Validation and Extension of the Endophenotype Model in ADHD Patterns of Inheritance in a Family Study of Inhibitory Control
Bibliographic record
Abstract
OBJECTIVE: Endophenotypes, markers of underlying liability to psychiatric disorders, can improve the power to detect genetic risks relative to a complex clinical endpoint. Motor response inhibition is a prime candidate endophenotype in ADHD. In this study, the authors sought to extend the endophenotype model and further demonstrate its utility by investigating the parental origin of shared genetic risk in ADHD. METHOD: Inhibitory control was studied in children with ADHD, unaffected siblings, and their biological parents. Covariation in inhibitory control within families was investigated. Differential covariation as a function of parental sex was also studied. A number of validity criteria for inhibitory control as an endophenotype were assessed, including sensitivity to the disorder and presence in unaffected relatives. RESULTS: The results confirmed an inhibitory control deficit in children with ADHD as well as in their parents, independent of symptom severity in both generations. Inhibitory control ability in children was significantly predicted by the ability of their parents, particularly their fathers. CONCLUSIONS: These findings indicate that an inhibitory control deficit is a cognitive marker of genetic risk shared by parents and offspring. The endophenotype model is also extended by evidence of differential parental contributions to this risk, consistent with findings of parent-of-origin effects in the transmission of certain risk alleles observed in molecular analyses. The identification of these effects at the endophenotype level and their incorporation in genetic modeling can improve both linkage detection and localization of quantitative trait loci.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".