Interaction of serotonergic and noradrenergic gene variants in panic disorder
Bibliographic record
Abstract
OBJECTIVE: Panic disorder is an anxiety disorder with an estimated heritability of 48%. Associations findings have been obtained with candidate genes from both serotonergic and noradrenergic pathways including regulatory and coding variants of the serotonin receptor 1A gene, the monoamine oxidase A gene, the catechol-O-methyltransferase gene and the norepinephrine transporter gene. METHODS: In the present study, an analysis of interactions between the functional serotonin receptor 1A polymorphism, the norepinephrine transporter variants and the other respective polymorphisms of the above-mentioned genes is reported. The analysis is based on genotype results from 115 cases and 115 age and sex-matched controls. RESULTS: A nominally significant (P=0.04) interaction between the serotonin receptor 1A and the catechol-O-methyltransferase polymorphisms was observed. Stratified analysis revealed that the odds ratio of each polymorphism was highest in the presence of the low-risk genotype(s) of the other polymorphism and low in the presence of the high-risk genotype(s) of the other polymorphism. CONCLUSIONS: This is the first possible interaction of genetic variations in panic disorder that has been observed. As the sample size was small and no adjustment for multiple testing was made, the assessment of the interacting risk alleles needs replication in a larger sample with higher power.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".