SNPsFinder--a web-based application for genome-wide discovery of single nucleotide polymorphisms in microbial genomes

SUMMARY: Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variations in closely related microbial species, strains or isolates. Some SNPs confer selective advantages for microbial pathogens during infection and many others are powerful genetic markers for distinguishing closely related strains or isolates that could not be distinguished otherwise. To facilitate SNP discovery in microbial genomes, we have developed a web-based application, SNPsFinder, for genome-wide identification of SNPs. SNPsFinder takes multiple genome sequences as input to identify SNPs within homologous regions. It can also take contig sequences and sequence quality scores from ongoing sequencing projects for SNP prediction. SNPsFinder will use genome sequence annotation if available and map the predicted SNP regions to known genes or regions to assist further evaluation of the predicted SNPs for their functional significance. SNPsFinder can generate PCR primers for all predicted SNP regions according to user's input parameters to facilitate experimental validation. The results from SNPsFinder analysis are accessible through the World Wide Web. AVAILABILITY: The SNPsFinder program is available at http://snpsfinder.lanl.gov/. SUPPLEMENTARY INFORMATION: The user's manual is available at http://snpsfinder.lanl.gov/UsersManual/     

High-throughput genotyping of single nucleotide polymorphisms using new biplex invader technology

The feasibility of large-scale genome-wide association studies of complex human disorders depends on the availability of accurate and efficient genotyping methods for single nucleotide polymorphisms (SNPs). We describe a new platform of the invader assay, a biplex assay, where both alleles are interrogated in a single reaction tube. The assay was evaluated on over 50 different SNPs, with over 20 SNPs genotyped in study cohorts of over 1500 individuals. We assessed the usefulness of the new platform in high-throughput genotyping and compared its accuracy to genotyping results obtained by the traditional monoplex invader assay, TaqMan genotyping and sequencing data. We present representative data for two SNPs in different genes (CD36 and protein tyrosine phosphatase 1β) from a study cohort comprising over 1500 individuals with high or low-normal blood pressure. In this high-throughput application, the biplex invader assay is very accurate, with an error rate of <0.3% and a failure rate of 1.64%. The set-up of the assay is highly automated, facilitating the processing of large numbers of samples simultaneously. We present new analysis tools for the assignment of genotypes that further improve genotyping success. The biplex invader assay with its automated set-up and analysis offers a new efficient high-throughput genotyping platform that is suitable for association studies in large study cohorts.     

A genome-wide detection of copy number variation using SNP genotyping arrays in Beijing-You chickens

Copy number variation (CNV) has been recently examined in many species and is recognized as being a source of genetic variability, especially for disease-related phenotypes. In this study, the PennCNV software, a genome-wide CNV detection system based on the 60 K SNP BeadChip was used on a total sample size of 1,310 Beijing-You chickens (a Chinese local breed). After quality control, 137 high confidence CNVRs covering 27.31 Mb of the chicken genome and corresponding to 2.61 % of the whole chicken genome. Within these regions, 131 known genes or coding sequences were involved. Q-PCR was applied to verify some of the genes related to disease development. Results showed that copy number of genes such as, phosphatidylinositol-5-phosphate 4-kinase II alpha, PHD finger protein 14, RHACD8 (a CD8α- like messenger RNA), MHC B-G, zinc finger protein, sarcosine dehydrogenase and ficolin 2 varied between individual chickens, which also supports the reliability of chip-detection of the CNVs. As one source of genomic variation, CNVs may provide new insight into the relationship between the genome and phenotypic characteristics.     

Detecting single nucleotide polymorphisms using DNA arrays for plant pathogen diagnosis

The lack of a rapid and reliable means for routine pathogen identification has been one of the main limitations in plant disease management, and has pushed the development of culture-independent, molecular approaches. Currently, DNA array technology is the most suitable technique for high-throughput detection and identification, as well as quantification, of multiple pathogens in a single assay. Closely related pathogens that may have completely different host ranges or pathogenicity often differ in only a single to a few base pairs in genes that may be targeted for identification. Therefore, the ability to discriminate single nucleotide polymorphisms (SNPs) should be pursued in any diagnostic assay. In this paper, we demonstrate the utility of DNA array technology to detect SNPs while accounting for specific criteria such as the position of the mismatch, the sequence of the oligonucleotide, and the length and amount of labeled amplicons that are hybridized. When disregarding mismatches at the extreme ends of the oligonucleotides, cross hybridization to single mismatch oligonucleotides is rare when processing environmental samples that contain genetic material from unknown sources. In addition to plant pathology, this study is relevant for any field of research where DNA arrays are used to detect mutations or polymorphisms, ranging from human diagnostics to environmental microbiology and microbial ecology.     

