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Figure 1.
Phenotype Validation Against Established Clinical Characteristics
Phenotype Validation Against Established Clinical Characteristics

Differences in sex, age at onset, number of DSM-IV symptoms, and sex-adjusted body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) are shown across subgroups of patients with major depressive disorder (MDD). The patients were compared by subgroups with increased appetite and/or weight symptoms (A/W), decreased A/W, or no change using generalized linear mixed models. A, Raw data for female patients include 3758 (70.3%) for the decreased A/W subgroup, 2141 (62.6%) for the no-change subgroup, and 1430 (76.4%) for the increased A/W subgroup. B, Estimated mean (SE) age at onset was 30.1 (2.3) years in the decreased A/W subgroup, 29.7 (2.3) years in the no-change subgroup, and 29.2 (2.3) years in the increased A/W subgroup. C, Estimated mean (SE) number of symptoms was 7.9 (0.1) in the decreased A/W subgroup, 6.7 (0.1) in the no-change subgroup, and 7.9 (0.1) in the increased A/W subgroup. D, Sex-adjusted BMI analyses included 9 of 14 data sets providing BMI data for more than 80% of patients. Estimated mean (SE) sex-adjusted BMIs were 27.3 (0.5) in the decreased A/W subgroup, 27.4 (0.5) in the no-change subgroup, and 30.2 (0.5) in the increased A/W. Error bars indicate SEs.

aCompared with the no-change subgroup.

bCompared with the increased A/W subgroup.

Figure 2.
Genetic Correlations Between Major Depressive Disorder (MDD) and Body Mass Index (BMI)
Genetic Correlations Between Major Depressive Disorder (MDD) and Body Mass Index (BMI)

Results from bivariate genomic relationship matrix–restricted maximum likelihood analyses were adjusted for sex, 10 ancestry-informative principal components, and 8 data set dummy variables. Analyses were based on 7 data sets providing BMI data in more than 80% of the samples, including control individuals. P values test the hypothesis that h2 values are significantly greater than 0. A/W indicates appetite and/or weight symptoms. Error bars indicate SEs; rg, genetic correlation.

Figure 3.
Associations of Polygenic Risk for Obesity-Related Traits With Major Depressive Disorder (MDD)
Associations of Polygenic Risk for Obesity-Related Traits With Major Depressive Disorder (MDD)

Results (odds ratios [ORs] and 95% CIs) from binary (using controls for reference values) logistic mixed models adjusted for sex and 5 ancestry-informative principal components. BMI indicates body mass index; CRP, C-reactive protein.

aFalse discovery rate, q < 0.05.

bAnalyses were based on 13 of 14 data sets available that are not included in the discovery genome-wide association study.

Table 1.  
Characteristics of the PGC Data Sets Selected
Characteristics of the PGC Data Sets Selected
Table 2.  
SNP Heritability and Genetic Correlation Estimates for MDD Subgroups
SNP Heritability and Genetic Correlation Estimates for MDD Subgroups
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Original Investigation
December 2017

Genetic Association of Major Depression With Atypical Features and Obesity-Related Immunometabolic Dysregulations

Author Affiliations
  • 1Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Vrije Universiteit Medical Center and GGZ inGeest, Amsterdam, the Netherlands
  • 2Discipline of Psychiatry, University of Adelaide, Adelaide, Australia
  • 3Medical Research Council Social Genetic and Developmental Psychiatry Centre, King’s College London, London, England
  • 4National Institute for Health Research Biomedical Research Centre for Mental Health, King’s College London, London, England
  • 5Department of Epidemiology and Biostatistics, Imperial College London, London, England
  • 6Institute of Human Genetics, University of Bonn, Bonn, Germany
  • 7Life Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
  • 8Department of Psychiatry, University of Basel, Basel, Switzerland
  • 9Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
  • 10Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
  • 11Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
  • 12Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany
  • 13Department of Psychological Medicine, King’s College London, London, England
  • 14South London and Maudsley National Health Service Foundation, London, England
  • 15German Centre for Cardiovascular Research, Partner Site Greifswald, University Medicine, University Medicine Greifswald, Greifswald, Germany
  • 16Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
  • 17Department of Psychiatry, University Hospital of Lausanne, Prilly, Switzerland
  • 18Department of Biochemistry and Molecular Biology II, Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, Spain
  • 19Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
  • 20Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
  • 21Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
  • 22Queensland Brain Institute, University of Queensland, Brisbane, Australia
  • 23Department of Biological Psychology, VU University Amsterdam, Amsterdam, the Netherlands
JAMA Psychiatry. 2017;74(12):1214-1225. doi:10.1001/jamapsychiatry.2017.3016
Key Points

Question  Is the genetic overlap with obesity-related traits (body mass index and levels of C-reactive protein and leptin) stronger in patients with major depression and the atypical symptoms of increased appetite and/or weight during an active episode?

Findings  Data from a large international consortium showed that patients with depression and increased appetite and/or weight (approximately 15% of cases) carried a higher number of genetic risk variants for body mass index and levels of C-reactive protein and leptin.

Meaning  The established phenotypic associations between atypical depressive symptoms and obesity-related traits may arise from shared pathophysiologic mechanisms, and development of treatments effectively targeting immunometabolic dysregulations may benefit this subgroup of patients.

Abstract

Importance  The association between major depressive disorder (MDD) and obesity may stem from shared immunometabolic mechanisms particularly evident in MDD with atypical features, characterized by increased appetite and/or weight (A/W) during an active episode.

Objective  To determine whether subgroups of patients with MDD stratified according to the A/W criterion had a different degree of genetic overlap with obesity-related traits (body mass index [BMI] and levels of C-reactive protein [CRP] and leptin).

Design, Setting, and Patients  This multicenter study assembled genome-wide genotypic and phenotypic measures from 14 data sets of the Psychiatric Genomics Consortium. Data sets were drawn from case-control, cohort, and population-based studies, including 26 628 participants with established psychiatric diagnoses and genome-wide genotype data. Data on BMI were available for 15 237 participants. Data were retrieved and analyzed from September 28, 2015, through May 20, 2017.

Main Outcomes and Measures  Lifetime DSM-IV MDD was diagnosed using structured diagnostic instruments. Patients with MDD were stratified into subgroups according to change in the DSM-IV A/W symptoms as decreased or increased.

Results  Data included 11 837 participants with MDD and 14 791 control individuals, for a total of 26 628 participants (59.1% female and 40.9% male). Among participants with MDD, 5347 (45.2%) were classified in the decreased A/W and 1871 (15.8%) in the increased A/W subgroups. Common genetic variants explained approximately 10% of the heritability in the 2 subgroups. The increased A/W subgroup showed a strong and positive genetic correlation (SE) with BMI (0.53 [0.15]; P = 6.3 × 10−4), whereas the decreased A/W subgroup showed an inverse correlation (−0.28 [0.14]; P = .06). Furthermore, the decreased A/W subgroup had a higher polygenic risk for increased BMI (odds ratio [OR], 1.18; 95% CI, 1.12-1.25; P = 1.6 × 10−10) and levels of CRP (OR, 1.08; 95% CI, 1.02-1.13; P = 7.3 × 10−3) and leptin (OR, 1.09; 95% CI, 1.06-1.12; P = 1.7 × 10−3).

