Deciphering the mediating role of childhood maltreatment in the association between genetic risk and developmental trajectories of school-age reactive and proactive aggression
Bibliographic record
Abstract
Abstract Background: Childhood maltreatment is a robust predictor of aggression. Research indicates that both maltreatment experiences and aggression are moderately heritable. It has been hypothesized that gene–environment correlation may be at play, whereby genetic predispositions to aggression in parents and children may be confounded with family environments conducive to its expression. Building on this framework, we tested whether maltreatment mediates the association between a polygenic score for aggression (PGS AGG ) and school-age aggression, and whether this varied for reactive and proactive aggression. Methods: The sample comprised 721 participants (44.9% males; 99.0% White) with prospective assessments of maltreatment from 5 months to 12 years (10 assessments;1998–2010), and teachers-reported aggression from ages 6 to 13 (6 assessments; 2004–2011). The PGS AGG was derived using a Bayesian estimation method (PRS-CS). Results: PGS AGG was associated with most aggression measures across specific ages and trajectories. Maltreatment experiences partially mediated the association between PGS AGG and the Childhood-Limited trajectory of reactive – but not proactive – aggression. Conclusion: Children with higher genetic propensities for aggression were more likely to experience maltreatment, which partly explained the association between PGS AGG and a Childhood-Limited trajectory of reactive aggression during elementary school. This finding reinforces the possibility of confounding influences between genetic liability for aggression and maltreatment experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".