{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T01:25:20Z","timestamp":1793841920251,"version":"4.6.1"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mol Psychiatry"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Antidepressants are an effective treatment for major depressive disorder (MDD), although individual response is unpredictable and highly variable. Whilst the mode of action of antidepressants is incompletely understood, many medications are associated with changes in DNA methylation in genes that are plausibly linked to their mechanisms. Studies of DNA methylation may therefore reveal the biological processes underpinning the efficacy and side effects of antidepressants. We performed a methylome-wide association study (MWAS) of self-reported antidepressant use accounting for lifestyle factors and MDD in Generation Scotland (GS:SFHS,\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u20096428, EPIC array) and the Netherlands Twin Register (NTR,\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u20092449, 450\u2009K array) and ran a meta-analysis of antidepressant use across these two cohorts. We found ten CpG sites significantly associated with self-reported antidepressant use in GS:SFHS, with the top CpG located within a gene previously associated with mental health disorders,\n                    <jats:italic>ATP6V1B2<\/jats:italic>\n                    (\n                    <jats:italic>\u03b2<\/jats:italic>\n                    \u2009=\u2009\u22120.055,\n                    <jats:italic>p<\/jats:italic>\n                    <jats:sub>corrected<\/jats:sub>\n                    \u2009=\u20090.005). Other top loci were annotated to genes including\n                    <jats:italic>CASP10, TMBIM1, MAPKAPK3<\/jats:italic>\n                    , and\n                    <jats:italic>HEBP2<\/jats:italic>\n                    , which have previously been implicated in the innate immune response. Next, using penalised regression, we trained a methylation-based score of self-reported antidepressant use in a subset of 3799 GS:SFHS individuals that predicted antidepressant use in a second subset of GS:SFHS (\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u20093360,\n                    <jats:italic>\u03b2<\/jats:italic>\n                    \u2009=\u20090.377,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20093.12 \u00d7 10\n                    <jats:sup>\u221211<\/jats:sup>\n                    ,\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20092.12%). In an MWAS analysis of prescribed selective serotonin reuptake inhibitors, we showed convergent findings with those based on self-report. In NTR, we did not find any CpGs significantly associated with antidepressant use. The meta-analysis identified the two CpGs of the ten above that were common to the two arrays used as being significantly associated with antidepressant use, although the effect was in the opposite direction for one of them. Antidepressants were associated with epigenetic alterations in loci previously associated with mental health disorders and the innate immune system. These changes predicted self-reported antidepressant use in a subset of GS:SFHS and identified processes that may be relevant to our mechanistic understanding of clinically relevant antidepressant drug actions and side effects.\n                  <\/jats:p>","DOI":"10.1038\/s41380-021-01412-7","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T08:04:58Z","timestamp":1638950698000},"page":"1647-1657","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Methylome-wide association study of antidepressant use in Generation Scotland and the Netherlands Twin Register implicates the innate immune system"],"prefix":"10.1038","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8967-683X","authenticated-orcid":false,"given":"Miruna C.","family":"Barbu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Floris","family":"Huider","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0198-5078","authenticated-orcid":false,"given":"Archie","family":"Campbell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmen","family":"Amador","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3599-6018","authenticated-orcid":false,"given":"Mark