Single-Nucleotide Polymorphism Phylotyping of Escherichia coli

We describe a rapid and easily automated phylogenetic grouping technique based on analysis of bacterial genome single-nucleotide polymorphisms (SNPs). We selected 13 SNPs derived from a complete sequence analysis of 11 essential genes previously used for multilocus sequence typing (MLST) of 30 Escherichia coli strains representing the genetic diversity of the species. The 13 SNPs were localized in five genes, trpA, trpB, putP, icdA, and polB, and were selected to allow recovery of the main phylogenetic groups (groups A, B1, E, D, and B2) and subgroups of the species. In the first step, we validated the SNP approach in silico by extracting SNP data from the complete sequences of the five genes for a panel of 65 pathogenic strains belonging to different E. coli pathovars, which were previously analyzed by MLST. In the second step, we determined these SNPs by dideoxy single-base extension of unlabeled oligonucleotide primers for a collection of 183 commensal and extraintestinal clinical E. coli isolates and compared the SNP phylotyping method to previous well-established typing methods. This SNP phylotyping method proved to be consistent with the other methods for assigning phylogenetic groups to the different E. coli strains. In contrast to the other typing methods, such as multilocus enzyme electrophoresis, ribotyping, or PCR phylotyping using the presence/absence of three genomic DNA fragments, the SNP typing method described here is derived from a solid phylogenetic analysis, and the results obtained by this method are more meaningful. Our results indicate that similar approaches may be used for a wide variety of bacterial species.     

EST-SNP genotyping of citrus species using high-resolution melting curve analysis

Citrus taxonomy is very complex mainly due to specific aspects of its reproductive biology. A number of studies have been performed using various molecular markers in order to evaluate the level of genetic variability in Citrus. SNP markers have been used for genetic diversity assessment using a variety of different methods. Recently, the availability of EST database and whole genome sequences has made it possible to develop more markers such as SNPs. In the present study, the high-resolution melting curve analysis (HRM) was used to detect SNPs or INDELs in Citrus genus for the first time. We aimed to develop a panel of SNPs to differentiate Citrus genotypes which can also be applied to Citrus biodiversity studies. The results showed that 21 SNP containing markers produced distinct polymorphic melting curves among the Citrus spp. investigated through HRM analysis. It was proved that HRM is an efficient, cost-effective, and accurate method for discriminating citrus SNPs as well as a method to analyze more polymorphisms in a single PCR amplicon, representing a useful tool for genetic, biodiversity, and breeding studies. SNPs developed based on Citrus sinensis EST database showed a good transferability within the Citrus genus. Moreover, HRM analysis allowed the discrimination of citrus genotypes at specific level and the resulting genetic distance analysis clustered these genotypes into three main branches. The results suggested that the panel of SNP markers could be used in a variety of applications in citrus biodiversity assessment and breeding programs using HRM analysis.     

Large scale SNP unearthing and genetic architecture analysis in sea-captured and cultured populations of Cynoglossus semilaevis

Knowledge on population structure and genetic diversity is a focal point for association mapping studies and genomic selection. Genotyping by sequencing (GBS) represents an innovative method for large scale SNP detection and genotyping of genetic resources. Here we used the GBS approach for the genome-wide identification of SNPs in a collection of Cynoglossus semilaevis and for the assessment of the level of genetic diversity in C. semilaevis genotypes. GBS analysis generated a total of 55.12 Gb high-quality sequence data, with an average of 0.63 Gb per sample. The total number of SNP markers was 563, 109. In order to explore the genetic diversity of C. semilaevis and to select a minimal core set representing most of the total genetic variation with minimum redundancy, C. semilaevis sequences were analyzed using high quality SNPs. Based on hierarchical clustering, it was possible to divide the collection into 2 clusters. The marine fishing populations were clustered and clearly separated from the cultured populations, and the cultured populations from Hebei was also distinct from the other two local populations. These analyses showed that genotypes were clustered based on species-related features. Differential significant SNPs were also captured and validated by GBS and SNaPshot, with linkage disequilibrium and haplotype analysis, seven SNPs have been confirmed to have obvious differentiation in two populations, which may be used as the characteristic evaluation sites of sea-captured and cultured Cynoglossus semilaevis populations. And SNP markers and information on population structure developed in this study will undoubtedly support genome-wide association mapping studies and marker-assisted selection programs. These differential SNPs could be also employed as the characteristic evaluation sites of sea-captured and cultured Cynoglossus semilaevis populations in future.     

