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Identifying the Common Genetic Basis of Antidepressant Response

Open AccessPublished:August 02, 2021DOI:https://doi.org/10.1016/j.bpsgos.2021.07.008

      Abstract

      Background

      Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction.

      Methods

      Genome-wide analysis of remission (nremit = 1852, nnonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism–based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA.

      Results

      Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism–based heritability was significantly different from zero for remission (h2 = 0.132, SE = 0.056) but not for percentage improvement (h2 = −0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response.

      Conclusions

      This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.

      Keywords

      Major depressive disorder (MDD) is the third leading cause of years lived with disability worldwide (
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      • Mattisson C.
      • Bogren M.
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      Long-term suicide risk of depression in the Lundby cohort 1947–1997—Severity and gender.
      ). MDD confers a major personal, societal, and economic burden (
      • Sobocki P.
      • Jönsson B.
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      • Rehnberg C.
      Cost of depression in Europe.
      ), partly because of the limited efficacy of treatment options.
      In 2011 to 2014, 12.7% of individuals in the United States 12 years of age and over reported antidepressant medication use (
      • Pratt L.A.
      • Brody D.J.
      • Gu Q.
      Antidepressant use among persons aged 12 and over: United States, 2011-2014.
      ). The rate of antidepressant prescriptions is also increasing, with the number of prescriptions doubling in the United Kingdom in the decade prior to 2018 (
      • Iacobucci G.
      NHS prescribed record number of antidepressants last year.
      ). Antidepressants are robustly linked to a reduction in depressive symptoms (
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      Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: A systematic review and network meta-analysis.
      ), but they are often ineffective: approximately 35% of patients remit after their primary treatment (
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      Evaluation of outcomes with citalopram for depression using measurement-based care in STAR∗ D: Implications for clinical practice.
      ) and approximately 40% develop treatment-resistant depression (TRD), defined as not remitting after two or more antidepressants (
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      • Calati R.
      • Oswald P.
      • Massat I.
      • Konstantinidis A.
      • et al.
      Switching antidepressant class does not improve response or remission in treatment-resistant depression.
      ). For patients, the process of trialing antidepressants can be lengthy and demoralizing, delaying recovery and exposing patients to a range of potential side effects that reduce adherence and willingness to try new drugs (
      • Wang S.-M.
      • Han C.
      • Bahk W.-M.
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      Addressing the side effects of contemporary antidepressant drugs: A comprehensive review.
      ). There is therefore great potential to improve treatment of depression through better understanding of the factors that control response to antidepressants and implementing this knowledge through individually tailored treatment.
      Pharmacogenetic studies were expected to uncover loci with large effects on drug response and adverse events due to effects of pharmacokinetic or pharmacodynamic mechanisms. While associations between antidepressant plasma levels and drug-metabolizing enzymes CYP2D6 and CYP2C19 have been identified (
      • Grasmäder K.
      • Verwohlt P.L.
      • Rietschel M.
      • Dragicevic A.
      • Müller M.
      • Hiemke C.
      • et al.
      Impact of polymorphisms of cytochrome-P450 isoenzymes 2C9, 2C19 and 2D6 on plasma concentrations and clinical effects of antidepressants in a naturalistic clinical setting.
      ,
      • McAlpine D.E.
      • Biernacka J.M.
      • Mrazek D.A.
      • O’Kane D.J.
      • Stevens S.R.
      • Langman L.J.
      • et al.
      Effect of cytochrome P450 enzyme polymorphisms on pharmacokinetics of venlafaxine.
      ,
      • Huezo-Diaz P.
      • Perroud N.
      • Spencer E.P.
      • Smith R.
      • Sim S.
      • Virding S.
      • et al.
      CYP2C19 genotype predicts steady state escitalopram concentration in GENDEP.
      ), previous research suggests that genes encoding these enzymes and other candidate genes account for a small proportion of variation in drug response (
      • van Westrhenen R.
      • Aitchison K.J.
      • Ingelman-Sundberg M.
      • Jukić M.M.
      Pharmacogenomics of antidepressant and antipsychotic treatment: How far have we got and where are we going?.
      ,
      • Solomon H.V.
      • Cates K.W.
      • Li K.J.
      Does obtaining CYP2D6 and CYP2C19 pharmacogenetic testing predict antidepressant response or adverse drug reactions?.
      ). However, genotyping complexities for such candidate genes may contribute to limited findings.
      Several genome-wide association studies (GWASs) have been performed to identify genetic predictors of antidepressant response. Although no robustly replicated associations have been detected to date (
      GENDEP InvestigatorsMARS InvestigatorsSTAR∗D Investigators
      Common genetic variation and antidepressant efficacy in major depressive disorder: A meta-analysis of three genome-wide pharmacogenetic studies.
      ,
      • Biernacka J.M.
      • Sangkuhl K.
      • Jenkins G.
      • Whaley R.M.
      • Barman P.
      • Batzler A.
      • et al.
      The International SSRI Pharmacogenomics Consortium (ISPC): A genome-wide association study of antidepressant treatment response.
      ,
      • Fabbri C.
      • Kasper S.
      • Kautzky A.
      • Bartova L.
      • Dold M.
      • Zohar J.
      • et al.
      Genome-wide association study of treatment-resistance in depression and meta-analysis of three independent samples, 2018/11/23.
      ,
      • Tansey K.E.
      • 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.
      ,
      • Fabbri C.
      • Tansey K.E.
      • Perlis R.H.
      • Hauser J.
      • Henigsberg N.
      • Maier W.
      • et al.
      New insights into the pharmacogenomics of antidepressant response from the GENDEP and STAR∗ D studies: Rare variant analysis and high-density imputation.
      ), common single nucleotide polymorphisms (SNPs) are reported to explain 42% (SE = 18%; 95% confidence interval [CI], 7%–77%) of the variance (
      • Tansey K.E.
      • Guipponi M.
      • Hu X.
      • Domenici E.
      • Lewis G.
      • Malafosse A.
      • et al.
      Contribution of common genetic variants to antidepressant response.
      ). Pharmacogenetic studies are intensive to perform, requiring disease severity measures at baseline pretreatment and then longitudinally, with many studies being performed as part of a randomized controlled trial (
      GENDEP InvestigatorsMARS InvestigatorsSTAR∗D Investigators
      Common genetic variation and antidepressant efficacy in major depressive disorder: A meta-analysis of three genome-wide pharmacogenetic studies.
      ,
      • Biernacka J.M.
      • Sangkuhl K.
      • Jenkins G.
      • Whaley R.M.
      • Barman P.
      • Batzler A.
      • et al.
      The International SSRI Pharmacogenomics Consortium (ISPC): A genome-wide association study of antidepressant treatment response.
      ,
      • Fabbri C.
      • Kasper S.
      • Kautzky A.
      • Bartova L.
      • Dold M.
      • Zohar J.
      • et al.
      Genome-wide association study of treatment-resistance in depression and meta-analysis of three independent samples, 2018/11/23.
      ,
      • Tansey K.E.
      • 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.
      ). This clinically assessed approach provides high-quality data, though it has led to previous studies being limited in sample size, with <3000 patients with MDD in the largest GWAS to date. Further efforts to combine these individual cohorts to increase sample size for genetic studies are therefore required. Use of lighter phenotyping approaches such as electronic health record–derived TRD (
      • Wigmore E.M.
      • Hafferty J.D.
      • Hall L.S.
      • Howard D.M.
      • Clarke T.-K.
      • Fabbri C.
      • et al.
      Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP.
      ) may also provide novel insight, though it is unclear whether these different measures of antidepressant response have a common genetic basis.
      In this study, we analyze genome-wide genetic data on clinically assessed antidepressant response from 5843 patients treated for MDD, combined from 13 international research studies. Using this novel data resource, we perform GWAS of remission and percentage improvement after receiving antidepressant medication, and undertake extensive post-GWAS analyses, made feasible through this increased sample size. This study aims to elucidate the genetic architecture of antidepressant response and use polygenic scores to establish the relationship between antidepressant response and mental health outcomes. We find, for the first time, a replicable polygenic signal of antidepressant response across studies.

