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Keywords:

  • antidepressant;
  • polygenic scoring;
  • bipolarity;
  • major depressive disorder

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGEMENTS
  9. REFERENCES
  10. Supporting Information

The high heterogeneity of response to antidepressant treatment in major depressive disorder (MDD) makes individual treatment outcomes currently unpredictable. It has been suggested that resistance to antidepressant treatment might be due to undiagnosed bipolar disorder or bipolar spectrum features. Here, we investigate the relationship between genetic susceptibility for bipolar disorder and response to treatment with antidepressants in MDD. Polygenic scores indexing risk for bipolar disorder were derived from the Psychiatric Genomics Consortium Bipolar Disorder whole genome association study. Linear regressions tested the effect of polygenic risk scores for bipolar disorder on proportional reduction in depression severity in two large samples of individuals with MDD, treated with antidepressants, NEWMEDS (n = 1,791) and STAR*D (n = 1,107). There was no significant association between polygenic scores for bipolar disorder and response to treatment with antidepressants. Our data indicate that molecular measure of genetic susceptibility to bipolar disorder does not aid in understanding non-response to antidepressants. © 2013 Wiley Periodicals, Inc.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGEMENTS
  9. REFERENCES
  10. Supporting Information

Response to antidepressants is heterogeneous with high levels of inter-individual variation in treatment outcome. There is the need to identify specific sub-groups of patients who may respond better or worse to currently available treatments in order to personalise treatment and reduce the time to remission of the disorder. Among factors to be involved in the lack of response to treatment with antidepressants, undiagnosed bipolar disposition has received the most attention [Ghaemi et al., 2002; Ghaemi, 2008; Smith et al., 2009; Perlis et al., 2011; Smith et al., 2011].

The distinction between major depressive disorder (MDD) and bipolar disorder can be difficult in individuals who present during a depressive episode and impossible in those who have not yet developed their first episode of mania but may have a latent bipolar disposition [Ghaemi et al., 2002]. Studies have suggested that individuals with bipolar disorder may not benefit from antidepressants more than from placebo [Sachs and Gardner-Schuster, 2007; McElroy et al., 2010]. Previous research has shown individuals who fail to respond to treatment with antidepressant are more likely to change diagnosis from MDD to bipolar disorder in the future [Sharma et al., 2005; Li et al., 2012]. Among individuals with MDD, sub-threshold manic symptoms predicted poor outcome of treatment with antidepressants [Smith et al., 2009], suggesting bipolar features in MDD result in resistance to treatment with antidepressants.

However, there have been contradictory findings. A large study of MDD individuals treated with antidepressants found no association between unrecognised bipolar spectrum disorder and treatment resistance [Perlis et al., 2011]. The lack of consistency in this area warrants further investigations.

Previous studies have relied on clinical features to define bipolar disposition in individuals diagnosed with MDD. This method relies on the manifestation and reporting of signs of a disorder and may not detect latent disposition to developing bipolar disorder. With advancements in psychiatric genetics, we can for the first time attempt to answer this question using specific genetic susceptibility for bipolar disorder, indexed by a score summing a large number of risk variants across the human genome, based on results of the largest genetic association study under taken to date, the Psychiatric Genomics Consortium Bipolar Disorder [PGC-BP; 7,481 individuals with bipolar disorder and 9,250 controls; Psychiatric GWAS Consortium Bipolar Disorder Working Group 2011]. Polygenic scores based on these results have been shown to significantly predict bipolar disorder and other phenotypes of interest (endophenotypes and clinical dimensions) in independent samples [Hamshere et al., 2011; Psychiatric GWAS Consortium Bipolar Disorder Working Group 2011; Whalley et al., 2012]. Furthermore, polygenic score derived from bipolar disorder have been able to discriminate between cases and controls in MDD [Schulze et al., 2012; Cross-Disorder Group of the Psychiatric Genomics Consortium et al. 2013]. We hypothesize that increased genetic susceptibility for bipolar disorder would be associated with poor response to antidepressant treatment. We investigate the effect of increased genetic loading for bipolar disorder on antidepressant treatment response in two large pharmacogenetic studies of individuals with MDD treated with antidepressants.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGEMENTS
  9. REFERENCES
  10. Supporting Information

Samples

We analyzed two large cohorts of adults with major depressive disorder (MDD), with prospectively recorded outcome of antidepressant treatment and genome-wide genotyping, the Novel Methods leading to New Medications in Depression and Schizophrenia (NEWMEDS) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D).

