title: Running spell-check to identify regulatory variants.
abstract: A major challenge in human genetics is pinpointing which non-coding genetic variants affect gene expression and disease risk. A new study in this issue describes a broadly applicable approach for this task that explicitly models cell type-specific regulatory motifs and generates variant effect predictions that are more accurate and interpretable than those of alternative tools.
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington, USA.
authors.forename: Martin
authors.identifier: 0000000192785471
authors.initials: M
authors.lastname: Kircher
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington, USA.
authors.forename: Jay
authors.initials: J
authors.lastname: Shendure
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*