Schizophrenia polygenic risk scores, clinical variables and genetic pathways as predictors of phenotypic traits of bipolar I disorder

• Published in: Journal of affective disorders 2024-07, Vol.356, p.507-518
• Main Authors: Grigoroiu-Serbanescu, Maria, van der Veen, Tracey, Bigdeli, Tim, Herms, Stefan, Diaconu, Carmen C., Neagu, Ana Iulia, Bass, Nicholas, Thygesen, Johan, Forstner, Andreas J., Nöthen, Markus M., McQuillin, Andrew
• Format: Article
• Language: English
• Subjects: Adult; Bipolar Disorder - genetics; Bipolar disorder subphenotypes; Case-Control Studies; Female; Genetic Predisposition to Disease - genetics; Genetic Risk Score; Genome-Wide Association Study; Humans; Individual pathway analysis; Male; Middle Aged; Multifactorial Inheritance - genetics; Phenotype; Polymorphism, Single Nucleotide; Psychosis; Psychotic Disorders - genetics; Romania; Schizophrenia - genetics; Schizophrenia polygenic score; United Kingdom
• ISSN: 0165-0327; 1573-2517; 1573-2517
• DOI: 10.1016/j.jad.2024.04.066

We investigated the predictive value of polygenic risk scores (PRS) derived from the schizophrenia GWAS (Trubetskoy et al., 2022) (SCZ3) for phenotypic traits of bipolar disorder type-I (BP-I) in 1878 BP-I cases and 2751 controls from Romania and UK. We used PRSice-v2.3.3 and PRS-CS for computing SCZ3-PRS for testing the predictive power of SCZ3-PRS alone and in combination with clinical variables for several BP-I subphenotypes and for pathway analysis. Non-linear predictive models were also used. SCZ3-PRS significantly predicted psychosis, incongruent and congruent psychosis, general age-of-onset (AO) of BP-I, AO-depression, AO-Mania, rapid cycling in univariate regressions. A negative correlation between the number of depressive episodes and psychosis, mainly incongruent and an inverse relationship between increased SCZ3-SNP loading and BP-I-rapid cycling were observed. In random forest models comparing the predictive power of SCZ3-PRS alone and in combination with nine clinical variables, the best predictions were provided by combinations of SCZ3-PRS-CS and clinical variables closely followed by models containing only clinical variables. SCZ3-PRS performed worst. Twenty-two significant pathways underlying psychosis were identified. The combined RO-UK sample had a certain degree of heterogeneity of the BP-I severity: only the RO sample and partially the UK sample included hospitalized BP-I cases. The hospitalization is an indicator of illness severity. Not all UK subjects had complete subphenotype information. Our study shows that the SCZ3-PRS have a modest clinical value for predicting phenotypic traits of BP-I. For clinical use their best performance is in combination with clinical variables. •Polygenic risk score based on Schizophrenia GWAS (Trubetskoy et al., 2022) (SCZ3-PRS) were computed using two PRS methods.•BP-I age of onset (AO), AO-mania, AO-depression, and rapid cycling had an inverse relationship with the SCZ3-SNP loading.•A negative correlation between the number of depressive episodes and psychosis, mainly incongruent psychosis was found.•The best predictions of BP-I traits were provided by combinations of SCZ3-PRS-CS and clinical variables.•SCZ3-PRS alone generated the worst predictions in machine learning models.•Psychosis pathway analysis in BP-I identified 22 genetic pathways; best associated were ZNF318, Apoptosis, Mitochondrion.