Mutation Burden of Rare Variants in Schizophrenia Candidate Genes
Bibliographic record
Abstract
BACKGROUND: Schizophrenia (SCZ) is a very heterogeneous disease that affects approximately 1% of the general population. Recently, the genetic complexity thought to underlie this condition was further supported by three independent studies that identified an increased number of damaging de novo mutations DNM in different SCZ probands. While these three reports support the implication of DNM in the pathogenesis of SCZ, the absence of overlap in the genes identified suggests that the number of genes involved in SCZ is likely to be very large; a notion that has been supported by the moderate success of Genome-Wide Association Studies (GWAS). METHODS: To further examine the genetic heterogeneity of this disease, we resequenced 62 genes that were found to have a DNM in SCZ patients, and 40 genes that encode for proteins known to interact with the products of the genes with DNM, in a cohort of 235 SCZ cases and 233 controls. RESULTS: We found an enrichment of private nonsense mutations amongst schizophrenia patients. Using a kernel association method, we were able to assess for association for different sets. Although our power of detection was limited, we observed an increased mutation burden in the genes that have DNM.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".