Annotation Of Single Nucleotide Polymorphisms Research Articles

Functional annotation of colon cancer risk SNPs.

Colorectal cancer (CRC) is a leading cause of cancer-related deaths in the United States. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with increased risk for CRC. A molecular understanding of the functional consequences of this genetic variation has been complicated because each GWAS SNP is a surrogate for hundreds of other SNPs, most of which are located in non-coding regions. Here we use genomic and epigenomic information to test the hypothesis that the GWAS SNPs and/or correlated SNPs are in elements that regulate gene expression, and identify 23 promoters and 28 enhancers. Using gene expression data from normal and tumour cells, we identify 66 putative target genes of the risk-associated enhancers (10 of which were also identified by promoter SNPs). Employing CRISPR nucleases, we delete one risk-associated enhancer and identify genes showing altered expression. We suggest that similar studies be performed to characterize all CRC risk-associated enhancers.

SNP@lincTFBS: an integrated database of polymorphisms in human LincRNA transcription factor binding sites.

Large intergenic non-coding RNAs (lincRNAs) are a new class of functional transcripts, and aberrant expression of lincRNAs was associated with several human diseases. The genetic variants in lincRNA transcription factor binding sites (TFBSs) can change lincRNA expression, thereby affecting the susceptibility to human diseases. To identify and annotate these functional candidates, we have developed a database SNP@lincTFBS, which is devoted to the exploration and annotation of single nucleotide polymorphisms (SNPs) in potential TFBSs of human lincRNAs. We identified 6,665 SNPs in 6,614 conserved TFBSs of 2,423 human lincRNAs. In addition, with ChIPSeq dataset, we identified 139,576 SNPs in 304,517 transcription factor peaks of 4,813 lincRNAs. We also performed comprehensive annotation for these SNPs using 1000 Genomes Project datasets across 11 populations. Moreover, one of the distinctive features of SNP@lincTFBS is the collection of disease-associated SNPs in the lincRNA TFBSs and SNPs in the TFBSs of disease-associated lincRNAs. The web interface enables both flexible data searches and downloads. Quick search can be query of lincRNA name, SNP identifier, or transcription factor name. SNP@lincTFBS provides significant advances in identification of disease-associated lincRNA variants and improved convenience to interpret the discrepant expression of lincRNAs. The SNP@lincTFBS database is available at http://bioinfo.hrbmu.edu.cn/SNP_lincTFBS.

SNP-SIG 2013: from coding to non-coding--new approaches for genomic variant interpretation.

Overview The last few years have seen an explosion in genomic sequencing and, consistently, exponential growth in the number of known variants [1]. One of the variant repositories, the dbSNP database [2], currently contains over 62 million human single nucleotide polymorphisms (SNPs) and many other short genomic variants. These variants are interesting as both markers of evolution and for their phenotypic effects (e.g. characteristic traits and diseases). However, due to the high count of variants per-genome, an even larger number of potential variant set interactions, and relatively low levels of experimental annotation, variant interpretation is still severely limited. In line with the continued strong interest of the computational biology community in genetic variation, the 3 SNP Special Interest Group (SNP-SIG) meeting [3,4] was held on July 19 at the ISMB/ECCB 2013 in Berlin (Germany). The meeting aimed to summarize the relevant (computational) research advances in the fields of “Annotation and prediction of structural/functional impacts of coding SNPs” and “SNPs and Personal Genomics: GWAS, populations and phylogenetic analysis”. The SNP-SIG is a venue for the development of a research network of scientists, necessary for facilitating the exchange of ideas and establishing new collaborations. The 2013 SNP-SIG attracted over 100 participants, with seven research talks and five presentations from the leading scientists in the field. The SIG topics covered in this proceedings issue address: the SNP annotation [5] from functional [6,7] and structural [8,9] perspectives, the prediction of pharmocogenomic variants [10] and new drug targets [11], the visualization of transcriptome genetic variants [12], and human population models to predict the rate of private variants [13]. [The complete program of SNP-SIG meeting 2013 with presentation and poster abstracts is available at http:// snpsig.biofold.org/2013/docs/snp-sig-2013-programme.pdf].

Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of large-scale protein-protein interaction networks, and can be useful for functional annotation of disease-associated SNPs. SNIP-IN tool is freely accessible as a web-server at http://korkinlab.org/snpintool/.

Identification of transcription factors and single nucleotide polymorphisms of Lrh1 and its homologous genes in Lrh1-knockout pancreas of mice.

BackgroundTo identify transcription factors (TFs) and single nucleotide polymorphisms (SNPs) of Lrh1 (also named Nr5a2) and its homologous genes in Lrh1-knockout pancreas of mice.MethodsThe RNA-Seq data GSE34030 were downloaded from Gene Expression Omnibus (GEO) database, including 2 Lrh1 pancreas knockout samples and 2 wild type samples. All reads were processed through TopHat and Cufflinks package to calculate gene-expression level. Then, the differentially expressed genes (DEGs) were identified via non-parametric algorithm (NOISeq) methods in R package, of which the homology genes of Lrh1 were identified via BLASTN analysis. Furthermore, the TFs of Lrh1 and its homologous genes were selected based on TRANSFAC database. Additionally, the SNPs were analyzed via SAM tool to record the locations of mutant sites.ResultsTotal 15683 DEGs were identified, of which 23 was Lrh1 homology genes (3 up-regulated and 20 down-regulated). Fetoprotein TF (FTF) was the only TF of Lrh1 identified and the promoter-binding factor of FTF was CYP7A. The SNP annotations of Lrh1 homologous genes showed that 92% of the mutation sites were occurred in intron and upstream. Three SNPs of Lrh1 were located in intron, while 1819 SNPs of Phkb were located in intron and 1343 SNPs were located in the upstream region.ConclusionFTF combined with CYP7A might play an important role in Lrh1 regulated pancreas-specific transcriptional network. Furthermore, the SNPs analysis of Lrh1 and its homology genes provided the candidate mutant sites that might affect the Lrh1-related production and secretion of pancreatic fluid.

Footprints of Directional Selection in Wild Atlantic Salmon Populations: Evidence for Parasite-Driven Evolution?

Mechanisms of host-parasite co-adaptation have long been of interest in evolutionary biology; however, determining the genetic basis of parasite resistance has been challenging. Current advances in genome technologies provide new opportunities for obtaining a genome-scale view of the action of parasite-driven natural selection in wild populations and thus facilitate the search for specific genomic regions underlying inter-population differences in pathogen response. European populations of Atlantic salmon (Salmo salar L.) exhibit natural variance in susceptibility levels to the ectoparasite Gyrodactylus salaris Malmberg 1957, ranging from resistance to extreme susceptibility, and are therefore a good model for studying the evolution of virulence and resistance. However, distinguishing the molecular signatures of genetic drift and environment-associated selection in small populations such as land-locked Atlantic salmon populations presents a challenge, specifically in the search for pathogen-driven selection. We used a novel genome-scan analysis approach that enabled us to i) identify signals of selection in salmon populations affected by varying levels of genetic drift and ii) separate potentially selected loci into the categories of pathogen (G. salaris)-driven selection and selection acting upon other environmental characteristics. A total of 4631 single nucleotide polymorphisms (SNPs) were screened in Atlantic salmon from 12 different northern European populations. We identified three genomic regions potentially affected by parasite-driven selection, as well as three regions presumably affected by salinity-driven directional selection. Functional annotation of candidate SNPs is consistent with the role of the detected genomic regions in immune defence and, implicitly, in osmoregulation. These results provide new insights into the genetic basis of pathogen susceptibility in Atlantic salmon and will enable future searches for the specific genes involved.

