the genome level and compared to seven other publicly available genomes to determine genetic variations that may contribute to their prevalence and host-adaptations. Variations between these genomes largely resulted from accumulation of SNPs. These genome-wide SNPs were also used to establish the phylogenetic relationships of 13 genome strains. Despite the presence of parallel/reverse mutations, the resulting phylogeny was generally consistent with our previous SNP typing study . Other v200 insertions which appeared mostly to be random. However, some IS200 insertions may provide a selective advantage including insertions in icdA, accA and dacB, all of which are related to antibiotic resistance.There was evidence of genome degradation, including pseudogene formation and some large indels. The pseudogenes mainly resulted from earlier termination codons or ISComparison of two host-adapted Typhimurium phage types, L796 (DT99) and DT2, did not reveal any unique genetic elements between them. SNP-based phylogeny grouped these strains together in GC II but they were clearly of separate lineages. This suggests that host-adaptation is a result of convergent evolution. However, factors contributing to the prevalence and host-adaptation in Typhimurium remain to be uncovered. In conclusion, genetic diversity within Typhimurium is mainly due to accumulation of SNPs, some of which led to pseudogenes. Unique genetic elements that were common between host-adapted phage types were not found.The authors declare that they have no competing interests.Experimental work and data collection were carried out by SP. SP, SO, RL, LF, BL and PRR contributed to data analysis and interpretation. The study was conceived and designed by RL and LW. The manuscript was drafted by SP, SO and RL. All authors have read and approved the final manuscript.


Date palm is a very important crop in western Asia and northern Africa, and it is the oldest domesticated fruit tree with archaeological records dating back 5000 years. The huge economic value of this crop has generated considerable interest in breeding programs to enhance production of dates. One of the major limitations of these efforts is the uncertainty regarding the number of date palm cultivars, which are currently based on fruit shape, size, color, and taste. Whole mitochondrial and plastid genome sequences were utilized to examine single nucleotide polymorphisms (SNPs) of date palms to evaluate the efficacy of this approach for molecular characterization of cultivars. Mitochondrial and plastid genomes of nine Saudi Arabian cultivars were sequenced. For each species about 60 million 100 bp paired-end reads were generated from total genomic DNA using the Illumina HiSeq 2000 platform. For each cultivar, sequences were aligned separately to the published date palm plastid and mitochondrial reference genomes, and SNPs were identified. The results identified cultivar-specific SNPs for eight of the nine cultivars. Two previous SNP analyses of mitochondrial and plastid genomes identified substantial intra-cultivar  polymorphisms in organellar genomes but these studies did not properly take into account the fact that nearly half of the plastid genome has been integrated into the mitochondrial genome. Filtering all sequencing reads that mapped to both organellar genomes nearly eliminated mitochondrial heteroplasmy but all plastid SNPs remained heteroplasmic. This investigation provides valuable insights into how to deal with interorganellar DNA transfer in performing SNP analyses from total genomic DNA. The results confirm recent suggestions that plastid heteroplasmy is much more common than previously thought. Finally, low levels of sequence variation in plastid and mitochondrial genomes argue for using nuclear SNPs for molecular characterization of date palm cultivars. Phoenix dactylifera L., Arecaceae) is the primary crop in many countries in western Asia and northern Africa Date palm  Is there heteroplasmy in the mitochondrial and plastid genomes?; (2) What is the effect of plastid DNA transfer to the mitochondrion on organellar SNP analyses? (3) Are organellar SNPs useful for identification of date palm cultivars?; and (4) What are the phylogenetic relationships among cultivars?Phoenix dactylifera was collected from Hada El-Sham Station, King Abdulaziz University, Saudi Arabia and frozen in liquid nitrogen. Isolation of total genomic DNA was performed using the modified procedure of Gawel and Jarret Approximately 500 mg of field-collected leaf tissue from a single plant