Genetic influences on delayed reward discounting: A genome-wide prioritized subset approach.
Bibliographic record
Abstract
Delayed reward discounting (DRD) is a behavioral economic measure of impulsivity that has been consistently associated with addiction. It has also been identified as a promising addiction endophenotype, linking specific sources of genetic variation to individual risk. A challenge in the studies to date is that levels of DRD are often confounded with prior drug use, and previous studies have also had limited genomic scope. The current investigation sought to address these issues by studying DRD in healthy young adults with low levels of substance use (N = 986; 62% female, 100% European ancestry) and investigating genetic variation genome-wide. The genome-wide approach used a prioritized subset design, organizing the tests into theoretically and empirically informed categories and apportioning power accordingly. Three subsets were used: (a) a priori loci implicated by previous studies; (b) high-value addiction (HVA) markers from the recently developed SmokeScreen array; and (c) an atheoretical genome-wide scan. Among a priori loci, a nominally significant association was present between DRD and rs521674 in ADRA2A. No significant HVA loci were detected. One statistically significant genome-wide association was detected (rs13395777, p = 2.8 × 10-8), albeit in an intergenic region of unknown function. These findings are generally not supportive of the previous candidate gene studies and suggest that DRD has a complex genetic architecture that will require considerably larger samples to identify genetic associations more definitively. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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".