Conclusions and Relevance  The phenotypic associations between atypical depressive symptoms and obesity-related traits may arise from shared pathophysiologic mechanisms in patients with MDD. Development of treatments effectively targeting immunometabolic dysregulations may benefit patients with depression and obesity, both syndromes with important disability.

Introduction

Epidemiologic evidence has identified robust associations between depression and obesity.1,2 This link may be attributable to shared pathophysiologic mechanisms, such as immunometabolic pathways characterized by increased proinflammatory response and dysregulation of homeostatic hormones responsible for energy metabolism.3 Obesity is characterized by low-grade proinflammatory activation.4 Peripheral immune activation could trigger brain inflammatory responses participating in depressive neurochemical and/or endocrine processes (ie, depletion and degradation of tryptophan toward neurotoxic end products5 or hyperactivation of the hypothalamic-pituitary-adrenal axis6). Disrupted central signaling of the adipocyte-derived hormone leptin (suppressing food intake and promoting energy expenditure7) may influence mood. Development of central functional resistance blunts leptin anorexigenic (disinhibiting feeding7), procognitive, and antidepressant effects.8-10 Leptin mood regulation may be exerted directly via receptors expressed in the hippocampus and amygdala by influencing neurogenesis and neuroplasticity in the hippocampus and cortex or by modulating the hypothalamic-pituitary-adrenal axis.11-13

Establishing the role in depression of such mechanisms has been difficult. Although meta-analytic evidence provides mean effect sizes, strength and direction of the associations of depression with obesity-related indexes and biomarkers may vary considerably across different subgroups of patients owing to clinical heterogeneity. In the past decade, evidence has emerged suggesting that the link between depression and immunometabolic dysregulations is stronger, or specific, for MDD with atypical features, such as increased appetite and/or weight (A/W) and hypersomnia.14-23 Other clinical characteristics linked to atypical depression are preponderance of female sex, earlier age at onset, and higher severity.24,25 Specific atypical symptoms may constitute major axes of variation for the associations of MDD with obesity-related features. Recently, the Netherlands Study of Depression and Anxiety (NESDA) showed that increased appetite during an active depressive episode was more strongly associated with body mass index (BMI) and levels of C-reactive protein (CRP)26 and leptin.27 Other atypical symptoms that commonly cluster with increased appetite showed a weaker association (increased weight)27 or no association (hypersomnia)26,27 with these markers. Whether this covariance between depressive symptoms and obesity-related traits reflects shared genetic liabilities needs to be established. In prior work28,29 including approximately 3000 Dutch individuals, the contribution of BMI risk variants could be specifically detected only in patients with MDD who endorsed increased A/W symptoms.

In the present study, we scaled up genetic analyses to more than 25 000 samples from different countries from the Psychiatric Genomics Consortium (PGC) with established psychiatric diagnoses and genome-wide association study (GWAS) data. Compared with the latest study,29 we were able to estimate the genetic correlations between BMI and MDD subgroups. Furthermore, we derived polygenic risk scores with enhanced predictive accuracy owing to larger discovery samples and newly developed methods. Finally, we included leptin polygenic risk scores previously not investigated. First, after stratifying patients according to A/W symptoms during the MDD index episodes, we validated the resulting subgroups against established clinical characteristics. Next, we estimated the contribution of common genetic variants to the underlying liabilities of the subgroups and their reciprocal correlations. Finally, we tested whether the subgroups had a differential genetic overlap with BMI and levels of CRP and leptin. Based on previous findings,28,29 we hypothesized a specific underlying connection between immunometabolic traits and patients with MDD endorsing the DSM-IV increased A/W symptoms.

Methods
Study Sample

Data were derived from the PGC MDD Working Group (PGC-MDD2) (http://www.biorxiv.org/content/early/2017/07/24/167577), which assembled genome-wide genotypic and phenotypic measures from 29 data sets (24 contributing cohorts), including 42 455 samples of European ancestry. Eleven data sets did not provide DSM items coding for A/W; in addition, 4 data sets were excluded owing to very low endorsement (<14 samples) of A/W symptoms. Thus, the main analytic sample (Table 1) was based on the remaining 14 data sets totaling 26 628 participants, of whom 11 837 had MDD (cases diagnosed according to DSM-IV lifetime MDD using structured diagnostic instruments, clinician-administered DSM-IV checklists, or medical record) and 14 791 were control individuals (screened in 11 of 14 data sets). Data collection was approved by each center’s local institutional review board or medical ethics committee (all participating centers are listed at the end of the article), which waived the need for informed consent for use of the deidentified data sets.

Stratification of MDD Cases

Data were retrieved and analyzed from September 28, 2015, through May 20, 2017. The selected data sets included information on DSM-IV MDD symptoms endorsed during the index depressive episode. Items on neurovegetative symptoms were disaggregated to code separately for increase and decrease. As in previous work,29 stratification of MDD cases in all data sets was based on 2 items coding separately for decrease in A/W and increase in A/W. Among the patients with MDD, the following 3 subgroups were identified (Table 1 and eMethods 1 in the Supplement): decreased A/W (5347 [45.2%]), no change (3421 [28.9%]), and increased A/W (1871 [15.8%]).

Additional Phenotypes

Information on age at onset was available for 10 452 patients with MDD (eTable 1 in the Supplement). The number of endorsed symptoms calculated as the sum of DSM-IV–positive criteria (range, 5-12; 3 symptoms disaggregated in 6 items coding separately for increase or decrease) to index overall severity was available in 10 991 cases. Analyses using BMI were based on 2 panels of data sets, including 9 data sets providing BMI for 15 237 samples (42%-100% of the samples across data sets; the GlaxoSmithKline/Max Planck Institute of Psychiatry and Sequenced Treatment Alternatives to Relieve Depression studies were excluded owing to lack of BMI data; the Bonn/Mannheim Study, RADIANT–United Kingdom, and RADIANT-Germany were excluded owing to availability of BMI in <18% of samples) and 7 data sets providing BMI for 13 448 samples (83%-100% of the samples across data sets; additional removal of Genetics of Recurrent Early-Onset Depression studies 1 and 2 providing BMI only in cases).

Genotype Data Selection

Genotype data underwent centralized quality control and imputation as extensively described in the PGC-MDD2 GWAS (http://www.biorxiv.org/content/early/2017/07/24/167577). Two panels of single-nucleotide polymorphisms (SNPs) were selected for the present analyses (eMethods 1 in the Supplement). The first panel of 1 169 543 SNPs passing postimputation quality control in at least 2 of 14 data sets and present in the HapMap3 reference was selected to build a genomic relationship matrix (eTable 2 in the Supplement). The second panel of 2 548 638 SNPs passing quality control in all 14 data sets constituted the base to build genomic profile risk scores (GPRSs).

