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Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology

Abstract

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

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Fig. 1: Manhattan plot of genome-wide association meta-analysis of 41,917 BD cases and 371,549 controls.
Fig. 2: Phenotypic variance in BD explained by PRSs.
Fig. 3: Relationships between BD and modifiable risk factors based on genetic correlations, GSMR and bivariate gaussian mixture modeling.

Data availability

GWAS summary statistics are publicly available on the PGC website (https://www.med.unc.edu/pgc/results-and-downloads). Individual-level data are accessible through collaborative analysis proposals to the Bipolar Disorder Working Group of the PGC (https://www.med.unc.edu/pgc/shared-methods/how-to/). This study included some publicly available datasets accessed through dbGaP (PGC bundle phs001254) and the HRC reference panel v1.0 (http://www.haplotype-reference-consortium.org/home). Databases used: Drug–Gene Interaction Database DGIdb v.2 (https://www.dgidb.org); Psychoactive Drug Screening Database Ki DB (https://pdsp.unc.edu/databases/kidb.php); DrugBank 5.0 (https://www.drugbank.ca); LD Hub (http://ldsc.broadinstitute.org); FUMA (https://fuma.ctglab.nl).

Code availability

All software used is publicly available at the URLs or references cited.

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Acknowledgements

We thank the participants who donated their time, life experiences and DNA to this research and the clinical and scientific teams that worked with them. We are deeply indebted to the investigators who make up the PGC. The PGC has received major funding from the US National Institute of Mental Health (PGC3: U01 MH109528; PGC2: U01 MH094421; PGC1: U01 MH085520). Statistical analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara and the Mount Sinai high-performance computing cluster (http://hpc.mssm.edu), which is supported by the Office of Research Infrastructure of the National Institutes of Health under award numbers S10OD018522 and S10OD026880. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Full acknowledgements are included in the Supplementary Note.

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