Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

New insights into the pharmacogenomics of antidepressant response from the GENDEP and STAR*D studies: rare variant analysis and high-density imputation

Abstract

Genome-wide association studies have generally failed to identify polymorphisms associated with antidepressant response. Possible reasons include limited coverage of genetic variants that this study tried to address by exome genotyping and dense imputation. A meta-analysis of Genome-Based Therapeutic Drugs for Depression (GENDEP) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D) studies was performed at the single-nucleotide polymorphism (SNP), gene and pathway levels. Coverage of genetic variants was increased compared with previous studies by adding exome genotypes to previously available genome-wide data and using the Haplotype Reference Consortium panel for imputation. Standard quality control was applied. Phenotypes were symptom improvement and remission after 12 weeks of antidepressant treatment. Significant findings were investigated in NEWMEDS consortium samples and Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) for replication. A total of 7062 950 SNPs were analyzed in GENDEP (n=738) and STAR*D (n=1409). rs116692768 (P=1.80e−08, ITGA9 (integrin α9)) and rs76191705 (P=2.59e−08, NRXN3 (neurexin 3)) were significantly associated with symptom improvement during citalopram/escitalopram treatment. At the gene level, no consistent effect was found. At the pathway level, the Gene Ontology (GO) terms GO: 0005694 (chromosome) and GO: 0044427 (chromosomal part) were associated with improvement (corrected P=0.007 and 0.045, respectively). The association between rs116692768 and symptom improvement was replicated in PGRN-AMPS (P=0.047), whereas rs76191705 was not. The two SNPs did not replicate in NEWMEDS. ITGA9 codes for a membrane receptor for neurotrophins and NRXN3 is a transmembrane neuronal adhesion receptor involved in synaptic differentiation. Despite their meaningful biological rationale for being involved in antidepressant effect, replication was partial. Further studies may help in clarifying their role.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2

Similar content being viewed by others

References

  1. Murray CJ, Atkinson C, Bhalla K, Birbeck G, Burstein R, Chou D et al. The state of US health, 1990-2010: burden of diseases, injuries, and risk factors. JAMA 2013; 310: 591–608.

    Article  CAS  PubMed  Google Scholar 

  2. Bradvik L, Mattisson C, Bogren M, Nettelbladt P . Long-term suicide risk of depression in the Lundby cohort 1947-1997—severity and gender. Acta Psychiatr Scand 2008; 117: 185–191.

    Article  CAS  PubMed  Google Scholar 

  3. Buist-Bouwman MA, De Graaf R, Vollebergh WA, Alonso J, Bruffaerts R, Ormel J et al. Functional disability of mental disorders and comparison with physical disorders: a study among the general population of six European countries. Acta Psychiatr Scand 2006; 113: 492–500.

    Article  CAS  PubMed  Google Scholar 

  4. Sobocki P, Jonsson B, Angst J, Rehnberg C . Cost of depression in Europe. J Ment Health Policy Econ 2006; 9: 87–98.

    PubMed  Google Scholar 

  5. Fabbri C, Di Girolamo G, Serretti A . Pharmacogenetics of antidepressant drugs: an update after almost 20 years of research. Am J Med Geneti B Neuropsychiatr Genet 2013; 162B: 487–520.

    Article  Google Scholar 

  6. O'Reilly RL, Bogue L, Singh SM . Pharmacogenetic response to antidepressants in a multicase family with affective disorder. Biol Psychiatry 1994; 36: 467–471.

    Article  CAS  PubMed  Google Scholar 

  7. Tansey KE, Guipponi M, Hu X, Domenici E, Lewis G, Malafosse A et al. Contribution of common genetic variants to antidepressant response. Biol Psychiatry 2013; 73: 679–682.

    Article  CAS  PubMed  Google Scholar 

  8. Perlis RH . Pharmacogenomic testing and personalized treatment of depression. Clin Chem 2014; 60: 53–59.

    Article  CAS  PubMed  Google Scholar 

  9. Garriock HA, Kraft JB, Shyn SI, Peters EJ, Yokoyama JS, Jenkins GD et al. A genomewide association study of citalopram response in major depressive disorder. Biol Psychiatry 2010; 67: 133–138.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Uher R, Perroud N, Ng MY, Hauser J, Henigsberg N, Maier W et al. Genome-wide pharmacogenetics of antidepressant response in the GENDEP project. Am J Psychiatry 2010; 167: 555–564.

