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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.
Coleman J.R.I., Gaspar H.A., Bryois J., Byrne E.M., Forstner A.J., Holmans P.A., et al. (2020). The Genetics of the Mood Disorder Spectrum: Genome-wide Association Analyses of More Than 185,000 Cases and 439,000 Controls. BIOLOGICAL PSYCHIATRY, 88(2), 169-184 [10.1016/j.biopsych.2019.10.015].
The Genetics of the Mood Disorder Spectrum: Genome-wide Association Analyses of More Than 185,000 Cases and 439,000 Controls
Coleman J. R. I.;Gaspar H. A.;Bryois J.;Byrne E. M.;Forstner A. J.;Holmans P. A.;de Leeuw C. A.;Mattheisen M.;McQuillin A.;Whitehead Pavlides J. M.;Pers T. H.;Ripke S.;Stahl E. A.;Steinberg S.;Trubetskoy V.;Trzaskowski M.;Wang Y.;Abbott L.;Abdellaoui A.;Adams M. J.;Adolfsson A. N.;Agerbo E.;Akil H.;Albani D.;Alliey-Rodriguez N.;Als T. D.;Andlauer T. F. M.;Anjorin A.;Antilla V.;Van der Auwera S.;Awasthi S.;Bacanu S. -A.;Badner J. A.;Baekvad-Hansen M.;Barchas J. D.;Bass N.;Bauer M.;Beekman A. T. F.;Belliveau R.;Bergen S. E.;Bigdeli T. B.;Binder E. B.;Boen E.;Boks M.;Boocock J.;Budde M.;Bunney W.;Burmeister M.;Buttenschon H. N.;Bybjerg-Grauholm J.;Byerley W.;Cai N.;Casas M.;Castelao E.;Cerrato F.;Cervantes P.;Chambert K.;Charney A. W.;Chen D.;Christensen J. H.;Churchhouse C.;St Clair D.;Clarke T. -K.;Colodro-Conde L.;Coryell W.;Couvy-Duchesne B.;Craig D. W.;Crawford G. E.;Cruceanu C.;Czerski P. M.;Dale A. M.;Davies G.;Deary I. J.;Degenhardt F.;Del-Favero J.;DePaulo J. R.;Derks E. M.;Direk N.;Djurovic S.;Dobbyn A. L.;Dolan C. V.;Dumont A.;Dunn E. C.;Eley T. C.;Elvsashagen T.;Escott-Price V.;Fan C. C.;Finucane H. K.;Fischer S. B.;Flickinger M.;Foo J. C.;Foroud T. M.;Forty L.;Frank J.;Fraser C.;Freimer N. B.;Frisen L.;Gade K.;Gage D.;Garnham J.;Giambartolomei C.;Goes F. S.;Goldstein J.;Gordon S. D.;Gordon-Smith K.;Green E. K.;Green M. J.;Greenwood T. A.;Grove J.;Guan W.;Hall L. S.;Hamshere M. L.;Hansen C. S.;Hansen T. F.;Hautzinger M.;Heilbronner U.;van Hemert A. M.;Herms S.;Hickie I. B.;Hipolito M.;Hoffmann P.;Holland D.;Homuth G.;Horn C.;Hottenga J. -J.;Huckins L.;Ising M.;Jamain S.;Jansen R.;Johnson J. S.;de Jong S.;Jorgenson E.;Jureus A.;Kandaswamy R.;Karlsson R.;Kennedy J. L.;Hassan Kiadeh F. F.;Kittel-Schneider S.;Knowles J. A.;Kogevinas M.;Kohane I. S.;Koller A. C.;Kraft J.;Kretzschmar W. W.;Krogh J.;Kupka R.;Kutalik Z.;Lavebratt C.;Lawrence J.;Lawson W. B.;Leber M.;Lee P. H.;Levy S. E.;Li J. Z.;Li Y.;Lind P. A.;Liu C.;Olde Loohuis L. M.;Maaser A.;MacIntyre D. J.;MacKinnon D. F.;Mahon P. B.;Maier W.;Maier R. M.;Marchini J.;Martinsson L.;Mbarek H.;McCarroll S.;McGrath P.;McGuffin P.;McInnis M. G.;McKay J. D.;Medeiros H.;Medland S. E.;Mehta D.;Meng F.;Middeldorp C. M.;Mihailov E.;Milaneschi Y.;Milani L.;Mirza S. S.;Mondimore F. M.;Montgomery G. W.;Morris D. W.;Mostafavi S.;Muhleisen T. W.;Mullins N.;Nauck M.;Ng B.;Nguyen H.;Nievergelt C. M.;Nivard M. G.;Nwulia E. A.;Nyholt