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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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.
References
GBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 390, 1211–1259 (2017).
Plans, L. et al. Association between completed suicide and bipolar disorder: a systematic review of the literature. J. Affect. Disord. 242, 111–122 (2019).
American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders 5th edn (American Psychiatric Association Publishing, 2013).
Merikangas, K. R. et al. Lifetime and 12-month prevalence of bipolar spectrum disorder in the national comorbidity survey replication. Arch. Gen. Psychiatry 64, 543–552 (2007).
Merikangas, K. R. et al. Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Arch. Gen. Psychiatry 68, 241–251 (2011).
Craddock, N. & Sklar, P. Genetics of bipolar disorder. Lancet 381, 1654–1662 (2013).
Song, J. et al. Bipolar disorder and its relation to major psychiatric disorders: a family-based study in the Swedish population. Bipolar Disord. 17, 184–193 (2015).
Bienvenu, O. J., Davydow, D. S. & Kendler, K. S. Psychiatric ‘diseases’ versus behavioral disorders and degree of genetic influence. Psychol. Med. 41, 33–40 (2011).
Psychiatric GWAS Consortium Bipolar Disorder Working Group. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat. Genet. 43, 977–983 (2011).
Baum, A. E. et al. A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder. Mol. Psychiatry 13, 197–207 (2008).
Charney, A. W. et al. Evidence for genetic heterogeneity between clinical subtypes of bipolar disorder. Transl. Psychiatry 7, e993 (2017).
Chen, D. T. et al. Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder. Mol. Psychiatry 18, 195–205 (2013).
Cichon, S. et al. Genome-wide association study identifies genetic variation in neurocan as a susceptibility factor for bipolar disorder. Am. J. Hum. Genet. 88, 372–381 (2011).
Ferreira, M. A. R. et al. Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat. Genet. 40, 1056–1058 (2008).
Green, E. K. et al. Association at SYNE1 in both bipolar disorder and recurrent major depression. Mol. Psychiatry 18, 614–617 (2013).
Green, E. K. et al. Replication of bipolar disorder susceptibility alleles and identification of two novel genome-wide significant associations in a new bipolar disorder case–control sample. Mol. Psychiatry 18, 1302–1307 (2013).
Hou, L. et al. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum. Mol. Genet. 25, 3383–3394 (2016).
Mühleisen, T. W. et al. Genome-wide association study reveals two new risk loci for bipolar disorder. Nat. Commun. 5, 3339 (2014).
Schulze, T. G. et al. Two variants in Ankyrin 3 (ANK3) are independent genetic risk factors for bipolar disorder. Mol. Psychiatry 14, 487–491 (2009).
Scott, L. J. et al. Genome-wide association and meta-analysis of bipolar disorder in individuals of European ancestry. Proc. Natl Acad. Sci. USA 106, 7501–7506 (2009).
Sklar, P. et al. Whole-genome association study of bipolar disorder. Mol. Psychiatry 13, 558–569 (2008).
Smith, E. N. et al. Genome-wide association study of bipolar disorder in European American and African American individuals. Mol. Psychiatry 14, 755–763 (2009).
Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).
Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).
Lee, S.-H., Zabolotny, J. M., Huang, H., Lee, H. & Kim, Y.-B. Insulin in the nervous system and the mind: functions in metabolism, memory, and mood. Mol. Metab. 5, 589–601 (2016).
McIntyre, R. S. et al. A randomized, double-blind, controlled trial evaluating the effect of intranasal insulin on neurocognitive function in euthymic patients with bipolar disorder. Bipolar Disord. 14, 697–706 (2012).
Nurnberger, J. I. Jr et al. Identification of pathways for bipolar disorder: a meta-analysis. JAMA Psychiatry 71, 657–664 (2014).
Gordovez, F. J. A. & McMahon, F. J. The genetics of bipolar disorder. Mol. Psychiatry 25, 544–559 (2020).
Zhang, C., Xiao, X., Li, T. & Li, M. Translational genomics and beyond in bipolar disorder. Mol. Psychiatry 26, 186–202 (2021).
Pedersen, C. B. et al. The iPSYCH2012 case–cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Mol. Psychiatry 1, 6–14 (2018).
Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).
Leitsalu, L. et al. Cohort Profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int. J. Epidemiol. 44, 1137–1147 (2015).
