transcripts and variants, including 38,500 characterized human genes. Arrays were washed on Affymetrix’s GeneChip Fluidics Station 450 using a primary streptavidin phycoerythrin (SAPE) stain, subsequent biotinylated antibody stain, and secondary SAPE stain. Arrays were scanned on Affymetrix’s GeneChip Scanner 3000 7G with AutoLoader. Scanned images obtained by the Affymetrix GeneChip Operating Software (GCOS) v1.2 were used to extract raw signal intensity values per probe set on the array. A scaling factor of 150 was used to normalize array signal intensity in MAS 5.0. Arrays were scanned over 1 day on 2 different machines. Arrays scanned on the same machine and in the same day were considered to be from the same scan batch. Rescanning of a limited number of samples indicated that there were no significant differences between machines, nonetheless, for all comparisons groups were balanced for the scan batch. Gene expression levels were not adjusted for possible batch effects as algorithms that attempt to adjust for batch effects also alter the gene expression distribution. When samples could not be prepped simultaneously they were balanced for group membership (autism vs. control). To statistically control for possible confounds related to scan batches in our analysis of gene expression variance, batch number was entered into an analysis of covariance. For traditional analysis of gene expression, experimental groups were balanced with respect to batch membership. Microarray data analysis Affymetrix .cel files were imported into Affymetrix Expression Console version 1.1. Data was pre-processed and summarized by Microarray Analysis Suite (MAS) 5.0 and Robust Multiarray Analysis (RMA). For the analysis of gene expression distributions, MAS 5.0 was used because the algorithm does not alter the gene expression distribution, whereas, RMA utilizes quantile normal- ization of probes prior to summarization and, therefore, has the potential to remove group level differences in gene expression distributions. Because of the numerous advantages in its handling of noise in the measurement of gene expression levels and background subtraction, RMA was used for traditional gene expression analyses looking for specific gene expression differences between groups. Because group level differences in the distribution of gene expression levels between groups were found, gene expression levels were also quantile normalized after the summarization step for traditional gene expression analyses. Quantile normalization adjusts all data sets such that they have identical distribution patterns. Probe sets were then filtered for those that were called present in at least 50 out of the 146 subjects (n = 25,146 probe sets). A p-value of .05 was used as a threshold for significance. A fold-change of 1.1 was used as a cut off for magnitude of change. All microarrays met manufacturers recommended quality control criteria. Present calls ranged from 37.4% to 49%, mean 43.7%, SD 2.7%. Actin 39to59 ratios ranged from .726 to 5.15, mean1.37, SD 0.5. There were no significant group level differences in quality control measures. Variance across the distribution of gene expression levels Variance across the distribution of gene expression levels was the primary measure used in the present study. The measure of overall variance compresses data from the expression levels of 54,675 probe sets covering approximately 47,000 unique tran- scripts including 38,500 human genes on the Affymetrix Human HG-U133 Array into a single variable. We previously found that overall variance in gene expression levels in the hippocampus of mice accurately predicted hippocampal-dependent behavior[20]. To calculate overall variance, gene expression levels for all 54,675 Autism Changes Global Gene Expression Regulation probe sets on the Affymetrix Human HG-U133 Array were log2 transformed. Variance in log2 transformed expression levels was calculated across all 54,675 probe sets in the total distribution as the average of the squares of the deviations from the mean. Pathway Analysis DAVID (david.abcc.ncifcrf.gov) Functional Annotation Bioin- formatics Microarray Analysis[31,32] was used to assess gene lists for enriched biological themes. A user supplied background list was uploaded which consisted of the list of all probe sets that were called present by Affy MAS 5.0 in at least 50 of 146 subjects (n = 25,146). Lists of significant probe sets met the following criteria: present in at least 50 subjects, p,.05, and absolute fold change of at least 1.1-fold. Statistical Cross Validation We found significant differences in variance in gene expression between children with autism and controls. When the paternal age of the control father was considered it was found that variance in gene expression of children with autism was only significantly different from children of younger fathers. Additionally, we found that variance in gene expression was significantly different between children of older fathers and children of younger fathers. To validate these findings we subdivided our data sets into 6 subgroups to allow for within study ‘‘replications’’. In all cases subgroups of the same type (e.g. autism, younger father, older father) were equally balanced for paternal age and scan batch. The validation sets were as follows: Autism1 (n = 41), Autism2 (n = 41), Control young father1 (n = 14), Control young father2 (n = 14), Control old father1 (n = 14), Control old father2 (n = 14), Control 1 (n = 28 - combination of control young father1 and control old father1), and Control 2 (n = 28 - combination of control young father2 and control old father2)(figure S1 and table S1). Transcription inhibitor dataset In order to assess for changes in gene expression variance in response to globally targeting transcription, we downloaded a publically available dataset in which a new class of transcription inhibitors, sappyrins, were compared to actinomycin D. The microarray study analyzed gene expression in A549 human lung cancer cells that were treated with transcriptional inhibitors[33]: [actinomycin D (5 ug/mL), sapphyrin (low – 1.5 uM), sapphyrin (high – 2.5 uM), or mannitol (5%) as a control]. Cel files were downloaded from the Gene Expression Omnibus (http://www. ncbi.nlm.nih.gov/geo) public database. The microarrays used in the transcription inhibitor study were the same used in the current study, Affymetrix Human Genome U133 Plus 2.0 Array. Microarray data was pre-processed and summarized as described above using Expression Console and the MAS 5.0 algorithm. Statistical Analyses JMP8 and Statview software (SAS, Cary, NC) were used for statistical analyses. The statistical analyses used in the study are listed below. Tests of normality To determine if parametric tests were appropriate, the Shapiro- Wilk Test was performed for the primary measures: variance in gene expression and paternal age. When assessed across the entire study or across all control subjects, variance in gene expression was not normally distributed, but was normally distributed within experimental groups: 1) autism, 2) children of older fathers, and 3) children of younger fathers. Paternal age was normally distributed PLoS ONE | www.plosone.org 3 February 2011 | Volume 6 | Issue 2 | e16715