Elsevier

Journal of Psychiatric Research

Volume 47, Issue 9, September 2013, Pages 1157-1165
Journal of Psychiatric Research

A genome-wide association study of a sustained pattern of antidepressant response

https://doi.org/10.1016/j.jpsychires.2013.05.002Get rights and content

Abstract

Genome-wide association studies (GWAS) have failed to replicate common genetic variants associated with antidepressant response, as defined using a single endpoint. Genetic influences may be discernible by examining individual variation between sustained versus unsustained patterns of response, which may distinguish medication effects from non-specific, or placebo responses to active medication. We conducted a GWAS among 1116 subjects with Major Depressive Disorder from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial who were characterized using Growth Mixture Modeling as showing a sustained versus unsustained pattern of clinical response over 12 weeks of treatment with citalopram. Replication analyses examined 585 subjects from the Genome-based Therapeutic Drugs for Depression (GENDEP) trial. The strongest association with sustained as opposed to unsustained response in STAR*D involved a single nucleotide polymorphism (SNP; rs10492002) within the acyl-CoA synthetase short-chain family member 3 gene (ACSS3, p-value = 4.5 × 10−6, odds ratio = 0.61). No SNPs met our threshold for genome-wide significance. SNP data were available in GENDEP for 18 of the top 25 SNPs in STAR*D. The most replicable association was with SNP rs7816924 (p = 0.008, OR = 1.58); no SNP met the replication p-value threshold of 0.003. Joint analysis of these 18 SNPs resulted in the strongest signal coming from rs7816924 (p = 2.11 × 10−7), which resides in chondroitin sulfate N-acetylgalactosaminyltransferase 1 gene (CSGALNACT1). An exploratory genetic pathway analysis revealed evidence for an involvement of the KEGG pathway of long-term potentiation (FDR = .02). Results suggest novel genetic associations to sustained response.

Introduction

Treatment with antidepressant medications is associated with significant improvements in clinical symptoms of Major Depressive Disorder (MDD), as well as improvements in functional status and quality of life. However, there is marked heterogeneity of outcomes including a subset of patients who show unsustained response (Muthén et al., 2011; Quitkin et al., 1984). Inter-individual variation in antidepressant response is under genetic influence (Tansey et al., 2012a), yet no genetic marker has shown a consistent association with clinical outcome (Tansey et al., 2012b; Uher et al., 2013). Limited progress in predicting drug efficacy may be in part due to heterogeneity in MDD related to complex gene–environment etiology (Keers and Aitchison, 2011), or the inability to separate specific response to antidepressants from naturalistic course or placebo response (Malhotra, 2010; Malhotra et al., 2012), among other factors.

The discovery of predictors of clinical response may also depend critically on the classification of outcomes. MDD trials commonly define outcomes using a predetermined cutoff score assessed at a single primary endpoint. This approach fails to account for patterns of change in clinical symptoms over time (Muthén et al., 2008) and may not reflect clinically or physiologically meaningful distinctions (Uher et al., 2010a, Uher et al., 2010b). Clinical changes over time are especially relevant when subjects exhibit alternating improvement and worsening of symptoms (Hunter et al., 2010) or a U-shaped pattern of outcome (Muthén et al., 2011; Quitkin et al., 1984). Unsustained response is clinically undesirable and may represent a “placebo” response rather than a “true drug” effect (Quitkin et al., 1984). Insofar as differences between sustained and unsustained response patterns may reflect a physiological substrate, it is of interest to examine these phenotypes for genetic association.

Advanced statistical modeling techniques have identified various response patterns, including unsustained response during acute antidepressant treatment. Growth mixture modeling (GMM) is a systematic, data-driven approach that utilizes symptom severity measures from all available time points to identify distinct trajectories of response; cluster analytic features are incorporated into GMM to reveal latent “classes” or patterns of change in symptom severity over time (Muthén and Asparouhov, 2009; Muthén and Shedden, 1999). Such techniques have been successfully applied to longitudinal data to identify response patterns of clinical relevance during pharmacotherapy interventions in MDD (Gueorguieva et al., 2011; Hunter et al., 2010; Muthén et al., 2011; Power et al., 2012; Uher et al., 2009, Uher et al., 2010a, Uher et al., 2010b, 2011).

