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Single Nucleotide Polymorphism

Single Nucleotide Polymorphism (SNP) is a genetic variation where a single nucleotide in the DNA sequence differs between individuals.
SNPs are the most common type of genetic variation and play a key role in understanding genetic differences, disease susceptibility, and drug response.
PubCompare.ai can help optimize your SNP research by locating relevant protocols from literature, preprints, and patents, while leveraging AI-driven comparisons to identify the best protocols and produts.
This can enhance reproducibilty and take your SNP research to the nect level.

Most cited protocols related to «Single Nucleotide Polymorphism»

SAMtools is a library and software package for parsing and manipulating alignments in the SAM/BAM format. It is able to convert from other alignment formats, sort and merge alignments, remove PCR duplicates, generate per-position information in the pileup format (Fig. 1c), call SNPs and short indel variants, and show alignments in a text-based viewer. For the example alignment of 112 Gbp Illumina GA data, SAMtools took about 10 h to convert from the MAQ format and 40 min to index with <30 MB memory. Conversion is slower mainly because compression with zlib is slower than decompression. External sorting writes temporary BAM files and would typically be twice as slow as conversion.
SAMtools has two separate implementations, one in C and the other in Java, with slightly different functionality.

Mandatory fields in the SAM format

No.NameDescription
1QNAMEQuery NAME of the read or the read pair
2FLAGBitwise FLAG (pairing, strand, mate strand, etc.)
3RNAMEReference sequence NAME
4POS1-Based leftmost POSition of clipped alignment
5MAPQMAPping Quality (Phred-scaled)
6CIGARExtended CIGAR string (operations: MIDNSHP)
7MRNMMate Reference NaMe (‘=’ if same as RNAME)
8MPOS1-Based leftmost Mate POSition
9ISIZEInferred Insert SIZE
10SEQQuery SEQuence on the same strand as the reference
11QUALQuery QUALity (ASCII-33=Phred base quality)
Publication 2009
Decompression DNA Library INDEL Mutation Memory Single Nucleotide Polymorphism
Table 1 illustrates the wide range of operations that BEDTools support. Many of the tools have extensive parameters that allow user-defined overlap criteria and fine control over how results are reported. Importantly, we have also defined a concise format (BEDPE) to facilitate comparisons of discontinuous features (e.g. paired-end sequence reads) to each other (pairToPair), and to genomic features in traditional BED format (pairToBed). This functionality is crucial for interpreting genomic rearrangements detected by paired-end mapping, and for identifying fusion genes or alternative splicing patterns by RNA-seq. To facilitate comparisons with data produced by current DNA sequencing technologies, intersectBed and pairToBed compute overlaps between sequence alignments in BAM format (Li et al., 2009 (link)), and a general purpose tool is provided to convert BAM alignments to BED format, thus facilitating the use of BAM alignments with all other BEDTools (Table 1). The following examples illustrate the use of intersectBed to isolate single nucleotide polymorphisms (SNPs) that overlap with genes, pairToBed to create a BAM file containing only those alignments that overlap with exons and intersectBed coupled with samtools to create a SAM file of alignments that do not intersect (-v) with repeats.

Summary of supported operations available in the BEDTools suite

UtilityDescription
intersectBed*Returns overlaps between two BED files.
pairToBedReturns overlaps between a BEDPE file and a BED file.
bamToBedConverts BAM alignments to BED or BEDPE format.
pairToPairReturns overlaps between two BEDPE files.
windowBedReturns overlaps between two BED files within a user-defined window.
closestBedReturns the closest feature to each entry in a BED file.
subtractBed*Removes the portion of an interval that is overlapped by another feature.
mergeBed*Merges overlapping features into a single feature.
coverageBed*Summarizes the depth and breadth of coverage of features in one BED file relative to another.
genomeCoverageBedHistogram or a ‘per base’ report of genome coverage.
fastaFromBedCreates FASTA sequences from BED intervals.
maskFastaFromBedMasks a FASTA file based upon BED coordinates.
shuffleBedPermutes the locations of features within a genome.
slopBedAdjusts features by a requested number of base pairs.
sortBedSorts BED files in useful ways.
linksBedCreates HTML links from a BED file.
complementBed*Returns intervals not spanned by features in a BED file.