Metabolic flux balance analysis and the in silicoanalysis of Escherichia coliK-12 gene deletions

Background

Genome sequencing and bioinformatics are producing detailed lists of the molecular components contained in many prokaryotic organisms. From this 'parts catalogue' of a microbial cell, in silicorepresentations of integrated metabolic functions can be constructed and analyzed using flux balance analysis (FBA). FBA is particularly well-suited to study metabolic networks based on genomic, biochemical, and strain specific information.

Results

Herein, we have utilized FBA to interpret and analyze the metabolic capabilities of Escherichia coli. We have computationally mapped the metabolic capabilities of E. coliusing FBA and examined the optimal utilization of the E. colimetabolic pathways as a function of environmental variables. We have used an in silicoanalysis to identify seven gene products of central metabolism (glycolysis, pentose phosphate pathway, TCA cycle, electron transport system) essential for aerobic growth of E. colion glucose minimal media, and 15 gene products essential for anaerobic growth on glucose minimal media. The in silico tpi ^-, zwf, and pta ^-mutant strains were examined in more detail by mapping the capabilities of these in silicoisogenic strains.

Conclusions

We found that computational models of E. colimetabolism based on physicochemical constraints can be used to interpret mutant behavior. These in silicaresults lead to a further understanding of the complex genotype-phenotype relation. Supplementary information: 10.1186/1471-2105-1-1     

Dissemination of Cephalosporin Resistance Genes between Escherichia coli Strains from Farm Animals and Humans by Specific Plasmid Lineages

Third-generation cephalosporins are a class of β-lactam antibiotics that are often used for the treatment of human infections caused by Gram-negative bacteria, especially Escherichia coli. Worryingly, the incidence of human infections caused by third-generation cephalosporin-resistant E. coli is increasing worldwide. Recent studies have suggested that these E. coli strains, and their antibiotic resistance genes, can spread from food-producing animals, via the food-chain, to humans. However, these studies used traditional typing methods, which may not have provided sufficient resolution to reliably assess the relatedness of these strains. We therefore used whole-genome sequencing (WGS) to study the relatedness of cephalosporin-resistant E. coli from humans, chicken meat, poultry and pigs. One strain collection included pairs of human and poultry-associated strains that had previously been considered to be identical based on Multi-Locus Sequence Typing, plasmid typing and antibiotic resistance gene sequencing. The second collection included isolates from farmers and their pigs. WGS analysis revealed considerable heterogeneity between human and poultry-associated isolates. The most closely related pairs of strains from both sources carried 1263 Single-Nucleotide Polymorphisms (SNPs) per Mbp core genome. In contrast, epidemiologically linked strains from humans and pigs differed by only 1.8 SNPs per Mbp core genome. WGS-based plasmid reconstructions revealed three distinct plasmid lineages (IncI1- and IncK-type) that carried cephalosporin resistance genes of the Extended-Spectrum Beta-Lactamase (ESBL)- and AmpC-types. The plasmid backbones within each lineage were virtually identical and were shared by genetically unrelated human and animal isolates. Plasmid reconstructions from short-read sequencing data were validated by long-read DNA sequencing for two strains. Our findings failed to demonstrate evidence for recent clonal transmission of cephalosporin-resistant E. coli strains from poultry to humans, as has been suggested based on traditional, low-resolution typing methods. Instead, our data suggest that cephalosporin resistance genes are mainly disseminated in animals and humans via distinct plasmids.     