Technical reproducibility of genotyping SNP arrays used in genome-wide association studies

During the last several years, high-density genotyping SNP arrays have facilitated genome-wide association studies (GWAS) that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. Moreover, discordance observed in results between independent GWAS indicates the potential for Type I and II errors. High reliability of genotyping technology is needed to have confidence in using SNP data and interpreting GWAS results. Therefore, reproducibility of two widely genotyping technology platforms from Affymetrix and Illumina was assessed by analyzing four technical replicates from each of the six individuals in five laboratories. Genotype concordance of 99.40% to 99.87% within a laboratory for the sample platform, 98.59% to 99.86% across laboratories for the same platform, and 98.80% across genotyping platforms was observed. Moreover, arrays with low quality data were detected when comparing genotyping data from technical replicates, but they could not be detected according to venders' quality control (QC) suggestions. Our results demonstrated the technical reliability of currently available genotyping platforms but also indicated the importance of incorporating some technical replicates for genotyping QC in order to improve the reliability of GWAS results. The impact of discordant genotypes on association analysis results was simulated and could explain, at least in part, the irreproducibility of some GWAS findings when the effect size (i.e. the odds ratio) and the minor allele frequencies are low.     

Microarray-based method for genotyping of functional single nucleotide polymorphisms using dual-color fluorescence hybridization

Screening disease-related single nucleotide polymorphism (SNP) markers in the whole genome has great potential in complex disease genetics and pharmacogenetics researches. It has led to a requirement for high-throughput genotyping platforms that can maximize the efficient screening functional SNPs with respect to accuracy, speed and cost. In this study, we attempted to develop a microarray-based method for scoring a number of genomic DNA in parallel for one or more molecular markers on a glass slide. Two SNP markers localized to the methylenetetrahydrofolate reductase gene (MTHFR) were selected as the investigated targets. Amplified PCR products from nine genomic DNA specimens were spotted and immobilized onto a poly-l-lysine coated glass slide to fabricate a microarray, then interrogated by hybridization with dual-color probes to determine the SNP genotype of each sample. The results indicated that the microarray-based method could determine the genotype of 677 and 1298 MTHFR polymorphisms. Sequencing was performed to validate these results. Our experiments successfully demonstrate that PCR products subjected to dual-color hybridization on a microarray could be applied as a useful and a high-throughput tool to analyze molecular markers.     

Multiplex single nucleotide polymorphisms genotyping using solid-phase single base extension on magnetic nanoparticles

To fulfill the increasing need for large-scale genetic research, we have developed a new solid-phase single base extension (SBE) protocol on magnetic nanoparticles (MNPs) for multiplex SNP detection using adapter polymerase chain reaction (PCR) products as templates. Extension primers were covalently immobilized on the MNPs, and allele-specific extension took place along the stretch of target DNA for one-color ddNTP incorporation. The MNPs with fluorophores were spotted on a glass slide to fabricate a “bead array” to discriminate their genotypes. Eight SNP loci of three DNA samples were interrogated, and the experiment demonstrated that it is an efficient method for large-scale SNP genotyping.     

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.     

Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy

A great promise of publicly sharing genome-wide association data is the potential to create composite sets of controls. However, studies often use different genotyping arrays, and imputation to a common set of SNPs has shown substantial bias: a problem which has no broadly applicable solution. Based on the idea that using differing genotyped SNP sets as inputs creates differential imputation errors and thus bias in the composite set of controls, we examined the degree to which each of the following occurs: (1) imputation based on the union of genotyped SNPs (i.e., SNPs available on one or more arrays) results in bias, as evidenced by spurious associations (type 1 error) between imputed genotypes and arbitrarily assigned case/control status; (2) imputation based on the intersection of genotyped SNPs (i.e., SNPs available on all arrays) does not evidence such bias; and (3) imputation quality varies by the size of the intersection of genotyped SNP sets. Imputations were conducted in European Americans and African Americans with reference to HapMap phase II and III data. Imputation based on the union of genotyped SNPs across the Illumina 1M and 550v3 arrays showed spurious associations for 0.2 % of SNPs: ~2,000 false positives per million SNPs imputed. Biases remained problematic for very similar arrays (550v1 vs. 550v3) and were substantial for dissimilar arrays (Illumina 1M vs. Affymetrix 6.0). In all instances, imputing based on the intersection of genotyped SNPs (as few as 30 % of the total SNPs genotyped) eliminated such bias while still achieving good imputation quality.     

