Identifying genetic differences between bipolar disorder and major depression through multiple genome-wide association analyses.
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Abstract | BACKGROUND: Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).AIMS: We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.METHOD: Based on individual genotypes from case-control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.RESULTS: Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, = 25 966). We observe strong genetic correlation between our case-case GWAS and that of case-control BPD.CONCLUSIONS: We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD. |
Year of Publication | 2025
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Journal | The British journal of psychiatry : the journal of mental science
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Pages | 1-12
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Date Published | 01/2025
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ISSN | 1472-1465
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DOI | 10.1192/bjp.2024.125
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PubMed ID | 39806801
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