Double hits in schizophrenia
Bibliographic record
Abstract
The co-occurrence of a copy number variant (CNV) and a functional variant on the other allele may be a relevant genetic mechanism in schizophrenia. We hypothesized that the cumulative burden of such double hits-in particular those composed of a deletion and a coding single-nucleotide variation (SNV)-is increased in patients with schizophrenia. We combined CNV data with coding variants data in 795 patients with schizophrenia and 474 controls. To limit false CNV-detection, only CNVs called by two algorithms were included. CNV-affected genes were subsequently examined for coding SNVs, which we termed "CNV-SNVs." Correcting for total queried sequence, we assessed the CNV-SNV-burden and the combined predicted deleterious effect. We estimated P-values by permutation of the phenotype. We detected 105 CNV-SNVs; 67 in duplicated and 38 in deleted genic sequence. Although the difference in CNV-SNVs rates was not significant, the combined deleteriousness inferred by CNV-SNVs in deleted sequence was almost 4-fold higher in cases compared with controls (nominal P = 0.009). This effect may be driven by a higher number of CNV-SNVs and/or by a higher degree of predicted deleteriousness of CNV-SNVs. No such effect was observed for duplications. We provide early evidence that deletions co-occurring with a functional variant may be relevant, albeit of modest impact, for the genetic etiology of schizophrenia. Large-scale consortium studies are required to validate our findings. Sequence-based analyses would provide the best resolution for detection of CNVs as well as coding variants genome-wide.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".