of each of the nine cultivars  of Phoenhttp://bio-bwa.sourceforge.net/). Reads were then run through samtools version mpileup (http://samtools.sourceforge.net/) and bcftools pipelines to identify SNPs that are unique to the mitochondria or plastid genomes. Only SNPs with a read depth of ≥ 10, mapping quality≥20, and SNP quality≥15 were retained. Initially, all reads were included in the mapping but a separate mapping was performed after filtering out of all reads that aligned to both plastid and mitochondrial genomes; only filtered reads were used in all subsequent SNP comparisons.Total genomic DNA was sequenced using the Illumina HiSeq 2000 platform at BGI. For each species, about 60 million 100 bp paired-end reads were generated from a sequencing library with 500 bp inserts. The raw data was processed in two steps: adapter sequences in reads were trimmed and then reads that contained more than 50% low quality bases  were removed. The remaining sequencing reads from the nine samples were aligned separately to the date palm plastid (NC_013991) and mitochondrial (NC_016740.1) reference genomes using BWA  covered 100% of the genome for all nine cultivars. The number of reads mapped to the mitochondrial genome varied from 854,270–1,495,892 depending on the cultivar, which included 1.41–2.07% of the total reads . MappingSince 10.3% of the 715,001 bp mitochondrial genome represents plastid insertions matR gene at coordinate 559,552 in the cultivar Moshwaq Al-Riyadh (MOS-A).The number of mitochondrial SNPs detected for each of the nine Saudi Arabian date palm cultivars relative to the reference genome ranged from 18–25 for a total of 188 SNPs . For theThe number of plastid SNPs ranged from one to eight per cultivar with a total of 30 among the nine date palm cultivars and all but two cultivars (DEK and SHA) had at least one unique SNP . One halUnrooted maximum likelihood (ML) trees were generated independently for mitochondrial and plastid SNPs to estimate relationships among the 10 cultivars of date palm . The mitThe ML tree for the plastid SNPs was also not well-resolved or supported, however, bootstrap values for two nodes were slightly higher than those in the mitochondrial tree . The lowThree previous studies within and among one or three date palm cultivars reported intra- and inter-cultivar organellar SNPs et al.et al. Straub et al. ActinidiaCoreopsisCynomoriumEpilobiumMedicagoGossypiumOenotheraOryzaPassifloraPelargoniumSenecioThe traditional view has been that heteroplasmy in plastids is uncommon et al. Integration of plastid DNA into the mitochondrial genome can cause difficulties in utilizing organellar genomes for SNP analyses. In the previous studies of date palm organellar SNPs heteroplasmy was greatly overestimated because 10.3% of the mitochondrial genome represents plastid DNA transfers. The transfer of plastid DNA to the mitochondrion is a common phenomenon with 1–12% of published angiosperm mitochondrial genomes representing plastid DNA Another issue with using total genomic DNA for SNP analyses from genome sequence data is the prevalence of both plastid and mitochondrial DNA in the nucleus, which is commonly referred to as NUMTS (nuclear mtDNA) or NUPTs (nuclear ptDNA). In flowering plants, it is well known that large fragments of DNA from both of these genomes are transferred to the nucleus et al.Date palm cultivar identification is complicated by the fact that there are so many named cultivars, and most of these are characterized by fruit size, color, shape, and taste. This has resulted in different cultivar names for the same morphological type in different countries. Also, reliance on characters that are only present on female plants has caused considerable confusion since it takes 8–10 years before plants flower. Thus, there has been an increasing effort to utilize molecular markers to define cultivars, and most of these studies have used fragment data from RAPD, ISSR, and AFLP comparisons. These approaches are problematic in terms of producing a well-characterized molecular signature for each cultivar, largely because of their limited repeatability. Even though the SNP comparison of the mitochondrial and plastid genomes was limited by cross compartment DNA transfer, our results were successful in detecting unique SNPs for eight of the nine cultivars examined . The maiPhylogenetic analyses of mitochondrial and plastid SNPs generated incongruent tree topologies that provided only limited resolution among cultivars with low support values . This re