      Methods and Materials

      Primary Samples and Measures

      This study analyzed 13 cohorts (Table 1). Ten cohorts were of European ancestry and 3 were of East Asian ancestry (Supplement 1). All subjects provided written informed consent for pharmacogenetic analyses. These primary cohorts include individuals with a clinical diagnosis of MDD, who were assessed for depressive symptoms before and after treatment with antidepressants.
      Table 1Cohorts of Individuals Diagnosed With Major Depressive Disorder and Assessed for Depressive Symptoms Before and After Treatment With Antidepressant Medication
      Study (Reference)Country, RegionStudy DesignStudy Length, WeeksMedication(s)MeasureMedian Age, YearsIQR for Age, YearsFemaleN
      Number of participants included after quality control of genetic and clinical data.
      npercentage improvementnremitnnonremit
      European Ancestry
      STAR∗D (
      • Garriock H.A.
      • Kraft J.B.
      • Shyn S.I.
      • Peters E.J.
      • Yokoyama J.S.
      • Jenkins G.D.
      • et al.
      A genomewide association study of citalopram response in major depressive disorder.
      )
      United StatesOpen label12CitalopramQIDSC4432–5358%11631163506657
      GSRD (
      • Fabbri C.
      • Kasper S.
      • Kautzky A.
      • Bartova L.
      • Dold M.
      • Zohar J.
      • et al.
      Genome-wide association study of treatment-resistance in depression and meta-analysis of three independent samples, 2018/11/23.
      )
      EuropeNaturalistic>4VariousMADRS52.543–6166%11521152189963
      GENDEP (
      • Uher R.
      • Perroud N.
      • Ng M.Y.M.
      • Hauser J.
      • Henigsberg N.
      • Maier W.
      • et al.
      Genome-wide pharmacogenetics of antidepressant response in the GENDEP project.
      )
      EuropePartially randomized RCT12Escitalopram, nortriptylineMADRS4333–5163%783783291365
      DAST (see Supplement 1)GermanyNaturalistic inpatient6VariousHAMD-215037–6257%586586245303
      PGRN-AMPS (
      • Mrazek D.A.
      • Biernacka J.M.
      • McAlpine D.E.
      • Benitez J.
      • Karpyak V.M.
      • Williams M.D.
      • et al.
      Treatment outcomes of depression: The pharmacogenomic research network antidepressant medication pharmacogenomic study.
      )
      United StatesOpen label8Citalopram, escitalopramQIDSC38.528–4963%490392200290
      GENPOD (
      • Tansey K.E.
      • 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.
      )
      United KingdomOpen label12Citalopram, reboxetineBDI3830–4869%474474169305
      PFZ (
      • Tansey K.E.
      • 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.
      )
      United StatesRCT6-8Sertraline, fluoxetine, paroxetineHAMD-174332–5467%30930999210
      Mayo (
      • Biernacka J.M.
      • Sangkuhl K.
      • Jenkins G.
      • Whaley R.M.
      • Barman P.
      • Batzler A.
      • et al.
      The International SSRI Pharmacogenomics Consortium (ISPC): A genome-wide association study of antidepressant treatment response.
      )
      United StatesOpen label8Citalopram, escitalopramHAMD-173729–5162%1561568076
      GSK (
      • Tansey K.E.
      • 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.
      )
      United StatesRCT8EscitalopramHAMD-173625.75–4555%1321325676
      GODS (
      • Tansey K.E.
      • 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.
      )
      SwitzerlandOpen label8ParoxetineMADRS3729.5–43.552%71711754
      East Asian Ancestry
      Miaoli (
      • Biernacka J.M.
      • Sangkuhl K.
      • Jenkins G.
      • Whaley R.M.
      • Barman P.
      • Batzler A.
      • et al.
      The International SSRI Pharmacogenomics Consortium (ISPC): A genome-wide association study of antidepressant treatment response.
      )
      TaiwanOpen label8Escitalopram, paroxetineHAMD-174130–5282%233233103130
      Taipei (
      • Biernacka J.M.
      • Sangkuhl K.
      • Jenkins G.
      • Whaley R.M.
      • Barman P.
      • Batzler A.
      • et al.
      The International SSRI Pharmacogenomics Consortium (ISPC): A genome-wide association study of antidepressant treatment response.
      )
      TaiwanOpen label8Fluoxetine, citalopramHAMD-174634–5955%17417445129
      Japan (
      • Biernacka J.M.
      • Sangkuhl K.
      • Jenkins G.
      • Whaley R.M.
      • Barman P.
      • Batzler A.
      • et al.
      The International SSRI Pharmacogenomics Consortium (ISPC): A genome-wide association study of antidepressant treatment response.
      )
      JapanRCT6Fluvoxamine, paroxetineHAMD-1744.532–5647%1201207842
      Total5843574520783600
      BDI, Beck Depression Inventory; DAST, Depression and Sequence of Treatment; GENDEP, Genome Based Therapeutic Drugs for Depression; GENPOD, GENetic and clinical Predictors Of treatment response in Depression; GODS, Geneva Outpatient Depression Study; GSK, Glaxo Smith Kline; GSRD, Group for the Study of Resistant Depression; HAMD-17, 17-item Hamilton Depression Rating Scale; HAMD-21, 21-item Hamilton Depression Rating Scale; IQR, interquartile range; MADRS, Montgomery–Åsberg Depression Rating Scale; PFZ, Pfizer; PGRN-AMPS, Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study; QIDSC, Quick Inventory of Depressive Symptomatology; RCT, randomized controlled trial; STAR∗D, Sequenced Treatment Alternatives to Relieve Depression.
      a Number of participants included after quality control of genetic and clinical data.
      Two measures of antidepressant response were defined: remission and percentage improvement. Remission is a binary measure attained when a patient’s depression symptom score decreases to a prespecified threshold for the rating scale (Supplement 1).
      All analyses included covariates of the first 20 principal components of population structure, age, and gender. Analyses using the remission measure of response also included the baseline symptom score as a covariate, to control for depression severity.
      Each cohort underwent standard quality control and 1000 Genomes Project phase 3 imputation using the RICOPILI pipeline on the LISA server (
      • Lam M.
      • Awasthi S.
      • Watson H.J.
      • Goldstein J.
      • Panagiotaropoulou G.
      • Trubetskoy V.
      • et al.
      RICOPILI: Rapid Imputation for COnsortias PIpeLIne.
      ) (Supplement 1 and Table S1 in Supplement 2).

