ArticleGeneral psychiatry2026
Dissecting the polygenic architecture of psychopathology via singular value decomposition of eight psychiatric genome-wide association studies and evaluation of component-based polygenic scores.
Article in General psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Current research suggests that genetic risk for psychiatric disorders is largely due to distinct combinations of many common variants shared by different disorders. This points to the existence of latent components affecting different dimensions of psychopathology. Aims: The aim of this study is to identify and characterise latent genetic components involved in psychopathology using a data-driven approach and evaluate their potential as principal component-based polygenic scores (PC-PGSs). Methods: Singular value decomposition was applied to a matrix of summary statistics from the largest available genome-wide association studies (GWASs) for eight psychiatric disorders to identify latent components. The components were characterised by gene mapping of the top contributing variants, enrichment analysis and genetic correlation with external traits. PC-PGSs were evaluated in the FinnGen dataset by computing group-wise PGSs from summary statistics using Reconstructing Allelic Count. Results: The different components were mainly involved in synapse organisation and neurodevelopment. The first latent component (PC1) explained 30.5% of the total variance and represented a broad transdiagnostic dimension. Neuroticism was the most strongly correlated external trait. Substance use traits and other psychiatric disorders were positively correlated, whereas cognitive traits were negatively correlated. The second latent component (14.7% of the variance) contrasted thought disorders with childhood-onset neurodevelopmental disorders. Educational attainment and creativity were the most correlated external traits. Other components were more related to a single disorder or differentiated between two related disorders. PC-PGSs in FinnGen largely showed associations in the expected direction, indicating a consistent overall trend for the different PC-PGSs. PC1-PGS was associated with all disorders. Other PC-PGSs showed the expected association in case-case comparisons. Conclusions: Decomposing GWAS summary statistic matrices can reveal functionally coherent dimensions of psychiatric genetic risk that could be clinically relevant, offering a potential framework to refine diagnosis, improve prediction and inform personalised treatment.
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