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

  • External authors:
  • Rebecca Elliott
  • Neil Burnet
  • et al

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Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants. / Muir, Kenneth; Lophatananon, Artitaya; West, Catharine; Elliott, Rebecca; Townsend, Paul A; Burnet, Neil; al, et.

In: Nature Communications, Vol. 9, 2256, 01.12.2018.

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@article{6ca24cb420834a87bc00c5ad0eff0d6a,
title = "Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants",
abstract = "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.",
keywords = "African Continental Ancestry Group/genetics, Algorithms, Bayes Theorem, Chromosome Mapping, European Continental Ancestry Group/genetics, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Molecular Sequence Annotation, Multivariate Analysis, Polymorphism, Single Nucleotide, Prostatic Neoplasms/genetics, Quantitative Trait Loci, Risk",
author = "Kenneth Muir and Artitaya Lophatananon and Catharine West and Rebecca Elliott and Townsend, {Paul A} and Neil Burnet and et al",
year = "2018",
month = dec,
day = "1",
doi = "10.1038/s41467-018-04109-8",
language = "English",
volume = "9",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Springer Nature",

}

RIS

TY - JOUR

T1 - Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

AU - Muir, Kenneth

AU - Lophatananon, Artitaya

AU - West, Catharine

AU - Elliott, Rebecca

AU - Townsend, Paul A

AU - Burnet, Neil

AU - al, et

PY - 2018/12/1

Y1 - 2018/12/1

N2 - 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.

AB - 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.

KW - African Continental Ancestry Group/genetics

KW - Algorithms

KW - Bayes Theorem

KW - Chromosome Mapping

KW - European Continental Ancestry Group/genetics

KW - Genetic Predisposition to Disease

KW - Genome-Wide Association Study

KW - Humans

KW - Male

KW - Molecular Sequence Annotation

KW - Multivariate Analysis

KW - Polymorphism, Single Nucleotide

KW - Prostatic Neoplasms/genetics

KW - Quantitative Trait Loci

KW - Risk

U2 - 10.1038/s41467-018-04109-8

DO - 10.1038/s41467-018-04109-8

M3 - Article

C2 - 29892050

VL - 9

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 2256

ER -