Prediction of depressive symptoms in young adults by polygenic score and childhood maltreatment: Results from a population-based birth cohort
Bibliographic record
Abstract
Abstract Childhood maltreatment is linked with later depressive symptoms, but not every maltreated child will experience symptoms later in life. Therefore, we investigate whether genetic predisposition for depression (i.e., polygenic score for depression, PGSDEP) modifies the association between maltreatment and depressive symptoms, while accounting for different types of maltreatment and whether it was evaluated through prospective and retrospective reports. The sample included 541–617 participants from the Quebec Longitudinal Study of Child Development with information on maltreatment, including threat, deprivation, assessed prospectively (5 months–17 years) and retrospectively (reported at 23 years), PGSDEP and self-reported depressive symptoms (20–23 years). Using hierarchical linear regressions, we found that retrospective, but not prospective indicators of maltreatment (threat/deprivation/cumulative) were associated with later depressive symptoms, above and beyond the PGSDEP. Our findings also show the presence of gene–environment interactions, whereby the association between maltreatment (retrospective cumulative maltreatment/threat, prospective deprivation) and depression was strengthened among youth with higher PGSDEP scores. Consistent with the Diathesis-Stress hypothesis, our findings suggest that a genetic predisposition for depression may exacerbate the putative impact of maltreatment on later depressive symptoms, especially when maltreatment is retrospective. Understanding the gene–environment interplay emerging in the context of maltreatment has the potential to guide prevention efforts.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".