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Specificity of polygenic signatures across symptom dimensions in bipolar disorder: an analysis of UK Bipolar Disorder Research Network data

Allardyce, Judith ORCID:, Cardno, Alastair G., Gordon-Smith, Katherine, Jones, Lisa, Di Florio, Arianna ORCID:, Walters, James T. R. ORCID:, Holmans, Peter A. ORCID:, Craddock, Nicholas J. ORCID:, Jones, Ian, Owen, Michael J. ORCID:, Escott-Price, Valentina ORCID: and O'Donovan, Michael C. ORCID: 2023. Specificity of polygenic signatures across symptom dimensions in bipolar disorder: an analysis of UK Bipolar Disorder Research Network data. The Lancet Psychiatry 10 (8) , pp. 623-631. 10.1016/S2215-0366(23)00186-4

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Background Current definitions and clinical heterogeneity in bipolar disorder are major concerns as they obstruct aetiological research and impede drug development. Therefore, stratification of bipolar disorder is a high priority. To inform stratification, our analysis aimed to examine the patterns and relationships between polygenic liability for bipolar disorder, major depressive disorder (MDD), and schizophrenia with multidimensional symptom representations of bipolar disorder. Methods In this analysis, data from the UK Bipolar Disorder Research Network (BDRN) were assessed with the Operational Checklist for Psychotic Disorders. Individuals with bipolar disorder as defined in DSM-IV, of European ancestry (self-reported), aged 18 years or older at time of interview, living in the UK, and registered with the BDRN were eligible for inclusion. Psychopathological variables obtained via interview by trained research psychologists or psychiatrists and psychiatric case notes were used to identify statistically distinct symptom dimensions, calibrated with exploratory factor analysis and validated with confirmatory factor analysis (CFA). CFA was extended to include three polygenic risk scores (PRSs) indexing liability for bipolar disorder, MDD, and schizophrenia in a multiple indicator multiple cause (MIMIC) structural equation model to estimate PRS relationships with symptom dimensions. Findings Of 4198 individuals potentially eligible for inclusion, 4148 (2804 [67·6%] female individuals and 1344 [32·4%] male individuals) with a mean age at interview of 45 years (SD 12·03) were available for analysis. Three reliable dimensions (mania, depression, and psychosis) were identified. The MIMIC model fitted the data well (root mean square error of approximation 0·021, 90% CI 0·019–0·023; comparative fit index 0·99) and suggests statistically distinct symptom dimensions also have distinct polygenic profiles. The PRS for MDD was strongly associated with the depression dimension (standardised β 0·125, 95% CI 0·080–0·171) and the PRS for schizophrenia was strongly associated with the psychosis dimension (0·108, 0·082–0·175). For the mania dimension, the PRS for bipolar disorder was weakly associated (0·050, 0·002–0·097). Interpretation Our findings support the hypothesis that genetic heterogeneity underpins clinical heterogeneity, suggesting that different symptom dimensions within bipolar disorder have partly distinct causes. Furthermore, our results suggest that a specific symptom dimension has a similar cause regardless of the primary psychiatric diagnosis, supporting the use of symptom dimensions in precision psychiatry.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Advanced Research Computing @ Cardiff (ARCCA)
MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Publisher: Elsevier
ISSN: 2215-0366
Date of First Compliant Deposit: 30 August 2023
Date of Acceptance: 17 May 2023
Last Modified: 10 Jun 2024 08:33

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