The association between temperament and polygenic score for psychopathology from infancy to middle childhood
Bibliographic record
Abstract
BACKGROUND: Certain temperament characteristics, such as low effortful control and high negative affectivity, are linked to an elevated likelihood for later psychopathology. Although genetic vulnerability has been associated with a number of psychiatric conditions, little work has examined the genetic architecture underlying temperament or the genetic overlap between early temperament profiles and later mental health outcomes. The present study examined associations of polygenic scores for anxiety (PGS-Anxiety) and ADHD (PGS-ADHD) with temperament characteristics in a longitudinal sample of children assessed from infancy through age 7 years. METHODS: Analyses were conducted in a sample of children (European Ancestry n = 476; Full Sample [European and other ancestries] N = 606). RESULTS: We observed an age-by-PGS interaction on effortful control. As children aged, there appeared to be stronger negative associations between PGS-ADHD and effortful control. No associations were observed between PGS-Anxiety and negative affectivity. CONCLUSIONS: Overall, the findings suggest some support for associations between genetic underpinnings for externalizing psychopathology and temperament that increase over time.
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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".