BMC Cancer 2009, 9:397 http://www.biomedcentral.com/1471-2407/9/397 tribution of average consistencies under the hypoth- esis of no association. Statistically significant single and multi-locus models may be adjusted for potential con- founders by placing the MDR output variables into a LR model. Furthermore, validation of models as effective pre- dictors of prostate cancer susceptibility was derived empirically from 1,000 permutations. This approach accounted for multiple testing issues as long as the entire model fitting procedure was repeated for each rand- omized dataset to provide an opportunity to identify false-positives. We considered MDR permutation results to be statistically significant at the 0.05 level. LR analysis was used to perform two-way interaction models for risk estimates terms identified by MDR. Interaction Entropy Graphs Interaction entropy was used as a third strategy to verify, visualize, and interpret combination effects identified by LR and MDR [36,37]. Interaction entropy uses informa- tion gain (IG) to gauge whether interactions between two or more variables provide more information about a class variable relative to each variable considered independ- ently [36] and has been applied to several recent epidemi- ological studies [34,36,38,39]. The colors range from red representing a high degree of synergy (positive informa- tion gain), orange a lesser degree, and gold representing independence and a midway point between synergy and redundancy. Blue represents the highest level of redun- dancy (negative information gain), followed by green. Results The patient and tumor characteristics in the current study are summarized in Table 1. Cases were significantly older than controls and had higher PSA levels. Although there was a small portion of controls (5.6%) who had PSA lev- els that exceeded 4.0 ng/ml, these individuals did not have an abnormal DRE or irregular biopsy. There was a significant difference in median West African genetic ancestry estimates comparing cases and controls (p = 0.020). Cigarette smoking data was available for approxi- mately 25% of our study subjects (n = 216, 136 cases and 80 controls). These subjects were classified by cigarette smoking: current (n = 38), former (n = 71) and never (n = 107). Cigarette smoking did not differ significantly (p > 0.05) between cases and controls. Within our study set, inheritance of two GST (M1, T1) deletions or at least one GSTP1 105 Val allele was fairly common among controls with frequencies ranging from 17.5-47.9%, as detailed in Table 2. The genotype frequen- cies among controls were comparable to those observed in other African descendent sub-groups[26,40,41]. The genotype frequencies among controls did not deviate from expected distributions based on the Hardy-Weinberg equilibrium (p ≥ 0.138). We evaluated the independent effects of genetic variations in highly variant GST genes in relation to PCA susceptibil- ity using unconditional LR models, as detailed in Table 2. Inheritance of at least one GSTP1 105 Val allele (linked with decreased capacity to conjugate electrophilic com- pounds) was associated with a 1.6-fold increase in pros- tate cancer risk (OR = 1.56; 95%CI = 0.95-2.58; p = 0.049). The GSTP1 Val loci was also identified as the best single factor predictor of PCA based on a cross-validation consistency of 100%, an average testing accuracy of 53% Table 2: Association between prostate cancer risk and selected GST gene variations Gene SNP GSTP1 A313G Ile105Val GSTM1 Deletion GSTT1 Deletion AA (Ile/Ile) AG (Ile/Val) GG(Val/Val) AG+GG missing *1/*1 + *1/*0 *0/*0 missing *1/*1 + *1/*0 *0/*0 missing Case n (%) 55 (29.0) 85 (44.7) 50 (26.3) 140 (73.7) 6 (3.2) 141 (75.0) 47 (25.0) 8 (4.3) 153 (81.0) 36 (19.0) 7 (3.7) Control n (%) 186 (32.5) 274 (47.9) 112 (19.6) 460 (80.4) 35 (6.1) 441 (76.3) 137 (23.7) 29 (5.0) 482 (82.5) 102 (17.5) 23 (3.9) Risk Estimate† OR (95% CI) 1.00 (Reference) 1.12 (0.77-1.63) 1.62 (1.04-2.51) 1.47 (1.00-2.15) Risk Estimate†† Adj OR (95% CI) 1.00 (Reference) 1.09 (0.67-1.81) 1.66 (0.94-2.96) 1.56 (0.95-2.58) 1.00 (Reference) 1.07 (0.73-15.7) 1.00 (Reference) 1.08 (0.65-1.82) 1.00 (Reference) 1.11 (0.73-1.69) 1.00 (Reference) 1.15 (0.66-2.02) p-value††† p-value for Trend 0.080 0.141 0.049 0.718 0.622 †Associations were determined using multivariate logistic regression models to estimate the risk of developing PCA using GSTP1 105 Ile/Ile, GSTM1 *1/*1 + *1/*0 and GSTT1 *1/*1 + *1/*0 as the reference genotypes. ††Risk estimates adjusted for age (continuous variable); prostate specific antigen (continuous variable); and West African Ancestry, WAA or admixture (continuous variable). †††Differences in the frequency of high-risk and low-risk genotypes between cases and controls were determined using the chi-square test of association with a significance level of 0.05. Page 5 of 10 (page number not for citation purposes)