GMM was recently applied to data from the Sequenced Treatment Alternatives to Relieve Depression trial (STAR*D) (Trivedi et al., 2006), a large open-label multi-site study that, because of its size and inclusion of “real world” patients, is especially well suited to this technique. Analyses that examined all available scores on the 16-item clinician-rated Quick Inventory of Depressive Symptoms (QIDS-C) (Rush et al., 2003b) obtained at baseline and over 12 weeks during Level 1 treatment with citalopram yielded fundamental trajectory shapes providing evidence of four classes: ‘non-responders’; ‘partial improvers’; ‘sustained responders’ (SUS) showing monotonic improvement culminating in response at week 12; and ‘unsustained responders’ (UNS), showing U-shaped response-level improvement by week 6 but with a return of baseline-level symptoms by week 12 (Fig. 1) (Muthén et al., 2011). SUS and UNS responder class sizes ranged from 32% to 45%, and 6% to19%, respectively, depending on the model (Muthén et al., 2011).

We hypothesize that sustained and unsustained response trajectories represent biologically distinct types of response to antidepressants. To test this hypothesis, we conducted a genome-wide association study (GWAS) contrasting STAR*D subjects in SUS versus UNS response trajectory classes to determine whether common DNA variation determines durability of response to antidepressant treatment. Identification of individuals unlikely to sustain antidepressant response would have great clinical utility, providing incentive for aggressive optimization of treatment in susceptible individuals.

Section snippets

Overview

GWAS was conducted in the STAR*D dataset to test for association between single-locus SNP variants and durability of response ('sustained' versus 'unsustained' response class outcomes defined using GMM). SNPs with the strongest association were then examined prospectively for replication in subjects from the Genome-based Therapeutic Drugs for Depression (GENDEP) study. Secondary, gene-based analyses were conducted to determine the association between combined effects of SNPs within individual

STAR*D clinical and demographic features

A total of 1116 subjects from STAR*D were analyzed (Supplemental Table 2). There were 869 subjects who were classified as SUS, while there were 247 subjects who were classified as UNS using our GMM algorithm. These latter subjects are described by a pattern of initial response to treatment with return of symptoms over time. Clinical and demographic variables were tested for association with the sustained response phenotype. Those that were associated with the sustained response pattern at a

Discussion

We carried out a GWAS to address the hypothesis that DNA variation influences a pattern of unsustained as compared to sustained antidepressant response in individuals with unipolar major depression in the STAR*D sample. Our strongest finding involved ACSS3, a gene not previously linked with antidepressant biology. The risk allele increased the likelihood of the unsustained response. There is no overlap between the top findings from this analysis and our previous GWAS of citalopram response in

Financial disclosures

Drs. Garriock, Hamilton, Lewis, and Power reported no biomedical financial interests or potential conflicts of interest.

Dr. Hunter is an inventor of a UCLA-assigned patent pending method to predict antidepressant effects.

Dr. Leuchter has received research support from Covidien, Neuronetics, NeuroSigma, NIMH, and Shire Pharmaceuticals. Dr. Leuchter further serves, or has served, in the following capacities for biomedical companies: Chair, Neuroscience Advisory Board, Covidien, Newton, MA

Contributors

Drs. Hunter, Leuchter, and Hamilton conceptualized the study.

Dr. Hunter managed the literature searches and analyses.

Drs. Hunter and Hamilton wrote the first draft of the manuscript with contributions from Drs. Leuchter, Power, Muthèn, Cook, and Uher.

Drs. Power, Uher, and Hamilton conducted statistical analyses.

Drs. Muthèn, McGrath, Lewis, Garriock, and McGuffin contributed critically to the design of the underlying studies and/or analyses. All authors contributed to revisions and have approved

Role of the funding source

Clinical data used in the GWAS were obtained from the limited access datasets distributed from the NIMH-supported “Sequenced Treatment Alternatives to Relieve Depression” (STAR*D) (Contract # N01MH90003 to the University of Texas Southwestern Medical Center). Genotyping of the STAR*D sample was supported by NIMH grant MH072802 to S.P. Hamilton. Analysis of STAR*D clinical data and response trajectories was supported by NIMH grant 1R34MH085933-01 to A.M. Hunter. This manuscript reflects the

Acknowledgments

A portion of this paper was presented in poster format at the 11th Annual Pharmacogenetics in Psychiatry meeting March 31, 2012, New York, NY. The trajectories that form the basis for sustained and unsustained patterns of response in STAR*D were previously reported in Muthén et al. (2011).

References (41)

  • S. Purcell et al.

    PLINK: a tool set for whole-genome association and population based linkage analyses

    American Journal of Human Genetics

    (2007)
  • A.J. Rush et al.