Genomic Profile Risk Scores

We used GWAS meta-analyses from large international consortia to generate the following obesity-related trait GPRSs (eMethods 1 in the Supplement): BMI (approximately 160 000 samples)30 and circulating blood concentrations of CRP (approximately 70 000 samples),31 leptin, and BMI-adjusted leptin (approximately 32 000 samples).32 Psychiatric trait GPRSs were also built, including schizophrenia (approximately 36 000 patients and 113 000 controls)33 and MDD (approximately 50 000 patients and 110 000 controls) (http://www.biorxiv.org/content/early/2017/07/24/167577). Overlapping SNPs (approximately 400 000 to 700 000) between the approximately 2.5 million selected and those filtered from the discovery GWAS were used to build GPRSs with linkage disequilibrium (LD) according to the LDpred method,34 which has shown an improved predictive performance compared with other methods by modeling a prior on effect sizes and LD information. The fraction of causal SNPs was set at 5%, consistent with the estimate for schizophrenia.35

Statistical Analysis

Analyses are extensively described in eMethods 1 and URLs of the software used in the analyses are given in eMethods 2 in the Supplement. In brief, differences in sex, age at onset, number of DSM-IV symptoms, and sex-adjusted BMI across the subgroups of patients were examined. Data were pooled using a multilevel analysis approach with (generalized) linear mixed models with random intercept (data set). Analyses based on genomic relationship matrix–restricted maximum likelihood methods were applied to estimate (1) the variance in liability to MDD subgroups explained by the joint effect of all SNPs (h2 SNP value) and (2) genetic correlations (rg value) across MDD subgroups and with BMI. The association of GPRS-LDpred with MDD subgroups was estimated using logistic mixed models with random intercept (data set). The proportion of variance explained by GPRS on the liability scale for MDD subgroups was estimated according to Lee at al.36 Statistical significance level was set at P < .05 (2-tailed).

Results
Phenotype Validation

Data included 11 837 patients with MDD and 14 791 controls, for a total of 26 628 participants (59.1% female and 40.9% male). We validated MDD subgroup phenotypes against established clinical characteristics (Figure 1). The increased A/W subgroup was more likely to be female compared with the decreased A/W (odds ratio [OR], 1.39; 95% CI, 1.22-1.57) and no-change (OR, 1.94; 95% CI, 1.71-2.22) subgroups and had slightly earlier age at onset compared with the decreased A/W subgroup (β = –0.91; SE = 0.32). The increased A/W (β = 1.24; SE = 0.04) and decreased A/W (β = 1.23; SE = 0.03) subgroups had higher similar severity indexed by number of endorsed symptoms than the no-change subgroup. Finally, the increased A/W subgroup had higher BMI than the decreased A/W (β = 2.94; SE = 0.18) and no-change (β = 2.78; SE = 0.19) subgroups. In addition, the increased A/W subgroup had higher BMI than controls (β = 2.00; SE = 0.70) (eFigure 1 in the Supplement); all other subgroups did not differ from controls. Direct comparisons of the increased A/W with decreased A/W subgroups by using meta-analyses highlighted moderate to substantial heterogeneity across data sets (I2 = 15%-91%) (eMethods 1 and eFigure 2 in the Supplement).

MDD Subgroup SNP Heritability and Correlations

The genomic relationship matrix–restricted maximum likelihood analyses (Table 2) showed that the joint effect of common SNPs (h2-SNP) significantly captured approximately 10% of variance in liability for MDD and the subgroups (eTable 3 in the Supplement for h2-SNP at varying lifetime risk). The h2-SNP estimates were consistent with those obtained from concurrent techniques (eMethods 1 in the Supplement). Bivariate genomic relationship matrix–restricted maximum likelihood analyses across subgroups showed an rg of 1.00 between the no-change subgroup and the increased and decreased A/W subgroups. A smaller correlation (rg = 0.82), although with large SEs owing to restricted sample sizes, was found between the decreased and increased A/W and MDD subgroups, suggesting the possibility of a specific small divergence between these 2 subgroups that otherwise shared most of their genetic background.

Differential Associations With Obesity-Related Liability

To investigate whether the smaller resemblance between the decreased and increased A/W subgroups could be attributed to a different underlying liability to obesity, we estimated their genetic correlation with BMI (estimated h2-SNP = 0.18; SE = 0.02; P = 2.9 × 10−15). As depicted in Figure 2 (MDD reported as the benchmark), BMI was significantly correlated with increased A/W (rg = 0.53; SE = 0.16) and inversely correlated with decreased A/W (rg = –0.28; SE = 0.15).

Furthermore, associations with GPRS-LDpred for obesity-related traits confirmed partially distinct polygenic signatures (Figure 3 and eTable 4 in the Supplement) for both A/W subgroups. The GPRS-LDpred for BMI was significantly associated with a higher likelihood of increased A/W (OR, 1.18; 95% CI, 1.12-1.25; h2 for liability = 0.56%) and with a slightly reduced likelihood of decreased A/W (OR, 0.96; 95% CI, 0.93-0.99; h2 for liability = 0.03%; false discovery rate, q <0.05, accounting for multiple testing) (eMethods 1 in the Supplement). Moreover, the association between GPRS-LDpred for CRP levels and MDD (OR, 1.01; 95% CI, 1.01-1.06; h2 for liability = 0.03%) seemed to be completely driven by increased A/W (OR, 1.08; 95% CI, 1.02-1.13; h2 for liability = 0.09%). Finally, increased A/W was associated with GPRS-LDpred for leptin levels (OR, 1.09; 95% CI, 1.06-1.12; h2 for liability = 0.14%) and, although with a reduced effect size, GPRS-LDpred for leptin levels adjusted for BMI (OR, 1.06; 95% CI, 1.01-1.12; h2 for liability = 0.07%). Of note, GPRS-LDpred for leptin levels adjusted for BMI was not associated with BMI (β = 0.07; SE = 0.04; P = .08). The associations between increased A/W and the GPRS-LDpred for obesity-related traits were similar across sex (GPRS-LDpred × sex interaction terms, P > .3) In supplementary analyses, GPRS-LDpred for MDD and schizophrenia were similarly associated across subgroups (eTable 4 in the Supplement). For completeness of information, eTable 5 in the Supplement reports the association between the no-change A/W MDD subgroup and GPRS-LDpred.

Additional Analyses With GWAS

The association of decreased and increased A/W with single genetic variants was estimated in the following 3 GWAS meta-analyses: (1) decreased A/W subgroup vs controls, (2) increased A/W subgroup vs controls, and (3) decreased vs increased A/W subgroups (eMethods 1 in the Supplement). Owing to small sample size, the 3 GWASs performed were substantially underpowered to detect significant association with single genetic variants. Only when comparing the decreased and increased A/W subgroups, 1 SNP reached genome-wide statistical significance (rs7598414; P = 4.7 × 10−8), harbored in a locus on chromosome 2 overlapping several genes, including SOS1 (NG_007530.1), CDKL4 (NC_000002.12), and MAP4K3 (NG_028007.1) (eFigures 3-5 in the Supplement); CDKL4 was statistically significant in gene-based test analyses (P = 8.3 × 10−7).

