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Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P <0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance ( P <5 × 10 −8 ), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083–53 822 102, minimum P =5.9 × 10 −9 at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.

FK506 binding protein 51 (FKBP51) is an Hsp90 co-chaperone and regulator of the glucocorticoid receptor, and consequently of stress physiology. Clinical studies suggest a genetic link between FKBP51 and antidepressant response in mood disorders; however, the underlying mechanisms remain elusive. The objective of this study was to elucidate the role of FKBP51 in the actions of antidepressants, with a particular focus on pathways of autophagy. Established cell lines, primary neural cells, human blood cells of healthy individuals and patients with depression, and mice were treated with antidepressants. Mice were tested for several neuroendocrine and behavioral parameters. Protein interactions and autophagic pathway activity were mainly evaluated by co-immunoprecipitation and Western blots. We first show that the effects of acute antidepressant treatment on behavior are abolished in FKBP51 knockout (51KO) mice. Autophagic markers, such as the autophagy initiator Beclin1, were increased following acute antidepressant treatment in brains from wild-type, but not 51KO, animals. FKBP51 binds to Beclin1, changes decisive protein interactions and phosphorylation of Beclin1, and triggers autophagic pathways. Antidepressants and FKBP51 exhibited synergistic effects on these pathways. Using chronic social defeat as a depression-relevant stress model in combination with chronic paroxetine (PAR) treatment revealed that the stress response, as well as the effects of antidepressants on behavior and autophagic markers, depends on FKBP51. In human blood cells of healthy individuals, FKBP51 levels correlated with the potential of antidepressants to induce autophagic pathways. Importantly, the clinical antidepressant response of patients with depression (n = 51) could be predicted by the antidepressant response of autophagic markers in patient-derived peripheral blood lymphocytes cultivated and treated ex vivo (Beclin1/amitriptyline: r = 0.572, p = 0.003; Beclin1/PAR: r = 0.569, p = 0.004; Beclin1/fluoxetine: r = 0.454, p = 0.026; pAkt/amitriptyline: r =  -0.416, p = 0.006; pAkt/PAR: r =  -0.355, p = 0.021; LC3B-II/PAR: r = 0.453, p = 0.02), as well as by the lymphocytic expression levels of FKBP51 (r = 0.631, p<0.0001), pAkt (r =  -0.515, p = 0.003), and Beclin1 (r = 0.521, p = 0.002) at admission. Limitations of the study include the use of male mice only and the relatively low number of patients for protein analyses. To our knowledge, these findings provide the first evidence for the molecular mechanism of FKBP51 in priming autophagic pathways; this process is linked to the potency of at least some antidepressants. These newly discovered functions of FKBP51 also provide novel predictive markers for treatment outcome, consistent with physiological and potential clinical relevance. Please see later in the article for the Editors' Summary.

Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1–3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response.

Selective Serotonin Reuptake Inhibitors (SSRIs) are commonly used drugs for the treatment of psychiatric diseases including major depressive disorder (MDD). For unknown reasons a substantial number of patients do not show any improvement during or after SSRI treatment. We treated DBA/2J mice for 28 days with paroxetine and assessed their behavioral response with the forced swim test (FST). Paroxetine-treated long-time floating (PLF) and paroxetine-treated short-time floating (PSF) groups were stratified as proxies for drug non-responder and responder mice, respectively. Proteomics and metabolomics profiles of PLF and PSF groups were acquired for the hippocampus and plasma to identify molecular pathways and biosignatures that stratify paroxetine-treated mouse sub-groups. The critical role of purine and pyrimidine metabolisms for chronic paroxetine treatment response in the mouse was further corroborated by pathway protein expression differences in both mice and patients that underwent chronic antidepressant treatment. The integrated -omics data indicate purine and pyrimidine metabolism pathway activity differences between PLF and PSF mice. Furthermore, the pathway protein levels in peripheral specimens strongly correlated with the antidepressant treatment response in patients. Our results suggest that chronic SSRI treatment differentially affects purine and pyrimidine metabolisms, which may explain the heterogeneous antidepressant treatment response and represents a potential biosignature.

