Genetic testing for suicide risk assessment: Theoretical premises, research challenges and ethical concerns
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, intertwined with the heritability of mental disorders, and possibly other traits. Current genetics research has abandoned searching for single gene Mendelian determinants, in favour of complex probabilistic epigenetic models. Genome-Wide Association Studies (GWAS) might identify thousands of single nucleotide polymorphisms (SNPs), each contributing very little to the variance associated with behavioral phenotypes. However, suicide is a behavioral outcome rather than a phenotype, with so many different causal aetiologies, that it is impossible to predict the behaviors of individuals. We analyse practical and ethical issues that would arise if future research were to identify genetic information that will accurately predict suicide. Applying ACCE guidelines that specify when genetic tests should and should not be used, we examine the Analytic Validity, Clinical Validity, Clinical Utility and Ethical, Legal, and Social Implications. Low sensitivity and specificity for predicting suicide diminish potential advantages and exacerbate risks. Key considerations include the likelihood that testing will result in effective preventive interventions, which are not currently available, and unreliable positive results increasing hopelessness, stigma, and psychosocial risks. If the unregulated direct-to-consumer genetic testing services include suicide risk assessments, their use risks negative impacts. In the future, if genetic testing could accurately identify suicide risk in individuals, its use would be contraindicated if we cannot provide effective preventive interventions and mitigate the negative impacts of informing people about their risk level.
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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 | 0.260 | 0.387 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.064 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".