In addition, subjects underwent a working memory task (n-back) and red blood cell membrane (RBC) fatty acid composition analysis pre- and post-supplementation. RBC analysis showed a significant increase in both DHA and EPA post-supplementation. In contrast, no significant change in [11C]DTBZ binding potential (BPND) in striatum and its subdivisions were observed after supplementation with n–3 PUFA. No correlation was evident between n–3 PUFA induced change in RBC DHA or EPA levels and change in [11C]DTBZ BPND in striatal subdivisions. However, pre-supplementation RBC DHA levels was predictive of baseline performance  on the n-back task . In addition, subjects AHR performance improved on 3-back post-supplementation . The correlation between n-back performance, and DHA levels are consistent with reports in which higher DHA levels is related to improved cognitive performance. However, the lack of change in [11C]DBTZ BPND indicates that striatal VMAT2 regulation is not the mechanism of action by which n–3 PUFA improves cognitive performance.Studies in rodents indicate that diets deficient in omega-3 polyunsaturated fatty acids (n–3 PUFA) lower dopamine neurotransmission as measured by striatal vesicular monoamine transporter type 2 (VMAT2) density and amphetamine-induced dopamine release. This suggests that dietary supplementation with fish oil might increase VMAT2 availability, enhance dopamine storage and release, and improve dopamine-dependent cognitive functions such as working memory. To investigate this mechanism in humans, positron emission tomography (PET) was used to measure VMAT2 availability pre- and post-supplementation of n–3 PUFA in healthy individuals. Healthy young adult subjects were scanned with PET using [ Previous studies in humans suggest that n–3 PUFA deficiency is associated with impairment in mood Basic science investigations involving rodents indicate that n–3 PUFA deficiency alters the transmission of monoamines such as dopamine and serotonin in the brain 11C]DTBZ both before and after six-months of n–3 PUFA supplementation . Our primary hypothesis was that n–3 PUFA would increase VMAT2 availability  in healthy individuals after six months of supplementation. In addition, we hypothesized that this increased availability of VMAT2 will lead to greater vesicular dopamine stores and improve dopamine-dependent working memory, which was measured using a verbal n-back task and three working memory loads .To evaluate this hypothesis we evaluated 11 healthy individuals with the selective VMAT2 PET radioligand, [The study was conducted following the approvals of the University of Pittsburgh Institutional Review Board and Radioactive Drug Research Committee. All subjects provided written informed consent.Study criteria for healthy controls were 11C]DTBZ PET scan, n-back task and RBC membrane extraction for fatty acid analysis after an overnight fast. Then, subjects were supplemented with n–3 PUFA (Lovaza), 2 g once daily for six months after which they were eligible for the post-supplementation evaluations (conducted a minimum of five months and maximum of 6 months after n–3 PUFA supplementation), which included a repeat [11C]DTBZ PET scan, n-back task and RBC for fatty acid analysis. Subjects were monitored on a monthly basis during the n–3 PUFA supplementation period for subjective complaints, medication adherence (via pill counts), illicit drug abuse and pregnancy. In addition to this subjects underwent a complete blood cell count, liver function test, fasting lipid profile and RBC for fatty acid analysis at 1-month, 3-months, 5-months to monitor for abnormal lab results (such as elevations in liver function tests and low-density lipoproteins and reduction in platelet count) and confirmed adherence to n–3 PUFA. The monitoring led to discontinuation on n–3 PUFA supplementation in two individuals –one for elevated low-density lipoproteins (193 mg/dl) and another for low platelet counts  at 1-month and 5-months post-supplementation respectively . Thus, the final sample includes pre- and post-supplementation data in only 11 out of the 13 enrolled subjects.A total of thirteen subjects who met inclusion/exclusion criteria  were enrolled to participate in the study that was conducted in the outpatient setting. All enrolled subjects underwent a pre-supplementation DTBZ was synthesized using the methodology reported previously by Kilbourn, et al. 11C]DTBZ was injected as a bolus plus constant infusion consistent with that reported in 11C]DTBZ dose (∼ 15 mCi) was administered as an intravenous