PROJECT SUMMARY/ABSTRACT Although genome-wide association studies (GWAS) have been extremely successful in identifying numerous germline variants associated to risk for prostate cancer, the causal mechanism between genetic variation and disease risk remains largely unknown at the vast majority of these loci. This prohibits the full realization of novel drug targets and/or personalized treatments. In the quest to address this gap, post-GWAS studies are experiencing a “big data” revolution driven by the exponentially decreasing costs of high-throughput genomic assays. Multiple layers of data (genetic variation, transcriptome levels, epigenetic modifications, localization of tissue-specific regulatory sites, 3D interactions, etc.) are routinely collected in increasingly large cohorts of individuals. This raises the need for rigorous computational and experimental frameworks that integrate various types of data to identify and validate causal genes and variants in prostate cancer. Here we propose a rigorous framework aimed at loci where risk is mediated through alteration in gene expression levels. We deliberately and exhaustively propose to examine all risk loci for prostate cancer to prioritize causal variants and genes and to functionally validate them in prostate cancer tissue and cell lines.