Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

Alzheimer's disease (AD) is one of the most serious neurodegenerative diseases in the world and has a strong genetic predisposition. At present, there is still no effective method for the early diagnosis and prevention of AD. Accumulating evidence shows the association of several loci with AD risk, such as apolipoprotein E (APOE) and translocase of outer mitochondrial membrane 40 (TOMM40). However, for routine disease diagnosis in clinics, genotype detection methods based on gene sequencing technology are time-consuming and excessively costly. Thus, in this study, we developed a high-sensitivity, low-cost, and convenient single nucleotide polymorphism(SNP) detection assay method based on allele-specific quantitative polymerase chain reaction (AS-qPCR) technology, which can be used to determine the SNP genotype in APOE and TOMM40. A total of 40 patients were recruited from the outpatient department of the memory clinic of Dongzhimen Hospital, Beijing University of Chinese Medicine. The SNP detection assay method includes three steps. First, positive plasmids with different genotypes (TT/CC/TC) in APOE rs429358, rs7412, and TOMM40 rs11556505 were prepared. Second, 3'-T/3'-C primers were designed to amplify these positive plasmids for each SNP site. Finally, we calculated the log10 of the copy number ratio for each positive plasmid, and the genotype interpretation interval was established. Based on this method, we investigated whether the SNPs in 40 patients could be accurately calculated using AS-qPCR technology. The accuracy of SNP detection was verified by PCR-Pooling sequencing. The results showed that SNP genotypes assessed by AS-qPCR technology corresponded perfectly to the results obtained by conventional DNA sequencing. We have developed a genotype detection method for AD based on AS-qPCR, which can be performed easily, rapidly, accurately, and at low cost. The method will contribute to the early diagnosis of patients with late-onset Alzheimer's and the detection of large clinical samples in the future.

Next-generation sequencing technologies have made it possible to sequence targeted regions of the human genome in hundreds of individuals. Deep sequencing represents a powerful approach for the discovery of the complete spectrum of DNA sequence variants in functionally important genomic intervals. Current methods for single nucleotide polymorphism (SNP) detection are designed to detect SNPs from single individual sequence data sets. Here, we describe a novel method SNIP-Seq (single nucleotide polymorphism identification from population sequence data) that leverages sequence data from a population of individuals to detect SNPs and assign genotypes to individuals. To evaluate our method, we utilized sequence data from a 200-kilobase (kb) region on chromosome 9p21 of the human genome. This region was sequenced in 48 individuals (five sequenced in duplicate) using the Illumina GA platform. Using this data set, we demonstrate that our method is highly accurate for detecting variants and can filter out false SNPs that are attributable to sequencing errors. The concordance of sequencing-based genotype assignments between duplicate samples was 98.8%. The 200-kb region was independently sequenced to a high depth of coverage using two sequence pools containing the 48 individuals. Many of the novel SNPs identified by SNIP-Seq from the individual sequencing were validated by the pooled sequencing data and were subsequently confirmed by Sanger sequencing. We estimate that SNIP-Seq achieves a low false-positive rate of approximately 2%, improving upon the higher false-positive rate for existing methods that do not utilize population sequence data. Collectively, these results suggest that analysis of population sequencing data is a powerful approach for the accurate detection of SNPs and the assignment of genotypes to individual samples.

Single nucleotide polymorphisms (SNPs) constitute the most common types of genetic variations in the human genome. A number of SNPs have been linked to the development of life threatening diseases including cancer, cardiovascular diseases and neurodegenerative diseases. The ability for ultrasensitive and accurate detection of low abundant disease-related SNPs in bodily fluids (e.g. blood, serum, etc.) holds a significant value in the development of non-invasive future biodiagnostic tools. Over the past two decades, nanomaterials have been utilized in a myriad of biosensing applications due to their ability of detecting extremely low quantities of biologically important biomarkers with high sensitivity and accuracy. Of particular interest is the application of such technologies in the detection of SNPs. The use of various nanomaterials, coupled with different powerful signal amplification strategies, has paved the way for a new generation of ultrasensitive SNP biodiagnostic assays. Over the past few years, several ultrasensitive SNP biosensors capable of detecting specific targets down to the ultra-low regimes (ca. aM and below) and therefore holding great promises for early clinical diagnosis of diseases have been developed. This mini review will highlight some of the most recent, significant advances in nanomaterial-based ultrasensitive SNP sensing technologies capable of detecting specific targets on the attomolar (10(-18) M) regime or below. In particular, the design of novel, powerful signal amplification strategies that hold the key to the ultrasensitivity is highlighted.

