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Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.

Original publication

DOI

10.1038/s41467-018-04109-8

Type

Journal article

Journal

Nat commun

Publication Date

11/06/2018

Volume

9

Keywords

African Continental Ancestry Group, Algorithms, Bayes Theorem, Chromosome Mapping, European Continental Ancestry Group, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Molecular Sequence Annotation, Multivariate Analysis, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Quantitative Trait Loci, Risk