Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies.
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In a genome-wide association study (GWAS) meta-analysis of 688,808 individuals with major depression (MD) and 4,364,225 controls from 29 countries across diverse and admixed ancestries, we identify 697 associations at 635 loci, 293 of which are novel. Using fine-mapping and functional tools, we find 308 high-confidence gene associations and enrichment of postsynaptic density and receptor clustering. A neural cell-type enrichment analysis utilizing single-cell data implicates excitatory, inhibitory, and medium spiny neurons and the involvement of amygdala neurons in both mouse and human single-cell analyses. The associations are enriched for antidepressant targets and provide potential repurposing opportunities. Polygenic scores trained using European or multi-ancestry data predicted MD status across all ancestries, explaining up to 5.8% of MD liability variance in Europeans. These findings advance our global understanding of MD and reveal biological targets that may be used to target and develop pharmacotherapies addressing the unmet need for effective treatment.
Overview of MD GWAS and downstream analysesFigure shows the 3 meta-analyses conducted (middle, deeper blue). Predictive testing using polygenic risk scores was conducted using both European and all ancestries GWAS summary statistics (left-hand side of the figure). Bioinformatic and mechanistic analyses were conducted using European-only GWAS summary statistics because many of the methods depend on a single suitable linkage equilibrium reference panel, and methods to generalize these approaches to trans-ancestry summary statistics were still in development at the time of submission.
Manhattan plot of GWAS meta-analysis of 688,808 MD cases and 4,364,225 controlsManhattan plot displaying the significance of each SNP’s association with MD across the genome (vertical axis shows −log10 p value). Chromosomal position of each SNP is shown on the horizontal axis. The horizontal line at 7.3 (−log10(5 × 10−8)) indicates the genome-wide statistical significance threshold.
Broad brain cell category enrichment analysisCell-type enrichment analysis. 20 categories of brain cell types are listed (from a total of 39 broad brain cell-type categories tested) along the vertical axis, and horizontal bar size represents the significance of the enrichment measured using MAGMA gene set enrichment test or partitioned LDSC. Color encodes results that were significant after false discovery rate correction. Bars in salmon color represent enrichments significant using both methods; green, MAGMA only; blue, partitioned LDSC only; and purple when neither method showed significant enrichment. 19 broad categories not displayed were not significant using either method. Columns represent the results of each test using summary statistics from MDD2013, MDD2018, and this study. The dotted line shows threshold of nominal (uncorrected) statistical significance.
MD polygenic score prediction into European ancestry studies(A) Comparison of liability R2 by input summary statistics by availability (full dataset with 23andMe versus public dataset without 23andMe, using p value clumping + thresholding at p ≤ 0.05 [P+CT]), PGS method (P+CT versus SBayesR), and discovery dataset (previous Howard et al.2 versus current MDD2024 SBayesR). The R2 are estimated across 42 cohorts with individual-level data. For the discovery panel, the R2 are estimated from the 20 cohorts with individual-level data contributed to the PGC after the Howard et al.2 study. The rl2 was calculated using a lifetime prevalence of 0.15.(B) Odds ratio by decile, with reference to decile 1, for clinical and community-ascertained studies (SBayesR). Bars reflecting the 95% confidence interval (CI) are based on estimates from the logistic regression.
Polygenic prediction of MD status from European and multi-ancestry GWAS into ancestrally diverse non-European studiesDetails of cohorts found in Table S1. The rl2 was calculated using a prevalence of 0.15 with the P+CT method. The error bars are confidence intervals calculated using bootstrap. The training data did not include 23andMe because of access limitations. AFR, African ancestry; AMR, Hispanic and Latin American ethnicities; EAS, East Asian ancestries; EUR, European ancestries; SAS, South Asian ancestries.