Discussion

Using data from more than 25 000 samples from the PGC-MDD2, we examined whether subgroups of patients with MDD stratified according to the DSM A/W symptoms had a different degree of genetic overlap with obesity-related traits. The findings showed that the derived subgroups largely shared their genetic background but were specifically divergent in their liability to immunometabolic traits, with approximately 15% of patients reporting increased A/W symptoms during an active episode carrying a higher number of common risk variants for BMI and CRP and leptin levels.

Despite the simple clinical subphenotyping strategy, the subgroups recapitulated some clinical features that were reported in previous research on classic MDD subtypes; patients with increased A/W (approximating an atypical subtype) were more likely to be female and had an earlier age at onset, higher severity, and higher BMI.24,25 Common variants explained approximately 10% of MDD heritability, in line with the estimation from the latest PGC-MDD2 GWAS (http://www.biorxiv.org/content/early/2017/07/24/167577). The SNP heritability was similar for MDD subgroups, indicating an overall common genetic background. Reciprocal genetic correlations confirmed the close association of the subgroups. Nevertheless, the correlation of 0.82 (affected by large uncertainty owing to limited sample size) for the decreased vs increased A/W subgroups was suggestive of a possible small divergence between these 2 subgroups otherwise sharing most of their genetic background. We tested whether this divergence was attributable to obesity-related genetic variants of traits. Results confirmed that BMI had a genetic correlation with increased A/W. Similarly, the increased A/W subgroup had a higher genetic risk load for CRP and leptin levels.

Evidence from observational studies suggests stronger links with obesity immunometabolic dysregulations in depression with atypical features14-23; in particular, the role of increased appetite in connection to BMI and levels of CRP and leptin has been highlighted.26,27 The present findings suggest that these phenotypic interrelationships may be rooted in a shared genetic base and common pathophysiologic mechanisms. Adipose tissue produces proinflammatory cytokines, which in turn act on peripheral cellular targets, leading to the synthesis of acute phase proteins (CRP from hepatocytes) responsible for systemic inflammation.3 Systemic inflammation in turn could trigger brain inflammatory responses participating in depression neurochemical and endocrine processes.5 Obesity-related inflammation can disrupt the central signaling of leptin (CRP directly inhibits the binding of leptin to its receptors37), blunting its anorexigenic effect and consequently disinhibiting feeding.7 In parallel, reduced central signaling affects leptin’s antidepressant effects.8-10 The hypothesis of a divergent mechanism acting in MDD along the A/W axis is strengthened by the finding of a dissociable neural response among subgroups of patients stratified according to appetite symptom, in particular with those endorsing increased appetite showing a specific hyperactivation of mesocorticolimbic reward circuitry in response to food stimuli.38

The present findings indicate genetic overlap between increased A/W and immunometabolic traits. Therefore, the phenotypic association between increased A/W and obesity-related features may have resulted from shared genetic and biological pathways (ie, pleiotropy), such as increased inflammation or leptin system dysregulation, rather than from an overrepresentation of participants with higher BMI among this MDD subgroup independent from these pathways (ie, confounding). The 2 processes cannot be disentagled statistically in observational data; if a pleiotropic action between 2 traits is subsumed, reciprocal adjustment for the other trait (eg, BMI) may represent a potential bias-inducing overadjustment.39 The example of leptin is emblematic of consistent pleiotropic (and BMI-independent) actions at different levels. In animal models, leptin resistance induced through selective deletion of leptin receptor in hypothalamus or hippocampus and cortex produces hyperleptinemia with obesity40 or depressionlike phenotypes, respectively.41,42 Previous results from NESDA27 showed that hyperleptinemia was linked with increased appetite in patients with MDD independently from BMI. In the present study, increased A/W was also associated with a polygenic score for BMI-adjusted leptin levels.

Although leptin may be an example of pleiotropic action of common genetic variants influencing increased A/W and obesity through specific pathways (eg, leptin effect in different brain areas40-42), pleiotropy also may occur when one trait is causally associated with another trait43,44; in this case, a genetic overlap with inflammation may occur if, for instance, the genetically determined increase in appetite and feeding would be the main driver of proinflammatory activation. Reliable discrimination of different pleiotropy scenarios with recently developed techniques43 will become possible with the availability in the future of adequately powered GWAS for MDD subgroups. By assuming the example of leptin dysregulation as a shared mechanism, the hypothetical model is that of a subgroup of patients expected to show phenotypically average higher appetite and weight compared with others.15 When depression ensues, the shared mechanism may become more dysregulated, and therefore, the behavioral phenotype will be more pronounced during an active episode. We confirmed using NESDA data (eResults in the Supplement) that the increased A/W subgroup had significantly higher BMI compared with other patients when the disorder was nonactive, but this difference was enhanced at the beginning of an active episode mainly owing to BMI increases in the increased A/W subgroup and decreases in the decreased A/W subgroup. This theoretical model is also consistent with the reported moderation effect of depression on the association of BMI and the FTO gene (NG_012969.1).45,46 Furthermore, this model is consistent with a conceptual framework similar to Research Domain Criteria,47 in which biology-based multilevel dimensions cut through symptom-based clinical categories. Obesity and atypical depression may partially overlap on the dimension of energy homeostasis, which could be measured at genetic, biomarker (immunometabolic mediators), and behavior (appetite and/or feeding) levels.

Limitations

In interpreting results from the present study, some limitations should be considered. First, A/W symptoms used for case stratification were based on a single MDD episode (index lifetime episode, generally with the highest severity). Nevertheless, these symptoms are consistently 75% to 85% stable across MDD episodes, especially atypical-like increase,48-50 indicating that they may be considered stable features of depression in an individual. Second, the data structure did not allow us to dissociate the specific effects of appetite from weight. Nevertheless, the correlation between these 2 symptoms during an active episode is high, and previous studies26,29,51 showed that higher genetic burden for BMI or immunometabolic biomarkers were detected in patients identified only by appetite. Finally, results cannot be generalized to a population of non-European ancestry.

Conclusions

The present findings showed that the increased A/W subgroup had a specific genetic overlap with obesity-related traits. An important follow-up study would be the identification of the specific genetic loci involved using unbiased genome-wide scans. The present results indicate that at the current stage, stratification of the existing MDD GWAS data set is not yet a viable option, because the reduction in power owing to reduced sample size would not be compensated by a higher heritability of the trait (similar to MDD), as shown by the substantially null results obtained using the present data. The significant locus detected in a case-only GWAS contrasting the decreased and increased A/W subgroups should be considered mainly as a stimulus to further pursue this analytic strategy in larger data sets. Furthermore, the present results identified only associations. Once the specific loci are identified, follow-up functional studies will be needed to elucidate the potential causal role of the related pathways. Finally, although we specifically focused on A/W symptoms, future studies should consider other axes of variation in the association between MDD and genetic and biological factors, including other symptoms such as sleep disturbances or interaction with environmental factors.

