Advertisement

The Genetics of the Mood Disorder Spectrum: Genome-wide Association Analyses of More Than 185,000 Cases and 439,000 Controls

      Abstract

      Background

      Mood disorders (including major depressive disorder and bipolar disorder) affect 10% to 20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Multiple approaches have shown considerable sharing of risk factors across mood disorders despite their diagnostic distinction.

      Methods

      To clarify the shared molecular genetic basis of major depressive disorder and bipolar disorder and to highlight disorder-specific associations, we meta-analyzed data from the latest Psychiatric Genomics Consortium genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; nonoverlapping N = 609,424).

      Results

      Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More loci from the Psychiatric Genomics Consortium analysis of major depression than from that for bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single-episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment—the relationship is positive in bipolar disorder but negative in major depressive disorder.

      Conclusions

      The mood disorders share several genetic associations, and genetic studies of major depressive disorder and bipolar disorder can be combined effectively to enable the discovery of variants not identified by studying either disorder alone. However, we demonstrate several differences between these disorders. Analyzing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Biological Psychiatry
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Craddock N.
        • Owen M.J.
        The Kraepelinian dichotomy—going, going. . . but still not gone.
        Br J Psychiatry. 2010; 196: 92-95
        • Kessler R.C.
        • Berglund P.
        • Demler O.
        • Jin R.
        • Merikangas K.R.
        • Walters E.E.
        Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication.
        Arch Gen Psychiatry. 2005; 62: 593-602
        • Weissman M.M.
        • Bland R.C.
        • Canino G.J.
        • Faravelli C.
        • Greenwald S.
        • Hwu H.G.
        • et al.
        Cross-national epidemiology of major depression and bipolar disorder.
        JAMA. 1996; 276: 293-299
        • Steel Z.
        • Marnane C.
        • Iranpour C.
        • Chey T.
        • Jackson J.W.
        • Patel V.
        • Silove D.
        The global prevalence of common mental disorders: A systematic review and meta-analysis 1980–2013.
        Int J Epidemiol. 2014; 43: 476-493
        • American Psychiatric Association
        Diagnostic and Statistical Manual of Mental Disorders.
        3rd ed. American Psychiatric Press, Washington, DC1980
        • Reed G.M.
        • First M.B.
        • Kogan C.S.
        • Hyman S.E.
        • Gureje O.
        • Gaebel W.
        • et al.
        Innovations and changes in the ICD-11 classification of mental, behavioural and neurodevelopmental disorders.
        World Psychiatry. 2019; 18: 3-19
        • American Psychiatric Association
        Diagnostic and Statistical Manual of Mental Disorders.
        5th ed. American Psychiatric Association Publishing, Arlington, VA2013
        • Cassano G.B.
        • Rucci P.
        • Frank E.
        • Fagiolini A.
        • Dell’Osso L.
        • Shear M.K.
        • Kupfer D.J.
        The mood spectrum in unipolar and bipolar disorder: Arguments for a unitary approach.
        Am J Psychiatry. 2004; 161: 1264-1269
        • Fiedorowicz J.G.
        • Endicott J.
        • Leon A.C.
        • Solomon D.A.
        • Keller M.B.
        • Coryell W.H.
        Subthreshold hypomanic symptoms in progression from unipolar major depression to bipolar disorder.
        Am J Psychiatry. 2011; 168: 40-48
        • Ratheesh A.
        • Davey C.
        • Hetrick S.
        • Alvarez-Jimenez M.
        • Voutier C.
        • Bechdolf A.
        • et al.
        A systematic review and meta-analysis of prospective transition from major depression to bipolar disorder.
        Acta Psychiatr Scand. 2017; 135: 273-284
        • National Collaborating Centre for Mental Health (United Kingdom)
        Common Mental Health Disorders: Identification and Pathways to Care.
        British Psychological Society, Leicester, United Kingdom2012
        • National Collaborating Centre for Mental Health (United Kingdom)
        Bipolar Disorder: The NICE Guideline on the Assessment and Management of Bipolar Disorder in Adults, Children and Young People in Primary and Secondary Care.
        British Psychological Society, Leicester, United Kingdom2018
        • Polderman T.J.C.
        • Benyamin B.
        • de Leeuw C.A.
