Support for association between ADHD and two candidate genes: <i>NET1</i> and <i>DRD1</i>
Bibliographic record
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common, multifactorial disorder with significant genetic contribution. Multiple candidate genes have been studied in ADHD, including the norepinephrine transporter (NET1) and dopamine D1 receptor (DRD1). NET1 is implicated in ADHD because of the efficacy of atomoxetine, a selective noradrenergic reuptake inhibitor, in the treatment of ADHD. DRD1 is primarily implicated through mouse models of ADHD. DNA from 163 ADHD probands, 192 parents, and 129 healthy controls was used to investigate possible associations between ADHD and polymorphisms in 12 previously studied candidate genes (5-HT1B, 5-HT2A, 5-HT2C, ADRA2A, CHRNA4, COMT, DAT1, DRD1, DRD4, DRD5, NET1, and SNAP-25). Analyses included case-control and family-based methods, and dimensional measures of behavior, cognition, and anatomic brain magnetic resonance imaging (MRI). Of the 12 genes examined, two showed a significant association with ADHD. Transmission disequilibrium test (TDT) analysis revealed significant association of two NET1 single nucleotide polymorphisms (SNPs) with ADHD (P < or = 0.009); case-control analysis revealed significant association of two DRD1 SNPs with ADHD (P < or = 0.008). No behavioral, cognitive, or brain MRI volume measurement significantly differed across NET1 or DRD1 genotypes at an alpha of 0.01. This study provides support for an association between ADHD and polymorphisms in both NET1 and DRD1; polymorphisms in ten other candidate genes were not associated with ADHD. Because family-based and case-control methods gave divergent results, both should be used in genetic studies of ADHD.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".