Modelling Co-Occurring Mental Health Conditions Among Autistic Individuals Using Polygenic Scores
Bibliographic record
Abstract
Aims This study investigated the relationship between common genetic variation and co-occurring mental health conditions among autistic individuals. Methods The study was conducted with the Simons Foundation Powering Autism Research (SPARK) dataset, V9 release, and included probands [n = 17,582] with confirmed diagnosis of autism, who were also in the SPARK iWES1 array genotyping dataset. Six co-occurring mental health conditions (attention deficit hyperactivity disorder or ADHD, bipolar disorder, depression, schizophrenia, anxiety disorder and disruptive behaviour disorders) were analysed. Polygenic scores (PRS) were generated with PRScs software, using summary statistics from the most recent genome wide association studies (GWAS) for autism, ADHD, schizophrenia, bipolar disorder, depression, anxiety, neuroticism, p-factor, intelligence, educational attainment and hair colour (negative control). General linear models (GLM) and Cox proportional hazards models were computed, with age at registration, sex, cognitive impairment and genetic principal components included in both sets of models. Multiple testing correction was done using the Benjamini-Yekutieli method. Results were calculated using odds ratios (OR), 95% Confidence Intervals (CI) and corrected p values (p). Results There were similar patterns of association and interaction for both GLMs and Cox models. Polygenic scores for educational attainment were significantly lower for those with co-occurring ADHD (GLM: OR=8.85E-01, 95% CI=8.48e-01–9.23e-01, p = 2.91E-07; Cox: OR=8.94E-01, 95% CI=8.66e-01–9.22e-01, p = 4.76E-11), bipolar disorder (GLM: OR=7.45E-01, 95% CI=6.54e-01–8.49e-01, p = 2.40E-04; Cox: OR=7.25E-01, 95% CI=6.39e-01–8.23e-01, p = 3.96E-05), depression (GLM: OR=8.63E-01, 95% CI=8.04e-01–9.26e-01, p = 5.13E-04; Cox: OR=8.56E-01, 95% CI=8.03e-01–9.12e-01, p = 2.80E-05), schizophrenia (GLM: OR=6.94E-01, 95% CI=5.71e-01–8.42e-01, p = 3.99E-03; Cox: OR=6.67E-01, 95% CI=5.52e-01–8.05e-01, p = 1.41E-03), anxiety disorder (GLM: OR=8.77E-01, 95% CI=8.37e-01–9.20e-01, p = 9.88E-07; Cox: OR=8.81E-01, 95% CI=8.49e-01–9.15e-01, p = 1.46E-09) and disruptive behaviour disorders (GLM: OR=7.10E-01, 95% CI=6.63e-01–7.60e-01, p = 3.22E-21; Cox: OR=7.10E-01, 95% CI=6.67e-01–7.57e-01, p = 1.35E-24). Conclusion Polygenic scores for educational attainment were associated with the co-occurrence of several mental health conditions among autistic individuals.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".