Post genome-wide association studies functional characterization of prostate cancer risk loci

BackgroundOver the last decade, genome-wide association studies (GWAS) have discovered many risk associated single nucleotide polymorphisms (SNPs) of prostate cancer (PCa). However, the majority of the associated PCa SNPs, including those in linkage disequilibrium (LD) blocks, are generally not located in protein coding regions. The systematical investigation of the functional roles of these SNPs, especially the non-coding SNPs, becomes very necessary and helpful to the understanding of the molecular mechanism of PCa.ResultsIn this work, we proposed a comprehensive framework at network level to integrate the SNP annotation, target gene assignment, gene ontology (GO) classification, pathway enrichment analysis and regulatory network reconstruction to illustrate the molecular functions of PCa associated SNPs. By LD expansion, we first identified 1828 LD SNPs using 49 reported GWAS SNPs as a start. We carefully annotated these 1828 LD SNPs via either UCSC known genes, UCSC regulation elements, or expression Quantitative Trait Loci (eQTL) data. As a result, we found 1154 SNPs were functionally annotated and obtained 205 unique PCa genes for further enrichment analysis. The enriched GO biological processes and pathways were found mainly related to regulation of cell death, apoptosis, cell proliferation, and metabolic process, which have been proved essential to cancer development. We constructed PCa genes specific transcription regulatory networks, finding several important genetic regulators for PCa, such as IGF-1/IGF-2 receptors, SP1, CREB1, and androgen receptor (AR).ConclusionsA comprehensive framework was proposed for integrative and systematic analysis of PCa SNPs, the analysis can provide essential information for the understanding of the regulatory function of GWAS SNPs in PCa, and will facilitate the discovery of novel candidate biomarkers for diagnosis and prognosis of PCa.

Tool for rapid annotation of microbial SNPs (TRAMS): a simple program for rapid annotation of genomic variation in prokaryotes

Next generation sequencing (NGS) has been widely used to study genomic variation in a variety of prokaryotes. Single nucleotide polymorphisms (SNPs) resulting from genomic comparisons need to be annotated for their functional impact on the coding sequences. We have developed a program, TRAMS, for functional annotation of genomic SNPs which is available to download as a single file executable for WINDOWS users with limited computational experience and as a Python script for Mac OS and Linux users. TRAMS needs a tab delimited text file containing SNP locations, reference nucleotide and SNPs in variant strains along with a reference genome sequence in GenBank or EMBL format. SNPs are annotated as synonymous, nonsynonymous or nonsense. Nonsynonymous SNPs in start and stop codons are separated as non-start and non-stop SNPs, respectively. SNPs in multiple overlapping features are annotated separately for each feature and multiple nucleotide polymorphisms within a codon are combined before annotation. We have also developed a workflow for Galaxy, a highly used tool for analysing NGS data, to map short reads to a reference genome and extract and annotate the SNPs. TRAMS is a simple program for rapid and accurate annotation of SNPs that will be very useful for microbiologists in analysing genomic diversity in microbial populations.