NEWMEDS (http://www.newmeds-europe.com) included 2,146 treatment seeking adults (age range 18–74 years) treated for up to 12 weeks with serotonin-reuptake inhibiting (SSRI: escitalopram, citalopram, paroxetine, sertraline, fluoxetine) or norepinephrine reuptake inhibiting (NRI: nortriptyline, reboxetine) antidepressants as part of several academic (GENDEP n = 868; GENPOD n = 601; GODS n = 131) and industry-led clinical trials [Pfizer n = 355, GSK n = 191; Tansey et al., 2012]. Individuals with self-reported white European ancestry, available high-quality blood DNA samples and valid information on treatment outcome with either SSRI or NRI were genotyped (n = 1,893). Gender and age did not differ substantially between drug groups (any antidepressant mean age 42.19 (SD 11.60) 62.91% female; serotonergic antidepressants mean age 42.18 (SD 11.54) 62.15% female; noradrenergic antidepressants mean age 42.20 (SD 11.70) 63.96% female). Each study included in NEWMEDS was approved by institutional review boards and all participants signed informed consent. Further information about individual samples can be found in Supplementary Materials.

STAR*D (http://www.nimh.nih.gov/trials/practical/stard/index.shtml) included 4,041 treatment-seeking adult outpatients (age range 18–75 years) with a diagnosis of non-psychotic MDD recruited across the United States [Rush et al., 2004]. Individuals were included if they had a minimal depression severity of 14 on Hamilton Rating Scale for Depression [HRSD-17; Hamilton, 1960]. This analysis focuses on the first treatment step, when all individuals were treated with protocol-guided citalopram 20–60 mg daily [Trivedi et al., 2006] in the 1,948 individuals with available DNA samples. Mean age of individuals included in this study was 43.17 (SD 13.72) and 57.54% were females. STAR*D was approved by institutional ethics review boards in all centers. All participants provided a written consent after the procedures and associated risks were explained.

Genotyping and Quality Control

Quality control was implemented in PLINK [Purcell et al., 2007].

NEWMEDS samples were genotyped on Illumina Human610-Quad BeadChip (n = 727) or Illumina Human 660W-Quad BeadChip (n = 1,166; Illumina, Inc., San Diego, CA). We included markers with a minor allele frequency of 0.01 or more and at least 97% complete genotyping. We excluded markers that differed significantly (P < 1 × 10−3) by genotyping center. We excluded individuals for ambiguous sex (n = 22), abnormal heterozygosity (n = 16), cryptic relatedness up to third-degree relatives by identity by descent (n = 20), genotyping completeness <97% (n = 9), non-European ethnicity admixture detected as outliers in an iterative EIGENSTRAT analyses of an LD-pruned dataset (n = 35), and invalid phenotypic information (n = 1), resulting in a sample of 1,790 genotyped individuals.

STAR*D samples were genotyped on the Human Mapping 500 K Array Set (n = 969) or Affymetrix Genome-Wide SNP Array 5.0 (n = 979; Affymetrix, South San Francisco, CA). We included makers with a minor allele frequency over 0.01, and at least 95% complete genotyping increasing to 99% if the minor allele frequency was below 0.05 following the criteria set by the Wellcome Trust Case Control Consortium when they used data from the same platform [Wellcome Trust Case Control Consortium, 2007]. To avoid batch artefacts, we excluded markers that differed significantly (P < 1 × 10−3) by genotyping center. We removed individuals from the analysis for ambiguous sex (n = 115), abnormal heterozygosity (n = 3), cryptic relatedness up to third-degree relatives by identity by descent (n = 13), genotyping completeness <97% (n = 5), non-European ethnicity admixture detected as outliers in an iterative EIGENSTRAT analyses of an LD-pruned dataset (n = 681), and invalid phenotypic information (n = 24), resulting in 1,107 genotyped individuals for the analysis.

