No evidence for genetic association between DARPP‐32 (<i>PP1R1B</i>) polymorphisms and attention deficit hyperactivity disorder
Bibliographic record
Abstract
Attention deficit hyperactivity disorder (ADHD) has a strong genetic basis, and evidence from human and animal studies suggests that a dopamine system dysfunction plays a role in the disorder pathophysiology. Several genes involved in dopamine neurotransmission have shown replicated genetic association with ADHD. These include the dopamine receptors D4 (DRD4), D5 (DRD5), and the dopamine transporter (DAT1) genes. Recently, evidence has also accumulated in favor of the dopamine receptor D1 gene (DRD1). The dopamine- and cAMP-regulated phosphoprotein of relative molecular mass of 32 kDa (DARPP-32) is a key component of dopamine signaling, acting as a converging point for several neurotransmitter systems influencing dopaminergic neurons and regulating a wide variety of downstream effectors. Here, we tested the DARPP-32 gene, PPP1R1B, for association with ADHD using four polymorphic markers selected across the gene in a sample of 255 ADHD families. We did not detect evidence of association of individual marker alleles and haplotype analysis did not reveal significant association in this sample of families. Moreover, we found no relationship between the same alleles or haplotypes and symptom scores of inattention or hyperactivity/impulsivity in these families using a quantitative approach. In conclusion, albeit a key regulatory role in dopamine signaling, our data do not support a major contribution of the DARPP-32 gene in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".