Testing the network hypothesis for schizophrenia and autism spectrum disorder using whole exome sequencing data
Bibliographic record
Abstract
Background Schizophrenia (SCZ) and autism spectrum disorder (ASD) are psychiatric diseases with complex inheritance.Genetic studies have not identified susceptibility genes to adequately explain the heritability and the etiology of these diseases is largely unknown. MethodsWe identified susceptibility genes enriched for de novo mutations (DNMs) in at least two independent whole exome sequencing (WES) publications.Genes associated with hypertrophic cardiomyopathy (HC) were used as control genes.We selected for rare inherited and DNMs in the ASD network using a WES dataset (2392 ASD families) and in the SCZ network using three independent WES datasets (35 trios; 598 trios; 5090 case controls).We compared the mutation load in the 'disease network' between affected and unaffected individuals for each dataset.The analyses were repeated using the 'HC network'.Results 14 SCZ genes and 143 ASD genes were identified.When using the 598 SCZ trios, probands were enriched in functional variants relative to the average mutation load of parents in the SCZ network (p = 0.04) but not in the HC genes (p = 0.23).All functional variants identified in the SCZ network were inherited.Similar results were obtained using the case control dataset (SCZ network: p = 0.02; HC network: p = 0.09).When analyzing ASD sibpairs, unaffected siblings were significantly enriched in functional variants in the ASD network (p = 0.02) but also throughout the exome based on a permutation analysis using all genes with functional variants.When controlling for sequencing depth through a conditional logistic regression and applying stricter filtering criteria, the difference was not statistically significant (p = 0.1358). ConclusionsWe provide preliminary evidence that the accumulation of rare variants (mainly inherited) in the identified SCZ susceptibility genes is associated with SCZ.However, this was not the case for the ASD dataset that we had access to.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".