    The 16-item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression

    Biological Psychiatry

    (2003)
  • D.M. Altshuler et al.

    Integrating common and rare genetic variation in diverse human populations

    Nature

    (2010)
  • W.J. Gauderman

    Sample size requirements for association studies of gene–gene interaction

    The American Journal of Epidemiology

    (2002)
  • R. Gueorguieva et al.

    Trajectories of depression severity in clinical trials of duloxetine: insights into antidepressant and placebo responses

    Archives of General Psychiatry

    (2011)
  • C.F. Kao et al.

    Enriched pathways for major depressive disorder identified from a genome-wide association study

    International Journal of Neuropsychopharmacology

    (2012)
  • R. Keers et al.

    Pharmacogenetics of antidepressant response

    Expert Review of Neurotherapeutics

    (2011)
  • G. Laje et al.

    Pharmacogenetics studies in STAR*D: strengths, limitations, and results

    Psychiatric Services

    (2009)
  • A.K. Malhotra

    The pharmacogenetics of depression: enter the GWAS

    American Journal of Psychiatry

    (2010)
  • A.K. Malhotra et al.

    Pharmacogenetics in psychiatry: translating research into clinical practice

    Molecular Psychiatry

    (2012)
  • Cited by (51)

    • Plasma microRNA expression levels and their targeted pathways in patients with major depressive disorder who are responsive to duloxetine treatment

      2019, Journal of Psychiatric Research
      Citation Excerpt :

      Further exploratory analysis was performed to identify pathways that may be involved in predisposing one to (baseline data) or change as a result of (time 2 data) remission with treatment in both duloxetine and placebo treatment groups, as it may identify processes that could be involved in treatment response regardless of the type of treatment used. Interestingly, the identified pathways included those involved in chondroitin sulfate synthesis, apoptosis, and nitric oxide synthesis and metabolism, which were suggested to be involved in the pathophysiology of MDD and antidepressant response in previous studies (Hunter et al., 2013; Oliveira et al., 2008; Shelton et al., 2011). The findings of this study must be interpreted in light of its limitations.

    • Genetic variants in major depressive disorder: From pathophysiology to therapy

      2019, Pharmacology and Therapeutics
      Citation Excerpt :

      A SNP near the NEDD4L gene was demonstrated to be associated with antidepressant response using the STAR*D population, but in Caucasians results became unconvincing (Antypa, Drago, & Serretti, 2014). No SNP reached GWS in an investigation of sustained vs non-sustained response, but KEGG pathway long-term potentiation remained significant after correction (Hunter et al., 2013). Another study also failed to demonstrate significantly associated SNPs with SSRI or NRI treatment response (Tansey et al., 2012).

    • Emerging roles of Fgf14 in behavioral control

      2019, Behavioural Brain Research
    • Roles of CSGalNAcT1, a key enzyme in regulation of CS synthesis, in neuronal regeneration and plasticity

      2018, Neurochemistry International
      Citation Excerpt :

      Human PNNs may play a role in psychiatric diseases such schizophrenia, depression, or attention deficit hyperactivity disorder (Pantazopoulos and Berretta, 2016). Genome-wide association studies for sensitivity to the antidepressant citalopram revealed that of 18 single nucleotide polymorphisms examined, the strongest signal was seen for rs7816924 (p = 2.11 × 10−7), which resides in T1 (Hunter et al., 2013). As mentioned above, T1 is necessary for normal skeletal development (Watanabe et al., 2010; Sato et al., 2011).

    • Pharmacogenetics in Psychiatry

      2018, Advances in Pharmacology
      Citation Excerpt :

      The most strongly supported pathways in antidepressant response are involved in neuroplasticity and inflammation processes. Indeed the long-term potentiation pathway, the inorganic cation transmembrane transporter activity pathway, and the GAP43 pathway (Fabbri et al., 2015) are involved in hippocampal plasticity and neurogenesis and they were associated with antidepressant efficacy (Cocchi et al., 2016; Fabbri et al., 2015; Hunter et al., 2013). These pathways include a number of genes coding for glutamate receptors (GRM1, GRM5, GRIA1, GRIN2A, GRIN2B, GRIN2C), postsynaptic L-type voltage-dependent Ca2 + channels (CACNA1C, CACNA1C, CACNB1, CACNB2), regulators of GABA and glutamatergic neurotransmission (e.g., ZDHHC7, NRG1, and HOMER1), and cell adhesion processes (e.g., FN1, EFNA5, and EPHA5).

    View all citing articles on Scopus
    View full text