Introduction

Most variants implicated in common human disease by genome-wide association studies (GWAS) lie in noncoding sequence intervals. Despite the suggestion that regulatory element disruption represents a common theme, identifying causal risk variants within implicated genomic regions remains a major challenge. Here we present a new sequence-based computational method to predict the effect of regulatory variation, using a classifier (gkm-SVM) that encodes cell type-specific regulatory sequence vocabularies. The induced change in the gkm-SVM score, deltaSVM, quantifies the effect of variants. We show that deltaSVM accurately predicts the impact of SNPs on DNase I sensitivity in their native genomic contexts and accurately predicts the results of dense mutagenesis of several enhancers in reporter assays. Previously validated GWAS SNPs yield large deltaSVM scores, and we predict new risk-conferring SNPs for several autoimmune diseases. Thus, deltaSVM provides a powerful computational approach to systematically identify functional regulatory variants.

Publications

  1. A method to predict the impact of regulatory variants from DNA sequence.
    Cite this
    Lee D, Gorkin DU, Baker M, Strober BJ, Asoni AL, McCallion AS, Beer MA, 2015-08-01 - Nature genetics

Credits

  1. Dongwon Lee
    Developer

    McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, United States of America

  2. David U Gorkin
    Developer

    McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, United States of America

  3. Maggie Baker
    Developer

    McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, United States of America

  4. Benjamin J Strober
    Developer

    Department of Biomedical Engineering, Johns Hopkins University, United States of America

  5. Alessandro L Asoni
    Developer

    Department of Biomedical Engineering, Johns Hopkins University, United States of America

  6. Andrew S McCallion
    Developer

    McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, United States of America

  7. Michael A Beer
    Investigator

    1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, United States of America

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Summary
AccessionBT000178
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByMichael A Beer