Genetic Predictors of Response to Serotonergic and Nonadrenergic Antidepressants In Major Depressive Disorder: A Genome-Wide Analysis of Individual Level Data and a Meta-Analysis
Katherine E. Tansey1, Michel Guipponi2, Nader Perroud3,Guido Bondolfi3, Enrico Domenici4,5, David Evans6,Stephanie K. Hall7, Joanna Hauser8, Neven Henigsberg9,Xiaolan Hu7, Borut Jerman10,11, Wolfgang Maier12, Ole Mors13, Michael O'Donovan14, Tim J. Peters15, Anna Placentino16, Marcella Rietschel17, Daniel Souery18,Katherine J. Aitchison1,19, Ian Craig1, Anne Farmer1, Jens R. Wendland5, Alain Malafosse2,3, Peter Holmans14, Glyn Lewis20, Cathryn M. Lewis1, Tine Bryan Stensbøl21, Shitij Kapur1, Peter McGuffin1, Rudolf Uher1,22*
1 Institute of Psychiatry, King's College London, London, United Kingdom, 2Department of Genetic Medicine and Laboratories, University Hospitals of Geneva, Geneva, Switzerland, 3 Department of Psychiatry, University of Geneva, Geneva, Switzerland, 4 Center of Excellence for Drug Discovery in Psychiatry, GlaxoSmithKline Medicines Research Centre, Verona, Italy, 5 Pharma Research and Early Development, F. Hoffmann–La Roche, Basel, Switzerland, 6 Medical Research Council CAiTE Centre, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom, 7 Molecular Medicine, Pfizer, Groton, Connecticut, United States of America, 8 Laboratory of Psychiatric Genetics, Poznan University of Medical Sciences, Poznan, Poland, 9Croatian Institute for Brain Research, Medical School, University of Zagreb, Zagreb, Croatia, 10 Department of Molecular and Biomedical Sciences, Jozef Stefan Institute, Ljubljana, Slovenia, 11 Institute of Public Health of the Republic of Slovenia, Ljubljana, Slovenia, 12 Department of Psychiatry, University of Bonn, Bonn, Germany, 13 Centre for Psychiatric Research, Aarhus University Hospital, Risskov, Denmark, 14 Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Cardiff, United Kingdom, 15 School of Clinical Sciences, University of Bristol, Bristol, United Kingdom, 16 Psychiatric Unit 23, Department of Mental Health, Spedali Civili Hospital and Biological Psychiatry Unit, Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy, 17 Central Institute of Mental Health, Division of Genetic Epidemiology in Psychiatry, Mannheim, Germany, 18Department of Psychiatry, Erasme Academic Hospital, Université Libre de Bruxelles, Brussels, Belgium, 19 Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada, 20 School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom, 21 Discovery Pharmacology Research, H. Lundbeck A/S, Copenhagen, Denmark, 22Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada). No biological pathways were significantly overrepresented in the results. No significant associations (genome-wide significance p<5 sup="sup">−8) were detected in a meta-analysis of NEWMEDS and another large sample (STAR*D), with 2,897 individuals in total. Polygenic scoring found no convergence among multiple associations in NEWMEDS and STAR*D.5>
No single common genetic variant was associated with antidepressant response at a clinically relevant level in a European-ancestry cohort. Effects specific to particular antidepressant drugs could not be investigated in the current study.
Please see later in the article for the Editors' Summary) by genotyping centre were excluded. Overall, 520,978 (99.0%) of the 526,424 genotyped SNPs passed all stages of quality control and were included in pharmacogenetic analyses. Hardy–Weinberg equilibrium was tested, but was not used as an exclusion criterion for markers, since departures from Hardy–Weinberg equilibrium are expected in a case-only study .
Individuals were excluded for ambiguous sex (genotypic sex different from phenotypic sex) (n = 22), abnormal heterozygosity (n = 16), cryptic relatedness up to third-degree relatives by identity by descent (n= 20), genotyping completeness less than 97% (n = 9), and non-European ethnicity admixture detected as outliers in iterative EIGENSTRAT analyses of a linkage-disequilibrium-pruned dataset (n = 35). One additional individual was excluded because of invalid phenotypic information, leaving 1,790 (94.6%) of the 1,893 genotyped individuals for the pharmacogenetic analyses.
