Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: Genome‐wide association study of both common and rare variants
Bibliographic record
Abstract
Attention-deficit hyperactivity disorder (ADHD) is a complex polygenic disorder. This study aimed to discover common and rare DNA variants associated with ADHD in a large homogeneous Han Chinese ADHD case-control sample. The sample comprised 1,040 cases and 963 controls. All cases met DSM-IV ADHD diagnostic criteria. We used the Affymetrix6.0 array to assay both single nucleotide polymorphisms (SNPs) and copy number variants (CNVs). Genome-wide association analyses were performed using PLINK. SNP-heritability and SNP-genetic correlations with ADHD in Caucasians were estimated with genome-wide complex trait analysis (GCTA). Pathway analyses were performed using the Interval enRICHment Test (INRICH), the Disease Association Protein-Protein Link Evaluator (DAPPLE), and the Genomic Regions Enrichment of Annotations Tool (GREAT). We did not find genome-wide significance for single SNPs but did find an increased burden of large, rare CNVs in the ADHD sample (P = 0.038). SNP-heritability was estimated to be 0.42 (standard error, 0.13, P = 0.0017) and the SNP-genetic correlation with European Ancestry ADHD samples was 0.39 (SE 0.15, P = 0.0072). The INRICH, DAPPLE, and GREAT analyses implicated several gene ontology cellular components, including neuron projections and synaptic components, which are consistent with a neurodevelopmental pathophysiology for ADHD. This study suggested the genetic architecture of ADHD comprises both common and rare variants. Some common causal variants are likely to be shared between Han Chinese and Caucasians. Complex neurodevelopmental networks may underlie ADHD's etiology.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".