Both samples were imputed using Beagle 3.3 [Browning and Browning, 2007] and HapMap phase 3 CEU population as the reference dataset.

Phenotype Definition

We defined antidepressant response as a continuous variable reflecting proportional reduction in depression severity on the primary depression rating scale from baseline to the end of treatment, adjusted for age, sex and recruiting center/study. Proportional reduction in severity is the preferred measure for antidepressant response as it is uncorrelated with severity at baseline, relatively independent of which depression rating scale is used, and best reflects the clinician's impression of improvement while avoiding arbitrary cut-offs [Streiner, 2002; Mulder et al., 2003; Royston et al., 2006; Uher et al., 2010a, 2010b]. We used each study's primary depression rating scale (Montgomery-Asberg Depression Rating Scale, Hamilton Rating Scale for Depression, Beck Depression Inventory or the clinician-rated Quick Inventory for Depression Symptomatology), and minimized the effects of different scales and other study-specific factors by converting outcome measures within each study to standardized change score, adjusted for sex, age, and recruitment center within each contributing study, z-transformed within each study.

Polygenic Scoring

We used the publicly available results from the Psychiatric Genomics Consortium Bipolar Disorder (PGC-BP) meta-analysis [Psychiatric GWAS Consortium Bipolar Disorder Working Group 2011]. The PGC-BP meta-analysis included 7,481 individuals with bipolar disorder and 9,250 controls analyzed at over 2.4 million genetic markers. Each contributing study included in the PGC-BP used standardized semi-structured interviews to collect clinical information about lifetime history of psychiatric illness, and operational criteria were applied to make lifetime diagnoses which included Bipolar Disorder I (BPI), Bipolar Disorder II (BPII), Schizoaffective Disorder—Bipolar Type (SAB) and Bipolar Disorder—not otherwise specified (BD-NOS; see Supplementary Table 1 of Psychiatric GWAS Consortium Bipolar Disorder Working Group [2011] for further information). These polygenic scores explained 2.83% of the genetic variation in the bipolar phenotype in an independent replication sample (see Supplementary Table 9 of Psychiatric GWAS Consortium Bipolar Disorder Working Group [2011] for further information). While the proportion of genetic variation explained is low the results were highly significant (P = 1.71 × 10−9).

The polygenic score methodology follows the procedure described elsewhere [Purcell et al., 2009]. SNPs were removed from the PGC-BP results if they had a low minor allele frequency (MAF < 0.02) or resided in the MHC region, and were subsequently pruned by linkage disequilibrium (LD). We used the “score” command in PLINK to calculated the polygenic scores [Purcell et al., 2007]. We scored both NEWMEDS and STAR*D samples for their polygenic risk of Bipolar Disorder using imputed data. Polygenic scores are calculated by the averaging number of susceptibility alleles of the index SNP weighted by the logarithm of the SNP odds ratios. Five different progressive P-value thresholds (P < 0.0005, 0.05, 0.1, 0.3, 0.5) for SNPs in the PGC-BP study were used to calculate polygenic scores in each individual. The resulting scores were tested as predictors of improvement (proportional reduction of depression severity from baseline to study exit) using linear regressions which were performed using STATA/SE 10 [StataCorp, 2007]. Analyses were also run co-varying for principal components from the final iteration of the EIGENSTRAT analysis of linkage-disequilibrium-pruned genetic data to minimize the influence of population stratification [Price et al., 2006]. This was to ensure that subtle differences in population structure were not affecting the results. For NEWMEDS this resulted in four principal components being used, and for STAR*D, six principal components.

Power Analysis

We aimed to determine if genetic disposition to bipolar disorder affects response to antidepressants with an effect size that is clinically significant [Uher et al., 2012] or accounts for a significant proportion of the prediction bipolar disorder itself. We determine what effect size our samples were able to detect with 80% power using the pwr package [Power analysis functions along the lines of Cohen, 1988] in R [2011]. The entire NEWMEDS sample had 80% power to detect an effect size (R2) of 0.0044, an effect which is one order of magnitude smaller what could be considered a clinically significant prediction [Uher et al., 2012] and a sixth of the effect seen when using polygenic scoring between different bipolar samples [R2 = 0.0283 in the PGC Bipolar meta-analysis; Psychiatric GWAS Consortium Bipolar Disorder Working Group 2011]. The serotonergic only NEWMEDS analysis had 80% power to detect an effect size (R2) of 0.00639, less than a fourth of the effect reported in the PGC Bipolar meta-analysis. The noradrenergic only NEWMEDS analysis had 80% power to detect an effect size (R2) of 0.0137; about half of what is the reported prediction between different bipolar samples [Psychiatric GWAS Consortium Bipolar Disorder Working Group 2011]. The STAR*D sample had 80% power to detect an effect size (R2) of 0.00706.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGEMENTS
  9. REFERENCES
  10. Supporting Information

