Cucumis melo L.) is a highly diverse species that is cultivated worldwide. Recent advances in massively parallel sequencing have begun to allow the study of nucleotide diversity in this species. The Sanger method combined with medium-throughput 454 technology were used in a previous study to analyze the genetic diversity of germplasm representing 3 botanical varieties, yielding a collection of about 40,000 SNPs distributed in 14,000 unigenes. However, the usefulness of this resource is limited as the sequenced genotypes do not represent the whole diversity of the species, which is divided into two subspecies with many botanical varieties variable in plant, flowering, and fruit traits, as well as in stress response. As a first step to extensively document levels and patterns of nucleotide variability across the species, we used the high-throughput SOLiD™ system to resequence the transcriptomes of a set of 67 genotypes that had previously been selected from a core collection representing the extant variation of the entire species.Melon  yielded about 300 M reads. Short reads were mapped to the recently generated draft genome assembly of the DHL line Piel de Sapo (inodorus) x Songwhan Charmi (conomon) and to a new version of melon transcriptome. Regions with at least 6X coverage were used in SNV calling, generating a melon collection with 303,883 variants. These SNVs were dispersed across the entire C. melo genome, and distributed in 15,064 annotated genes. The number and variability of in silico SNVs differed considerably between pools. Our finding of higher genomic diversity in wild and exotic agrestis melons from India and Africa as compared to commercial cultivars, cultigens and landraces from Eastern Europe, Western Asia and the Mediterranean basin is consistent with the evolutionary history proposed for the species. Group-specific SNVs that will be useful in introgression programs were also detected. In a sample of 143 selected putative SNPs, we verified 93% of the polymorphisms in a panel of 78 genotypes.The deep transcriptome resequencing of all of the genotypes, grouped into 8 pools  melon transcriptomes, yielding the largest melon SNP collection available to date and representing a notable sample of the species diversity. This data provides a valuable resource for creating a catalog of allelic variants of melon genes and it will aid in future in-depth studies of population genetics, marker-assisted breeding, and gene identification aimed at developing improved varieties. Cucumis melo L., Cucurbitaceae) is an important fruit crop worldwide. It is considered to be the most variable species in the genus Cucumis, and one of the most diverse among the cultivated vegetables[Melon [cantalupensis, reticulatus, adana, chandalak, ameri, inodorus, chate, flexuosus, dudaim and tibish (subsp. melo), momordica, conomon, chinensis, makuwa, and acidulus (subsp. agrestis)). However, some of these botanical groups are not well defined, share characteristics and are quite heterogeneous. Despite many reported accessions accurately fit into one of these distinctive taxonomic groups, other accessions displaying intermediated or mixed features are difficult to classify. Cantalupensis and inodorus are the botanical groups of greatest commercial interest. Both include different cultivar-types that are highly popular in different parts of the world.Traditionally,  agrestis. One of g melon),7. More g melon) split thtibish has been included in the subspecies agrestis) and detecting a higher diversity among the agrestis types. In general, higher genetic diversity is reported in Africa and India than in the extremes of the distribution of melon (Mediterranean area and eastern Asia), which is consistent with the higher variation being maintained close to the center of domestication. The variability found in some groups of the subspecies agrestis (mostly conomon and momordica) has been used as a source of disease resistance for cantalupensis and inodorus cultivars and is also an underexploited reservoir of genetic variability for improving fruit quality in melon cultivars[Different marker systems have been used to assess the genetic diversity in melon by studying the genetic relationships among the different botanical groups  . Most oultivars.To date, the genetic basis of this diversity and the consequences of selection on genetic variation in the different wild and cultivated groups have not yet been studied on a genome-wide basis. The genomic abundance and amenability to cost-effective high throughput genotyping make single-nucleotide polymorphisms, SNPs, the most-used markers for genome-wide surveys of genetic diversity. Large SNPs collections have been identified in humans, several animals, and various model plants-19.The availability of SNPs collections for melon has increased in the past few years with the sequences produced by several national and international projects using the Sanger technology-22. SeveTM enables a reduction of the sequencing error rate. This reduction translates into more accurate polymorphism discovery[Second-generation sequencing (SGS) platforms, such as 454 GS FLX (Roche Applied Science), Solexa (Illumina Inc), and SOLiD (Life Technologies Inc), offer higher sequencing throughputs at greatly reduced costs. SGS platforms (mostly 454 and Solexa) are being used to resequence a number of genotypes in different crops , and are successfully generating vast amounts of SNPs. SGS is often combined with approaches to reduce genome complexity ,25. SGS iscovery.http://melogene.net).Blanca et al. used SGSinodorus ‘Piel de Sapo’ and the cantalupensis “Charentais”, as well as the exotic conomon variety, which is mostly used for breeding. These markers are turning out to be extremely useful in the genetic diversity assays and breeding programs that use these varieties. This collection has been already used to construct a high-density genetic map employed to anchor and orient scaffolds in the melon whole genome sequence[In the study by Blanca et al., the 454sequence. HoweverTo obtain a comprehensive overview of the sequence variation of melon genes, we have used SOLiD to resequence the transcriptome of 67 genotypes, grouped into 8 pools that represent all the botanical groups of the species. The completion of a draft of the genome sequence of melon gives usagrestis and exotic acidulus is analyzed here for the first time. Within the subsp. melo, we extended the study to better represent the variability of the cantalupensis group, the Spanish inodorus landraces, the Piel de Sapo commercial breeding lines, and also included the variability of melons from Eastern Europe and Western Asia that have not been represented in previous studies. Also, the intermediate group of flexuosus, dudaim and momordica, reservoir of resistance and quality genes for improving cultivated melons, has been analyzed. With this deep resequencing we captured a high number of SNVs between groups and detected some group-specific common variants. This new resource provides a unique opportunity to explore the genetic variation of melon and to identify sequence variants associated with phenotypes of interest.The diversity in African and Asian wild We used a core collection of 212 melon accessions, including wild relatives, feral types, landraces, breeding lines and commercial cultivars from 54 countries (representing the putative origin areas and diversity centers of the species). This collection was established on the framework of a previous project (MELRIP (2007–2010): ERA-PG project (GEN2006-27773-C2-2-E)), selfed, genotyped with AFLP markers and extensively phenotyped for plant and fruit traits at the COMAV. Fifty tinodorus lines, Piel de Sapo and Amarillo types, and one set of cantalupensis lines) were provided by Semillas Fitó  and included in the analysis. A total of 67 genotypes were resequenced. Some of these accessions have been used extensively as parental lines in breeding programs. The name, origin, and some phenotypic traits of the resequenced accessions are presented in TableAdditionally, 15 breeding lines belonging to 3 melon commercial market classes   and agrestis (momordica), but are often considered intermediate between the two subspecies based on molecular studies[We prepared 8 pooled RNA samples. Three pools represented the variability of the subsp. is Table: the firin India; the thiof China. The conng assay, and incng assay,31. The  studies,11,32. Tmelo , as well as other less know types representing the variability of the Spanish melon landraces; the most important inodorus market class, Piel de Sapo, was resequenced in a separate group, which included the cultivar T111 and 5 additional breeding lines provided by Semillas Fitó. The cultivar T111 was included in the previous massive sequencing assay, and is the parental of the genetic map of melon[The remaining four pools represented the variability of the cultivated types of subsp. lo Table: the fifis group; the sixof melon.TM mRNA Purification Kit . Quantification and quality analysis was performed by agarose electrophoresis and by using Spectrophotometer NanoDrop ND-1000 v 3.5.Total RNA was isolated from leaf tissue using the Trizol method in the 67 selected genotypes and stored at −80°C until library construction. Equivalent amounts of RNA from each genotype were combined into eight pools. mRNA was purified from the total RNA using the illustraTM PCR cDNA Synthesis Kit (Clontech). cDNA PCR products were purified using the RocheÂ´s High Pure PCR Cleanup MicroKit and a subsequent precipitation with sodium acetate. Another quantification step using electrophoresis and spectophotometry was also carried out. A normalization step was carried out with the TRIMMER cDNA normalization Kit (Evrogen) in order to prevent over-representation of the most common transcripts. cDNA was amplified with the Advantage 2 PCR Kit (Clontech) in order to obtain the required quantity. The performance of the normalization step was checked by quantitative PCR with FastStart Universal SYBR Green Master (ROX) (Roche). Samples to be sequenced were lyophilized after purification and precipitation. Approximately 10 μg of double-stranded cDNA from each of the eight normalized cDNA pools were used for sequencing on a SOLiD v4 following standard procedures.Double-stranded cDNA was then synthesized from the RNA pools with the SMART The Applied Biosystems SOLiD™ System uses the sequence-by-ligation technique to generate several gigabases of short sequence reads in a single run. Error rates are higher in comparison to those of Sanger sequencing reads, but the sequence-by-ligation technique takes advantage of a two-base encoding scheme to help identify these errors. Templated beads were prepared from each of the eight transcriptome libraries according to the manufacturer's instructions using the ePCR kit v.2 and the Bead Enrichment Kit from Applied Biosystems  for SOLiD3. Workflow Analysis was done after the first round of template bead preparation for each library according to the manufacturer's instructions using the Workflow Analysis kit from Applied Biosystems  to check library quality and the amount of templated beads generated per ePCR. An additional Workflow Analysis was done after it was estimated that a sufficient number of templated beads has been produced. Templated beads were deposited on slides according to the manufacturerÂ´s instructions using the Bead Deposition kit from Applied Biosystems . A 1/8 sequencing run was performed for each pooled transcriptome library (Sistemas Genomicos S.L).conomon variety Songwhan Charmi.Raw reads generated with SOLiD were processed using the ngs_backbone pipeline,34 with http://melonge.net build with the reads described in Blanca et al.