Figure 1 shows the flow of individuals through genotyping and quality control.
Figure 1. Flow of samples through quality control.doi:10.1371/journal.pmed.1001326.g001
Definition of Antidepressant Response Phenotype
Response to antidepressants involves changes in depressive symptoms over a number of weeks and is more accurately captured by continuous than by dichotomous variables ,–. We defined response as a continuous variable, reflecting proportional reduction in depression severity from baseline to end of treatment. This measure is uncorrelated with initial severity (−0.10<r<0 .10=".10" a="a" all="all" and="and" clinically="clinically" clinician="clinician" closely="closely" component="component" depression="depression" href="http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1001326#pmed.1001326-Uher3" impression="impression" improvement="improvement" in="in" independent="independent" is="is" it="it" nbsp="nbsp" of="of" rating="rating" reflects="reflects" relevant="relevant" s="s" scale="scale" since="since" studies="studies" style="color: #0066cc; text-decoration: underline;" used="used">0>,.
Studies included in NEWMEDS used several outcome measures. The Montgomery–Åsberg Depression Rating Scale  was the primary outcome measure in GENDEP and GODS, the 17-item Hamilton Rating Scale for Depression (HRSD-17)  was the primary outcome measure in the studies conducted by Pfizer and GlaxoSmithKline, and the Beck Depression Inventory  was the primary outcome measure in GENPOD. While the outcome measures used differ in details, here we are interested in generalizable effects related to depression as a whole rather than effects specific to a particular measure. Previous research has shown no difference between classes of antidepressants in response as measured by these three scales . In addition, we took the following steps to minimize the effects of scale differences. To allow for an unbiased analysis of the combined dataset, we converted the outcome measures within each study to a single continuous metric: a standardized change score, adjusted for sex, age, and recruitment centre within each contributing study. The adjusted change score was z-transformed within each study to remove any correlation between data origin and outcome prior to the genetic analysis, and to remove effects that are specific to individual contributing studies. Detailed information about the definition of the phenotype is provided in Text S1 (section 1.3).
Data analysis was carried out according to a protocol specified prior to data acquisition (Text S2). Joint analysis of individual-level data was conducted to allow for rigorous quality control and to retain maximum statistical power when combining studies that varied in size. The whole sample was analyzed jointly since this is a more efficient and powerful approach than discovery–replication design ,. Quality-controlled genotypes were tested for association with the adjusted percentage change in depression severity using linear regression under an additive genetic model in PLINK . Four genome-wide analyses were performed. A first linear regression searched for common genetic markers that predict response to both types of antidepressants in the whole sample of 1,790 individuals. The second and third analyses tested predictors of response to SRIs (n = 1,222) and NRIs (n = 568). A fourth analysis, of primary interest to our second aim, searched the genome for common variants that differentially predict response to SRIs and NRIs. To avoid confounding by covariation between antidepressant drug and genetic background due to different studies contributing unequal numbers of individuals to each antidepressant group, we tested this hypothesis as a drug-by-genotype interaction in a sample restricted to individuals that were randomly allocated to treatment with either an SRI or an NRI (n = 949), thus ensuring full comparability of the two drug groups on measured and unmeasured confounders. In all analyses, the influence of genetic population structure was controlled by the inclusion of the four significant principal components from the final iteration of the EIGENSTRAT analysis of linkage-disequilibrium-pruned genetic data. We defined genome-wide statistical significance at the generally accepted threshold p<5 sup="sup">−85>.
Associations reaching a less stringent threshold of p<5 sup="sup">−65>are reported in Text S1 (section 2). Analyses performed by component study, summary data meta-analysis (Text S1, section 6), and pharmacogenetic associations within genes reported in previous candidate gene and genome-wide studies (Text S1, section 7) are also provided.
