Positive association of the pericentrin (<i>PCNT</i>) gene with major depressive disorder in the Japanese population
Bibliographic record
Abstract
Background: Pericentrin ( PCNT) interacts with disruption-in-schizophrenia 1 ( DISC1), a known genetic risk factor for schizophrenia, bipolar disorder and major depressive disorder (MDD). We sought to determine whether the PCNT gene is implicated in MDD. Methods: We performed case–control association analyses in the Japanese population. We analyzed 9 single nucleotide polymorphisms (SNPs) in 173 patients with MDD and 348 healthy controls. Results: We found a significant allelic association between 3 SNPs (rs3788265, rs2073376 and rs2073380) of the PCNT gene and MDD ( p = 0.006, 0.005 and 0.021, respectively). After correction for multiple testing, 2 SNPs (rs3788265 and rs2073376) retained significant allelic associations with MDD. In addition, we found a significant association between the 2 marker haplotypes (r3788265 and rs2073376) and MDD (permutation p = 0.011). Limitations: Our sample was small and comprised only Japanese participants. In addition, owing to the late onset of MDD, it is possible that the disorder will develop in at least some participants in our control group. Finally, we did not show how SNPs of the PCNT gene alter its function. Conclusion: Our results suggest that genetic variations in the PCNT gene may play a significant role in the etiology of MDD in the Japanese population.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".