J.","family":"Adams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1939-7525","authenticated-orcid":false,"given":"Mary-Ellen","family":"Lynall","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6005-1972","authenticated-orcid":false,"given":"David M.","family":"Howard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1060-4479","authenticated-orcid":false,"given":"Rosie M.","family":"Walker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stewart W.","family":"Morris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jenny","family":"Van Dongen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1249-6106","authenticated-orcid":false,"given":"David J.","family":"Porteous","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn L.","family":"Evans","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward","family":"Bullmore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gonneke","family":"Willemsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7099-7972","authenticated-orcid":false,"given":"Dorret I.","family":"Boomsma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4505-8869","authenticated-orcid":false,"given":"Heather C.","family":"Whalley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0198-4588","authenticated-orcid":false,"given":"Andrew M.","family":"McIntosh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"key":"1412_CR1","doi-asserted-by":"crossref","unstructured":"World Health Organization. Depression and other common mental disorders. Geneva: World Health Organization; 2017; pp. 1\u201324.","DOI":"10.7591\/9780801463921-002"},{"key":"1412_CR2","doi-asserted-by":"crossref","unstructured":"Wray NR, Gottesman II. Using summary data from the Danish National Registers to estimate heritabilities for schizophrenia, bipolar disorder, and major depressive disorder. Front Genet. 2012;3:118.","DOI":"10.3389\/fgene.2012.00118"},{"key":"1412_CR3","doi-asserted-by":"crossref","unstructured":"Kirsch I. Antidepressants and the placebo effect. Z Psychol. 2014;222:128\u2013134.","DOI":"10.1027\/2151-2604\/a000176"},{"key":"1412_CR4","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1016\/S0140-6736(17)32802-7","volume":"391","author":"A Cipriani","year":"2018","unstructured":"Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet. 2018;391:1357\u201366. https:\/\/doi.org\/10.1016\/S0140-6736(17)32802-7.","journal-title":"Lancet"},{"key":"1412_CR5","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.1176\/ajp.2006.163.11.1905","volume":"163","author":"AJ Rush","year":"2006","unstructured":"Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: A STAR*D report. Am J Psychiatry. 2006;163:1905\u201317. https:\/\/pubmed.ncbi.nlm.nih.gov\/17074942\/.","journal-title":"Am J Psychiatry"},{"key":"1412_CR6","doi-asserted-by":"crossref","unstructured":"Keers R, Uher R. Gene-environment interaction in major depression and antidepressant treatment response. Curr Psychiatry Rep. 2012;14:129\u201337.","DOI":"10.1007\/s11920-011-0251-x"},{"key":"1412_CR7","doi-asserted-by":"crossref","unstructured":"Berton O, Nestler EJ. New approaches to antidepressant drug discovery: beyond monoamines. Nat Rev Neuroscience. 2006;7:137\u201351.","DOI":"10.1038\/nrn1846"},{"key":"1412_CR8","doi-asserted-by":"publisher","first-page":"395","DOI":"10.31887\/DCNS.2014.16.3\/amenke","volume":"16","author":"A Menke","year":"2014","unstructured":"Menke A, Binder EB. Epigenetic alterations in depression and antidepressant treatment. Dialogues Clin Neurosci. 2014;16:395\u2013404.","journal-title":"Dialogues Clin Neurosci"},{"key":"1412_CR9","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.nbd.2015.07.002","volume":"82","author":"B Le Fran\u00e7ois","year":"2015","unstructured":"Le Fran\u00e7ois B, Soo J, Millar AM, Daigle M, Le Guisquet AM, Leman S, et al. Chronic mild stress and antidepressant treatment alter 5-HT1A receptor expression by modifying DNA methylation of a conserved Sp4 site. Neurobiol Dis. 2015;82:332\u201341.","journal-title":"Neurobiol Dis"},{"key":"1412_CR10","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1042\/BJ20120674","volume":"448","author":"N Zimmermann","year":"2012","unstructured":"Zimmermann N, Zschocke J, Perisic T, Yu S, Holsboer F, Rein T. Antidepressants inhibit DNA methyltransferase 1 through reducing G9a levels. Biochem J. 2012;448:93\u2013102.","journal-title":"Biochem J"},{"key":"1412_CR11","doi-asserted-by":"crossref","unstructured":"Bird A. Perceptions of epigenetics. Nature. 