Our aim was to determine whether any common genetic variant predicts a clinically significant difference in the outcome of treatment with antidepressants, taking a difference of at least three points in the reduction of depression symptom severity on HRSD-17 as the benchmark for clinical significance . Specifically, we aimed to achieve 80% power to detect an additive genetic effect that explains 6.33% of variance in outcome, corresponding to an HRSD-17 three-point difference in a drug comparison study . Since not all common genetic variants were directly genotyped, we factored in imperfect tagging (at R2 = 0.8) to estimate power for detecting effects of genotyped and ungenotyped variants. The quality-controlled sample provided a power well above 80% to detect a clinically significant effect at the genome-wide significance level for three of the four analyses (overall, SRI, and genotype–drug interaction). The meta-analysis of NEWMEDS and STAR*D samples had an adequate power to detect even an effect that was half of what would be considered clinically significant. Details of the power analysis can be found in Text S1 (section 1.4).
Enrichment of genome-wide association signal in genes that belong to known biological pathways was tested using ALIGATOR . This method takes a predefined list of significant SNPs, and tests whether these SNPs cluster in genes belonging to a particular pathway more than would be expected by chance, allowing for varying numbers of SNPs per gene and non-independence of SNPs within and between genes. ALIGATOR corrects the significance levels of pathway-specific enrichment for the testing of multiple non-independent pathways. Further details of the pathway analysis can be found in Text S1 (section 3).
Meta-Analysis with STAR*D
A meta-analysis was undertaken between NEWMEDS and data subsequently obtained from the first level of the STAR*D (Sequenced Treatment Alternatives to Relieve Depression) trial, when all participants with MDD were treated with citalopram (an SRI), in the hope of finding genome-wide significant associations. For further information about the STAR*D sample, genotyping, and quality control procedures see Text S1(section 4.1).
To maximize the overlap between the two samples and genome coverage, both NEWMEDS and STAR*D were imputed to include over 1.4 million markers using BEAGLE 3.3  and the HapMap phase 3 CEU population as the reference dataset. Meta-analysis was undertaken using the meta command in PLINK in the entire samples and in a sample limited to individuals treated with a serotonergic antidepressant. More information about these methods used can be found in Text S1 (section 4).
While both our main analysis in NEWMEDS and the meta-analysis with STAR*D had sufficient power to detect clinically significant genetic associations, there could also exist an underlying weak signal from across the genome that could offer insight into the mechanism of antidepressant response. The methodology of polygene scoring allows for the detection of such weakly distributed signal . Details on this method can be found in Text S1 (section 5). Polygenic scores were created based on the NEWMEDS results and used to predict outcomes in STAR*D using linear regression. Two polygenic tests, one based on the entire NEWMEDS sample and the other restricted to SRI-treated participants, were carried out.
Response to Any Antidepressant
Linear regression assessed the influence of 520,978 SNPs on the adjusted percentage change in depression severity in the whole sample of 1,790 antidepressant-treated individuals. A quantile–quantile plot showed a uniform distribution of p-values, with no inflation of the test statistic (median lambda = 1.0034; Figure 2). No association reached the genome-wide level of significance (Figure 3).
Figure 2. Quantile–quantile plots for the four genome-wide analyses.
(A) Analysis of the whole sample (n = 1,790); (B) analysis of SRI-treated individuals (n = 1,222); (C) analysis of NRI-treated individuals (n = 568); (D) gene-by-drug interaction analysis in the randomly allocated individuals (n = 949). The shaded area is the 95% confidence interval of values expected under a uniform distribution.doi:10.1371/journal.pmed.1001326.g002
Figure 3. Manhattan plots for the genome-wide pharmacogenetic analyses showing results by −log10 p-value and chromosome location.
The red line indicates the genome-wide significance level (p<5 .0=".0" sup="sup">−85>
Response to Serotonergic Antidepressants
A linear regression tested association between 520,978 SNPs and the adjusted percentage change in depression severity in 1,222 SRI-treated individuals. The quantile–quantile plot showed a uniform distribution of p-values, indicating no inflation of the test statistic (median lambda = 1.0094; Figure 2). No SNP was associated at the genome-wide level of significance (Figure 3).
Response to Noradrenergic Antidepressants
A linear regression tested association between 520,978 SNPs and adjusted percentage change in depression severity in 568 NRI-treated individuals. The quantile–quantile plot showed a uniform distribution of p-values, with no inflation of the test statistic (median lambda = 0.9875; Figure 2). There were no significant associations (Figure 3).