Polygenic scores were investigated as predictors of improvement using proportional reduction of depression severity corrected for age, sex, and center of recruitment, using linear regressions in the entire of NEWMEDS, NEWMEDS separated by drug class (SSRI or NRI) and STAR*D. Polygenic scores calculated from the PGC Bipolar Data were not significantly related to antidepressant response in any of the four analyses tested at any of the five different P values thresholds used (Table I). Results are presented for both regression models tested, with and without covariates for population stratification. There was no linear relationship between genetic susceptibility for bipolar disorder and antidepressant response (Fig. 1).

Table I. Results From Linear Regression Performed Between Polygenic Score for Bipolar Disorder and Response to Antidepressants From NEWMEDS and STAR*D
SampleSample sizeNumber of SNPsP-Value thresholdWithout population covariatesWith population covariates
R2P-ValueR2P-Value
NEWMEDS All17904050.0005>0.00010.837>0.00010.834
  14,1610.05>0.00010.837>0.00010.880
  24,0960.10.00020.5900.00010.624
  54,5770.30.00010.6060.00010.623
  77,3950.50.00010.6210.00010.633
NEWMEDS SSRI only12224050.0005>0.00010.829>0.00010.782
  14,1610.050.00020.6380.00020.613
  24,0960.1>0.00010.911>0.00010.878
  54,5770.3>0.00010.934>0.00010.999
  77,3950.5>0.00010.865>0.00010.931
NEWMEDS NRI only5684050.0005>0.00010.960>0.00010.999
  14,1610.050.00270.2140.00310.184
  24,0960.10.00290.2020.00340.168
  54,5770.30.00150.3550.00240.252
  77,3950.50.00100.4630.00170.338
STAR*D11074050.0005>0.00010.918>0.00010.982
  14,1610.050.00010.774>0.00010.876
  24,0960.1>0.00010.9560.00010.828
  54,5770.30.00010.6860.00010.826
  77,3950.5>0.00010.815>0.00010.942
image

Figure 1. Scatter plots of the relationship between polygenic score for bipolar disorder and response to antidepressants. Top left is NEWMEDS ALL, top right NEWMEDS SSRI, bottom left NEWMEDS NRI and bottom right STAR*D.

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DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGEMENTS
  9. REFERENCES
  10. Supporting Information

It has been previously suggested that failure to respond to treatment with antidepressants in individuals diagnosed with MDD is due to partly to latent bipolarity. In this manuscript, we tested the hypothesis that individuals with a higher polygenic risk score for bipolar disorder respond poorly to treatment with antidepressants. Our results suggest that failure to respond to antidepressant treatment in MDD is not due to increased genetic susceptibility to bipolar disorder.

Our results align with previous work undertaken in the largest investigation of clinical features of bipolar spectrum in individuals with MDD treated with antidepressants, which found no effect of multiple bipolar features, including family history of bipolar disorder, on outcome of antidepressant treatment [Perlis et al., 2011]. The results of this previous large investigation along with our negative findings for a specific genetic effect mean that latent bipolar disposition is unlikely to significantly affect antidepressant efficacy in individuals diagnosed with MDD.