Krokstad, S. et al. Cohort Profile: the HUNT Study, Norway. Int. J. Epidemiol. 42, 968–977 (2013).
Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
Bryois, J. et al. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease. Nat. Genet. 52, 482–493 (2020).
Wagner, A. H. et al. DGIdb 2.0: mining clinically relevant drug–gene interactions. Nucleic Acids Res. 44, D1036–D1044 (2016).
Roth, B. L., Lopez, E., Patel, S. & Kroeze, W. K. The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrassment of riches? Neuroscientist 6, 252–262 (2000).
Gaspar, H. A. & Breen, G. Drug enrichment and discovery from schizophrenia genome-wide association results: an analysis and visualisation approach. Sci. Rep. 7, 12460 (2017).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).
Mancuso, N. et al. Probabilistic fine-mapping of transcriptome-wide association studies. Nat. Genet. 51, 675–682 (2019).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Wu, Y. et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9, 918 (2018).
Võsa, U. et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. Preprint at bioRxiv https://doi.org/10.1101/447367 (2018).
Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).
Frei, O. et al. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nat. Commun. 10, 2417 (2019).
Holland, D. et al. Beyond SNP heritability: polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model. PLoS Genet. 16, e1008612 (2020).
Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).
Steardo, L. Jr et al. Sleep disturbance in bipolar disorder: neuroglia and circadian rhythms. Front. Psychiatry 10, 501 (2019).
Hunt, G. E., Malhi, G. S., Cleary, M., Lai, H. M. X. & Sitharthan, T. Prevalence of comorbid bipolar and substance use disorders in clinical settings, 1990–2015: systematic review and meta-analysis. J. Affect. Disord. 206, 331–349 (2016).
Heffner, J. L., Strawn, J. R., DelBello, M. P., Strakowski, S. M. & Anthenelli, R. M. The co-occurrence of cigarette smoking and bipolar disorder: phenomenology and treatment considerations. Bipolar Disord. 13, 439–453 (2011).
Vreeker, A. et al. High educational performance is a distinctive feature of bipolar disorder: a study on cognition in bipolar disorder, schizophrenia patients, relatives and controls. Psychol. Med. 46, 807–818 (2016).
MacCabe, J. H. et al. Excellent school performance at age 16 and risk of adult bipolar disorder: national cohort study. Br. J. Psychiatry 196, 109–115 (2010).
Broome, M. R., Saunders, K. E. A., Harrison, P. J. & Marwaha, S. Mood instability: significance, definition and measurement. Br. J. Psychiatry 207, 283–285 (2015).
Ward, J. et al. The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function. Mol. Psychiatry 25, 3091–3099 (2020).
Ikeda, M. et al. A genome-wide association study identifies two novel susceptibility loci and trans population polygenicity associated with bipolar disorder. Mol. Psychiatry 23, 639–647 (2018).
Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).
Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).
Smeland, O. B., Frei, O., Dale, A. M. & Andreassen, O. A. The polygenic architecture of schizophrenia – rethinking pathogenesis and nosology. Nat. Rev. Neurol. 16, 366–379 (2020).
Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).
GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
Brainstorm Consortium et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
Cross-Disorder Group of the Psychiatric Genomics Consortium. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482 (2019).
Lewis, K. J. S. et al. Comparison of genetic liability for sleep traits among individuals with bipolar disorder I or II and control participants. JAMA Psychiatry 77, 303–310 (2020).
Zhou, H. et al. Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat. Neurosci. 23, 809–818 (2020).
Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
Vermeulen, J. M. et al. Smoking and the risk for bipolar disorder: evidence from a bidirectional Mendelian randomisation study. Br. J. Psychiatry 218, 88–94 (2021).
Peyrot, W. J. et al. The association between lower educational attainment and depression owing to shared genetic effects? Results in ~25 000 subjects. Mol. Psychiatry 20, 735–743 (2015).
Swanson, C. L. Jr, Gur, R. C., Bilker, W., Petty, R. G. & Gur, R. E. Premorbid educational attainment in schizophrenia: association with symptoms, functioning, and neurobehavioral measures. Biol. Psychiatry 44, 739–747 (1998).
Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).
Mizuno, A. & Okada, Y. Biological characterization of expression quantitative trait loci (eQTLs) showing tissue-specific opposite directional effects. Eur. J. Hum. Genet. 27, 1745–1756 (2019).
Schrode, N. et al. Synergistic effects of common schizophrenia risk variants. Nat. Genet. 51, 1475–1485 (2019).
