Ing p-values. Moreover, we purchase K162 tested for association of advanced prostate cancer risk with the 320 SNPs individually using unconditional multivariate logistic regression adjusting for age, institution, and genetic ancestry. Odds ratios (ORs), 95 confidence intervals (95 CI) and P-values were estimated using both co-dominant and logadditive models. To adjust for genetic ancestry in all analyses, we included the first principal component of the principal component analysis of the 39 AIMs as covariate. Moreover, to identify SNPs with potential opposite effects in African Americans and Caucasians, we also stratified all analyses by reported ethnicity. Our strategy evaluated disease risk association at multiple levels of SNP groupings (whole set, sub-pathways, genes, and individual SNPs). To account for the multiple tests done while incorporating the correlation between SNPs and genotype coding, we used apermutation procedure to obtain the empirical distribution of statistical tests under the null hypothesis of no association with the set of SNPs or SNP. Then for each level of SNP groupings, we calculated a family-wise error rate by comparing the P-value of each test to the distribution of the minimum P-values obtained from 1000 permuted data sets. Reported P-values are two-sided and analyses were done using R v2.13.1 [43].Results Study Subject CharacteristicsThe case-control sample included 1,030 subjects whose average age at diagnosis or recruitment was 65.87 (SD: 8.46) years, and was comprised of 194 African Americans (18.8 ) and 836 Caucasians (81.2 ). Age and ethnicity were similarly distributed in advanced prostate cancer cases and controls (Table 1).Association with Advanced Prostate Cancer RiskTaken together, the whole set of 320 SNPs in the innate immunity and inflammation pathway was significantly associated with advanced prostate cancer risk (P = 0.02). Of the 6 subpathways analyzed, the intracellular antiviral molecules and the extracellular pattern recognition sub-pathways were Indolactam V nominally associated with advanced prostate cancer risk (P = 0.02 for both) but not associated after correction for multiple testing (P = 0.12 and P = 0.11, respectively). Interestingly, 4 genes in these 2 sub-pathways were also nominally associated with prostate cancer risk: TLR1 and TLR6 in the extracellular pattern recognition sub-pathway (P = 0.002 and P = 0.04, respectively), and OAS1 and OAS2 in the intracellular antiviral molecules sub-pathway (P = 0.015 and P = 0.019, respectively). In addition, IFNGR1 in the cytokine signaling sub-pathway and COX-2, which is the sole member of the eicosanoid signaling sub-pathway represented in our data set, had nominal P-values of 0.006.and 0.044, respectively (Table 2). However, none of these associations are robust to correction for multiple testing (P = 0.10 for the association with TLR1). The results of the individual SNP analyses supported the findings obtained with the sub-pathway and gene sets. Indeed, most of the SNPs having a nominal association P-value below 0.01, belong to TLR1, TLR6, OAS1, OAS2 or COX-2 (Table 3). Moreover, many of the other SNPs in these genes have a p-value between 0.01 and 0.05 (Table S2). Interestingly, for all these SNPs, results indicate a protective effect of the minor allele with additive ORs between 0.73 and 0.77. But again, when correcting for multiple testing, these were no longer 24272870 significant (P = 0.42 for the most significant association).Innate Immunity Inflammation in.Ing p-values. Moreover, we tested for association of advanced prostate cancer risk with the 320 SNPs individually using unconditional multivariate logistic regression adjusting for age, institution, and genetic ancestry. Odds ratios (ORs), 95 confidence intervals (95 CI) and P-values were estimated using both co-dominant and logadditive models. To adjust for genetic ancestry in all analyses, we included the first principal component of the principal component analysis of the 39 AIMs as covariate. Moreover, to identify SNPs with potential opposite effects in African Americans and Caucasians, we also stratified all analyses by reported ethnicity. Our strategy evaluated disease risk association at multiple levels of SNP groupings (whole set, sub-pathways, genes, and individual SNPs). To account for the multiple tests done while incorporating the correlation between SNPs and genotype coding, we used apermutation procedure to obtain the empirical distribution of statistical tests under the null hypothesis of no association with the set of SNPs or SNP. Then for each level of SNP groupings, we calculated a family-wise error rate by comparing the P-value of each test to the distribution of the minimum P-values obtained from 1000 permuted data sets. Reported P-values are two-sided and analyses were done using R v2.13.1 [43].Results Study Subject CharacteristicsThe case-control sample included 1,030 subjects whose average age at diagnosis or recruitment was 65.87 (SD: 8.46) years, and was comprised of 194 African Americans (18.8 ) and 836 Caucasians (81.2 ). Age and ethnicity were similarly distributed in advanced prostate cancer cases and controls (Table 1).Association with Advanced Prostate Cancer RiskTaken together, the whole set of 320 SNPs in the innate immunity and inflammation pathway was significantly associated with advanced prostate cancer risk (P = 0.02). Of the 6 subpathways analyzed, the intracellular antiviral molecules and the extracellular pattern recognition sub-pathways were nominally associated with advanced prostate cancer risk (P = 0.02 for both) but not associated after correction for multiple testing (P = 0.12 and P = 0.11, respectively). Interestingly, 4 genes in these 2 sub-pathways were also nominally associated with prostate cancer risk: TLR1 and TLR6 in the extracellular pattern recognition sub-pathway (P = 0.002 and P = 0.04, respectively), and OAS1 and OAS2 in the intracellular antiviral molecules sub-pathway (P = 0.015 and P = 0.019, respectively). In addition, IFNGR1 in the cytokine signaling sub-pathway and COX-2, which is the sole member of the eicosanoid signaling sub-pathway represented in our data set, had nominal P-values of 0.006.and 0.044, respectively (Table 2). However, none of these associations are robust to correction for multiple testing (P = 0.10 for the association with TLR1). The results of the individual SNP analyses supported the findings obtained with the sub-pathway and gene sets. Indeed, most of the SNPs having a nominal association P-value below 0.01, belong to TLR1, TLR6, OAS1, OAS2 or COX-2 (Table 3). Moreover, many of the other SNPs in these genes have a p-value between 0.01 and 0.05 (Table S2). Interestingly, for all these SNPs, results indicate a protective effect of the minor allele with additive ORs between 0.73 and 0.77. But again, when correcting for multiple testing, these were no longer 24272870 significant (P = 0.42 for the most significant association).Innate Immunity Inflammation in.