2007;447:396\u20138.","DOI":"10.1038\/nature05913"},{"key":"1412_CR12","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1001\/jamapsychiatry.2018.1725","volume":"75","author":"OS Jovanova","year":"2018","unstructured":"Jovanova OS, Nedeljkovic I, Spieler D, Walker RM, Liu C, Luciano M, et al. DNA methylation signatures of depressive symptoms in middle-aged and elderly persons: meta-analysis of multiethnic epigenome-wide studies. JAMA Psychiatry. 2018;75:949\u201359.","journal-title":"JAMA Psychiatry"},{"key":"1412_CR13","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-018-1514-1","volume":"19","author":"DL McCartney","year":"2018","unstructured":"McCartney DL, Hillary RF, Stevenson AJ, Ritchie SJ, Walker RM, Zhang Q, et al. Epigenetic prediction of complex traits and death. Genome Biol. 2018;19:136.","journal-title":"Genome Biol"},{"key":"1412_CR14","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1038\/s41380-019-0516-z","volume":"25","author":"SL Clark","year":"2019","unstructured":"Clark SL, Hattab MW, Chan RF, Shabalin AA, Han LKM, Zhao M, et al. A methylation study of long-term depression risk. Mol Psychiatry. 2019;25:1334\u201343.","journal-title":"Mol Psychiatry"},{"key":"1412_CR15","unstructured":"Barbu MC, Shen X, Walker RM, Howard DM, Evans KL, Whalley HC, et al. Epigenetic prediction of major depressive disorder. Mol Psychiatry. 2020;1\u201312. http:\/\/www.nature.com\/articles\/s41380-020-0808-3."},{"key":"1412_CR16","unstructured":"Hansen K. IlluminaHumanMethylationEPICanno.ilm10b2.hg19: annotation for illumina\u2019s EPIC methylation arrays. R Packag version 060. 2016. https:\/\/bitbucket.com\/kasperdanielhansen\/Illumina_EPIC."},{"key":"1412_CR17","unstructured":"Hansen KD. IlluminaHumanMethylation450kanno.ilmn12.hg19: Annotation for Illumina\u2019s 450k methylation arrays. 2016; R package version 0.6.0."},{"key":"1412_CR18","doi-asserted-by":"crossref","unstructured":"Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, et al. Epigenetic signatures of cigarette smoking. Circ Cardiovasc Genet. 2016;9:436\u201347.","DOI":"10.1161\/CIRCGENETICS.116.001506"},{"key":"1412_CR19","first-page":"1","volume":"9","author":"BH Smith","year":"2006","unstructured":"Smith BH, Campbell H, Blackwood D, Connell J, Connor M, Deary IJ, et al. Generation Scotland: the Scottish Family Health Study; a new resource for researching genes and heritability. BMC Med Genomics. 2006;9:1\u20139.","journal-title":"BMC Med Genomics"},{"key":"1412_CR20","doi-asserted-by":"crossref","unstructured":"Smith BH, Campbell A, Linksted P, Fitzpatrick B, Jackson C, Kerr SM, et al. Cohort profile: generation Scotland: Scottish Family Health Study (GS: SFHS). The study, its participants and their potential for genetic research on health and illness. Int J Epidemiol. 2013;42:689\u2013700.","DOI":"10.1093\/ije\/dys084"},{"key":"1412_CR21","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1375\/twin.13.3.231","volume":"13","author":"G Willemsen","year":"2010","unstructured":"Willemsen G, de Geus EJ, Bartels M, van Beijsterveldt CE, Brooks AI, Estourgie-van Burk GF, et al. The Netherlands Twin Register Biobank: a resource for genetic epidemiological studies. Twin Res Hum Genet. 2010;13:231\u201345. https:\/\/www.cambridge.org\/core\/journals\/twin-research-and-human-genetics\/article\/netherlands-twin-register-biobank-a-resource-for-genetic-epidemiological-studies\/94AA0448D70BFEA4780D0CD01EB7DF05.","journal-title":"Twin Res Hum Genet"},{"key":"1412_CR22","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1017\/thg.2019.93","volume":"22","author":"L Ligthart","year":"2019","unstructured":"Ligthart L, van Beijsterveldt CEM, Kevenaar ST, de Zeeuw E, van Bergen E, Bruins S, et al. The Netherlands Twin Register: longitudinal research based on twin and twin-family designs. Twin Res Hum Genet. 2019;22:623\u201336. https:\/\/www.cambridge.org\/core\/journals\/twin-research-and-human-genetics\/article\/netherlands-twin-register-longitudinal-research-based-on-twin-and-twinfamily-designs\/C68488FDD92716CB9ED7C040B9776E2F.","journal-title":"Twin Res Hum Genet"},{"key":"1412_CR23","unstructured":"British Medical Association, Royal Pharmaceutical Society of Great Britain. British national formulary. 58 edn. UK: BMJ Publishing Group. 2009."