Pathway analysis of a genome-wide association study in schizophrenia

ObjectiveThe aim of this study was to identify the candidate single nucleotide polymorphisms (SNPs) and candidate mechanisms that contribute to schizophrenia susceptibility and to generate a SNP to gene to pathway hypothesis using an analytical pathway-based approach. MethodsWe used schizophrenia GWAS data of the genotypes of 660,259 SNPs in 1378 controls and 1351 cases of European descent after quality control filtering. ICSNPathway (Identify candidate Causal SNPs and Pathways) analysis was applied to the schizophrenia GWAS dataset. The first stage involved the pre-selection of candidate SNPs by linkage disequilibrium analysis and the functional SNP annotation of the most significant SNPs found. The second stage involved the annotation of biological mechanisms for the pre-selected candidate SNPs using improved-gene set enrichment analysis. ResultsICSNPathway analysis identified fifteen candidate SNPs, ten candidate pathways, and nine hypothetical biological mechanisms. The most strongly associated potential pathways were as follows. First, rs1644731 and rs1644730 to RDH8 to estrogen biosynthetic process (p<0.001, FDR<0.001). The genes involved in this pathway are RDH8 and HSD3B1 (p<0.05). All-trans-retinol dehydrogenase (RDH8) is a visual cycle enzyme that reduces all-trans-retinal to all-trans-retinol in the presence of NADPH. The chemical reactions and pathways involved result in the formation of estrogens, which are C18 steroid hormones that can stimulate the development of female sexual characteristics. Second, rs1146031 to ACVR1 to mesoderm formation and activin binding (p<0.001, FDR=0.032, 0.034). Two of 15 candidate genes are known genes associated with schizophrenia: KCNQ2 and APOL2. One of the 10 candidate pathways, estrogen biosynthetic process, is known to be associated with schizophrenia (p<0.001, FDR<0.001). However, 13 of candidate genes (RDH8, ACVR1, PSMD9, KCNAB1, SLC17A3, ARCN1, COG7, STAB2, LRPAP1, STAB1, CXCL16, COL4A4, EXOSC3) and 9 of candidate pathways were novel. ConclusionBy applying ICSNPathway analysis to schizophrenia GWAS data, we identified candidate SNPs, genes like KCNQ2 and APOL2 and pathways involving the estrogen biosynthetic process may contribute to schizophrenia susceptibility. Further analyses are needed to validate the results of this analysis.

Snpdat: Easy and rapid annotation of results from de novo snp discovery projects for model and non-model organisms

BackgroundSingle nucleotide polymorphisms (SNPs) are the most abundant genetic variant found in vertebrates and invertebrates. SNP discovery has become a highly automated, robust and relatively inexpensive process allowing the identification of many thousands of mutations for model and non-model organisms. Annotating large numbers of SNPs can be a difficult and complex process. Many tools available are optimised for use with organisms densely sampled for SNPs, such as humans. There are currently few tools available that are species non-specific or support non-model organism data.ResultsHere we present SNPdat, a high throughput analysis tool that can provide a comprehensive annotation of both novel and known SNPs for any organism with a draft sequence and annotation. Using a dataset of 4,566 SNPs identified in cattle using high-throughput DNA sequencing we demonstrate the annotations performed and the statistics that can be generated by SNPdat.ConclusionsSNPdat provides users with a simple tool for annotation of genomes that are either not supported by other tools or have a small number of annotated SNPs available. SNPdat can also be used to analyse datasets from organisms which are densely sampled for SNPs. As a command line tool it can easily be incorporated into existing SNP discovery pipelines and fills a niche for analyses involving non-model organisms that are not supported by many available SNP annotation tools. SNPdat will be of great interest to scientists involved in SNP discovery and analysis projects, particularly those with limited bioinformatics experience.

Whole genome resequencing in tomato reveals variation associated with introgression and breeding events

BackgroundOne of the goals of genomics is to identify the genetic loci responsible for variation in phenotypic traits. The completion of the tomato genome sequence and recent advances in DNA sequencing technology allow for in-depth characterization of genetic variation present in the tomato genome. Like many self-pollinated crops, cultivated tomato accessions show a low molecular but high phenotypic diversity. Here we describe the whole-genome resequencing of eight accessions (four cherry-type and four large fruited lines) chosen to represent a large range of intra-specific variability and the identification and annotation of novel polymorphisms.ResultsThe eight genomes were sequenced using the GAII Illumina platform. Comparison of the sequences with the reference genome yielded more than 4 million single nucleotide polymorphisms (SNPs). This number varied from 80,000 to 1.5 million according to the accessions. Almost 128,000 InDels were detected. The distribution of SNPs and InDels across and within chromosomes was highly heterogeneous revealing introgressions from wild species and the mosaic structure of the genomes of the cherry tomato accessions. In-depth annotation of the polymorphisms identified more than 16,000 unique non-synonymous SNPs. In addition 1,686 putative copy-number variations (CNVs) were identified.ConclusionsThis study represents the first whole genome resequencing experiment in cultivated tomato. Substantial genetic differences exist between the sequenced tomato accessions and the reference sequence. The heterogeneous distribution of the polymorphisms may be related to introgressions that occurred during domestication or breeding. The annotated SNPs, InDels and CNVs identified in this resequencing study will serve as useful genetic tools, and as candidate polymorphisms in the search for phenotype-altering DNA variations.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-14-791) contains supplementary material, which is available to authorized users.

SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS

BackgroundThe capability of correlating specific genotypes with human diseases is a complex issue in spite of all advantages arisen from high-throughput technologies, such as Genome Wide Association Studies (GWAS). New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects.ResultsWe propose a new bioinformatics approach to support biological data mining in the analysis and interpretation of SNPs associated to pathologies. This system can be employed to design custom genotyping chips for disease-oriented studies and to re-score GWAS results. The proposed method relies (1) on the data integration of public resources using a gene-centric database design, (2) on the evaluation of a set of static biomolecular annotations, defined as features, and (3) on the SNP scoring function, which computes SNP scores using parameters and weights set by users. We employed a machine learning classifier to set default feature weights and an ontological annotation layer to enable the enrichment of the input gene set. We implemented our method as a web tool called SNPranker 2.0 (http://www.itb.cnr.it/snpranker), improving our first published release of this system. A user-friendly interface allows the input of a list of genes, SNPs or a biological process, and to customize the features set with relative weights. As result, SNPranker 2.0 returns a list of SNPs, localized within input and ontologically enriched genes, combined with their prioritization scores.ConclusionsDifferent databases and resources are already available for SNPs annotation, but they do not prioritize or re-score SNPs relying on a-priori biomolecular knowledge. SNPranker 2.0 attempts to fill this gap through a user-friendly integrated web resource. End users, such as researchers in medical genetics and epidemiology, may find in SNPranker 2.0 a new tool for data mining and interpretation able to support SNPs analysis. Possible scenarios are GWAS data re-scoring, SNPs selection for custom genotyping arrays and SNPs/diseases association studies.

Pathway analysis of genome-wide association studies for Parkinson’s disease

The study was done to identify the candidate causal single nucleotide polymorphisms (SNPs) and candidate causal mechanisms that contribute to Parkinson's disease (PD) susceptibility and to generate a SNP to ene to pathway hypothesis using an analytical pathway-based approach. We used a PD genome-wide association study (GWAS) meta-analysis data of the genotypes of 2,525,705 SNPs in 4,238 PD cases and 4,239 controls. Identify candidate Causal SNPs and Pathways (ICSNPathway) analysis was applied to the PD GWAS dataset. The first stage involved the pre-selection of candidate causal SNPs by linkage disequilibrium analysis and the functional SNP annotation of the most significant SNPs found. The second stage involved the annotation of biological mechanisms for the pre-selected candidate causal SNPs using improved-gene set enrichment analysis. ICSNPathway analysis identified three candidate SNPs, two genes, twenty-one pathways, and three hypothetical biological mechanisms: (1) rs17651549 to microtubule-associated protein tau (MAPT) to protein domain specific binding (nominal p < 0.001, false discovery rate (FDR) < 0.001), neurogenesis (nominal p < 0.001, FDR < 0.001), regulation of neurogenesis (nominal p < 0.001, FDR = 0.001), positive regulation of axonogenesis (nominal p < 0.001, FDR = 0.001), regulation of protein polymerization (nominal p < 0.001, FDR = 0.004), negative regulation of organelle organization (nominal p < 0.001, FDR = 0.004), hsa01510 (nominal p < 0.001, FDR = 0.005), neuron differentiation (nominal p < 0.001, FDR = 0.009), and axonogenesis (nominal p < 0.001, FDR = 0.009); (2) rs10445337 to MAPT to protein domain specific binding (nominal p < 0.001, FDR < 0.001), neurogenesis (nominal p < 0.001, FDR < 0.001), regulation of neurogenesis (nominal p < 0.001, FDR = 0.001), and positive regulation of axonogenesis (nominal p < 0.001, FDR = 0.001); (3) rs9938550 to HSD3B7 to hsa00363 (nominal p < 0.001, FDR = 0.004), bile acid metabolic process (nominal p = 0.005, FDR = 0.019), and steroid metabolic process (nominal p = 0.010, FDR = 0.039). By applying the ICSNPathway analysis to PD GWAS meta-analysis data, three candidate SNPs, two genes (MAPT and HSD3B7), and 21 pathways involving protein domain specific binding and neurogenesis were identified, which may contribute to PD susceptibility.

GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies

GWASTools is an R/Bioconductor package for quality control and analysis of genome-wide association studies (GWAS). GWASTools brings the interactive capability and extensive statistical libraries of R to GWAS. Data are stored in NetCDF format to accommodate extremely large datasets that cannot fit within R's memory limits. The documentation includes instructions for converting data from multiple formats, including variants called from sequencing. GWASTools provides a convenient interface for linking genotypes and intensity data with sample and single nucleotide polymorphism annotation.

Single nucleotide polymorphism discovery from wheat next‐generation sequence data

Single nucleotide polymorphisms (SNPs) are the most abundant type of molecular genetic marker and can be used for producing high-resolution genetic maps, marker-trait association studies and marker-assisted breeding. Large polyploid genomes such as wheat present a challenge for SNP discovery because of the potential presence of multiple homoeologs for each gene. AutoSNPdb has been successfully applied to identify SNPs from Sanger sequence data for several species, including barley, rice and Brassica, but the volume of data required to accurately call SNPs in the complex genome of wheat has prevented its application to this important crop. DNA sequencing technology has been revolutionized by the introduction of next-generation sequencing, and it is now possible to generate several million sequence reads in a timely and cost-effective manner. We have produced wheat transcriptome sequence data using 454 sequencing technology and applied this for SNP discovery using a modified autoSNPdb method, which integrates SNP and gene annotation information with a graphical viewer. A total of 4,694,141 sequence reads from three bread wheat varieties were assembled to identify a total of 38 928 candidate SNPs. Each SNP is within an assembly complete with annotation, enabling the selection of polymorphism within genes of interest.

Pathway analysis of genome-wide association study for bone mineral density

The aim of this study was to identify the candidate causal single nucleotide polymorphisms (SNPs) and candidate causal mechanisms that contribute to bone mineral density (BMD) and to generate a SNP to gene to pathway hypothesis using an analytical pathway-based approach. We used hip BMD GWAS data of the genotypes of 301,019 SNPs in 5,715 Europeans. ICSNPathway (identify candidate causal SNPs and pathways) analysis was applied to the BMD GWAS dataset. The first stage involved the pre-selection of candidate causal SNPs by linkage disequilibrium analysis and the functional SNP annotation of the most significant SNPs found. The second stage involved the annotation of biological mechanisms for the pre-selected candidate causal SNPs using improved-gene set enrichment analysis. ICSNPathway analysis identified seven candidate SNPs, eight candidate pathways, and seven hypothetical biological mechanisms. Eight pathways are as follows; gamma-hexachlorocyclohexane degradation (nominal p-value < 0.001, false discovery rate (FDR) <0.001), regulation of the smoothened signaling pathway (nominal p-value < 0.001, FDR = 0.016), TACI and BCMA stimulation of B cell immune response (nominal p-value < 0.001, FDR = 0.021), endonuclease activity (nominal p-value = 0.001, FDR = 0,026), regulation of defense response to virus (nominal p-value = 0.001, FDR = 0.028), serine_type_endopeptidase_inhibitor_activity (nominal p-value = 0.001, FDR = 0.044), endoribonuclease activity (nominal p-value = 0.002, FDR = 0.045), and myeloid leukocyte differentiation (nominal p-value = 0.001, FDR = 0.050). The most significant causal pathway was gamma-hexachlorocyclohexane degradation. CYP3A5, PON2, PON3, CMBL, PON1, ALPL, CYP3A43, CYP3A7, ACP6, ACPP, and ALPI (p < 0.05) are involved in the pathway of gamma-hexachlorocyclohexane degradation. Further examination of the gene contents revealed that DBR1, DICER1, EXO1, FEN1, POP1, POP4, RPP30, and RPP38 were involved in 2 of the 8 pathways (p < 0.05). By applying ICSNPathway analysis to BMD GWAS data, we identified seven candidate SNPs and eight pathways involving gamma-hexachlorocyclohexane degradation, which may contribute to low BMD.

