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; Van Os, Jim; Morgan, Craig; Di Forti, Marta; Murray, Robin M; Vassos, Evangelos
(2023) Psychological medicine, volume 53, issue 8, pp. 3396 - 3405
(Article)
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
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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.
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Keywords: Affective psychosis, bipolar disorder, diagnosis, genetics, polygenic score, psychosis, psychotic depression, schizophrenia-spectrum disorder, Psychiatry and Mental health, Applied Psychology, Journal Article
ISSN: 0033-2917
Publisher: Cambridge University Press
Note: Funding Information: This work was supported by funding from the European Community's Seventh Framework Programme under grant agreement No. HEALTH-F2-2010-241909 (Project EU-GEI). VR was funded by a PhD scholarship supported by Lord Leverhulme's Charitable Trust and the Velvet Foundation. EV is funded by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. CA was supported by the Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/024), co-financed by ERDF Funds from the European Commission, ‘A way of making Europe’, CIBERSAM. Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), Fundación Familia Alonso and Fundación Alicia Koplowitz. MB was supported by the Ministry od Economy and Competitivity (PI08/0208; PI11/00325; PI14/00612), Instituto de Salud Carlos III – ERDF Funds from the European Commission, ‘A way of making Europe’, CIBERSAM, by the CERCA Programme/Generalitat de Catalunya and Secretaria d'Universitats i Recerca del Departament d'Economia I Coneixement (2017SGR1355). Departament de Salut de la Generalitat de Catalunya, en la convocatoria corresponent a l'any 2017 de concessió de subvencions del PERIS 2016-2020, modalitat Projectes de recerca orientats a l'atenció primària, amb el codi d'expedient SLT006/17/00345; and grateful for the support of the Institut de Neurociències, Universitat de Barcelona. Publisher Copyright: Copyright © The Author(s), 2022. Published by Cambridge University Press.
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