},{"key":"1412_CR24","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1093\/aje\/kwf122","volume":"156","author":"K Leffondr\u00e9","year":"2002","unstructured":"Leffondr\u00e9 K, Abrahamowicz M, Siemiatycki J, Rachet B. Modeling smoking history: a comparison of different approaches. Am J Epidemiol. 2002;156:813\u201323.","journal-title":"Am J Epidemiol"},{"key":"1412_CR25","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1521\/pedi.1995.9.2.92","volume":"9","author":"MB First","year":"1995","unstructured":"First MB, Spitzer RL, Gibbon M, Williams JBW, Davies M, Borus J, et al. The structured clinical interview for DSM-III-R personality disorders (SCID-II). Part II: multi-site test-retest reliablity study. J Pers Disord. 1995;9:92\u2013104.","journal-title":"J Pers Disord"},{"key":"1412_CR26","doi-asserted-by":"crossref","unstructured":"Fernandez-Pujals AM, Adams MJ, Thomson P, McKechanie AG, Blackwood DHR, Smith BH, et al. Epidemiology and heritability of major depressive disorder, stratified by age of onset, sex, and illness course in generation Scotland: Scottish family health study (GS: SFHS). PLoS ONE. 2015;10:e0142197.","DOI":"10.1371\/journal.pone.0142197"},{"key":"1412_CR27","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1002\/mpr.33","volume":"7","author":"RC Kessler","year":"1998","unstructured":"Kessler RC, Wittchen H-U, Abelson JM, Mcgonagle K, Schwarz N, Kendler KS, et al. Methodological studies of the Composite International Diagnostic Interview (CIDI) in the US national comorbidity survey (NCS). Int J Methods Psychiatr Res. 1998;7:33\u201355. https:\/\/onlinelibrary.wiley.com\/doi\/full\/10.1002\/mpr.33.","journal-title":"Int J Methods Psychiatr Res"},{"key":"1412_CR28","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1017\/S0033291716002312","volume":"47","author":"M Bot","year":"2017","unstructured":"Bot M, Middeldorp CM, de Geus EJ, Lau HM, Sinke M, van Nieuwenhuizen B, et al. Validity of LIDAS (LIfetime Depression Assessment Self-report): a self-report online assessment of lifetime major depressive disorder. Psychol Med. 2017;47:279\u201389. https:\/\/www.cambridge.org\/core\/journals\/psychological-medicine\/article\/validity-of-lidas-lifetime-depression-assessment-selfreport-a-selfreport-online-assessment-of-lifetime-major-depressive-disorder\/97602A258E2DABA0745F8039C0F20907.","journal-title":"Psychol Med"},{"key":"1412_CR29","unstructured":"The Achenbach System of Empirically Based Assessment (ASEBA) for Ages 18 to 90 Years. - PsycNET. 2021. https:\/\/psycnet.apa.org\/record\/2004-14941-004."},{"key":"1412_CR30","doi-asserted-by":"publisher","first-page":"175","DOI":"10.12688\/f1000research.4680.1","volume":"3","author":"J-P Fortin","year":"2014","unstructured":"Fortin J-P, Fertig E, Hansen K. shinyMethyl: interactive quality control of Illumina 450k DNA methylation arrays in R. F1000Research. 2014;3:175 https:\/\/doi.org\/10.12688\/f1000research.4680.1.","journal-title":"F1000Research"},{"key":"1412_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2164-14-293","volume":"14","author":"R Pidsley","year":"2013","unstructured":"Pidsley R, Y Wong CC, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics. 2013;141\u201310. https:\/\/bmcgenomics.biomedcentral.com\/articles\/10.1186\/1471-2164-14-293.","journal-title":"BMC Genomics"},{"key":"1412_CR32","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1038\/sj.ejhg.5201979","volume":"16","author":"DI Boomsma","year":"2008","unstructured":"Boomsma DI, Willemsen G, Sullivan PF, Heutink P, Meijer P, Sondervan D, et al. Genome-wide association of major depression: description of samples for the GAIN Major Depressive Disorder Study: NTR and NESDA biobank projects. Eur J Hum Genet. 2008;16:335\u201342. https:\/\/www.nature.com\/articles\/5201979.","journal-title":"Eur J Hum Genet"},{"key":"1412_CR33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms11115","volume":"7","author":"J van Dongen","year":"2016","unstructured":"van Dongen J, Nivard MG, Willemsen G, Hottenga J-J, Helmer Q, Dolan CV, et al. Genetic and environmental influences interact with age and sex in shaping the human methylome. Nat Commun. 2016;7:1\u201313. https:\/\/www.nature.com\/articles\/ncomms11115.","journal-title":"Nat Commun"},{"key":"1412_CR34","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1038\/ng.3721","volume":"49","author":"MJ Bonder","year":"2017","unstructured":"Bonder MJ, Luijk R, Zhernakova DV, Moed M, Deelen P, Vermaat M, et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat Genet. 