Polygenic Risk Scores for Subtyping of Schizophrenia

Schizophrenia is a complex disorder with many comorbid conditions. In this study, we used polygenic risk scores (PRSs) from schizophrenia and comorbid traits to explore consistent cluster structure in schizophrenia patients. With 10 comorbid traits, we found a stable 4-cluster structure in two datas...

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Main Authors: Jingchun Chen, Travis Mize, Jain-Shing Wu, Elliot Hong, Vishwajit Nimgaonkar, Kenneth S. Kendler, Daniel Allen, Edwin Oh, Alison Netski, Xiangning Chen
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Schizophrenia Research and Treatment
Online Access:http://dx.doi.org/10.1155/2020/1638403
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spelling doaj-9db418c04a974eb4a2f37c06f0ce436f2020-11-25T03:21:59ZengHindawi LimitedSchizophrenia Research and Treatment2090-20852090-20932020-01-01202010.1155/2020/16384031638403Polygenic Risk Scores for Subtyping of SchizophreniaJingchun Chen0Travis Mize1Jain-Shing Wu2Elliot Hong3Vishwajit Nimgaonkar4Kenneth S. Kendler5Daniel Allen6Edwin Oh7Alison Netski8Xiangning Chen9Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, NV 89154, USANevada Institute of Personalized Medicine, University of Nevada, Las Vegas, NV 89154, USANevada Institute of Personalized Medicine, University of Nevada, Las Vegas, NV 89154, USADepartment of Psychiatry, University of Maryland, Baltimore, MD 21228, USADepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USADepartment of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USADepartment of Psychiatry and Behavioral Health, University of Nevada, Las Vegas, School of Medicine, NV 89102, USANevada Institute of Personalized Medicine, University of Nevada, Las Vegas, NV 89154, USADepartment of Psychiatry and Behavioral Health, University of Nevada, Las Vegas, School of Medicine, NV 89102, USA410 AI, LLC, Germantown, MD 20876, USASchizophrenia is a complex disorder with many comorbid conditions. In this study, we used polygenic risk scores (PRSs) from schizophrenia and comorbid traits to explore consistent cluster structure in schizophrenia patients. With 10 comorbid traits, we found a stable 4-cluster structure in two datasets (MGS and SSCCS). When the same traits and parameters were applied for the patients in a clinical trial of antipsychotics, the CATIE study, a 5-cluster structure was observed. One of the 4 clusters found in the MGS and SSCCS was further split into two clusters in CATIE, while the other 3 clusters remained unchanged. For the 5 CATIE clusters, we evaluated their association with the changes of clinical symptoms, neurocognitive functions, and laboratory tests between the enrollment baseline and the end of Phase I trial. Class I was found responsive to treatment, with significant reduction for the total, positive, and negative symptoms (p=0.0001, 0.0099, and 0.0028, respectively), and improvement for cognitive functions (VIGILANCE, p=0.0099; PROCESSING SPEED, p=0.0006; WORKING MEMORY, p=0.0023; and REASONING, p=0.0015). Class II had modest reduction of positive symptoms (p=0.0492) and better PROCESSING SPEED (p=0.0071). Class IV had a specific reduction of negative symptoms (p=0.0111) and modest cognitive improvement for all tested domains. Interestingly, Class IV was also associated with decreased lymphocyte counts and increased neutrophil counts, an indication of ongoing inflammation or immune dysfunction. In contrast, Classes III and V showed no symptom reduction but a higher level of phosphorus. Overall, our results suggest that PRSs from schizophrenia and comorbid traits can be utilized to classify patients into subtypes with distinctive clinical features. This genetic susceptibility based subtyping may be useful to facilitate more effective treatment and outcome prediction.http://dx.doi.org/10.1155/2020/1638403
collection DOAJ
language English
format Article
sources DOAJ
author Jingchun Chen
Travis Mize
Jain-Shing Wu
Elliot Hong
Vishwajit Nimgaonkar
Kenneth S. Kendler
Daniel Allen
Edwin Oh
Alison Netski
Xiangning Chen
spellingShingle Jingchun Chen
Travis Mize
Jain-Shing Wu
Elliot Hong
Vishwajit Nimgaonkar
Kenneth S. Kendler
Daniel Allen
Edwin Oh
Alison Netski
Xiangning Chen
Polygenic Risk Scores for Subtyping of Schizophrenia
Schizophrenia Research and Treatment
author_facet Jingchun Chen
Travis Mize
Jain-Shing Wu
Elliot Hong
Vishwajit Nimgaonkar
Kenneth S. Kendler
Daniel Allen
Edwin Oh
Alison Netski
Xiangning Chen
author_sort Jingchun Chen
title Polygenic Risk Scores for Subtyping of Schizophrenia
title_short Polygenic Risk Scores for Subtyping of Schizophrenia
title_full Polygenic Risk Scores for Subtyping of Schizophrenia
title_fullStr Polygenic Risk Scores for Subtyping of Schizophrenia
title_full_unstemmed Polygenic Risk Scores for Subtyping of Schizophrenia
title_sort polygenic risk scores for subtyping of schizophrenia
publisher Hindawi Limited
series Schizophrenia Research and Treatment
issn 2090-2085
2090-2093
publishDate 2020-01-01
description Schizophrenia is a complex disorder with many comorbid conditions. In this study, we used polygenic risk scores (PRSs) from schizophrenia and comorbid traits to explore consistent cluster structure in schizophrenia patients. With 10 comorbid traits, we found a stable 4-cluster structure in two datasets (MGS and SSCCS). When the same traits and parameters were applied for the patients in a clinical trial of antipsychotics, the CATIE study, a 5-cluster structure was observed. One of the 4 clusters found in the MGS and SSCCS was further split into two clusters in CATIE, while the other 3 clusters remained unchanged. For the 5 CATIE clusters, we evaluated their association with the changes of clinical symptoms, neurocognitive functions, and laboratory tests between the enrollment baseline and the end of Phase I trial. Class I was found responsive to treatment, with significant reduction for the total, positive, and negative symptoms (p=0.0001, 0.0099, and 0.0028, respectively), and improvement for cognitive functions (VIGILANCE, p=0.0099; PROCESSING SPEED, p=0.0006; WORKING MEMORY, p=0.0023; and REASONING, p=0.0015). Class II had modest reduction of positive symptoms (p=0.0492) and better PROCESSING SPEED (p=0.0071). Class IV had a specific reduction of negative symptoms (p=0.0111) and modest cognitive improvement for all tested domains. Interestingly, Class IV was also associated with decreased lymphocyte counts and increased neutrophil counts, an indication of ongoing inflammation or immune dysfunction. In contrast, Classes III and V showed no symptom reduction but a higher level of phosphorus. Overall, our results suggest that PRSs from schizophrenia and comorbid traits can be utilized to classify patients into subtypes with distinctive clinical features. This genetic susceptibility based subtyping may be useful to facilitate more effective treatment and outcome prediction.
url http://dx.doi.org/10.1155/2020/1638403
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