bioRxivGenomicsPreprint
Genetic and Cortical Cell-Type Liability Architecture of Autism
Thomas RenneiD, Florian BénitièreiD, Cécile PoulainiD, Alma DubuciD, Vincent-Raphaël BourqueiD, Guillaume HuguetiD, Tomasz Jan NowakowskiiD, Sébastien JacquemontiD
Abstract
Autism Spectrum Disorders (ASD) can result from rare genetic variants interfering with brain development. Whether their effects converge on specific cortical cell types remains unresolved. Previous studies have focused on a narrow set of high-confidence ASD (hcASD) genes, which were enriched in neuronal cell types during prenatal development. By contrast, studies of postnatal cerebral cortex have repeatedly associated ASD with transcriptional changes in both neurons and glia. To comprehensively map ASD genetic liability across cortical cell types, we conducted a functional genetic burden analysis with 124,416 individuals, including ASD probands and unaffected family members. We examined six classes of rare gene-disrupting variants aggregated across a complete spectrum of transcriptomic cell types of the human prefrontal cortex throughout development. We show that cellular liabilities in ASD delineate a broad and developmentally dynamic architecture. Likewise, we uncover high dependency on classes of variants with Loss-of-Function (LoF) and de novo linked to prenatal cells, while duplications, missense, and inherited variants increase liability through postnatal and glial cell types. Notably, inherited LoF variants uncover the contribution of microglia to ASD liability, also supported by transcriptomic evidence from postmortem ASD brains. Finally, we show that overall, variants disrupting genes differentially expressed in postmortem ASD brains significantly contribute to ASD liability, demonstrating convergence between disrupted transcriptomes and genetic liability. Together, our study offers an integrative, cell-type-aware framework for interpreting ASD risk genetics.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
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