Genome‐Wide Association Studies of Delay Discounting and Impulsive Personality Traits in Children From the Adolescent Behavior and Cognitive Development Study
Bibliographic record
Abstract
ABSTRACT Impulsivity, often operationalized as delay discounting (DD) and as impulsive personality traits via the UPPS‐P scales, is a key transdiagnostic construct across psychiatric disorders. Recent genome‐wide association studies (GWAS) have studied the genetic basis of impulsivity in adults, but it remains unclear how similar the genetic architecture of DD is in children. The present study conducted GWAS of DD and impulsivity traits in 5548 children (ages 9–10 years old) of genetically inferred European ancestry from the Adolescent Brain Cognitive Development (ABCD) Study. Heritability estimates for DD ( h 2 = 0.20, S.E. = 0.10) and UPPS‐P subscales ( h 2 = 0.08–0.11 S.E. = 0.05) were comparable to adult estimates. Genetic correlations between adult and child impulsivity were modest ( r g = 0.28–0.46), with positive urgency showing the strongest correlation ( r g = 0.83). While no genome‐wide significant associations were identified, the top associated variants were mapped to genes previously linked to smoking initiation (rs3820908; p = 6.5 × 10 −8 ) and UPPS‐P Lack of Premeditation (rs17292179; p = 4.2 × 10 −7 ). Polygenic score (PGS) associations were used to compare the genetic signals in children with those reported in adults. Adult PGSs for DD and positive and negative urgency indicators explained small but significant variance in the respective child impulsivity phenotypes (0.36%–0.44%, p < 7.5 × 10 −4 ). Additionally, UPPS‐P indices were broadly associated with PGSs derived from adult externalizing (0.42%–1.02%) and ADHD (0.23%–0.79%). This first GWAS of impulsivity in children offers a developmentally informed comparison of genetic influences, revealing both similarities and differences by developmental stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".