Oligogenic heterozygosity in individuals with high-functioning autism spectrum disorders

Autism spectrum disorders (ASDs) are a heterogeneous group of neuro-developmental disorders. While significant progress has been made in the identification of genes and copy number variants associated with syndromic autism, little is known to date about the etiology of idiopathic non-syndromic autism. Sanger sequencing of 21 known autism susceptibility genes in 339 individuals with high-functioning, idiopathic ASD revealed de novo mutations in at least one of these genes in 6 of 339 probands (1.8%). Additionally, multiple events of oligogenic heterozygosity were seen, affecting 23 of 339 probands (6.8%). Screening of a control population for novel coding variants in CACNA1C, CDKL5, HOXA1, SHANK3, TSC1, TSC2 and UBE3A by the same sequencing technology revealed that controls were carriers of oligogenic heterozygous events at significantly (P < 0.01) lower rate, suggesting oligogenic heterozygosity as a new potential mechanism in the pathogenesis of ASDs

High-Throughput Sequencing of mGluR Signaling Pathway Genes Reveals Enrichment of Rare Variants in Autism

Identification of common molecular pathways affected by genetic variation in autism is important for understanding disease pathogenesis and devising effective therapies. Here, we test the hypothesis that rare genetic variation in the metabotropic glutamate-receptor (mGluR) signaling pathway contributes to autism susceptibility. Single-nucleotide variants in genes encoding components of the mGluR signaling pathway were identified by high-throughput multiplex sequencing of pooled samples from 290 non-syndromic autism cases and 300 ethnically matched controls on two independent next-generation platforms. This analysis revealed significant enrichment of rare functional variants in the mGluR pathway in autism cases. Higher burdens of rare, potentially deleterious variants were identified in autism cases for three pathway genes previously implicated in syndromic autism spectrum disorder, TSC1, TSC2, and SHANK3, suggesting that genetic variation in these genes also contributes to risk for non-syndromic autism. In addition, our analysis identified HOMER1, which encodes a postsynaptic density-localized scaffolding protein that interacts with Shank3 to regulate mGluR activity, as a novel autism-risk gene. Rare, potentially deleterious HOMER1 variants identified uniquely in the autism population affected functionally important protein regions or regulatory sequences and co-segregated closely with autism among children of affected families. We also identified rare ASD-associated coding variants predicted to have damaging effects on components of the Ras/MAPK cascade. Collectively, these findings suggest that altered signaling downstream of mGluRs contributes to the pathogenesis of non-syndromic autism

A Brain Region-Specific Predictive Gene Map for Autism Derived by Profiling a Reference Gene Set

Molecular underpinnings of complex psychiatric disorders such as autism spectrum disorders (ASD) remain largely unresolved. Increasingly, structural variations in discrete chromosomal loci are implicated in ASD, expanding the search space for its disease etiology. We exploited the high genetic heterogeneity of ASD to derive a predictive map of candidate genes by an integrated bioinformatics approach. Using a reference set of 84 Rare and Syndromic candidate ASD genes (AutRef84), we built a composite reference profile based on both functional and expression analyses. First, we created a functional profile of AutRef84 by performing Gene Ontology (GO) enrichment analysis which encompassed three main areas: 1) neurogenesis/projection, 2) cell adhesion, and 3) ion channel activity. Second, we constructed an expression profile of AutRef84 by conducting DAVID analysis which found enrichment in brain regions critical for sensory information processing (olfactory bulb, occipital lobe), executive function (prefrontal cortex), and hormone secretion (pituitary). Disease specificity of this dual AutRef84 profile was demonstrated by comparative analysis with control, diabetes, and non-specific gene sets. We then screened the human genome with the dual AutRef84 profile to derive a set of 460 potential ASD candidate genes. Importantly, the power of our predictive gene map was demonstrated by capturing 18 existing ASD-associated genes which were not part of the AutRef84 input dataset. The remaining 442 genes are entirely novel putative ASD risk genes. Together, we used a composite ASD reference profile to generate a predictive map of novel ASD candidate genes which should be prioritized for future research