    Article  PubMed  Google Scholar 

  11. Ising M, Lucae S, Binder EB, Bettecken T, Uhr M, Ripke S et al. A genomewide association study points to multiple loci that predict antidepressant drug treatment outcome in depression. Arch Gen Psychiatry 2009; 66: 966–975.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. GENDEP Investigators, MARS Investigators, STAR*D Investigators. Common genetic variation and antidepressant efficacy in major depressive disorder: a meta-analysis of three genome-wide pharmacogenetic studies. Am J Psychiatry 2013; 170: 207–217.

    Article  Google Scholar 

  13. Biernacka JM, Sangkuhl K, Jenkins G, Whaley RM, Barman P, Batzler A et al. The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response. Transl Psychiatry 2015; 5: e553.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ji Y, Biernacka JM, Hebbring S, Chai Y, Jenkins GD, Batzler A et al. Pharmacogenomics of selective serotonin reuptake inhibitor treatment for major depressive disorder: genome-wide associations and functional genomics. Pharmacogenomics J 2013; 13: 456–463.

    Article  CAS  PubMed  Google Scholar 

  15. Li QS, Tian C, Seabrook GR, Drevets WC, Narayan VA . Analysis of 23andMe antidepressant efficacy survey data: implication of circadian rhythm and neuroplasticity in bupropion response. Transl Psychiatry 2016; 6: e889.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. McCarthy S, Das S, Kretzschmar W, Durbin R, Abecasis G, Marchini J . A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 2016; 48: 1279–1283.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Tansey KE, Guipponi M, Perroud N, Bondolfi G, Domenici E, Evans D et al. Genetic predictors of response to serotonergic and noradrenergic antidepressants in major depressive disorder: a genome-wide analysis of individual-level data and a meta-analysis. PLoS Med 2012; 9: e1001326.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Montgomery SA, Asberg M . A new depression scale designed to be sensitive to change. Br J Psychiatry 1979; 134: 382–389.

    Article  CAS  PubMed  Google Scholar 

  19. Hamilton M . Development of a rating scale for primary depressive illness. Br J Soc Clin Psychol 1967; 6: 278–296.

    Article  CAS  PubMed  Google Scholar 

  20. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J . An inventory for measuring depression. Arch Gen Psychiatry 1961; 4: 561–571.

    Article  CAS  PubMed  Google Scholar 

  21. Trivedi MH, Rush AJ, Ibrahim HM, Carmody TJ, Biggs MM, Suppes T et al. The Inventory of Depressive Symptomatology, Clinician Rating (IDS-C) and Self-Report (IDS-SR), and the Quick Inventory of Depressive Symptomatology, Clinician Rating (QIDS-C) and Self-Report (QIDS-SR) in public sector patients with mood disorders: a psychometric evaluation. Psychol Med 2004; 34: 73–82.

    Article  CAS  PubMed  Google Scholar 

  22. Rush AJ, Fava M, Wisniewski SR, Lavori PW, Trivedi MH, Sackeim HA et al. Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design. Control Clin Trials 2004; 25: 119–142.

    Article  PubMed  Google Scholar 

  23. Hughes B . Novel consortium to address shortfall in innovative medicines for psychiatric disorders. Nat Rev Drug Discov 2009; 8: 523–524.

    Article  CAS  PubMed  Google Scholar 

  24. Thomas L, Mulligan J, Mason V, Tallon D, Wiles N, Cowen P et al. GENetic and clinical predictors of treatment response in depression: the GenPod randomised trial protocol. Trials 2008; 9: 29.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bondolfi G, Aubry JM, Golaz J, Gex-Fabry M, Gervasoni N, Bertschy G . A stepwise drug treatment algorithm to obtain complete remission in depression: a Geneva study. Swiss Med Wkly 2006; 136: 78–85.

