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We studied 1,030 white outpatients with MDD<\/jats:styled-content> treated with citalopram\/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN<\/jats:styled-content>\u2010AMPS<\/jats:styled-content>; n<\/jats:italic>\u00a0=\u00a0398), Sequenced Treatment Alternatives to Relieve Depression (STAR<\/jats:styled-content>*D; n<\/jats:italic>\u00a0=\u00a0467), and International SSRI Pharmacogenomics Consortium (ISPC<\/jats:styled-content>; n<\/jats:italic>\u00a0=\u00a0165) trials. A genomewide association study for PGRN<\/jats:styled-content>\u2010AMPS<\/jats:styled-content> plasma metabolites associated with SSRI<\/jats:styled-content> response (serotonin) and baseline MDD<\/jats:styled-content> severity (kynurenine) identified single nucleotide polymorphisms (SNP<\/jats:styled-content>s) in DEFB<\/jats:styled-content>1<\/jats:italic>, ERICH<\/jats:styled-content>3<\/jats:italic>, AHR<\/jats:styled-content><\/jats:italic>, and TSPAN<\/jats:styled-content>5<\/jats:italic> that we tested as predictors. Supervised machine\u2010learning methods trained using SNP<\/jats:styled-content>s and total baseline depression scores predicted remission and response at 8\u00a0weeks with area under the receiver operating curve (AUC<\/jats:styled-content>)\u00a0>\u00a00.7 (P<\/jats:italic>\u00a0<\u00a00.04) in PGRN<\/jats:styled-content>\u2010AMPS<\/jats:styled-content> patients, with comparable prediction accuracies\u00a0>\u00a069% (P<\/jats:italic>\u00a0\u2264\u00a00.07) in STAR<\/jats:styled-content>*D and ISPC<\/jats:styled-content>. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI<\/jats:styled-content> therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.<\/jats:p>","DOI":"10.1002\/cpt.1482","type":"journal-article","created":{"date-parts":[[2019,4,23]],"date-time":"2019-04-23T09:46:19Z","timestamp":1556012779000},"page":"855-865","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":99,"title":["Pharmacogenomics\u2010Driven Prediction of Antidepressant Treatment Outcomes: A Machine\u2010Learning Approach With Multi\u2010trial Replication"],"prefix":"10.1111","volume":"106","author":[{"given":"Arjun P.","family":"Athreya","sequence":"first","affiliation":[{"name":"Department of Electrical & Computer Engineering University of Illinois at Urbana\u2010Champaign Urbana Illinois USA"},{"name":"Department of Molecular Pharmacology and Experimental Therapeutics Mayo Clinic Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Drew","family":"Neavin","sequence":"additional","affiliation":[{"name":"Department of Molecular Pharmacology and Experimental Therapeutics Mayo Clinic Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tania","family":"Carrillo\u2010Roa","sequence":"additional","affiliation":[{"name":"Department of Translational Research in Psychiatry Max Planck Institute of Psychiatry Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle","family":"Skime","sequence":"additional","affiliation":[{"name":"Department of Psychiatry & Psychology Mayo Clinic Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joanna","family":"Biernacka","sequence":"additional","affiliation":[{"name":"Department of Psychiatry & Psychology Mayo Clinic Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark A.","family":"Frye","sequence":"additional","affiliation":[{"name":"Department of Psychiatry & Psychology Mayo Clinic Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. 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