Use of VeraCode 384-plex assays for watermelon diversity analysis and integrated genetic map of watermelon with single nucleotide polymorphisms and simple sequence repeats

Watermelon (Citrullus lanatus var. lanatus) is one of the most important vegetable crops in the world. Molecular markers have become the tools of choice for resolving watermelon taxonomic relationships and evolution. Increased numbers of single nucleotide polymorphism (SNP) markers together with simple sequence repeat (SSR) markers would be useful for phylogenetic analyses of germplasm accessions and for linkage mapping for marker-assisted breeding with quantitative trait loci and single genes. We aimed to construct a genetic map based on SNPs (generated by Illumina Veracode multiplex assays for genotyping) and SSR markers and evaluate relationships inferred from SNP genotypes between 130 watermelon accessions collected throughout the world. We incorporated 282 markers (232 SNPs and 50 SSRs) into the linkage map. The genetic map consisted of 11 linkage groups spanning 924.72 cM with an average distance of 3.28 cM between markers. Because all of the SNP-containing sequences were assembled with the whole-genome sequence draft for watermelon, chromosome numbers could be readily assigned for all the linkage groups. We found that 134 SNPs were polymorphic in 130 watermelon accessions chosen for diversity studies. The current 384-plex SNP set is a powerful tool for characterizing genetic relatedness and for developing medium-resolution genetic maps.     

When Whole-Genome Alignments Just Won't Work: kSNP v2 Software for Alignment-Free SNP Discovery and Phylogenetics of Hundreds of Microbial Genomes

Effective use of rapid and inexpensive whole genome sequencing for microbes requires fast, memory efficient bioinformatics tools for sequence comparison. The kSNP v2 software finds single nucleotide polymorphisms (SNPs) in whole genome data. kSNP v2 has numerous improvements over kSNP v1 including SNP gene annotation; better scaling for draft genomes available as assembled contigs or raw, unassembled reads; a tool to identify the optimal value of k; distribution of packages of executables for Linux and Mac OS X for ease of installation and user-friendly use; and a detailed User Guide. SNP discovery is based on k-mer analysis, and requires no multiple sequence alignment or the selection of a single reference genome. Most target sets with hundreds of genomes complete in minutes to hours. SNP phylogenies are built by maximum likelihood, parsimony, and distance, based on all SNPs, only core SNPs, or SNPs present in some intermediate user-specified fraction of targets. The SNP-based trees that result are consistent with known taxonomy. kSNP v2 can handle many gigabases of sequence in a single run, and if one or more annotated genomes are included in the target set, SNPs are annotated with protein coding and other information (UTRs, etc.) from Genbank file(s). We demonstrate application of kSNP v2 on sets of viral and bacterial genomes, and discuss in detail analysis of a set of 68 finished E. coli and Shigella genomes and a set of the same genomes to which have been added 47 assemblies and four “raw read” genomes of H104:H4 strains from the recent European E. coli outbreak that resulted in both bloody diarrhea and hemolytic uremic syndrome (HUS), and caused at least 50 deaths.     

Coverage and characteristics of the Affymetrix GeneChip Human Mapping 100K SNP set

Improvements in technology have made it possible to conduct genome-wide association mapping at costs within reach of academic investigators, and experiments are currently being conducted with a variety of high-throughput platforms. To provide an appropriate context for interpreting results of such studies, we summarize here results of an investigation of one of the first of these technologies to be publicly available, the Affymetrix GeneChip Human Mapping 100K set of single nucleotide polymorphisms (SNPs). In a systematic analysis of the pattern and distribution of SNPs in the Mapping 100K set, we find that SNPs in this set are undersampled from coding regions (both nonsynonymous and synonymous) and oversampled from regions outside genes, relative to SNPs in the overall HapMap database. In addition, we utilize a novel multilocus linkage disequilibrium (LD) coefficient based on information content (analogous to the information content scores commonly used for linkage mapping) that is equivalent to the familiar measure r² in the special case of two loci. Using this approach, we are able to summarize for any subset of markers, such as the Affymetrix Mapping 100K set, the information available for association mapping in that subset, relative to the information available in the full set of markers included in the HapMap, and highlight circumstances in which this multilocus measure of LD provides substantial additional insight about the haplotype structure in a region over pairwise measures of LD.     