Utilities in bold support sequence alignments in BAM. Utilities with an asterisk were compared with Galaxy and found to yield identical results.

Other notable tools include coverageBed, which calculates the depth and breadth of genomic coverage of one feature set (e.g. mapped sequence reads) relative to another; shuffleBed, which permutes the genomic positions of BED features to allow calculations of statistical enrichment; mergeBed, which combines overlapping features; and utilities that search for nearby yet non-overlapping features (closestBed and windowBed). BEDTools also includes utilities for extracting and masking FASTA sequences (Pearson and Lipman, 1988 (link)) based upon BED intervals. Tools with similar functionality to those provided by Galaxy were directly compared for correctness using the ‘knownGene’ and ‘RepeatMasker’ tracks from the hg19 build of the human genome. The results from all analogous tools were found to be identical (Table 1).
Publication 2010
Exons Gene Fusion Gene Rearrangement Genes Genome Genome, Human Sequence Alignment Single Nucleotide Polymorphism
While reading variants from input file, ANNOVAR scans the gene annotation database stored at local disk, and identifies intronic variants, exonic variants, intergenic variants, 5′/3′-UTR variants, splicing site variants and upstream/downstream variants (less than a threshold away from a transcript, by default 1 kb). For intergenic variants, the closest two genes and the distances to them are reported. For exonic variants, ANNOVAR scans annotated mRNA sequences to identify and report amino acid changes, as well as stop-gain or stop-loss mutations. ANNOVAR can also perform region-based annotations on many types of annotation tracks, such as the most conserved elements and the predicted transcription factor binding sites. These annotations must be downloaded by ANNOVAR, before they can be utilized. Finally, ANNOVAR can filter specific variants such as SNPs with >1% frequency in the 1000 Genomes Project, or non-synonymous SNPs with SIFT scores >0.05.
To automate the procedure of reducing large amounts of variants into a small subset of functionally important variants, a script (auto_annovar.pl) is provided in the ANNOVAR package. By default, auto_annovar.pl performs a multi-step procedure by executing ANNOVAR multiple times, each time with several different command line parameters, and generates a final output file containing the most likely causal variants and their corresponding candidate genes. For recessive diseases, this list can be further trimmed down to include genes with multiple variants that are predicted to be functionally important.
Publication 2010
5' Untranslated Regions Amino Acids Binding Sites Exons Gene Annotation Genes Genetic Diversity Genome Introns Mutation Radionuclide Imaging RNA, Messenger Single Nucleotide Polymorphism Transcription Factor
In order to understand the modeling choices underlying our new imputation algorithm, it is crucial to consider the statistical issues that arise in imputation datasets. For simplicity, we will discuss these issues in the context of Scenario A, although we will also extend them to Scenario B in the Results section. Fundamentally, imputation is very similar to phasing, so it is no surprise that most imputation algorithms are based on population genetic models that were originally used in phasing methods. The most important distinction between phasing and imputation datasets is that the latter include large proportions of systematically missing genotypes.
Large amounts of missing data greatly increase the space of possible outcomes, and most phasing algorithms are not able to explore this space efficiently enough to be useful for inference in large studies. A standard way to overcome this problem with HMMs [6] (link),[11] (link) is to make the approximation that, conditional on the reference panel, each study individual's multilocus genotype is independent of the genotypes for the rest of the study sample. This transforms the inference problem into a separate imputation step for each study individual, with each step involving only a small proportion of missing data since the reference panel is assumed to be missing few, if any, genotypes.
In motivating our new imputation methodology, we pointed out that modeling the study individuals independently, rather than jointly, sacrifices phasing accuracy at typed SNPs; this led us to propose a hybrid approach that models the study haplotypes jointly at typed SNPs but independently at untyped SNPs. We made the latter choice partly to improve efficiency – it is fast to impute untyped alleles independently for different haplotypes, which allows us to use all of the information in large reference panels – but also because of the intuition that there is little to be gained from jointly modeling the study sample at untyped SNPs.
By contrast, the recently published BEAGLE [13] (link) imputation approach fits a full joint model to all individuals at all SNPs. To overcome the difficulties caused by the large space of possible genotype configurations, BEAGLE initializes its model using a few ad-hoc burn-in iterations in which genotype imputation is driven primarily by the reference panel. The intuition is that this burn-in period will help the model reach a plausible part of parameter space, which can be used as a starting point for fitting a full joint model.
This alternative modeling strategy raises the question of whether, and to what extent, it is advantageous to model the study sample jointly at untyped SNPs. One argument [20] (link) holds that there is no point in jointly modeling such SNPs because all of the linkage disequilibrium information needed to impute them is contained in the reference panel. A counterargument is that, as with any statistical missing data problem, the “correct” inference approach is to create a joint model of all observed and missing data. We have found that a full joint model may indeed improve accuracy on small, contrived imputation datasets (data not shown), and this leads us to believe that joint modeling could theoretically increase accuracy in more realistic datasets.
However, a more salient question is whether there is any useful information to be gained from jointly modeling untyped SNPs, and whether this information can be obtained with a reasonable amount of computational effort. Most imputation methods, including our new algorithm, implicitly assume that such information is not worth pursuing, whereas BEAGLE assumes that it is. We explore this question further in the sections that follow.
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Publication 2009
Alleles Genotype Haplotypes Hybrids Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Intuition Joints Seizures Single Nucleotide Polymorphism
VCF is flexible and allows to express virtually any type of variation by listing both the reference haplotype (the REF column) and the alternate haplotypes (the ALT column). This permits redundancy such that the same event can be expressed in multiple ways by including different numbers of reference bases or by combining two adjacent SNPs into one haplotype (Fig. 1g). Users are advised to follow recommended practice whenever possible: one reference base for SNPs and insertions, and one alternate base for deletions. The lowest possible coordinate should be used in cases where the position is ambiguous. When comparing or merging indel variants, the variant haplotypes should be reconstructed and reconciled, such as in the Figure 1g example, although the exact nature of the reconciliation can be arbitrary. For larger, more complex, variants, quoting large sequences becomes impractical, and in these cases the annotations in the INFO column can be used to describe the variant (Fig. 1f). The full VCF specification also includes a set of recommended practices for describing complex variants.
Publication 2011
Gene Deletion Haplotypes INDEL Mutation Insertion Mutation Single Nucleotide Polymorphism

