de Jong S et al. BMC Genomics 2010, 11:20 http://www.biomedcentral.com/1471-2164/11/20 The Netherlands. All mice used for the experiment were bred under standard conditions (21 ± 1.0°C and 50% humidity) in Macrolon cages (type II-extended, type number: 1284 L.) maintained in a 12-hr light/12-hr dark cycle. Four weeks after birth, mice were weaned and socially housed (2-4 same sex littermates per cage) with ad libitum access to food and water. Mice were sacri- ficed at 3-4 months of age and their brains were quickly removed, frozen in liquid nitrogen and stored at -80°C. After dissection of selected brain areas we had 17 amyg- dala samples (8 A/J and 9 C57BL/6J) and 18 hippocam- pus samples (9 A/J and 9 C57BL/6J). Dissection procedure Brain samples were thawed from -80°C storage to -8°C in cryostat. Coronal sections of 300 μm thickness were taken. Frozen sections were laid down on a cooled steel plate, covered with parafilm and immediately covered with RNAlater® (Ambion, #AM7024). Selected brain regions were punched out using a stainless steel punch needle (1 mm in diameter) filled with RNAlater con- nected to a syringe. The amygdala were captured in 5 sections starting at -0.58 mm Bregma with one punch taken bilaterally for a total of 10 punches. Hippocampal tissue was taken in 6 sections starting at -2.06 mm Bregma with two punches taken bilaterally in the first three sections, and three in the last three, for a total of 15 punches. Tissue from the left hippocampus was used in the current study. RNA isolation RNAlater was pipetted off the samples. Punches were homogenized using disposable pestels (Fisher Scientific Pellet Stamp, #749521-1500). Phase separation was achieved using 750 μl TRIzol reagent (Invitrogen, #15596-018) and 200 μl chloroform (Merck #8.222.65.1000), after which samples were precipitated in 500 μl isopropanol (Merck #1.0934.2500). DNAse treatment (Qiagen Rnase-Free Dnase Set, #79254) was applied using the manufacturer’s protocol, followed by an RNA clean-up procedure (Qiagen RNeasy MinElute columns, #74204). Samples were stored at -80°C. Total RNA concentration was checked using the Nanodrop ND-1000 and RNA quality was checked using Bioanaly- zer RNA chips (Agilent Technologies RNA Nano kit, #5067-1511). Microarrays Genome-wide RNA expression profiling was obtained with the Illumina MouseRef-8 V1.1 arrays using Illumi- na’s standard protocol. In short, RNA samples were pre- pared with the Illumina TotalPrep kit amplification and labeling protocol (Ambion, #IL1791). Amplified and bio- tinylated cRNA was measured with a ribogreen assay (Invitrogen Quant-it™ Ribogreen, #R11490), and 750 ng of labeled cRNA was then used for array hybridization. Individual mouse samples were hybridized to separate Page 11 of 14 arrays. BeadChips were scanned using an Illumina Bea- dArray reader. Data is made available at Gene Expres- sion Omnibus (GSE17955; http://www.ncbi.nlm.nih.gov/ geo/). Statistical analysis BeadStudio© software version 3.2.3 was used to extract raw data and generate background-corrected gene- expression data. Background correction was performed by subtracting the average value of negative control beads present on the array. To demonstrate that our findings are not dependent on the background correc- tion method, we repeated the analysis with non-back- ground corrected gene expression data. Our conclusions remain unchanged: we find highly similar grouping of the genes and relationships to the phenotypes. Further pre-processing was done using the Lumi package for R [30]. A variance stabilizing transformation was applied to preserve much of the gene-expression variance. Data were normalized using the robust spline normalization method [52]. Chip quality and outlier detection was per- formed by assessing quality statistics and plots (hier- archical clustering, box plots, density distribution plots, pair-wise correlations) before and after transformation and normalization. Modules of co-expressed genes were identified using weighted gene co-expression network analysis (WGCNA) developed by Zhang and Horvath, implemented in a freely available R packge [26,29]. First, a correlation matrix for these genes was constructed. This matrix was then raised to a power (beta = 8 in this study) to achieve an adjacency matrix holding connec- tion strengths. Connectivity is defined as the sum of connection strengths with the other network genes. A topological overlap measure is calculated based on the number of shared neighbors. A dendrogram is produced by hierarchical clustering of 1 minus the topological overlap; branches of the tree are cut using a dynamic tree cut algorithm to define modules [34]. The module eigengene is the first principal component of a module and can therefore be thought of as an average gene expression value for all genes in a module per sample. Module significance for either a continuous or dichoto- mous outcome is determined assessing eigengene mod- ule significance. The importance of individual genes could be assessed by looking at both gene significance and connectivity measures in the whole network and within modules. Polymorphic SNPs in genes and probes Analyses assessing polymorphisms between mouse inbred strains were performed in silico. Gene start and end positions were obtained by entering Illumina identi- fiers in Biomart http://www.biomart.org, which uses ENSMBL data. To include possible regulatory regions, a margin of 10 kb before and after each gene was used. This region was then entered in the Mouse Phenome