Tune H Pers1, Pascal Timshel1, Joel N Hirschhorn1. 1. Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA 2142, USA, Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA 2142, USA, Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA 2142, USA, Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
Abstract
SUMMARY: An important computational step following genome-wide association studies (GWAS) is to assess whether disease or trait-associated single-nucleotide polymorphisms (SNPs) enrich for particular biological annotations. SNP-based enrichment analysis needs to account for biases such as co-localization of GWAS signals to gene-dense and high linkage disequilibrium (LD) regions, and correlations of gene size, location and function. The SNPsnap Web server enables SNP-based enrichment analysis by providing matched sets of SNPs that can be used to calibrate background expectations. Specifically, SNPsnap efficiently identifies sets of randomly drawn SNPs that are matched to a set of query SNPs based on allele frequency, number of SNPs in LD, distance to nearest gene and gene density. AVAILABILITY AND IMPLEMENTATION: SNPsnap server is available at http://www.broadinstitute.org/mpg/snpsnap/. CONTACT: joelh@broadinstitute.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: An important computational step following genome-wide association studies (GWAS) is to assess whether disease or trait-associated single-nucleotide polymorphisms (SNPs) enrich for particular biological annotations. SNP-based enrichment analysis needs to account for biases such as co-localization of GWAS signals to gene-dense and high linkage disequilibrium (LD) regions, and correlations of gene size, location and function. The SNPsnap Web server enables SNP-based enrichment analysis by providing matched sets of SNPs that can be used to calibrate background expectations. Specifically, SNPsnap efficiently identifies sets of randomly drawn SNPs that are matched to a set of query SNPs based on allele frequency, number of SNPs in LD, distance to nearest gene and gene density. AVAILABILITY AND IMPLEMENTATION: SNPsnap server is available at http://www.broadinstitute.org/mpg/snpsnap/. CONTACT: joelh@broadinstitute.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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