Most recents protocols related to «Single Nucleotide Polymorphism»

Example 5

Three tobacco lines, FC401 wild type (Wt); FC40-M207 mutant line fourth generation (M4) and FC401-M544 mutant line fourth generation (M4) were used for candidate gene screening. Low anatabine traits were confirmed for the two tobacco mutant lines (M207 and M544) in root and leaf before screening (see FIG. 3).

RNA was extracted from root tissues of wild type (Wt) FC401, M207 and M544 with RNeasy Plus Mini kit from Quiagen Inc. following the manufacturer's protocol. cDNA libraries were prepared from the RNAs using In-Fusion® SMARTer® Directional cDNA Library Construction Kit from Clontech Inc. cDNA libraries were diluted to 100 ng/μl and used as the template for candidate gene PCR screening.

PCR amplifications were performed in 50 μl final volumes that contained 50-100 ng of template DNA (i.e., the cDNA library) and 0.2 μM of primers (Fisher Scientific) using the Platinum® Taq DNA Polymerase High Fidelity kit (Life Technology Inc.). Thermocycling conditions included a 5 min incubation at 94° C.; followed by 34 cycles of 30 seconds at 94° C., 30 seconds at 58° C., 1 min 30 seconds at 68° C.; with a final reaction step of 68° C. for 7 mins. The PCR products were evaluated by agarose gel electrophoresis, and desired bands were gel purified and sequenced using an ABI 3730 DNA Analyzer (ABI).

51 candidate genes (listed in Table 4) were cloned from F401, Wt, M207 and M544 lines, and sequenced for single nucleotide polymorphism (SNP) detection.