    CAS  PubMed  Google Scholar 

  26. Gaynes BN, Warden D, Trivedi MH, Wisniewski SR, Fava M, Rush AJ . What did STAR*D teach us? Results from a large-scale, practical, clinical trial for patients with depression. Psychiatr Serv 2009; 60: 1439–1445.

    Article  PubMed  Google Scholar 

  27. Streiner DL . Breaking up is hard to do: the heartbreak of dichotomizing continuous data. Can J Psychiatry 2002; 47: 262–266.

    Article  PubMed  Google Scholar 

  28. Mrazek DA, Biernacka JM, McAlpine DE, Benitez J, Karpyak VM, Williams MD et al. Treatment outcomes of depression: the pharmacogenomic research network antidepressant medication pharmacogenomic study. J Clin Psychopharmacol 2014; 34: 313–317.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Frank E, Prien RF, Jarrett RB, Keller MB, Kupfer DJ, Lavori PW et al. Conceptualization and rationale for consensus definitions of terms in major depressive disorder. Remission, recovery, relapse, and recurrence. Arch Gen Psychiatry 1991; 48: 851–855.

    Article  CAS  PubMed  Google Scholar 

  30. Li H, Gu N, Zhang H, Wang G, Tan Q, Yang F et al. Efficacy and safety of quetiapine extended release monotherapy in bipolar depression: a multi-center, randomized, double-blind, placebo-controlled trial. Psychopharmacology 2016; 233: 1289–1297.

    Article  CAS  PubMed  Google Scholar 

  31. Jacobsen PL, Mahableshwarkar AR, Serenko M, Chan S, Trivedi MH . A randomized, double-blind, placebo-controlled study of the efficacy and safety of vortioxetine 10mg and 20mg in adults with major depressive disorder. J Clin Psychiatry 2015; 76: 575–582.

    Article  PubMed  Google Scholar 

  32. Moller HJ, Demyttenaere K, Olausson B, Szamosi J, Wilson E, Hosford D et al. Two Phase III randomised double-blind studies of fixed-dose TC-5214 (dexmecamylamine) adjunct to ongoing antidepressant therapy in patients with major depressive disorder and an inadequate response to prior antidepressant therapy. World J Biol Psychiatry 2015; 16: 483–501.

    Article  PubMed  Google Scholar 

  33. Svensson S, Mansfield PR . Escitalopram: superior to citalopram or a chiral chimera? Psychother Psychosom 2004; 73: 10–16.

    Article  PubMed  Google Scholar 

  34. Fournier JC, DeRubeis RJ, Hollon SD, Dimidjian S, Amsterdam JD, Shelton RC et al. Antidepressant drug effects and depression severity: a patient-level meta-analysis. JAMA 2010; 303: 47–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Anderson CA, Pettersson FH, Clarke GM, Cardon LR, Morris AP, Zondervan KT . Data quality control in genetic case-control association studies. Nat Protoc 2010; 5: 1564–1573.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Patterson N, Price AL, Reich D . Population structure and eigenanalysis. PLoS Genet 2006; 2: e190.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D . Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38: 904–909.

    Article  CAS  PubMed  Google Scholar 

  38. Wittke-Thompson JK, Pluzhnikov A, Cox NJ . Rational inferences about departures from Hardy-Weinberg equilibrium. Am J Hum Genet 2005; 76: 967–986.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR . MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010; 34: 816–834.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Pistis G, Porcu E, Vrieze SI, Sidore C, Steri M, Danjou F et al. Rare variant genotype imputation with thousands of study-specific whole-genome sequences: implications for cost-effective study designs. Eur J Hum Genet 2015; 23: 975–983.

    Article  PubMed  Google Scholar 

  41. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–575.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Fabbri C, Crisafulli C, Gurwitz D, Stingl J, Calati R, Albani D et al. Neuronal cell adhesion genes and antidepressant response in three independent samples. Pharmacogenomics J 2015; 15: 538–548.

    Article  CAS  PubMed  Google Scholar 

  43. deLeeuw CA, Mooij JM, Heskes T, Posthuma D . MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol 2015; 11: e1004219.

