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Recent genome-wide association studies have identified independent susceptibility loci for prostate cancer that could influence risk through interaction with other, possibly undetected, susceptibility loci. We explored evidence of interaction between pairs of 13 known susceptibility loci and single nucleotide polymorphisms (SNP) across the genome to generate hypotheses about the functionality of prostate cancer susceptibility regions. We used data from Cancer Genetic Markers of Susceptibility: Stage I included 523,841 SNPs in 1,175 cases and 1,100 controls; Stage II included 27,383 SNPs in an additional 3,941 cases and 3,964 controls. Power calculations assessed the magnitude of interactions our study is likely to detect. Logistic regression was used with alternative methods that exploit constraints of gene-gene independence between unlinked loci to increase power. Our empirical evaluation demonstrated that an empirical Bayes (EB) technique is powerful and robust to possible violation of the independence assumption. Our EB analysis identified several noteworthy interacting SNP pairs, although none reached genome-wide significance. We highlight a Stage II interaction between the major prostate cancer susceptibility locus in the subregion of 8q24 that contains POU5F1B and an intronic SNP in the transcription factor EPAS1, which has potentially important functional implications for 8q24. Another noteworthy result involves interaction of a known prostate cancer susceptibility marker near the prostate protease genes KLK2 and KLK3 with an intronic SNP in PRXX2. Overall, the interactions we have identified merit follow-up study, particularly the EPAS1 interaction, which has implications not only in prostate cancer but also in other epithelial cancers that are associated with the 8q24 locus.

Large-scale Exploration of Gene–Gene Interactions in Prostate Cancer Using a Multistage Genome-wide Association Study