Autism spectrum disorder (ASD) is a heterogeneous disease where efforts to define subtypes behaviorally have met with limited success. Hypothesizing that genetically based subtype identification may prove more productive, we resequenced the ASD-associated gene CHD8 in 3,730 children with developmental delay or ASD. We identified a total of 15 independent mutations; no truncating events were identified in 8,792 controls, including 2,289 unaffected siblings. In addition to a high likelihood of an ASD diagnosis among patients bearing CHD8 mutations, characteristics enriched in this group included macrocephaly, distinct faces, and gastrointestinal complaints. chd8 disruption in zebrafish recapitulates features of the human phenotype, including increased head size as a result of expansion of the forebrain/midbrain and impairment of gastrointestinal motility due to a reduction in post-mitotic enteric neurons. Our findings indicate that CHD8 disruptions define a distinct ASD subtype and reveal unexpected comorbidities between brain development and enteric innervation.

Gene-disruptive mutations contribute to the biology of neurodevelopmental disorders (NDDs), but most pathogenic genes are not known. We sequenced 208 candidate genes from >11,730 patients and >2,867 controls. We report 91 genes with an excess of de novo mutations or private disruptive mutations in 5.7% of patients, including 38 novel NDD genes. Drosophila functional assays of a subset bolster their involvement in NDDs. We identify 25 genes that show a bias for autism versus intellectual disability and highlight a network associated with high-functioning autism (FSIQ>100). Clinical follow-up for NAA15, KMT5B, and ASH1L reveals novel syndromic and non-syndromic forms of disease.

Context Clinical best estimate diagnoses of specific autism spectrum disorders (autistic disorder, pervasive developmental disorder-not otherwise specified, Asperger’s disorder) have been used as the diagnostic gold standard, even when information from standardized instruments is available. Objective To determine if the relationships between behavioral phenotypes and clinical diagnoses of different autism spectrum disorders vary across 12 university-based sites. Design Multi-site observational study collecting clinical phenotype data (diagnostic, developmental and demographic) for genetic research. Classification trees were employed to identify characteristics that predicted diagnosis across and within sites. Setting Participants were recruited through 12 university-based autism service providers into a genetic study of autism. Participants 2102 probands (1814 males) between 4 and 18 years of age (M age=8.93, SD=3.5 years) who met autism spectrum criteria on the Autism Diagnostic Interview–Revised and Autism Diagnostic Observation Schedule and had a clinical diagnosis of an autism spectrum disorder. Main Outcome Measures Best estimate clinical diagnoses predicted by standardized scores from diagnostic, cognitive, and behavioral measures. Results Though distributions of scores on standardized measures were similar across sites, significant site differences emerged in best estimate clinical diagnoses of specific autism spectrum disorders. Relationships between clinical diagnoses and standardized scores, particularly verbal IQ, language level and core diagnostic features, varied across sites in weighting of information and cut-offs. Conclusions Clinical distinctions among categorical diagnostic subtypes of autism spectrum disorders were not reliable even across sites with well-documented fidelity using standardized diagnostic instruments. Results support the move from existing sub-groupings of autism spectrum disorders to dimensional descriptions of core features of social affect and fixated, repetitive behaviors, together with characteristics such as language level and cognitive function.

The Simons Foundation Autism Research Initiative (SFARI) has launched SPARKForAutism.org, a dynamic platform that is engaging thousands of individuals with autism spectrum disorder (ASD) and connecting them to researchers. By making all data accessible, SPARK seeks to increase our understanding of ASD and accelerate new supports and treatments for ASD.