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F-SNP: computationally predicted functional SNPs for disease association studies.

ABSTRACT: The Functional Single Nucleotide Polymorphism (F-SNP) database integrates information obtained from 16 bioinformatics tools and databases about the functional effects of SNPs. These effects are predicted and indicated at the splicing, transcriptional, translational and post-translational level. As such, the database helps identify and focus on SNPs with potential deleterious effect to human health. In particular, users can retrieve SNPs that disrupt genomic regions known to be functional, including splice sites and transcriptional regulatory regions. Users can also identify non-synonymous SNPs that may have deleterious effects on protein structure or function, interfere with protein translation or impede post-translational modification. A web interface enables easy navigation for obtaining information through multiple starting points and exploration routes (e.g. starting from SNP identifier, genomic region, gene or target disease). The F-SNP database is available at http://compbio.cs.queensu.ca/F-SNP/.

SUBMITTER: Lee PH

PROVIDER: S-EPMC2238878 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

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F-SNP: computationally predicted functional SNPs for disease association studies.

Lee Phil Hyoun PH Shatkay Hagit H

Nucleic acids research 20071105 Database issue

The Functional Single Nucleotide Polymorphism (F-SNP) database integrates information obtained from 16 bioinformatics tools and databases about the functional effects of SNPs. These effects are predicted and indicated at the splicing, transcriptional, translational and post-translational level. As such, the database helps identify and focus on SNPs with potential deleterious effect to human health. In particular, users can retrieve SNPs that disrupt genomic regions known to be functional, includ ...[more]

PMID: 17986460

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Project description:BackgroundGenetic association studies are conducted to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype. To date, functional annotations of the genetic variants have rarely played more than an indirect role in assessing evidence for association. Here, we demonstrate how these data can be systematically integrated into an association study's analysis plan.ResultsWe developed a Bayesian statistical model for the prior probability of phenotype-genotype association that incorporates data from past association studies and publicly available functional annotation data regarding the susceptibility variants under study. The model takes the form of a binary regression of association status on a set of annotation variables whose coefficients were estimated through an analysis of associated SNPs in the GWAS Catalog (GC). The functional predictors examined included measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative: summaries of the UCSC Human Genome Browser ENCODE super-track data, dbSNP function class, sequence conservation summaries, proximity to genomic variants in the Database of Genomic Variants and known regulatory elements in the Open Regulatory Annotation database, PolyPhen-2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotations would contribute to predicting association, we employed a penalized likelihood method to reduce the impact of non-informative predictors and evaluated the model's ability to predict GC SNPs not used to construct the model. We show that the functional data alone are predictive of a SNP's presence in the GC. Further, using data from a genome-wide study of ovarian cancer, we demonstrate that their use as prior data when testing for association is practical at the genome-wide scale and improves power to detect associations.ConclusionsWe show how diverse functional annotations can be efficiently combined to create 'functional signatures' that predict the a priori odds of a variant's association to a trait and how these signatures can be integrated into a standard genome-wide-scale association analysis, resulting in improved power to detect truly associated variants.

Dataset Information

F-SNP: computationally predicted functional SNPs for disease association studies.

Publications

F-SNP: computationally predicted functional SNPs for disease association studies.

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