    Article  Google Scholar 

  44. McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 2016; 48: 1279–1283.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Dudbridge F, Gusnanto A . Estimation of significance thresholds for genomewide association scans. Genet Epidemiol 2008; 32: 227–234.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Feng S, Wang S, Chen CC, Lan L . GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits. BMC Genet 2011; 12: 12.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Skol AD, Scott LJ, Abecasis GR, Boehnke M . Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 2006; 38: 209–213.

    Article  CAS  PubMed  Google Scholar 

  48. Maranville JC, Cox NJ . Pharmacogenomic variants have larger effect sizes than genetic variants associated with other dichotomous complex traits. Pharmacogenomics J 2016; 16: 388–392.

    Article  CAS  PubMed  Google Scholar 

  49. Mendell JT, Sharifi NA, Meyers JL, Martinez-Murillo F, Dietz HC . Nonsense surveillance regulates expression of diverse classes of mammalian transcripts and mutes genomic noise. Nat Genet 2004; 36: 1073–1078.

    Article  CAS  PubMed  Google Scholar 

  50. Yepiskoposyan H, Aeschimann F, Nilsson D, Okoniewski M, Muhlemann O . Autoregulation of the nonsense-mediated mRNA decay pathway in human cells. RNA 2011; 17: 2108–2118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Tani H, Imamachi N, Salam KA, Mizutani R, Ijiri K, Irie T et al. Identification of hundreds of novel UPF1 target transcripts by direct determination of whole transcriptome stability. RNA Biol 2012; 9: 1370–1379.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Werner A, Berdal A . Natural antisense transcripts: sound or silence? Physiol Genomics 2005; 23: 125–131.

    Article  CAS  PubMed  Google Scholar 

  53. Smith AC, Scofield MD, Kalivas PW . The tetrapartite synapse: extracellular matrix remodeling contributes to corticoaccumbens plasticity underlying drug addiction. Brain Res 2015; 1628 (Pt A): 29–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lynch G, Kramar EA, Gall CM . Protein synthesis and consolidation of memory-related synaptic changes. Brain Res 2015; 1621: 62–72.

    Article  CAS  PubMed  Google Scholar 

  55. Probst-Schendzielorz K, Scholl C, Efimkina O, Ersfeld E, Viviani R, Serretti A et al. CHL1, ITGB3 and SLC6A4 gene expression and antidepressant drug response: results from the Munich Antidepressant Response Signature (MARS) study. Pharmacogenomics 2015; 16: 689–701.

    Article  CAS  PubMed  Google Scholar 

  56. Bot N, Schweizer C, Ben Halima S, Fraering PC . Processing of the synaptic cell adhesion molecule neurexin-3beta by Alzheimer disease alpha- and gamma-secretases. J Biol Chem 2011; 286: 2762–2773.

    Article  CAS  PubMed  Google Scholar 

  57. Christoforou A, McGhee KA, Morris SW, Thomson PA, Anderson S, McLean A et al. Convergence of linkage, association and GWAS findings for a candidate region for bipolar disorder and schizophrenia on chromosome 4p. Mol Psychiatry 2011; 16: 240–242.

    Article  CAS  PubMed  Google Scholar 

  58. Fabbri C, Serretti A . Genetics of long-term treatment outcome in bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 2016; 65: 17–24.

    Article  CAS  PubMed  Google Scholar 

  59. Dean B, Gibbons AS, Boer S, Uezato A, Meador-Woodruff J, Scarr E et al. Changes in cortical N-methyl-D-aspartate receptors and post-synaptic density protein 95 in schizophrenia, mood disorders and suicide. Aust N Z J Psychiatry 2016; 50: 275–283.

    Article  PubMed  Google Scholar 

  60. Nivard MG, Mbarek H, Hottenga JJ, Smit JH, Jansen R, Penninx BW et al. Further confirmation of the association between anxiety and CTNND2: replication in humans. Genes Brain Behav 2014; 13: 195–201.

    Article  CAS  PubMed  Google Scholar 

  61. Hunter AM, Leuchter AF, Power RA, Muthen B, McGrath PJ, Lewis CM et al. A genome-wide association study of a sustained pattern of antidepressant response. J Psychiatr Res 2013; 47: 1157–1165.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Hashimoto R, Numakawa T, Ohnishi T, Kumamaru E, Yagasaki Y, Ishimoto T et al. Impact of the DISC1 Ser704Cys polymorphism on risk for major depression, brain morphology and ERK signaling. Hum Mol Genet 2006; 15: 3024–3033.