[In order to make the best use of the short sequence reads for SNVs (Single variants: short INDELs and SNPs) discovery, processed SOLiD reads were aligned to this available melon genome assembly (v3.5). Alternaa et al.. The metReads were mapped using BWA run withmelos (pool 6) and African agrestis (pool 1) groups retaining the lowest and the highest numbers of useful sequences, respectively. Pool 6 was the one with the lowest sequencing quality. Changes in read number and average quality after read cleaning are detailed in Additional fileThe 8 pooled libraries were sequenced separately in one SOLiD run, generating a total of 260 million (M) reads of 49-bp (12.737 Gb of sequence). These reads were deposited in the NCBI Sequence Read Archive (SRA) with code SRA050003.2. An average of 32 M reads was generated per library. After cleaning with ngs_backbone, a total of 150 M reads were obtained with an average length of 44 bp, comprising 6.654 Gb. The total yield of sequences per pool was variable, ranging from 8.4 to 30.6 M, with the http://melogene.net.The cleaned reads were mapped by BWA. About 5We identified a large number of genetic variants across the transcriptomes. A total of 303,883 SNVs, including SNPs and INDELs, were detected. Information about this SNVs collection is included in Additional filehttp://melogene.net.Information about the 239,521 SNVs identified by mapping SOLiD reads against the reference transcriptome instead of the genome is also included in Additional fileSNVs were distributed in 245 different scaffolds and contigs of the reference genome. Most 283,206, 93%) were located in annotated genes. The list of SNVs located in annotated genes is included in Additional file, 93% werThe annotation of the newly assembled genome predicted 27,427 protein-coding genes, 15,064 of which contained variants, with an average of 18.8 SNVs per gene. 65.7% of the detected variants in genes were in CDS and the remainder in UTRs, with the UTRs displaying a higher SNV density, 14.9 SNVs/Kb, than in the ORF, 9.5/Kb.The errors that occur in SNVs discovery when using massive sequencing technologies have several major causes: (1) PCR artifacts, (2) sequencing errors, and (3) errors in the mapping of short reads to the reference sequence. You et al., after cversus 9.3 SNPs/Kb, respectively. Due to the mapping procedure used, we did not identify any SNPs in intron-exon junctions. Further analysis of these regions would increase the total number of SNPs in the collection.A 93%  of the SNVs detected by mapping SOLiD reads against the melon genome were SNPs. 94%  were located in annotated genes of the melon genome, distributed in UTRs (28.4%) and ORFs (67.6%), with an average density of 13.3 SNPs/Kb For each SNP, the major allele frequency (MAF) was estimated from the available sequences. The proportion of SNPs with MAF <0.9 was 25.94%. FigureC. melo to date. A collection of several thousand SNPs  was reported, generated from a much more limited collection of sequences obtained using traditional Sanger methods[melo, var. inodorus (including the Piel de Sapo market class) and var. cantalupensis, and the conomon variety of the subspecies agrestis. Blanca et al.[This is the largest SNP collection available in  methods,21. Mass methods. This pra et al. reporteda et al.,38,39, ba et al.,40,41. Hmelo) to 15.7 Mb . The number, density and variability of in silico-detected SNPs, varied among groups.Tableagrestis were similar to those of the subspecies melo (ranging from 4.9 to 9.2 SNPs/Kb). However, the percentage of highly variable SNPs (with MAF under 0.7) was higher in agrestis pools including wild and exotic accessions from Africa and Southern Asia (pools 1 and 2)  even when the included accessions were quite phenotipically variable  , but also for promoting their own conservation and for starting commercial breeding activities for these exotic crops. In this sense, Fergany et al.[acidulus and conomon melons, which are in high demand in India and China.The previously described pools, 1 to 4, mostly include non-sweet melons found growing wild or locally cultivated as exotic vegetables in different parts of the world. We present here for the first time a deep understanding of their genetic variation. This knowledge can be used to provide the basis not only for breeding commercial sweet melons   was the most variable, with MAF values similar to those of the agrestis group , was less variable, as expected. Despite this low variability, 3.2%  of the 43,363 SNPs detected in this group were highly informative with MAF < 0.7, and represent the largest set of SNPs detected for this group to date.Unlike other crops for which a extremely narrow genetic basis is reported in cultivated material after resequencing, such as cereals, or tomap Figure. All thecantalupensis and inodorus groups are thought to have originated from genotypes distributed in Eastern Europe and Western Asia. The current variability of landraces and local cultivars in this area, including Turkey, Iran, Iraq, Russia, Ukraine and surrounding countries has only started to be analyzed[inodorus and cantalupensis groups in Turkey due to the traditional farming practices employed by some local small-scale melon producers. Kohpayegani and Behbahani[ameri, adana and chandalack from Ukraine, considered to be the ancestors of the cantalupensis group. Most of these groups of cultivars are represented in pool 6. Even though this highly heterogeneous group had the lowest percentage of mapped reads  might prove useful for breeding commercial melons.momordica group was the group with the highest percentage of SNPs in common with other libraries. Between 16 and 40% of the SNPs found in this group of exotic accessions were also variable in the commercial melons and landraces  were shared among all libraries, with only 6 with MAF <0.7, which suggests the existence of differential variation in the different groups. Table% were shbsp. melo suggestebsp. melo. Momordimomordica group were not detected in Piel de Sapo and the cantalupensis commercial cultivarsrespectively. Tablemomordica group has thousands of fixed positions with different alleles in groups of subsp. melo , but this number is much higher in wild African  and even in Far Eastern conomon . These two groups were the most divergent from the subspecies melo. The largest differences were detected between inodorus and Piel de Sapo and the wild African agrestis group . This suggests that a large portion of the genetic variability found within this melon collection has not yet been used for the development of new cultivars. Both, the African agrestis and conomon groups appear to represent essential reservoirs of underexploited variation. The large number of variants in which the two groups differ  suggests that they are rich complementary sources of genetic diversity for cultivated melons. The number of SNPs still present in the cultigens and landraces pools (6 and 7) that are absent from commercial cultivars (pools 5 and 8) are worthy of note as they may be useful for breeding melons using these sources that share similar genetic backgrounds.Despite the high level of shared variation, all the groups retained a number of exclusive SNPs. For example, 111,226 and 80,278 SNPs that were variable within the in silico SNP mining, we compared our results to those previously obtained using EcoTILLING in the same germplasm collection[Cm-ACO1 (1-aminocyclopropane-1-carboxylate oxidase 1) which is involved in melon ripening through the alteration of ethylene synthesis[Cm-eIF(iso)4E  which has been suggested to be involved in recessive resistance to viruses[In order to validate the efficiency of this llection. EcoTILLynthesis, and Cm- viruses,62. In t viruses all muta viruses,64 whichCm-ACO-1  (CM3.5_scaffold00022: 3015744 and 3016016), one was exclusive to the conomon pool (3) (CM3.5_scaffold00022: 3016091), and one was fixed in agrestis and appeared with a low frequency in the momordica and melo pools  (CM3.5_scaffold00022: 3015944). According to EcoTILLING, the mutation CM3.5_scaffold00022: 3016304, the only predicted not to be tolerated by SIFT, was present in only one genotype, the snake melon from Arabia  is loca14437 at. The allCm-ACO-1 occurs in exon 1, 2 and 3[momordica and the two groups with commercial varieties, cantalupensis and Piel de Sapo. It has been demonstrated that two artificially induced missense mutations found in exon 3  [EcoTILLING studies show that most natural variation in  2 and 3. The onl 2 and 3. SOLiD ry194Asp) delay thCm-eiF(iso)4E . Exons 1, 2, and 3 of Cm-eIF(iso)4E were also tilled in the Piel de Sapo and Charentais TILLING populations described above[23037 at) is locaed above. Only onagrestis, momordica and commercial cultivars respectively, the last of which was predicted to alter protein function according to SIFT .In the re-sequencing assay we also analyzed exons 4 and 5, which have not been analyzed by EcoTILLING. We found 3 rare mutations in in silico-detected SNPs, our results confirm that resequencing strategy provides a large catalog of alleles in genes of interest, some of which may potentially alter gene function.Although it is necessary to validate by sequencing or genotyping these Cm-ACO-1 gene, mutation C/T in nucleotide 747 from the ATG, and the second in Cm-eIF(iso)4E, mutation G/A in nucleotide 26 from the ATG, both detected in the Wild chibbar accession of pool 2. Problems with the sequencing of the cDNA of this accession may explain these results.Only two of the mutations detected by EcoTILLING in the accessions used for resequencing were missed by SOLiD: one in the To validate some of the putative SNPs found by resequencing we designed a Sequenom genotyping array with 143Using the subset of SNPs with no IS60 and no CS60_MAF, we randomly selected several sets of SNPs that met different within- and between-group variation criteria for validation. The number of SNPs selected from each group and the validation percentage is included in Tableagrestis or conomon, and variable with conomon or African agrestis, respectively, were successfully validated , and the lower percentage of validation was found in the group variable within Piel de Sapo. However, the lower ratio of validation found in the latter group can be due to the fact that only 2 genotypes of this market class were included in the genotyping array due to technical problems.The ratio of validation varied among SNPs groups. Nearly 100% of the SNPs selected for being common between Piel de sapo and African ed Table. Nearly in silico selected markers will be useful for different melon breeding objectives.Polymorphism Information Content (PIC) for every SNP validated was calculated by using Power Marker v. software Additio. In geneagrestis and conomon, which is absent in the subspecies melo, may prove useful in breeding commercial types. The variation detected in landraces shows that these are also reservoirs of polymorphism for breeding melons with similar genetic backgrounds. The high percentage of validation confirms the utility of the SNP-mining process and the stringent quality criteria for distinguishing sequence variations from sequencing errors and mutations introduced during the cDNA synthesis step. The availability of this information will aid in carrying out future studies of population genetics, marker-assisted breeding, and QTL dissection. Some of the resequenced genotypes are donors of agronomic traits, with available mapping population’s with will enable the rapid application of the discovered SNPs in mapping experiments.This study provides the first comprehensive resequencing data of wild, exotic, and cultivated