The present findings may have important translational implications, providing molecular ground to the observed heterogeneity of response observed in intervention studies. For instance, anti-inflammatory agents have been proposed for depression treatment, and a recent meta-analysis52 of randomized placebo-controlled trials indicated substantial heterogeneity in their antidepressant effects. Of interest, post hoc analyses of a previous trial showed that anti-inflammatory agents exerted antidepressant effects only in patients with high baseline CRP levels.53 These data underline the need to identify subgroups of patients who may benefit most from a specific treatment according to a personalized medicine approach. In the present study, increased A/W symptoms identified a subgroup of approximately 15% of patients with MDD with a higher genetic risk for immunometabolic dysregulations. Future clinical trials should plan to test treatment efficacy across patients with depression stratified according to this criterion, especially for treatments targeting immunometabolic pathways (eg, anti-inflammatory agents5 or weight reduction pharmacologic or behavioral interventions with or without recombinant leptin7). Development of tailored treatments effectively targeting immunometabolic dysregulations may benefit this specific subgroup of patients with MDD and obesity, both of which are associated with disability.

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Article Information

Corresponding Author: Yuri Milaneschi, PhD, Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Vrije Universiteit Medical Center and GGZ inGeest, AJ Ernststraat 1187, 1081 HL Amsterdam, the Netherlands (y.milaneschi@ggzingeest.nl).

Accepted for Publication: August 24, 2017.

Correction: This article was corrected on December 6, 2017, for a missing degree for Dr Van der Auwera in the byline and an error in the abbreviations footnote in Table 1.

Published Online: October 18, 2017. doi:10.1001/jamapsychiatry.2017.3016

Author Contributions: Dr Lamers and Mr Peyrot contributed equally to this study. Dr Milaneschi had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analyses.

Study concept and design: Milaneschi, Lamers, Peyrot, Boomsma, Penninx.

Acquisition, analysis, or interpretation of data: Milaneschi, Lamers, Peyrot, Boomsma. Pennix.

Drafting of the manuscript: Milaneschi, Lamers, Peyrot, Boomsma, Penninx.

Critical revision of the manuscript for important intellectual content: Lamers, Peyrot, Baune, Breen, Dehghan, Forstner, Grabe, Homuth, Kan, Lewis, Mullins, Nauck, Pistis, Preisig, Rivera, Rietschel, Streit, Strohmaier, Teumer, Van der Auwera, Wray, Boomsma, Penninx.

Statistical analysis: Milaneschi, Peyrot.

Obtained funding: Lewis, Breen, Grabe, Preisig, Rietschel, Wray, Boomsma, Pennix.

Administrative, technical, or material support: Milaneschi, Peyrot, Lewis, Wray.

Study supervision: Boomsma, Penninx.

Conflict of Interest Disclosures: Dr Kan reports receiving salary support from Novo Nordisk UK Research Foundation and National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health at South London and Maudsley National Health Service (NHS) Foundation Trust in the past. Dr Penninx reports receiving grant support from Jansen Research, Boehringer Ingelheim, Netherlands Organisation for Scientific Research, Netherlands Organization for Health Research and Development, the National Institutes of Health (NIH), and European Community not directly related to the conduct of this study. No other disclosures were reported.