TABLE 4
Listing of Candidate Genes for Screening
Quinolinate Synthase A-1Pathogenesis related protein 1
Allene oxide synthaseAllene oxide cyclase
ET861088.1 Methyl esteraseFH733463.1 TGACG-sequence specific transcription factor
FH129193.1 Aquaporin-TransportFH297656.1 Universal stress protein
Universal stress protein Tabacum sequenceFH077657.1 Scarecrow-like protein
FH864888.1 EIN3-binding F-box proteinFH029529.1 4,5 DOPA dioxygenase
FI010668.1 Ethylene-responsive transcription EB430189 Carboxylesterase
factor
DW001704 Glutathione S transferaseEB683763 Bifunctional inhibitor/lipid transfer protein/seed
storage 2S albumin
DW002318 Serine/threonine protein kinaseDW004086 Superoxide dismutase
DW001733 Lipid transfer protein DIRIDW001944 Protein phosphatase 2C
DW002033EB683763 Bifunctional inhibitor/lipid transfer protein/seed
storage 2S albumin
DW002318 Serine/threonine protein kinaseDW002576 Glycosyl hydrolase of unknown function DUF1680
EB683279EB683763
EB683951FG141784 (FAD Oxidoreductase)
BBLa-Tabacum sequencesBBLb
BBLeBBLd
PdrlPdr2
Pdr3Pdr5a
Pdr5bNtMATEl
NtMATE2NtMATE3
WRKY8EIG-I24
WRKY3WRKY9
EIG-E17AJ748263.1 QPT2 quinolinate phosphoribosyltransferase
AJ748262.1 QPT1

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Patent 2024
Albumins allene oxide cyclase allene oxide synthase Amino Acid Sequence anatabine Carboxylesterase cDNA Library Dioxygenases Dopa Electrophoresis, Agar Gel Esterases Ethylenes Genes Glutathione S-Transferase Heat Shock Proteins Histocompatibility Testing Hydrolase lipid transfer protein Neoplasm Metastasis Nicotiana Nicotinate-nucleotide pyrophosphorylase (carboxylating) NOS1 protein, human Oligonucleotide Primers Oxidoreductase pathogenesis Plant Leaves Plant Roots Platinum Protein-Serine-Threonine Kinases Protein-Threonine Phosphatase Protein Kinases protein methylesterase Protein Phosphatase Protein Phosphatase 2C Proteins Quinolinate RNA Single Nucleotide Polymorphism Superoxide Dismutase Synapsin I Taq Polymerase Transcription, Genetic Transcription Factor Transfer Factor Water Channel

Example 3

We generated and analyzed a collection of 14 early-passage (passage ≤9) human pES cell lines for the persistence of haploid cells. All cell lines originated from activated oocytes displaying second polar body extrusion and a single pronucleus. We initially utilized chromosome counting by metaphase spreading and G-banding as a method for unambiguous and quantitative discovery of rare haploid nuclei. Among ten individual pES cell lines, a low proportion of haploid metaphases was found exclusively in a single cell line, pES10 (1.3%, Table 1B). We also used viable FACS with Hoechst 33342 staining, aiming to isolate cells with a DNA content corresponding to less than two chromosomal copies (2c) from four additional lines, leading to the successful enrichment of haploid cells from a second cell line, pES12 (Table 2).

Two individual haploid-enriched ES cell lines were established from both pES10 and pES12 (hereafter referred to as h-pES10 and h-pES12) within five to six rounds of 1c-cell FACS enrichment and expansion (FIG. 1C (pES10), FIG. 5A (pES12)). These cell lines were grown in standard culture conditions for over 30 passages while including cells with a normal haploid karyotype (FIG. 1D, FIG. 5B). However, since diploidization occurred at a rate of 3-9% of the cells per day (FIG. 1E), cell sorting at every three to four passages was required for maintenance and analysis of haploid cells. Further, visualization of ploidy in adherent conditions was enabled by DNA fluorescence in situ hybridization (FISH) (FIG. 1F, FIG. 5c) and quantification of centromere protein foci (FIG. 1G, FIG. 5D; FIG. 6). In addition to their intact karyotype, haploid ES cells did not harbor significant copy number variations (CNVs) relative to their unsorted diploid counterparts (FIG. 5E). Importantly, we did not observe common duplications of specific regions in the two cell lines that would result in pseudo-diploidy. Therefore, genome integrity was preserved throughout haploid-cell isolation and maintenance. As expected, single nucleotide polymorphism (SNP) array analysis demonstrated complete homozygosity of diploid pES10 and pES12 cells across all chromosomes.

