March 12–13, 2026

LeeAnne Green Snyder, Ph.D.

Licensed Psychologist

Dr. LeeAnne Green Snyder is a clinical psychologist who advises the Simons Foundation Autism Research Initiative (SFARI) on protocol design, outcome measures and database needs for its research cohorts of individuals with autism. LeaAnne received her Ph.D. in clinical psychology from the University of Connecticut, where her research involved the first studies of oxytocin in children with autism. She completed her pediatric psychology internship and postdoctoral training in pediatric neuropsychology at Wayne State University School of Medicine in Detroit. There, she led the multi-disciplinary autism clinic and autism training rotation and collaborated in research at the Children’s Hospital of Michigan. She also maintained a private practice in neuropsychology, providing legal consultation, as well as assessments for families and schools. 

Toward Understanding Individual Differences: Data-Driven Decomposition of Autism at the Phenotypic, Clinical and Genetic Levels

LeeAnne Green Snyder, Ph.D., and Aviya Litman
Thursday, March 12 | 8:15AM to 9:45AM | 60 MINUTES | LIVE

Unraveling the behavioral and genetic complexity of autism is extremely challenging, yet critical for understanding its characteristics, biology, inheritance and life course. In this study, we applied a machine learning framework for uncovering distinct subgroups in a large cohort of autistic children (n = 5,392) with matched phenotypic and genomic data. Our analyses identified and independently replicated four subgroups characterized by distinct patterns of core and co-occurring traits and found that those traits correspond to different types of either common, rare or inherited genetic programs. We then further characterized distinct pathways and biological processes disrupted in each subgroup. Remarkably, children with higher support needs who get diagnosed earlier had disruptions in certain types of genes that get activated in the brain at different times than the genes disrupted in later-diagnosed children with lower support needs. Together, these analyses demonstrate the complexity of children with autism, identify genetic programs underlying their diversity, and offer a framework for identifying reliable biologically distinct autism subtypes.


Objective 1:
Participants will be able to state two reasons why understanding heterogeneity with objective methods including computational approaches is important to autism research. 

Objective 2:
Participants will be able to discuss two differences between common vs. rare variants and their potential impacts in autism and presentation. 

Objective 3:
Participants will be able to describe four subgroups uncovered by the current analysis, their phenotypes, and how they correspond with previous clinically defined groups.