Single-nucleotide polymorphisms (SNPs) in the FKBP5 , GRIK4 , and HTR2A genes have been shown to be associated with response to citalopram treatment in the STAR * D sample, but only associations with FKBP5 have so far been tested in the Munich Antidepressant Response Signature (MARS) project. Response and remission of depressive symptoms after 5 weeks of antidepressant treatment were tested against 82 GRIK4 and 37 HTR2A SNPs. Association analysis was conducted in about 300 depressed patients from the MARS project, 10% of whom had bipolar disorder. The most predictive SNPs from these two genes and rs1360780 in FKBP5 were then genotyped in a total of 387 German depressed in-patients to analyze potential additive and interactive effects of these variants. We could not replicate previous findings of the Sequenced Treatment Alternatives to Relieve Depression (STAR * D) study in our sample. Although not statistically significant, the effect for the best GRIK4 SNP of STAR * D (rs1954787, p =0.076, p corrected =0.98) seemed to be in the same direction. On the other hand, the nominally significant association with the top HTR2A SNPs of STAR * D (rs7997012, allelic, p =0.043, p corrected =0.62) was with the opposite risk allele. The GRIK4 SNP (rs12800734, genotypic, p =0.0019, p corrected =0.12) and the HTR2A SNP (rs17288723, genotypic, p =0.0011, p corrected =0.02), which showed the strongest association with remission in our sample, had not been reported previously. Associations across all genetic markers within the GRIK4 (genotypic, p =0.022) or HTR2A (genotypic, p =0.012) locus using the Fisher's product method (FPM) were also significant. In all 374 patients, the best predictive model included a main effect for GRIK4 rs12800734 and two significant interactions between GRIK4 rs12800734 and FKBP5 rs1360780, and GRIK4 rs12800734 and HTR2A rs17288723. This three SNP model explained 13.1% of the variance for remission after 5 weeks ( p =0.00051 for the model). Analyzing a sub-sample of 194 patients, plasma ACTH ( p =0.002) and cortisol ( p =0.021) responses of rs12800734 GG ( GRIK4 ) carriers, who also showed favorable treatment response, were significantly lower in the second combined dexamethasone (dex)/corticotrophin-releasing hormone (CRH) test before discharge compared with the other two genotype groups. Despite large differences in ethnicity and design compared with the STAR * D study, our results from the MARS study further support both independent and interactive involvement of GRIK4 , HTR2A and FKBP5 in antidepressant treatment response.

Background Panic disorder (PD) is characterized by recurrent panic attacks and higher affection of women as compared to men. The lifetime prevalence of PD is about 2–3% in the general population leading to tremendous distress and disability. Etiologically, genetic and environmental factors, such as stress, contribute to the onset and relapse of PD. In the present study, we investigated epigenome-wide DNA methylation (DNAm) in respond to a cumulative, stress-weighted life events score (wLE) in patients with PD and its boundary to major depressive disorder (MDD), frequently co-occurring with symptoms of PD. Methods DNAm was assessed by the Illumina HumanMethylation450 BeadChip. In a meta-analytic approach, epigenome-wide DNAm changes in association with wLE were first analyzed in two PD cohorts (with a total sample size of 183 PD patients and 85 healthy controls) and lastly in 102 patients with MDD to identify possible overlapping and opposing effects of wLE on DNAm. Additionally, analysis of differentially methylated regions (DMRs) was conducted to identify regional clusters of association. Results Two CpG-sites presented with p -values below 1 × 10 −05 in PD: cg09738429 ( p  = 6.40 × 10 −06 , located in an intergenic shore region in next proximity of PYROXD1 ) and cg03341655 ( p  = 8.14 × 10 −06 , located in the exonic region of GFOD2 ). The association of DNAm at cg03341655 and wLE could be replicated in the independent MDD case sample indicating a diagnosis independent effect. Genes mapping to the top hits were significantly upregulated in brain and top hits have been implicated in the metabolic system. Additionally, two significant DMRs were identified for PD only on chromosome 10 and 18, including CpG-sites which have been reported to be associated with anxiety and other psychiatric phenotypes. Conclusion This first DNAm analysis in PD reveals first evidence of small but significant DNAm changes in PD in association with cumulative stress-weighted life events. Most of the top associated CpG-sites are located in genes implicated in metabolic processes supporting the hypothesis that environmental stress contributes to health damaging changes by affecting a broad spectrum of systems in the body.