        • Sullivan P.F.
        • van Bochoven A.
        • Visscher P.M.
        • Posthuma D.
        Meta-analysis of the heritability of human traits based on fifty years of twin studies.
        Nat Genet. 2015; 47: 702-709
        • McGuffin P.
        • Rijsdijk F.
        • Andrew M.
        • Sham P.
        • Katz R.
        • Cardno A.
        The heritability of bipolar affective disorder and the genetic relationship to unipolar depression.
        Arch Gen Psychiatry. 2003; 60: 497-502
        • Wray N.R.
        • Ripke S.
        • Mattheisen M.
        • Trzaskowski M.
        • Byrne E.M.
        • Abdellaoui A.
        • et al.
        Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
        Nat Genet. 2018; 50: 668-681
        • Stahl E.A.
        • Breen G.
        • Forstner A.J.
        • McQuillin A.
        • Ripke S.
        • Trubetskoy V.
        • et al.
        Genome-wide association study identifies 30 loci associated with bipolar disorder.
        Nat Genet. 2019; 55: 793-803
        • Anttila V.
        • Bulik-Sullivan B.
        • Finucane H.K.
        • Walters R.K.
        • Bras J.
        • et al.
        • The Brainstorm Consortium
        Analysis of shared heritability in common disorders of the brain.
        Science. 2018; 360eaap8757
        • Davis K.A.S.
        • Coleman J.R.I.
        • Adams M.
        • Allen N.
        • Breen G.
        • Cullen B.
        • et al.
        Mental health in UK Biobank. Revised.
        medrxiv. 2019; (19001214; doi: https://doi.org/10.1101/19001214.)
        • Coleman J.R.I.
        • Peyrot W.J.
        • Purves K.L.
        • Davis K.A.S.
        • Rayner C.
        • Choi S.W.
        • et al.
        Genome-wide gene-environment analyses of depression and reported lifetime traumatic experiences in UK Biobank.
        bioRxiv. 2019; (247353; doi: https://doi.org/10.1101/247353.)
        • Hyde C.L.
        • Nagle M.W.
        • Tian C.
        • Chen X.
        • Paciga S.A.
        • Wendland J.R.
        • et al.
        Identification of 15 genetic loci associated with risk of major depression in individuals of European descent.
        Nat Genet. 2016; 48: 1031-1036
        • Gilly A.
        • Tachmazidou I.
        • Zeggini E.
        Meta-analysis of summary statistics from quantitative trait association studies with unknown sample overlap.
        Genet Epidemiol. 2015; 39: 552-553
        • Southam L.
        • Gilly A.
        • Süveges D.
        • Farmaki A.-E.
        • Schwartzentruber J.
        • Tachmazidou I.
        • et al.
        Whole genome sequencing and imputation in isolated populations identify genetic associations with medically-relevant complex traits.
        Nat Commun. 2017; 8: 15606
        • Turley P.
        • Walters R.K.
        • Maghzian O.
        • Okbay A.
        • Lee J.J.
        • Fontana M.A.
        • et al.
        Multi-trait analysis of genome-wide association summary statistics using MTAG.
        Nat Genet. 2018; 50: 229-237
        • Hill W.D.
        Comment on “Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets” by Lam et al.
        Twin Res Hum Genet. 2018; 21: 84-88
        • Bulik-Sullivan B.K.
        • Loh P.-R.
        • Finucane H.K.
        • Ripke S.
        • Yang J.
        • Schizophrenia Working Group of the Psychiatric Genomics Consortium
        • et al.
        LD score regression distinguishes confounding from polygenicity in genome-wide association studies.
        Nat Genet. 2015; 47: 291-295
        • Nievergelt C.M.
        • Maihofer A.X.
        • Klengel T.
        • Atkinson E.G.
        • Chen C.-Y.
        • Choi K.W.
        • et al.
        International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci.
        Nat Commun. 2019; 10: 4558
        • Shi H.
        • Kichaev G.
        • Pasaniuc B.
        Contrasting the genetic architecture of 30 complex traits from summary association data.
        Am J Hum Genet. 2016; 99: 139-153
        • Shi H.
        • Mancuso N.
        • Spendlove S.
        • Pasaniuc B.
        Local genetic correlation gives insights into the shared genetic architecture of complex traits.
        Am J Hum Genet. 2017; 101: 737-751
        • R Core Team
        R: A language and environment for statistical computing.
        R Foundation for Statistical Computing, Vienna, Austria2015 (Available at:) (Accessed September 6, 2019)
        • Warnes G.R.
        • Bolker B.
        • Bonebakker L.
        • Gentleman R.
        • Liaw W.H.
        • Lumley T.
        • et al.
        Gplots: Various R programming tools for plotting data, version 3.0.1.
        (Available at:) (Accessed September 6, 2019)
        • Purves K.L.
        • Coleman J.R.I.