QTL linkage mapping of wing length in zebra finch using genome-wide single nucleotide polymorphisms markers

Avian wing length is an important trait that covaries with the ecology and migratory behaviour of a species and tends to change rapidly when the conditions are altered. Long-distance migrants typically have longer wings than short-distance migrants and sedentary species, and long-winged species also tend to be more dispersive. Although the substantial heritability of avian wing length is well established, the identification of causal genes has remained elusive. Based on large-scale genotyping of 1404 informative single nucleotide polymorphisms (SNP) in a captive population of 1067 zebra finches, we here show that the within-population variation of relative wing length (h(2) = 0.74 ± 0.05) is associated with standing genetic variation in at least six genomic regions (one genome-wide significant and five suggestive). The variance explained by these six quantitative trait loci (QTL) sums to 36.8% of the phenotypic variance (half of the additive genetic variance), although this likely is an overestimate attributable to the Beavis effect. As avian wing length is primarily determined by the length of the primary feathers, we then searched for candidate genes that are related to feather growth. Interestingly, all of the QTL signals co-locate with Wnt growth factors and closely interacting genes (Wnt3a, Wnt5a, Wnt6, Wnt7a, Wnt9a, RhoU and RhoV). Our findings therefore suggest that standing genetic variation in the Wnt genes might be linked to avian wing morphology, although there are many other genes that also fall within the confidence regions.     

A novel procedure for efficient genotyping of single nucleotide polymorphisms

Due to the surge in interest in using single nucleotide polymorphisms (SNPs) for genotyping a facile and affordable method for this is an absolute necessity. Here we introduce a procedure that combines an easily automatable single tube sample preparation with an efficient high throughput mass spectrometric analysis technique. Known point mutations or single nucleotide polymorphisms are easily analysed by this procedure. It starts with PCR amplification of a short stretch of genomic DNA, for example an exon of a gene containing a SNP. By shrimp alkaline phosphatase digest residual dNTPs are destroyed. Allele-specific products are generated using a special primer, a conditioned set of α-S-dNTPs and α-S-ddNTPs and a fresh DNA polymerase in a primer extension reaction. Unmodified DNA is removed by 5′-phosphodiesterase digestion and the modified products are alkylated to increase the detection sensitivity in the mass spectrometric analysis. All steps of the preparation are simple additions of solutions and incubations. The procedure operates at the lowest practical sample volumes and in contrast to other genotyping protocols with mass spectrometric detection requires no purification. This reduces the cost and makes it easy to implement. Here it is demonstrated in a version using positive ion detection on described mutations in exon 17 of the amyloid precursor protein gene and in a version using negative ion detection on three SNPs of the granulocyte-macrophage colony stimulating factor gene. Preparation and analysis of SNPs is shown separately and simultaneously, thus demonstrating the multiplexibility of this genotyping procedure. The preparation protocol for genotyping is adapted to the conditions used for the SNP discovery method by denaturing HPLC, thus demonstrating a facile link between protocols for SNP discovery and SNP genotyping. Results corresponded unanimously with the control sequencing. The procedure is useful for high throughput genotyping as it is required for gene identification and pharmacogenomics where large numbers of DNA samples have to be analysed. We have named this procedure the ‘GOOD Assay’ for SNP analysis.     

Single nucleotide polymorphisms generated by genotyping by sequencing to characterize genome-wide diversity,linkage disequilibrium,and selective sweeps in cultivated watermelon

Background

A large single nucleotide polymorphism (SNP) dataset was used to analyze genome-wide diversity in a diverse collection of watermelon cultivars representing globally cultivated, watermelon genetic diversity. The marker density required for conducting successful association mapping depends on the extent of linkage disequilibrium (LD) within a population. Use of genotyping by sequencing reveals large numbers of SNPs that in turn generate opportunities in genome-wide association mapping and marker-assisted selection, even in crops such as watermelon for which few genomic resources are available. In this paper, we used genome-wide genetic diversity to study LD, selective sweeps, and pairwise F_ST distributions among worldwide cultivated watermelons to track signals of domestication.

