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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 amon...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41467-018-04109-8

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Role:
Author
ORCID:
0000-0002-8268-0438
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Role:
Author
ORCID:
0000-0002-3486-3168
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Publisher:
Springer Nature Publisher's website
Journal:
Nature Communications Journal website
Volume:
9
Article number:
2256
Publication date:
2018-06-11
Acceptance date:
2018-04-05
DOI:
EISSN:
2041-1723
Pmid:
29892050
Source identifiers:
857086
Language:
English
Pubs id:
pubs:857086
UUID:
uuid:d974ce2d-d20d-4772-83ce-119e39383717
Local pid:
pubs:857086
Deposit date:
2019-01-11

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