Both h-pES10 and h-pES12 exhibited classical human pluripotent stem cell features, including typical colony morphology and alkaline phosphatase activity (FIG. 2A, FIG. 2B). Single haploid ES cells expressed various hallmark pluripotency markers (NANOG, OCT4, SOX2, SSEA4 and TRA1-60), as confirmed in essentially pure haploid cultures by centromere foci quantification (>95% haploids) (FIG. 2C, FIG. 7). Notably, selective flow cytometry enabled to validate the expression of two human ES-cell-specific cell surface markers (TRA-1-60 and CLDN618) in single haploid cells (FIG. 2D). Moreover, sorted haploid and diploid ES cells showed highly similar transcriptional and epigenetic signatures of pluripotency genes (FIG. 2E, FIG. 2F). Since the haploid ES cells were derived as parthenotes, they featured distinct transcriptional and epigenetic profiles of maternal imprinting, owing to the absence of paternally-inherited alleles (FIG. 8).

Haploid cells are valuable for loss-of-function genetic screening because phenotypically-selectable mutants can be identified upon disruption of a single allele. To demonstrate the applicability of this principle in haploid human ES cells, we generated a genome-wide mutant library using a piggyBac transposon gene trap system that targets transcriptionally active loci (FIG. 2G, FIG. 8E), and screened for resistance to the purine analog 6-thioguanine (6-TG). Out of six isolated and analyzed 6-TG-resistant colonies, three harbored a gene trap insertion localizing to the nucleoside diphosphate linked moiety X-type motif 5 (NUDT5) autosomal gene (FIG. 2H). NUDT5 disruption was recently confirmed to confer 6-TG resistance in human cells,51 by acting upstream to the production of 5-phospho-D-ribose-1-pyrophosphate (PRPP), which serves as a phosphoribosyl donor in the hypoxanthine phosphoribosyltransferase 1 (HPRT1)-mediated conversion of 6-TG to thioguanosine monophosphate (TGMP) (FIG. 2I). Detection of a loss-of-function phenotype due to an autosomal mutation validates that genetic screening is feasible in haploid human ES cells.

