Polymorphisms of <i>IKBKE</i> gene are associated with major depressive disorder and panic disorder
Bibliographic record
Abstract
BACKGROUND: The immune system has been increasingly implicated in the development of mood and anxiety disorders. Inhibitor of kappa light polypeptide gene enhancer in B cells, kinase epsilon (IKBKE) gene encodes IKKε protein that is involved in innate immunity, predominantly antiviral response generation. It also bears pro-inflammatory properties that could affect psychiatric outcomes. In order to investigate the possible role of IKBKE gene in major depressive disorder (MDD) and panic disorder (PD), we conducted a case-control genetic association study concerning these disorders. METHODS: In all, 14 SNPs of IKBKE gene were genotyped in groups of 391 patients with MDD and 190 patients with PD together with respective 389 and 371 healthy control individuals. The given groups were further divided by gender for additional analyses. RESULTS: Substantial genetic associations were revealed between IKBKE SNPs and MDD (multiple testing adjusted P < 0.05) and suggestive associations in case of PD (P(adj) > 0.05). In addition, two SNPs that were only associated with PD among males, also displayed significantly different allele frequencies compared to PD females. This may indicate a specific role of these SNPs in male PD, but caution should be applied here due to the small size of the studied PD males group. CONCLUSIONS: The results of this study confirm our initial findings and indicate a possible role of IKBKE gene in mood and anxiety disorders.
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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".