| Literature DB >> 26162851 |
Ying Han1, Dennis J Hazelett1, Fredrik Wiklund2, Fredrick R Schumacher3, Daniel O Stram3, Sonja I Berndt4, Zhaoming Wang5, Kristin A Rand1, Robert N Hoover4, Mitchell J Machiela4, Merideth Yeager6, Laurie Burdette5, Charles C Chung4, Amy Hutchinson5, Kai Yu4, Jianfeng Xu7, Ruth C Travis8, Timothy J Key8, Afshan Siddiq9, Federico Canzian10, Atsushi Takahashi11, Michiaki Kubo12, Janet L Stanford13, Suzanne Kolb14, Susan M Gapstur15, W Ryan Diver15, Victoria L Stevens15, Sara S Strom16, Curtis A Pettaway17, Ali Amin Al Olama18, Zsofia Kote-Jarai19, Rosalind A Eeles20, Edward D Yeboah21, Yao Tettey21, Richard B Biritwum21, Andrew A Adjei21, Evelyn Tay21, Ann Truelove22, Shelley Niwa22, Anand P Chokkalingam23, William B Isaacs24, Constance Chen25, Sara Lindstrom25, Loic Le Marchand26, Edward L Giovannucci27, Mark Pomerantz28, Henry Long29, Fugen Li29, Jing Ma30, Meir Stampfer27, Esther M John31, Sue A Ingles3, Rick A Kittles32, Adam B Murphy33, William J Blot34, Lisa B Signorello16, Wei Zheng35, Demetrius Albanes4, Jarmo Virtamo36, Stephanie Weinstein4, Barbara Nemesure37, John Carpten38, M Cristina Leske37, Suh-Yuh Wu37, Anselm J M Hennis39, Benjamin A Rybicki40, Christine Neslund-Dudas40, Ann W Hsing31, Lisa Chu31, Phyllis J Goodman41, Eric A Klein42, S Lilly Zheng43, John S Witte44, Graham Casey3, Elio Riboli45, Qiyuan Li46, Matthew L Freedman28, David J Hunter25, Henrik Gronberg2, Michael B Cook4, Hidewaki Nakagawa47, Peter Kraft48, Stephen J Chanock4, Douglas F Easton18, Brian E Henderson3, Gerhard A Coetzee49, David V Conti3, Christopher A Haiman50.
Abstract
Interpretation of biological mechanisms underlying genetic risk associations for prostate cancer is complicated by the relatively large number of risk variants (n = 100) and the thousands of surrogate SNPs in linkage disequilibrium. Here, we combined three distinct approaches: multiethnic fine-mapping, putative functional annotation (based upon epigenetic data and genome-encoded features), and expression quantitative trait loci (eQTL) analyses, in an attempt to reduce this complexity. We examined 67 risk regions using genotyping and imputation-based fine-mapping in populations of European (cases/controls: 8600/6946), African (cases/controls: 5327/5136), Japanese (cases/controls: 2563/4391) and Latino (cases/controls: 1034/1046) ancestry. Markers at 55 regions passed a region-specific significance threshold (P-value cutoff range: 3.9 × 10(-4)-5.6 × 10(-3)) and in 30 regions we identified markers that were more significantly associated with risk than the previously reported variants in the multiethnic sample. Novel secondary signals (P < 5.0 × 10(-6)) were also detected in two regions (rs13062436/3q21 and rs17181170/3p12). Among 666 variants in the 55 regions with P-values within one order of magnitude of the most-associated marker, 193 variants (29%) in 48 regions overlapped with epigenetic or other putative functional marks. In 11 of the 55 regions, cis-eQTLs were detected with nearby genes. For 12 of the 55 regions (22%), the most significant region-specific, prostate-cancer associated variant represented the strongest candidate functional variant based on our annotations; the number of regions increased to 20 (36%) and 27 (49%) when examining the 2 and 3 most significantly associated variants in each region, respectively. These results have prioritized subsets of candidate variants for downstream functional evaluation.Entities:
Mesh:
Year: 2015 PMID: 26162851 PMCID: PMC4572069 DOI: 10.1093/hmg/ddv269
Source DB: PubMed Journal: Hum Mol Genet ISSN: 0964-6906 Impact factor: 6.150