Polygenic dissection of suicidal thoughts and behaviours in high-risk mood-disorder cohorts
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Updated: Oct 31, 2026

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Published on: December 2, 2015
Zuriel Ceja1,2, Luis M García-Marín1,2, Ian B Hickie3
1Brain & Mental Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia.
Abstract:
Suicidal thoughts and behaviours (STB) span ideation (SI), attempt (SA), and death (SD), yet the factors distinguishing ideation from attempt remain poorly understood. Genome-wide studies suggest shared yet partially distinct genetic liabilities. However, few analyses have tested the differential contributions of SI-, SA-, and SD-related genetic liability across STB phenotypes, particularly in high-risk mood-disorder populations. We analysed up to 17,622 participants from the Australian Genetics of Depression Study and the Australian Genetics of Bipolar Disorder study, jointly modelling polygenic scores (PGS) for SI, SA, and SD across mutually exclusive STB phenotypes using multinomial regression, alongside dimensional outcomes, age of onset, and the ideation-to-attempt transition. SI-PGS and SA-PGS showed independent and distinct associations: SI-PGS showed reduced odds of SI-only but increased odds of SI + SA, and SA-PGS robustly predicted attempt-related outcomes. Transition models captured the clearest clinical signal: SA-PGS predicted a 23% higher odds of attempt among ideators, with absolute risk differences of 9-12% between the top and bottom deciles-larger than those of the onset models (5-7%). Predictive discrimination improved incrementally (ΔAUC ≈ 0.02-0.03; ΔC ≈ 0.01). SD-PGS showed no robust effects. Sex-stratified analyses yielded broadly consistent findings across sexes. These findings support ideation-to-action models at the genetic level, demonstrating partially distinct polygenic liabilities for ideation and attempt. Although absolute predictive utility remains modest, these results indicate that PGS sharpen the stratification of ideators at risk of attempt, providing incremental value for future integrative risk models that combine genetics with mood disorders and environmental predictors.
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