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Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Citation

Al Olama, AA and Dadaev, T and Hazelett, DJ and Li, Q and Leongamornlert, D and Saunders, EJ and Stephens, S and Cieza-Borrella, C and Whitmore, I and Benlloch Garcia, S and Giles, GG and Southey, MC and Fitzgerald, LM and Gronberg, H and Wiklund, F and Aly, M and Henderson, BE and Schumacher, F and Haiman, CA and Schleutker, J and Wahlfors, T and Tammela, TL and Nordestgaard, BG and Key, TJ and Travis, RC and Neal, DE and Donovan, JL and Hamdy, FC and Pharoah, P and Pashayan, N and Khaw, K-T and Stanford, JL and Thibodeau, SN and Mcdonnell, SK and Schaid, DJ and Maier, C and Vogel, W and Luedeke, M and Herkommer, K and Kibel, AS and Cybulski, C and Wokolorczyk, D and Kluzniak, W and Cannon-Albright, L and Brenner, H and Butterbach, K and Arndt, V and Park, JY and Sellers, T and Lin, H-Y and Slavov, C and Kaneva, R and Mitev, V and Batra, J and Clements, JA and Spurdle, A and Teixeira, MR and Paulo, P and Maia, S and Pandha, H and Michael, A and Kierzek, A and Govindasami, K and Guy, M and Lophatonanon, A and Muir, K and Vinuela, A and Brown, AA and Freedman, M and Conti, DV and Easton, D and Coetzee, GA and Eeles, RA and Kote-Jarai, Z and The PRACTICAL Consortium, COGS-CRUK GWAS-ELLIPSE (Part of GAME-ON) Initiative, The Australian Prostate Cancer BioResource, The UK Genetic Prostate Cancer Study Collaborators, The UK ProtecT Study Collaborators, Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans, Human Molecular Genetics, 24, (19) pp. 5589-5602. ISSN 0964-6906 (2015) [Refereed Article]


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Copyright 2015 The Authors Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1093/hmg/ddv203

Abstract

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.

Item Details

Item Type:Refereed Article
Keywords:Prostate Cancer
Research Division:Medical and Health Sciences
Research Group:Oncology and Carcinogenesis
Research Field:Cancer Genetics
Objective Division:Health
Objective Group:Clinical Health (Organs, Diseases and Abnormal Conditions)
Objective Field:Cancer and Related Disorders
UTAS Author:Fitzgerald, LM (Dr Liesel Fitzgerald)
ID Code:104091
Year Published:2015
Web of Science® Times Cited:40
Deposited By:Menzies Institute for Medical Research
Deposited On:2015-11-03
Last Modified:2017-11-06
Downloads:387 View Download Statistics

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