across the entire study population and within relevant experimen- tal groups: 1) autism and 2) controls. Based on these analyses of normality, parametric tests were used when appropriate. Pearson’s correlation coefficient (r) Pearson’s correlation coefficient (r) between two variables is defined as the covariance of the two variables divided by the product of their standard deviations. The square of the Pearson’s correlation coefficient (R2) estimates the proportion of variance in a dependent variable (e.g. overall variance) that is accounted for by an independent variable (e.g. paternal age). Fisher’s transforma- tion was used to calculate a p-value from the Pearson’s r. For our analyses we considered a p-value of .05 to be statistically significant. Spearman’s rank correlation (rho) Spearman’s rank correlation assesses how well the relationship between two variables can be described using a monotonic function. It is used to assess the relationship between 2 variables when data is not normally distributed. Unpaired Student’s T-tests Compares two groups of normally distributed data to test whether the means of the distributions are different. The p-value represents the probability that the distributions are actually different. For our analyses we considered a p-value of .05 to be statistically significant. Analysis of covariance Analysis of covariance was used to examine the potential confounding effects of subject age and scan batch on a significant association of autism with decreased overall variance. Variance was the dependent variable, diagnosis (21 = autism, +1 = control), subject age, and scan batch (21 = batch 1, +1 = batch 2). All possible interaction terms were also included in the model. For our analyses we considered a p-value of .05 to be statistically significant. Chi square An internet based 262 chi square contingency table (http:// www.graphpad.com/quickcalcs/contingency1.cfm) was used to assess for a significant overlap between gene lists. A chi-square with Yates correction was used to calculate chi squared and a two- tailed p-value. Results Decreased overall variance in log-transformed measures of gene expression predicts the diagnosis of autism (figures 1 and 2) We used microarrays to measure the expression levels of greater than 47,000 unique transcripts including 38,500 well-character- ized human genes using the Affymetrix Human U133 Plus 2.0 microarray with purified RNA from peripheral blood lymphocytes from each of 82 children with sporadic cases of autism and 64 control subjects (figure 1). In contrast to comparing the expression levels of individual genes, we compared the pattern of the overall distribution of gene expression levels between children with autism and controls. Measurement of the variance in the distribution of gene expression levels assessed for differences at the global level of gene expression regulation. To obtain a normal-like distribution, gene expression levels were log2-transformed. The overall variance of the total distribution was measured for each microarray (schematic in figure 1). The distribution of gene Autism Changes Global Gene Expression Regulation expression levels on microarrays from individuals with autism had a decreased variance when compared to microarrays from controls (figure 2, p = .006, parameter estimate = 2.45 Std Dev lower in autism). Because the group of children with autism was significantly younger than the control group (autism: mean - 5.5 years SD - 2.1; control: mean - 7.9 SD - 2.2, p,.0001), we tested whether subject age had an effect on the relationship between diagnosis and overall variance. An analysis of covariance (ANCOVA) demonstrated that even when controlling for subject age, variance in gene expression continued to be significantly decreased by the same amount in the blood of children with autism compared to controls (p = .018, parameter estimate = 2.45 Std Dev lower in autism). When scan batch was included with subject age in the ANCOVA the results also remained significant (p = .03, parameter estimate = 2.42 Std Dev lower in autism). Importantly, parameter estimates for the relationship of diagnosis to variance were virtually unchanged in the ANCOVAs indicating that increasing p-values were related to increases in the degrees of freedom in the analysis, and not to a decreased association. Increased paternal age is associated with decreased overall variance in gene expression levels (figure 3) Previous work in mice indicated that factors or interventions that modified mouse hippocampal-dependent behavior also modified overall variance in the predicted direction [20]. We hypothesized, therefore, that factors that modified risk for autism in the general population might alter the variance in gene expression in a manner that resembled the variance in gene expression in children with autism. Paternal age was found in multiple studies to be a risk factor for autism and other neurodevelopmental disorders such as schizophrenia and mental retardation [26,28,34,35]. We found that in controls, but not in children with autism, overall variance in gene expression was significantly and negatively associated with paternal age (Pearson r = 2.283, R2 = .08, p = .03, parameter estimate = 2.054 Std Figure 2. Overall variance in gene expression in peripheral blood lymphocytes (PBL) was decreased in children with autism. We used microarrays to measure the expression levels of greater than 47,000 transcripts including 38,500 well-characterized human genes using the Affymetrix Human U133 Plus 2.0 microarray on RNA from peripheral blood lymphocytes from each of 82 children with autism and 64 control subjects. Microarrays showed no group level differences in quality control measures. Microarray expression levels were log-transformed and the overall variance was calculated across the total distribution of expression levels on each microarray. Variance in gene expression was significantly decreased in the blood of children with autism (p = .006). Error bars represent standard error. doi:10.1371/journal.pone.0016715.g002 PLoS ONE | www.plosone.org 4 February 2011 | Volume 6 | Issue 2 | e16715