Polygenic risk score and peer victimisation independently predict depressive symptoms in adolescence: results from the Quebec Longitudinal Study of Children Development
Bibliographic record
Abstract
BACKGROUND: Peer victimisation has been associated with depressive symptoms during adolescence, however not all peer victimised adolescents will exhibit such symptoms. This study tested whether having a genetic predisposition to developing depression increased the risk of experiencing depressive symptoms in peer victimised youth. To date, no study has explored such gene-environment interaction using a polygenic risk score for depression (PRS-depression) in the context of peer victimisation and depressive symptoms in adolescence. METHODS: The sample included 748 participants born in 1997/98 from the Quebec Longitudinal Study of Child Development with genotype data and prospectively collected information on peer victimisation (12-13 years) obtained from both self- and teacher-reports, as well as self-reported depressive symptoms (15-17 years). The PRS-depression was based on the genome-wide association meta-analysis of broad depression by Howard et al. (2019). RESULTS: Self- and teacher-reported peer victimisation in early adolescence were both associated with depressive symptoms in adolescence (β = 0.34, p < .001; β = 0.14, p = .001 respectively), and this association remained significant when accounting for PRS-depression (β = 0.33, p < .001; β = 0.13, p = .002 respectively). PRS-depression was independently associated with depressive symptoms, but there was no significant PRS-depression by peer victimisation interaction (self-reported and teacher-reported). PRS-depression was correlated with self-reported, but not teacher-reported, peer victimisation. CONCLUSIONS: Our findings suggested that a partial measure of an individual's genetic predisposition to depression, as measured by PRS-depression, and being exposed to peer victimisation (self- and teacher-reported) were independently associated with depressive symptoms in adolescence. Furthermore, PRS-depression did not exacerbate the risk of depressive symptoms among adolescents who had been peer victimised. Lastly, we found evidence of a gene-environment correlation between PRS-depression and self-reported peer victimisation. Future studies are needed to replicate this finding and to further understand the role of genetic predispositions in experiencing depressive symptoms following peer victimisation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".