Clinical applications of schizophrenia genetics: genetic diagnosis, risk, and counseling in the molecular era
Bibliographic record
Abstract
Gregory Costain1,2, Anne S Bassett1–41Clinical Genetics Research Program, Centre for Addiction and Mental Health, 2Institute of Medical Science, University of Toronto, 3Division of Cardiology, Department of Medicine and Department of Psychiatry, University Health Network, 4Department of Psychiatry, University of Toronto, Toronto, Ontario, CanadaAbstract: Schizophrenia is a complex neuropsychiatric disease with documented clinical and genetic heterogeneity, and evidence for neurodevelopmental origins. Driven by new genetic technologies and advances in molecular medicine, there has recently been concrete progress in understanding some of the specific genetic causes of this serious psychiatric illness. In particular, several large rare structural variants have been convincingly associated with schizophrenia, in targeted studies over two decades with respect to 22q11.2 microdeletions, and more recently in large-scale, genome-wide case-control studies. These advances promise to help many families afflicted with this disease. In this review, we critically appraise recent developments in the field of schizophrenia genetics through the lens of immediate clinical applicability. Much work remains in translating the recent surge of genetic research discoveries into the clinic. The epidemiology and basic genetic parameters (such as penetrance and expression) of most genomic disorders associated with schizophrenia are not yet well characterized. To date, 22q11.2 deletion syndrome is the only established genetic subtype of schizophrenia of proven clinical relevance. We use this well-established association as a model to chart the pathway for translating emerging genetic discoveries into clinical practice. We also propose new directions for research involving general genetic risk prediction and counseling in schizophrenia.Keywords: schizophrenia, genetics, 22q11 deletion syndrome, copy number variation, genetic counseling, genetic predisposition to disease
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| 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".