Abstract
In the last decade, Autism has broadened and often shifted its diagnostics criteria, allowing several neuropsychiatric and neurological disorders of known etiology. This has resulted in a highly heterogeneous spectrum with apparent exponential rates in prevalence. I ask if it is possible to leverage existing genetic information about those disorders making up Autism today and use it to stratify this spectrum. To that end, I combine genes linked to Autism in the SFARI database and genomic information from the DisGeNET portal on 25 diseases, inclusive of non-neurological ones. I use the GTEx data on genes’ expression on 54 human tissues and ask if there are overlapping genes across those associated to these diseases and those from SFARI-Autism. I find a compact set of genes across all brain-disorders which express highly in tissues fundamental for somatic-sensory-motor function, self-regulation, memory, and cognition. Then, I offer a new stratification that provides a distance-based orderly clustering into possible Autism subtypes, amenable to design personalized targeted therapies within the framework of Precision Medicine. I conclude that viewing Autism through this physiological (Precision) lens, rather than viewing it exclusively from a psychological behavioral construct, may make it a more manageable condition and dispel the Autism epidemic myth.
Highlights
According to the CDC, in the span of 16 years, the US moved from 6.7/1000 to 18.5/1000 autistics in the population of school age children [1]
While the Simons Foundation Research Initiative (SFARI) genes circularly depend on the ADOS and the DSM behavioral Autism criteria, the latter genes come from the disease-association network which did not rely on an Autism diagnosis
I included post-traumatic stress syndrome (PTSD) owing to the tendency of trauma reported in Autism [49,54,55] and found 55 genes of those linked to PTSD in the SFARIAutism set
Summary
According to the CDC, in the span of 16 years, the US moved from 6.7/1000 to 18.5/1000 autistics in the population of school age children [1] This increase continues to move along an exponential rate, while maintaining a near 5:1 males-to-females ratio [2,3]. Motor features derived from endogenous neural signals in motor patterns, do identify the female phenotype [4,5,6,7]. This is the case even when digitizing the current clinical criteria that would otherwise miss females because of exclusive reliance on external observation [8,9]. Current interventions are far from being inclusive, or advocating for neurodiversity in the clinical data driving best-practices and evidence-based criteria for treatment recommendation [10,11]
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