Funding/Support: This study was supported by grants MH085520, MH080403, and U01 MH109532 from the National Institute of Mental Health (NIMH). The Bonn/Mannheim (BoMa) genome-wide association study (GWAS) was supported by the German Federal Ministry of Education and Research, within the context of the National Genome Research Network 2 (NGFN-2); National Genome Research Network plus (NGFNplus); grants 01GS08144 and 01GS08147 from the Integrated Genome Research Network (IG) MooDS; and grants BMBF–01ZX1314A, 01ZX1314D, 01ZX1314D, 01ZX1314G, and 01ZX1314K from Integrated Understanding of Causes and Mechanisms in Mental Disorders, under the auspices of the e:Med Programme. The Cohorte Lausannoise (CoLaus) and psychiatric arm of CoLaus was supported by research grants from GlaxoSmithKline; the Faculty of Biology and Medicine of Lausanne; and grants 3200B0-105993, 3200B0-118308, 33CSCO-122661, 33CS30-139468, and 33CS30-148401 from the Swiss National Science Foundation. The Genetics of Recurrent Early-Onset Depression Study GWAS project was supported by grants R01 MH061686, MH059542, MH075131, MH059552, MH059541, and MH060912 from the NIMH. Genotyping was performed by the Broad Institute Center for Genotyping and Analysis with support from grant U54 RR020278 from the NIH. The Netherlands Study of Depression and Anxiety (NESDA) and the Netherlands Twin Register (NTR) were supported by the Netherlands Organization for Scientific Research and grants Middelgroot-911-09-032 and Spinozapremie 56-464-14192 from MagW/ZonMW; Center for Medical Systems Biology (Netherlands Organization for Scientific Research Genomics); grant 912-10-02 from ZonMW (Genetic Influences on Stability and Change in Psychopathology From Childhood to Young Adulthood); grant 2008.024 from Netherlands Bioinformatics Centre/BioAssist/RK; grant BBMRI-NL 184.021.007 from Biobanking and Biomolecular Resources Research Infrastructure; VU University’s Institute for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam; and European Research Council Advanced grant 230374 from the European Science Council. The infrastructure for the NESDA study is supported by grant 10-000-1002 from the Geestkracht program of the ZonMw and is supported by participating universities and mental health care organizations (VU University Medical Centre, GGZ inGeest, Arkin, Leiden University Medical Centre, GGZ Rivierduinen, University Medical Centre Groningen, Lentis, GGZ Friesland, GGZ Drenthe, Institute for Quality of Health Care, Netherlands Institute for Health Services Research, and Netherlands Institute of Mental Health and Addiction). Part of the genotyping and analyses were supported by the Genetic Association Information Network of the Foundation for the NIH, Rutgers University Cell and DNA Repository (grant U24 MH068457-06 from the NIMH); the Avera Institute, and grants R01 HD042157-01A and MH081802 and Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995 from the NIH. Computing was supported by BiG Grid, the Dutch e-Science Grid, which is supported by the Netherlands Organization for Scientific Research. Femke Lamers is supported by FP7–Marie Curie Career Integration Grant PCIG12-GA-2012-334065 from the European Union Seventh Framework Programme. The QIMR samples were supported by grants 241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496675, 496739, 552485, 552498, 613602, 613608, 613674, and 619667 from the Australian National Health and MRC; grants FT0991360 and FT0991022 from the Australian Research Council; grant QLG2-CT- 2002-01254 from the FP-5 GenomEUtwin Project; grants AA07535, AA10248, AA13320, AA13321, AA13326, AA14041, MH66206, DA12854, and DA019951 from the NIH; and the Center for Inherited Disease Research. RADIANT was supported by joint grant G0701420 from the UK MRC and GlaxoSmithKline; NIHR Specialist Biomedical Research Centre (BRC) for Mental Health at the South London and Maudsley NHS Foundation Trust and the Institute of Psychiatry, King’s College London; and grant G0000647 from the UK MRC. The GENDEP study was supported by EC Contract LSHB-CT-2003-503428 from the European Commission Framework 6. The Study of Health in Pomerania (SHIP) is part of the Community Medicine Research net of the University of Greifswald, Germany, which is supported by research grants 01ZZ9603, 01ZZ0103, and 01ZZ0403 from the Federal Ministry of Education; the Ministry of Cultural Affairs; and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Genome-wide data were supported by research grant 03ZIK012 from the Federal Ministry of Education and a joint grant from Siemens Healthineers and the Federal State of Mecklenburg-West Pomerania. SHIP—Life-Events and Gene-Environment Interaction in Depression (LEGEND) is supported by grant GR 1912/5-1 from the German Research Foundation. Dr Van der Auwera was supported by grant 01ZX1314E from the German Federal Ministry of Education and Research within the framework of the e:Med research and funding concept (IntegraMent). The Sequenced Treatment Alternatives to Relieve Depression Study was supported by contract N01MH90003 from the NIMH to the University of Texas Southwestern Medical Center at Dallas. The TwinGene study was supported by the Swedish Ministry for Higher Education, grant M-2005-1112 from the Swedish Research Council; grants EU/QLRT-2001-01254 and QLG2-CT-2002-01254 from GenomEUtwin; the Swedish Foundation for Strategic Research; and grant U01 DK066134 from the NIH.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: Abbas Degan is a member of the CHARGE Inflammation Working Group. The Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, which provided the data infrastructure for the present study, include the following: Naomi R. Wray, Institute for Molecular Bioscience and Queensland Brain Institute, The University of Queensland, Brisbane, Australia; Stephan Ripke, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte, Berlin, Germany, and Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts; Manuel Mattheisen, Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden, Department of Biomedicine and iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark, and iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; Maciej Trzaskowski, Institute for Molecular Bioscience, The University of Queensland; Enda M. Byrne, Institute for Molecular Bioscience, The University of Queensland; Abdel Abdellaoui, Department of Biological Psychology and EMGO, Institute for Health and Care Research, Vrije Universiteit (VU) Amsterdam, Amsterdam, the Netherlands; Mark J Adams, Division of Psychiatry, University of Edinburgh, Edinburgh, Scotland; Esben Agerbo, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, and Centre for Integrated Register-based Research and National Centre for Register-Based Research, Aarhus University; Tracy M. Air, Discipline of Psychiatry, University of Adelaide, Adelaide, Australia; Till F. M. Andlauer, Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Silviu-Alin Bacanu, Department of Psychiatry, Virginia Commonwealth University, Richmond; Marie Bækvad-Hansen, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, and Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark; Aartjan T. F. Beekman, Department of Psychiatry, VU Medical Center and GGZ inGeest; Tim B. Bigdeli, Department of Psychiatry, Virginia Commonwealth University, and Virginia Institute for Psychiatric and Behavior Genetics, Richmond; Elisabeth B. Binder, Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, and Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia; Douglas H. R. Blackwood, Division of Psychiatry, University of Edinburgh; Julien Bryois, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm; Henriette N. Buttenschøn, iSEQ, Centre for Integrative Sequencing, Aarhus University, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, and Department of Clinical Medicine, Translational Neuropsychiatry Unit, Aarhus University, and Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut; Jonas Bybjerg-Grauholm, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, and Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut; Na Cai, Human Genetics, Wellcome Trust Sanger Institute, and Statistical Genomics and Systems Genetics, European Bioinformatics Institute, Cambridge, England; Enrique Castelao, Department of Psychiatry, University Hospital of Lausanne, Prilly, Switzerland; Jane Hvarregaard Christensen, Department of Biomedicine and iSEQ, Centre for Integrative Sequencing, Aarhus University, and iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research; Toni-Kim Clarke, Division of Psychiatry, University of Edinburgh; Jonathan R. I. Coleman, Medical Research Council (MRC) Social Genetic and Developmental Psychiatry Centre, King’s College London, London, England; Lucía Colodro-Conde, Genetics and Computational Biology, Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute, Herston, Australia; Baptiste Couvy-Duchesne, Centre for Advanced Imaging and Queensland Brain Institute, The University of Queensland, Saint Lucia, Australia; Nick Craddock, Psychological Medicine, Cardiff University, Cardiff, Wales; Gregory E. Crawford, Center for Genomic and Computational Biology and Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, North Carolina; Gail Davies, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh; Ian J. Deary, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh; Franziska Degenhardt, Institute