2017;49:131\u20138.","journal-title":"Nat Genet"},{"key":"1412_CR35","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1093\/nar\/gkv007","volume":"43","author":"ME Ritchie","year":"2015","unstructured":"Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:47.","journal-title":"Nucleic Acids Res"},{"key":"1412_CR36","doi-asserted-by":"publisher","first-page":"2190","DOI":"10.1093\/bioinformatics\/btq340","volume":"26","author":"CJ Willer","year":"2010","unstructured":"Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190\u20131. https:\/\/pubmed.ncbi.nlm.nih.gov\/20616382\/.","journal-title":"Bioinformatics"},{"key":"1412_CR37","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1093\/bioinformatics\/btv560","volume":"32","author":"B Phipson","year":"2016","unstructured":"Phipson B, Maksimovic J, Oshlack A. MissMethyl: An R package for analyzing data from Illumina\u2019s HumanMethylation450 platform. Bioinformatics. 2016;32:286\u20138.","journal-title":"Bioinformatics"},{"key":"1412_CR38","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1093\/bioinformatics\/bty892","volume":"35","author":"X Ren","year":"2019","unstructured":"Ren X, Kuan PF. methylGSA: a Bioconductor package and Shiny app for DNA methylation data length bias adjustment in gene set testing. Bioinformatics 2019;35:1958\u20139.","journal-title":"Bioinformatics"},{"key":"1412_CR39","doi-asserted-by":"crossref","unstructured":"Dolgalev I msigdbr: MSigDB gene sets for multiple organisms in a tidy data format. R package version 7.1.1. 2020. https:\/\/cran.r-project.org\/web\/packages\/msigdbr\/index.html.","DOI":"10.32614\/CRAN.package.babelgene"},{"key":"1412_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-017-01297-7","volume":"8","author":"AY Chu","year":"2017","unstructured":"Chu AY, Tin A, Schlosser P, Ko YA, Qiu C, Yao C, et al. Epigenome-wide association studies identify DNA methylation associated with kidney function. Nat Commun. 2017;8:1\u201312. www.nature.com\/naturecommunications.","journal-title":"Nat Commun"},{"key":"1412_CR41","doi-asserted-by":"publisher","first-page":"4767","DOI":"10.1093\/bioinformatics\/btz456","volume":"35","author":"CE Breeze","year":"2019","unstructured":"Breeze CE, Reynolds AP, Van Dongen J, Dunham I, Lazar J, Neph S, et al. EFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data. Bioinformatics. 2019;35:4767\u20139.","journal-title":"Bioinformatics."},{"key":"1412_CR42","doi-asserted-by":"publisher","unstructured":"Suderman M, Staley J, French R, Arathimos R, Simpkin A, Tilling K. dmrff: identifying differentially methylated regions efficiently with power and control. bioRxiv. 2018. https:\/\/doi.org\/10.1101\/508556.","DOI":"10.1101\/508556"},{"key":"1412_CR43","unstructured":"Liu C, Marioni RE, Hedman \u00c5K, Pfeiffer L, Tsai P, Reynolds LM, et al. A DNA methylation biomarker of alcohol consumption. Mol Psychiatry. 2018:23:422\u201333."},{"key":"1412_CR44","doi-asserted-by":"crossref","unstructured":"Bohlin J, H\u00e5berg SE, Magnus P, Reese SE, Gjessing HK, Magnus MC, et al. Prediction of gestational age based on genome-wide differentially methylated regions. Genome Biol. 2016;17:1\u20139.","DOI":"10.1186\/s13059-016-1063-4"},{"key":"1412_CR45","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1093\/nar\/gky942","volume":"47","author":"D Liu","year":"2019","unstructured":"Liu D, Zhao L, Wang Z, Zhou X, Fan X, Li Y, et al. EWASdb: epigenome-wide association study database. Nucleic Acids Res. 2019;47:989\u201393.","journal-title":"Nucleic Acids Res"},{"key":"1412_CR46","doi-asserted-by":"crossref","unstructured":"Li Y, Qi X, Liu B, Huang H. The STAT5\u2013GATA2 pathway is critical in basophil and mast cell differentiation and maintenance. J. Immunol. 2015;194:4328\u201338. http:\/\/www.jimmunol.org\/content\/194\/9\/4328http:\/\/www.jimmunol.org\/content\/194\/9\/4328.full#ref-list-1.","DOI":"10.4049\/jimmunol.1500018"},{"key":"1412_CR47","doi-asserted-by":"crossref","unstructured":"Collin M, Dickinson R, Bigley V. Haematopoietic and immune defects associated with GATA2 mutation. Br J Haematol. 