melons. It demonstrates that pooling RNA samples from several genotypes combined with high-throughput transcriptome sequencing is an efficient and effective way to identify large numbers of SNPs. This collection of variants dramatically improves the previously available SNP collection by increasing the total number of useful SNPs and by identifying new ones in groups of melons from the area of origin and diversification analyzed here for the first time. Our results show the divergence between wild and cultivated melons. The huge amount of variation present in wild African The authors declare that they have no competing interests.BP, JB and JC were involved in the conception and design of the study. BP provided the melon core collection and selected the genotypes for sequencing. CE, CR and JC prepared the normalized cDNA libraries for sequencing. VF-P, CC and RR were involved in the sequencing of normalized cDNA libraries in SOLiD platform: construction of SOLiD barcoded libraries from cDNA, pooling of the libraries, emulsion PCR and sequencing in SOLiD 4.0., and AB was involved in coordination activities related with sequencing throughout the project. JB, JC, PZ and DP conducted the bioinformatic analysis, reads processing, SNP mining and mapping to the melon genome and trsnacriptome. BP selected the SNPs and genotypes for validation. CE, CR and BP validated the SNPs. CE and BP performed EcoTILLING and analyzed mutations. BP was primarily responsible for drafting and revising the manuscript with contributions from co-authors. All authors read and approved the final manuscript.Resequenced melon genotypes. Photographs of the fruits of the genotypes resequenced, in eight pools, using SOLiD are included. A. Pools 1–4. B. Pools 5–8.Click here for fileThe configuration of the ngs_backbone pipeline used for processing raw reads generated with SOLiD, for mapping, SNV calling and filtering is included.Click here for fileChanges in number and quality of reads after processing with ngs_backbone.Click here for fileSNVs detected by mapping SOLiD sequences against melon genome. All SNVs detected in all eight resequenced pools are included, their position in the reference genome (scaffold or contig), referred to the whole genome draft version 3.5 available in MELONOMICS[LONOMICS, their MClick here for fileSNVs detected by mapping SOLiD sequences against melon transcriptome. All SNVs detected in all eight resequenced pools are included, their position in the reference transcriptome available inhttp://melogene.net, their allelic frequency in each group are detailed. Alleles in reads from genotypes previously sequenced with Sanger and 454 are also indicated.Click here for fileLocation of SNVs in melon genes. Correspondence of the SNVs located in melon genes annotated in the melon genome version 3.5 available in MELONOMICS[LONOMICS is listeClick here for fileValidation of SNPs. Information about the SNPs selected for validation is included along with genotyping results obtained with Sequenom with 78 varieties. PIC for each SNP along with the MAF estimated by SOLiD and by genotyping is indicated.Click here for file


A typical bacterial pathogen genome mapping project can identify thousands of single nucleotide polymorphisms (SNP). Interpreting SNP data is complex and it is difficult to conceptualise the data contained within the large flat files that are the typical output from most SNP calling algorithms. One solution to this problem is to construct a database that can be queried using simple commands so that SNP interrogation and output is both easy and comprehensible.Here we present snp-search, a tool that manages SNP data and allows for manipulation and searching of SNP data. After creation of a SNP database from a VCF file, snp-search can be used to convert the selected SNP data into FASTA sequences, construct phylogenies, look for unique SNPs, and output contextual information about each SNP. The FASTA output from snp-search is particularly useful for the generation of robust phylogenetic trees that are based on SNP differences across the conserved positions in whole genomes. Queries can be designed to answer critical genomic questions such as the association of SNPs with particular phenotypes.snp-search is a tool that manages SNP data and outputs useful information which can be used to test important biological hypotheses. Next Generation Sequencing (NGS) technologies have facilitated large scale resequencing of genomes for the purpose of Single Nucleotide polymorphism (SNP) discovery. The thousands of discovered SNPs during such projects can cause some difficulty for biological interpretation and computational analyses.To address this challenge, various databases and tools have been created that annotate SNPs with information derived from a reference genome and store that information within a database -7.et al for SNP management includes limited search functions and does not allow for advanced user database querying. Some databases like that described by Dereeper et al have implemented a web tool for SNP discovery and analysis. However, it does not accept Variant Call Format (VCF) files as input and is only applicable to diploid species. Another tool is SNPpy by Mitha et al which provides a database management system that allows for SQL querying and data manipulation. The SNPpy system is dependent on a relational database (PostgreSQL) that requires considerable expertise to administer and does not accept VCF. VCF files were an initiative from the 1000 Genomes Project [While these tools have excelled at the management of the SNP data, less attention has been directed towards simple processing and searching of the stored data. For example, the tool developed by Wang  Project  and are  Project , Samtool Project .There is a need therefore for a tool that extracts SNPs from VCF files, stores them into a simple database and provides multiple output options for analysis. To meet such a need, we have developed snp-search, an easy to use tool for management of SNPs generated from haploid next generation sequencing data. Given a VCF file, snp-search stores the SNPs generated by the variant calling algorithm into a sqlite database. Sqlite is a relational database contained in a single file and does not require complex management software. snp-search can then be used to extract useful information from the database. For example, by running snp-search using the command line syntax, the user can extract unique SNPs for a specified set of strains; generate a SNP phylogeny and provide detailed information about each individual SNP such as if it is a synonymous or non-synonymous SNP, gene information, etc  and a VCF file. snp-search requires the reference genome used in the mapping process to import the features  and annotations into the database and requires the VCF file to populate the database tables with the SNP information.annotations table which is related to the features table which contains information on the type of the feature and its nucleotide sequence. SNP positions from the VCF file are imported to the snps table which is related to the features table by the features_snps table. A SNP that has occurred in each resequenced genome is recorded as an allele in the alleles table. The strain name and description (strains table) is related to alleles via the genotype table which contains the allele_id and strain_id. Thus queries that relate strains to snps or annotations may be easily composed. These relationships are schematised in Figure The database schema created by snp-search is designed to facilitate construction of simple queries that will address complex biological questions. Annotations from the genbank file are populated in the 2. Output requested objects from databaseOnce the database has been populated, snp-search provides several filtering and output options Figure .SNPs for each sample are collected from the database and concatenated and converted to FASTA format.Information for individual SNPs are provided in tab-delimited tabular format.snp-search uses FastTree 2  to generA Phred-scaled score that the SNP exists at the given site (default 90).A Phred-scaled score that the genotype is true .The ratio of the unfiltered count of all reads that carried that specific allele compared to other REF and ALT alleles in that site (default 0.9).Ignoring particular SNP calls in features, such as phages, transposases, and insertion sequences to remove horizontally transferred DNA as a result of a recombination event.SNPs in a defined specified range will be ignored.Certain samples can be excluded from the SNP output.A SNP that is found in all samples is not included in the output.Given a set of defined strains as input, snp-search queries the database for unique SNPs shared by the defined strains and outputs the results in concatenated FASTA or tabular format.Concatenated FASTA files are used to generate robust phylogenetic trees that are based on SNP differences across whole core genomes.snp-search processes the data in the database and outputs information on each SNP. The following are the description of this output:Synonymous or non-synonymous SNP. This is calculated by translating the coding sequence and report if the SNP has whether caused an amino acid change or not.– Function of coding region at the SNP position. This is extracted from the annotated genbank file.– Possible pseudogene. If a stop codon is detected in the coding region (other than the end of the sequence).– Original amino acid. Provide the amino acid for the SNP region in the non-mutated sequence.– Has amino acid has changed due to the mutation? An answer of 'yes’ will be given if there are at least one amino acid changes between the original non-mutated sequence and the mutated sequence.– Is there a change in hydrophobicity of the amino acid? The answer to this question is based on the pre-defined set of hydrophobic and non-hydrophobic amino acids taken from Livingstone et al.,[– e et al.,: hydrophIs there a change in the polarity of the amino acid? The answer to this question is based on the pre-defined set of polarised and non-polarised amino acids taken from Hausman et al., [–  et al., : polar aIs there a change in size of the amino acid? The answer to this question is based on the pre-defined set of small or non-small set of amino acids taken from Livingstone et al.,[– e et al.,: small aSQLite database can be interrogated by user defined queries (requires some knowledge of SQL). To view, administer and query the database, one may download a SQL GUI tool or simply use the Unix version of sqlite3.Streptococcus pyogenes strains (GAS) sequenced using 50 bp reads on the Illumina HiSeq platform. The reads were mapped onto the MGAS315 core genome using the bowtie tool. Samtools mpileup [-output -all_or_filtered_snps was used to generate an 'alignment’ of the concatenated SNPs from each strain in FASTA format. These SNPs represent positions found in positions conserved in all the genomes, sometimes described as the core genome.The snp-search tool was used to generate a SNP database of 200 Group A  mpileup  was used-tree option was used that runs the phylogenetic tree algorithm FastTree 2 to generate a maximum-likelihood (ML) phylogenetic tree of the 200 genomes based on the SNP alignment from the core genome : ST15, ST315 and ST406  to test snp-search functionalities. Two files are provided in the example test set directory: plasmid_test_set.vcf and Reference_file_plasmid_test_set.gbk. The following are some commands that can be used when using snp-search (the required fields are in bold):Snp-search is implemented as an easy-to-use command-line tool. The user does not need experience in Unix, a programming language, or SQL . The user can use the example test sets of plasmid samples  provided in the github repository. • Only select SNPs with a GQ quality cuttoff of greater than 25• Only select SNPs with a SNP QUAL cuttoff of greater than 90 snp-search -output -all_or_filtered_snps -name_of_database plasmid_test_set.sqlite3 -fasta -cuttoff_genotype 25 -cuttoff_snp_qual 90 -remove_non_informative_snps -ignore_snps_in_range 2000..2500 -ignore_strains plasmid1,plasmid4,plasmid9 -ignore_snps_on_annotation phage,transposase,transposon,insertion -out plasmid_SNPs_concatenated_file.fasta -tree -fasttree_path /usr/local/bin/fastTree• • You may then use any phylogenetic tree viewing software to view your tree.