Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.

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The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

Autism has a strong genetic background with a higher frequency of affected males suggesting involvement of X-linked genes and possibly also other factors causing the unbalanced sex ratio in the etiology of the disorder. We have identified two missense mutations in the ribosomal protein gene RPL10 located in Xq28 in two independent families with autism. We have obtained evidence that the amino-acid substitutions L206M and H213Q at the C-terminal end of RPL10 confer hypomorphism with respect to the regulation of the translation process while keeping the basic translation functions intact. This suggests the contribution of a novel, possibly modulating aberrant cellular function operative in autism. Previously, we detected high expression of RPL10 by RNA in situ hybridization in mouse hippocampus, a constituent of the brain limbic system known to be afflicted in autism. Based on these findings, we present a model for autistic disorder where a change in translational function is suggested to impact on those cognitive functions that are mediated through the limbic system.