title: Quantifying deleterious effects of regulatory variants.
abstract: The majority of genome-wide association study (GWAS) risk variants reside in non-coding DNA sequences. Understanding how these sequence modifications lead to transcriptional alterations and cell-to-cell variability can help unraveling genotype-phenotype relationships. Here, we describe a computational method, dubbed CAPE, which calculates the likelihood of a genetic variant deactivating enhancers by disrupting the binding of transcription factors (TFs) in a given cellular context. CAPE learns sequence signatures associated with putative enhancers originating from large-scale sequencing experiments (such as ChIP-seq or DNase-seq) and models the change in enhancer signature upon a single nucleotide substitution. CAPE accurately identifies causative cis-regulatory variation including expression quantitative trait loci (eQTLs) and DNase I sensitivity quantitative trait loci (dsQTLs) in a tissue-specific manner with precision superior to several currently available methods. The presented method can be trained on any tissue-specific dataset of enhancers and known functional variants and applied to prioritize disease-associated variants in the corresponding tissue.
authors.affiliation: Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: Shan
authors.initials: S
authors.lastname: Li
authors.affiliation: Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: Roberto Vera
authors.initials: RV
authors.lastname: Alvarez
authors.affiliation: School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
authors.forename: Roded
authors.initials: R
authors.lastname: Sharan
authors.affiliation: Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: David
authors.initials: D
authors.lastname: Landsman
authors.affiliation: Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: Ivan
authors.initials: I
authors.lastname: Ovcharenko
mesh_terms: D001402:B-Lymphocytes / Q000166:cytology / Q000378:metabolism; D001483:Base Sequence; D003850:Deoxyribonuclease I / Q000378:metabolism; D004742:Enhancer Elements, Genetic; D056726:Genetic Association Studies; D015894:Genome, Human; D055106:Genome-Wide Association Study; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D016013:Likelihood Functions; D000069550:Machine Learning; D009928:Organ Specificity; D020641:Polymorphism, Single Nucleotide; D011485:Protein Binding; D040641:Quantitative Trait Loci; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*; D014158:Transcription, Genetic
chemical_list: D014157:Transcription Factors; D003850:Deoxyribonuclease I