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The speaker is on the autism spectrum and lives between the worlds of science and self-advocacy. In interviewing experts and advocates, several themes emerge. The first is that the public disagreements in the autism world belie a growing convergence of the views that autism is a biological condition subject to intervention and a social difference that requires only acceptance. Scientific evidence, interviews with experts and advocates, and aspects of the speaker’s experience will be shared.
Our group identified the first genes associated with autism pointing at a key role of the synapse in this complex condition. These findings underscore the genetic diversity of autism while revealing shared biological mechanisms. The genetic architecture of autism involves a complex combination of de novo, rare and common genetic variants shared with other traits such as attention deficit hyperactivity disorders (ADHD), cognitive skills, and epilepsy. In this presentation, I will introduce recent results that shed new light on different subgroups of people with autism and how they differ at the clinical, brain imaging and genetic levels. I will also illustrate how we can explore synaptic genes such as SHANK2 and SHANK3 and their roles in cognition and social motivation. Finally, I will present how we are currently using participatory research to study Risk, Resilience and Developmental Diversity in Mental Health (The R2D2-MH project) to understand why some carriers of genetic variants seem to be protected from adverse symptoms while others have more difficulties to thrive in the society.
Autism is currently diagnosed as a singular behavioral entity, yet its profound clinical and
genetic heterogeneity suggests distinct underlying etiologies that demand a precision medicine
approach. This talk presents a data-driven framework for turning that heterogeneity into
actionable subtypes, by combining massive electronic health record (EHR) analytics with deep-
phenotyped cohort studies.
First, we explore a data-driven phenotypic analysis of 63,673 children with autism derived from
a national EHR repository. By applying dimensionality reduction (PaCMAP) and density-based
clustering (HDBSCAN) to standardized diagnosis codes (PheCodes), we identified 36 distinct
clinical clusters with interpretable comorbidity signatures spanning neurologic, developmental,
psychiatric, and metabolic profiles. To translate these findings into clinical practice, we
introduced a gradient-boosting prediction model (LightGBM) capable of assigning new patients
to these clusters with high accuracy (AUC 0.9) at their first diagnostic encounter, enabling early,
individualized intervention trajectories.
We then connect the EHR-derived subtypes to mechanistically informative case studies. The first
is a dyslipidemia-associated autism subtype that we have identified using multi-dimensional
genomic analyses. By integrating whole exome sequence data from 3,531 individuals with
spatiotemporal brain gene expression data, we identified a convergence of autism-segregating
deleterious variants within lipid regulation pathways. Validation of this dyslipidemia subtype in
an independent cohort of 34 million individuals confirmed that ~5% of children with autism have
altered blood lipid profiles and a significantly higher prevalence of dyslipidemia diagnoses
compared to unaffected siblings and controls.
We also used the Simons Simplex Collection to characterize the subgroup of autistic children
reported to demonstrate marked behavioral improvements during febrile episodes. We found that
fever responsiveness is associated with maternal infection during pregnancy and gastrointestinal
dysfunction, pointing to immune–gut interactions as plausible modulators of core autism
features. Moreover, we addressed diagnostic timing in the SPARK cohort. Despite improved
screening, ~25% of children receive an autism diagnosis after age six, preventing them from
achieving optimal outcomes. Moreover, analyzing 23,632 participants in the SPARK cohort, we
addressed the paradox of delayed diagnosis (post-age 6). We identified two diametrically
opposed late-diagnosed groups: one characterized by camouflaging (lower support needs and
fewer comorbidities) and another by clinical overshadowing (severe comorbidities and high
support needs that masked the underlying autism).
Together, these results show that autism heterogeneity can be partitioned into scalable,
interpretable subtypes from routine clinical data and refined with cohort studies. By prioritizing
interpretability, we connect machine-learning outputs to clinical reasoning and enable earlier risk
stratification, targeted screening, and better-defined cohorts for mechanistic research and clinical
Israel’s research ecosystem enables the large-scale integration of Big Data with deep, theory-driven developmental research. This approach generates rich, fine-grained insights into children’s developmental trajectories that are rarely achievable elsewhere. Translating these insights into national tools and policies, however, requires moving beyond research settings into complex, real-world systems. Along this path, challenges emerge around scalability, data integration, ethics, and alignment between scientific evidence and policy implementation.
