Genetic Testing for Suicide Risk Assessment
Bibliographic record
Abstract
We explore ethical premises and practical implications of using genetic testing to predict suicide risk. Twin studies indicate heritable components of suicide risk, and associated heritability of mental disorders. Currently, genetics research has abandoned seeking single gene Mendelian determinants, in favour of complex probabilistic epigenetic models. Genome-Wide Association Studies (GWAS) may identify thousands of single nucleotide polymorphisms (SNPs), each contributing very little to the variance in behavioural phenotypes. Since suicide is a behaviour rather than a phenotype, with many different causal aetiologies, it is impossible to predict the behaviours of individuals. We analyse practical and ethical issues that would arise if future research were to identify genetic information that accurately predicts suicide. We examine analytical validity, clinical validity, clinical utility and ethical, legal and social implications. Low sensitivity and specificity for predicting suicide diminish potential advantages and exacerbate risks. We discuss risks of unregulated direct-to-consumer genetic testing services. If someday genetic testing can accurately identify suicide risk in individuals, its use would be contraindicated if we cannot provide effective preventive interventions and mitigate negative impacts of informing people of their suicide risk.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".