Common genetic variants are associated with increased likelihood of some co-occurring mental health conditions among autistic individuals
Bibliographic record
Abstract
ABSTRACT IMPORTANCE Autism frequently co-occurs with other mental health conditions. In the general population, these co-occurring mental health conditions are highly heritable and genetically correlated; however, the genetic architecture of co-occurring mental health conditions among autistic individuals is unclear. OBJECTIVE To investigate the relationship between common and rare genetic variation and co-occurring mental health conditions and latent factors among autistic individuals. DESIGN Cross-sectional SETTING The study was conducted with the Simons Foundation Powering Autism Research (SPARK) dataset, V9 release (12 December 2022). PARTICIPANTS Phenotypic data exploration and factor analyses was conducted in 74,204 autistic individuals, and genetic analyses were conducted in a maximum of 17,582 individuals with genetic data. MAIN OUTCOMES AND MEASURES Genetic analysis was limited to those probands included in the SPARK iWES1 dataset [n=17,582]. SNP heritability estimates and genetic correlations were computed for three factor scores and six diagnostic categories (attention deficit hyperactivity disorder or ADHD, bipolar disorder, depression, schizophrenia, anxiety disorder and disruptive behaviour disorders or DBD) using bivariate GCTA-GREML and LDSC. Polygenic scores were generated using summary statistics from the most recent genome wide association studies (GWAS) for five traits and six conditions. Associations with factor scores and categorical diagnoses were tested separately for polygenic scores (PGS), de novo variants (DNVs) and copy number variants (CNVs). RESULTS 56% of autistic individuals presented a co-occurring psychiatric condition. Confirmatory factor analysis identified three minimally correlated factors: a behavioural factor, cothymic factor, and a ‘Kraepelin’ or thought disorder factor, with SNP heritabilities ranging from 0.21 (s.e. 0.02) for behavioural to 0.09 (s.e. 0.03) for cothymic factor. Among conditions, moderate and significant SNP heritabilites were observed for ADHD (0.18, s.e. = 0.04) and DBD (0.52, s.e. = 0.08). Moderate positive genetic correlations were found between co-occurring ADHD, DBD, anxiety and the three factors in autistic individuals and corresponding conditions in external population cohorts. PGS for ADHD, depression, and educational attainment were significantly associated with all mental health factors and some of the conditions tested. We found no evidence for an association between common variants for autism, rare CNVs, and DNVs in highly constrained genes with increased likelihood of mental health phenotypes among autistic individuals. CONCLUSION AND RELEVANCE Among autistic individuals, some mental health conditions and all mental health factors are heritable, but have a distinct genetic architecture from autism itself. Key Points Question Do genetic variants contribute to co-occurring mental health conditions and latent factors in autistic individuals? Findings In this cross-sectional study of 17,582 autistic individuals with and without co-occurring conditions, we found significant single nucleotide polymorphism (SNP) heritability for co-occurring ADHD, Disruptive Behaviour Disorders (DBD), and mental-health latent factors. Co-occurring ADHD, DBD, anxiety, and all mental health factors among autistic individuals had moderate genetic correlations with corresponding case-control GWAS. We found no evidence linking common genetic variants linked to autism and rare genetic variants with increased likelihood for mental health conditions among autistic individuals. Meaning Among autistic individuals, the genetic correlates of co-occurring mental health conditions are distinct from that of autism, suggesting that additional genetic factors contribute to the development of these conditions among autistic individuals.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".