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Nature Research, Nature Communications, 1(9), 2018

DOI: 10.1038/s41467-018-04109-8

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Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants.

Journal article published in 2018 by Zhuo Zhang, Ron H. N. van Schaik, Z. Zhang, Alicja Wolk, Stephanie Weinstein, Catharine M. L. West, Hong-Wei Zhang, Guomin Wang, Dominika Wokolorczyk, Maren Weisher, I. Whitmore, Jianfeng Xu, T. Yeadon, A. R. Zlotta, S.-C. Zhao ORCID and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

AbstractProstate 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.