ArticleJournal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology2018
Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits.
Article in Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for genetic prediction of psychiatric disorders: a systematic review.Molecular psychiatry · 2021Pooled it
- Research advances on Cordyceps sinensis and its components in relation to omics biomarkers for the neurological disorders.Die Naturwissenschaften · 2026Review
- SNP-based prediction of schizophrenia using machine learning.Bioinformatics advances · 2026Article
- Metabolic syndrome in patients with schizophrenia: Underlying mechanisms and therapeutic approaches (Review).Molecular medicine reports · 2025Review
- Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms.Schizophrenia (Heidelberg, Germany) · 2025Article
- Easy, bias-free Bayesian hierarchical modeling of the psychometric function using the Palamedes Toolbox.Behavior research methods · 2024Article
- A review of model evaluation metrics for machine learning in genetics and genomics.Frontiers in bioinformatics · 2024Review
- A greedy regression algorithm with coarse weights offers novel advantages.Scientific reports · 2022Article
- Clinical Approaches to Late-Onset Psychosis.Journal of personalized medicine · 2022Review
- Prediction of transition to psychosis from an at-risk mental state using structural neuroimaging, genetic, and environmental data.Frontiers in psychiatry · 2022Article
- Artificial image objects for classification of schizophrenia with GWAS-selected SNVs and convolutional neural network.Patterns (New York, N.Y.) · 2021Article
- Editors' Commentary for Special Issue: The 2017 CALDAR Summer Institute and International Conference Promoting Global Health-Precision Research in Substance Abuse, HIV, and Care.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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