Gene–environment interplay between peer rejection and depressive behavior in children
Bibliographic record
Abstract
BACKGROUND: Genetic risk for depressive behavior may increase the likelihood of exposure to environmental stressors (gene-environment correlation, rGE). By the same token, exposure to environmental stressors may moderate the effect of genes on depressive behavior (gene-environment interaction, GxE). Relating these processes to a peer-related stressor in childhood, the present study examined (1) whether genetic risk for depressive behavior in children is related to higher levels of rejection by the peer group (rGE) and (2) whether peer rejection moderates the effect of genetic factors on children's depressive behavior (GxE). METHODS: The sample comprised 336 twin pairs (MZ pairs = 196, same-sex DZ pairs = 140) assessed in kindergarten (mean age 72.7 months). Peer acceptance/rejection was measured via peer nominations. Depressive behavior was measured through teacher ratings. RESULTS: Consistent with rGE, a moderate overlap of genetic effects was found between peer acceptance/rejection and depressive behavior. In line with GxE, genetic effects on depressive behavior varied across levels of peer acceptance/rejection. CONCLUSIONS: An increased genetic disposition for depressive behavior is related to a higher risk of peer rejection (rGE). However, genes play a lesser role in explaining individual differences in depressive behavior in rejected children than in accepted children (GxE).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".