Schizophrenia is genetically heterogeneous and comorbid with many conditions. In this study, we explored polygenic scores (PGSs) from genetically related conditions and traits to predict schizophrenia diagnosis using both logistic regression and deep neural network (DNN) models. We used the combined Molecular Genetics of Schizophrenia and Swedish Schizophrenia Case Control Study (MGS + SSCCS) data for training and testing the models, and used the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) data as independent validation. We screened 28 conditions and traits comorbid with schizophrenia to identify traits as potential predictors and used LASSO regression to select predictors for model construction. We investigated how PGS calculation influenced model performance. We found that the inclusion of comorbid traits improved model performance and PGSs calculated from two traits were more generalizable in independent validation. With a DNN model using 19 PGS predictors, we accomplished a prediction accuracy of 0.813 and an AUC of 0.905 in the MGS + SSCCS data. When this model was validated with the CATIE data, it achieved an accuracy of 0.721 and AUC of 0.747. Our results indicate that PGSs alone may not be sufficient to predict schizophrenia accurately and the inclusion of behavioral and clinical data may be necessary for more accurate prediction model.
Keywords: Deep neural network; Polygenic risk score; Risk prediction; Schizophrenia.