Polygenic risk for aggressive behaviour from late childhood through early adulthood
Bibliographic record
Abstract
Twin studies suggest a substantial role for genes in explaining individual differences in aggressive behaviour across development. It is unclear, however, how directly measured genetic risk is associated with aggressive behaviour at different moments across adolescence and how genes might distinguish developmental trajectories of aggressive behaviour. Here, a polygenic risk score derived from the EAGLE-Consortium genome-wide association study of aggressive behaviour in children was tested as predictor of latent growth classes derived from those measures in an adolescent population (n = 2229, of which n = 1259 with genetic information) and a high-risk sample (n = 543, of which n = 339 with genetic information). In the population sample, the polygenic risk score explained variation in parent-reported aggressive behaviour at all ages and distinguished between stable low aggressive behaviour and moderate and high-decreasing trajectories based on parent-report. In contrast, the polygenic risk score was not associated with self- and teacher-reported aggressive behaviour, and no associations were found in the high-risk sample. This pattern of results suggests that methodological choices made in genome-wide association studies impact the predictive power of polygenic risk scores, not just with respect to power but likely also in terms of generalizability and specificity.
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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) | 0.002 | 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".