bolus injection over the first 30 seconds, while the remaining 45% of the dose was continuously infused over the next 60 minutes. This infusion ratio allowed for [11C]DTBZ to reach steady state thirty minutes after the beginning of the bolus plus constant infusion 11C]DTBZ. In addition, four venous blood samples  were obtained to measure plasma concentration of [11C]DTBZ as described in 11C]DTBZ concentration at steady state . Plasma clearance  was calculated as the rate of infusion/CSS and the free fraction (fp) was measured using previously described methods DTBZ concentration in the region of interest  to the concentration of un-metabolized [11C]DTBZ in venous plasma  at equilibrium was derived as.In this section we use the consensus nomenclature for in vivo imaging of reversibly binding radioligands to describe all outcome measures T in the occipital cortex (VT OCC ). Thus, VTOCC was assumed to be equal to the non-displaceable distribution volume (VND).The concentration of VMAT2 is negligible in the occipital cortex 11C]DTBZ BPND, i.e., binding potential relative to non-displaceable uptake. This was computed as.ND is the free fraction of radiotracer in brain expressed relative to the non-displaceable concentration (fND  =  fp/VND), Bavail is the density of VMAT2 available to bind to [11C]DTBZ in vivo and KD is the equilibrium disassociation constant of [11C]DTBZ.VMAT2 availability in the striatal regions of interest was estimated as DTBZ injected dose, specific activity at time of injection, and injected mass did not differ between the pre- and post-n–3 PUFA supplementation conditions. No significant between-condition differences were observed in the plasma free fraction and clearance rate of [11C]DTBZ, or in [11C]DTBZ occipital cortex distribution volume, VND measure .[11C]DTBZ BPND in the striatal subdivisions . In addition, a test of between-condition differences in each region of interest failed to reach significance in all five striatal subdivisions  or EPA  levels. Also, no significant associations were noted between the change in [11C]DTBZ BPND in the striatum and change in RBC DHA  or EPA  levels following supplementation. No significant associations were noted when the above correlations were performed using [11C]DTBZ BPND and Δ BPND from the functional or anatomical subdivisions of the striatum.Correlation analyses revealed no significant relationship between pre-supplementation [11C]DTBZ and PET in a group of healthy young adults before and after six months of supplementation of a FDA approved formulation of n–3 PUFA . Despite the fact that the formulation used in this study led to significant elevations in RBC DHA (1.75-fold) and EPA (4.5-fold) levels relative to pre-supplementation values, we failed to detect an effect for it on striatal VMAT2 availability. The mean change in [11C]DTBZ BPND in the striatal subdivisions (range −1 to −4%) after n–3 PUFA supplementation was well within the reported test-retest variability (4 to 7%) for this radioligand 11C]DTBZ binding might be related to the fact that healthy humans were supplemented with n–3 PUFA in this study, as opposed to the rodent studies in which a group of animals were developmentally deprived of n–3 PUFA and compared to controls. Thus, the possibility of dietary depletion of n–3 PUFA leading to a reduction in striatal VMAT2 availability in humans cannot be excluded based on the six-month supplementation data. Because individuals with diets deficient in n–3 PUFA are likely to have less RBC DHA/EPA, we evaluated whether lower RBC DHA/EPA levels are associated with lower striatal VMAT2 availability in subjects before supplementation. Contrary to this hypothesis, we found no relationship between the RBC DHA/EPA levels and striatal [11C]DTBZ BPND. Taken together these data do not support an effect for n–3 PUFA on striatal VMAT2 in healthy adults.In this study, we evaluated VMAT2 availability with DTBZ and PET. Higher RBC DHA levels were associated with better working memory performance in this cohort of young adults, which is consistent with that previously shown in middle-aged adults. Also, n–3 PUFA supplementation improved working memory performance, which is consistent with some but not all clinical trials that have evaluated the pro-cognitive effects of n–3 PUFA in humans. Further research is warranted to elucidate the mechanisms by which n–3 PUFA enhances cognitive performance in healthy individuals.In summary, we found no effect for n–3 PUFA supplementation on striatal VMAT2 availability in healthy humans using [