Interplay between polygenic risk for mood disorders and stressful life events in bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Although genetic and environmental factors are involved in the aetiology of bipolar disorder [BD], studies focused on their interplay are lacking. The current investigation examines interactions and correlations between polygenic risk scores [PRS] for BD and major depressive disorder [MDD] with stressful life events [SLEs] in liability for BD. METHODS: This study used data from 1715 participants (862 bipolar cases and 853 controls) taken from UK and Canadian samples. The List of Threatening Experiences Questionnaire recorded SLEs that occurred 6 months before interview for controls and 6 months prior to the first (Canadian sample) and worst (UK sample) depressive and manic episodes for bipolar cases. PRS-BD and PRS-MDD were calculated from the Psychiatric Genomics Consortium. RESULTS: For the worst depressive episode, the PRS-MDD was significantly correlated with total number of SLEs (β = 0.13, 95 % CI:0.04-0.22, p = 0.003) and dependent SLEs (β = 0.09, 95 % CI:0.02-0.16, p = 0.007). After correction for multiple testing nominally significant correlations were detected for PRS-BD with total number of SLEs (β = 0.11, 95 % CI:0.02-0.20, p = 0.015) and dependent SLEs (β = 0.08, 95 % CI:0.01-0.15, p = 0.019). Among bipolar cases, these associations were slightly stronger but were only of nominal significance for total number of SLEs (PRS-MDD: β = 0.19, 95 % CI:0.04-0.35, p = 0.015; PRS-BD: β = 0.16, 95 % CI:0.01-0.32, p = 0.042) and dependent SLEs (PRS-MDD: β = 0.14, 95 % CI:0.03-0.26, p = 0.015; PRS-BD: β = 0.12, 95 % CI:0.004-0.24, p = 0.043). No other significant gene-environment correlations or interactions were found. LIMITATIONS: Use of a larger sample size would be beneficial. CONCLUSIONS: The relationship between SLEs and genetic risk for mood disorders may be best explained through correlations rather than interactions.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".