Development and validation of polygenic risk profiles of schizophrenia

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Abstract 1. Importance Schizophrenia is clinically and biologically heterogeneous, with marked variability in course and treatment response. Stratification of patients may advance individualized therapy and clarify underlying mechanisms. 2. Objective To identify, validate, and replicate genetic risk factors distinct to each other in schizophrenia patients using polygenic risk scores of large numbers of psychiatry-relevant phenotypes. 3. Design We analyzed genetic and phenotypic data from FinnGen (discovery dataset) and two independent cohorts for validation (PsyCourse, Bari). Using PRScope, a framework for standardizing polygenic score calculation and for patient stratification, we calculated 413 psychiatry-related polygenic scores for individuals with schizophrenia and controls. The resulting multi-PGS matrix was used to stratify patients through a data-driven approach. The findings were validated via cross-validation in FinnGen, and replicated in PsyCourse and Bari. 4. Setting We used available large-scale datasets containing genetic and phenotypic information and publicly available GWAS summary statistics. 5. Participants Data from FinnGen (7,486 schizophrenia cases; 27,288 controls), PsyCourse (419 cases; 299 controls), and Bari (531 cases; 738 controls) were included in this study. 6. Main outcome and measures We examined the validity of the genetic differences among patients, predictive accuracy across datasets, contributing genetic domains, and phenotypic differences, including clinical severity proxies. 7. Results Two data-driven clusters of patients with differing genetic risk profiles were identified. Both showed comparable genetic liability for schizophrenia, but diverged strongly in genetic risk for multiple psychiatry-related traits. The profile of the first cluster was characterized by a higher risk of depression, neuroticism, and low cognitive performance, whereas the second cluster showed a profile similar to that of healthy controls in these dimensions. Despite equal genetic liability for schizophrenia, the first profile showed higher predictability for schizophrenia in a case–control prediction model and was associated with significant differences in clinical severity indicators, such as higher clozapine use. 8. Conclusions and Relevance A data-driven, polygenic risk–based approach revealed two biologically and clinically distinct genetic risk profiles. Genetic liability for depression traits, neuroticism, and low cognition beyond schizophrenia liability itself, appears to shape disease penetrance and severity of schizophrenia. These findings highlight the potential of multivariate polygenic risk stratification for refining schizophrenia nosology and tailoring interventions. Keypoints 1. Question Can polygenic scores derived from many psychiatry-relevant GWAS be used to stratify schizophrenia patients in a biologically and clinically meaningful way? 2. Findings We identified two clusters of schizophrenia patients and characterized them as distinct genetic risk profiles based on their polygenic risk contributions. While both profiles showed comparable risk for schizophrenia, one profile was associated with higher liability for neuroticism and depression, reduced cognitive performance, and more complex clinical manifestations compared with patients in the second profile showing the opposite characteristics. 3. Meaning Our results provide replicable insights into the genetic architecture of schizophrenia and have the potential to inform future personalized treatments.

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