Multi-tissue probabilistic fine-mapping of transcriptome-wide association study identifies cis-regulated genes for miserableness
Bibliographic record
Abstract
Abstract Miserableness is a behavioural trait that is characterized by strong negative feelings in an individual. Although environmental factors tend to invoke miserableness, it is common to feel miserable ‘for no reason’, suggesting an innate, potential genetic component. Currently, little is known about the functional relevance of common variants associated with miserableness. To further characterize the trait, we conducted a transcriptome-wide association study (TWAS) on 373,733 individuals and identified 104 signals across brain tissue panels with 37 unique genes. Subsequent probabilistic fine-mapping prioritized 95 genes into 90%-credible sets. Amongst these prioritized hits, C7orf50 had the highest posterior inclusion probability of 0.869 in the brain cortex. Furthermore, we demonstrate that many GWAS hits for miserableness are driven by expression. To conclude, we successfully identified several genes implicated in miserableness and highlighted the power of TWAS to prioritize genes associated with a trait. Short summary The first transcriptome-wide association study of miserableness identifies many genes including c7orf50 implicated in the trait.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".