        • Meier S.M.
        • Rayner C.
        • Davis K.A.S.
        • Cheesman R.
        • et al.
        A major role for common genetic variation in anxiety disorders [published online ahead of print Nov 20]..
        Mol Psychiatry. 2019;
        • Schizophrenia Working Group of the Psychiatric Genomics Consortium
        Biological insights from 108 schizophrenia-associated genetic loci.
        Nature. 2014; 511: 421-427
        • Demontis D.
        • Walters R.K.
        • Martin J.
        • Mattheisen M.
        • Als T.D.
        • Agerbo E.
        • et al.
        Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder.
        Nat Genet. 2019; 51: 63-75
        • Baselmans B.M.L.
        • Bartels M.
        A genetic perspective on the relationship between eudaimonic and hedonic well-being.
        Sci Rep. 2018; 8: 14610
        • Baselmans B.M.L.
        • Jansen R.
        • Ip H.F.
        • van Dongen J.
        • Abdellaoui A.
        • van de Weijer M.P.
        • et al.
        Multivariate genome-wide analyses of the well-being spectrum.
        Nat Genet. 2019; 51: 445-451
        • Choi S.W.
        • O’Reilly P.F.
        PRSice-2: Polygenic risk score software for biobank-scale data.
        Gigascience. 2019; 8 (giz082)
        • de Leeuw C.A.
        • Mooij J.M.
        • Heskes T.
        • Posthuma D.
        MAGMA: Generalized gene-set analysis of GWAS data.
        PLoS Comput Biol. 2015; 11e1004219
        • Skene N.G.
        • Bryois J.
        • Bakken T.E.
        • Breen G.
        • Crowley J.J.
        • Gaspar H.A.
        • et al.
        Genetic identification of brain cell types underlying schizophrenia.
        Nat Genet. 2018; 50: 825-833
        • GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI
        • et al.
        Genetic effects on gene expression across human tissues.
        Nature. 2017; 550: 204-213
        • Finucane H.K.
        • Reshef Y.A.
        • Anttila V.
        • Slowikowski K.
        • Gusev A.
        • Byrnes A.
        • et al.
        Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.
        Nat Genet. 2018; 50: 621-629
        • Zhu Z.
        • Zheng Z.
        • Zhang F.
        • Wu Y.
        • Trzaskowski M.
        • Maier R.
        • et al.
        Causal associations between risk factors and common diseases inferred from GWAS summary data.
        Nat Commun. 2018; 9: 224
        • Yang J.
        • Lee S.H.
        • Goddard M.E.
        • Visscher P.M.
        GCTA: A tool for genome-wide complex trait analysis.
        Am J Hum Genet. 2011; 88: 76-82
        • Savage J.E.
        • Jansen P.R.
        • Stringer S.
        • Watanabe K.
        • Bryois J.
        • de Leeuw C.A.
        • et al.
        Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence.
        Nat Genet. 2018; 50: 912-919
        • Lee J.J.
        • Wedow R.
        • Okbay A.
        • Kong E.
        • Maghzian O.
        • Zacher M.
        • et al.
        Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.
        Nat Genet. 2018; 50: 1112-1121
        • Locke A.E.
        • Kahali B.
        • Berndt S.I.
        • Justice A.E.
        • Pers T.H.
        • Day F.R.
        • et al.
        Genetic studies of body mass index yield new insights for obesity biology.
        Nature. 2015; 518: 197-206
        • Nelson C.P.
        • Goel A.
        • Butterworth A.S.
        • Kanoni S.
        • Webb T.R.
        • Marouli E.
        • et al.
        Association analyses based on false discovery rate implicate new loci for coronary artery disease.
        Nat Genet. 2017; 49: 1385-1391
        • Loh P.-R.
        • Kichaev G.
        • Gazal S.
        • Schoech A.P.
        • Price A.L.
        Mixed-model association for biobank-scale datasets.
        Nat Genet. 2018; 50: 906-908
        • Howard D.M.
        • Adams M.J.
        • Shirali M.
        • Clarke T.-K.
        • Marioni R.E.
        • Davies G.
        • et al.
        Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways.
        Nat Commun. 2018; 9: 1470
        • Li X.
        • Luo Z.
        • Gu C.
        • Hall L.S.
        • McIntosh A.M.
        • Zeng Y.
        • et al.
        Common variants on 6q16.2, 12q24.31 and 16p13.3 are associated with major depressive disorder.
        Neuropsychopharmacology. 2018; 43: 2146-2153
        • Okbay A.
        • Baselmans B.M.L.
        • De Neve J.-E.
        • Turley P.
        • Nivard M.G.
        • Fontana M.A.
        • et al.
        Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses.
        Nat Genet. 2016; 48: 624-633
        • Amare A.T.
        • Vaez A.
        • Hsu Y.H.
        • Direk N.
        • Kamali Z.
        • Howard D.M.
