The term ‘autism spectrum disorder’ exemplifies the tremendous heterogeneity in this neurodevelopmental disorder at both the clinical and underlying genetic levels. This heterogeneity is a major obstacle toward identifying the pathophysiology of autism. Genetic variations affecting brain proteins are directly expressed in brain function, connectivity and temporal dynamics, but indirectly in behavior.

Kevin Pelphrey and his colleagues at Yale University leveraged the Simons Simplex Collection (SSC) to apply a novel multidisciplinary approach involving cognitive neuroscience and molecular genetics to reveal how genetic differences manifest at the phenotypic level. Their project generated a dataset of clinical measures, electrophysiology and functional magnetic resonance imaging (fMRI) data that is freely available to both the SSC community and the broader scientific community.

The fMRI and electroencephalography datasets include resting-state data as well as task-based data from scanning sessions involving paradigms designed to engage key aspects of social perception and social cognition (for example, the perception of biological motion). These data inform multilevel analysis associating genetic variation and endophenotypes — measurable traits that connect the symptoms of a disorder to underlying genetic risk factors — across participants to reveal how individual genetic differences manifest at the endophenotypic level. Understanding these manifestations may allow researchers to bridge the gene-cognition gap in autism.