GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach

• Published in: Bioinformatics Vol. 31; no. 16; pp. 2601 - 2606
• Main Authors: Schmidt, Ellen M, Zhang, Ji, Zhou, Wei, Chen, Jin, Mohlke, Karen L, Chen, Y Eugene, Willer, Cristen J
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15-08-2015
• Subjects: Algorithms; Bioinformatics; Collection; Computer programs; Enrichment; Epigenomics; Genetic Variation - genetics; Genetics; Genome-Wide Association Study; Genomics - methods; Humans; Loci; Organ Specificity; Original Papers; Phenotype; Programming Languages; Quantitative Trait Loci; Regulatory Sequences, Nucleic Acid - genetics; Software
• ISSN: 1367-4803; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btv201

The majority of variation identified by genome wide association studies falls in non-coding genomic regions and is hypothesized to impact regulatory elements that modulate gene expression. Here we present a statistically rigorous software tool GREGOR (Genomic Regulatory Elements and Gwas Overlap algoRithm) for evaluating enrichment of any set of genetic variants with any set of regulatory features. Using variants from five phenotypes, we describe a data-driven approach to determine the tissue and cell types most relevant to a trait of interest and to identify the subset of regulatory features likely impacted by these variants. Last, we experimentally evaluate six predicted functional variants at six lipid-associated loci and demonstrate significant evidence for allele-specific impact on expression levels. GREGOR systematically evaluates enrichment of genetic variation with the vast collection of regulatory data available to explore novel biological mechanisms of disease and guide us toward the functional variant at trait-associated loci.
GREGOR, including source code, documentation, examples, and executables, is available at http://genome.sph.umich.edu/wiki/GREGOR.
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Supplementary data are available at Bioinformatics online.