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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
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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277719/ https://www.ncbi.nlm.nih.gov/pubmed/35076361 http://dx.doi.org/10.1017/S0033291721005456 |
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author | 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 |
author_facet | 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 |
author_sort | Rodriguez, Victoria |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10277719 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-102777192023-06-20 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 Psychol Med Original Article 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. Cambridge University Press 2023-06 2022-01-25 /pmc/articles/PMC10277719/ /pubmed/35076361 http://dx.doi.org/10.1017/S0033291721005456 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |
spellingShingle | Original Article 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 Use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the EU-GEI study |
title | Use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the EU-GEI study |
title_full | Use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the EU-GEI study |
title_fullStr | Use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the EU-GEI study |
title_full_unstemmed | Use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the EU-GEI study |
title_short | Use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the EU-GEI study |
title_sort | use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the eu-gei study |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277719/ https://www.ncbi.nlm.nih.gov/pubmed/35076361 http://dx.doi.org/10.1017/S0033291721005456 |
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