      Genome-wide Association Study

      GWAS was performed using the RICOPILI pipeline (
      • Lam M.
      • Awasthi S.
      • Watson H.J.
      • Goldstein J.
      • Panagiotaropoulou G.
      • Trubetskoy V.
      • et al.
      RICOPILI: Rapid Imputation for COnsortias PIpeLIne.
      ) separately for studies with participants of European and of East Asian ancestry (Supplement 1). All other analyses were performed using only the European ancestry cohorts due to the limited sample size of the East Asian cohorts.

      Gene-Level Association Analysis

      Gene associations were estimated using MAGMA (
      • de Leeuw C.A.
      • Mooij J.M.
      • Heskes T.
      • Posthuma D.
      MAGMA: Generalized gene-set analysis of GWAS data.
      ) and transcriptome-wide association study (TWAS) (
      • Gusev A.
      • Ko A.
      • Shi H.
      • Bhatia G.
      • Chung W.
      • Penninx B.W.J.H.
      • et al.
      Integrative approaches for large-scale transcriptome-wide association studies.
      ).
      The MAGMA v1.06b SNP-wise mean model (±10-kb window) was used to perform gene-level association analysis based on the remission and percentage improvement GWAS p values. The analysis was based on genetic variants and linkage disequilibrium in the 1000 Genomes Project phase 3 dataset available on the MAGMA website (g1000_eur.bed/bim/fam). SNPs were assigned to genes using the MAGMA NCBI37.3.gene.loc file with a 10-kb window. False discovery rate (FDR) correction was used to control for multiple testing. See Supplement 1 for a description of gene set enrichment analysis using MAGMA.
      TWAS integrates GWAS associations with external expression quantitative trait loci data to infer whether differential gene expression estimated from SNP data is associated with the GWAS phenotype. TWAS was performed using FUSION software (http://gusevlab.org/projects/fusion/) and precomputed multi-SNP predictors of gene expression based on data collected from multiple specific brain regions, thyroid tissue, pituitary gland, liver, and blood (Table S2 in Supplement 2). The transcriptome-wide significance threshold of p < 2.51 × 10−6 was estimated using a permutation procedure (
      • Pain O.
      • Pocklington A.J.
      • Holmans P.A.
      • Bray N.J.
      • O’Brien H.E.
      • Hall L.S.
      • et al.
      Novel insight into the aetiology of autism spectrum disorder gained by integrating expression data with genome-wide association statistics.
      ). To test whether the same causal SNP affects both the GWAS phenotype and gene expression, colocalization analysis was performed using the coloc package in R software (version 3.5.0; R Foundation for Statistical Computing) (
      • Giambartolomei C.
      • Vukcevic D.
      • Schadt E.E.
      • Franke L.
      • Hingorani A.D.
      • Wallace C.
      • Plagnol V.
      Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.
      ), as implemented by FUSION software.

      Estimation of SNP-Based Heritability

      The SNP-based heritability of remission and percentage improvement was estimated using individual-level data by genomic relatedness–based restricted maximum likelihood (GREML) in the software GCTA (genome-wide complex trait analysis) (
      • Yang J.
      • Lee S.H.
      • Goddard M.E.
      • Visscher P.M.
      GCTA: A tool for genome-wide complex trait analysis.
      ,
      • Lee S.H.
      • Yang J.
      • Chen G.-B.
      • Ripke S.
      • Stahl E.A.
      • Hultman C.M.
      • et al.
      Estimation of SNP heritability from dense genotype data.
      ). The analysis was performed 1) across all cohorts, including a study covariate (mega-GREML); and 2) separately within each cohort and then inverse variance meta-analyzed (meta-GREML) (Supplement 1). Comparison of mega- and meta-GREML estimates can provide insight into the heterogeneity between cohorts, as only mega-GREML accounts for genetic covariances between cohorts. We converted SNP-based heritability estimates for remission to the liability scale using assuming a population prevalence of 0.357, reflecting the prevalence of remission across the cohorts in this study.

