Facing the Methodological Challenge in Dissecting the Genetics of ADHD: A Case for Deep Phenotyping and Heterogeneity Reduction.
Bibliographic record
Abstract
OBJECTIVE: The aetiology of ADHD is complex, with genetic and environmental factors both implicated in the disorder. The most recent ADHD genome-wide association study identified 12 loci that showed significant association with the disorder. However, as highlighted by the authors, these loci "only capture a tiny fraction" of the risk for ADHD. It has been suggested that it may be important to disentangle: (1) the clinical complexity of the disorder, and (2) the complex interaction between genetic and environmental factors, in order to better dissect the aetiology of the disorder. METHOD: We have conducted a clinically-relevant Pharmaco-Behavioural Genetic study in a large group of children with ADHD (~850 families) over the last 15 years. The study includes detailed evaluation of quantitative behavioural and neuropsychological phenotypes, as well as short-term response of these phenotypes to treatment with a fixed dose of methylphenidate (0.5mg/kg in a b.i.d. dose). Specific genetic markers and environmental factors were examined for their association with these dimensions. RESULTS: Here we present results that highlight the importance of examining genetic association with quantitative traits, including those constructs having relevance to Research Domain Criteria (RDoC). Further, we demonstrate that by conducting association analysis in groups of children stratified based on exposure to key environmental exposure (maternal smoking or stress during pregnancy), we are able to increase the sensitivity for finding genes involved in the disorder. CONCLUSION: These results suggest that deep phenotyping and heterogeneity reduction may be imperative in order to uncover the "missing heritability" of the disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.304 | 0.577 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".