Results

We examined 183 Citrullus lanatus var. lanatus accessions representing domesticated watermelon and generated a set of 11,485 SNP markers using genotyping by sequencing. With a diverse panel of worldwide cultivated watermelons, we identified a set of 5,254 SNPs with a minor allele frequency of ≥ 0.05, distributed across the genome. All ancestries were traced to Africa and an admixture of various ancestries constituted secondary gene pools across various continents. A sliding window analysis using pairwise F_ST values was used to resolve selective sweeps. We identified strong selection on chromosomes 3 and 9 that might have contributed to the domestication process. Pairwise analysis of adjacent SNPs within a chromosome as well as within a haplotype allowed us to estimate genome-wide LD decay. LD was also detected within individual genes on various chromosomes. Principal component and ancestry analyses were used to account for population structure in a genome-wide association study. We further mapped important genes for soluble solid content using a mixed linear model.

Conclusions

Information concerning the SNP resources, population structure, and LD developed in this study will help in identifying agronomically important candidate genes from the genomic regions underlying selection and for mapping quantitative trait loci using a genome-wide association study in sweet watermelon.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-767) contains supplementary material, which is available to authorized users.     

High-throughput discovery of rare human nucleotide polymorphisms by Ecotilling

Human individuals differ from one another at only ~0.1% of nucleotide positions, but these single nucleotide differences account for most heritable phenotypic variation. Large-scale efforts to discover and genotype human variation have been limited to common polymorphisms. However, these efforts overlook rare nucleotide changes that may contribute to phenotypic diversity and genetic disorders, including cancer. Thus, there is an increasing need for high-throughput methods to robustly detect rare nucleotide differences. Toward this end, we have adapted the mismatch discovery method known as Ecotilling for the discovery of human single nucleotide polymorphisms. To increase throughput and reduce costs, we developed a universal primer strategy and implemented algorithms for automated band detection. Ecotilling was validated by screening 90 human DNA samples for nucleotide changes in 5 gene targets and by comparing results to public resequencing data. To increase throughput for discovery of rare alleles, we pooled samples 8-fold and found Ecotilling to be efficient relative to resequencing, with a false negative rate of 5% and a false discovery rate of 4%. We identified 28 new rare alleles, including some that are predicted to damage protein function. The detection of rare damaging mutations has implications for models of human disease.     

Identification of genome-wide single nucleotide polymorphisms in allopolyploid crop Brassica napus

Background

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation. Identification of large numbers of SNPs is helpful for genetic diversity analysis, map-based cloning, genome-wide association analyses and marker-assisted breeding. Recently, identifying genome-wide SNPs in allopolyploid Brassica napus (rapeseed, canola) by resequencing many accessions has become feasible, due to the availability of reference genomes of Brassica rapa (2n = AA) and Brassica oleracea (2n = CC), which are the progenitor species of B. napus (2n = AACC). Although many SNPs in B. napus have been released, the objective in the present study was to produce a larger, more informative set of SNPs for large-scale and efficient genotypic screening. Hence, short-read genome sequencing was conducted on ten elite B. napus accessions for SNP discovery. A subset of these SNPs was randomly selected for sequence validation and for genotyping efficiency testing using the Illumina GoldenGate assay.

Results

A total of 892,536 bi-allelic SNPs were discovered throughout the B. napus genome. A total of 36,458 putative amino acid variants were located in 13,552 protein-coding genes, which were predicted to have enriched binding and catalytic activity as a result. Using the GoldenGate genotyping platform, 94 of 96 SNPs sampled could effectively distinguish genotypes of 130 lines from two mapping populations, with an average call rate of 92%.

Conclusions

Despite the polyploid nature of B. napus, nearly 900,000 simple SNPs were identified by whole genome resequencing. These SNPs were predicted to be effective in high-throughput genotyping assays (51% polymorphic SNPs, 92% average call rate using the GoldenGate assay, leading to an estimated >450 000 useful SNPs). Hence, the development of a much larger genotyping array of informative SNPs is feasible. SNPs identified in this study to cause non-synonymous amino acid substitutions can also be utilized to directly identify causal genes in association studies.     

