Se.168 We10,11 and others194 have performed antidepressant response candidate gene and genome-wide association studies (GWAS), but with only limited good results and with few replicated findings.17,25Relative lack of power, variation in study design and phenotypic heterogeneity may well all contribute to this state of affairs. The addition of other `omics’ to genomics could make it attainable to attain enhanced patient subclassification, therefore making it possible to identify novel genetic elements that contribute to variation in SSRI response. We’ve previously employed pharmacometabolomics to help guide and inform genomic research of SSRI clinical response.28,29 Metabolomics is becoming made use of increasingly to recognize `biosignatures’ for disease subclassification and/or drug response phenotype(s).302 Pharmacometabolomics is an emerging field that utilizes `metabolic profiles’ to characterize biological response to drug treatment.28,29,335 In the present study, 306 MDD sufferers were randomly selected from the Mayo Clinic Pharmacogenomics Investigation Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS) SSRI trial who had been included in our `Clinical SSRI Response’ and `Citalopram and Escitalopram Metabolism’ GWA research.11,36,37 Plasma samples from those individuals were utilized to carry out metabolomic research via the Pharmacometabolomics Study Network at baseline and right after 4 and1 Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA; 2Department of Biomedical Statistics and Bioinformatics Genetics and Bioinformatics, Mayo Clinic, Rochester, MN, USA; 3Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA; 4Bedford VA Healthcare Center, Bedford, MA, USA; five Division of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC, USA; 6RIKEN Center for Genomic Medicine, Yokohama, Japan and 7Department of Medicine, University of Chicago, Chicago, IL, USA. Correspondence: Dr RM Weinshilboum, Division of Clinical Pharmacology, Division of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 Very first Street SW, Rochester, MN 55905, USA.Grubbs 1st site E-mail: weinshilboum.Formula of 3-Amino-4-methylpicolinic acid richard@mayo.PMID:24059181 edu eight These authors contributed equally to this study. Received 4 August 2015; revised 7 December 2015; accepted 7 January 2016; published on the internet 23 FebruaryTSPAN5, ERICH3 and big depressive disorder M Gupta et al1718 8 weeks of SSRI therapy, for any total of 918 samples assayed. Among the metabolites analyzed, plasma serotonin concentrations and alterations in plasma serotonin concentrations have been associated with the largest number of SSRI remedy outcome measures. Especially, sufferers with greater baseline plasma serotonin concentrations and/or greater decreases in plasma serotonin concentrations responded far better to SSRI therapy. We then moved from metabolomics to genomics by performing GWAS to identify genes related with variation in plasma serotonin concentrations or modifications in serotonin concentrations through SSRI therapy, followed by the functional pursuit of those genes in neuronal cell models. Especially, when GWAS was performed with baseline plasma serotonin concentrations because the phenotype, a genome-wide important (P = 7.84E-09) single nucleotide polymorphism (SNP) signal that was 5′ with the Tetraspanin five (TSPAN5) gene on chromosome 4 as well as a cluster of SNPs across the Glutamaterich three (ERICH3) gene on chromosome 1 (P = 9.28E-08) have been identified. These similar SNP s.