The polygenic scores used in the this analysis for bipolar disorder come from a large consortium and offer the best evidence for genetic associations of bipolar disorder [Psychiatric GWAS Consortium Bipolar Disorder Working Group 2011]. Our use of multiple progressive P-value thresholds takes into consideration the polygenic nature of the traits and the possibility of true genetic association existing with too small an effect size to be currently considered significant. Polygenic risk scores for bipolar disorder reliably predict bipolar disorder in independent samples [Psychiatric GWAS Consortium Bipolar Disorder Working Group 2011], and are able to demonstrate the share genetic architecture between bipolar disorder and other psychiatric disorders like schizophrenia and major depressive disorder [Schulze et al., 2012; Cross-Disorder Group of the Psychiatric Genomics Consortium et al. 2013]. Furthermore, bipolar polygenic risk scores are significantly associated with other phenotypes of interest like altered neural activation [Whalley et al., 2012] as well as the personality dimension extraversion [Middeldorp et al., 2011]. Polygenic scores derived from similar disorders that have a similar genetic architecture, such as schizophrenia and major depressive disorder, significantly predict a wide range of phenotypes including psychosis dimensions [Derks et al., 2012], negative cognitive change [McIntosh et al., 2013], total brain and white matter volume [Terwisscha van Scheltinga et al., 2013], and white matter integrity [Whalley et al., 2013]. These studies demonstrate the power and utility of this method to significantly predict the shared genetic architecture of a wide range of phenotypes.

We utilized data from the largest samples available for both deriving the polygenic scores for bipolar disorder as well as antidepressant pharmacogenetic studies, giving our study sufficient power to detect even an effect one order of magnitude smaller than what could be considered clinically significant [Uher et al., 2012]. The NEWMEDS and STAR*D samples included serotonergic (citalopram, escitalopram, sertraline, fluoxetine, and paroxetine) and noradrenergic (nortriptyline and reboxentine) antidepressants. The interpretation of the present results are therefore limited to these types of antidepressants and to White Caucasian population. Our results may not generalize to other types of antidepressants or to individuals from ethnic backgrounds other than white European. Future studies of other types of antidepressants and different populations may offer important insights. Polygenic risk scores created from the PGC-BP dataset are not being suggested as tools for personalizing treatment for individuals with MDD, but were used to decipher potential reasons about non-response to antidepressant treatment. The present negative findings, taking together with previous positive findings with other phenotypes using the same methodology and bipolar polygenic risk scores, constitute evidence against the hypothesis that latent bipolarity is responsible for substantial amount of treatment resistance among individuals diagnosed with MDD.

CONCLUSIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGEMENTS
  9. REFERENCES
  10. Supporting Information

Differentiating between bipolar disorder and major depressive disorder remains an important element of clinical practice. However, we found no evidence that testing for latent genetic disposition to bipolar disorder could contribute to response to antidepressant treatment among individuals with apparent MDD. It may be time to rethink the hypothesis about the causes of treatment resistance in MDD, and open new investigations into the reasons for treatment failure at both the clinical and biological level.

ACKNOWLEDGEMENTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGEMENTS
  9. REFERENCES
  10. Supporting Information

The research leading to these results has received support from 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, Glaxo Smith Kline, 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. GENDEP was funded by the European Commission Framework 6 grant, EC Contract Ref.: LSHB-CT-2003-503428. Lundbeck provided nortriptyline and escitalopram for the GENDEP study. GlaxoSmithKline and the UK National Institute for Health Research of the Department of Health contributed to the funding of the sample collection at the Institute of Psychiatry, London. GENDEP genotyping was funded by a joint grant from the U.K. Medical Research Council (MRC, UK) and GlaxoSmithKline (G0701420). GenPod was funded by the Medical Research Council (MRC, UK) and supported by the Mental Health Research Network. GODS study was partly supported by external funding provided by the Swiss branches of the following pharmaceutical companies: GlaxoSmithKline, Wyeth-Lederle, Bristol-Myers-Squibb, and Sanofi Aventis. R.U. is supported by the Canada Research Chairs program (http://www.chairs-chaires.gc.ca/). K.E.T. and R.U. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. The authors thank the National Institute of Mental Health (NIMH) for providing access to the STAR*D genetic dataset. STAR*D was funded by NIMH via contract (N01MH90003) to the University of Texas Southwestern Medical Center at Dallas (A. John Rush, principal investigator). STAR*D genotyping was supported by the National Institute of Mental Health (NIMH) grant to SPH (Grant No. MH072802).

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGEMENTS
  9. REFERENCES
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGEMENTS
  9. REFERENCES
  10. Supporting Information

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