Genetic architecture and risk of childhood maltreatment across five psychiatric diagnoses

Importance: Childhood Maltreatment (CM) is associated with psychiatric disorders. 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 and participants: The population-based case-cohort iPSYCH was used to analyse 13 polygenic scores (PGS) in CM-exposed individuals across five psychiatric 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. Main outcome 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. 3 Results: This study included 102,856 individuals (47,938 females, 54,918 males) 8-35 years old. We analysed 2,179 CM-exposed individuals across individuals with attention deficit hyperactivity disorder (ADHD; N=22,674), autism (N=18,941), schizophrenia (N=6,103), bipolar disorder (N=3,061), 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, e.g., for ADHD the absolute CM risk was 5.6% in the highest ADHD-PGS quartile while only 3,3% in the lowest ADHD-PGS quartile (hazard rate ratio quantile-4 vs quantile-1 = 1.81; CI = 1.47 – 2.22). CM risk was more than twice as high for children with psychiatric diagnosed parents (5.7%) than for children with non-diagnosed parents (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 striking sex-differences in absolute CM risk, which reached 5.6% for females in the highest ADHD-PGS quartile while it was 2.0% for males. Conclusions and relevance: Individuals with high a ADHD-PRS and/or low educational attainment-PRS are at elevated risk of CM. Extra attention should be given to CM-high-risk individuals across all five psychiatric diagnoses i.e. females with a high ADHD-PGS and/or a parent diagnosed with a psychiatric disorder.

Publication Details

Journal
Spectrum Research Repository (Concordia University)
Published
2025-01-01
Primary Topic
Child Abuse and Trauma
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Genetic architecture and risk of childhood maltreatment across five psychiatric diagnoses

Spectrum Research Repository (Concordia University)
Child Abuse and Trauma
article

Genetic architecture and risk of childhood maltreatment across five psychiatric diagnoses

article en

Abstract

Importance: Childhood Maltreatment (CM) is associated with psychiatric disorders. 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 and participants: The population-based case-cohort iPSYCH was used to analyse 13 polygenic scores (PGS) in CM-exposed individuals across five psychiatric 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. Main outcome 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. 3 Results: This study included 102,856 individuals (47,938 females, 54,918 males) 8-35 years old. We analysed 2,179 CM-exposed individuals across individuals with attention deficit hyperactivity disorder (ADHD; N=22,674), autism (N=18,941), schizophrenia (N=6,103), bipolar disorder (N=3,061), 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, e.g., for ADHD the absolute CM risk was 5.6% in the highest ADHD-PGS quartile while only 3,3% in the lowest ADHD-PGS quartile (hazard rate ratio quantile-4 vs quantile-1 = 1.81; CI = 1.47 – 2.22). CM risk was more than twice as high for children with psychiatric diagnosed parents (5.7%) than for children with non-diagnosed parents (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 striking sex-differences in absolute CM risk, which reached 5.6% for females in the highest ADHD-PGS quartile while it was 2.0% for males. Conclusions and relevance: Individuals with high a ADHD-PRS and/or low educational attainment-PRS are at elevated risk of CM. Extra attention should be given to CM-high-risk individuals across all five psychiatric diagnoses i.e. females with a high ADHD-PGS and/or a parent diagnosed with a psychiatric disorder.

Spectrum Research Repository (Concordia University)
Openalex Percentile: Top 39%
Child Abuse and Trauma
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.