title: A method to predict the impact of regulatory variants from DNA sequence.
abstract: 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.
authors.affiliation: McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Dongwon
authors.initials: D
authors.lastname: Lee
authors.affiliation: McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: David U
authors.initials: DU
authors.lastname: Gorkin
authors.affiliation: McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Maggie
authors.initials: M
authors.lastname: Baker
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Strober
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Alessandro L
authors.initials: AL
authors.lastname: Asoni
authors.affiliation: McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Andrew S
authors.initials: AS
authors.lastname: McCallion
authors.affiliation: 1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA. [2] Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Beer
mesh_terms: D000818:Animals; D001327:Autoimmune Diseases / Q000235:genetics; D001483:Base Sequence; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D003850:Deoxyribonuclease I / Q000378:metabolism; D004742:Enhancer Elements, Genetic / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D015894:Genome, Human / Q000235:genetics; D055106:Genome-Wide Association Study / Q000379:methods; D056945:Hep G2 Cells; D006801:Humans; D051379:Mice; D009154:Mutation; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci / Q000235:genetics; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D015203:Reproducibility of Results; D012307:Risk Factors; D060388:Support Vector Machine
chemical_list: D003850:Deoxyribonuclease I