Genome-wide characterization of genetic and functional dysregulation in autism spectrum disorder
Bibliographic record
Abstract
Abstract Autism spectrum disorder (ASD) is a range of major neurodevelopmental disabilities with a strong genetic basis. Yet, owing to extensive genetic heterogeneity, multiple modes of inheritance and limited study sizes, sequencing and quantitative genetics approaches have had limited success in characterizing the complex genetics of ASD. Currently, only a small fraction of potentially causal genes—about 65 genes out of an estimated severalhundred—are known based on strong genetic evidence. Hence, there isa critical need for complementary approaches to further characterize the genetic basis of ASD, enabling development of better screening and therapeutics. Here, we use a machine-learning approach based on a human brain-specific functional gene interaction network to present a genome-wide prediction of autism-associated genes, including hundreds of candidate genes for which there is minimal or no prior genetic evidence. Our approach is validated in an independent case-control sequencing study of approximately 2,500families. Leveraging these genome-wide predictions and the brain-specificnetwork, we demonstrate that the large set of ASD genes converges on a smaller number of key cellular pathways and specific developmental stages of the brain. Specifically, integration with spatiotemporal transcriptome expression data implicates early fetal and midfetal stages of the developing human brain in ASD etiology. Likewise, analysis of the connectivity of topautism genes in the brain-specific interaction network reveals the breadthof autism-associated functional modules, processes, and pathways in the brain. Finally, we identify likely pathogenic genes within the most frequent autism-associated copy-number-variants (CNVs) and propose genes and pathways that are likely mediators of autism across multiple CNVs. All the predictions, interactions, and functional insights from this work are available to biomedical researchers at asd.princeton.edu .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".