Differential Response to Serotonergic and Noradrenergic Antidepressants
A linear regression tested the interaction between 520,978 SNPs and antidepressant type (SRI versus NRI) in their effects on the adjusted percentage change in depression severity among the 949 individuals randomly allocated to SRI or NRI antidepressant. The quantile–quantile plot showed a uniform distribution of p-values, with no inflation of the test statistic (median lambda = 1.0015; Figure 2). No genotype–drug interactions were detected at the genome-wide level of significance (Figure 3).
For all four analyses, a meta-analysis of results from contributing studies also gave negative results (seeText S1, section 6).
Pathway analysis tested whether any biological pathways had more genes in the top 5% of genes (ranked by their most significant SNP) than expected by chance. None of the four analyses (response to any antidepressant, serotonergic antidepressants, noradrenergic antidepressants, and differential response to serotonergic and noradrenergic antidepressants) showed a significant excess in the number of enriched pathways, and no single pathway was significantly enriched after correcting for multiple testing. Full results are given in Text S1 (section 3.2).
Meta-Analysis with STAR*D
A meta-analysis tested percentage improvement in 2,897 individuals from NEWMEDS and STAR*D using over 1.1 million genotyped and imputed SNPs and found no genome-wide significant results. A meta-analysis restricted to SRI-treated individuals (n = 2,329) found no genome-wide significant results. For more information see Text S1 (section 4.4).
Polygenic scores were calculated to test the combined effect of multiple weak associations across the genome. Scores were created using NEWMEDS and used to predict outcomes in STAR*D. In the analysis that included all individuals (NEWMEDS, n = 1,790; STAR*D, n = 1,107), there was no significant prediction across the 13 progressive p-value thresholds. In a sample restricted to SRI-treated individuals (NEWMEDS, n = 1,222; STAR*D, n = 1,107), there was no significant prediction from any of the 13 progressive p-value thresholds either. Further information about the results can be found in Text S1(section 5.2).
In a large pharmacogenetic analysis, including 1,790 antidepressant-treated individuals with MDD, none of the more than 500,000 genetic markers predicted treatment outcome after genome-wide correction. Since our study had adequate statistical power to detect common genetic variants with a clinically significant predictive effect, the results suggest that single marker prediction will not contribute to personalizing prescription of currently available antidepressants. Increasing sample size may aid in obtaining positive results in future studies, which may provide insight into the mechanism of the therapeutic action of antidepressants, even though the effect size will likely be too small to translate into clinical applications.
The lack of genome-wide significant or even moderately strong associations among a comprehensive list of candidate genes (details in Text S1, section 7) puts previously reported positive results from smaller candidate gene studies , into a sobering perspective. The current study also fails to strengthen associations found previously in the genome-wide association study of the GENDEP sample , the largest sub-sample in the NEWMEDS consortium, or other genome-wide pharmacogenetic studies ,,. Furthermore, the current investigation fails to find a genome-wide significant association in the largest pharmacogenetic meta-analysis to date, which included 2,897 individuals. Pathway analysis has not shown any enrichment for a known biological pathway. Polygenic prediction has not found any evidence for a distributed convergent signal between the two largest pharmacogenetic samples collected to date. It is therefore possible that common polymorphisms will not help predict the outcome of treatment with commonly used antidepressants in a clinical meaningful way.
The present study benefited from a large sample through the combination of participants from multiple studies. This limits the interpretation of the present results in several ways. One limitation is the use of multiple antidepressants, each differing slightly in its chemical structure, transporter affinity, and receptor binding profile. Based on previous pharmacogenetic data , we hypothesized the existence of common genetic predictors of response to antidepressants with broadly defined modes of action, such as serotonin and noradrenaline reuptake inhibition. While we found no predictors of response to SRI or NRI types of antidepressants, our results are compatible with the existence of pharmacogenetic effects that are specific to a particular antidepressant compound. Large groups treated with the same drug may uncover genetic predictors that were not detected in the present study. However, the largest existing sample treated with the same antidepressant has also failed to detect significant genetic predictors in a genome-wide analysis, and it is unlikely that even larger samples with homogeneous treatment will be collected in the near future. The studies included in NEWMEDS also differed in other aspects, e.g., in the depression rating scale used and in the way participants were recruited. The adjusted change score used as the phenotype in this study removed effects specific to each individual contributing study. This measure was intended to minimize the risk of spurious findings, but it could have reduced the impact of a genuinely larger treatment effect in a particular study. However, our aim was to detect pragmatic predictors that generalize to multiple settings rather than effects specific to a particular homogeneous group. It is unlikely that pharmacogenetic predictions limited to a particular depression rating scale or to a more homogeneous subgroup of patients would be clinically meaningful or commercially viable. Furthermore, a meta-analysis conducted across the individual studies provided similar results (see Text S1, section 6). A related limitation was the smaller size of the NRI-treated sample, meaning that only relatively strong predictors could be identified in this arm of the study. Additional limitations pertain to the scope of the present study. Our results are limited to the influence of common polymorphisms on the therapeutic effects of several monoaminergic antidepressants under study in individuals of European ancestry. Studies in other populations and studies of antidepressants with a non-monoaminergic mode of action are needed to extend the scope of pharmacogenetic exploration.
