Z Kote-Jarai, DF Easton, JL Stanford, EA Ostrander, J Schleutker, SA Ingles, D Schaid, S Thibodeau, T Dörk, D Neal, J Donovan, F Hamdy, A Cox, C Maier, W Vogel, M Guy, K Muir, A Lophatananon, M-A Kedda, A Spurdle, S Steginga, EM John, G Giles, J Hopper, PO Chappuis, P Hutter, WD Foulkes, N Hamel, CA Salinas, JS Koopmeiners, DM Karyadi, B Johanneson, T Wahlfors, TL Tammela, MC Stern, R Corral, SK McDonnell, P Schürmann, A Meyer, R Kuefer, DA Leongamornlert, M Tymrakiewicz, J-F Liu, T O'Mara, RAF Gardiner, J Aitken, AD Joshi, G Severi, DR English, M Southey, SM Edwards, AA Al Olama, PRACTICAL Consortium, RA Eeles
Journal name: 
Cancer Epidemiol Biomarkers Prev
Citation info: 
A recent genome-wide association study found that genetic variants on chromosomes 3, 6, 7, 10, 11, 19 and X were associated with prostate cancer risk. We evaluated the most significant single-nucleotide polymorphisms (SNP) in these loci using a worldwide consortium of 13 groups (PRACTICAL). Blood DNA from 7,370 prostate cancer cases and 5,742 male controls was analyzed by genotyping assays. Odds ratios (OR) associated with each genotype were estimated using unconditional logistic regression. Six of the seven SNPs showed clear evidence of association with prostate cancer (P = 0.0007-P = 10(-17)). For each of these six SNPs, the estimated per-allele OR was similar to those previously reported and ranged from 1.12 to 1.29. One SNP on 3p12 (rs2660753) showed a weaker association than previously reported [per-allele OR, 1.08 (95% confidence interval, 1.00-1.16; P = 0.06) versus 1.18 (95% confidence interval, 1.06-1.31)]. The combined risks associated with each pair of SNPs were consistent with a multiplicative risk model. Under this model, and in combination with previously reported SNPs on 8q and 17q, these loci explain 16% of the familial risk of the disease, and men in the top 10% of the risk distribution have a 2.1-fold increased risk relative to general population rates. This study provides strong confirmation of these susceptibility loci in multiple populations and shows that they make an important contribution to prostate cancer risk prediction.
E-pub date: 
31 Aug 2008
Users with this publication listed: 
David Neal