of Human Genetics and Life and Brain Center, Department of Genomics, University of Bonn, Bonn, Germany; Eske M. Derks, Genetics and Computational Biology, QIMR Berghofer Medical Research Institute; Nese Direk, Epidemiology, Erasmus MC, Rotterdam, Zuid-Holland, the Netherlands, and Psychiatry, Dokuz Eylul University School of Medicine, Izmir, Turkey; Conor V. Dolan, Department of Biological Psychology and EMGO, Institute for Health and Care Research, Vrije Universiteit; Erin C. Dunn, Department of Psychiatry and Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, Massachusetts, and Stanley Center for Psychiatric Research, Broad Institute; Thalia C. Eley, MRC Social Genetic and Developmental Psychiatry Centre, King's College London; Valentina Escott-Price, Neuroscience and Mental Health, Cardiff University; Farnush Farhadi Hassan Kiadeh, Bioinformatics, University of British Columbia, Vancouver, Canada; Hilary K. Finucane, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, and Department of Mathematics, Massachusetts Institute of Technology, Cambridge; Andreas J. Forstner, Institute of Human Genetics and Life and Brain Center, Department of Genomics, University of Bonn, and Department of Psychiatry (UPK) and Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland; Josef Frank, Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Héléna A. Gaspar, MRC Social Genetic and Developmental Psychiatry Centre, King's College London; Michael Gill, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland; Fernando S. Goes, Psychiatry and Behavioral Sciences, The Johns Hopkins University, Baltimore, Maryland; Scott D. Gordon, Genetics and Computational Biology, QIMR Berghofer Medical Research Institute; Jakob Grove, Department of Biomedicine, iSEQ, Centre for Integrative Sequencing, and Bioinformatics Research Centre, Aarhus University, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research; Lynsey S. Hall, Division of Psychiatry, University of Edinburgh, and Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, England; Christine Søholm Hansen, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, and Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut; Thomas F. Hansen, iPSYCH, The Lundbeck Foundation Initiative for Psychiatric Research, Danish Headache Centre, Department of Neurology, Rigshospitalet, Glostrup, and Institute of Biological Psychiatry, Mental Health Center Sankt Hans, Mental Health Services Capital Region of Denmark, Copenhagen; Stefan Herms, Institute of Human Genetics and Life and Brain Center, Department of Genomics, University of Bonn, and Human Genomics Research Group, Department of Biomedicine, University of Basel; Ian B. Hickie, Brain and Mind Centre, University of Sydney, Sydney, Australia; Per Hoffmann, Institute of Human Genetics and Life and Brain Center, Department of Genomics, University of Bonn, and Human Genomics Research Group, Department of Biomedicine, University of Basel; Georg Homuth, Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany; Carsten Horn, Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche, Ltd, Basel; Jouke-Jan Hottenga, Department of Biological Psychology and EMGO, Institute for Health and Care Research, VU Amsterdam; David M. Hougaard, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, and Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut; Marcus Ising, Max Planck Institute of Psychiatry, Munich, Germany; Rick Jansen, Department of Psychiatry, VU Medical Center and GGZ inGeest; Eric Jorgenson, Division of Research, Kaiser Permanente Northern California, Oakland; James A. Knowles, Psychiatry and the Behavioral Sciences, University of Southern California, Los Angeles; Isaac S. Kohane, Department of Biomedical Informatics, Harvard Medical School, Department of Medicine, Brigham and Women's Hospital, and Informatics Program, Boston Children’s Hospital, Boston, Massachusetts; Julia Kraft, Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte; Warren W. Kretzschmar, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, England; Jesper Krogh, Department of Endocrinology at Herlev University Hospital, University of Copenhagen; Zoltán Kutalik, Institute of Social and Preventive Medicine, University Hospital of Lausanne, and Swiss Institute of Bioinformatics, Lausanne, Switzerland; Yihan Li, Informatics Program, Boston Children’s Hospital; Penelope A. Lind, Genetics and Computational Biology, QIMR Berghofer Medical Research Institute; Donald J. MacIntyre, Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, and Mental Health, NHS 24, Glasgow, Scotland; Dean F. MacKinnon, Psychiatry and Behavioral Sciences, The Johns Hopkins University; Robert M. Maier, Queensland Brain Institute, The University of Queensland; Wolfgang Maier, Department of Psychiatry and Psychotherapy, University of Bonn; Jonathan Marchini, Statistics, University of Oxford; Hamdi Mbarek, Department of Biological Psychology and EMGO, Institute for Health and Care Research, VU Amsterdam; Patrick McGrath, Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York; Peter McGuffin, MRC Social Genetic and Developmental Psychiatry Centre, King’s College London; Sarah E. Medland, Genetics and Computational Biology, QIMR Berghofer Medical Research Institute; Divya Mehta, Queensland Brain Institute, The University of Queensland, and School of Psychology and Counseling, Queensland University of Technology; Christel M. Middeldorp, Department of Biological Psychology and EMGO, Institute for Health and Care Research, VU Amsterdam, Child and Youth Mental Health Service, Children’s Health Queensland Hospital and Health Service, South Brisbane, and Child Health Research Centre, University of Queensland; Evelin Mihailov, Estonian Genome Center, University of Tartu, Tartu; Yuri Milaneschi, Department of Psychiatry, VU Medical Center and GGZ inGeest; Lili Milani, Estonian Genome Center, University of Tartu; Francis M. Mondimore, Psychiatry and Behavioral Sciences, The Johns Hopkins University; Grant W. Montgomery, Institute for Molecular Bioscience, The University of Queensland; Sara Mostafavi, Medical Genetics and Statistics, University of British Columbia, Vancouver, Canada; Niamh Mullins, MRC Social Genetic and Developmental Psychiatry Centre, King’s College London; Matthias Nauck, German Centre for Cardiovascular Research, Partner Site Greifswald, and Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany; Bernard Ng, Statistics, University of British Columbia; Michel G. Nivard, Department of Biological Psychology and EMGO, Institute for Health and Care Research, VU Amsterdam; Dale R. Nyholt, Institute of Health and Biomedical Innovation, Queensland University of Technology; Paul F. O’Reilly, MRC Social Genetic and Developmental Psychiatry Centre, King’s College London; Hogni Oskarsson, Humus, Reykjavik, Iceland; Michael J. Owen, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University; Jodie N. Painter, Genetics and Computational Biology, QIMR Berghofer Medical Research Institute; Carsten Bøcker Pedersen, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, and Centre for Integrated Register-based Research and National Centre for Register-Based Research, Aarhus University; Marianne Giørtz Pedersen, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, and Centre for Integrated Register-based Research and National Centre for Register-Based Research, Aarhus University; Roseann E. Peterson, Department of Psychiatry and Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University; Erik Pettersson, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet; Wouter J. Peyrot, Department of Psychiatry, VU Medical Center and GGZ inGeest; Giorgio Pistis, Department of Psychiatry, University Hospital of Lausanne; Danielle Posthuma, Clinical Genetics and Complex Trait Genetics, VU Amsterdam; Jorge A. Quiroz, Solid Biosciences, Boston, Massachusetts; Per Qvist, Department of Biomedicine and iSEQ, Centre for Integrative Sequencing, Aarhus University, and iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research; John P. Rice, Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, Missouri; Brien P. Riley, Department of Psychiatry, Virginia Commonwealth University; Margarita Rivera, MRC Social Genetic and Developmental Psychiatry Centre, King’s College London, and Department of Biochemistry and Molecular Biology II, Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, Spain; Saira Saeed Mirza, Epidemiology, Erasmus MC; Robert Schoevers, Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Eva C. Schulte, Department of Psychiatry and Psychotherapy and Institute of Psychiatric Phenomics and Genomics (IPPG), Medical Center of the University of Munich, Campus Innenstadt, Munich, Germany; Ling Shen, Division of Research, Kaiser Permanente Northern California, Oakland; Jianxin Shi, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland; Stanley I. Shyn, Behavioral Health Services, Kaiser Permanente Washington, Seattle; Engilbert Sigurdsson, Faculty of Medicine, Department of Psychiatry, University of Iceland, Reykjavik; Grant C. B. Sinnamon, School of Medicine and Dentistry, James Cook University, Townsville, Australia; Johannes H. Smit, Department of Psychiatry, VU Medical Center and GGZ inGeest; Daniel J. Smith, Institute of Health and Wellbeing, University of Glasgow; Hreinn Stefansson, deCODE Genetics/Amgen, Reykjavik; Stacy Steinberg, deCODE Genetics/Amgen; Fabian