Linking disease associations with regulatory information in the human genome

  1. Michael Snyder2,3,4
  1. 1Department of Computer Science, Stanford University, Stanford, California 94305, USA;
  2. 2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
    1. 3 These authors contributed equally to this work.

    Abstract

    Genome-wide association studies have been successful in identifying single nucleotide polymorphisms (SNPs) associated with a large number of phenotypes. However, an associated SNP is likely part of a larger region of linkage disequilibrium. This makes it difficult to precisely identify the SNPs that have a biological link with the phenotype. We have systematically investigated the association of multiple types of ENCODE data with disease-associated SNPs and show that there is significant enrichment for functional SNPs among the currently identified associations. This enrichment is strongest when integrating multiple sources of functional information and when highest confidence disease-associated SNPs are used. We propose an approach that integrates multiple types of functional data generated by the ENCODE Consortium to help identify “functional SNPs” that may be associated with the disease phenotype. Our approach generates putative functional annotations for up to 80% of all previously reported associations. We show that for most associations, the functional SNP most strongly supported by experimental evidence is a SNP in linkage disequilibrium with the reported association rather than the reported SNP itself. Our results show that the experimental data sets generated by the ENCODE Consortium can be successfully used to suggest functional hypotheses for variants associated with diseases and other phenotypes.

    Footnotes

    • 4 Corresponding author

      E-mail mpsnyder{at}stanford.edu

    • [Supplemental material is available for this article.]

    • Article and supplemental material are at http://www.genome.org/cgi/doi/10.1101/gr.136127.111.

      Freely available online through the Genome Research Open Access option.

    • Received December 16, 2011.
    • Accepted May 24, 2012.

    This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at http://creativecommons.org/licenses/by-nc/3.0/.

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