Association of NTRK2 gene with suicidality: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that genes in brain development pathways may have important roles in affecting risk of suicidal behaviors, with our previous meta-analysis supporting a role of the brain-derived neurotrophic factor (BDNF) gene. NTRK2 is a gene that encodes the neurotrophic receptor tyrosine kinase 2, which is a receptor for BDNF. In the current study, we aim to examine the potential association between NTRK2 single nucleotide polymorphism (SNPs) and suicidal ideation/behaviors. METHODS: We first conducted a literature search using keywords like 'NTRK2', 'TRKB', and 'suicid*' to identify papers on NTRK2 SNPs and suicidal ideation/behaviors. In addition, we have individual-level genotype data for all the identified SNPs in literature search. We used the R meta package to perform meta-analyses on both the genotype count and the allele count data. Moreover, we performed meta-analyses on specific haplotypes within each haplotype block. MAIN RESULTS: Following our literature search and meta-analyses on 20 NTRK2 SNPs across up to 8467 samples, we found three SNPs, rs10868235 [N = 5,318, odds ratio (OR) = 1.34, P = 0.02], rs1867283 (N = 5,134, OR = 0.73, P = 0.04), and rs1147198 (N = 5,132, OR = 1.36, P = 0.03) to be nominally associated with suicidal attempts. Those three findings, however, did not survive multiple-testing corrections. Also, none of the haplotype blocks showed significant involvement in suicidality. CONCLUSION: Our results suggest that the NTRK2 gene may not have a major role in suicidality. Future efforts, however, should explore gene-gene interaction and pathway analyses.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.041 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".