A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds

Single nucleotide polymorphisms (SNPs) able to describe population differences can be used for important applications in livestock, including breed assignment of individual animals, authentication of mono-breed products and parentage verification among several other applications. To identify the most discriminating SNPs among thousands of markers in the available commercial SNP chip tools, several methods have been used. Random forest (RF) is a machine learning technique that has been proposed for this purpose. In this study, we used RF to analyse PorcineSNP60 BeadChip array genotyping data obtained from a total of 2737 pigs of 7 Italian pig breeds (3 cosmopolitan-derived breeds: Italian Large White, Italian Duroc and Italian Landrace, and 4 autochthonous breeds: Apulo-Calabrese, Casertana, Cinta Senese and Nero Siciliano) to identify breed informative and reduced SNP panels using the mean decrease in the Gini Index and the Mean Decrease in Accuracy parameters with stability evaluation. Other reduced informative SNP panels were obtained using Delta, Fixation index and principal component analysis statistics, and their performances were compared with those obtained using the RF-defined panels using the RF classification method and its derived Out Of Bag rates and correct prediction proportions. Therefore, the performances of a total of six reduced panels were evaluated. The correct assignment of the animals to its breed was close to 100% for all tested approaches. Porcine chromosome 8 harboured the largest number of selected SNPs across all panels. Many SNPs were included in genomic regions in which previous studies identified signatures of selection or genes (e.g. ESR1, KITL and LCORL) that could contribute to explain, at least in part, phenotypically or economically relevant traits that might differentiate cosmopolitan and autochthonous pig breeds. Random forest used as preselection statistics highlighted informative SNPs that were not the same as those identified by other methods. This might be due to specific features of this machine learning methodology. It will be interesting to explore if the adaptation of RF methods for the identification of selection signature regions could be able to describe population-specific features that are not captured by other approaches.     

IIb‐RAD‐sequencing coupled with random forest classification indicates regional population structuring and sex‐specific differentiation in salmon lice (Lepeophtheirus salmonis)

The aquaculture industry has been dealing with salmon lice problems forming serious threats to salmonid farming. Several treatment approaches have been used to control the parasite. Treatment effectiveness must be optimized, and the systematic genetic differences between subpopulations must be studied to monitor louse species and enhance targeted control measures. We have used IIb‐RAD sequencing in tandem with a random forest classification algorithm to detect the regional genetic structure of the Norwegian salmon lice and identify important markers for sex differentiation of this species. We identified 19,428 single nucleotide polymorphisms (SNPs) from 95 individuals of salmon lice. These SNPs, however, were not able to distinguish the differential structure of lice populations. Using the random forest algorithm, we selected 91 SNPs important for geographical classification and 14 SNPs important for sex classification. The geographically important SNP data substantially improved the genetic understanding of the population structure and classified regional demographic clusters along the Norwegian coast. We also uncovered SNP markers that could help determine the sex of the salmon louse. A large portion of the SNPs identified to be under directional selection was also ranked highly important by random forest. According to our findings, there is a regional population structure of salmon lice associated with the geographical location along the Norwegian coastline.     

Shrunken methodology to genome-wide SNPs selection and construction of SNPs networks

Background

Recent development of high-resolution single nucleotide polymorphism (SNP) arrays allows detailed assessment of genome-wide human genome variations. There is increasing recognition of the importance of SNPs for medicine and developmental biology. However, SNP data set typically has a large number of SNPs (e.g., 400 thousand SNPs in genome-wide Parkinson disease data set) and a few hundred of samples. Conventional classification methods may not be effective when applied to such genome-wide SNP data.

Results

In this paper, we use shrunken dissimilarity measure to analyze and select relevant SNPs for classification problems. Examples of HapMap data and Parkinson disease (PD) data are given to demonstrate the effectiveness of the proposed method, and illustrate it has a potential to become a useful analysis tool for SNP data sets. We use Parkinson disease data as an example, and perform a whole genome analysis. For the 367440 SNPs with less than 1% missing percentage from all 22 chromosomes, we can select 357 SNPs from this data set. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that has a similarity value being greater than a threshold are selected to construct several groups of genes. For the SNPs that involved in these groups of genes, a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and SNPs with significance of P < 0.01 are chosen to identify SNPs networks based on their P values. Here SNPs networks are constructed based on Gene Ontology knowledge, and therefore each SNP network plays a role in the biological process. An analysis shows that such networks have relationships directly or indirectly to Parkinson disease.

Conclusions

Experimental results show that our approach is suitable to handle genetic variations, and provide useful knowledge in a genome-wide SNP study.