Evaluating probabilistic genotyping for low-pass DNA sequencing

IL28B single nucleotide polymorphisms in the treatment of hepatitis C

Detection of nucleic acids and single nucleotide polymorphisms (SNPs) is of pivotal importance in biology and medicine. Given that the biological effect of SNPs often is enhanced in combination with other SNPs, multiplexed SNP detection is desirable. We show proof of concept of the multiplexed detection of SNPs based on the template-directed native chemical ligation (NCL) of PNA-probes carrying a metal tag allowing detection using ICP-MS. For the detection of ssDNA oligonucleotides (30 bases), two probes, one carrying the metal tag and a second one carrying biotin for purification, are covalently ligated. The methodological limit of detection is of 29 pM with RSD of 6.7% at 50 pM (n=5). Detection of SNPs is performed with the combination of two sets of reporter probes. The first probe set targets the SNP, and its yield is compared with a second set of probes targeting a neighboring sequence. The assay was used to simultaneously differentiate between alleles of three SNPs at 5-nM concentration.

DNA sequence alignment and single-nucleotide polymorphism (SNP) detection are two important tasks in genomics research. A common genome resequencing analysis workflow is to first perform sequence alignment and then detect SNPs among the aligned sequences. In practice, the performance bottleneck in this workflow is usually the intermediate result I/O due to the separation of the two components, especially when the in-memory computation has been accelerated, e.g., by graphics processors. To address this bottleneck, we propose to integrate the two tasks tightly so as to eliminate the I/O of intermediate results in the workflow. Specifically, we make the following three changes for the tight integration: (1) we adopt a partition-based approach so that the external sorting of alignment results, which was required for SNP detection, is eliminated; (2) we perform customized compression on alignment results to reduce memory footprint; and (3) we move the computation of a global matrix from SNP detection to sequence alignment to save a file scan. We have developed a GPU-accelerated system that tightly integrates sequence alignment and SNP detection. Our results with human genome data sets show that our GPU-acceleration of individual components in the traditional workflow improves the overall performance by 18 times and that the tight integration further improves the performance of the GPU-accelerated system by 2.3 times.

As the amount of re-sequencing genome data grows, minimizing the execution time of an analysis is required. For this purpose, recent computing systems have been adopting both high-performance coprocessors and host processors. However, there are few applications that efficiently utilize these heterogeneous computing resources. This problem equally refers to the work of single nucleotide polymorphism (SNP) detection, which is one of the bottlenecks in genome data processing. In this paper, we propose a method for speeding up an SNP detection by enhancing the utilization of heterogeneous computing resources often used in recent high-performance computing systems. Through the measurement of workload in the detection procedure, we divide the SNP detection into several task groups suitable for each computing resource. These task groups are scheduled using a window overlapping method. As a result, we improved upon the speedup achieved by previous open source applications by a magnitude of 10.

SNPpattern: A Genetic Tool to Derive Haplotype Blocks and Measure Genomic Diversity in Populations Using SNP Genotypes

Many species on Earth can carry up to two different versions of a given gene, with each of these ‘alleles’ having only a 50/50 chance of being transmitted to the next generation via sexual reproduction. Certain ‘selfish’ sequences, however, can hijack this process and increase their probability of being passed on to an offspring. Known as transmission distortion, this phenomenon may result in alleles spreading through the population even if they are detrimental to fertility. Transmission distortion has been detected in many species such as flies, mice and some plants. It can take place at various stages during reproduction; for example, the selfish alleles may become overrepresented among eggs or sperm. However, scientists need to study a large number of offspring or reproductive cells to be able to detect whether an allele is inherited more often than expected. This has made it difficult to determine whether transmission distortion also happens in humans, and research so far has resulted in conflicting conclusions. A recently published dataset of human sperm from 25 donors offered Carioscia, Weaver et al. the opportunity to examine this question. Every volunteer had produced between 969 and 3377 sperm cells, each with about 1% of their genome sequenced. Carioscia, Weaver et al. developed a computational method, which they named rhapsodi, that allowed them to ‘fill in the gaps’ and infer missing regions of the genome for each cell. To do so, they relied on the fact that sperm cells from a given individual are highly related to one another. With this more complete data at hand, it became possible to look for evidence of transmission distortion by searching for alleles that were overrepresented in sperm from a given donor. No selfish sequence could be detected in any of the 25 individuals, suggesting that human sperm may not be subject to pervasive transmission distortion. Signatures of selfish alleles detected in previous human studies may have therefore not resulted from this mechanism taking place at the sperm level. Instead, transmission distortion in humans could primarily target eggs or happen at later stages (for instance, if embryos carrying the selfish allele have better chances of survival). The ‘rhapsodi’ method developed by Carioscia, Weaver et al. should allow other scientists to work with datasets for which large portions of the genetic information is missing. It may therefore become easier for researchers to track selfish alleles which are difficult to detect, and to examine bigger, more diverse samples which also include individuals with known fertility challenges.

