Microarray quality was assessed by inspecting the distributions of log-transformed probe intensity values, as well as scanning for outlier chips using a standard battery of quality measurements, including: average background, scaling factor, percentage of probe-sets called present and 3′/5′ ratios for Actin and Gapdh. Bioconductor's implementation of the MAS 5.0 Detection Calls Algorithm, available in the affy package [22] for R [23], was used to generate absent/marginal/present calls across all samples. We excluded any probe-sets called absent in ≥95% of samples from all subsequent analyses to improve the ratio of true positives in downstream statistical filtering [24]. This removed 14,096, 12,970 and 13,312 probe-sets from the PFC, NAc and VMB, respectively. The lists of ‘absent’ probe-sets were largely overlapping, with 11,343 probe-sets filtered out of all 3 regional datasets, suggesting this filtering step largely removes probe-sets targeting genes unexpressed in brain tissue. Expression data from the saline and ethanol treatment groups were background corrected, quantile normalized and summarized using the robust multi-array average (RMA) expression measure [25]. For analysis of SNPs possibly affecting microarray probe performance, the D2 genome sequence