     

Grapevine powdery mildew resistance and susceptibility loci identified on a high-resolution SNP map

Improved efficacy and durability of powdery mildew resistance can be enhanced via knowledge of the genetics of resistance and susceptibility coupled with the development of high-resolution maps to facilitate the stacking of multiple resistance genes and other desirable traits. We studied the inheritance of powdery mildew (Erysiphe necator) resistance and susceptibility of wild Vitis rupestris B38 and cultivated V. vinifera ‘Chardonnay’, finding evidence for quantitative variation. Molecular markers were identified using genotyping-by-sequencing, resulting in 16,833 single nucleotide polymorphisms (SNPs) based on alignment to the V. vinifera ‘PN40024’ reference genome sequence. With an average density of 36 SNPs/Mbp and uniform coverage of the genome, this 17K set was used to identify 11 SNPs on chromosome 7 associated with a resistance locus from V. rupestris B38 and ten SNPs on chromosome 9 associated with a locus for susceptibility from ‘Chardonnay’ using single marker association and linkage disequilibrium analysis. Linkage maps for V. rupestris B38 (1,146 SNPs) and ‘Chardonnay’ (1,215 SNPs) were constructed and used to corroborate the ‘Chardonnay’ locus named Sen1 (Susceptibility to Erysiphe necator 1), providing the first insight into the genetics of susceptibility to powdery mildew from V. vinifera. The identification of markers associated with a susceptibility locus in a V. vinifera background can be used for negative selection among breeding progenies. This work improves our understanding of the nature of powdery mildew resistance in V. rupestris B38 and ‘Chardonnay’, while applying next-generation sequencing tools to advance grapevine genomics and breeding.     

Genotyping-by-Sequencing for Populus Population Genomics: An Assessment of Genome Sampling Patterns and Filtering Approaches

Continuing advances in nucleotide sequencing technology are inspiring a suite of genomic approaches in studies of natural populations. Researchers are faced with data management and analytical scales that are increasing by orders of magnitude. With such dramatic advances comes a need to understand biases and error rates, which can be propagated and magnified in large-scale data acquisition and processing. Here we assess genomic sampling biases and the effects of various population-level data filtering strategies in a genotyping-by-sequencing (GBS) protocol. We focus on data from two species of Populus, because this genus has a relatively small genome and is emerging as a target for population genomic studies. We estimate the proportions and patterns of genomic sampling by examining the Populus trichocarpa genome (Nisqually-1), and demonstrate a pronounced bias towards coding regions when using the methylation-sensitive ApeKI restriction enzyme in this species. Using population-level data from a closely related species (P. tremuloides), we also investigate various approaches for filtering GBS data to retain high-depth, informative SNPs that can be used for population genetic analyses. We find a data filter that includes the designation of ambiguous alleles resulted in metrics of population structure and Hardy-Weinberg equilibrium that were most consistent with previous studies of the same populations based on other genetic markers. Analyses of the filtered data (27,910 SNPs) also resulted in patterns of heterozygosity and population structure similar to a previous study using microsatellites. Our application demonstrates that technically and analytically simple approaches can readily be developed for population genomics of natural populations.     

A Multi-Marker Genetic Association Test Based on the Rasch Model Applied to Alzheimer’s Disease

Results from Genome-Wide Association Studies (GWAS) have shown that the genetic basis of complex traits often include many genetic variants with small to moderate effects whose identification remains a challenging problem. In this context multi-marker analysis at the gene and pathway level can complement traditional point-wise approaches that treat the genetic markers individually. In this paper we propose a novel statistical approach for multi-marker analysis based on the Rasch model. The method summarizes the categorical genotypes of SNPs by a generalized logistic function into a genetic score that can be used for association analysis. Through different sets of simulations, the false-positive rate and power of the proposed approach are compared to a set of existing methods, and shows good performances. The application of the Rasch model on Alzheimer’s Disease (AD) ADNI GWAS dataset also allows a coherent interpretation of the results. Our analysis supports the idea that APOE is a major susceptibility gene for AD. In the top genes selected by proposed method, several could be functionally linked to AD. In particular, a pathway analysis of these genes also highlights the metabolism of cholesterol, that is known to play a key role in AD pathogenesis. Interestingly, many of these top genes can be integrated in a hypothetic signalling network.     

