Convergent coexpression of autism associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies
Bibliographic record
Abstract
Abstract Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder characterized by deficits in social interactions and communication. Protein function altering variants in many genes have been shown to contribute to ASD risk; however, understanding the biological convergence across so many genes has been difficult and genetic studies depending on presence of deleterious variation may be limited in implicating highly intolerant genes with shorter coding sequences. Here, we demonstrate that coexpression patterns from human post-mortem brain samples (N = 993) are significantly correlated with the transcriptional consequences of CRISPR perturbations (gene editing, interference and activation) in human neurons (N = 17). Across 71 ASD risk genes, there is significant tissue-specific transcriptional convergence that implicates synaptic pathways. Tissue specific convergence of risk genes is a generalizable phenomenon, shown additionally in schizophrenia (brain) and atrial fibrillation (heart). The degree of this convergence in ASD is significantly correlated with the level of association to ASD from sequencing studies (rho = -0.32, P = 3.03 ×10 −65 ) as well as differential expression in post-mortem ASD brains (rho = -0.23, P = 2.39×10 −43 ). After removing all genes statistically associated with ASD, the remaining positively convergent genes showed intolerance to functional mutations, had shorter coding lengths than the ASD genes and were enriched for genes with clinical reports of potential pathogenic contribution to ASD. These results indicate that leveraging convergent coexpression can identify potentially novel risk genes that are unlikely to be discovered by sequencing studies. Overall, this work provides a simple approach to functionally proxy CRISPR perturbation, demonstrates significant context-specific transcriptional convergence among known risk genes of multiple diseases, and proposes novel ASD risk gene candidates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".