Gene–environment interplay linking friends’ antisociality with different developmental trajectories of antisocial behavior during adolescence.
Bibliographic record
Abstract
This study investigated gene-environment correlations and interactions underlying the association between distinct developmental trajectories of adolescents' antisocial behavior and their friends' antisociality. Participants were 398 twin pairs (53% girls; 87% European descent) followed from age 13 to 19 years. Self-reported antisocial behavior was obtained from the twins and from their friends. Latent class growth modeling identified three antisocial behavior trajectories: nonantisocial, stable-low, and persistent-high. Biometric modeling revealed significant genetic influence on the probabilities of following either the nonantisocial or the persistent-high antisocial behavior trajectory. In contrast, the probability of following the stable-low trajectory was almost entirely explained by environmental influences. All three trajectories were correlated with friends' antisociality and these correlations were entirely explained by shared underlying environmental-not genetic-pathways. Moreover, friends' antisociality moderated the relative influence of genetic factors on the probability of following the persistent-high trajectory, as well as the relative influence of environmental factors on the probability of following the stable-low trajectory. The discussion stresses the importance of distinguishing distinct developmental trajectories of antisocial behavior during adolescence and the differential moderating role played by friends' antisociality. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".