Developing genomic resources for the common bottlenose dolphin (Tursiops truncatus): isolation and characterization of 153 single nucleotide polymorphisms and 53 genotyping assays

Although single nucleotide polymorphisms (SNPs) are commonly used in human genetics, they have only recently been incorporated into genetic studies of non‐model organisms, including cetaceans. SNPs have several advantages over other molecular markers for studies of population genetics: they are quicker and more straightforward to score, cross‐laboratory comparisons of data are less complicated, and they can be used successfully with low‐quality DNA. We screened portions of the genome of one of the most abundant cetaceans in U.S. waters, the common bottlenose dolphin (Tursiops truncatus), and identified 153 SNPs resulting in an overall average of one SNP every 463 base pairs. Custom TaqMan^® Assays were designed for 53 of these SNPs, and their performance was tested by genotyping a set of bottlenose dolphin samples, including some with low‐quality DNA. We found that in 19% of the loci examined, the minor allele frequency (MAF) estimated during initial SNP ascertainment using a DNA pool of 10 individuals differed significantly from the final MAF after genotyping over 100 individuals, suggesting caution when making inferences about MAF values based on small data sets. For two assays, we also characterized the basis for unusual clustering patterns to determine whether their data could still be utilized for further genetic studies. Overall results support the use of these SNPs for accurate analysis of both poor and good‐quality DNA. We report the first SNP markers and genotyping assays for use in population and conservation genetic studies of bottlenose dolphins.     

Genetic Dissection of Drought and Heat Tolerance in Chickpea through Genome-Wide and Candidate Gene-Based Association Mapping Approaches

To understand the genetic basis of tolerance to drought and heat stresses in chickpea, a comprehensive association mapping approach has been undertaken. Phenotypic data were generated on the reference set (300 accessions, including 211 mini-core collection accessions) for drought tolerance related root traits, heat tolerance, yield and yield component traits from 1–7 seasons and 1–3 locations in India (Patancheru, Kanpur, Bangalore) and three locations in Africa (Nairobi, Egerton in Kenya and Debre Zeit in Ethiopia). Diversity Array Technology (DArT) markers equally distributed across chickpea genome were used to determine population structure and three sub-populations were identified using admixture model in STRUCTURE. The pairwise linkage disequilibrium (LD) estimated using the squared-allele frequency correlations (r²; when r²<0.20) was found to decay rapidly with the genetic distance of 5 cM. For establishing marker-trait associations (MTAs), both genome-wide and candidate gene-sequencing based association mapping approaches were conducted using 1,872 markers (1,072 DArTs, 651 single nucleotide polymorphisms [SNPs], 113 gene-based SNPs and 36 simple sequence repeats [SSRs]) and phenotyping data mentioned above employing mixed linear model (MLM) analysis with optimum compression with P3D method and kinship matrix. As a result, 312 significant MTAs were identified and a maximum number of MTAs (70) was identified for 100-seed weight. A total of 18 SNPs from 5 genes (ERECTA, 11 SNPs; ASR, 4 SNPs; DREB, 1 SNP; CAP2 promoter, 1 SNP and AMDH, 1SNP) were significantly associated with different traits. This study provides significant MTAs for drought and heat tolerance in chickpea that can be used, after validation, in molecular breeding for developing superior varieties with enhanced drought and heat tolerance.     

A set of EST-SNPs for map saturation and cultivar identification in melon

Background

There are few genomic tools available in melon (Cucumis melo L.), a member of the Cucurbitaceae, despite its importance as a crop. Among these tools, genetic maps have been constructed mainly using marker types such as simple sequence repeats (SSR), restriction fragment length polymorphisms (RFLP) and amplified fragment length polymorphisms (AFLP) in different mapping populations. There is a growing need for saturating the genetic map with single nucleotide polymorphisms (SNP), more amenable for high throughput analysis, especially if these markers are located in gene coding regions, to provide functional markers. Expressed sequence tags (ESTs) from melon are available in public databases, and resequencing ESTs or validating SNPs detected in silico are excellent ways to discover SNPs.

