Leveraging related health phenotypes for polygenic prediction of impulsive choice, impulsive action, and impulsive personality traits in 1534 European ancestry community adults
Bibliographic record
Abstract
Abstract Impulsivity refers to a number of conceptually related phenotypes reflecting self‐regulatory capacity that are considered promising endophenotypes for mental and physical health. Measures of impulsivity can be broadly grouped into three domains, namely, impulsive choice, impulsive action, and impulsive personality traits. In a community‐based sample of ancestral Europeans ( n = 1534), we conducted genome‐wide association studies (GWASs) of impulsive choice (delay discounting), impulsive action (behavioral inhibition), and impulsive personality traits (UPPS‐P), and evaluated 11 polygenic risk scores (PRSs) of phenotypes previously linked to self‐regulation. Although there were no individual genome‐wide significant hits, the neuroticism PRS was positively associated with negative urgency (adjusted R 2 = 1.61%, p = 3.6 × 10 −7 ) and the educational attainment PRS was inversely associated with delay discounting (adjusted R 2 = 1.68%, p = 2.2 × 10 −7 ). There was also evidence implicating PRSs of attention‐deficit/hyperactivity disorder, externalizing, risk‐taking, smoking cessation, smoking initiation, and body mass index with one or more impulsivity phenotypes (adjusted R 2 s: 0.35%–1.07%; FDR adjusted p s = 0.05–0.0006). These significant associations between PRSs and impulsivity phenotypes are consistent with established genetic correlations. The combined PRS explained 0.91%–2.46% of the phenotypic variance for individual impulsivity measures, corresponding to 8.7%–32.5% of their reported single‐nucleotide polymorphism (SNP)‐based heritability, suggesting a non‐negligible portion of the SNP‐based heritability can be recovered by PRSs. These results support the predictive validity and utility of PRSs, even derived from related phenotypes, to inform the genetics of impulsivity phenotypes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".