• Further note: In addition to the concatenated FASTA file, SNP positions are provided in two formats: a simple text file and a tsv file.3 To select SNPs that are shared only between a set of samples, example samples plasmid2, plasmid7, plasmid10, and GQ quality cuttoff of greater than 25 and SNP QUAL cuttoff of greater than 90 you may run the following command :snp-search -output -name_of_database plasmid_test_set.sqlite3 -unique_snps -strain samples.txt -cuttoff_genotype 25 -cuttoff_snp_qual 90-out unique_snps. tx.t4 If you like to output all SNPs, with information about each SNP (e.g. if it is a synonymous or non-synonymous SNP) and with the quality cuttoff values used in the previous commands, you may run the following command:snp-search -output -name_of_database plasmid_test_set.sqlite3 -info-cuttoff_genotype 25 -cuttoff_snp_qual 90 -out plasmid_info_for_my_snps. txt.5 Furthermore, if you like to query the database, you may do so by opening the SQLite3 database generated by snp-search with sqlite3 in your Unix environment. The following is an example of a SQL query that can be performed on the snp-search database to select all snps that are associated with features annotated with a term containing the phrase 'antibiotic’:snps INNER JOIN features_snps ON snps.id = features_snps.snp_id INNER JOIN features ON features.id = features_snps.feature_id WHERE features.id IN );SELECT * FROM SNP data is now commonly generated, at least for smaller genomes, by mapping whole genome sequencing reads to a reference where both known and novel SNPs can be found. To facilitate and understand these findings, we have developed snp-search, a tool that manages SNP data from high throughput haploid sequence datasets and outputs useful information which in turn can be used to test important biological hypotheses. The main advantages of snp-search are:Parses VCF file and stores SNPs in a databaseDesigned simple database schema but powerful in answering complex biological queriesProvides detailed information about each SNPMultiple SNPs filtering steps provided including filtering on the function of genes within which the SNPs are found.Has no size limitOnly dependent on sqlite3 to build the database.Sub-populations of SNPs can be extracted from the database to address specific biological questions, such as whether variation in specific genes is linked to phenotypic differences or does the exclusion of SNPs from horizontally transferred elements generate more reliable phylogenetic inferences.Project name: snp-searchProject home page:https://github.com/phe-bioinformatics/snp-search.Operating system(s): UnixProgramming language: RubyOther requirements: SQLite, Ruby 1.8.7 or greater and optionally FastTree 2 for phylogenetic tree constructionLicense: GNU General Public License (GPL)The authors declare that they have no competing interests.AA drafted the manuscript, AU supervised the project and edited the manuscript and AA and AU implemented the snp-search algorithm. All authors read and approved the final manuscript.


Genomic technologies, such as high-throughput genotyping based on SNP arrays, have great potential to decipher the genetic architecture of complex traits and provide background information concerning genome structure in domestic animals, including the extent of linkage disequilibrium (LD) and haplotype blocks. The objective of this study was to estimate LD, the population evolution (past effective population size) and the level of inbreeding in Spanish Churra sheep.r2 between all pairs of loci. For SNPs up to 10 kb apart, the average r2 was 0.329; for SNPs separated by 200–500 kb the average r2 was 0.061. When SNPs are separated by more than 50 Mbp, the average r2 is the same as between non-syntenic SNP pairs (0.003). The effective population size has decreased through time, faster from 1,000 to 100 years ago and slower since the selection scheme started (15–25 generations ago). In the last generation, four years ago, the effective population size was estimated to be 128 animals. Inbreeding coefficients, although differed depending on the estimation approaches, were generally low and showed the same trend, which indicates that since 2003, inbreeding has been slightly increasing in the studied resource population.A total of 43,784 SNPs distributed in the ovine autosomal genome was analyzed in 1,681 Churra ewes. LD was assessed by measuring The extent of LD in Churra sheep persists over much more limited distances than reported in dairy cattle and seems to be similar to other ovine populations. Churra sheep show a wide genetic base, with a long-term viable effective population size that has been slightly decreasing since selection scheme began in 1986. The genomic dataset analyzed provided useful information for identifying low-level inbreeding in the sample, whereas based on the parameters reported here, a higher marker density than that analyzed here will be needed to successfully conduct accurate mapping of genes underlying production traits and genomic selection prediction in this sheep breed. Although the Ovine Assembly development is still in a draft stage and future refinements will provide a more accurate physical map that will improve LD estimations, this work is a first step towards the understanding of the genetic architecture in sheep. The application of recently developed genomic technology, such as genome-wide SNP genotyping has great potential to increase our understanding of the genetic architecture of complex traits and to improve selection efficiency in domestic animals through genomic selection. However, the success of these approaches depends on the extent of the linkage disequilibrium (LD) across the genome, which may vary between populations. As an example, the extent of linkage disequilibrium serves to assess the number of markers required to associate genetic variation with economically important traits. A population with extensive LD will require a lower marker density; in contrast, if LD persists over short distances many more markers will be required to obtain the same power to detect association ; the samNe) [The linkage disequilibrium pattern can also provide insight into the evolutionary history of a population. The extension of LD in the genome could be used to infer ancestral effective population size (Ne) -7. This Ne) .Another aspect of interest while studying a commercial population under selection pressure is to study the level of inbreeding. Traditional estimation of the inbreeding coefficient based on pedigree data  is depenAn increasing number of studies have analyzed LD features in livestock species, especially in cattle ,14,15 buThe objective of this study was to characterize LD in a Spanish Churra sheep commercial population using data generated with the Illumina Ovine SNP50 BeadChip. This genomic tool is currently being used in this dairy sheep breed to fine-map previously reported QTLs  and to oA commercial population of Spanish Churra sheep was analyzed in this research. Blood samples were collected from 1,710 Spanish Churra ewes belonging to 16 half-sib families and distributed across 20 different flocks. Semen straws were obtained for the 16 sires. The use of animals was performed in compliance with the guidelines approved by the University of Leon ethical commission.260/280 ratio above 1.8 were used for Illumina Ovine SNP50 BeadChip genotyping. Genotyping was performed commercially at AROS Applied Biotechnology AS  and LABOGENA . Quality control (QC) of the raw genotypes consisted of checking the GenCall Score (GCscore) using the GenomeStudio software (Illumina Inc. San DiegoCA). Genotypes with a GCscore < 0.15 were set as missing genotypes.DNA was extracted from blood and semen samples using standard protocols, as described in . A contrThe SNPs included in the Illumina Ovine SNP50 BeadChip were mapped using the Ovine Genome Assembly v2.0 . The marWe performed QC of the genotypes in two stages, first implementing the control on a ‘per-individual’ basis prior to conducting QC on a ‘per-marker’ basis to maximize the number of markers that remained in the study ,28. Firsfixed option of a modified version of CRI-MAP [The initial locus order between adjacent markers, which was based on the Sheep Genome Assembly v2.0, was assessed using the  CRI-MAP , v2.503 The ideal scenario to measure the extent of LD within a population is to analyze “non-related” individuals. Our resource population of half-sib families had initially been selected to perform linkage-based QTL mapping studies using a daughter design , and theD’[r2[D’ and r2) were estimated for each non-syntenic pair. To assess how far LD extends, we average r2 based on the SNP distance in 1-Mb intervals and calculated the half-length of r2[Reconstructed haplotypes were selected to not have an overrepresentation of the sires’ haplotypes . Sire haD’ and r2[3D’[r2, were cogth of r2. This hagth of r2.D’ is higher than 0.98 and if the lower bound is above 0.7. In contrast, ‘strong evidence for historical recombination’ is defined if the upper confidence bound on D’ is less than 0.9 [HAPLOVIEW v4.1 was also used to define the haplotype blocks present in the genome. The method followed for block definition was previously described by Gabriel et al. . A pair than 0.9 .Ne) was calculated for 11 time points. Based on the physical map used for the LD analysis, genetic distances between adjacent markers were calculated using three conversion rates: (i) considering a 1 cM ~ 1 Mb conversion rate for all the chromosomes, (ii) considering the specific cM/Mb ratio calculated for each chromosome by comparing the genetic and physical map in the CRI-MAP analysis and (iii) considering the average conversion rate estimated across the genome for all the chromosomes. Each genetic distance  corresponds to a value of T generations in the past. This value was calculated as T = 1/(2c). The following formula was used to estimate Ne[Past effective population size  used in the analysis. According to the genetic distances between markers, SNP pairs were stacked into bins of 1,000 pairs, and the average distance and r2 estimated for each bin were used to calculate the Ne.where F) within the Spanish Churra population. The pedigree-based F (FPED) was estimated using the Relax2 software [F values, we used the population under study as the base population. Allele frequencies were estimated across the 1,681 animals, and the GCTA software was used to obtain F values. Three different metrics were obtained using the --ibc option of the program: a) based on the variance of the additive genotype (F1), b) based on the excess of homozygosity (F2) and c) based upon the correlation between uniting gametes (F3) [Two different approaches were used to estimate the coefficient of inbreeding  .Out of a total of 54,241 SNPs genotyped in this study, 1,516 SNPs were unmapped, and 215 were located on sex chromosomes as per Ovine Genome Assembly v2.0. Thus, 52,510 SNPs mapped onto the 26 sheep autosomes