Genetic and Cortical Cell-Type Liability Architecture of Autism
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".