Full flexibility genotyping of single nucleotide polymorphisms by the GOOD assay

Recently a facile method for genotyping single nucleotide polymorphisms (SNPs) using MALDI mass spectrometry, termed the GOOD assay, was developed. It does not require any purification and is performed with simple liquid handling, thermal incubation and cycling steps. Although this method is well suited to automation and high-throughput analysis of SNPs, it did not allow full flexibility due to lack of certain reagents. A complete set of β-cyanoethyl phosphoramidites is presented herein that give this SNP genotyping method full sequence and multiplex capabilities. Applications to SNP genotyping in the prion protein gene, the β-2-adrenergic receptor gene and the angiotensin converting enzyme gene using the GOOD assay are demonstrated. Because SNP genotyping technologies are generally very sensitive to varying DNA quality, the GOOD assay has been stabilised and optimised for low quality DNA. A template extraction method is introduced that allows genotyping from tissue that was taken while placing an ear tag on an animal. This dramatically facilitates the application of genotyping to animal agricultural applications, as it demonstrates that expensive and cumbersome DNA extraction procedures prior to genotyping can be avoided.     

Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays

Background

The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference.

Results

The performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection.

Conclusions

We demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples.     

Probability of detecting disease-associated single nucleotide polymorphisms in case-control genome-wide association studies

Some case-control genome-wide association studies (CCGWASs) select promising single nucleotide polymorphisms (SNPs) by ranking corresponding p-values, rather than by applying the same p-value threshold to each SNP. For such a study, we define the detection probability (DP) for a specific disease-associated SNP as the probability that the SNP will be "T-selected," namely have one of the top T largest chi-square values (or smallest p-values) for trend tests of association. The corresponding proportion positive (PP) is the fraction of selected SNPs that are true disease-associated SNPs. We study DP and PP analytically and via simulations, both for fixed and for random effects models of genetic risk, that allow for heterogeneity in genetic risk. DP increases with genetic effect size and case-control sample size and decreases with the number of nondisease-associated SNPs, mainly through the ratio of T to N, the total number of SNPs. We show that DP increases very slowly with T, and the increment in DP per unit increase in T declines rapidly with T. DP is also diminished if the number of true disease SNPs exceeds T. For a genetic odds ratio per minor disease allele of 1.2 or less, even a CCGWAS with 1000 cases and 1000 controls requires T to be impractically large to achieve an acceptable DP, leading to PP values so low as to make the study futile and misleading. We further calculate the sample size of the initial CCGWAS that is required to minimize the total cost of a research program that also includes follow-up studies to examine the T-selected SNPs. A large initial CCGWAS is desirable if genetic effects are small or if the cost of a follow-up study is large.     

Facile method for automated genotyping of single nucleotide polymorphisms by mass spectrometry

In the future, analysis of single nucleotide polymorphisms (SNPs) should become a powerful tool for many genetic applications in areas such as association studies, pharmacogenetics and traceability in the agro-alimentary sector. A number of technologies have been developed for high-throughput genotyping of SNPs. Here we present the simplified GOOD assay for SNP genotyping by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI). The simplified GOOD assay is a single-tube, purification-free, three-step procedure consisting of PCR, primer extension and phosphodiesterase II digestion followed by mass spectrometric analysis. Due to the application of charge-tag technology, no sample purification is required prior to the otherwise very impurity-sensitive MALDI analysis. The use of methylphosphonate containing primers and ddNTPs or α-S-ddNTPs together with a novel DNA polymerase derived from Thermotoga maritima for primer extension allow the fluent preparation of negatively charge-tagged, allele-specific products. A key feature of this polymerase is its preference for ddNTPs and α-S-ddNTPs over dNTPs. The simplified GOOD assay was run with automatic liquid handling at the lowest manageable volumes, automatic data acquisition and interpretation. We applied this novel procedure to genotyping SNPs of candidate genes for hypertension and cardiovascular disease.     

Cost-effective procedures for genotyping of human FCN2 gene single nucleotide polymorphisms

L-ficolin (ficolin-2) is a complement-activating pattern-recognition lectin taking part in the innate immune response. Both its serum concentration and sugar binding capacity are influenced by single nucleotide polymorphisms (SNP) of the corresponding FCN2 gene. Cost-effective and simple procedures, based on polymerase chain reaction (PCR) or PCR-restriction fragment length polymorphism for an investigation of four FCN2 SNPs are proposed: ?64 A?>?C (rs7865453), ?4 A?>?G (rs17514136; both located in the promoter region), +6359 C?>?T (rs17549193), +6424 G?>?T (rs7851696; both in exon 8). Variant alleles of ?64 and +6424 (in strong linkage disequlibrium) are known to be associated with low L-ficolin level or activity. In contrast, variant alleles at positions ?4 and +6359 (also in strong linkage disequlibrium) correspond to higher values. Since several L-ficolin clinical associations have been reported, FCN2 genotyping seems to be a valuable tool for disease association studies.