PurposeBioMD-Y is a comprehensive biobank study of children and adolescents with major depression (MD) and their healthy peers in Germany, collecting a host of both biological and psychosocial information from the participants and their parents with the aim of exploring genetic and environmental risk and protective factors for MD in children and adolescents.ParticipantsChildren and adolescents aged 8–18 years are recruited to either the clinical case group (MD, diagnosis of MD disorder) or the typically developing control group (absence of any psychiatric condition).Findings to dateTo date, four publications on both genetic and environmental risk and resilience factors (including FKBP5, glucocorticoid receptor activation, polygenic risk scores, psychosocial and sociodemographic risk and resilience factors) have been published based on the BioMD-Y sample.Future plansData collection is currently scheduled to continue into 2026. Research questions will be further addressed using available measures.

Antidepressant therapy is still associated with delays in symptomatic improvement and low response rates. Incomplete understanding of molecular mechanisms underlying antidepressant effects hampered the identification of objective biomarkers for antidepressant response. In this work, we studied transcriptome-wide expression followed by pathway analysis in lymphoblastoid cell lines (LCLs) derived from 17 patients documented for response to SSRI antidepressants from the Munich Antidepressant Response Signatures (MARS) study upon short-term incubation (24 and 48 h) with citalopram. Candidate transcripts were further validated with qPCR in MARS LCLs from responders ( n  = 33) vs. non-responders ( n  = 36) and afterward in an independent cohort of treatment-resistant patients ( n  = 20) vs. first-line responders ( n  = 24) from the STAR*D study. In MARS cohort we observed significant associations of GAD1 (glutamate decarboxylase 1; p  = 0.045), TBC1D9 (TBC1 Domain Family Member 9; p  = 0.014–0.021) and NFIB (nuclear factor I B; p  = 0.015–0.025) expression with response status, remission status and improvement in depression scale, respectively. Pathway analysis of citalopram-altered gene expression indicated response-status-dependent transcriptional reactions. Whereas in clinical responders neural function pathways were primarily up- or downregulated after incubation with citalopram, deregulated pathways in non-responders LCLs mainly involved cell adhesion and immune response. Results from the STAR*D study showed a marginal association of treatment-resistant depression with NFIB ( p  = 0.068) but not with GAD1 ( p  = 0.23) and TBC1D9 ( p  = 0.27). Our results propose the existence of distinct pathway regulation mechanisms in responders vs. non-responders and suggest GAD1, TBC1D9 , and NFIB as tentative predictors for clinical response, full remission, and improvement in depression scale, respectively, with only a weak overlap in predictors of different therapy outcome phenotypes.

The current inability of clinical psychiatry to objectively select the most appropriate treatment is a major factor contributing to the severity and clinical burden of major depressive disorder (MDD). Here, we have attempted to identify plasma protein signatures in 39 MDD patients to predict response over a 6-week treatment period with antidepressants. LC-MS/MS analysis showed that differences in the levels of 29 proteins at baseline were found in the group with a favorable treatment outcome. Most of these proteins were components of metabolism or immune response pathways as well as multiple components of the coagulation cascade. After 6 weeks of treatment, 43 proteins were altered in responders of which 2 (alpha-actinin and nardilysin) had been identified at baseline. In addition, 46 proteins were altered in non-responders and 9 of these (alpha-actinin, alpha-2-macroglobulin, apolipoprotein B-100, attractin, C-reactive protein, fibrinogen alpha chain, fibrinogen beta chain, nardilysin and serine/threonine-protein kinase Chk1) had been identified at baseline. However, it should be stressed that the small sample size precludes generalization of the main results. Further studies to validate these as potential biomarkers of antidepressant treatment response are warranted considering the potential importance to the field of psychiatric disorders. This study provides the groundwork for development of novel objective clinical tests that can help psychiatrists in the clinical management of MDD through improved prediction and monitoring of patient responses to antidepressant treatments.