Disorder‐specific genetic factors in obsessive‐compulsive disorder: A comprehensive meta‐analysis
Bibliographic record
Abstract
Much remains to be learned about the etiology of obsessive-compulsive disorder (OCD). Twin studies suggest that it arises from both disorder-specific and non-specific genetic factors. To understand the etiology of OCD per se, it is necessary to identify disorder-specific factors. Previous research shows that OCD is associated with serotonin-related polymorphisms (5-HTTLPR coded as triallelic and HTR2A rs6311/rs6313) and, in males, a polymorphism involved in catecholamine modulation; COMT (rs4680). The present study is the first comprehensive meta-analysis to investigate whether these polymorphisms are specific to OCD. A meta-analysis was conducted for genetic association studies of OCD or any other psychiatric disorder, published in any language, in any country. A total of 551 studies were examined, of which 290 were included, consisting of 47,358 cases and 68,942 controls from case control studies, and 2,443 trios from family based studies. The main meta-analysis was limited to those disorders in which there were at least five datasets (studies or sub-studies) per disorder. Results confirmed that OCD is associated with polymorphisms of 5-HTTLPR, HTR2A, and, in males only, COMT. These polymorphisms were not associated with almost all other forms of psychopathology, including unipolar mood disorders, bipolar disorder, panic disorder, schizophrenia, and alcohol dependence. OCD, compared to most other disorders, had a significantly stronger association with particular alleles of each of the polymorphisms. Results did not differ across ancestral groups (Asian vs. Caucasian), designs (case control vs. family based), or diagnostic systems. Results suggest that the polymorphisms investigated in this study are relatively specific to OCD.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.045 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".