covariates included age, gender, ethnicity, DSM-IV diagnosis of the form of autism, Autism Diagnosis Observation Schedule (ADOS) or Autism Diagnostic Interview-Revised (ADI-R) scores, Intelligence Quotient (IQ) scores, seizure history, medication history, birth history, brain neuropathology findings, cause of death, and postmortem interval where available. Inclusion criteria included: male gender; autism diagnosis by a validated psychiat- ric/psychologic instrument; and the availability of sufficient fresh frozen tissue available for genome-wide methylation analysis, bisulfite sequencing, and gene expression studies. Exclusion criteria included: formalin-fixation of brains, brains from individ- uals with a medication history of medications known or suspected to have effects on methylation (eg, valproic acid, olanzapine, and sulpiride); gross structural abnormalities of the brain; brains from individuals with a complicated birth history and/or evidence of pre- or perinatal hypoxia; history of major head trauma; diagnosis of Rett syndrome, Fragile X syndrome, tuberous sclerosis, or other syndromic process; or any known or likely pathologic cytogenetic abnormality identified by either routine karyotyping or chromo- somal microarray analysis. Samples from male controls with no known developmental disorder or syndromic process were age- matched to each case. Tissues were procured from a total of 9 autism and 9 control subjects. The characteristics of the subjects are presented in Tables S1 and S2. This study was approved by the Institutional Review Board of the Cleveland Clinic and is in accord with the principles of the Declaration of Helsinki. The gene expression and DNA methylation data presented in this publication have been deposited in NCBIs Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession numbers GSE38322 and GSE38608, respectively. Copy Number Evaluation All 9 cases had high-resolution single nucleotide polymorphism (SNP) hybridization array data available from 2 platforms, the Affymetrix Genome-Wide Human SNP Array 6.0 and the Illumina Human1M-Duo DNA Analysis BeadChip. These assays were performed in the laboratory of S. Scherer, Hospital for Sick Children, Toronto [17]. We analyzed the stringent call list for each potential subject for pathological copy number variants (CNVs) by evaluating each call against the Database of Genomic Variants (DGV) (http://projects.tcag.ca/variation/) and the DECIPHER database (http://decipher.sanger.ac.uk/) as of Jan- uary 2011. Subjects were included if they did not harbor CNVs that overlapped with those listed in the DECIPHER database or if they had CNVs that were not present in the DGV or DECIPHER databases but which did not contain genes with likely haploinsuf- ficiency or strongly implicated in autism in the literature. Sample Processing Total DNA and RNA were co-extracted from ,40 mg of cerebellar cortex and ,40 mg BA19 cortex using the Allprep DNA/RNA Kit (Qiagen) according to the manufacturer’s instructions. RNA and DNA were tested for purity and concentration on a Nanodrop device (Thermo Scientific). One microgram of genomic DNA per sample was bisulfite-converted using the EZ DNA Methylation Kit (Zymo). Samples of RNA or bisulfite-converted DNA were submitted to the Genomics Core Laboratory of the Lerner Research Institute at the Cleveland Clinic for further processing and application to either Illumina HumanRefHT12 v4 BeadChip gene expression microarrays or Illumina HumanMethylation27 BeadChip DNA methylation microarrays according to manufacturer’s instructions. Transcriptomic and Epigenetic Findings in Autism Fragile X Syndrome Testing All subjects had DNA analyzed for FMR1 expanded repeats [18]. No autistic or control subjects were found to have pathologic expansions of CGG repeats in the 59 untranslated region (Table S2). Statistical Analyses Data were imported from Genome Studio (Illumina) to the R statistical environment (http://www.r-project.org/) and processed via standard Bioconductor packages: lumi, methylumi, made4, and limma (www.Bioconductor.org/packages/release/bioc/). Methylation data were color-balanced, quantile-normalized, and filtered for probes with #0.01 detection p-values. Expression data were quantile-normalized, variance-stabilized, and filtered from probes with #0.01 detection p-values. Correspondence analysis was carried out with the made4 Bioconductor package. Due to heterogeneity of expression data, we either included all samples to identify robust phenotype-associated differential expression or performed differential expression analysis on the set of 3 autistic cerebellar outliers compared with age-matched, non-outlier controls, as noted in the text. The 3 autistic cerebellar outliers were defined by visual inspection based on the correspondence analysis, and the two most extreme outlier samples were re- assessed for sampling or processing errors by re-extracting tissue RNA and evaluating the samples on new microarrays, which showed similar patterns of gene expression when compared with the original samples. Array data for methylation were analyzed in two ways: (1) targeted candidate probes based on prior literature were compared between cases and matched controls using limma with a paired design. P-values were Bonferroni corrected to a familywise type I error rate of 5% and (2) genome-wide analysis was carried out comparing cases and controls in limma using a paired design. Top gene lists of up to 300 probes were generated using a filter of false discovery rate #5%. If no genes were differentially expressed or methylated, we also attempted to relax criteria to FDR #25%. Expression data were analyzed with a genome-wide discovery approach, controlling for brain region with FDR threshold of #5%. Analyses of subject characteristics and global methylation values were performed in SAS 9.2 (http:// www.sas.com/) and JMP 8 (http://www.jmp.com/) software packages. A priori power analysis pertaining to methylation data in candidate genes indicated at least 95% power to detect a change of 0.3 in beta value between autistic and control brain with a family- wise error rate of ,0.05 (based on a two-tailed, two sample t test). WGCNA Analysis We used Weighted Genome Co-Expression Network Analysis (WGCNA) to determine whether expression or methylation of particular gene modules was associated with any of the covariates under study and to assess potential confounding variables. WGCNA was implemented using the WGCNA Bioconductor package (http://www.genetics.ucla.edu/labs/horvath/ CoexpressionNetwork/Rpackages/WGCNA/) for R. We used block-wise network construction of the full gene expression dataset (including both brain regions) with maximum block size of 5000, power threshold of 10, merge cutting height of 0.25, and minimum module size of 60. We chose the power threshold based on visual analysis of the scale-free topology fit index as a function of power. We performed similar analysis on the full DNA methylation dataset, but with a minimum module size of 40, given the smaller probe set for the whole genome methylation microarray. There were 21 gene modules generated. A matrix of gene modules and PLOS ONE | www.plosone.org 2 September 2012 | Volume 7 | Issue 9 | e44736