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


Dadaev, T and Saunders, EJ and Newcombe, PJ and Anokian, E and Leongamornlert, DA and FitzGerald, LM and Kote-Jarai, Z, et al, Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants, Nature Communications, 9, (1) Article 2256. ISSN 2041-1723 (2018) [Refereed Article]


Copyright Statement

2018 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

DOI: doi:10.1038/s41467-018-04109-8


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.

Item Details

Item Type:Refereed Article
Keywords:algorithm, ancestry, cancer, disease, meta-analysis, risk assessment, prostate cancer
Research Division:Biological Sciences
Research Group:Genetics
Research Field:Epigenetics (incl. genome methylation and epigenomics)
Objective Division:Health
Objective Group:Clinical health
Objective Field:Clinical health not elsewhere classified
UTAS Author:FitzGerald, LM (Dr Liesel Fitzgerald)
ID Code:127048
Year Published:2018
Web of Science® Times Cited:56
Deposited By:Menzies Institute for Medical Research
Deposited On:2018-07-07
Last Modified:2019-02-25
Downloads:93 View Download Statistics

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