2015;169:173\u201387. https:\/\/onlinelibrary.wiley.com\/doi\/full\/10.1111\/bjh.13317.","DOI":"10.1111\/bjh.13317"},{"key":"1412_CR48","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/s41588-018-0309-3","volume":"51","author":"R Karlsson Linn\u00e9r","year":"2019","unstructured":"Karlsson Linn\u00e9r R, Biroli P, Kong E, Meddens SFW, Wedow R, Fontana MA, et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat Genet. 2019;51:245\u201357. \/pmc\/articles\/PMC6713272\/?report=abstract","journal-title":"Nat Genet"},{"key":"1412_CR49","doi-asserted-by":"publisher","first-page":"5725","DOI":"10.1523\/JNEUROSCI.14-10-05725.1994","volume":"14","author":"GD Frantz","year":"1994","unstructured":"Frantz GD, Weimann JM, Levin ME, McConnell SK. Otx1 and Otx2 define layers and regions in developing cerebral cortex and cerebellum. J Neurosci. 1994;14:5725\u201340. https:\/\/www.jneurosci.org\/content\/14\/10\/5725.","journal-title":"J Neurosci"},{"key":"1412_CR50","doi-asserted-by":"crossref","unstructured":"Petschner P, Gonda X, Baksa D, Eszlari N, Trivaks M, Juhasz G, et al. Genes linking mitochondrial function, cognitive impairment and depression are associated with endophenotypes serving precision medicine. Neuroscience. 2018;370:207\u201317.","DOI":"10.1016\/j.neuroscience.2017.09.049"},{"key":"1412_CR51","doi-asserted-by":"crossref","unstructured":"Wang KS, Liu XF, Aragam N. A genome-wide meta-analysis identifies novel loci associated with schizophrenia and bipolar disorder. Schizophr Res. 2010;124:192\u20139.","DOI":"10.1016\/j.schres.2010.09.002"},{"key":"1412_CR52","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1038\/mp.2009.125","volume":"16","author":"SI Shyn","year":"2011","unstructured":"Shyn SI, Shi J, Kraft JB, Potash JB, Knowles JA, Weissman MM, et al. Novel loci for major depression identified by genome-wide association study of sequenced treatment alternatives to relieve depression and meta-analysis of three studies. Mol Psychiatry. 2011;16:202\u201315.","journal-title":"Mol Psychiatry"},{"key":"1412_CR53","doi-asserted-by":"publisher","DOI":"10.1038\/tp.2016.221","volume":"6","author":"X Gonda","year":"2016","unstructured":"Gonda X, Eszlari N, Anderson IM, Deakin JFW, Bagdy G, Juhasz G. Association of ATP6V1B2 rs1106634 with lifetime risk of depression and hippocampal neurocognitive deficits: possible novel mechanisms in the etiopathology of depression. Transl Psychiatry. 2016;6:e945.","journal-title":"Transl Psychiatry"},{"key":"1412_CR54","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1289\/ehp.1205412","volume":"120","author":"BR Joubert","year":"2012","unstructured":"Joubert BR, H\u00e5berg SE, Nilsen RM, Wang X, Vollset SE, Murphy SK, et al. 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy. Environ Health Perspect. 2012;120:1425\u201331.","journal-title":"Environ Health Perspect"},{"key":"1412_CR55","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.1093\/ije\/dyv048","volume":"44","author":"LK K\u00fcpers","year":"2015","unstructured":"K\u00fcpers LK, Xu X, Jankipersadsing SA, Vaez A, La Bastide-van Gemert S, Scholtens S, et al. DNA methylation mediates the effect of maternal smoking during pregnancy on birthweight of the offspring. Int J Epidemiol. 2015;44:1224\u201337.","journal-title":"Int J Epidemiol"},{"key":"1412_CR56","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1038\/nature20784","volume":"541","author":"S Wahl","year":"2017","unstructured":"Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017;541:81\u20136. https:\/\/www.nature.com\/articles\/nature20784.","journal-title":"Nature"},{"key":"1412_CR57","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1038\/mp.2016.192","volume":"23","author":"C Liu","year":"2018","unstructured":"Liu C, Marioni RE, Hedman AK, Pfeiffer L, Tsai PC, Reynolds LM, et al. A DNA methylation biomarker of alcohol consumption. Mol Psychiatry. 2018;23:422\u201333.","journal-title":"Mol Psychiatry"},{"key":"1412_CR58","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1161\/CIRCGENETICS.116.001506","volume":"9","author":"R Joehanes","year":"2016","unstructured":"Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, et al. Epigenetic signatures of cigarette smoking. Circ Cardiovasc Genet. 2016;9:436\u201347. http:\/\/circgenetics.ahajournals.org\/lookup\/suppl\/.","journal-title":"Circ Cardiovasc Genet"},{"key":"1412_CR59","doi-asserted-by":"crossref","unstructured":"\u00c1lvarez-Errico D, Vento-Tormo R, Sieweke M, Ballestar E. Epigenetic control of myeloid cell differentiation, identity and function. Nat Rev Immunol. 