BMC Cancer 2009, 9:397 http://www.biomedcentral.com/1471-2407/9/397 TaqMan allelic discrimination of GSTM1, GSTP1 and GSTT1 sequence variants Polymorphisms in three glutathione S-transferases genes were ascertained using TaqMan Polymerase Chain Reac- tion (PCR) allelic discrimination assays. The following alleles were detected: (1) GSTM1 (*1/*1 + *1/*0); (2) GSTT1 (*1/*0 + *1/*0); and (3) GSTP1 (Ile105Val). The albumin reference gene served as a positive control for GST (M1, T1) deletions. The discrimination assay con- tained approximately 40 ng of germ-line DNA, 1× Univer- sal Master Mix (Applied Biosystems), 900 nM of each primer (forward and reverse), and 400 nM of each probe (FAM and VIC) to comprise a 10 μl reaction. The primers and probes used to detect a deletion or single nucleotide polymorphisms (SNP) detected in GSTM1, GSTP1 and GSTT1 alleles were obtained using the NCI SNP500 data- base, published reports and Primer Express 3.0 software (Applied Biosystems, Foster City, CA) [29-31]. The PCR amplification conditions consisted of the following: an initial 2 step hold (50°C for 2 min, followed by 95°C for 10 min) and 40 cycles of a two-step PCR (95°C for 15 s, 60°C for 1 min)[30]. The fluorescent intensity emitted from the fluorogenic probes were measured using the ABI 7900HT sequence detection system and assigned geno- types using SDS 2.2.1 software (Applied Biosystems, Fos- ter City, CA). To minimize misclassification bias, laboratory technicians were blinded to the case status of study participants. Based on 24 non-DNA template con- trols per batch analysis, percent cross-contamination dur- ing sample handling was negligent (<0%). Duplicate genotyping performed on 72 randomly selected samples for quality control purposes resulted in concordance rates of 96-97% for GST (M1, T1) and 100% for GSTP1. Call rates were >94% for the GST deletion (M1, T1) and GSTP1 allelic discrimination assays. In addition, we tested whether the aforementioned GST polymorphisms were in Hardy-Weinberg equilibrium among controls using a sig- nificance level of p < 0.05. Ancestry markers One hundred previously validated autosomal ancestry markers were included to account for potential popula- tion stratification among our admixed population of self- reported African-Americans, West African, East African, Afro-Caribbean, as previously described[32]. Study partic- ipants were grouped from lowest to highest genetic West African Ancestry (WAA), with scores ranging from 0- 100%. These 100 markers were assembled using DNA from self-identified African-Americans (Coriell Institute for Medical Research, n = 96), Yoruban West Africans (HapMap, n = 60), West Africans (Bantu and Nilo Saharan speakers, n = 72), Europeans (New York City, n = 24), and CEPH Europeans (HapMap Panel, n = 60), as previously reported[32]. Individuals (n = 873) with a high degree of WAA greater than or equal to 25% were considered in the final analysis. Evaluation of individual GST loci and PCA risk using LR analysis To assess whether inheritance of at least one GST (M1, T1) deletion or GSTP1 Val allele was associated with elevated risk of developing PCA, we tested for significant differ- ences in the distribution of variant genotypes between 208 cases and 665 controls using the chi-square test of homogeneity. Associations between PCA risk and candi- date polymorphic genes, expressed as odds ratios (ORs) and corresponding 95% confidence intervals (CIs), were estimated using unconditional multivariate logistic regression (LR) models adjusted for potential confound- ers [age (yrs), PSA (ng/ml), and WAA (continuous)]. All reported risk estimates and 95% CIs for the selected poly- morphic GST genes used the following as reference geno- types: GSTM1 (*1/*1 + *1/*0), GSTT1 (*1/*1 + *1/*0), GSTP1 (Ile/Ile). Test for trend included genotypes as ordi- nal variables. Statistical significance was assessed using a p-value < 0.05. All chi-square test and LR analyses were conducted using SAS 9.1.3 (SAS Institute, Cary, NC). Gene combination effects MDR was used to evaluate gene-gene interactions associ- ated with PCA risk. This tool aids in the identification of high-risk markers using a cross validation strategy to esti- mate the classification and prediction accuracy of individ- ual and multifactor models [33-35]. MDR is a data mining platform that readily overcomes sample size limitations often encountered by parametric statistical methods (e.g., LR analysis) by collapsing high-dimensional genetic data into a single dimension [33-35]. Also, MDR is a "model- free" (it does not assume a specified genetic model) and "non-parametric" method (it does not estimate parame- ters) that is effective with relatively small sample sizes [33- 35]. With MDR, we pooled multi-locus factors into high- risk and low-risk groups, thereby reducing the otherwise high-dimensional data to a single variable and permitting an investigation of individual GST genes, gene combina- tion effects, and GST-tobacco smoking interactions. This one-dimensional multi-locus variable was evaluated for its ability to classify and predict PCA susceptibility through cross-validation and permutation testing. MDR was utilized to generate a single model that maximizes the number of individuals with the proper risk assignment. Single best factor models were selected among each of the one- or two- factor combinations based on those that minimize the prediction error as well as maximize cross validation consistency (CVC) and average testing accuracy (ATA). To evaluate the number of times the same individ- ual loci or set of genes was identified in each possible 9/ 10ths of the data, the average CVC (based on a scale from 0-100%) from the observed data was compared to the dis- Page 4 of 10 (page number not for citation purposes)