Decomposition of phenotypic heterogeneity in autism reveals underlying genetic programs

作者：Aviya Litman, Natalie Sauerwald, LeeAnne Green Snyder, Jennifer H. Foss‐Feig, Christopher Y. Park, Yun Hao, Ilan Dinstein, Chandra L. Theesfeld, Olga G. Troyanskaya · 发表于：Nature Genetics · 年份：2025 · DOI：10.1038/s41588-025-02224-z · 被引用次数：76 · 研究领域：Autism Spectrum Disorder Research、Genetics and Neurodevelopmental Disorders、Genomics and Rare Diseases

Unraveling the phenotypic and genetic complexity of autism is extremely challenging yet critical for understanding the biology, inheritance, trajectory and clinical manifestations of the many forms of the condition. Using a generative mixture modeling approach, we leverage broad phenotypic data from a large cohort with matched genetics to identify robust, clinically relevant classes of autism and their patterns of core, associated and co-occurring traits, which we further validate and replicate in an independent cohort. We demonstrate that phenotypic and clinical outcomes correspond to genetic and molecular programs of common, de novo and inherited variation and further characterize distinct pathways disrupted by the sets of mutations in each class. Remarkably, we discover that class-specific differences in the developmental timing of affected genes align with clinical outcome differences. These analyses demonstrate the phenotypic complexity of children with autism, identify genetic programs underlying their heterogeneity, and suggest specific biological dysregulation patterns and mechanistic hypotheses.