Strong evidence from family and twin studies demonstrates that major depressive disorder (MDD) is heritable, yet there has been limited progress in identifying the actual genes involved. A separate, perhaps overlapping set of genes is expected to play a role in individual variation in treatment response in MDD. We seek to characterize genetically, using a large set of markers in many genes, patients who differ in their response to standard treatments as defined in the STAR*D protocol. Our initial focus was on two sets of genes most likely to play a role in the etiology of major depression: 1) genes selected on neurobiological grounds because of their known involvement in pathways thought to be important in mood disorders;and 2) genes implicated primarily by their positions within genomic regions implicated by genetic linkage or association studies of major mood disorders and related conditions. Our ultimate goal is to study markers representing every common functional variation in the human genome, once such genome-wide studies become technologically feasible. We utilize state-of-the-art, high-throughput genotyping methods as well as sophisticated methods of genetic analysis that take into account haplotypes and multi-locus interactions in addition to standard, single-marker analyses. Primary comparisons are performed with the group of cases who respond to citalopram and those who do not, but analyses are done in other treatment groups as well, depending on sample size. In the first years of this project, we completed genotyping 738 markers in a set of 68 candidate genes selected by an expert panel. Results implicated several of these genes in treatment outcome and other genes that contribute to adverse effects. For example, we identified a marker in the gene encoding the serotonin 2A receptor (HTR2A) that was reproducibly associated with both response and remission during 6-12 weeks of therapy with citalopram. We also identified markers near two genes encoding the ionotropic glutamate receptors, GRIK2 and GRIA3, that seem to be associated with treatment-emergent suicidal ideation. We also discovered additional markers that predict treatment outcome. One marker is in the gene GRIK4, that encodes yet another ionotropic glutamate receptor. A second marker is in the gene FKBP5, which encodes a protein involved in the trafficking of glucocorticoid receptors, key molecules in the stress response system. This latter finding confirms an earlier report implicating this gene in an independent sample of inpatients with major depression. We also discovered genetic markers that help identify those at risk for sexual dysfunction during antidepressant therapy. Sexual dysfunction, such as erectile dysfunction, is one of the most common complaints during treatment with modern selective serotonin reuptake inhibitor antidepressants. We also expanded our study of treatment-emergent suicidal ideation, and identified additional markers that increase risk of this worrisome event. Taken together with our previous findings, these markers can identify a few individuals at substantially increased risk, who may benefit from closer monitoring or alternative treatments. In 2011, we joined a collaboration with investigators from Harvard, the Max Planck Institute Munich, UCSF, and University College London to carry out a meta-analysis of STAR*D along with the two other genome-wide association studies of antidepressant outcome that have been completed, known as MARS and GENDEP. Despite greater power of this combined sample to confer to uncover association with common genetic markers, no genome-wide significant associations were uncovered. We conclude that no common alleles of large effect on antidepressant outcome exist in these samples. In the past year, we used new, high-throughput sequencing methods to test for rarer alleles that may exert larger effects, at least on treatment resistance. Patients who respond to antidepressant treatment constitute a mixture of true responders, placebo responders, and spontaneous remitters. Patients who fail to respond to multiple treatments may be less heterogeneous, since placebo responders and spontaneous remitters are removed. Truly treatment-resistant depression (TRD) still needs to be differentiated from non-adherence, intolerance, delayed response, and comorbidities that interfere with treatment, but this distinction can generally be accomplished in prospective studies like STAR*D. Over 4,000 patients enrolled in STAR*D and of these, only 10% were deemed treatment-resistant, and only 3% were deemed highly treatment-resistant. Rarer alleles may show larger effects, especially among patients with unusual treatment outcomes. Sequencing studies may uncover alleles that play a major role in a minority of patients. Few patients will carry such alleles, but the genes involved will point to attractive new drug targets. Although family designs are impractical for pharmacogenetic studies, unrelated cases who sit at the extremes of the response distribution may be particularly informative. This rare outcomes/rare alleles strategy has already been successful in other fields, but has not yet been tried in antidepressant outcome studies. Exome sequencing has now been completed on 25 treatment-resistant and 25 "typically responsive" patients. Initial analyses have uncovered some promising leads, but additional studies in larger samples are needed. Additional work led by extramurally-funded fellow Eleanor Murphy is investigating why minority participants in clinical trials like STAR*D drop out of treatment and experience poorer treatment response than non-minorities. The goal here is to boost minority retention in clinical trials and identify genetic markers of treatment response and associated adverse effects, which often vary by ancestry. In the past year the research focused primarily on pharmacological treatment outcomes for major depressive disorder (MDD), showing that race, ethnicity, genetic ancestry, and other factors affect SSRI treatment response, but genetic African ancestry remains a significant risk factor for poor response, even after other factors were taken into account. This research resulted in two first-authored publications in widely-read peer reviewed journals. In the coming year, we will follow up this work with further analyses on functional genetic variants differentially prevalent in diverse human populations. In collaboration with other NIMH Intramural Investigators, we will further investigate the genetic basis of treatment-resistant depression and the response of such patients to novel antidepressant agents such as ketamine.

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McMahon, Francis J (2015) Clinically Useful Genetic Markers of Antidepressant Response: How Do We Get There From Here? Am J Psychiatry 172:697-9
Xu, Meng Yuan; Umbach, David M; Murphy, Eleanor et al. (2015) A Novel Mixture Model to Estimate the Time to Drug Effect Onset and Its Association with Covariates. Hum Hered 80:90-9
McMahon, Francis J (2014) Prediction of treatment outcomes in psychiatry--where do we stand ? Dialogues Clin Neurosci 16:455-64
Lekman, Magnus; Hössjer, Ola; Andrews, Peter et al. (2014) The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study. BioData Min 7:19
Kreisl, William C; Jenko, Kimberly J; Hines, Christina S et al. (2013) A genetic polymorphism for translocator protein 18 kDa affects both in vitro and in vivo radioligand binding in human brain to this putative biomarker of neuroinflammation. J Cereb Blood Flow Metab 33:53-8
Murphy, Eleanor; McMahon, Francis J (2013) Pharmacogenetics of antidepressants, mood stabilizers, and antipsychotics in diverse human populations. Discov Med 16:113-22
Murphy, Eleanor; Hou, Liping; Maher, Brion S et al. (2013) Race, genetic ancestry and response to antidepressant treatment for major depression. Neuropsychopharmacology 38:2598-606
Murphy, Eleanor J; Kassem, Layla; Chemerinski, Anat et al. (2013) Retention and attrition among African Americans in the STAR*D study: what causes research volunteers to stay or stray? Depress Anxiety 30:1137-44
Cutler, Jessica A; Rush, A John; McMahon, Francis J et al. (2012) Common genetic variation in the indoleamine-2,3-dioxygenase genes and antidepressant treatment outcome in major depressive disorder. J Psychopharmacol 26:360-7
Laje, Gonzalo; Lally, Níall; Mathews, Daniel et al. (2012) Brain-derived neurotrophic factor Val66Met polymorphism and antidepressant efficacy of ketamine in depressed patients. Biol Psychiatry 72:e27-8

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