Results

EST-based SNPs were discovered after resequencing ESTs between the parental lines of the PI 161375 (SC) × 'Piel de sapo' (PS) genetic map or using in silico SNP information from EST databases. In total 200 EST-based SNPs were mapped in the melon genetic map using a bin-mapping strategy, increasing the map density to 2.35 cM/marker. A subset of 45 SNPs was used to study variation in a panel of 48 melon accessions covering a wide range of the genetic diversity of the species. SNP analysis correctly reflected the genetic relationships compared with other marker systems, being able to distinguish all the accessions and cultivars.

Conclusion

This is the first example of a genetic map in a cucurbit species that includes a major set of SNP markers discovered using ESTs. The PI 161375 × 'Piel de sapo' melon genetic map has around 700 markers, of which more than 500 are gene-based markers (SNP, RFLP and SSR). This genetic map will be a central tool for the construction of the melon physical map, the step prior to sequencing the complete genome. Using the set of SNP markers, it was possible to define the genetic relationships within a collection of forty-eight melon accessions as efficiently as with SSR markers, and these markers may also be useful for cultivar identification in Occidental melon varieties.     

SNP genotyping in melons: genetic variation, population structure, and linkage disequilibrium

Novel sequencing technologies were recently used to generate sequences from multiple melon (Cucumis melo L.) genotypes, enabling the in silico identification of large single nucleotide polymorphism (SNP) collections. In order to optimize the use of these markers, SNP validation and large-scale genotyping are necessary. In this paper, we present the first validated design for a genotyping array with 768 SNPs that are evenly distributed throughout the melon genome. This customized Illumina GoldenGate assay was used to genotype a collection of 74 accessions, representing most of the botanical groups of the species. Of the assayed loci, 91 % were successfully genotyped. The array provided a large number of polymorphic SNPs within and across accessions. This set of SNPs detected high levels of variation in accessions from this crop’s center of origin as well as from several other areas of melon diversification. Allele distribution throughout the genome revealed regions that distinguished between the two main groups of cultivated accessions (inodorus and cantalupensis). Population structure analysis showed a subdivision into five subpopulations, reflecting the history of the crop. A considerably low level of LD was detected, which decayed rapidly within a few kilobases. Our results show that the GoldenGate assay can be used successfully for high-throughput SNP genotyping in melon. Since many of the genotyped accessions are currently being used as the parents of breeding populations in various programs, this set of mapped markers could be used for future mapping and breeding efforts.     

The development of genome-wide single nucleotide polymorphisms in blue wildebeest using the DArTseq platform

Blue wildebeest (Connochaetes taurinus taurinus) are economically important antelope that are widely utilised in the South African wildlife industry. However, very few genomic resources are available for blue wildebeest that can assist in breeding management and facilitate research. This study aimed to develop a set of genome-wide single nucleotide polymorphism (SNP) markers for blue wildebeest. The DArTseq genotyping platform, commonly used in polyploid plant species, was selected for SNP discovery. A limited number of published articles have described the use of the DArTseq platform in animals and, therefore, this study also provided a unique opportunity to assess the performance of the DArTseq platform in an animal species. A total of 20,563 SNPs, each located within a 69 bp sequence, were generated. The developed SNP markers had a high average scoring reproducibility (>99%) and a low percentage missing data (~9.21%) compared to other reduced representation sequencing approaches that have been used in animal studies. Furthermore, the number of candidate SNPs per nucleotide position decreased towards the 3′ end of sequence reads, and the ratio of transitions (Ts) to transversions (Tv) remained similar for each read position. These observations indicate that there was no read position bias, such as the identification of false SNPs due to low sequencing quality, towards the tail-end of sequencing reads. The DArTseq platform was also successful in identifying a large number of informative SNPs with desirable polymorphism parameters such as a high minor allele frequency (MAF). The Bos taurus genome was used for the in silico mapping of the marker sequences and a total of 6020 (29.28%) sequences were successfully mapped against the bovine genome. The marker sequences mapped to all of the bovine chromosomes establishing the genome-wide distribution of the SNPs. Moreover, the high observed Ts:Tv ratio (2.84:1) indicate that the DArTseq platform targeted gene-rich regions of the blue wildebeest genome. Finally, functional annotation of the marker sequences revealed a wide range of different putative functions indicating that these SNP markers can be useful in functional gene studies. The DArTseq platform, therefore, represents a high-throughput, robust and cost-effective genotyping platform, which may find adoption in several other African antelope and animal species.