Sequence Analysis of Drug Target Genes with Suicidal Behavior in Bipolar Disorder Patients
Bibliographic record
Abstract
Background: A number of genes have been implicated in recent genome-wide association studies of suicide attempt in bipolar disorder. More focused investigation of genes coding for protein targets of existing drugs may lead to drug repurposing for the treatment and/or prevention of suicide. Methods: We analyzed 2,457 DNA variants across 197 genes of interest to GlaxoSmithKline across the pipeline in our sample of European patients suffering from bipolar disorder (N = 219). We analyzed these variants for a possible association with the suicide severity score (ranging from suicidal ideation/plan to serious suicide attempt) from the Schedule for Clinical Assessment in Neuropsychiatry. We conducted tests of individual variants and gene-based tests. Results: We found a number of DNA variants in the transforming growth factor beta receptor 1 gene (TGFBR1) to be suggestively associated with suicide severity scores (p < 0.005). The gene-based tests also pointed to TGFBR1 to be associated with suicide severity (p = 0.0001). However, these findings were not replicated in an independent bipolar disorder sample. Conclusions: We report no significant association between DNA sequences of drug target genes and suicidal behavior. Additional larger sequencing studies could further interrogate associations between variants in drug target genes and suicidal behavior.
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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 | 0.001 |
| 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".