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Motivation: The majority of variation identified by genome wide association studies falls in noncoding 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. [ABSTRACT FROM AUTHOR]

Most cases of breast and prostate cancer are not associated with mutations in known high-penetrance genes, indicating the involvement of multiple low-penetrance risk alleles. Studies that have attempted to identify these genes have met with limited success. The National Cancer Institute Breast and Prostate Cancer Cohort Consortium--a pooled analysis of multiple large cohort studies with a total of more than 5,000 cases of breast cancer and 8,000 cases of prostate cancer--was therefore initiated. The goal of this consortium is to characterize variations in approximately 50 genes that mediate two pathways that are associated with these cancers--the steroid-hormone metabolism pathway and the insulin-like growth factor signalling pathway--and to associate these variations with cancer risk. [ABSTRACT FROM AUTHOR]