Hierarchical investigation of genetic influences on response inhibition in healthy young adults.
Bibliographic record
Abstract
Poor inhibitory control is a known risk factor for substance use disorders, making it a priority to identify the determinants of these deficits. The aim of the current study was to identify genetic associations with inhibitory control using the stop signal task in a large sample (n = 934) of healthy young adults of European ancestry. We genotyped the subjects genome-wide and then used a hierarchical approach in which we tested seven a priori single nucleotide polymorphisms (SNPs) previously associated with stop signal task performance, approximately 9,000 SNPs designated as high-value addiction (HVA) markers by the SmokeScreen array, and approximately five million genotyped and imputed SNPs, followed by a gene-based association analysis using the resultant p values. A priori SNP analyses revealed nominally significant associations between response inhibition and one locus in HTR2A (rs6313; p = .04, dominance model, uncorrected) in the same direction as prior findings. A nominally significant association was also found in one locus in ANKK1 (rs1800497; p = .03, uncorrected), although in the opposite direction of previous reports. After accounting for multiple comparisons, the HVA, genome-wide, and gene-based analyses yielded no significant findings. This study implicates variation in serotonergic and dopaminergic genes while underscoring the difficulty of detecting the influence of individual SNPs, even when biological information is used to prioritize testing. Although such small effect sizes suggest limited utility of individual SNPs in predicting risk for addiction or other impulse control disorders, they may nonetheless shed light on complex biological processes underlying poor inhibitory control. (PsycINFO Database Record
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".