    Article  CAS  PubMed  Google Scholar 

  63. Thomson PA, Parla JS, McRae AF, Kramer M, Ramakrishnan K, Yao J et al. 708 Common and 2010 rare DISC1 locus variants identified in 1542 subjects: analysis for association with psychiatric disorder and cognitive traits. Mol Psychiatry 2014; 19: 668–675.

    Article  CAS  PubMed  Google Scholar 

  64. Arias B, Fabbri C, Serretti A, Drago A, Mitjans M, Gasto C et al. DISC1-TSNAX and DAOA genes in major depression and citalopram efficacy. J Affect Disord 2014; 168: 91–97.

    Article  CAS  PubMed  Google Scholar 

  65. Schosser A, Gaysina D, Cohen-Woods S, Chow PC, Martucci L, Craddock N et al. Association of DISC1 and TSNAX genes and affective disorders in the depression case-control (DeCC) and bipolar affective case-control (BACCS) studies. Mol Psychiatry 2010; 15: 844–849.

    Article  CAS  PubMed  Google Scholar 

  66. Okuda A, Kishi T, Okochi T, Ikeda M, Kitajima T, Tsunoka T et al. Translin-associated factor X gene (TSNAX) may be associated with female major depressive disorder in the Japanese population. Neuromolecular Med 2010; 12: 78–85.

    Article  CAS  PubMed  Google Scholar 

  67. Kaiser VB, Svinti V, Prendergast JG, Chau YY, Campbell A, Patarcic I et al. Homozygous loss-of-function variants in European cosmopolitan and isolate populations. Hum Mol Genet 2015; 24: 5464–5474.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Menashe I, Man O, Lancet D, Gilad Y . Different noses for different people. Nat Genet 2003; 34: 143–144.

    Article  CAS  PubMed  Google Scholar 

  69. Belzeaux R, Bergon A, Jeanjean V, Loriod B, Formisano-Treziny C, Verrier L et al. Responder and nonresponder patients exhibit different peripheral transcriptional signatures during major depressive episode. Transl Psychiatry 2012; 2: e185.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Alrahbeni T, Sartor F, Anderson J, Miedzybrodzka Z, McCaig C, Muller B . Full UPF3B function is critical for neuronal differentiation of neural stem cells. Mol Brain 2015; 8: 33.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Meneghini V, Bortolotto V, Francese MT, Dellarole A, Carraro L, Terzieva S et al. High-mobility group box-1 protein and beta-amyloid oligomers promote neuronal differentiation of adult hippocampal neural progenitors via receptor for advanced glycation end products/nuclear factor-kappaB axis: relevance for Alzheimer's disease. J Neurosci 2013; 33: 6047–6059.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Colleoni S, Galli C, Gaspar JA, Meganathan K, Jagtap S, Hescheler J et al. Development of a neural teratogenicity test based on human embryonic stem cells: response to retinoic acid exposure. Toxicol Sci 2011; 124: 370–377.

    Article  CAS  PubMed  Google Scholar 

  73. Gaier ED, Eipper BA, Mains RE . Pam heterozygous mice reveal essential role for Cu in amygdalar behavioral and synaptic function. Ann NY Acad Sci 2014; 1314: 15–23.

    Article  CAS  PubMed  Google Scholar 

  74. Gaier ED, Rodriguiz RM, Ma XM, Sivaramakrishnan S, Bousquet-Moore D, Wetsel WC et al. Haploinsufficiency in peptidylglycine alpha-amidating monooxygenase leads to altered synaptic transmission in the amygdala and impaired emotional responses. J Neurosci 2010; 30: 13656–13669.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Anacker C, Zunszain PA, Carvalho LA, Pariante CM . The glucocorticoid receptor: pivot of depression and of antidepressant treatment? Psychoneuroendocrinology 2011; 36: 415–425.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Grover D, Verma R, Goes FS, Mahon PL, Gershon ES, McMahon FJ et al. Family-based association of YWHAH in psychotic bipolar disorder. Am J Med Genet B Neuropsychiatr Genet 2009; 150B: 977–983.