      Leave-One-Out Polygenic Scoring

      To determine whether polygenic scores derived from the remission and percentage improvement GWAS summary statistics predict antidepressant response in an independent sample, a leave-one-out polygenic scoring approach was used. This involves calculating polygenic scores within each cohort based on GWAS summary statistics derived using all other cohorts. Polygenic scores were calculated using PRSice V2 (
      • Choi S.W.
      • O’Reilly P.F.
      PRSice-2: Polygenic Risk Score software for biobank-scale data.
      ) (Supplement 1). One-sided p values were used to assess statistical significance, as we are testing the one-sided hypothesis that the polygenic score has a positive association with the outcome in the target sample.

      Estimation of Genetic Overlap With Mental Health Phenotypes

      We tested for evidence of genetic overlap between antidepressant response measures and seven mental health phenotypes: major depression (
      • Wray N.R.
      • Ripke S.
      • Mattheisen M.
      • Trzaskowski M.
      • Byrne E.M.
      • Abdellaoui A.
      • et al.
      Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
      ), bipolar disorder (
      • Stahl E.A.
      • Breen G.
      • Forstner A.J.
      • McQuillin A.
      • Ripke S.
      • Trubetskoy V.
      • et al.
      Genome-wide association study identifies 30 loci associated with bipolar disorder.
      ), schizophrenia (
      • Pardiñas A.F.
      • Holmans P.
      • Pocklington A.J.
      • Escott-Price V.
      • Ripke S.
      • Carrera N.
      • et al.
      Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection.
      ), attention-deficit/hyperactivity disorder (
      • Demontis D.
      • Walters R.K.
      • Martin J.
      • Mattheisen M.
      • Als T.D.
      • Agerbo E.
      • et al.
      Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder.
      ), autism spectrum disorder (ASD) (
      • Grove J.
      • Ripke S.
      • Als T.D.
      • Mattheisen M.
      • Walters R.K.
      • Won H.
      • et al.
      Identification of common genetic risk variants for autism spectrum disorder.
      ), anxiety (
      • Purves K.L.
      • Coleman J.R.I.
      • Meier S.M.
      • Rayner C.
      • Davis K.A.S.
      • Cheesman R.
      • et al.
      A major role for common genetic variation in anxiety disorders.
      ), and problematic drinking (Alcohol Use Disorders Identification Test problem subscale) (
      • Sanchez-Roige S.
      • Palmer A.A.
      • Fontanillas P.
      • Elson S.L.
      • 23andMe Research Team, the Substance Use Disorder Working Group of the Psychiatric Genetics Consortium
      • et al.
      Genome-wide association study meta-analysis of the Alcohol Use Disorders Identification Test (AUDIT) in two population-based cohorts.
      ). Educational attainment (
      • Lee J.J.
      • Wedow R.
      • Okbay A.
      • Kong E.
      • Maghzian O.
      • Zacher M.
      • et al.
      Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.
      ) was also included, as it has strong correlations with the mental health disorders tested. Evidence of genetic overlap was assessed using polygenic scoring with AVENGEME (
      • Palla L.
      • Dudbridge F.
      A fast method that uses polygenic scores to estimate the variance explained by genome-wide marker panels and the proportion of variants affecting a trait.
      ), and linkage disequilibrium score regression (
      • Bulik-Sullivan B.
      • Finucane H.K.
      • Anttila V.
      • Day F.R.
      • ReproGen Consortium, Psychiatric Genetics Consortium
      • et al.
      An atlas of genetic correlations across human diseases and traits.
      ). To avoid sample overlap between the major depression GWAS and the antidepressant response cohorts in this study, we used major depression GWAS summary statistics excluding overlapping cohorts (STAR∗D [Sequenced Treatment Alternatives to Relieve Depression], GENPOD [GENetic and clinical Predictors Of treatment response in Depression], GENDEP [Genome Based Therapeutic Drugs for Depression], PFZ [Pfizer]).
      AVENGEME aggregates polygenic score association results across p-value thresholds to estimate genetic covariance between antidepressant response and the eight mental health phenotypes. AVENGEME parameters are provided in Table S3 in Supplement 2. Bonferroni correction was used to account for multiple testing for the eight discovery GWASs used.

      Replication Cohorts and Analyses

      Out-of-Sample Prediction

      External validation of polygenic scores derived using the full GWAS results was also carried out. Five independent samples were used (Supplement 1). In brief, Janssen (N = 190, remission rate = 11.8%) (
      • Rapaport M.H.
      • Gharabawi G.M.
      • Canuso C.M.
      • Mahmoud R.A.
      • Keller M.B.
      • Bossie C.A.
      • et al.
      Effects of risperidone augmentation in patients with treatment-resistant depression: Results of open-label treatment followed by double-blind continuation.
      ), the Douglas Biomarker Study (N = 127, remission rate = 23.6%) (
      • Ju C.
      • Fiori L.M.
      • Belzeaux R.
      • Theroux J.-F.
      • Chen G.G.
      • Aouabed Z.
      • et al.
      Integrated genome-wide methylation and expression analyses reveal functional predictors of response to antidepressants.
      ), and the IRL-GREY (Incomplete Response in Late Life Depression: Getting to Remission) study (N = 307, remission rate = 52.4%) (
      • Lenze E.J.
      • Mulsant B.H.
      • Blumberger D.M.
      • Karp J.F.
      • Newcomer J.W.
      • Anderson S.J.
      • et al.
      Efficacy, safety, and tolerability of augmentation pharmacotherapy with aripiprazole for treatment-resistant depression in late life: A randomised, double-blind, placebo-controlled trial.
      ) prospectively assessed depressive symptoms, concordant with the discovery GWAS samples. In contrast, Generation Scotland (ntreatment resistant = 177, nnon–treatment resistant = 2455) (
      • Wigmore E.M.
      • Hafferty J.D.
      • Hall L.S.
      • Howard D.M.
      • Clarke T.-K.
      • Fabbri C.
      • et al.
      Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP.
      ) assessed electronic prescription data, and the AGDS (Australian Genetics of Depression Study) study (nresponders = 4368, nnonresponders = 6879) (
      • Byrne E.M.
      • Kirk K.M.
      • Medland S.E.
      • McGrath J.J.
      • Colodro-Conde L.
      • Parker R.
      • et al.
      Cohort profile: The Australian genetics of depression study.
      ) collected retrospective self-report questionnaire data. Polygenic score association results were meta-analyzed across the prospectively assessed cohorts given their more comparable study design and antidepressant measures. One-sided p values were used to assess statistical significance.

