Association of serotonin transporter gene polymorphisms with obsessive-compulsive disorder (OCD) in a south Indian population.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Serotonin transporter polymorphisms, 5-HTTVNTR and 5-HTTLPR, have been found to be associated with obsessive-compulsive disorder (OCD) and particularly with neurotic characteristics. In the present study we looked for an association between OCD and these polymorphisms in OCD patients and controls of south Indian origin. METHODS: 5-HTTVNTR and 5-HTTLPR/rs25531 were genotyped in 93 OCD patients and 92 healthy controls. The allelic distribution and genotype frequency in cases and controls were compared using chi square test. In order to test for the effects of genotype on heterogeneity of the illness, linear regression analysis was undertaken for co-morbid depression status and YBOCS score (severity index). RESULTS: There was no significant association with the 5-HTTVNTR or the 5-HTTLPR polymorphism. No significant association of OCD with the 5-HTTLPR genotype was found even on inclusion of the rs25531 locus, which is part of the transcription factor binding site as reported in earlier studies. However, severity of the illness showed a modest association with the dominant model. INTERPRETATION AND CONCLUSIONS: Our data show that genetic variation in the SLC6A4 gene regulatory region may not have a significant effect on OCD in the present population. Further replication in a large and independent cohort with an equal number of female subjects would help to ascertain if the absence of association in this cohort is due to the nullifying effect of the larger proportion of male subjects in our sample population. The marginal effect of the 5-HTTLPR (A/G) genotype obtained on linear regression with disease severity is suggestive of a potential role for this locus in the disease process.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".