    Article  CAS  PubMed  Google Scholar 

  77. Gratten J, Wray NR, Keller MC, Visscher PM . Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat Neurosci 2014; 17: 782–790.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Uher R, Tansey KE, Malki K, Perlis RH . Biomarkers predicting treatment outcome in depression: what is clinically significant? Pharmacogenomics 2012; 13: 233–240.

    Article  PubMed  Google Scholar 

  79. Holmes RD, Tiwari AK, Kennedy JL . Mechanisms of the placebo effect in pain and psychiatric disorders. Pharmacogenomics J 2016; 16: 491–500.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the NIMH for having had the possibility of analyzing their data on the STAR-D sample. We also thank the authors of previous publications in this data set, and foremost, we thank the patients and their families who accepted to be enrolled in the study. Data and biomaterials were obtained from the limited access datasets distributed from the NIH-supported ‘Sequenced Treatment Alternatives to Relieve Depression’ (STAR*D). The study was supported by NIMH Contract No. N01MH90003 to the University of Texas Southwestern Medical Center. The ClinicalTrials.gov identifier is NCT00021528.

This paper represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. The GENDEP project was supported by a European Commission Framework 6 grant (contract reference: LSHB-CT-2003-503428). The Medical Research Council, United Kingdom, and GlaxoSmithKline (G0701420) provided support for genotyping.

The NEWMEDS study was funded by the Innovative Medicine Initiative Joint Undertaking (IMI-JU) under grant agreement no. 115008 of which resources are composed of European Union and the European Federation of Pharmaceutical Industries and Associations (EFPIA) in-kind contribution and financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013). EFPIA members Pfizer, GlaxoSmithKline and F Hoffmann La-Roche have contributed work and samples to the project presented here.

The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

The PGRN-AMPS data set used for the analyses described in this manuscript was obtained from dbGaP (study accession phs000670.v1.p1). PGRN-AMPS was supported, in part, by NIH grants RO1 GM28157, U19 GM61388 (The Pharmacogenomics Research Network), U01 HG005137, R01 CA138461, P20 1P20AA017830-01 (The Mayo Clinic Center for Individualized Treatment of Alcohol Dependence) and a PhRMA Foundation Center of Excellence in Clinical Pharmacology Award.

RU is supported by the Canada Research Chairs Program.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C M Lewis.

Ethics declarations

Competing interests

NH participated in clinical trials sponsored by pharmaceutical companies including GlaxoSmithKline and Lundbeck. DS is serving in national advisory boards or consulting for Janssen, TEVA, GlaxoSmithKline. His center is receiving unrestricted financial support from Lundbeck and Fondation René de Spoellberghe. WM, KJA, AEF and PMcG have received consultancy fees and honoraria for participating in expert panels from pharmaceutical companies including Lundbeck and GlaxoSmithKline and Roche Diagnostics. RHP reported serving on scientific advisory boards or consulting for Genomind LLC, Healthrageous, Pfizer, Perfect Health, Proteus Biomedical, PsyBrain and RID Ventures LLC and reported receiving royalties through Massachusetts General Hospital from Concordant Rater Systems (now Bracket/Medco). NP received honoraria for participating in expert panels from pharmaceutical companies including Lundbeck. GB is a member of a national advisory board for Bristol-Myer Squibb and Pfizer and has received research funding from GlaxoSmithKline, Wyeth-Lederle, Bristol-Myers-Squibb and Sanofi Aventis. The department of MO’D received £2000 in lieu of an honorarium to MO’D from Lilly as a result of his participation in sponsored symposia in 2012. Those symposia were unrelated to the contents of this manuscript. The other authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website

Supplementary information

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fabbri, C., Tansey, K., Perlis, R. et al. New insights into the pharmacogenomics of antidepressant response from the GENDEP and STAR*D studies: rare variant analysis and high-density imputation. Pharmacogenomics J 18, 413–421 (2018). https://doi.org/10.1038/tpj.2017.44

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/tpj.2017.44

This article is cited by

Search

Quick links