      Comparison of Genetic Covariance With Mental Health Phenotypes

      Individual-level data were available for Generation Scotland enabling estimation of genetic covariance between TRD and mental health-related phenotypes using AVENGEME, as described above. Analyses in Generation Scotland were controlled for age, gender, and 20 principal components of population structure. When estimating genetic covariance between TRD and major depression, we used major depression GWAS summary statistics excluding Generation Scotland to avoid sample overlap.

      Results

      Descriptive statistics for the cohorts used in this study are available in Table 1 and in Figures S1 to S5 in Supplement 1.

      GWAS of Antidepressant Response

      Across the 10 European studies, 5151 individuals with remission data (1852 [36.0%] patients remitting) and 5218 participants with percentage improvement data were available. No variants were significantly associated with remission or percentage improvement (Figures S6 and S7 in Supplement 1, Tables S4 and S5 in Supplement 2). There was no evidence of confounding (Figures S8 and S9 in Supplement 1, Table S6 in Supplement 2)
      No significant associations were identified in the East Asian GWASs (N = 527) (Figures S10 and S11 in Supplement 1). A comparison between East Asian and European GWAS results is shown in Supplement 1.

      Gene-Level Association Results

      MAGMA identified a significant association on chromosome 17 for ETV4 with both remission (pFDR = .016) and percentage improvement (pFDR = .016). Within the same region, DHX8 was also significantly associated with remission (pFDR = .046). The SNP associations within this region span multiple genes (Figure S12 in Supplement 1). Full MAGMA gene-based association results are shown in Tables S7 and S8 in Supplement 2.
      TWAS identified no association achieving transcriptome-wide significance (p < 2.51 × 10−6). Further inspection of TWAS associations within the chromosome 17 region implicated by MAGMA highlighted SNP-associations with upregulation of BRCA1 (remission p = 1.96 × 10−4; percentage improvement p = 9.21 × 10−5; GTeX brain–caudate [basal ganglia]) and upregulation of TMEM106A (remission p = .0011; percentage improvement p = .0018; Young Finns Study [blood]). Colocalization analysis of these associations indicated shared casual variants for these genes’ differential expression and antidepressant response. Full TWAS results are given in Tables S9 and S10 in Supplement 2.
      See Supplement 1 for gene set enrichment analysis results.

      SNP-Based Heritability

      Analysis across all samples (mega-GREML) showed remission to have a significant nonzero SNP-based heritability (h2 = 0.132; SE = 0.056; 95% CI, 0.022 to 0.241; p = .009, liability scale assuming population prevalence of 0.357), whereas the SNP-based heritability for percentage improvement was not significantly different from zero (h2 = −0.018; SE = 0.032; 95% CI, −0.080 to 0.045; p = .303) (Figure 1).
      Figure thumbnail gr1
      Figure 1Single nucleotide polymorphism–based heritability (SNP-h2) estimates for remission and percentage improvement with SE bars. Figure shows across (mega-) and within (meta-) sample genomic relatedness–based restricted maximum likelihood (GREML) estimates. ∗Estimate is significantly different from zero, at p < .05.
      The SNP-based heritability estimates from meta-analysis of within-sample estimates (meta-GREML) were significant for both remission (h2 = 0.396; SE = 0.153; 95% CI, 0.096 to 0.696; p = .010, liability scale assuming population prevalence of 0.357) and percentage improvement (h2 = 0.215; SE = 0.105; 95% CI, 0.009 to 0.421; p = .041) (Figure 1). See Figures S18 and S19 in Supplement 1 for meta-analysis forest plots.
      See Supplement 1 for SNP-based heritability sensitivity analyses.

      Out-of-Sample Prediction

      Leave-one-out polygenic score analysis provided evidence that polygenic scores derived using remission and percentage improvement GWAS results could both explain a statistically significant amount of variance out-of-sample (Figure 2). Both remission and percentage improvement explained ∼0.1% of the variance, with polygenic scores for multiple p-value thresholds associated at nominal significance.
      Figure thumbnail gr2
      Figure 2Polygenic prediction of antidepressant response from leave-one-out polygenic scoring for (A) remission and (B) percentage improvement. R2 estimates are signed to indicate positive or negative association. One-sided p values are shown above or below the bars, with p values < .05 highlighted in red.
      Validation of polygenic scores based on the full antidepressant response GWAS summary statistics was carried out using five samples. Meta-analysis of polygenic score associations across the three prospectively assessed cohorts (Janssen, Douglas Biomarker Study, and IRL-GREY study) showed nominally significant evidence of association for the remission polygenic score (maximum liability R2 = 0.8%, p = .015) and a nonsignificant association for the percentage improvement score (maximum R2 = 0.2%, p = .091) (Figure S21 in Supplement 1). Results were highly variable across each prospectively assessed cohort. No association was found between polygenic scores in Generation Scotland or AGDS study cohorts. Full polygenic score replication results are in Tables S14 to S17 in Supplement 2.

