Rare copy number variations associated with schizophrenia and intellectual disability
Bibliographic record
Abstract
Schizophrenia is a severe psychiatric disorder associated with significant impairments in cognitive functioning. Extensive evidence supports the importance of genetic aetiology, similar to other neurodevelopmental disorders like intellectual disability (ID). Of particular importance are large rare pathogenic copy number variations (CNVs), which have been independently associated with schizophrenia and ID. To date, there have been no studies systematically investigating the genome-wide burden and/or functional impact of rare CNVs on intellect (IQ) in schizophrenia. In this thesis, I used high resolution CNV data from a sample of 546 unrelated subjects of European descent with schizophrenia to investigate multiple IQ groups. The results demonstrated that the yield of pathogenic CNVs increased with decreasing IQ. Notably, the yield of pathogenic CNVs was similar for those with ID and those with a non-verbal learning disability (NVLD). There was a significantly greater burden of rare genic duplications that overlapped genes involved in neurodevelopment in individuals with schizophrenia in the lower IQ group compared to those with higher IQ that persisted after removing all subjects with a pathogenic CNV. I also investigated the variable expression and incomplete penetrance of two rare pathogenic CNVs by compiling every case with a 15q13.3 or a 3q13.31 microdeletion reported in the literature to date. The results of these studies showed ID and schizophrenia to be features of both, with the collective penetrance of 15q13.3 deletions for any neuropsychiatric disorder at over 80.0%. Finally, using two large clinically ascertained cohorts, I demonstrated that the burden of additional rare CNVs located elsewhere in the genome shapes the expression of schizophrenia in 22q11.2 deletion syndrome (22q11.2DS), and, together with the location of the deletion itself, the penetrance of ID in NRXN1 deletions. Collectively, these novel data represent important contributions towards understanding the genetic architecture of schizophrenia and ID.
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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 | 0.001 |
| Research integrity | 0.000 | 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".