Jiang, X. et al. Sodium valproate rescues expression of TRANK1 in iPSC-derived neural cells that carry a genetic variant associated with serious mental illness. Mol. Psychiatry 24, 613–624 (2019).
Huckins, L. M. et al. Transcriptomic imputation of bipolar disorder and bipolar subtypes reveals 29 novel associated genes. Preprint at bioRxiv https://doi.org/10.1101/222786 (2017).
Finan, C. et al. The druggable genome and support for target identification and validation in drug development. Sci. Transl. Med. 9, eaag1166 (2017).
von Wegerer, J., Hesslinger, B., Berger, M. & Walden, J. A calcium antagonistic effect of the new antiepileptic drug lamotrigine. Eur. Neuropsychopharmacol. 7, 77–81 (1997).
Cipriani, A. et al. A systematic review of calcium channel antagonists in bipolar disorder and some considerations for their future development. Mol. Psychiatry 21, 1324–1332 (2016).
Harrison, P. J., Tunbridge, E. M., Dolphin, A. C. & Hall, J. Voltage-gated calcium channel blockers for psychiatric disorders: genomic reappraisal. Br. J. Psychiatry 216, 250–253 (2020).
Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci. 18, 199–209 (2015).
Forstner, A. J. et al. Identification of shared risk loci and pathways for bipolar disorder and schizophrenia. PLoS ONE 12, e0171595 (2017).
Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715 (2018).
Lee, Y., Zhang, Y., Kim, S. & Han, K. Excitatory and inhibitory synaptic dysfunction in mania: an emerging hypothesis from animal model studies. Exp. Mol. Med. 50, 1–11 (2018).
Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).
Lewis, C. M. & Vassos, E. Polygenic risk scores: from research tools to clinical instruments. Genome Med. 12, 44 (2020).
Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018).
Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).
Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).
Coleman, J. R. I. et al. The genetics of the mood disorder spectrum: genome-wide association analyses of more than 185,000 cases and 439,000 controls. Biol. Psychiatry 88, 169–184 (2020).
Moon, S. et al. The Korea Biobank Array: design and identification of coding variants associated with blood biochemical traits. Sci. Rep. 9, 1382 (2019).
Bigdeli, T. B. et al. Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry. Mol. Psychiatry 25, 2455–2467 (2020).
Lam, M. et al. RICOPILI: Rapid Imputation for COnsortias PIpeLIne. Bioinformatics 36, 930–933 (2020).
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Loh, P.-R. et al. Reference-based phasing using the haplotype reference consortium panel. Nat. Genet. 48, 1443–1448 (2016).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).
Nievergelt, C. M. et al. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nat. Commun. 10, 4558 (2019).
Purves, K. L. et al. A major role for common genetic variation in anxiety disorders. Mol. Psychiatry 25, 3292–3303 (2020).
Yu, D. et al. Interrogating the genetic determinants of Tourette’s syndrome and other tic disorders through genome-wide association studies. Am. J. Psychiatry 176, 217–227 (2019).
Watson, H. J. et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat. Genet. 51, 1207–1214 (2019).
Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).
Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).
Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).
1000 Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Kamitaki, N. et al. Complement genes contribute sex-biased vulnerability in diverse disorders. Nature 582, 577–581 (2020).
Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).
Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).
Lee, S. H., Goddard, M. E., Wray, N. R. & Visscher, P. M. A better coefficient of determination for genetic profile analysis. Genet. Epidemiol. 36, 214–224 (2012).
Hemani, G., Tilling, K. & Davey Smith, G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 13, e1007081 (2017).
Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 7, e34408 (2018).
Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).
Hübel, C. et al. Genomics of body fat percentage may contribute to sex bias in anorexia nervosa. Am. J. Med. Genet. B Neuropsychiatr. Genet. 180, 428–438 (2019).
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.
