Genetic Architecture and Risk of Childhood Maltreatment Across 5 Psychiatric Diagnoses
Bibliographic record
Abstract
Importance: Childhood maltreatment (CM) is associated with psychiatric disorders. The underlying mechanisms are complex and involve genetics. Objective: To investigate the polygenic architecture of CM-exposed individuals across psychiatric conditions and if genetics modulates absolute CM risk in the presence of high-impact risk factors such as parental psychiatric diagnoses. Design, Setting, and Participants: The population-based case-cohort iPSYCH was used to analyze 13 polygenic scores (PGS) in CM-exposed individuals across 5 psychiatric International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnoses benchmarked against controls. Individuals were stratified into PGS quantiles, and absolute CM risk was calculated using Cox regression. Sex-specific analyses were also performed. Data were analyzed from June 2022 to December 2024. Exposures: PGS of phenotypes of psychiatric disorders, CM, educational attainment, and substance use. Main Outcomes and Measures: PGSs were generated using summary statistics from genome-wide association studies of phenotypes representing psychiatric disorders, CM, educational attainment, and substance use and tested for their association with CM across psychiatric disorders. Results: This study included 102 856 individuals (mean [SD] age, 22.6 [7.1] years; 54 918 male [53.4%]) 8 to 35 years old. A total of 2179 CM-exposed individuals were analyzed across individuals with attention-deficit/hyperactivity disorder (ADHD; n = 22 674), autism (n = 18 941), schizophrenia (n = 6103), bipolar disorder (n = 3061), depression (n = 28 896), and controls (n = 34 689). PGSs for ADHD and educational attainment were associated with CM across all psychiatric diagnoses. The absolute CM risk was increased in the highest PGS groups, eg, for ADHD, the absolute CM risk was 5.6% in the highest ADHD-PGS quartile whereas it was only 3.3% in the lowest ADHD-PGS quartile (hazard rate ratio quantile 4 vs quantile 1 = 1.81; 95% CI, 1.47-2.22). CM risk was more than twice as high for children with parents with a psychiatric diagnosis (5.7%) than for children with parents without a psychiatric diagnosis (2.5%), but even in the presence of this risk factor, individuals could still be stratified into risk groups based on their genetics. No genetic differences between CM-exposed males and females were observed, but there were striking sex differences in absolute CM risk, which reached 5.6% for females in the highest ADHD-PGS quartile and 2.0% for males. Conclusions and Relevance: Results of this case-control study suggest that individuals with high ADHD-PRS and/or low educational attainment-PRS had an associated elevated risk of CM. Extra attention should be given to individuals at high risk for CM across all 5 psychiatric diagnoses, ie, females with a high ADHD-PGS and/or a parent diagnosed with a psychiatric disorder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".