expression and reduced glutathione binding efficiency of PAH epoxides and other geno- toxic substrates (Hayes and Pulford 1995). The GSTP 1le105Val polymorphism [GSTP1; Unigene Hs.523836 (NCBI 2009b)] is associ- ated with reduced isoenzyme (pi) expression and a reduced capacity to inactivate carcino- gens, including the diol epoxides created dur- ing phase I metabolism of PAHs (Coles and Kadlubar 2003; Pavanello and Clonfero 2000; Ryberg et al. 1997; Watson et al. 1998). The GSTT1 polymorphism (Genbank X79389) results in the absence of (theta) isoen- zyme (Hayes and Pulford 1995), but the role of this polymorphism in relation to PAH metabo- lism is uncertain (Pavanello and Clonfero 2000). On the one hand, the theta isoenzyme is thought to activate alkylating agents, such as nitrosamines present in tobacco smoke (Hecht 1999; Pavanello and Clonfero 2000); on the other hand, in human erythrocytes, the theta isoenzyme binds reactive conjugates to gluta- thione, inhibiting genotoxic damage to DNA (Pelin et al. 1996). GSTs conjugate PAH diol epoxides into water-soluble products, and GST enzymes also protect against products of oxida- tive stress (Mitrunen and Hirvonen 2003). GSTA1 (Unigene Hs. 446309) is thought to decrease reactive oxygen species and, via an increase in antioxidant levels, to also play an active role in PAH metabolism (Ahn et al. 2006; Coles et al. 2005; Korashy and El-Kadi 2006). For GSTA1, two alleles, hGSTA1*A and hGSTA1*B, differ in three linked base changes in the 5´ promoter region of the gene: T→G at nucleotide 567, C→T at nucleotide 69, and G→A at nucleotide 52 (Coles et al 2001a, 2001b). These substitutions result in differen- tial expression (Coles et al. 2001b), with lower transcriptional activation with GSTA1*B (vari- ant) than with GSTA1*A (common) alleles. It has been demonstrated by our group that the variant GSTA1*B/B genotype is associated with higher risk of breast cancer among smok- ers compared with nonsmokers with the A/A* genotype (Ahn et al. 2006). Only one breast cancer study (Rundle et al. 2000b) has reported on a potential interaction between GSTM1 genotype and PAH– DNA adducts. However, the sample size was small (< 100 cases), yielding unstable effect estimates and prohibiting exploration of more than one single nucleotide polymorphism from among the many that contribute to this com- plex cascade of activation and detoxification. Investigating whether genetically determined differences in multiple GST genotypes mod- ify the association between breast cancer risk and PAH exposure may improve our ability to identify women who are susceptible to the car- cinogenic effects of PAHs on breast tissue. The goal of our present analysis was to investigate whether multiple GST polymor- phisms modified the relationship between PAH exposure and breast cancer, using the lymphocyte PAH–DNA adduct biomarker as our primary exposure measure of interest using a large, population-based sample of cases and controls from the LIBCSP. We also evaluated the interaction between GST polymorphisms and smoking status, because smoking behav- ior was previously identified as the strongest predictor of detectable PAH–DNA adducts (Shantakumar et al. 2005). Finally, we inves- tigated the joint effects of smoking status and GST polymorphisms on detectable PAH– DNA adducts among control women. Methods Study population. The population-based sample of cases and controls from the LIBCSP has been described previously (Gammon et al. 2002a). Briefly, cases were defined as English-speaking adult women newly diagnosed with breast cancer residing in Nassau County or Suffolk County in Long Island, New York. Population- based controls were identified through random- digit dialing for women < 65 years of age, and by Health Care Finance rosters for women ≥ 65 years of age within the same counties, and frequency matched them (by 5-year age groups) to cases. Participants were between 20 and 98 years of age; 94% were white, 4% were black, and 2% other races/ethnicities. The study protocol was approved by the institutional review boards of the collaborating institutions, and written informed consent was obtained from the LIBCSP participants. Data collection. Respondents to the case– control interview included 1,508 cases and 1,556 controls (Gammon et al. 2002a). The 2-hr in- person structured interview collected data on history of cigarette smoking (current, past), exposure to environmental tobacco smoke in the residential home throughout the life course (Gammon et al. 2004a), and other characteristics potentially relevant to breast cancer risk. Blood samples were obtained from 73% of cases and 73% of controls (Gammon et al. 2004b). Laboratory assays. We processed the blood samples collected by Gammon et al. (2004b), isolated the DNA and completed genotyping for GSTT1 and GSTM1 as pre- viously described (Steck et al. 2007b), by a multiplex polymerase chain reaction method as previously described (Bell et al. 1992). Genotyping for GSTP1 (Ile105Val; rs1695) (Steck et al. 2007b) and GSTA1 (Ahn et al. 2006) was completed as previously described by matrix-assisted laser desorption/ioniza- tion time-of-flight assay (Fannon 2002). We included positive and negative controls in each batch, and a random 10% repeated sam- pling yielded a 97% concordance. Genotype distributions of GSTA1 (p = 0.81) and GSTP1 (p = 0.38) were in Hardy-Weinberg equilibrium among controls. The number of cases and controls, respectively, for whom Environmental Health Perspectives • volume 117 | number 4 | April 2009 PAH–DNA adducts, GSTs, and breast cancer genotyping was successful varied with geno- type (n = 1,041 and 1,090 for GSTA1, 983 and 1,016 for GSTT1, 975 and 1,001 for GSTM1, and 1,027 and 1,069 for GSTP1). Missing genotype data were primarily due to insufficient DNA (Ahn et al. 2006; Steck et al. 2007b). PAH–DNA adducts were measured in peripheral mononuclear cell DNA using com- petitive enzyme-linked immunosorbent assay as previously described (Gammon et al. 2002b). PAH–DNA adduct measurements were available for 873 cases and 941 controls who donated sufficient blood volume for the assay, as described previously (Gammon et al. 2004b). Statistical methods. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) using unconditional logistic regression using SAS version 9.0 (SAS Institute Inc., Cary, NC). All models were adjusted for age at reference (date of diagnosis for cases and date of identification for controls), the frequency-matching factor. Covariates consid- ered as potential confounders included race, family history of breast cancer, parity, age at menarche, age at first pregnancy, menopausal status, lifetime alcohol intake, education, and smoking status. We considered a covariate a confounder and included it in the models if it changed the ORs by more than 10% (Hosmer and Lemeshow 1989); however, adjustment by these potential confounders did not appre- ciably change the ORs, and therefore only the age-adjusted estimates are shown. For our first set of analyses, our goal was to determine whether breast cancer risk is associated with the GST polymorphisms and whether they interact with PAH exposures, either alone or jointly, to affect breast cancer risk. We previously reported that the ORs for the association between breast cancer and individual GST genotypes were not substan- tially elevated (Ahn et al. 2006; Steck et al. 2007b), although risk was elevated when we considered multiple GSTs (Ahn et al. 2006; Steck et al 2007b). Here, we focus primarily on the analyses that examine the interaction of GSTs with adducts and smoking, using the following methods. We estimated the ORs for breast cancer in relation to potential interactions between various PAH measures and the GST polymor- phisms, a) considering each polymorphism separately (Table 1), and (b) by grouping indi- viduals based on the number of variant alleles for any of the four GST polymorphisms con- sidered (Tables 2 and 3). For this group of analyses, the reference group included women with GSTT1 present, GSTM1 present, GSTP1 common (AG or GG), or GSTA1 common (A/A*) genotypes. Using this approach, we explored the effect of even a single at-risk genotype, among those who carried com- mon alleles for the other GSTs considered. 553