SNP-SIG Meeting 2011: Identification and annotation of SNPs in the context of structure, function, and disease

Overview Advances in high-throughput sequencing, genotyping, and characterization of haplotype diversity are consistently generating vast amounts of genomic data. Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation. In the recent years the number of known SNPs has been increasing exponentially; the last release of the NCBI’s dbSNP database contained more than 50 million human SNPs. SNPs are interesting as both markers of evolutionary history and in the context of their phenotypic manifestations (e.g. characteristic traits and diseases). For some diseases, e.g. sickle-cell anemia, the causative SNPs are well documented. In most other cases, the detection of disease-causing variants is still a problem. The genome-wide association studies (GWAS) provide insight into SNP-disease relationships. However, GWAS analysis is both experimentally and computationally expensive and fails to properly consider the rare variants, i.e. individual-specific SNPs that have yet to be documented on a population scale. This discrepancy between the deluge of SNP data and the lack of its interpretation spurs the development of the SNP impact annotation/prediction algorithms. In the near future, the study of genetic variation in disease and treatment options will be key for the development of the field of personalized medicine. In 2010, the first edition of the Critical Assessment of Genomic Interpretation (CAGI; Berkeley, California) was organized to evaluate the ability of available computational methods to predict the phenotypic impacts of genomic variation. Annotation of SNPs was also a hot topic in many other meetings, such as AIMM at ECCB 2010 (Ghent, Belgium), the HGVS 2010 meeting (Washington, DC) and PSB 2011 (Big Island of Hawaii, USA). In line with the increasing interest in the genetic variation analysis and annotation, on July 15, 2011 we organized the first SNP Special Interesting Group (SNP-SIG) meeting at ISMB/ECCB’2011 in Vienna, Austria (http://snps.uib.es/ snp-sig/2011). This meeting attempted to summarize the field’s research advances in the directions of “Annotation and prediction of structural/functional impacts of coding SNPs” and “SNPs and Personal Genomics: GWAS, populations and phylogenetic analysis”. Over 70 scientists actively working in the field and strongly interested in its development have officially registered for the SIG. On the date of the meeting, an even larger number of ISMB participants have gathered to discuss their work, the state of the art, and future perspectives. In all, 17 presentation proposals and 13 posters were submitted to the SIG and eight works where selected for an oral presentation at the meeting. Distinguished scientists were invited to share their visions of the field past, present, and future: Steven Brenner (University of California at Berkeley), Atul Butte (Stanford University), John Moult (University of Maryland, College Park), Burkhard Rost (Techinal University of Munich) and Mauno Vihinen (Lund University). A round table discussion on the most timely and important problems of SNP annotation was held, directed by Christopher Baker (University of New Brunswick), Maricel Kann (University of Maryland, Baltimore), Sean Mooney (Buck Institute), Pauline Ng (Genome Institute of Singapore) and Mauno Vihinen (Lund University). * Correspondence: YanaB@rci.rutgers.edu; emidio.capriotti@uab.es Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences, Rutgers University, New Brunswick 76 Lipman Drive, NJ 08901, USA Department of Mathematics and Computer Science, University of Balearic Islands, Ctra. de Valldemossa Km 7.5, Palma de Mallorca, 07122 Spain Full list of author information is available at the end of the article Bromberg and Capriotti BMC Genomics 2012, 13(Suppl 4):S1 http://www.biomedcentral.com/1471-2164/13/S4/S1