Author information
Authors and Affiliations
Consortia
Contributions
Writing group: N.M., A.J.F., K.S.O’C., B.C., J.R.I.C., J.M.B., J.I.N., S. Cichon, H.J.E., E.A.S., A. McQuillin, A.D.F., R.A.O., O.A.A. PGC BD PI group: A.J.F., M.I., H.-H.W., D.C., R.A., I.A., M.A., L. Alfredsson, G. Babadjanova, L.B., B.T.B., F.B., S. Bengesser, W.H.B., D.H.R.B., M. Boehnke, A.D.B., G. Breen, V.J.C., S. Catts, A.C., N.C., U.D., D.D., T. Esko, B.E., P.F., M.F., J.M.F., M.G., E.S.G., F.S.G., M. J. Green, M.G.-S., J. Hauser, F.H., J. Hillert, K.S.H., D.M.H., C.M.H., K. Hveem, N.I., A.V.J., I.J., L.A.J., R.S.K., J.R.K., G.K., M. Landén, M. Leboyer, C.M.L., Q.S.L., J. Lissowska, C. Lochner, C. Loughland, N.G.M., C.A.M., F.M., S.L.M., A.M.M., F.J.M., I.M., P. Michie, L. Milani, P. B. Mitchell, G.M., O.M., P. B. Mortensen, B.M., B.M.-M., R.M.M., B.M.N., C.M.N., M.N., M.M.N., M.C.O’D., K.J.O., T.O., M.J.O., S.A.P., C. Pantelis, C. Pato, M.T.P., G.P.P., R.H.P., D.P., J.A.R.-Q., A.R., E.Z.R., M. Ribasés, M. Rietschel, S.R., G.A.R., T.S., U.S., M.S., P.R.S., T.G.S., L.J.S., R.J.S., A.S., C.S.W., J.W.S., H.S., K.S., E. Stordal, F. Streit, P.F.S., G.T., A.E.V., E.V., J.B.V., I.D.W., T.W.W., T.W., N.R.W., J.-A.Z., J.M.B., J.I.N., S. Cichon, H.J.E., E.A.S., A. McQuillin, A.D.F., R.A.O., O.A.A. Bioinformatics: N.M., A.J.F., J.R.I.C., S. Børte, M.J. Gandal, M. Kim, B.M.S., L.G.S., B.S.W., H.-H.W., N.A.-R., S.E.B., B.M.B., V.E.-P., S.H., P.A.H., Y.K., M. Koromina, M. Kubo, M. Leber, P.H.L., C. Liao, L.M.O.L., T.R., P.R., P.D.S., M.S.A., C. Terao, T.E.T., S.X., H.Y., P.P.Z., S. Bengesser, G. Breen, P.F., E.S.G., Q.S.L., G.A.R., H.S., T.W., E.A.S. Clinical: O.K.D., M.I., L. Abramova, K.A., E.A., N.A.-R., A. Anjorin, A. Antoniou, J.H.B., N.B., M. Bauer, A.B., C.B.P., E.B., M.P.B., R.B., M. Brum, N.B.-K., M. Budde, W.B., M. Cairns, M. Casas, P.C., A.C.-B., D.C., P.M.C., N.D., A.D., T. Elvsåshagen, L. Forty, L. Frisén, K.G., J. Garnham, M.G.P., I.R.G., K.G.-S., J. Grove, J.G.-P., K. Ha, M. Haraldsson, M. Hautzinger, U.H., D.H., J. L. Kalman, J. L. Kennedy, S.K.-S., M. Kogevinas, T.M.K., R.K., S.A.K., J. Lawrence, H.-J.L., C. Lewis, S.L., M. Lundberg, D.J.M., W.M., D.M., L. Martinsson, M.M., P. McGuffin, H.M., V.M., C.O’D., L.O., S.P., A. Perry, A. Pfennig, E.P., J.B.P., D.Q., M.H.R., J.R.D., E.J.R., J.P.R., F.R., J.R., E.C.S., F. Senner, E. Sigurdsson, L.S., C.S., O.B.S., D. J. Smith, J.L.S., A.T.S., J.S.S., B.