Family-based association study of ADHD and genes increasing the risk for smoking behaviours
Bibliographic record
Abstract
OBJECTIVE: To investigate five top single nucleotide polymorphisms (SNPs) located in different genes and loci (CHRNA3, BDNF, DBH and LOC100188947) that were highly associated with different dimensions of smoking behaviour, in relation to attention-deficit hyperactivity disorder (ADHD). DESIGN: Cohort study consisting of a clinical sample of children with ADHD. SETTING: Douglas Institute ADHD Clinic, Montreal, Canada. PATIENTS: Families of 454 children with ADHD aged 6-12 years old. INTERVENTIONS: Family-based association tests used to study the transmission of risk alleles within these five genetic markers. MAIN OUTCOME MEASURES: Clinical diagnosis of ADHD, and a number of behavioural and neurocognitive phenotypes relevant to the disorder. RESULTS: One SNP (rs1329650) from a non-coding RNA (LOC100188947) was significantly associated with overall ADHD diagnosis with the C* risk allele being over-transmitted from parents to children with ADHD (p=0.02). It was also over-transmitted to children with higher scores on Conners' Parents (p=0.01) and Conners' Teacher (p=0.002) index scores, and Child Behaviour Checklist withdrawn (p=0.001) and aggressive (p=0.007) behaviours. Children with poorer performances on executive and attention tasks were more likely to inherit the risk allele. CONCLUSIONS: The C* allele of rs1329650 may be increasing the risk for ADHD and smoking behaviour through a common mechanism, possibly externalising behaviours and specific cognitive deficits that manifest as ADHD in childhood and are the gateway to smoking behaviour later in life. This exploratory study illustrates the use of comorbid disorders to investigate ADHD genetics. In spite of its relatively large sample size, replication in future studies is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".