were used in the described analyses.Of the 1,696 genotyped animals, 15 individuals did not pass the QC. Thus, a total of 1,681 animals were used in the analyses. The number of markers removed during QC was 8,726 SNPs: 4,140 SNPs were deleted due to low call rate (< 0.95); 3,044 SNPs did not reach minimum MAF (< 0.05); and 1,542 markers were not in HWE (P ≤ 0.00001). The total number of markers used in the analyses was 43,784 SNPs. The distribution of these SNPs per chromosome is described in Table D’ and r2 values, pooled over autosomes in different categories of map distances, are presented in Table D’ and r2 with respect to the physical distance separating loci is presented in Figure r2 with increasing physical distance between SNPs: for SNPs up to 10 kb apart, the average r2 is 0.329; for SNPs separated by 200–500 kb, the average r2 is 0.061. When SNPs are separated by more than 50 Mbp, the average r2 (0.003) is the same as that found between non-syntenic SNP pairs (0.003). The distribution of D' is similar to that observed for r2. The half-length of r2 was 0.033, which corresponds to a distance between SNPs of 2.5 Mbp  was 1,692. A total of 42,381,374 syntenic pairs of SNPs was analyzed for all the autosomes. Average A summary of the distribution, size, number and SNPs involved in the haploblocks per chromosome are presented in Table Ne values at each time point, from 250 to 1 generation ago, and for each of the three different cM/Mb conversion rates used to calculate genetic distance between markers is given in Figure Ne has decreased through time, faster at 1,000 to 100 years ago and slower since selection scheme started (15–25 generations ago)  Figure . The effFPED, was estimated to be null (no inbreeding) for 5,856 out of the 5,956 animals, with an average of 0.001. For the remaining 100 ‘inbred animals’, the mean value was 0.064. To compare these results with marker-based inbreeding values, we extracted pedigree-based inbreeding coefficients for the 1,681 genotyped animals. For these animals, a mean value of 0.003 was obtained. Using molecular information, we did not obtain any ‘non-inbred’ animal (F = 0), but some results were negative. With the aim of comparing results between the marker-based and the pedigree-based methods, we transformed negative values to 0 and studied the differences in values across time. Average values for the positive estimates were 0.015 (F1), 0.009 (F2) and 0.005 (F3). Figure F1 method was found for year 2002. A more detailed study of the animals born that year showed more than half of these animals had an inbreeding coefficient higher than 0.0625, which is very high according to the results obtained across the population. Although the estimates from various approaches were different, they all show the same trend and indicate that in the studied resource population inbreeding has increased slightly since 2003.Available pedigree used in this analysis included 5,956 animals, which represents a pedigree depth of 6 generations. From the 11,912 expected parents, 41% were missing data. The inbreeding coefficient calculated using this pedigree information, D’ and r2. D’ values were higher than those estimated for r2. This might be because rare alleles and unobserved haplotypes tend to inflate D’ but not r2[This study presents an analysis of the extent of LD in Spanish Churra sheep using 43,784 SNPs distributed across the autosomal genome, although the draft stage of the version of the Ovine Assembly it is based on should be taken into account. Future refinements and updates in the physical maps can lead to changes in the estimations reported here. To enable comparison with previous studies in sheep and other domestic species, we estimated two pairwise statistics: ut not r2.r2 of 4.6 Mb), which contrasts with the short extension of LD reported here for Churra sheep . A recent assessment of LD based on the analysis of 51,446 SNPs in a sample of Sarda rams showed a similar level of LD than in Churra sheep, with an average r2 value over 1,000 kb of 0.072 [r2 between nonsyntenic SNP pairs provides an idea of the LD that can be expected by chance. None of the nonsyntenic SNP pairs tested showed a ‘high’ LD value (r2 > 0.25).Comparing the level of LD obtained in different studies is difficult because of different sample sizes, LD measures, marker types, marker densities and recent and historical population demographics . Previouof 0.072 . Compareof 0.072 ,15,41, Lof 0.072  were in of 0.072 . This anof 0.072 . AverageDifferences in LD between chromosomes have already been reported in Holstein cattle ,15. Thespolled locus (RXFP2 gene), which is related to the presence of horns in sheep [HERC2 gene, which has already been related to pigmentation in cattle [Also within the framework of the SheepHapMap project genomic regions containing signals of selection have been identified across a wide range of sheep breeds . Higher in sheep . Howevern cattle .Ne) in the Churra sheep commercial population under study. First historical references from the existence of Spanish Churra sheep date from Middle Ages (thirteenth century) approximately 800 years ago [Ne value has been descending through time until the selection scheme began. From that point on, no major changes are found. Effective population size estimated 50 generations ago in Churra  is in agreement with the observations reported within the framework of the SheepHapMap project, where most of the sheep breeds displayed high Ne, and only two populations showed a narrow genetic base comparable to the current Ne of domestic cattle breeds (Ne < 150) [Ne estimations have been reported so far in sheep. High selection pressure and the use of artificial insemination are the main reasons for the low Ne values obtained in cattle [Ne = 100 has been given [Ne = 128) are above the threshold, but care should be taken on this regard to ensure that the effective population size is maintained.We also investigated the past effective population size (ears ago . Therefoe < 150) . No othen cattle . To ensuen given . Our resr2 was 0.15-0.2; considering a bovine genome length of 2.87 Gb, they concluded that 28,700 fully informative markers would be needed to saturate the cattle genome at an average resolution of 100 kb. Considering the lower value of LD estimates reported in this study, one can easily estimate that to obtain similar resolution in Churra sheep the marker density needs to be at least two times higher than the currently analyzed dataset. Hence, to implement genomic selection in this population with appropriate accuracy, a SNP array of higher density would be valuable. In this regard, following previous reported estimations [Ne of 128 animals and a genome of 30 Morgans, the SNP chip should include approximately 95,000 SNPs (assuming the same percentage of successful genotyping obtained in this study) to improve the accuracy of genetic breeding values estimation.The LD estimates reported in this work can serve to assess the utility of the Ovine SNP50 Beadchip to address fine-mapping studies in Churra sheep. In cattle, McKay et al.  showed timations  we can eimations , a populF1vs. F2) to 0.83 (F2vs. F3), with F1 as the most different. In general, values calculated using pedigree information were lower than those obtained through marker analysis. The latter could be inflated because we assumed a homogeneous population [F1) to 1.6% (F3), which is lower than that described for Finnsheep [F1), 50% of the animals had an F1 > 0.0625. Moreover, we were not able to compare between methods as most of the animals for F2 and F3 had negative values which might also explain the low correlation found between F1 and F2. In agreement with previous studies, the results presented here show that genomic data sets can provide useful information on a per sample basis in cases of complex genealogies or in the absence of genealogical data [Pedigree-based inbreeding calculations rely on the completeness and accuracy of the available pedigree. The results reported herein based on the available Churra pedigree showed 94% of the animals included in the analysis were ‘non-inbred’, although this is due to the lack of a deep pedigree. We obtained some negative values for inbreeding coefficients, which corresponded to animals with lower homozygosity than the average population. This could be because we estimated the allele frequencies from the currently genotyped population instead of the base population. Correlation between the different methods to estimate inbreeding ranged from 0.27 . This pinnsheep . This pecal data .In the studied Churra sheep population, LD decayed with increasing genomic distance and the analysis yielded similar values than previously reported in other sheep populations. An estimation of genetic distance from physical position showed few differences between chromosomes, which did not affect the past effective population size results. Effective population size seems to have been decreasing until the recent selection scheme in Spanish Churra began. Marker information aided the estimation of the level of inbreeding present in the sample, although more accurate results could be obtained if we had a base population to estimate allele frequencies. In conclusion, the results reported herein are a first step toward understanding the genomic architecture of a domestic sheep breed and can be used to access the feasibility of direct future selection based on genomic data. The level of LD estimated in Churra sheep indicates that the present Illumina Ovine SNP50 BeadChip is not an optimum and the future availability of a high-density SNP-array or the use of next-generation sequencing methods will improve the performance of QTL fine-mapping studies and genomic selection accuracy in this population.The authors declare that they have no competing interests.EG-G performed the quality control, analyses and drafted the manuscript. BG-G and GS participated in the design and coordination of the study and helped to draft the manuscript. JJA conceived of the study and participated in its design and coordination. All authors have read and approved the final manuscript.Physical and linkage map information used in the analysis. This file contains information about physical and genetic position of the markers included in this analysis. This table contains three columns: SNP identification, chromosome, physical position (in base pairs) and genetic position (in cM) obtained using the fixed option of a modified version of CRI-MAP v2.503 (kindly provided by JF Maddox).Click here for file