Conclusions and Future Directions
Our study adds to the growing literature of genome-wide pharmacogenetic studies, offering, to our knowledge, the largest body of pharmacogenetic data available to date. The absence of pharmacogenetic associations with clinically meaningful effect suggests that common genetic variation is not ready to inform personalization of treatment for depression. Future studies may need to combine clinical, genetic, epigenetic, transcriptomic, and proteomic information to obtain clinically meaningful prediction of how an individual with major depression will respond to antidepressant treatment.
Supporting Information TopText S1.
Supplementary methods and results. Supplementary text, tables, and figures including further information about the individual studies included in the analysis, suggestive (p<5 sup="sup">−65>
Author Contributions Top
Conceived and designed the experiments: RU PM JRW TBS SK. Performed the experiments: MG. Analyzed the data: KET PH. Contributed reagents/materials/analysis tools: NP GB ED DE SKH JH NH XH BJ WM OM MO'D TJP AP MR DS KJA IC AF AM GL PM. Wrote the first draft of the manuscript: KET RU. Contributed to the writing of the manuscript: KET MG NP GB ED DE SKH JH NH XH BJ WM OM MO'D TJP AP MR DS KJA IC AF JRW AM PH GL CML TBS SK PM RU. ICMJE criteria for authorship read and met: KET MG NP GB ED DE SKH JH NH XH BJ WM OM MO'D TJP AP MR DS KJA IC AF JRW AM PH GL CML TBS SK PM RU. Agree with manuscript results and conclusions: KET MG NP GB ED DE SKH JH NH XH BJ WM OM MO'D TJP AP MR DS KJA IC AF JRW AM PH GL CML TBS SK PM RU. Supervised the statistical analyses: CML.
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Hat Tip to Mad In America
Right, and how many More of these incredibly involved Studies are Humanity expected to wade through, to discover Yet Again, that human experience is Not a Genetically caused Disease?
And how much More Money is going to be thrown down the Junk Science Rat Hole conducting them, all for the purposes of
1: trying to sell More Drugs which cure, absolutely, nothing.
2: justifying the heavenly budgets of the researchers who keep coming up with, FAIL.
Even With the commercial concerns Funding this study, the results were so weak that they couldn't be spun into a positive light.
pic cred of Heath Ledger as the Joker, Burning the Money to Warner Brothers
Autopsy and toxicology report
After two weeks of intense media speculation about possible causes of Ledger's death, on 6 February 2008, the Office of the Chief Medical Examiner of New York released its conclusions, based on an initial autopsy of 23 January 2008, and a subsequent complete toxicological analysis. The report concludes, in part, "Mr. Heath Ledger died as the result of acute intoxication by the combined effects of oxycodone, hydrocodone, diazepam, temazepam, alprazolam and doxylamine." It states definitively: "We have concluded that the manner of death is accident, resulting from the abuse of prescription medications." The medications found in the toxicological analysis are commonly prescribed in the United States for insomnia, anxiety, depression, pain, or common cold symptoms, or any combination thereof. Although the Associated Press and other media reported that "police estimate Ledger's time of death between 1 pm and 2:45 pm" (on 22 January 2008), the Medical Examiner's Office announced that it would not be publicly disclosing the official estimated time of death. The official announcement of the cause and manner of Ledger's death heightened concerns about the growing problems of prescription drug abuse or misuse and combined drug intoxication (CDI).