Ś., P.T., M.P.V., H.V., A.H.Y., L.Z., HUNT All-In Psychiatry, R.A., I.A., M.A., G. Babadjanova, L.B., B.T.B., F.B., S. Bengesser, D.H.R.B., A.D.B., A.C., N.C., U.D., D.D., B.E., P.F., M.F., M.G., E.S.G., F.S.G., M. J. Green, M.G.-S., J. Hauser, K.S.H., N.I., I.J., L.A.J., R.S.K., G.K., M. Landén, C.M.L., J. Lissowska, N.G.M., C.A.M., F.M., S.L.M., A.M.M., I.M., P. B. Mitchell, G.M., O.M., P. B. Mortensen, M.C.O’D., K.J.O., M.J.O., C. Pato, M.T.P., R.H.P., J.A.R.-Q., A.R., E.Z.R., M. Rietschel, T.S., T.G.S., A.S., C.S.W., J.W.S., E. Stordal, F. Streit, A.E.V., E.V., J.B.V., I.D.W., T.W.W., T.W., J.I.N., A. McQuillin, A.D.F. Genomic assays/data generation: A.J.F., M.I., E.A., M.A.E., D.A., M.B.-H., E.C.B., C.B.P., J.B.-G., M. Cairns, T.-K.C., C.C., J.C., F.S.D., F.D., S.D., A.F., J.F., N.B.F., J. Gelernter, M.G.P., P.H., S.J., Y.K., H.R.K., M. Kubo, S.E.L., C. Liao, E.M., N.W.M., J.D.M., G.W.M., J.L.M., D.W.M., T.W.M., N.O’B., M. Rivera, C.S.-M., S. Sharp, C.S.H., C. Terao, C. Toma, E.-E.T., S.H.W., HUNT All-In Psychiatry, G. Breen, A.C., T. Esko, J.M.F., E.S.G., D.M.H., N.I., F.J.M., L. Milani, R.M.M., M.M.N., M. Ribasés, G.A.R., T.S., G.T., S. Cichon. Obtained funding for BD samples: M.I., M. Cairns, I.N.F., L. Frisén, S.J., Y.K., J.A.K., M. Kubo, C. Lavebratt, S.L., D.M., P. McGuffin, G.W.M., J.B.P., M.H.R., J.R.D., D. J. Stein, J.S.S., C. Terao, A.H.Y., P.P.Z., M.A., L. Alfredsson, L.B., B.T.B., F.B., W.H.B., M. Boehnke, A.D.B., G. Breen, A.C., N.C., B.E., M.F., J.M.F., E.S.G., M. J. Green, M.G.-S., K.S.H., K. Hveem, N.I., I.J., L.A.J., M. Landén, M. Leboyer, N.G.M., F.J.M., P. B. Mitchell, O.M., P. B. Mortensen, B.M.N., M.N., M.M.N., M.C.O’D., T.O., M.J.O., C. Pato, M.T.P., G.P.P., M. Rietschel, G.A.R., T.S., M.S., P.R.S., T.G.S., C.S.W., J.W.S., G.T., J.B.V., T.W.W., T.W., J.M.B., J.I.N., H.J.E., R.A.O., O.A.A. Statistical analysis: N.M., K.S.O’C., B.C., J.R.I.C., Z.Q., T.D.A., T.B.B., S. Børte, J.B., A.W.C., O.K.D., M. J. Gandal, S.P.H., N.K., M. Kim, K.K., G.P., B.M.S., L.G.S., S. Steinberg, V.T., B.S.W., H.-H.W., V.A., S.A., S.E.B., B.M.B., A.M.D., A.L.D., V.E.-P., T.M.F., O.F., S.D.G., T.A.G., J. Grove, P.A.H., L.H., J.S.J., Y.K., M. Kubo, C. Lavebratt, M. Leber, P.H.L., S.H.M., A. Maihofer, M.M., S.A.M., S.E.M., L.M.O.L., A.F.P., T.R., P.R., D.M.R., O.B.S., C. Terao, T.E.T., J.T.R.W., W.X., J.M.K.Y., H.Y., P.P.Z., H.Z., A.D.B., G. Breen, E.S.G., F.S.G., Q.S.L., B.M.-M., C.M.N., D.P., S.R., H.S., P.F.S., T.W., N.R.W., J.M.B., E.A.S. K.S.O’C., B.C., J.R.I.C. and Z.Q. contributed equally to this work and should be regarded as joint second authors.