SNP (Single Nucleotide Polymorphism) markers are rapidly becoming the markers of choice for applications in breeding because of next generation sequencing technology developments. For SNP development by NGS technologies, correct assembly of the huge amounts of sequence data generated is essential. Little is known about assembler's performance, especially when dealing with highly heterogeneous species that show a high genome complexity and what the possible consequences are of differences in assemblies on SNP retrieval. This study tested two assemblers (CAP3 and CLC) on 454 data from four lily genotypes and compared results with respect to SNP retrieval.CAP3 assembly resulted in higher numbers of contigs, lower numbers of reads per contig, and shorter average read lengths compared to CLC. Blast comparisons showed that CAP3 contigs were highly redundant. Contrastingly, CLC in rare cases combined paralogs in one contig. Redundant and chimeric contigs may lead to erroneous SNPs. Filtering for redundancy can be done by blasting selected SNP markers to the contigs and discarding all the SNP markers that show more than one blast hit. Results on chimeric contigs showed that only four out of 2,421 SNP markers were selected from chimeric contigs.In practice, CLC performs better in assembling highly heterogeneous genome sequences compared to CAP3, and consequently SNP retrieval is more efficient. Additionally a simple flow scheme is suggested for SNP marker retrieval that can be valid for all non-model species. In the last few years, the development of next-generation sequencing technologies that have the capacity to generate millions of short reads in a single run, has led to a revolution in sequencing applications. The NGS technologies not only boosted re-sequencing and allele mining studies in model species, but are also very useful for the development of SNP markers in species with no or hardly any genetic resources.de novo assembly of a nematode [http://soap.genomics.org.cn/soapdenovo.html], and SOAovo.html]. SimilarLilium transcriptome was covered as could be estimated from the transcriptome size of the monocot model species rice, which has 41,000 genes with average gene length of 2,000 bp [Lilium transcriptome while the CLC contigs cover 38%, regardless of the singletons that could be added to the total coverage.A reasonable percentage of the 2,000 bp . AssuminNotable differences between the assembler's performance were recorded in this study. Similarly, differences in assembler's performance were also found in another study . The perIn order to choose the best assembler with respect to the identification of high quality reliable SNP markers for genetic mapping, we performed several tests to compare the performance of the assemblers.Redundancy is a main parameter in which the quality of assemblies can be evaluated. Redundancy occurs when different contigs are likely to originate from the same locus as defined by the degree of similarity . This isk-mers, 21 in our case) and combines all similar reads in one contig. This difference in algorithms may have made CLC more able to assemble reads with a high level of heterozygosity compared to CAP3 which showed to be highly discriminating. These differences between the performances of the two assemblers were not recorded in a previous study [Visual inspection of the blast hits showed that in some cases both assemblers have mapped the same reads and constructed identical contigs  into 381 UniGenes. These UniGenes were used to compare the performance of the two assemblers by aligning the contig consensus sequences of each assembler with the 381 UniGenes and analyzing the results. CAP3-contigs showed a total of 251 hits, 86% of them were redundant (more than one BLAST-hit) compared to CLC that showed 260 hits of which 64% showed redundancy  vs. the contigs from the assembly they originated, provided an additional criterion for SNP markers selection. Many species have undergone genome duplications during their evolution. Assuming paralogs are assembled in different contigs it is still possible that SNP markers selected from one of these contigs will also be present in a paralogous gene assembled in another contig. Thus, is vital to check that SNP markers only map back to the contig from which they were selected. This paralog detection is important in any study aiming to generate SNP markers for which other genetic resources are missing. Selected SNP marker sequences (101 bp) of each assembler were blasted against all contigs using a threshold of E-20.Lilium genome or due to poor assembly and thus cannot be used for mapping studies. However, around 83% of the SNP markers generated in a CAP3 assembly of 454 transcriptome pyrosequencing in the inbreeding species Solanum lycopersicum, in which the level of polymorphism is low, showed to be unique . From this, we can conclude that the performance of CAP3 is negatively correlated to the polymorphism level present in the genome studied. In the case of high heterozygosity as in the present study, CAP3 software might separate alleles of highly polymorphic loci into different contigs which means that these contigs are not unique and thus it is highly risky to use them to generate SNP markers. Redundant contigs can either be related to paralogs or they can be alleles of the same locus (among the four genotypes) that were split up into different contigs. In both cases the SNP markers should not be used for mapping purposes. In case of paralogs, SNP markers will cause problems in SNP detection. In the latter case, there is a chance that SNP markers will either give no call or will work poorly because the risk of secondary SNPs close to the SNP of interest is overlooked. So, the most trustworthy SNP markers will be the ones that were generated when all alleles of the same locus are grouped in one contig. CAP3 generated 5775 contigs (8%) that include sequences of the four cultivars, compared with 9234 contigs (17%) for CLC.The CAP3-SNP blasting showed that only 22% of the generated 'SNP markers' (defined here as the SNP and 50 bp flanking sequence on each side) uniquely mapped to the contigs from which they were derived, 78% of the SNP markers had more than one blast hit, and 198 SNP markers had more than 10 blast hits Figure . This inLilium. The percentage of paralogous genes differs between species. For example, in rice around 15 to 62% were expected to be duplicated genes [de novo sequence assembly. In contrast, CAP3 could not handle such high levels of heterogeneity [In CLC, 77% of SNP markers were unique. Only 13 SNP marker sequences had more than 5 blast hits Figure . The 22%ed genes . Using aed genes  where CLogeneity .This step was done to visualize on the individual contig level the influence of polymorphism: on the assembly, on the SNP selection processes in each assembly and on the Quality value D. The Quality D-value is the standard deviation of the normalized number of potential SNPs among haplotypes and it is calculated and used to assess the probability that a cluster contains paralogs . RandomlIn the second example, contig 63 of CAP3 containing four reads; two of 'Connecticut King', one 'Trumpet' and one 'White Fox' indicated a reliable inter (G/A) SNP marker with D = 0.2 Figure . The samThis example showed that since CLC assembly adds more reads to a contig compared to CAP3, the risk of grouping paralogs into one chimeric contig might be higher. This also indicates the importance of filtering for haplotype number per cultivar and low D-values before selecting the SNP. A maximum of eight haplotypes can be expected in the assembly of four diploid heterozygous cultivars. Out of the 2,421 SNP markers selected by CLC, 4 contigs have haplotype numbers higher than 8. Number of haplotypes in contigs gives a clue on the frequency with which paralogs are incorporated into single contigs and QualitySNP uses it in SNP identification. In an ideal situation having the full genome sequence, all SNP flanking regions can be blasted to the genome and thus SNP markers can be identified for which paralogs are present. Unfortunately, for many species in which researchers would like to use the advantages of NGS technologies to develop SNP markers, whole genome sequences will not be readily available and blasting of SNP 101 bp flanking regions vs. contigs is the best alternative. To sum up, the main steps which are needed to generate SNP markers of a non-model species are summarized in Figure Lilium cDNAs are likely also valid in other highly heterogeneous species. There seems to be a strong correlation between the level of heterozygosity in the studied species and the performance of the assemblers.SNP markers are becoming the markers of choice in genetic studies and as such for many species researchers are likely to start up SNP retrieval from NGS data. Our results clearly showed that sequence assembly and consequently the SNP markers retrieval are affected significantly by the assembler. In our study, we tested two widely used assemblers that use different algorithms. Procedures followed can be used in any species that has little genetic resources to view assembly quality. Importantly, blasting the selected SNP markers vs. the contigs from where they generated from (in case of missing the support information from the databases) or against the whole genome, if available, is very essential to avoid false positive SNPs. Results obtained with Overall, we believe that for inbreeding species both assemblers can be used, while in an outbreeding and highly heterozygote species CLC is preferred.Lilium were used for sequencing: cv 'Star Gazer' , breeding line 'Trumpet 061099' (Trumpet), cv 'White Fox' (Longiflorum), and cv 'Connecticut King' (Asiatic). Young leaves (500 mg) were collected and kept at -80°C upon RNA isolation.Four lily genotypes that represent the four main hybrid groups of the genus Using the Trizol protocol , the RNA of the four genotypes was isolated and subsequently purified using the RNeasy MinElute kit .RNA library processing i.e. cDNA synthesis, normalization of the cDNA and adaptor ligation for GS FLX Titanium sequencing, was performed by Vertis Biotechnologie AG . In short, 45 ug of total RNA of each of the four samples was treated with DNase and then primed with 6 nucleotide random primers for first strand cDNA synthesis. Next, 454 adapters A and B with an unique 6 nucleotides barcode for each cultivar were ligated to the 5' and 3' ends of the cDNAs. These cDNAs were subjected to two steps of PCR: one before the normalization step (around 18 cycles) and one after it (around 8 cycles) using a proof reading enzyme. Normalization was carried out by one cycle of denaturation and re-association of the cDNAs and subsequent column purification. For Titanium sequencing the cDNAs in the size range of 500-600 bp were eluted from preparative agarose gels.The four cDNA libraries were mixed in equal concentrations and sequenced on a Life Sciences GS-FLX Titanium according to standard procedures (454 Life Sciences) at Wageningen UR Greenomics .Raw sequence data are available at ENA-SRA (European Nucleotide Archive-Sequence Read Archive) with the accession number ERP001106.Raw unprocessed sequences were cleaned before assembly using both the reads and the accompanying sequence quality information (SFF files). Trimming was done by removing: 5' and 3' adapters sequences, low quality bases (limit 0.05), ambiguous nucleotides , terminal nucleotides (one nucleotide from the 5' end and 15 nucleotides from the 3' end), and removal of all reads that have less than 100 and more than 800 nucleotides.http://www.clcbio.com/). The de novo assembly using CLC was done using the following parameters: conflict resolution (vote), similarity 95% 100 bp over read length and alignment mode . Through this study few terms will be used frequently such as:Next, all the duplicated reads, i.e. reads that have the same first 6 nucleotides and exactly the same sequence (>98% similarity), were excluded  using CD-HIT . After t• Assembler's performance: refer to the number of contigs with average contig's length, the number of singletons, and assembly redundancy.• Assembly redundancy: when the assembler tend to separate sequence related to the same locus over different contigs.All the contigs resulting from CAP3 and CLC were submitted to an updated version of QualitySNP  to detecThe authors declare that they have no competing interests.TvG performed the assembly using CLC software, and SP performed the assembly using CAP3 software. AS and PA participated in the design and interpretation of the results and writing the manuscript. AS performed the comparison between the two assemblers and their effects on SNP markers detection. JvT and RV participated in the coordination of the study. All authors read and proved the final manuscript.