D. R.;O'Donovan C.;O'Reilly P. F.;Ori A. P. S.;Oruc L.;Osby U.;Oskarsson H.;Painter J. N.;Parra J. G.;Pedersen C. B.;Pedersen M. G.;Perry A.;Peterson R. E.;Pettersson E.;Peyrot W. J.;Pfennig A.;Pistis G.;Purcell S. M.;Quiroz J. A.;Qvist P.;Regeer E. J.;Reif A.;Reinbold C. S.;Rice J. P.;Riley B. P.;Rivas F.;Rivera M.;Roussos P.;Ruderfer D. M.;Ryu E.;Sanchez-Mora C.;Schatzberg A. F.;Scheftner W. A.;Schoevers R.;Schork N. J.;Schulte E. C.;Shehktman T.;Shen L.;Shi J.;Shilling P. D.;Shyn S. I.;Sigurdsson E.;Slaney C.;Smeland O. B.;Smit J. H.;Smith D. J.;Sobell J. L.;Spijker A. T.;Steffens M.;Strauss J. S.;Streit F.;Strohmaier J.;Szelinger S.;Tansey K. E.;Teismann H.;Teumer A.;Thompson R. C.;Thompson W.;Thomson P. A.;Thorgeirsson T. E.;Traylor M.;Treutlein J.;Uitterlinden A. G.;Umbricht D.;Vedder H.;Viktorin A.;Visscher P. M.;Wang W.;Watson S. J.;Webb B. T.;Weickert C. S.;Weickert T. W.;Weinsheimer S. M.;Wellmann J.;Willemsen G.;Witt S. H.;Wu Y.;Xi H. S.;Xu W.;Yang J.;Young A. H.;Zandi P.;Zhang P.;Zhang F.;Zollner S.;Adolfsson R.;Agartz I.;Alda M.;Arolt V.;Backlund L.;Baune B. T.;Bellivier F.;Berger K.;Berrettini W. H.;Biernacka J. M.;Blackwood D. H. R.;Boehnke M.;Boomsma D. I.;Corvin A.;Craddock N.;Daly M. J.;Dannlowski U.;Domenici E.;Domschke K.;Esko T.;Etain B.;Frye M.;Fullerton J. M.;Gershon E. S.;de Geus E. J. C.;Gill M.;Goes F.;Grabe H. J.;Grigoroiu-Serbanescu M.;Hamilton S. P.;Hauser J.;Hayward C.;Heath A. C.;Hougaard D. M.;Hultman C. M.;Jones I.;Jones L. A.;Kahn R. S.;Kendler K. S.;Kirov G.;Kloiber S.;Landen M.;Leboyer M.;Lewis G.;Li Q. S.;Lissowska J.;Lucae S.;Madden P. A. F.;Magnusson P. K.;Martin N. G.;Mayoral F.;McElroy S. L.;McIntosh A. M.;McMahon F. J.;Melle I.;Metspalu A.;Mitchell P. B.;Morken G.;Mors O.;Mortensen P. B.;Muller-Myhsok B.;Myers R. M.;Neale B. M.;Nimgaonkar V.;Nordentoft M.;Nothen M. M.;O'Donovan M. C.;Oedegaard K. J.;Owen M. J.;Paciga S. A.;Pato C.;Pato M. T.;Pedersen N. L.;Penninx B. W. J. H.;Perlis R. H.;Porteous D. J.;Posthuma D.;Potash J. B.;Preisig M.;Ramos-Quiroga J. A.;Ribases M.;Rietschel M.;Rouleau G. A.;Schaefer C.;Schalling M.;Schofield P. R.;Schulze T. G.;Serretti A.;Smoller J. W.;Stefansson H.;Stefansson K.;Stordal E.;Tiemeier H.;Turecki G.;Uher R.;Vaaler A. E.;Vieta E.;Vincent J. B.;Volzke H.;Weissman M. M.;Werge T.;Andreassen O. A.;Borglum A. D.;Cichon S.;Edenberg H. J.;Di Florio A.;Kelsoe J.;Levinson D. F.;Lewis C. M.;Nurnberger J. I.;Ophoff R. A.;Scott L. J.;Sklar P.;Sullivan P. F.;Wray N. R.;Breen G.
2020
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.
Coleman J.R.I., Gaspar H.A., Bryois J., Byrne E.M., Forstner A.J., Holmans P.A., et al. (2020). The Genetics of the Mood Disorder Spectrum: Genome-wide Association Analyses of More Than 185,000 Cases and 439,000 Controls. BIOLOGICAL PSYCHIATRY, 88(2), 169-184 [10.1016/j.biopsych.2019.10.015].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/793189
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