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Use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the EU-GEI study

Rodriguez, Victoria, Alameda, Luis, Quattrone, Diego, Tripoli, Giada, Gayer-Anderson, Charlotte, Spinazzola, Edoardo, Trotta, Giulia, Jongsma, Hannah E., Stilo, Simona, La Cascia, Caterina, Ferraro, Laura, La Barbera, Daniele, Lasalvia, Antonio, Tosato, Sarah, Tarricone, Ilaria, Bonora, Elena, Jamain, Stéphane, Selten, Jean-Paul, Velthorst, Eva, de Haan, Lieuwe, Llorca, Pierre-Michel, Arrojo, Manuel, Bobes, Julio, Bernardo, Miguel, Arango, Celso, Kirkbride, James, Jones, Peter B., Rutten, Bart P., Richards, Alexander, Sham, Pak C., O'Donovan, Michael ORCID: https://orcid.org/0000-0001-7073-2379, Van Os, Jim, Morgan, Craig, Di Forti, Marta, Murray, Robin M. and Vassos, Evangelos 2022. Use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the EU-GEI study. Psychological Medicine 10.1017/S0033291721005456

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Abstract

Background Schizophrenia (SZ), bipolar disorder (BD) and depression (D) run in families. This susceptibility is partly due to hundreds or thousands of common genetic variants, each conferring a fractional risk. The cumulative effects of the associated variants can be summarised as a polygenic risk score (PRS). Using data from the EUropean Network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) first episode case–control study, we aimed to test whether PRSs for three major psychiatric disorders (SZ, BD, D) and for intelligent quotient (IQ) as a neurodevelopmental proxy, can discriminate affective psychosis (AP) from schizophrenia-spectrum disorder (SSD). Methods Participants (842 cases, 1284 controls) from 16 European EU-GEI sites were successfully genotyped following standard quality control procedures. The sample was stratified based on genomic ancestry and analyses were done only on the subsample representing the European population (573 cases, 1005 controls). Using PRS for SZ, BD, D, and IQ built from the latest available summary statistics, we performed simple or multinomial logistic regression models adjusted for 10 principal components for the different clinical comparisons. Results In case–control comparisons PRS-SZ, PRS-BD and PRS-D distributed differentially across psychotic subcategories. In case–case comparisons, both PRS-SZ [odds ratio (OR) = 0.7, 95% confidence interval (CI) 0.54–0.92] and PRS-D (OR = 1.31, 95% CI 1.06–1.61) differentiated AP from SSD; and within AP categories, only PRS-SZ differentiated BD from psychotic depression (OR = 2.14, 95% CI 1.23–3.74). Conclusions Combining PRS for severe psychiatric disorders in prediction models for psychosis phenotypes can increase discriminative ability and improve our understanding of these phenotypes. Our results point towards the potential usefulness of PRSs in specific populations such as high-risk or early psychosis phases.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Medicine
MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Additional Information: This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/)
Publisher: Cambridge University Press
ISSN: 0033-2917
Date of First Compliant Deposit: 4 May 2022
Date of Acceptance: 15 December 2021
Last Modified: 30 Nov 2022 07:41
URI: https://orca.cardiff.ac.uk/id/eprint/149529

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