Polygenic Risk for Schizophrenia and Global Assessment of Functioning—A Comparison with In-Silico Data

Abstract

In psychiatry, polygenic risk scores (PRSs) have recently been exploited to uncover the shared genetic components in distinct psychiatric disorders. Summary data of large-scale discovery genome-wide association studies (GWASs) on traits such as schizophrenia (SZ) are available. In addition, clinical deep phenotyping includes several correlated phenotypes for psychosocial functioning such as the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF). PANSS evaluates acute symptom severity, thus adjusting for this effect when measuring overall assessment and progression of patients with the GAF. A far-reaching understanding of the properties of PRS in such phenotypes is critical to interpreting such analyses, especially when the intermediate phenotype limits sample size. We conducted a simulation study to investigate the performance of PRS in the correlated target phenotypes using sample sizes n = 200, 500, and 1000 (100 replicates) in terms of explained variance in the simulated target phenotypes. We investigated performance of SZ-PRS in the PsyCourse study involving 653 patients (psychotic n = 387, affective n = 266), in which SZ-PRS was derived from the results of a large GWAS of schizophrenia by the Psychiatric Genomics Consortium. Our simulation results reveal that decreasing correlation between target phenotypes indicates a definable decrease in shared genetic burden with the discovery phenotype. However, with a small sample size, there is already a loss in retrieved R2 with an identical generation model. Our PsyCourse results portrayed that for all patients and for psychotic subgroup, SZ-PRS explained 1% R2 for GAF