Variation in the <i>DRD2</i> gene affects impulsivity in intertemporal choice
Bibliographic record
Abstract
Introduction: Impulsivity in intertemporal choice has been operationalized as “delay discounting,” referring to the preference for a sooner, smaller reward in neuroeconomics. It is reportedly associated with the dopaminergic systems. Dopamine receptor D2 (DRD2) is the D2 subtype of the dopamine receptor of the G-protein coupled receptor family. The aim of this study was to explore the effect of single nucleotide polymorphisms (SNPs) in DRD2 gene on delay discounting. Methods: The participants consisted of 91 healthy Japanese people (66 males and 25 females with a mean age of 40.9 ± 6.9 years). Each participant completed the Kirby’s monetary choice questionnaire (MCQ) for delayed gain and donated a whole blood sample. Two SNPs (C957T (rs6277) and TaqI A (rs1800497)) in DRD2 were genotyped by using the DigiTag2 assay. SNP linear regression analyses with 100,000 permutations were conducted for the hyperbolic time-discount rate (k). Results: The SNP C957T showed a significant association; participants with more minor alleles (T) were more impulsive in intertemporal choice for delayed gain (multiplicity-corrected P = 0.041 with a small effect size). Conclusion: The variation in the DRD2 gene is associated with impulsive decision-making. This is the first study to demonstrate an association between DRD2 and impulsivity in intertemporal choice with a multiplicity-corrected significance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".