Autism spectrum disorder (ASD), a chronic and heterogeneous condition with many traits and co-occurring conditions, is poorly understood. Diagnosis is commonly accompanied by other conditions like epilepsy, gastrointestinal disturbances and motor issues. People with autism have a wide variety of distinct experiences, likely due to differences in genes, environment and lifestyle, suggesting the existence of different subtypes of ASD. Thus, a comprehensive understanding of ASD landscape and subtypes is crucial to personalized treatment of ASD.
In the current project, Michael Snyder and colleagues plan to initiate a study known as COUNT. COUNT’s precision approach will leverage longitudinal deep multiomic profiling, combining genomic, proteomic, metabolomic, lipidomic and physiological profiles of individuals with ASD. Multiomic associations will reveal molecular and cellular differentiators of those with and without particular ASD characteristics or with varying frequencies of occurrence. Intra-individual markers of occurrence among the real-time physiological data will reveal potential physiological predictors. We expect this approach to illuminate our understanding of the biology, characterize its heterogeneity and identify predictors of ASD traits, with the ultimate goal of better symptom management and supports for the disorder at the individual level.