title: Which Genetics Variants in DNase-Seq Footprints Are More Likely to Alter Binding?
abstract: Large experimental efforts are characterizing the regulatory genome, yet we are still missing a systematic definition of functional and silent genetic variants in non-coding regions. Here, we integrated DNaseI footprinting data with sequence-based transcription factor (TF) motif models to predict the impact of a genetic variant on TF binding across 153 tissues and 1,372 TF motifs. Each annotation we derived is specific for a cell-type condition or assay and is locally motif-driven. We found 5.8 million genetic variants in footprints, 66% of which are predicted by our model to affect TF binding. Comprehensive examination using allele-specific hypersensitivity (ASH) reveals that only the latter group consistently shows evidence for ASH (3,217 SNPs at 20% FDR), suggesting that most (97%) genetic variants in footprinted regulatory regions are indeed silent. Combining this information with GWAS data reveals that our annotation helps in computationally fine-mapping 86 SNPs in GWAS hit regions with at least a 2-fold increase in the posterior odds of picking the causal SNP. The rich meta information provided by the tissue-specificity and the identity of the putative TF binding site being affected also helps in identifying the underlying mechanism supporting the association. As an example, the enrichment for LDL level-associated SNPs is 9.1-fold higher among SNPs predicted to affect HNF4 binding sites than in a background model already including tissue-specific annotation.
authors.affiliation: Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America.
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Moyerbrailean
authors.affiliation: Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America.
authors.forename: Cynthia A
authors.initials: CA
authors.lastname: Kalita
authors.affiliation: Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America.
authors.forename: Chris T
authors.initials: CT
authors.lastname: Harvey
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Xiaoquan
authors.initials: X
authors.lastname: Wen
authors.affiliation: Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America.|Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, United States of America.
authors.forename: Francesca
authors.initials: F
authors.lastname: Luca
authors.affiliation: Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America.|Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, United States of America.
authors.forename: Roger
authors.initials: R
authors.lastname: Pique-Regi
mesh_terms: D000483:Alleles; D001665:Binding Sites; D019295:Computational Biology; D018983:DNA Footprinting; D003851:Deoxyribonucleases / Q000378:metabolism*; D017930:Genes, Reporter; D055106:Genome-Wide Association Study; D006801:Humans; D058977:Molecular Sequence Annotation; D059372:Nucleotide Motifs / Q000235:genetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D011485:Protein Binding; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics; D015203:Reproducibility of Results; D014157:Transcription Factors
chemical_list: D014157:Transcription Factors; D003851:Deoxyribonucleases