Mutation screen and association analysis of the glucocorticoid receptor gene (<i>NR3C1</i>) in childhood‐onset mood disorders (COMD)
Bibliographic record
Abstract
Depressive disorders are highly heterogeneous psychiatric disorders involving deficits to cognitive, psychomotor, and emotional processing. Considerable evidence links disruption to the hypothalamic-pituitary-adrenal (HPA) axis to the etiology of depression, with specific deficits reported in glucocorticoid receptor (GR)-mediated negative feedback. Given the role of GR-mediated negative feedback in mediating response to stress, and the clear link between stress and depression, it is plausible that polymorphisms in the GR gene (NR3C1) act to increase susceptibility. Maternal behavior in rats epigenetically alters a NGF1-A transcription factor binding-site in the promoter region of the GR gene, providing a mechanism by which environmental cues can regulate GR expression and thus response to stress. The analogous region of the human GR gene (NR3C1) has not been studied, but it is possible that polymorphisms in this region may alter the binding of transcription factors known to regulate GR expression. In this study, we have performed bioinformatic analyses on the promoter region of NR3C1 to identify conserved promoter sequences and predicted transcription factor binding sites. These regions were screened with denaturing high-performance liquid chromatography (DHPLC) and direct re-sequencing, and several novel polymorphic variants were identified. We genotyped nine polymorphisms across NR3C1 in a large sample of Hungarian nuclear families ascertained through affected probands with a diagnosis of childhood-onset mood disorders (COMD). Single-marker analysis provided little evidence for an association of this gene with COMD, but multi-marker analysis across a region of high linkage disequilibrium revealed modest evidence for the biased transmission of several haplotypes.
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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 | 0.000 |
| 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) | 0.001 | 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".