      Genetic Overlap With Mental Health Phenotypes

      Both remission and percentage improvement showed a significant negative genetic covariance with schizophrenia, and significant positive genetic covariance with educational attainment (Figure 3; Tables S18 and S19 in Supplement 2). Percentage improvement also showed a significant negative covariance with major depression and bipolar disorder, and a significant positive genetic covariance with ASD. Linkage disequilibrium score regression genetic correlation estimates were broadly concordant, although they were nonsignificant (Figure S22 in Supplement 1). Subsequent conditional analysis, covarying for educational attainment polygenic scores, showed that the associations with psychiatric disorders were independent of the association with educational attainment (Figure S23 in Supplement 1).
      Figure thumbnail gr3
      Figure 3Genetic covariance (gcov) estimates between antidepressant response phenotypes and seven mental health phenotypes and educational attainment. Confidence intervals (CIs) were corrected for multiple testing. ADHD, attention-deficit/hyperactivity disorder; ASD, autism spectrum disorder.
      Genetic overlap estimates between TRD in Generation Scotland and mental health phenotypes were congruent with results from primary samples, showing that genetic risk for schizophrenia was greater among individuals with TRD, and educational attainment genetic propensity was greater among individuals with non-TRD (Figure S24 in Supplement 1).

      Discussion

      Antidepressants are a common and effective strategy for treating MDD; however, remission rates are typically low, and factors affecting antidepressant response are poorly understood. This study is the largest genetic investigation of antidepressant response based on clinically defined cohorts. For the first time, we identify a polygenic profile for antidepressant response, which can predict across cohorts, and shows genetic correlations with traits that reflect clinical observations.
      This study finds significant evidence that antidepressant response is influenced by common genetic variation. Meta-analysis of SNP-based heritability estimates within each cohort indicates that 20% to 40% of the variance in antidepressant response is attributable to common genetic variation, consistent with a previous analysis of a subset of these studies (
      • Tansey K.E.
      • Guipponi M.
      • Hu X.
      • Domenici E.
      • Lewis G.
      • Malafosse A.
      • et al.
      Contribution of common genetic variants to antidepressant response.
      ). However, the SNP-based heritability decreased substantially when estimating across cohorts simultaneously. Although the change in SNP-heritability was not statistically significant, these results suggest that antidepressant response in a broad context has a heritable component, but genetic differences can explain additional variability in antidepressant response within more specific contexts. Despite the apparent heterogeneity across individual cohorts, the sample sizes for antidepressant response are sufficiently large to detect a polygenic signal. Genetic studies for susceptibility to psychiatric disorders show that findings accrue after an inflection point in sample size is reached (
      • Wray N.R.
      • Ripke S.
      • Mattheisen M.
      • Trzaskowski M.
      • Byrne E.M.
      • Abdellaoui A.
      • et al.
      Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
      ,
      • Stahl E.A.
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      ,
      • Pardiñas A.F.
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      • Escott-Price V.
      • Ripke S.
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      • et al.
      Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection.
      ). This study’s findings for SNP-based heritability and out-of-sample polygenic prediction indicate that sample sizes for antidepressant response are reaching the inflection point and that larger studies will uncover more of the genetic component (
      • Kim Y.
      • Zerwas S.
      • Trace S.E.
      • Sullivan P.F.
      Schizophrenia genetics: Where next?.
      ). Power calculations for detecting genome-wide significant variation, and the variance explained by corresponding polygenic scores, are provided in Figure S25 in Supplement 1. Interestingly, our findings suggest that the SNP-based heritability of remission is higher than for percentage improvement. The percentage improvement score might have lower heritability because of increased noise, in which this measure is more susceptible to random variation in depressive symptoms, is less comparable across the different depressive symptom scales used, or captures increases in depressive symptoms.
      This study provides novel insight into the shared genetic basis between antidepressant response and mental health phenotypes. We show an association between high genetic liability of psychiatric disorders and poorer response, which mirrors conclusions of clinical studies (
      • Perlman K.
      • Benrimoh D.
      • Israel S.
      • Rollins C.
      • Brown E.
      • Tunteng J.-F.
      • et al.
      A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder.
      ). The schizophrenia polygenic risk score was negatively associated with antidepressant response, which is replicated in the TRD phenotype in Generation Scotland. Previous studies have shown that individuals with TRD may respond to antipsychotic medication (
      • Zhou X.
      • Keitner G.I.
      • Qin B.
      • Ravindran A.V.
      • Bauer M.
      • Del Giovane C.
      • et al.
      Atypical antipsychotic augmentation for treatment-resistant depression: A systematic review and network meta-analysis.
      ). Our findings extend those reports by suggesting that individuals with antidepressant resistance also have a higher burden of schizophrenia genetic risk. We found some evidence that genetic liability to major depression is associated with poorer response to antidepressants. However, this association was only statistically significant for percentage improvement, and it requires replication. In addition, we report a novel finding that high ASD genetic liability increased the chance of remission. Another recent study reported that ASD genetic liability is associated with poorer response to cognitive behavioral therapy (
      • Andersson E.
      • Crowley J.J.
      • Lindefors N.
      • Ljótsson B.
      • Hedman-Lagerlöf E.
      • Boberg J.
      • et al.
      Genetics of response to cognitive behavior therapy in adults with major depression: A preliminary report.
      ). If both these findings are replicated, it would suggest ASD genetic liability could serve as a differential predictor of response to antidepressants and cognitive behavioral therapy. We also identified a significant association between genetic propensity for educational attainment and improved antidepressant response as well as between genetic propensity for educational attainment and non-TRD. This may reflect the indirect measurement of socioeconomic status captured by educational attainment, which is supported by previous literature showing a positive association between antidepressant response and socioeconomic status (
      • Cohen A.
      • Gilman S.E.
      • Houck P.R.
      • Szanto K.
      • Reynolds C.F.
      Socioeconomic status and anxiety as predictors of antidepressant treatment response and suicidal ideation in older adults.