        • et al.
        Bivariate genome-wide association analyses of the broad depression phenotype combined with major depressive disorder, bipolar disorder or schizophrenia reveal eight novel genetic loci for depression [published online ahead of print Jan 9].
        Mol Psychiatry. 2019;
        • Howard D.M.
        • Adams M.J.
        • Clarke T.-K.
        • Hafferty J.D.
        • Gibson J.
        • Shirali M.
        • et al.
        Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions.
        Nat Neurosci. 2019; 22: 343-352
        • Jiang Y.
        • Zhang H.
        Propensity score-based nonparametric test revealing genetic variants underlying bipolar disorder.
        Genet Epidemiol. 2011; 35: 125-132
        • Ikeda M.
        • Takahashi A.
        • Kamatani Y.
        • Okahisa Y.
        • Kunugi H.
        • Mori N.
        • et al.
        A genome-wide association study identifies 2 novel susceptibility loci and trans population polygenicity associated with bipolar disorder.
        Mol Psychiatry. 2018; 23: 639-647
        • 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. 2011; 43: 977-983
        • Charney A.W.
        • Ruderfer D.M.
        • Stahl E.A.
        • Moran J.L.
        • Chambert K.
        • Belliveau R.A.
        • et al.
        Evidence for genetic heterogeneity between clinical subtypes of bipolar disorder.
        Transl Psychiatry. 2017; 7: e993
        • Nagel M.
        • Jansen P.R.
        • Stringer S.
        • Watanabe K.
        • de Leeuw C.A.
        • Bryois J.
        • et al.
        Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways.
        Nat Genet. 2018; 50: 920-927
        • Smith D.J.
        • Escott-Price V.
        • Davies G.
        • Bailey M.E.S.
        • Colodro-Conde L.
        • Ward J.
        • et al.
        Genome-wide analysis of over 106 000 individuals identifies 9 neuroticism-associated loci.
        Mol Psychiatry. 2016; 21: 749-757
        • Luciano M.
        • Hagenaars S.P.
        • Davies G.
        • Hill W.D.
        • Clarke T.-K.
        • Shirali M.
        • et al.
        Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism.
        Nat Genet. 2018; 50: 6-11
        • Li Z.
        • Chen J.
        • Yu H.
        • He L.
        • Xu Y.
        • Zhang D.
        • et al.
        Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia.
        Nat Genet. 2017; 49: 1576-1583
        • Cross-Disorder Group of the Psychiatric Genomics Consortium
        Identification of risk loci with shared effects on five major psychiatric disorders: A genome-wide analysis.
        Lancet. 2013; 381: 1371
        • National Institute for Healthcare and Excellence
        Depression in Adults: Recognition and Management: Clinical Guideline [CG90].
        (Available at) (Accessed September 6, 2019)
        • Coleman J.R.I.
        • Bryois J.
        • Gaspar H.A.
        • Jansen P.R.
        • Savage J.E.
        • Skene N.
        • et al.
        Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals.
        Mol Psychiatry. 2019; 24: 182-197
        • 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. 2018; 50: 693-698
        • Hemani G.
        • Bowden J.
        • Davey Smith G.
        Evaluating the potential role of pleiotropy in Mendelian randomization studies.
        Hum Mol Genet. 2018; 27: R195-R208
        • Carney R.M.
        • Freedland K.E.
        Depression and coronary heart disease.
        Nat Rev Cardiol. 2017; 14: 145-155
        • Weissbrod O.
        • Flint J.
        • Rosset S.
        Estimating SNP-based heritability and genetic correlation in case-control studies directly and with summary statistics.
        Am J Hum Genet. 2018; 103: 89-99
        • Sullivan P.F.
        • Agrawal A.
        • Bulik C.M.
        • Andreassen O.A.
        • Børglum A.D.
        • Breen G.
        • et al.
        Psychiatric genomics: An update and an agenda.
        Am J Psychiatry. 2018; 175: 15-27

      Linked Article

      • Delineating the Shared Genetics Across the Mood Disorders Spectrum
        Biological PsychiatryVol. 88Issue 2
        • Preview
          An important question in psychiatry is the extent of overlap between various psychiatric conditions. It has been more than a century since Emil Kraepelin first suggested a broad division between schizophrenia and bipolar disorder (BD) [see (1)], an idea that has largely endured until today. However, the distinction between major depressive disorder (MDD) and BD and their subtypes (together, known as mood disorders) is less clear, with differences between the ICD-11 and the DSM-5 in how these conditions are grouped.
        • Full-Text
        • PDF