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.
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- The DCCPS Team.