Dataset Information

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS.

ABSTRACT:

Background

Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD).

Aims

Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis.

Methods

Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses.

Results

While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD.

Conclusions

We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD 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 BD and, importantly, BD with depressive onset from MDD.

SUBMITTER: Panagiotaropoulou G

PROVIDER: S-EPMC10896417 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS.

Panagiotaropoulou Georgia G Hellberg Kajsa-Lotta Georgii KG Coleman Jonathan R I JRI Seok Darsol D Kalman Janos J Mitchell Philip B PB Schofield Peter R PR Forstner Andreas J AJ Bauer Michael M Scott Laura J LJ Pato Carlos N CN Pato Michele T MT Li Qingqin S QS Kirov George G Landén Mikael M Jonsson Lina L Müller-Myhsok Bertram B Smoller Jordan W JW Binder Elisabeth B EB Brückl Tanja M TM Czamara Darina D der Auwera Sandra Van SV Grabe Hans J HJ Homuth Georg G Schmidt Carsten O CO Potash James B JB DePaulo Raymond J RJ Goes Fernando S FS MacKinnon Dean F DF Mondimore Francis M FM Weissman Myrna M MM Shi Jianxin J Frye Mark A MA Biernacka Joanna M JM Reif Andreas A Witt Stephanie H SH Kahn René R RR Boks Marco M MM Owen Michael J MJ Gordon-Smith Katherine K Mitchell Brittany L BL Martin Nicholas G NG Medland Sarah E SE Jones Lisa L Knowles James A JA Levinson Douglas F DF O'Donovan Michael C MC Lewis Cathryn M CM Breen Gerome G Werge Thomas T Schork Andrew J AJ Ophoff Roel R Ripke Stephan S Loohuis Loes Olde LO

medRxiv : the preprint server for health sciences 20240130

<h4>Background</h4>Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD).<h4>Aims</h4>Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk s ...[more]

PMID: 38410442

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Project description:ImportanceUnderstanding the origins of clinical heterogeneity in bipolar disorder (BD) will inform new approaches to stratification and studies of underlying mechanisms.ObjectiveTo identify components of genetic liability that are shared between BD, schizophrenia, and major depressive disorder (MDD) and those that differentiate each disorder from the others and to examine associations between heterogeneity for key BD symptoms and each component.Design, setting, and participantsUsing data from the Bipolar Disorder Research Network in the United Kingdom, components of liability were identified by applying genomic structural equation modeling to genome-wide association studies of schizophrenia, BD, and MDD. Polygenic risk scores (PRS) representing each component were tested for association with symptoms in an independent BD data set. Adults with DSM-IV BD or schizoaffective disorder, bipolar type, were included. Data were collected from January 2000 to December 2013, and data were analyzed from June 2020 to February 2022.Main outcomes and measuresPRS representing the components of liability were tested for association with mania and depression, psychosis, and mood incongruence of psychosis in participants with BD, measured using the Bipolar Affective Disorder Dimensional Scale.ResultsOf 4429 included participants, 3012 (68.0%) were female, and the mean (SD) age was 46.2 (12.3) years. Mania and psychosis were associated with the shared liability component (mania β = 0.29; 95% CI, 0.23-0.34; P = 3.04 × 10-25; psychosis β = 0.05; 95% CI, 0.04-0.07; P = 2.33 × 10-13) and the components that differentiate each of schizophrenia (mania β = 0.08; 95% CI, 0.03-0.14; P = .002; psychosis β = 0.03; 95% CI, 0.01-0.04; P = 1.0 × 10-4) and BD (mania β = 0.14; 95% CI, 0.09-0.20; P = 1.99 × 10-7; psychosis β = 0.02; 95% CI, 0.01-0.03; P = .006) from the other disorders. The BD differentiating component was associated with mania independently of effects on psychosis (β = 0.14; 95% CI, 0.08-0.20; P = 4.32 × 10-6) but not with psychosis independently of mania. Conversely, the schizophrenia differentiating component was associated with psychosis independently of effects on mania (β = 0.01; 95% CI, 0.003-0.03; P = .02), but not with mania independently of psychosis. Mood incongruence of psychosis was associated only with the schizophrenia differentiating component (β = 0.03; 95% CI, 0.01-0.05; P = .005). Depression was associated with higher MDD differentiating component (β = 0.07; 95% CI, 0.01-0.12; P = .01) but lower BD differentiating component (β = -0.11; 95% CI, -0.17 to -0.06; P = 7.06 × 10-5).Conclusions and relevanceIn this study of BD, clinical heterogeneity reflected the burden of liability to BD and the contribution of alleles that have differentiating effects on risk for other disorders; mania, psychosis, and depression were associated with the components of genetic liability differentiating BD, MDD, and schizophrenia, respectively. Understanding the basis of this etiological heterogeneity will be critical for identifying the different pathophysiological processes underlying BD, stratifying patients, and developing precision therapeutics.

Project description:Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.

Dataset Information

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS.

Background

Aims

Methods

Results

Conclusions

Publications

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS.

Similar Datasets

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