Corresponding authors
Ethics declarations
Competing interests
T.E.T., S. Steinberg, H.S. and K.S. are employed by deCODE Genetics/Amgen. Multiple additional authors work for pharmaceutical or biotechnology companies in a manner directly analogous to academic coauthors and collaborators. A.H.Y. has given paid lectures and served on advisory boards relating to drugs used in affective and related disorders for several companies (AstraZeneca, Eli Lilly, Lundbeck, Sunovion, Servier, Livanova, Janssen, Allergan, Bionomics and Sumitomo Dainippon Pharma), was Lead Investigator for Embolden Study (AstraZeneca), BCI Neuroplasticity study and Aripiprazole Mania Study, and is an investigator for Janssen, Lundbeck, Livanova and Compass. J.I.N. is an investigator for Janssen. P.F.S. reports the following potentially competing financial interests: Lundbeck (advisory committee), Pfizer (Scientific Advisory Board member) and Roche (grant recipient, speaker reimbursement). G. Breen reports consultancy and speaker fees from Eli Lilly and Illumina and grant funding from Eli Lilly. M. Landén has received speaker fees from Lundbeck. O.A.A. has received speaker fees from Lundbeck and Sunovion, and is a consultant to HealthLytix. J.A.R.-Q. was on the speakers bureau and/or acted as consultant for Eli Lilly, Janssen-Cilag, Novartis, Shire, Lundbeck, Almirall, Braingaze, Sincrolab and Rubió in the last 5 years. He also received travel awards (air tickets and hotel) for taking part in psychiatric meetings from Janssen-Cilag, Rubió, Shire and Eli Lilly. The Department of Psychiatry chaired by him received unrestricted educational and research support from the following companies in the last 5 years: Eli Lilly, Lundbeck, Janssen-Cilag, Actelion, Shire, Ferrer, Oryzon, Roche, Psious and Rubió. E.V. has received grants and served as a consultant, advisor or CME speaker for the following entities: AB-Biotics, Abbott, Allergan, Angelini, AstraZeneca, Bristol Myers Squibb, Dainippon Sumitomo Pharma, Farmindustria, Ferrer, Forest Research Institute, Gedeon Richter, GlaxoSmithKline, Janssen, Lundbeck, Otsuka, Pfizer, Roche, SAGE, Sanofi-Aventis, Servier, Shire, Sunovion, Takeda, the Brain and Behaviour Foundation, the Catalan Government (AGAUR and PERIS), the Spanish Ministry of Science, Innovation, and Universities (AES and CIBERSAM), the Seventh European Framework Programme and Horizon 2020 and the Stanley Medical Research Institute. T. Elvsåshagen has received speaker fees from Lundbeck. S.K.-S. received author’s and consultant honoraria from Medice Arzneimittel Pütter GmbH and Shire/Takeda. A.S. is or has been a consultant/speaker for: Abbott, Abbvie, Angelini, AstraZeneca, Clinical Data, Boheringer, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, Innovapharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi, Servier. J.R.D. has served as an unpaid consultant to Myriad – Neuroscience (formerly Assurex Health) in 2017 and 2019 and owns stock in CVS Health. H.R.K. serves as an advisory board member for Dicerna Pharmaceuticals, and is a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was sponsored in the past 3 years by AbbVie, Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Ethypharm, Indivior, Lilly, Lundbeck, Otsuka and Pfizer. H.R.K. is named as an inventor on PCT patent application no. 15/878,640 entitled: Genotype-guided dosing of opioid agonists, filed January 24, 2018. B.M.N. is a member of the scientific advisory board at Deep Genomics and consultant for Camp4 Therapeutics, Takeda Pharmaceutical and Biogen. All other authors declare no financial interests or potential conflicts of interest.
Additional information
Peer review information Nature Genetics thanks Na Cai, Qiang Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary Information
Supplementary Figs. 1–9 and Note
Supplementary Tables
Supplementary Tables 1–23
Supplementary Data 1
Regional association plots
Supplementary Data 2
Forest plots
Rights and permissions
About this article
Cite this article
Mullins, N., Forstner, A.J., O’Connell, K.S. et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat Genet 53, 817–829 (2021). https://doi.org/10.1038/s41588-021-00857-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41588-021-00857-4
This article is cited by
-
Associations between breakfast skipping and outcomes in neuropsychiatric disorders, cognitive performance, and frailty: a Mendelian randomization study
BMC Psychiatry (2024)
-
Causal links between sedentary behavior, physical activity, and psychiatric disorders: a Mendelian randomization study
Annals of General Psychiatry (2024)
-
Physical activity and lung function association in a healthy community-dwelling European population
BMC Pulmonary Medicine (2024)
-
The Mood and Resilience in Offspring (MARIO) project: a longitudinal cohort study among offspring of parents with and without a mood disorder
BMC Psychiatry (2024)
-
Hemorrhoidal disease and its genetic association with depression, bipolar disorder, anxiety disorders, and schizophrenia: a bidirectional mendelian randomization study
Human Genomics (2024)
