Gender differences in association between serotonin transporter gene polymorphism and personality traits
Bibliographic record
Abstract
Since Lesch and colleagues reported an association between anxiety-related traits (Neuroticism) and a functional polymorphism in the serotonin transporter gene regulatory region (5-HTTLPR), there have been several reports on 5-HTTLPR and personality traits with both positive and negative results. The present study was a further attempt to replicate the original findings of Lesch et al. in a population of well-defined normal healthy subjects. In addition, a variable number tandem repeat polymorphism in the second intron was included in this study because it has recently been shown to act as a transcriptional regulator. Personality traits were evaluated in 186 unrelated normal subjects by the NEO Five Factor Inventory. The most important and novel finding of this study was a significant association of mean Neuroticism scores with the short allele of 5-HTTLPR in male subjects (t = 2.4, P = 0.018). We were thus able to replicate the finding of Lesch et al. of an association between serotonin transporter gene polymorphism (5-HTTLPR) and Neuroticism, but only in a male population. We also found a significant effect of gender on mean scores of Neuroticism [F = 3.9, degrees of freedom (df) = 1, 180, P = 0.05] and Agreeableness (F = 6.8, df = 1, 180, P = 0.01), but no significant effect of 5-HTTLPR genotype on Neuroticism (F = 0.87, df= 2, 180, P = 0.42) or Agreeableness (F = 0.35, df = 2, 180, P = 0.7). These findings suggest that gender differences exist in contribution of genetic factors to behavioural phenotypes. They may also explain the inconsistencies in previous reports on association of Neuroticism with 5-HTTLPR from studies using different proportions of male and female subjects.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".