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BackgroundIn contrast to currently used single nucleotide polymorphism (SNP) panels, the use of whole-genome sequence data is expected to enable the direct estimation of the effects of causal mutations on a given trait. This could lead to higher reliabilities of genomic predictions compared to those based on SNP genotypes. Also, at each generation of selection, recombination events between a SNP and a mutation can cause decay in reliability of genomic predictions based on markers rather than on the causal variants. Our objective was to investigate the use of imputed whole-genome sequence genotypes versus high-density SNP genotypes on (the persistency of) the reliability of genomic predictions using real cattle data.MethodsHighly accurate phenotypes based on daughter performance and Illumina BovineHD Beadchip genotypes were available for 5503 Holstein Friesian bulls. The BovineHD genotypes (631,428 SNPs) of each bull were used to impute whole-genome sequence genotypes (12,590,056 SNPs) using the Beagle software. Imputation was done using a multi-breed reference panel of 429 sequenced individuals. Genomic estimated breeding values for three traits were predicted using a Bayesian stochastic search variable selection (BSSVS) model and a genome-enabled best linear unbiased prediction model (GBLUP). Reliabilities of predictions were based on 2087 validation bulls, while the other 3416 bulls were used for training.ResultsPrediction reliabilities ranged from 0.37 to 0.52. BSSVS performed better than GBLUP in all cases. Reliabilities of genomic predictions were slightly lower with imputed sequence data than with BovineHD chip data. Also, the reliabilities tended to be lower for both sequence data and BovineHD chip data when relationships between training animals were low. No increase in persistency of prediction reliability using imputed sequence data was observed.ConclusionsCompared to BovineHD genotype data, using imputed sequence data for genomic prediction produced no advantage. To investigate the putative advantage of genomic prediction using (imputed) sequence data, a training set with a larger number of individuals that are distantly related to each other and genomic prediction models that incorporate biological information on the SNPs or that apply stricter SNP pre-selection should be considered.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0149-x) contains supplementary material, which is available to authorized users.

The detection of single nucleotide polymorphisms (SNPs) is of great significance in the early diagnosis of diseases and the rational use of drugs. Thus, a novel biosensor based on the quenching effect of fluorescence-embedded SYBR Green I (SG) dye and graphene oxide (GO) was introduced in this study. The probe DNA forms a double helix structure with perfectly complementary DNA (pcDNA) and 15 single-base mismatch DNA (smDNA) respectively. SG is highly intercalated with perfectly complementary dsDNA (pc-dsDNA) and exhibits strong fluorescence emission. Single-base mismatch dsDNA (SNPs) has a loose double-stranded structure and exhibits poor SG intercalation and low fluorescence sensing. At this time, the sensor still showed poor SNP discrimination. GO has a strong effect on single-stranded DNA (ssDNA), which can reduce the fluorescence response of probe DNA and eliminate background interference. And competitively combined with ssDNA in SNPs, quenching the fluorescence of SG/SNP, while the fluorescence value of pc-dsDNA was retained, increasing the signal-to-noise ratio. At this time, the sensor has obtained excellent SNP resolution. Different SNPs detect different intensities of fluorescence in the near-infrared region to evaluate the sensor's identification of SNPs. The experimental parameters such as incubation time, incubation temperature and salt concentration were optimized. Under optimal conditions, 1 nM DNA with 0–10 nM linear range and differentiate 5% SNP were achieved. The detection method does not require labeling, is low cost, simple in operation, exhibits high SNP discrimination and can be distinguished by SNP at room temperature.

BackgroundDNA sequence diversity within the human genome may be more greatly affected by copy number variations (CNVs) than single nucleotide polymorphisms (SNPs). Although the importance of CNVs in genome wide association studies (GWAS) is becoming widely accepted, the optimal methods for identifying these variants are still under evaluation. We have previously reported a comprehensive view of CNVs in the HapMap DNA collection using high density 500 K EA (Early Access) SNP genotyping arrays which revealed greater than 1,000 CNVs ranging in size from 1 kb to over 3 Mb. Although the arrays used most commonly for GWAS predominantly interrogate SNPs, CNV identification and detection does not necessarily require the use of DNA probes centered on polymorphic nucleotides and may even be hindered by the dependence on a successful SNP genotyping assay.ResultsIn this study, we have designed and evaluated a high density array predicated on the use of non-polymorphic oligonucleotide probes for CNV detection. This approach effectively uncouples copy number detection from SNP genotyping and thus has the potential to significantly improve probe coverage for genome-wide CNV identification. This array, in conjunction with PCR-based, complexity-reduced DNA target, queries over 1.3 M independent NspI restriction enzyme fragments in the 200 bp to 1100 bp size range, which is a several fold increase in marker density as compared to the 500 K EA array. In addition, a novel algorithm was developed and validated to extract CNV regions and boundaries.ConclusionUsing a well-characterized pair of DNA samples, close to 200 CNVs were identified, of which nearly 50% appear novel yet were independently validated using quantitative PCR. The results indicate that non-polymorphic probes provide a robust approach for CNV identification, and the increasing precision of CNV boundary delineation should allow a more complete analysis of their genomic organization.