The Genetic Overlap of Attention-Deficit/Hyperactivity Disorder and Autistic-like Traits: an Investigation of Individual Symptom Scales and Cognitive markers
Bibliographic record
Abstract
Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorders (ASDs) frequently co-occur. However, due to previous exclusionary diagnostic criteria, little is known about the underlying causes of this covariation. Twin studies assessing ADHD symptoms and autistic-like traits (ALTs) suggest substantial genetic overlap, but have largely failed to take into account the genetic heterogeneity of symptom subscales. This study aimed to clarify the phenotypic and genetic relations between ADHD and ASD by distinguishing between symptom subscales that characterise the two disorders. Moreover, we aimed to investigate whether ADHD-related cognitive impairments show a relationship with ALT symptom subscales; and whether potential shared cognitive impairments underlie the genetic risk shared between the ADHD and ALT symptoms. Multivariate structural equation modelling was conducted on a population-based sample of 1312 twins aged 7-10. Social-communication ALTs correlated moderately with both ADHD symptom domains (phenotypic correlations around 0.30) and showed substantial genetic overlap with both inattention and hyperactivity-impulsivity (genetic correlation = 0.52 and 0.44, respectively). In addition to previously reported associations with ADHD traits, reaction time variability (RTV) showed significant phenotypic (0.18) and genetic (0.32) association with social-communication ALTs. RTV captured a significant proportion (24 %) of the genetic influences shared between inattention and social-communication ALTs. Our findings suggest that social-communication ALTs underlie the previously observed phenotypic and genetic covariation between ALTs and ADHD symptoms. RTV is not specific to ADHD symptoms, but is also associated with social-communication ALTs and can, in part, contribute to an explanation of the co-occurrence of ASD and 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".