Enrichment of gene variants associated with treatable genetic disorders in psychiatric populations
Bibliographic record
Abstract
ABSTRACT Purpose Many genetic conditions can mimic mental health disorders, with psychiatric symptoms that are difficult to treat with standard psychotropic medications. This study tests the hypothesis that psychiatric populations are enriched for pathogenic variants associated with selected treatable genetic disorders. Methods Using next-generation sequencing, 2046 psychiatric patients were screened for variants in genes associated with four inborn errors of metabolism (IEMs), Niemann-Pick disease type C (NPC), Wilson disease (WD), homocystinuria (HOM), and acute intermittent porphyria (AIP). Results Among the 2046 cases, carrier rates of 0·83%, 0·98%, 0·20%, and 0·24% for NPC, WD, HOM, and AIP were seen respectively. An enrichment of known and likely pathogenic variants in the genes associated with NPC and AIP was found in the psychiatric cohort, and especially in schizophrenia patients. Conclusion The results of this study support that rare genetic disease variants, such as those associated with IEMs, may contribute to the pathogenesis of psychiatric disorders. IEMs should be considered as possible causative factors for psychiatric presentations, especially in psychotic disorders, such as schizophrenia, and in the context of poor treatment response.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".