{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chen J"],"funding":["NIMH NIH HHS","NIGMS NIH HHS"],"pagination":["532-540"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7189428"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(4)"],"pubmed_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."],"journal":["Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology"],"pubmed_title":["Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits."],"pmcid":["PMC7189428"],"funding_grant_id":["R01 MH060879","R01 MH059587","R01 MH059565","R01 MH077139","R01 MH059588","R01 MH059566","R01 MH095034","R01 MH061675","R01 MH059586","R01 MH059571","U54 GM104944","R01 MH067257","R15 GM107983","P20 GM121325","R01 MH060870","R01 MH074027","R01 MH101054"],"pubmed_authors":["Wu JS","Chen J","Chen X","Mize T","Shui D"],"additional_accession":[]},"is_claimable":false,"name":"Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits.","description":"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.","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Dec","modification":"2020-10-29T10:03:48Z","creation":"2020-05-22T18:37:17Z"},"accession":"S-EPMC7189428","cross_references":{"pubmed":["30276764"],"doi":["10.1007/s11481-018-9811-8"]}}