      ). Future research should explore whether individuals with higher educational attainment have improved response due to factors such as adherence or joint psychological treatment.
      Polygenic scores derived from the remission and percentage improvement GWASs both significantly predicted antidepressant response out of sample using a leave-one-out design. This is the first GWAS of antidepressant response able to predict significantly out of sample, representing an important advance in the field of antidepressant response genetics. Although the variance explained is low (R2 = 0.1%) and p values are close to the nominal significance threshold, this result is encouraging given the sample size of this study. For example, a recent GWAS of MDD explains only 1.9% of the variance in MDD, despite having a sample size 100 times greater than this study (
      • Wray N.R.
      • Ripke S.
      • Mattheisen M.
      • Trzaskowski M.
      • Byrne E.M.
      • Abdellaoui A.
      • et al.
      Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
      ). Our finding suggests that a renewed effort to systematically collect new samples in which genetic associations with antidepressant response can be identified will improve the prediction of antidepressant response, helping to uncover its biological mechanisms and clinical associations, and eventually enable more accurate clinical predictors to be developed and applied.
      This study provided limited insight into the biological underpinnings of antidepressant response implicating one locus on chromosome 17 surrounding ETV4 and DHX8. A previous study using neuronal cell lines and mouse models found that ETV4 mediates brain-derived neurotrophic factor (BDNF) induced hippocampal dendrite development and plasticity (
      • Fontanet P.A.
      • Ríos A.S.
      • Alsina F.C.
      • Paratcha G.
      • Ledda F.
      Pea3 transcription factors, Etv4 and Etv5, are required for proper hippocampal dendrite development and plasticity.
      ), congruent with the hypothesis that the mechanism of action for antidepressants is via hippocampal neuroplasticity (
      • Liu W.
      • Ge T.
      • Leng Y.
      • Pan Z.
      • Fan J.
      • Yang W.
      • Cui R.
      The role of neural plasticity in depression: From hippocampus to prefrontal cortex.
      ). DHX8 has a less clear mechanistic link to antidepressant response with a broader function in messenger RNA splicing (
      • Jurica M.S.
      • Licklider L.J.
      • Gygi S.R.
      • Grigorieff N.
      • Moore M.J.
      Purification and characterization of native spliceosomes suitable for three-dimensional structural analysis.
      ). Replication of the association at this locus is required before further experimental investigation.
      In addition, no association was detected with genetic variation within classical pharmacokinetic candidate genes, such as CYP2D6 and CYP2C19, which have previously been robustly associated with antidepressant plasma levels (
      • McAlpine D.E.
      • Biernacka J.M.
      • Mrazek D.A.
      • O’Kane D.J.
      • Stevens S.R.
      • Langman L.J.
      • et al.
      Effect of cytochrome P450 enzyme polymorphisms on pharmacokinetics of venlafaxine.
      ). Although the enzymatic activity of CYP2D6 and CYP2C19 is largely regulated by common genetic variation, these variants include structural variants that are not well captured by GWAS arrays, and large effects on enzymatic activity are typically conferred by combinations of genetic variants (haplotypes), which GWAS does not assess. Therefore, the absence of an association at this point may be a false negative result. Furthermore, looking across individuals that have not been treated with a specific antidepressant or antidepressant class will reduce the likelihood of detecting pharmacokinetic effects.
      Owing to a limited sample size, it was not possible to estimate genetic correlations between longitudinally assessed antidepressant response and TRD defined using electronic health records. However, comparison of shared genetic etiology with other mental health phenotypes indicated that these distinct measures of antidepressant response have a shared genetic basis. Further comparison and integration of these two approaches is warranted and may prove fruitful given the large gains in sample size that electronic health record–derived phenotypes can provide.
      There are several limitations to this study that should be addressed in the future. First, large sample sizes are essential for robust identification of associated genetic variation and out-of-sample prediction. However, combining independently collected datasets inevitably introduces heterogeneity. Obtaining large homogeneous samples is particularly challenging for pharmacogenetic studies, as heterogeneity is driven not only by patient characteristics such as diagnosis and patient ascertainment, but also by differences in treatment such as the drug, dosage, duration, and co-pharmacotherapy. Although the cohorts within this study have many features in common, heterogeneity in antidepressant treatment is present. As sample sizes grow, analyses stratified by these factors will become more feasible, enabling detection of genetic effects relevant to each antidepressant, antidepressant class, or other treatment characteristics. Second, an important question to consider is whether the variance in depressive symptoms after treatment is due to antidepressant response or to other variables altering the course of depression. Although antidepressants have a significant effect on depressive symptoms, and their administration is the core feature of participants in this study, individuals may vary in depressive symptoms due to other factors affecting disease progression, such as clinical and sociodemographic variables and placebo response. This is a difficult issue to resolve but should be considered when interpreting the results. Future genetic studies incorporating the placebo arm of clinical trials may help identify genetic associations specific to antidepressant response. Third, this study has focused on changes in total depressive symptoms without considering symptom domain-specific changes or the presence of side effects. Given the wide range of depressive symptoms and the influence side effects can have on efficacy, consideration of these features may provide additional insights. Fourth, although this study included three cohorts of East Asian ancestry, further inclusion of cohorts with diverse ancestries is an important area. Genetic analysis within diverse populations helps to ensure that the findings are applicable to worldwide populations and can help fine-map causal variants underlying genetic associations.
      In summary, this study identifies a polygenic profile for antidepressant response that predicts across studies and is negatively correlated with genetic susceptibility to schizophrenia, which could be used for prognostic purposes. While the current results have no clinical utility as a pharmacogenetic test, they indicate that studies with larger sample sizes could provide predictions explaining a substantial proportion of antidepressant response. We note that a prognostic test that enables even a modest increase in the proportion of patients that respond to antidepressants would have a substantial impact on recovery for many patients, given the high prevalence of depression. We hope that this study prompts both replication and extension to accelerate the development of pharmacogenetic testing for psychiatry.

