Autism spectrum disorders (ASDs) are complex, heritable and highly heterogeneous neurodevelopmental conditions, where clinical symptoms often co-occur with other psychiatric conditions. The majority of genetic variance in ASD liability resides with common genetic variation, and research suggests that clusters of co-occurring ASD symptoms may reflect distinct combinations of multiple, overarching genetic factors. While such multivariate genetic architecture is largely unexplored, understanding the nature of co-occurring symptoms and their underlying etiological mechanisms is important, as this can affect the support that individuals with ASD receive and help to differentiate genuine comorbidities (involving shared genetic factors) from differential diagnoses (involving multiple unique genetic factors).

Beate St Pourcain and colleagues aim to disentangle ASD heterogeneity through multivariate genetic analysis of co-occurring ASD symptoms using structural equation modelling techniques, studying affected individuals from the SPARK (Simons Foundation Powering Autism Research for Knowledge) and SSC (Simons Simplex Collection) cohorts. Building on previous experiences¹, the researchers will model the common genetic architecture of co-occurring ASD symptoms using genetic-relationship-matrix structural equation modelling (GSEM). This analysis approach combines established twin study methodologies with genome-wide genotyping information in unrelated individuals.

Specifically, St Pourcain and colleagues’ studies will focus on: (i) the identification and characterization of the genetically predictable phenotypic spectrum of high- versus low-functioning ASD and (ii) the identification and characterization of shared and unique genetic influences among co-occurring ASD symptoms. Taken together, this explorative pilot project will provide insight into ASD as a collection of similar disorders with varying symptom clusters, rather than a single phenotype.