A method to predict the impact of regulatory variants from DNA sequence

Dongwon Lee, David U Gorkin, Maggie Baker, Benjamin J Strober, Alessandro L Asoni, Andrew S McCallion, Michael A Beer
<span title="2015-06-15">2015</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ndd3azjmwfh45iqb6bvdfttaf4" style="color: black;">Nature Genetics</a> </i> &nbsp;
Most variants implicated in common human disease by Genome-Wide Association Studies (GWAS) lie in non-coding sequence intervals. Despite the suggestion that regulatory element disruption represents a common theme, identifying causal risk variants within indicted genomic regions remains a significant challenge. Here we present a novel sequence-based computational method to predict the effect of regulatory variation, using a classifier (gkm-SVM) which encodes cell-specific regulatory sequence
more &raquo; ... bularies. 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 context, 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 novel risk SNPs for several autoimmune diseases. Thus, deltaSVM provides a powerful computational approach for systematically identifying functional regulatory variants. Sequence variation in DNA regulatory elements is hypothesized to contribute substantially to risk for common diseases. Variants associated with human disease by GWAS predominantly lie in non-coding genomic regions 1 and occur within putative regulatory Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
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