Malus germplasm showed that more than half the probes returned genotype clustering patterns that were difficult or impossible to interpret reliably, highlighting implications for the use of the array in genome-wide association studies.High throughput arrays for the simultaneous genotyping of thousands of single-nucleotide polymorphisms (SNPs) have made the rapid genetic characterisation of plant genomes and the development of saturated linkage maps a realistic prospect for many plant species of agronomic importance. However, the correct calling of SNP genotypes in divergent polyploid genomes using array technology can be problematic due to paralogy, and to divergence in probe sequences causing changes in probe binding efficiencies. An Illumina Infinium II whole-genome genotyping array was recently developed for the cultivated apple and used to develop a molecular linkage map for an apple rootstock progeny (M432), but a large proportion of segregating SNPs were not mapped in the progeny, due to unexpected genotype clustering patterns. To investigate the causes of this unexpected clustering we performed BLAST analysis of all probe sequences against the ‘Golden Delicious’ genome sequence and discovered evidence for paralogous annealing sites and probe sequence divergence for a high proportion of probes contained on the array. Following visual re-evaluation of the genotyping data generated for 8,788 SNPs for the M432 progeny using the array, we manually re-scored genotypes at 818 loci and mapped a further 797 markers to the M432 linkage map. The newly mapped markers included the majority of those that could not be mapped previously, as well as loci that were previously scored as monomorphic, but which segregated due to divergence leading to heterozygosity in probe annealing sites. An evaluation of the 8,788 probes in a diverse collection of  Vitis vinifera L. Rubus ideaus L. Malus pumila L. Borkh. Brassica napus L. Citrus spp. Cynara cardunculus L. As genome and transcriptome sequence data become more accessible due to the availability of affordable second-generation sequencing technologies, the characterisation of single nucleotide polymorphisms (SNPs) has become routine. Thus, large genomic SNP data sets have been reported for many diverse plant species including grapevine  assays permit the economical and reliable genotyping in a semi-automated system, that permits SNPs to be multiplexed to include many tens or hundreds of thousands of markers per assay Zea mays L.) Prunus persica (L.) Batsch Solanum tuberosum L. Helianthus annus L. Solanum lycopersicum L. Malus pumila Mill.) and pear (Pyrus communis L.) using re-sequencing data from 27 Malus genotypes Malus and 921 Pyrus SNPs was developed as part of the International RosBREED SNP Consortium, and hence is denoted IRSC. The array was subsequently used to construct a comprehensive genetic linkage map for an apple rootstock mapping progeny M432 (‘M.27′×‘M.116′) by Antanaviciute et al The Illumina Infinium II technology has been extensively applied to plant species and arrays have been developed for grapevine Malus pumila Mill. is a diploid species (2n = 2x = 34), however, the extant genome of the species is known to have been derived from an ancient whole genome duplication (WGD) event Malus genome sequence, and that an unprecedented number of paralogous gene loci exist within the apple genome. The Infinium array technology generates a signal for SNP calling from the hybridisation of DNA to 50-mer oligonucleotide sequences. The presence of a high degree of paralogy within the Malus genome could lead to non-preferential binding of paralogous gene sequences to what were thought to be locus-specific probes on the IRSC array, causing shifts in clustering patterns obtained from SNP genotyping, and the erroneous scoring of segregating loci in the investigation of Antanaviciute et al V. vinifera) SNPs using other genotyping technology In this investigation, we re-evaluated data generated using the IRSC array for an apple rootstock progeny. We identified widespread incidences of incorrectly assigned genotypes following automatic genotype calling, in both the mapping progeny, and in data from a selection of 192 apple varieties. We attribute the incorrectly assigned genotypes to probe binding sequence paralogy and divergence. The data we present in this analysis demonstrate the potential difficulties that can be encountered when using high-throughput microarray-based genotyping applications in complex, heterozygous, genomes such as apple, in which recent WGD have occurred, but also in polyploid species.Malus genome, probe sequences from the IRSC array were used as queries to BLAST the ‘Golden Delicious’ genome sequence at two different levels of stringency. Only regions that returned a perfect match between the 50 nucleotides of the probes and the genome sequence were considered as positive for the first BLAST analysis, following the Illumina guidelines for probe development. Subsequently, 24 nucleotides from the 3′ end of the probe were used as queries, since this is the section of the probe sited closest to the SNP under assay and thus the most critical section in which to conserve sequence integrity. Due to the physical-chemical properties of DNA oligonucleotide binding and tolerances permitted in nucleotide divergence To determine the proportion of probes that could potentially anneal to paralogous regions of the Malus pumila)   varietieAA genotype, 1 for a BB genotype and 0.5 for an AB genotype. Data generated for SNPs heterozygous in the M432 progeny were exported in text format from GenomeStudio (Illumina). A custom Python script was written to hierarchically cluster these data with a simple agglomerative strategy based on the Euclidian distance of the normalised Theta and normalised R coordinates for each genotype at each locus. The Python script is available upon request from the corresponding author. Several clustering runs were performed with similarity thresholds between clusters of 0.1, 0.15, 0.2, 0.3, 0.4 and 0.5. Clusters composed of fewer than three individuals were removed. The mean normalised Theta and R values for all markers that segregated in a 1∶1 or 1∶2:1 manner at a similarity threshold of 0.15, and for which BLAST data were available, were plotted according to whether the probe sequences returned a single BLAST hit to the ‘Golden Delicious’ genome, or multiple significant matches on multiple chromosomes, and the plots were compared.Single copy, heterozygous SNPs segregating in a diploid genome, cluster with mean normalised Theta values approximating to 0 for an Manually annotated markers were integrated into the consensus linkage map of Antanaviciute et al Of the 8,788 probes on the IRSC SNP array, 7,901 (90%) returned perfect matches to the ‘Golden Delicious’ genome sequence assembly ‘all contigs’ set when the full 50 nucleotides of each probe were used as queries; 6,767 (85.6%) of them returned a single significant association with the ‘Golden Delicious’ genome sequence, 44 (0.6%) returned multiple hits to a single linkage group, suggesting paralogous loci that may have arisen through copy-number variation (CNV) and 1,090 (13.8%) returned multiple hits to multiple linkage groups suggesting annealing to paralogous sequences . When thMalus SNP assays and the remaining 72 were derived from Pyrus SNP assays.Cluster data generated for the M432 mapping progeny using the IRSC array by Antanaviciute et al Malus SNP assays and the remaining 73 were derived from Pyrus SNP assays. Of the remaining 124 loci, 91 loci produced four clear clusters of genotypes, suggesting the segregation of three alleles at the locus (i.e. AB×AC) which could not be assigned genotypic classes for mapping, whilst 33 loci were not annotated because the number of genotype classes observed after cluster separation exceeded four, suggesting segregation of more than one heterozygous locus (i.e. ABAB×ABAB). Each of the misclassified loci for which clusters could be reliably differentiated, was manually annotated and each of the seedlings in the population was genotyped at each locus. Markers previously classified as monomorphic (i.e. AA×AA or BB×BB), and a number of those classified previously as segregating in a 1∶1 ratio (i.e. AA×AB or AB×BB) were reclassified variously as segregating either 1∶1 or 1∶2:1 for those displaying evidence of binding to paralogous sequences , and 1∶1Malus and 72 from Pyrus), 449 segregating approximating to a 1∶1 segregation ratio, 280 segregating approximating to a 1∶2:1 ratio and 68 segregating approximating to a 3∶1 ratio, mapped to one of the 17 linkage groups of the M432 map, bringing the total number of SNP markers mapped using the IRSC array in the M432 progeny to 3,069. The remaining 21 loci, all of which displayed cluster patterns suggesting annealing to paralogous loci, remained unlinked. The manually-annotated SNPs mapped across the 17 linkage groups of the M432 linkage map, and no clustering of re-scored loci was observed suggesting genome-wide incidences of probes binding to paralogous loci and reduced probe binding efficiency due to probe sequence divergence. All markers exhibiting recombination from previously mapped markers, and thus unique genotypes, were visually scrutinised to ensure no spurious genotyping calls had been made following which, a total of just 15.2% of the mapped SNPs revealed unique genotypes and thus map positions. The additional loci did not extend the M432 linkage map significantly, suggesting data for loci mapped in this investigation are robust and reliable. A total of 469 SNPs segregated approximating to a 1∶1 Mendelian ratio, 235 in the female ‘M.27′ genotype, 234 in the male ‘M.116′ genotype, whilst 68 and 281 SNPs segregated approximating to a 3∶1 and a 1∶2:1 Mendelian ratio respectively. Following co-segregation analysis with the data of Antanaviciute et al Of the 797 markers mapped in this investigation, 67 were not physically located to the ‘Golden Delicious’ genome. Of the remaining 730 markers, 470 (64.4%) mapped to the expected linkage group based on the physical location of the marker on the ‘Golden Delicious’ genome, 59 (8.1%) mapped to the identified homeologous linkage group BLAST and cluster analysis data were available for a total of 1,387 heterozygous marker clusters, of which 468 returned significant hits to multiple chromosomes in the ‘Golden Delicious’ genome sequence, whilst the remaining 919 returned a