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 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 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.