Copy Number Variations in Schizophrenia: Critical Review and New Perspectives on Concepts of Genetics and Disease
Bibliographic record
Abstract
OBJECTIVE: Structural variations of DNA, such as copy number variations (CNVs), are recognized to contribute both to normal genomic variability and to risk for human diseases. For example, schizophrenia has an established connection with 22q11.2 deletions. Recent genome-wide studies have provided initial evidence that CNVs at other loci may also be associated with schizophrenia. In this article, the authors provide a brief overview of CNVs, review recent findings related to schizophrenia, outline implications for clinical practice and diagnostic subtyping, and make recommendations for future reports on CNVs to improve interpretation of results. METHOD: The review included genome-wide surveys of CNVs in schizophrenia that included one or more comparison groups, were published before 2009, and used newer methods. Six studies were identified. RESULTS: Despite some limitations, these initial genome-wide studies of CNVs provide replicated associations of schizophrenia with rare 1q21.1 and 15q13.3 deletions. Collectively, the results point to a more general mutational mechanism involving rare CNVs that elevate risk for schizophrenia, especially more developmental forms of the disease. Including 22q11.2 deletions, rare risk-associated CNVs appear to account for up to 2% of schizophrenia. CONCLUSIONS: The more penetrant CNVs have direct implications for clinical practice and diagnostic subtyping. CNVs with lower penetrance promise to contribute to our genetic understanding of pathogenesis. The findings provide insight into a broader neuropsychiatric spectrum for schizophrenia than previously conceived and indicate new directions for genetic studies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".