significant match to a single position. When the mean Theta and R values were plotted for each of these loci, clear differences were observed regarding the position of the clusters within graph space , with thMalus probes IRSC array, a total of 157 (19.7%) were derived from suspected divergence in the probe sequence binding site in the M432 parents, 92 of which created a null allele, whilst the remaining 568 (80.3%) SNPs annealed to paralogous loci in the Malus genome. In contrast, 45 (63.4%) SNPs derived from the 921 Pyrus probes on the IRSC array segregated due to heterozygosity due to suspected divergence in the probe sequence binding site in the M432 parents, 14 of which created a null allele, whilst just 27 (36.6%) annealed to paralogous loci  in the M432 progeny that displayed robust, reliable genotypes containing no null alleles and clustering in one, two or three distinct genotype groups. A further 285 (9%) probes displayed a low level of ambiguity that may have been the result of too few genotypes in a particular class and could have revealed clearer clustering with the inclusion of further cultivars in the analysis. The remaining 2,199 (72%) probes revealed cluster patterns containing more than three clusters and could not be assigned to a genotype Malus in which WGD events have occurred relatively recently, high degrees of paralogy in the genome and high levels of sequence divergence between genotypes within probe sequences will render the interpretation of a large proportion of the data generated from genotyping arrays problematic if they have not been taken into consideration during the initial design of the array.Genotyping using high-throughput microarray technology has the potential to fundamentally change the way genomes are genetically characterised and has vastly increased the level of resolution at which genetic analyses are performed. High and ultra-high throughput mammalian Infinium arrays are available for genotyping cows, dogs, pigs, sheep, and goats, and high-throughput Infinium arrays available for maize, peach Previously, using automatic genotype calling with GenomeStudio (Illumina), Antanaviciute et al Malus to which the cultivated apple belongs is a member of the Pyreae tribe of the Spiraeoideae subfamily of the Rosaceae Pyrus spp.), quince (Cydonia oblonga Mill.) and loquat (Eriobotrya japonica Lindl.) and ornamental genera such as the rowans (Sorbus spp.), all of which have the basic diploid genome configuration 2n = 2x = 17. However, the 17 chromosomes of the Pyreae tribe are derived from a recent WGD event as was demonstrated recently by Velasco et al Malus LGs 5 and 10; 3 and 11; 9 and 17; 13 and 16; 6 and 14, and more complex relationships between 4, 12 and 14; 1, 7 and 2; and 8 and 15. As a result of this recent duplication, the Malus genome contains the largest reported number of genes of any plant genome sequenced to date and as such, an exceptionally high number of paralogous loci have been identified in the sequence. Besides the more recent WGD, sequencing of the ‘Golden Delicious’ genome sequence also demonstrated the presence of other more ancient duplication events, including the ancient hexaploidisation event in the lineage of the angiosperms The genus Malus genome sequence aligned to two distinct regions, indicative of the WGD event that occurred 30–45 Mya in the evolution of the Pyrea tribe Malus genome following the WGD event, or possible inconsistencies in the ‘Golden Delicious’ genome sequence assembly. A minority of probe sequences returned significant alignments to between three and six Malus linkage groups, consistent with the paleohexaploidisation event shown to have occurred in the evolutionary lineage of the angiosperms, prior to the WGD event in the Pyrea tribe In line with the findings of Velasco et al. Malus genome. BLAST analysis at two levels of stringency of the 8,788 probes contained on the IRSC array clearly demonstrated the potential for paralogy to lead to non-specific annealing of probes within the Malus genome. This was evidenced by a three-fold increase in probe annealing site paralogy when the criteria for the BLAST analysis were changed to evaluate just the first 24 nucleotides of probe sequences. This study identified erroneously scored data for a total of 595 SNP loci due to shifts in genotyping cluster positions caused by the binding of probe sequences to paralogous loci within the Malus genome. However, a further 487 probe sequences successfully scored using the automated genotype calling function of GenomeStudio (Illumina) and previously mapped by Antanaviciute et al The recent WGD event, more ancient polyploidisation events, and instances of expansion of gene families have all contributed to the high degree of paralogy observed within the Vitis 9k array Gene duplication and thus the occurrence of paralogous loci within a genome is a phenomenon that occurs in all eukaryotic genomes Brassica napus L.), SNP assays contained on a WGG array developed using GoldenGate technology were demonstrated to have amplified two paralogous loci, both of which were heterozygous Malus genome has affected significantly more loci contained on the IRSC array than other polyploid species for which Infinium arrays are available.In contrast, in the polyploid species oilseed rape . However, for such analyses to be successful, it is vital that reliable genotypic data can be generated for many tens of thousands of loci, particularly in species such as Malus, and in maize, the rate of failure has been strongly correlated to the genetic distance of the genotype being assayed from the maize reference genome Mismatches between the IRSC probe sequences and the DNA of the progeny from the M432 mapping population, due to the presence of InDels and additional SNPs, interfered with hybridization signals and caused the failure of a hybridisation assay due to reduced hybridisation efficiency between the probe and the DNA being assayed. This phenomenon is not unique to apple and has been shown to occur in other crop species such as maize and grape SNP) from Malus to Pyrus of between 1.2 and 3.0% for P. pyrifolia and P. communis respectively using the SNPlex platform. Likewise Antanaviciute et al Pyrus SNPs to the M432 progeny when automatic SNP genotype calling was used. In this investigation, we demonstrated that manual annotation of genotype clusters produced from probes developed from Pyrus genomic sequences recovered a far higher percentage of heterozygous loci in Malus germplasm than would have been expected from TSNP values reported for transferability from Pyrus to Malus. This was due to the presence of other polymorphisms associated with the probe sequence and the existence of such variation has implications for the use of these probes in GWAS.Micheletti et al. Malus provides numerous experimental advantages over genotyping using traditional PCR-based assays analysed by electrophoresis Malus since both phenomena, especially when they occur together at a locus, make genotyping data difficult to score reliably. This has been demonstrated in this study through the genotyping of a set of Malus varieties from a diverse germplasm collection, where the majority of loci scored displayed evidence of these phenomena and rendered genotyping unreliable or impossible. For the development of a widely applicable tool for genotyping and marker-trait association, it is thus important to consider the degree of sequence divergence within the probe sequences by developing probes with the assistance of the largest re-sequencing resource possible, encompassing as diverse a germplasm base as possible. This will minimise the level of undetected probe sequence divergence, and thus the prevalence of null alleles and additional multi-SNP alleles within genotyping datasets.The development of array-based genotyping for Figure S1Erroneously scored genotype clusters annealing to multiple genomic regions. Examples of erroneously scored genotype clusters following automatic genotyping using GenomeStudio (Illumina)of SNP that target multiple sites. Red circles and genotypes written in red indicate genotype assignment by GenomeStudio: (a) Data scored as monomorphic AA clearly clustering in three genotype groups (AA/AA:AB/AA:BB/AA); (b) data scored as segregating 1∶1 (AA:AB) clearly segregating in three genotype groups (AA/AA:AB/AA:BB/AA); (c) Data scored as monomorphic AA clearly segregating in two genotype groups (AA/AA:AB/AA); (d) Data scored as monomorphic BB clearly clustering in three genotype groups (AA/BB:AB/BB:BB/BB); (e) data scored as segregating 1∶1 (AB:BB) clearly segregating in three genotype groups (AA/BB:AB/BB:BB/BB); (f) Data scored as monomorphic BB clearly segregating in two genotype groups (AB/BB:BB/BB).(PNG)Click here for additional data file.Figure S2Erroneously scored genotype clusters containing additional SNPs. Examples of erroneously scored genotype clusters following automatic genotyping using GenomeStudio (Illumina) containing additional SNPs (hereafter reported as B’) and null (n) alleles. Red circles and genotypes indicate scores assigned by GenomeStudio, blue circles indicate genotypes assigned ‘no call’ (NC) by GenomeStudio: (a) Data scored as monomorphic BB clustering in two genotype groups (BB’:BB) resulting from BB’×BB; (b) Data scored as BB clustering in three genotype groups (BB:BB’:B’B’) resulting from BB’×BB’; (c) Data scored as BB clustering in two genotype groups (BB or BB’:B’B’) resulting from BB’×BB’; (d) Data scored as BB clustering in two genotype groups (Bn:nn) resulting from Bn×nn; (e) Data scored as BB clustering in three genotype groups (BB:Bn:nn) resulting from Bn×Bn; (f) Data scored as BB clustering in two genotype groups (BB or Bn:nn) resulting from Bn×Bn.(PNG)Click here for additional data file.Figure S3Linkage map of newly-mapped SNP markers. The M432 consensus linkage map detailing the map positions of the 797 novel SNP loci mapped in this investigation following manual re-annotation of data generated with the IRSC array. The scale in centi-Morgans (cM) is given at the left edge of the figure.(PNG)Click here for additional data file.Table S1The names of the 192 Malus varieties genotyped with the IRSC array.(PDF)Click here for additional data file.Table S2The genetic positions, marker names, segregation ratios and physical positions on the ‘Golden Delicious’ genome sequence of the 797 SNP markers mapped to the M432 linkage map.(XLS)Click here for additional data file.