Genome-wide prediction of splice-modifying SNPs in human genes using a new analysis pipeline called AASsites

BackgroundSome single nucleotide polymorphisms (SNPs) are known to modify the risk of developing certain diseases or the reaction to drugs. Due to next generation sequencing methods the number of known human SNPs has grown. Not all SNPs lead to a modified protein, which may be the origin of a disease. Therefore, the recognition of functional SNPs is needed. Because most SNP annotation tools look for SNPs which lead to an amino acid exchange or a premature stop, we designed a new tool called AASsites which searches for SNPs which modify splicing.ResultsAASsites uses several gene prediction programs and open reading frame prediction to compare the wild type (wt) and the variant gene sequence. The results of the comparison are combined by a handmade rule system to classify a change in splicing as “likely, probable, unlikely”. Having received good results from tests with SNPs known for changing the splicing pattern we checked 80,000 SNPs from the human genome which are located near splice sites for their ability to change the splicing pattern of the gene and hereby result in a different protein. We identified 301 “likely” and 985 “probable” classified SNPs with such characteristics. Within this set 33 SNPs are described in the ssSNP Target database to cause modified splicing.ConclusionsWith AASsites single SNPs can be checked for those causing splice modifications. Screening 80,000 known human SNPs we detected about 1,200 SNPs which probably modify splicing. AASsites is available at http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar using any web browser.

Characterization of single-nucleotide variation in Indian-origin rhesus macaques (Macaca mulatta)

BackgroundRhesus macaques are the most widely utilized nonhuman primate model in biomedical research. Previous efforts have validated fewer than 900 single nucleotide polymorphisms (SNPs) in this species, which limits opportunities for genetic studies related to health and disease. Extensive information about SNPs and other genetic variation in rhesus macaques would facilitate valuable genetic analyses, as well as provide markers for genome-wide linkage analysis and the genetic management of captive breeding colonies.ResultsWe used the available rhesus macaque draft genome sequence, new sequence data from unrelated individuals and existing published sequence data to create a genome-wide SNP resource for Indian-origin rhesus monkeys. The original reference animal and two additional Indian-origin individuals were resequenced to low coverage using SOLiD™ sequencing. We then used three strategies to validate SNPs: comparison of potential SNPs found in the same individual using two different sequencing chemistries, and comparison of potential SNPs in different individuals identified with either the same or different sequencing chemistries. Our approach validated approximately 3 million SNPs distributed across the genome. Preliminary analysis of SNP annotations suggests that a substantial number of these macaque SNPs may have functional effects. More than 700 non-synonymous SNPs were scored by Polyphen-2 as either possibly or probably damaging to protein function and these variants now constitute potential models for studying functional genetic variation relevant to human physiology and disease.ConclusionsResequencing of a small number of animals identified greater than 3 million SNPs. This provides a significant new information resource for rhesus macaques, an important research animal. The data also suggests that overall genetic variation is high in this species. We identified many potentially damaging non-synonymous coding SNPs, providing new opportunities to identify rhesus models for human disease.

GWAS Integrator: a bioinformatics tool to explore human genetic associations reported in published genome-wide association studies

Genome-wide association studies (GWAS) have successfully identified numerous genetic loci that are associated with phenotypic traits and diseases. GWAS Integrator is a bioinformatics tool that integrates information on these associations from the National Human Genome Research institute (NHGRI) Catalog, SNAP (SNP Annotation and Proxy Search), and the Human Genome Epidemiology (HuGE) Navigator literature database. This tool includes robust search and data mining functionalities that can be used to quickly identify relevant associations from GWAS, as well as proxy single-nucleotide polymorphisms (SNPs) and potential candidate genes. Query-based University of California Santa Cruz (UCSC) Genome Browser custom tracks are generated dynamically on the basis of users' selected GWAS hits or candidate genes from HuGE Navigator literature database (http://www.hugenavigator.net/HuGENavigator/gWAHitStartPage.do). The GWAS Integrator may help enhance inference on potential genetic associations identified from GWAS studies.