Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms
Abstract Many psychiatric disorders share genetic liabilities, but whether these shared liabilities can be utilized to classify and differentiate psychiatric disorders remains unclear. In this study, we use polygenic risk scores (PRSs) of 42 traits comorbid with schizophrenia (SCZ), bipolar disorder...
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| Format: | Article |
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Nature Portfolio
2025-02-01
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| Series: | Schizophrenia |
| Online Access: | https://doi.org/10.1038/s41537-025-00564-7 |
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| author | Xiangning Chen Yimei Lu Joan Manuel Cue Mira V. Han Vishwajit L. Nimgaonkar Daniel R. Weinberger Shizhong Han Zhongming Zhao Jingchun Chen |
| author_facet | Xiangning Chen Yimei Lu Joan Manuel Cue Mira V. Han Vishwajit L. Nimgaonkar Daniel R. Weinberger Shizhong Han Zhongming Zhao Jingchun Chen |
| author_sort | Xiangning Chen |
| collection | DOAJ |
| description | Abstract Many psychiatric disorders share genetic liabilities, but whether these shared liabilities can be utilized to classify and differentiate psychiatric disorders remains unclear. In this study, we use polygenic risk scores (PRSs) of 42 traits comorbid with schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) to evaluate their utilities. We found that combining target specific PRS with PRSs of comorbid traits can improve the classification of the target disorders. Importantly, without inclusion of PRSs from targeted disorders, we can still classify SCZ (accuracy 0.710 ± 0.008, AUC 0.789 ± 0.011), BIP (accuracy 0.782 ± 0.006, AUC 0.852 ± 0.004), and MDD (accuracy 0.753 ± 0.019, AUC 0.822 ± 0.010). Furthermore, PRSs from comorbid traits alone can effectively differentiate unaffected controls and patients with SCZ, BIP, and MDD (accuracy 0.861 ± 0.003, AUC 0.961 ± 0.041). Our results demonstrate that shared liabilities can be used effectively to improve the classification and differentiation of these disorders. The finding that PRSs from comorbid traits alone can classify and differentiate SCZ, BIP and MDD reasonably well implies that a majority of the risk variants composing target PRSs are shared with comorbid traits. Overall, our results suggest that a data-driven approach may be feasible to classify and differentiate these disorders. |
| format | Article |
| id | doaj-art-049a9ed41d8e4db49c42abe2258baddd |
| institution | Kabale University |
| issn | 2754-6993 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Schizophrenia |
| spelling | doaj-art-049a9ed41d8e4db49c42abe2258baddd2025-02-09T12:42:04ZengNature PortfolioSchizophrenia2754-69932025-02-0111111110.1038/s41537-025-00564-7Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithmsXiangning Chen0Yimei Lu1Joan Manuel Cue2Mira V. Han3Vishwajit L. Nimgaonkar4Daniel R. Weinberger5Shizhong Han6Zhongming Zhao7Jingchun Chen8Center for Precision Medicine, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at HoustonNevada Institute of Personalized Medicine, University of Nevada Las VegasNevada Institute of Personalized Medicine, University of Nevada Las VegasSchool of Life Sciences, University of Nevada Las VegasDepartment of Psychiatry, University of PittsburghLieber Institute for Brain DevelopmentLieber Institute for Brain DevelopmentCenter for Precision Medicine, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at HoustonNevada Institute of Personalized Medicine, University of Nevada Las VegasAbstract Many psychiatric disorders share genetic liabilities, but whether these shared liabilities can be utilized to classify and differentiate psychiatric disorders remains unclear. In this study, we use polygenic risk scores (PRSs) of 42 traits comorbid with schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) to evaluate their utilities. We found that combining target specific PRS with PRSs of comorbid traits can improve the classification of the target disorders. Importantly, without inclusion of PRSs from targeted disorders, we can still classify SCZ (accuracy 0.710 ± 0.008, AUC 0.789 ± 0.011), BIP (accuracy 0.782 ± 0.006, AUC 0.852 ± 0.004), and MDD (accuracy 0.753 ± 0.019, AUC 0.822 ± 0.010). Furthermore, PRSs from comorbid traits alone can effectively differentiate unaffected controls and patients with SCZ, BIP, and MDD (accuracy 0.861 ± 0.003, AUC 0.961 ± 0.041). Our results demonstrate that shared liabilities can be used effectively to improve the classification and differentiation of these disorders. The finding that PRSs from comorbid traits alone can classify and differentiate SCZ, BIP and MDD reasonably well implies that a majority of the risk variants composing target PRSs are shared with comorbid traits. Overall, our results suggest that a data-driven approach may be feasible to classify and differentiate these disorders.https://doi.org/10.1038/s41537-025-00564-7 |
| spellingShingle | Xiangning Chen Yimei Lu Joan Manuel Cue Mira V. Han Vishwajit L. Nimgaonkar Daniel R. Weinberger Shizhong Han Zhongming Zhao Jingchun Chen Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms Schizophrenia |
| title | Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms |
| title_full | Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms |
| title_fullStr | Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms |
| title_full_unstemmed | Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms |
| title_short | Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms |
| title_sort | classification of schizophrenia bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms |
| url | https://doi.org/10.1038/s41537-025-00564-7 |
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