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Patent 2024
Alkaline Phosphatase Alleles Cell Lines Cell Nucleus Cells Cell Separation Centromere Chromosomes Copy Number Polymorphism Diphosphates Diploid Cell Diploidy Embryonic Stem Cells Flow Cytometry Fluorescent in Situ Hybridization Genes Genes, vif Genitalia Genome Genomic Library Haploid Cell HOE 33342 Homo sapiens Homozygote Human Embryonic Stem Cells Hypoxanthine Phosphoribosyltransferase isolation Jumping Genes Karyotype Metaphase Mothers Mutation Nucleosides Oocytes Phenotype Pluripotent Stem Cells Polar Bodies POU5F1 protein, human Proteins purine Ribose Single Nucleotide Polymorphism SOX2 protein, human stage-specific embryonic antigen-4 Tissue Donors Transcription, Genetic
We downloaded short-read data for 1,057 accessions from the 1001 Genomes Project [19 (link)]. Raw paired-end reads were processed with cutadapt (v1.9) [51 (link)] to remove 3′ adapters, and to trim 5′-ends with quality 15 and 3′-ends with quality 10 or N-endings. All reads were aligned to the A. thaliana TAIR10 reference genome [52 (link)] with BWA-MEM (v0.7.8) [53 ], and both Samtools (v0.1.18) and Sambamba (v0.6.3) were used for various file format conversions, sorting and indexing [54 (link), 55 (link)], while duplicated reads where by marked by Markduplicates from Picard (v1.101; http://broadinstitute.github.io/picard/). Further steps were carried out with GATK (v3.4) functions [26 (link), 56 ]. Local realignment around indels were done with “RealignerTargetCreator” and “IndelRealigner,” and base recalibration with “BaseRecalibrator” by providing known indels and SNPS from The 1001 Genomes Consortium [19 (link)]. Genetic variants were called with “HaplotypeCaller” in individual samples followed by joint genotyping of a single cohort with “GenotypeGVCFs.” An initial SNP filtering was done following the variant quality score recalibration (VQSR) protocol. Briefly, a subset of ~181,000 high-quality SNPs from the RegMap panel [57 (link)] was used as the training set for VariantRecalibrator with a priori probability of 15 and four maximum Gaussian distributions. Finally, only bi-allelic SNPs within a sensitivity tranche level of 99.5 were kept, for a total of 7,311,237 SNPs.
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Publication 2023
Alleles Genetic Diversity Genome Hypersensitivity INDEL Mutation Joints Single Nucleotide Polymorphism
Bisulfite reads for the accessions were taken from 1001 methylomes (Kawakatsu et al. 2016). Reads were mapped to PacBio genomes using an nf-core pipeline (https://github.com/rbpisupati/methylseq). We filtered for cytosines with a minimum depth of 3. They methylation levels were calculated either on the gene-body or on 200bp windows using custom python scripts following guidelines from Schultz et al. [61 (link)]. Weighted methylation levels were used, i.e., if there are three cytosines with a depth of t1, t2, and t3 and number of methylated reads are c1, c2, and c3, the methylation level was calculated as (c1+c2+c3)/(t1+t2+t3). We called a gene “differentially methylated” if the difference in weighted methylation level was more than 0.05 for CG and 0.03 for CHG.
The sequencing coverage for each accession was extracted using the function bamCoverage (windows size of 50bp) from the program DeepTools [62 (link)]. The Bigwig files generated were then processed in R using the package rtracklayer. No correlation between the mean sequencing coverage and the number of pseudo-SNPs detected was observed (Additional file 1: Fig. S18).
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Publication 2023
Cytosine Epigenome Genes Human Body hydrogen sulfite Methylation Python Single Nucleotide Polymorphism
From the raw VCF files SNP positions containing heterozygous labels were extracted using GATK VariantFiltration. From the 3.3 million of heterozygous SNPs extracted, two filtering steps were then applied. Only SNPs with a frequency of at least 5% of the population and located in TAIR10-annotated coding regions were kept. After those filtering steps a core set of 26,647 SNPs were retained for further analysis (see Additional file 1: Fig. S17). Gene names and features containing those pseudo-SNPs were extracted from the TAIR10 annotation.
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Publication 2023
Genes Heterozygote Single Nucleotide Polymorphism

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The TaqMan SNP Genotyping Assays are a collection of pre-designed and validated assays used for the detection and analysis of single nucleotide polymorphisms (SNPs) in genetic samples. These assays utilize the TaqMan probe-based real-time PCR technology to accurately identify the specific genetic variations present in a sample.
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More about "Single Nucleotide Polymorphism"

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation, where a single nucleotide in the DNA sequence differs between individuals.
These genetic variations play a crucial role in understanding disease susceptibility, drug response, and genetic differences.
Optimizing SNP research can be achieved using tools like PubCompare.ai, which helps locate relevant protocols from literature, preprints, and patents.
By leveraging AI-driven comparisons, researchers can identify the best protocols and products, enhancing reproducibility and taking their SNP research to the next level.
To further enhance SNP research, tools like the HiSeq 2000 and HiSeq 2500 sequencing platforms, QIAamp DNA Blood Mini Kit and QIAamp DNA Mini Kit for DNA extraction, TaqMan SNP Genotyping Assays and TaqMan assays for SNP genotyping, and the GenomeStudio software and MassARRAY system for data analysis can be utilized.
These technologies and kits provide reliable and efficient methods for SNP detection, genotyping, and data analysis, empowering researchers to uncover insights and advance their understanding of genetic variations.
By incorporating these resources and techniques, researchers can optimize their SNP research, improve reproducibility, and gain deeper insights into the underlying genetic mechanisms that influence disease, drug response, and other important biological phenomena.
The combination of PubCompare.ai and these complementary tools and technologies can truly take SNP research to new heights.