2015;15:7\u201317. https:\/\/pubmed.ncbi.nlm.nih.gov\/25534619\/.","DOI":"10.1038\/nri3777"},{"key":"1412_CR60","doi-asserted-by":"crossref","unstructured":"Eyre HA, Lavretsky H, Kartika J, Qassim A, Baune BT. Modulatory effects of antidepressant classes on the innate and adaptive immune system in depression. Pharmacopsychiatry. 2016;49:85\u201396.","DOI":"10.1055\/s-0042-103159"},{"key":"1412_CR61","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.jclinepi.2017.10.013","volume":"94","author":"JD Hafferty","year":"2018","unstructured":"Hafferty JD, Campbell AI, Navrady LB, Adams MJ, MacIntyre D, Lawrie SM, et al. Self-reported medication use validated through record linkage to national prescribing data. J Clin Epidemiol. 2018;94:132\u201342.","journal-title":"J Clin Epidemiol"},{"key":"1412_CR62","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/s11096-013-9891-8","volume":"36","author":"I Arnet","year":"2014","unstructured":"Arnet I, Abraham I, Messerli M, Hersberger KE. A method for calculating adherence to polypharmacy from dispensing data records. Int J Clin Pharm. 2014;36:192\u2013201.","journal-title":"Int J Clin Pharm"},{"key":"1412_CR63","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1093\/ije\/dyz190","volume":"48","author":"T Battram","year":"2019","unstructured":"Battram T, Richmond RC, Baglietto L, Haycock PC, Perduca V, Bojesen SE, et al. Appraising the causal relevance of DNA methylation for risk of lung cancer. Int J Epidemiol. 2019;48:1493\u2013504. https:\/\/academic.oup.com\/ije\/article\/48\/5\/1493\/5573019.","journal-title":"Int J Epidemiol"},{"key":"1412_CR64","unstructured":"Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2020;51:237\u201344. \/pmc\/articles\/PMC6358542\/?report=abstract."},{"key":"1412_CR65","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1016\/j.cell.2020.06.045","volume":"182","author":"MH Chen","year":"2020","unstructured":"Chen MH, Raffield LM, Mousas A, Sakaue S, Huffman JE, Moscati A, et al. Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations. Cell. 2020;182:1198\u20131213.e14. https:\/\/pubmed.ncbi.nlm.nih.gov\/32888493\/.","journal-title":"Cell"},{"key":"1412_CR66","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.ajhg.2018.11.008","volume":"104","author":"G Kichaev","year":"2019","unstructured":"Kichaev G, Bhatia G, Loh PR, Gazal S, Burch K, Freund MK, et al. Leveraging polygenic functional enrichment to improve GWAS power. Am J Hum Genet. 2019;104:65\u201375. https:\/\/pubmed.ncbi.nlm.nih.gov\/30595370\/.","journal-title":"Am J Hum Genet"},{"key":"1412_CR67","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1016\/j.cell.2016.10.042","volume":"167","author":"WJ Astle","year":"2016","unstructured":"Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell. 2016;167:1415\u20131429.e19. https:\/\/pubmed.ncbi.nlm.nih.gov\/27863252\/.","journal-title":"Cell"},{"key":"1412_CR68","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/nature11582","volume":"491","author":"L Jostins","year":"2012","unstructured":"Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature. 2012;491:119\u201324. https:\/\/pubmed.ncbi.nlm.nih.gov\/23128233\/.","journal-title":"Nature"},{"key":"1412_CR69","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1038\/ng.3359","volume":"47","author":"JZ Liu","year":"2015","unstructured":"Liu JZ, Van Sommeren S, Huang H, Ng SC, Alberts R, Takahashi A, et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet. 2015;47:979\u201386. https:\/\/pubmed.ncbi.nlm.nih.gov\/26192919\/.","journal-title":"Nat Genet"},{"key":"1412_CR70","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1002\/gepi.22242","volume":"43","author":"L Din","year":"2019","unstructured":"Din L, Sheikh M, Kosaraju N, Smedby KE, Bernatsky S, Berndt SI, et al. Genetic overlap between autoimmune diseases and non-Hodgkin lymphoma subtypes. Genet Epidemiol. 2019;43:844\u201363. https:\/\/pubmed.ncbi.nlm.nih.gov\/31407831\/.","journal-title":"Genet Epidemiol"},{"key":"1412_CR71","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1038\/s41588-018-0147-3","volume":"50","author":"JJ Lee","year":"2018","unstructured":"Lee JJ, 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\u201321.","journal-title":"Nat Genet"},{"key":"1412_CR72","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1016\/j.jaci.2019.09.035","volume":"145","author":"Z Zhu","year":"2020","unstructured":"Zhu Z, Guo Y, Shi H, Liu CL, Panganiban RA, Chung W, et al. Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank. J Allergy Clin Immunol. 2020;145:537\u201349. https:\/\/pubmed.ncbi.nlm.nih.gov\/31669095\/.","journal-title":"J Allergy Clin Immunol"},{"key":"1412_CR73","doi-asserted-by":"publisher","first-page":"1670","DOI":"10.1038\/s41588-019-0512-x","volume":"51","author":"M Lam","year":"2019","unstructured":"Lam M, Chen CY, Li Z, Martin AR, Bryois J, Ma X, et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat Genet. 2019;51:1670\u20138. https:\/\/pubmed.ncbi.nlm.nih.gov\/31740837\/.","journal-title":"Nat Genet"},{"key":"1412_CR74","doi-asserted-by":"crossref","unstructured":"Edwards TL, Giri A, Hellwege JN, Hartmann KE, Stewart EA, Jeff JM, et al. A trans-ethnic genome-wide association study of uterine fibroids. Front Genet. 2019;10. https:\/\/pubmed.ncbi.nlm.nih.gov\/31249589\/.","DOI":"10.3389\/fgene.2019.00511"},{"key":"1412_CR75","doi-asserted-by":"publisher","first-page":"1412","DOI":"10.1038\/s41588-018-0205-x","volume":"50","author":"E Evangelou","year":"2018","unstructured":"Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018;50:1412\u201325. https:\/\/pubmed.ncbi.nlm.nih.gov\/30224653\/.","journal-title":"Nat Genet"},{"key":"1412_CR76","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.neurobiolaging.2020.04.025","volume":"93","author":"H Wang","year":"2020","unstructured":"Wang H, Yang J, Schneider JA, De Jager PL, Bennett DA, Zhang HY. Genome-wide interaction analysis of pathological hallmarks in Alzheimer\u2019s disease. Neurobiol Aging. 2020;93:61\u20138. https:\/\/pubmed.ncbi.nlm.nih.gov\/32450446\/.","journal-title":"Neurobiol Aging"},{"key":"1412_CR77","doi-asserted-by":"crossref","unstructured":"Gallois A, Mefford J, Ko A, Vaysse A, Julienne H, Ala-Korpela M, et al. A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context. Nat Commun. 2019;10. https:\/\/pubmed.ncbi.nlm.nih.gov\/31636271\/.","DOI":"10.1038\/s41467-019-12703-7"},{"key":"1412_CR78","doi-asserted-by":"crossref","unstructured":"Inouye M, Ripatti S, Kettunen J, Lyytik\u00e4inen LP, Oksala N, Laurila PP, et al. Novel loci for metabolic networks and multi-tissue expression studies reveal genes for atherosclerosis. PLoS Genet. 2012;8. https:\/\/pubmed.ncbi.nlm.nih.gov\/22916037\/.","DOI":"10.1371\/journal.pgen.1002907"},{"key":"1412_CR79","doi-asserted-by":"crossref","unstructured":"Li M, Maruthur NM, Loomis SJ, Pietzner M, North KE, Mei H, et al. Genome-wide association study of 1,5-anhydroglucitol identifies novel genetic loci linked to glucose metabolism. Sci Rep. 2017;7. https:\/\/pubmed.ncbi.nlm.nih.gov\/28588231\/.","DOI":"10.1038\/s41598-017-02287-x"},{"key":"1412_CR80","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1097\/FPC.0000000000000250","volume":"27","author":"Q Li","year":"2017","unstructured":"Li Q, Wineinger NE, Fu DJ, Libiger O, Alphs L, Savitz A, et al. Genome-wide association study of paliperidone efficacy. Pharmacogenet Genomics. 2017;27:7\u201318. https:\/\/pubmed.ncbi.nlm.nih.gov\/27846195\/.","journal-title":"Pharmacogenet Genomics"}],"container-title":["Molecular Psychiatry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41380-021-01412-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41380-021-01412-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41380-021-01412-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T20:42:30Z","timestamp":1726260150000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41380-021-01412-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,8]]},"references-count":80,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["1412"],"URL":"https:\/\/doi.org\/10.1038\/s41380-021-01412-7","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.10.06.20207621","asserted-by":"object"}]},"ISSN":["1359-4184","1476-5578"],"issn-type":[{"value":"1359-4184","type":"print"},{"value":"1476-5578","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,8]]},"assertion":[{"value":"1 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"AMM has previously received grant support from Pfizer, Lilly and Janssen. These studies are not connected to the current investigation. Remaining authors report no conflicts of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}