      Acknowledgments and Disclosures

      The collection of the sample from the Group for the Study of Resistant Depression (GSRD) Consortium was supported by an unrestricted grant from Lundbeck for the GSRD. Lundbeck had no further role in the study design and the collection, analysis, and interpretation of data. The GENDEP (Genome Based Therapeutic Drugs for Depression) study was funded by a European Commission Framework 6 grant (EC Contract Ref. No. LSHB-CT-2003-503428 ). H. 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 Illumina array genotyping was funded in part by a joint grant from the UK Medical Research Council and GlaxoSmithKline (Grant No. G0701420 ). The GENPOD (GENetic and clinical Predictors Of treatment response in Depression) trial was funded by the UK Medical Research Council and supported by the UK Mental Health Research Network . The genotyping of GENPOD samples was supported by the Innovative Medicine Initiative Joint Undertaking under Grant 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 (Grant No. FP7/2007-2013 ). EFPIA members Pfizer, GlaxoSmithKline, and F. Hoffmann-La Roche have contributed work and samples to the project presented here. The PFZ (Pfizer), GSK (GlaxoSmithKline), and GODS were supported by the Innovative Medicine Initiative Joint Undertaking (IMI-JU) under Grant No. 115008 of which resources are composed of European Union and 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 PGRN-AMPS (Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study) study data were obtained via the database of Genotypes and Phenotypes (dbGAP) (Accession No. phs000670.v1.p1). Funding support for the PGRN-AMPS study was provided by the National Institute of General Medical Sciences , National Institutes of Health , through the PGRN grant to Principal Investigators R. Weinshilboum and L. Wang (Grant No. U19 GM61388 ). D. Mrazek served as the Principal Investigator for the PGRN-AMPS study within the Mayo Clinic PGRN program. Genome-wide genotyping was performed at the RIKEN Center for Genomic Medicine, with funding provided by RIKEN. The datasets used for the analyses described in this manuscript were obtained from the dbGaP at http://www.ncbi.nlm.nih.gov/gap/. Generation Scotland is supported by the Wellcome Trust (Reference Nos. 104036/Z/14/Z and 216767/Z/19/Z ), the Chief Scientist Office of the Scottish Government Health Department (Grant No. CZD/16/6 ), and the Scottish Funding Council (Grant No. HR03006 ). Data linkage and analysis in Generation Scotland is supported by the Medical Research Council (Grant No. MC_PC_17209 ). For the Douglas Biomarker Study, the genotyping of the samples was funded and generated by Janssen Research & Development, LLC. Major funding for the Psychiatric Genetics Consortium (PGC) is from the U.S. National Institutes of Health (Grant Nos. U01 MH109528 and U01 MH109532 ). OP and CML were 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 authors acknowledge use of the research computing facility at King’s College London, Rosalind (https://rosalind.kcl.ac.uk), which is delivered in partnership with the NIHR Maudsley BRC, and part-funded by capital equipment grants from the Maudsley Charity (Award No. 980 ) and Guy's & St. Thomas' Charity (Grant Nos. TR130505 ). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.
      We are deeply indebted to the investigators who comprise the PGC, and to the hundreds of thousands of subjects who have shared their life experiences with PGC investigators. Statistical analyses for the PGC were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara. 23andMe, Inc. used GWAS summary statistics for major depression including 23andMe participants. We thank the research participants and employees of 23andMe for making the work regarding major depression possible.
      A previous version of this article was published as a preprint on medRxiv: https://www.medrxiv.org/content/10.1101/2020.12.11.20245035v1.
      The PGC’s policy is to make genome-wide summary results public. Summary statistics for remission and percentage improvement GWAS are available on the PGC website (https://www.med.unc.edu/pgc).
      CML has served on the scientific advisory board for Myriad Neuroscience. KJA has received two research grants in the last 2 years from Janssen Inc., Canada (fellowship grants for trainees), and provided consultancy services in the last three years for Otsuka Canada Pharmaceutical Inc., Lundbeck Canada, and HLS Therapeutics. AS is or has been consultant/speaker for: Abbott, AbbVie, Angelini, AstraZeneca, Clinical Data, Boehringer, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, InnovaPharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi, and Servier. AMM has received research support from the Sackler Trust and speakers fees from Janssen and Illumina. MK has received grant funding from the Japan Society for the Promotion of Science, SENSHIN Medical Research Foundation, and Japan Research Foundation for Clinical Pharmacology; and speaker honoraria from Dainippon-Sumitomo Pharma, Otsuka, Meiji-Seika Pharma, Eli Lilly, MSD K.K., GlaxoSmithKline, Pfizer, Janssen Pharmaceutical, Shionogi, Mitsubishi Tanabe Pharma, Takeda Pharmaceutical, Lundbeck, and Ono Pharmaceutical. QSL is an employee of Janssen Research & Development, LLC, and a shareholder in Johnson & Johnson, the parent company of the Janssen companies and declares that, except for income received from her primary employer, no financial support or compensation has been received from any individual or corporate entity over the past 3 years for research or professional service, and there is no personal financial holding that could be perceived as constituting a potential conflict of interest. DS has received grant/research support from GlaxoSmithKline and Lundbeck; and served as a consultant or on advisory boards for AstraZeneca, Bristol Myers Squibb, Eli Lilly, Janssen, and Lundbeck. Stuart Montgomery has served as a consultant or on advisory boards for AstraZeneca, Bristol Myers Squibb, Forest, Johnson & Johnson, Leo, Lundbeck, Medelink, Neurim, Pierre Fabre, and Richter. Siegfried Kasper has received grants/research support, consulting fees, and/or honoraria within the last 3 years from Angelini, AOP Orphan Pharmaceuticals AG, Celgene GmbH, Eli Lilly, Janssen-Cilag Pharma GmbH, KRKA-Pharma, Lundbeck A/S, Mundipharma, Neuraxpharm, Pfizer, Sanofi, Schwabe, Servier, Shire, Sumitomo Dainippon Pharma Co. Ltd., and Takeda. Joseph Zohar has received grant/research support from Lundbeck, Servier, Brainsway, and Pfizer; served as a consultant or on advisory boards for Servier, Pfizer, Abbott, Lilly, Actelion, AstraZeneca, and Roche; and served on speakers bureaus for Lundbeck, Roche, Lilly, Servier, Pfizer, and Abbott. Julien Mendlewicz has served on the Board of the Lundbeck International Neuroscience Foundation and on the advisory board for Servier. Patrick F. Sullivan has served on the scientific advisory board for Pfizer, Inc., and on the advisory committee for Lundbeck. All other authors report no biomedical financial interests or potential conflicts of interest.

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