Genetics of suicide attempts in individuals with and without mental disorders: a population-based genome-wide association study
Bibliographic record
Abstract
Family studies have shown an aggregation of suicidal behavior in families. Yet, molecular studies are needed to identify loci accounting for genetic heritability. We conducted a genome-wide association study and estimated single nucleotide polymorphisms (SNP) heritability for a suicide attempt. In a case-cohort study, national data on all individuals born in Denmark after 1981 and diagnosed with severe mental disorders prior to 2013 ( n = 57,377) and individuals from the general population ( n = 30,000) were obtained. After quality control, the sample consisted of 6024 cases with an incidence of suicide attempt and 44,240 controls with no record of a suicide attempt. Suggestive associations between SNPs, rs6880062 ( p -value: 5.4 × 10 −8 ) and rs6880461 ( p -value: 9.5 × 10 −8 ), and suicide attempt were identified when adjusting for socio-demographics. Adjusting for mental disorders, three significant associations, all on chromosome 20, were identified: rs4809706 ( p -value: 2.8 × 10 −8 ), rs4810824 ( p -value: 3.5 × 10 −8 ), and rs6019297 ( p -value: 4.7 × 10 8 ). Sub-group analysis of cases with affective disorders revealed SNPs associated with suicide attempts when compared to the general population for gene PDE4B. All SNPs explained 4.6% [CI-95: 2.9–6.3%] of the variation in suicide attempt. Controlling for mental disorders reduced the heritability to 1.9% [CI-95: 0.3–3.5%]. Affective and autism spectrum disorders exhibited a SNP heritability of 5.6% [CI-95: 1.9–9.3%] and 9.6% [CI-95: 1.1–18.1%], respectively. Using the largest sample to date, we identified significant SNP associations with suicide attempts and support for a genetic transmission of suicide attempt, which might not solely be explained by mental disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".