Polygenic risk and hostile environments: Links to stable and dynamic antisocial behaviors across adolescence
Bibliographic record
Abstract
Abstract Adverse environments are linked to elevated youth antisocial behavior. However, this relation is thought to depend, in part, on genetic susceptibility. The present study investigated whether polygenic risk for antisociality moderates relations between hostile environments and stable as well as dynamic antisocial behaviors across adolescence. We derived two antisocial-linked polygenic risk scores (PRS) (N = 721) based on previous genome-wide association studies. Forms of antisocial behavior (nonaggressive conduct problems, physical aggression, social aggression) and environmental hostility (harsh parenting and school violence) were assessed at age 13, 15, and 17 years. Relations to individual differences stable across adolescence (latent stability) vs. time-specific states (timepoint residual variance) of antisocial behavior were assessed via structural equation models. Higher antisocial PRS, harsh parenting, and school violence were linked to stable elevations in antisocial behaviors across adolescence. We identified a consistent polygenic-environment interaction suggestive of differential susceptibility in late adolescence. At age 17, harsher parenting was linked to higher social aggression in those with higher antisocial PRS, and lower social aggression in those with lower antisocial PRS. This suggests that genetics and environmental hostility relate to stable youth antisocial behaviors, and that genetic susceptibility moderates home environment-antisocial associations specifically in late adolescence.
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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 | 0.000 |
| 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".