Streit, Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University; Jana Strohmaier, Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University; Katherine E. Tansey, College of Biomedical and Life Sciences, Cardiff University; Henning Teismann, Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany; Alexander Teumer, Institute for Community Medicine, University Medicine Greifswald; Wesley Thompson, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Institute of Biological Psychiatry, Mental Health Center Sankt Hans, Mental Health Services Capital Region of Denmark, Department of Psychiatry, University of California, San Diego, and KG Jebsen Centre for Psychosis Research, Norway Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Pippa A. Thomson, Medical Genetics Section, Centre for Genomic and Experimental Medicine (CGEM), Institute of Genetics and Molecular Medicine (IGMM), University of Edinburgh; Thorgeir E. Thorgeirsson, deCODE Genetics/Amgen; Matthew Traylor, Clinical Neurosciences, University of Cambridge, Cambridge, England; Jens Treutlein, Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University; Vassily Trubetskoy, Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte; André G. Uitterlinden, Internal Medicine, Erasmus MC, Rotterdam; Daniel Umbricht, Roche Pharmaceutical Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases Discovery and Translational Medicine Area, Roche Innovation Center Basel, F. Hoffmann-La Roche, Ltd, Basel; Sandra Van der Auwera, Department of Psychiatry and Psychotherapy, University Medicine Greifswald; Albert M. van Hemert, Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands; Alexander Viktorin, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet; Peter M. Visscher, Institute for Molecular Bioscience and Queensland Brain Institute, The University of Queensland; Yunpeng Wang, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Institute of Biological Psychiatry, Mental Health Center Sankt Hans, Mental Health Services Capital Region of Denmark, Institute of Epidemiology and Social Medicine, University of Münster; Bradley T. Webb, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University; Shantel Marie Weinsheimer, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Institute of Biological Psychiatry, Mental Health Center Sankt Hans, Mental Health Services Capital Region of Denmark; Jürgen Wellmann, Institute of Epidemiology and Social Medicine, University of Münster; Gonneke Willemsen, Department of Biological Psychology and EMGO, Institute for Health and Care Research, Vrije Universiteit Amsterdam; Stephanie H. Witt, Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health,Medical Faculty Mannheim, Heidelberg University; Yang Wu, Institute for Molecular Bioscience, The University of Queensland; Hualin S. Xi, Computational Sciences Center of Emphasis, Pfizer Global Research and Development, Cambridge, Massachusetts; Jian Yang, Institute for Molecular Bioscience; Queensland Brain Institute, The University of Queensland; Futao Zhang, Institute for Molecular Bioscience, The University of Queensland; Volker Arolt, Department of Psychiatry, University of Münster; Bernhard T. Baune, Discipline of Psychiatry, University of Adelaide; Klaus Berger, Institute of Epidemiology and Social Medicine, University of Münster; Dorret I. Boomsma, Department of Biological Psychology and EMGO, Institute for Health and Care Research, VU Amsterdam; Sven Cichon, Institute of Human Genetics, University of Bonn, Human Genomics Research Group, Department of Biomedicine, and Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, and Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Juelich, Denmark; Udo Dannlowski, Department of Psychiatry, University of Münster; E. J. C. de Geus, Department of Biological Psychology and EMGO, Institute for Health and Care Research, and Amsterdam Public Health Institute, Vrije Universiteit Medical Center, Amsterdam; J. Raymond DePaulo, Psychiatry and Behavioral Sciences, The Johns Hopkins University; Enrico Domenici, Centre for Integrative Biology, Università degli Studi di Trento, Trento, Italy; Katharina Domschke, Department of Psychiatry and Psychotherapy, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Denmark; Tõnu Esko, Medical and Population Genetics, Broad Institute, and Estonian Genome Center, University of Tartu; Hans J. Grabe, Department of Psychiatry and Psychotherapy, University Medicine Greifswald; Steven P. Hamilton, Psychiatry, Kaiser Permanente Northern California, San Francisco; Caroline Hayward, MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh; Andrew C. Heath, Department of Psychiatry, Washington University in Saint Louis School of Medicine; Kenneth S. Kendler, Department of Psychiatry, Virginia Commonwealth University; Stefan Kloiber, Max Planck Institute of Psychiatry, and Department of Psychiatry, University of Toronto, and Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Glyn Lewis, Division of Psychiatry, University College London, London, England; Qingqin S. Li, Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, New Jersey; Susanne Lucae, Max Planck Institute of Psychiatry; Pamela A. F. Madden, Department of Psychiatry, Washington University in Saint Louis School of Medicine; Patrik K. Magnusson, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet; Nicholas G. Martin, Genetics and Computational Biology, QIMR Berghofer Medical Research Institute; Andrew M. McIntosh, Division of Psychiatry and Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh; Andres Metspalu, Estonian Genome Center and Institute of Molecular and Cell Biology, University of Tartu; Ole Mors, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research and Psychosis Research Unit, Aarhus University Hospital; Preben Bo Mortensen, iSEQ, Centre for Integrative Sequencing, Centre for Integrated Register-based Research, and National Centre for Register-Based Research, Aarhus University, and iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research; Bertram Müller-Myhsok, Discipline of Psychiatry, University of Adelaide, Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, and University of Liverpool, Liverpool, England; Merete Nordentoft, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, and Mental Health Center Copenhagen, Copenhagen University Hospital; Markus M. Nöthen, Institute of Human Genetics and Life and Brain Center, Department of Genomics, University of Bonn; Michael C. O’Donovan, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University; Sara A. Paciga, Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Groton, Connecticut; Nancy L. Pedersen, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet; Brenda W. J. H. Penninx, Department of Psychiatry, VU Medical Center and GGZ inGeest; Roy H. Perlis, Department of Psychiatry, Massachusetts General Hospital, and Psychiatry, Harvard Medical School, Boston, Massachusetts; David J. Porteous, Medical Genetics Section, CGEM, IGMM, University of Edinburgh; James B. Potash, Psychiatry, University of Iowa, Iowa City; Martin Preisig, Department of Psychiatry, University Hospital of Lausanne; Marcella Rietschel, Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University; Catherine Schaefer, Division of Research, Kaiser Permanente Northern California, Oakland; Thomas G. Schulze, Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, IPPG, Medical Center of the University of Munich, Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University, Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, and Human Genetics Branch, National Institute of Mental Health Division of Intramural Research Programs, Bethesda, Maryland; Jordan W. Smoller, Department of Psychiatry and PNGU, Massachusetts General Hospital, and Stanley Center for Psychiatric Research, Broad Institute; Kari Stefansson, deCODE Genetics/Amgen and Faculty of Medicine, University of Iceland, Reykjavik; Henning Tiemeier, Epidemiology, Child and Adolescent Psychiatry, and Psychiatry, Erasmus MC; Rudolf Uher, Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada; Henry Völzke, Institute for Community Medicine, University Medicine Greifswald; Myrna M. Weissman, Psychiatry, Columbia University College of Physicians and Surgeons, and Division of Epidemiology, New York State Psychiatric Institute, New York; Thomas Werge, iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Institute of Biological Psychiatry, Mental Health Center Sankt Hans, Mental Health Services Capital Region of Denmark, and Department of Clinical Medicine, University of Copenhagen; Cathryn M. Lewis, MRC Social Genetic and Developmental Psychiatry Centre and Department of Medical and Molecular Genetics, King’s College London; Douglas F. Levinson, Psychiatry and Behavioral Sciences, Stanford University, Stanford, California; Gerome Breen, MRC Social Genetic and Developmental Psychiatry Centre and NIHR BRC for Mental Health, King's College London; Anders D Børglum, Department of Biomedicine and iSEQ, Centre for Integrative Sequencing, Aarhus University, and iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research; and Patrick F Sullivan, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Genetics and Psychiatry, University of North Carolina at Chapel Hill.

Disclaimer: This report represents, in part, independent research funded by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.

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