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| 40 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Association / Meta-analysis in the core PGC dataset | In each cohort, association testing was based on an additive logistic regression model using… |
| 41 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Joint analysis model for ancestrally diverse cohorts | We fitted ancestry-aware mixed models for 12 cohorts with ancestrally diverse and admixed… |
| 42 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Joint analysis model for ancestrally diverse cohorts | variants as predictors. Due to computational limitations, the Million Veterans Program was… |
| 43 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Post-imputation quality control procedures | Summary statistics were aligned to chromosome-position scaffolds for Genome Reference Consortium… |
| 44 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Post-imputation quality control procedures | < 0.001; a minor allele count in cases and controls of < 20; imputation INFO score < 0.1; or that… |
| 45 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Post-imputation quality control procedures | least 5% was required; for larger studies, we required a minimum effective sample size (Neff) of 50,… |
| 46 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Genome-wide association and fixed effects meta-analysis | After quality control, we meta-analysed genotype and summary statistics samples together using… |
| 47 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Common-factor meta-analysis | To examine the role of how MDD status was ascertained and phenotyped,69 we meta-analysed European… |
| 48 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — SNP-based heritability and Genetic Correlation estimation | SNP-based heritability was estimated using SBayesS14 assuming lifetime risk of 15% for comparison… |
| 49 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Polygenic analysis — Out of sample prediction | Of the case-control studies in the meta-analysis of European cohorts, 48 provided individual level… |
| 50 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Polygenic analysis — Out of sample prediction | with previous publications, PGS were generated using the basic p-value clumping and thresholding… |
| 51 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Polygenic analysis — Out of sample prediction | genome-wide significant SNPs and their weights (bJ) estimated from a conditional/joint COJO analysis… |
| 52 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Polygenic analysis — Out of sample prediction | The PGS were evaluated in each cohort. Logistic regression of case/control status on PGS… |
| 53 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Polygenic analysis — Out of sample prediction | Results are reported per cohort and for the weighted mean across cohorts (weighted by effective… |
| 54 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — PGS association in participants with non-European ancestry | We conducted polygenic profiling in three cohorts of African ancestry (48,669 cases and 52,939… |
| 55 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — PGS association in participants with non-European ancestry | As above, PGS were calculated from the all-ancestries meta-analysis using the p-value P+CT method… |
| 56 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Tissue and cell-type enrichment analysis | We performed tissue and cell-type enrichment analysis aiming to identify relevant tissues and cell… |
| 57 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Tissue and cell-type enrichment analysis | RNA sequencing data of over 3 million high-quality nuclei from around 100 dissections across adult… |
| 58 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Tissue and cell-type enrichment analysis | We used two primary methods, partitioned LD Score regression (pLSDC)75 and MAGMA (v1.08),45 to test… |
| 59 | STAR★METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Gene finding analysis in fastBAT | A gene-based association analysis was conducted using fastBAT76 within GCTA version 1.94.0 beta.46… |
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In this knowledge base
| Title | Year | PMID |
|---|---|---|
| Genome-wide analyses identify 30 loci associated with obsessive-compulsive disorder. | 2025 | 40360802 |
External
| Title | Authors | Journal | Year | Link |
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| Acupuncture for depression: Decoding neuroimmune crosstalk and targeting anti-inflammatory mechanisms. | Shi A et al. | — | 2026 | → |
| A head-to-head comparison of gray matter morphological differences between early-onset and middle-to-late-onset depression in a large-scale multi-site cohort. | Fang K et al. | — | 2026 | → |
| Are polygenic scores for psychiatric and substance use outcomes "ready" for clinical application? Current state and next steps. | Dick DM et al. | — | 2026 | → |
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| Causal Relationships Between Major Depressive Disorder and Coronary Artery Disease Across Diverse Populations: A Bidirectional Mendelian Randomisation Study. | Silva S et al. | — | 2026 | → |
| Construction of depression risk prediction model using genetic markers and machine learning. | Cai Y et al. | — | 2026 | → |
| Cumulative adverse childhood experiences increase migraine risk in later life in China: evidence from CHARLS with mediation and Mendelian randomization. | Duan L et al. | — | 2026 | → |
| Deep Phenotyping at Scale: Study Protocol for the Korean Mood Disorder Genetic Study-Depression (KOMOGEN-D). | Min S et al. | — | 2026 | → |
| Detection of pleiotropic genetic factors and critical brain cell types linking insomnia with psychiatric disorders. | Xue B et al. | — | 2026 | → |
| Disrupted resting-state amygdala connectivity dynamics in major depressive disorder with suicidal ideation: Implications for emotional dysregulation and suicide risk. | Liu C et al. | — | 2026 | → |
| Dissecting the genetic relationship between severe mental disorders and autoimmune diseases. | Wiström ED et al. | — | 2026 | → |
| Disturbances of paraventricular thalamic nucleus neurons in bipolar disorder revealed by single-nucleus analysis. | Nishioka M et al. | — | 2026 | → |
| Ethnic Discrimination Moderates Genetic Influences on Adolescent Internalizing and Externalizing Psychopathology. | Su J et al. | — | 2026 | → |
| Functional polygenic risk score of glucocorticoid-dependent regulatory element activity and its relation to clinical and stress-related phenotypes. | Linsen F et al. | — | 2026 | → |
| Genetics of SSRI antidepressant use and relationship to psychiatric and medical traits. | Levey D et al. | — | 2026 | → |
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| Immunogenetic Variants in Major Mental and Neurodevelopmental Disorders. | Le Clerc S et al. | — | 2026 | → |
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| Intra-familial dynamics of mental distress during the Covid-19 lockdown. | Pettersen JH et al. | — | 2026 | → |
| Major depression and atherosclerotic disease: Linking shared genetics to pathways in blood, brain, heart, and atherosclerotic plaques. | Pruin E et al. | — | 2026 | → |
| Mapping phenotypic and genetic relationships among irritability, depression and ADHD in adolescence using network analysis. | Shakeshaft A et al. | — | 2026 | → |
| Mapping the genetic landscape across 14 psychiatric disorders. | Grotzinger AD et al. | — | 2026 | → |
| Multi-omics analysis reveals CXCL14<sup>+</sup> inhibitory neuron dysfunction in major depressive disorder. | Zhang L et al. | — | 2026 | → |
| Polygenic depression risk, childhood parental substance abuse, and G×E interaction in divergent depression trajectories from middle to late adulthood. | Chen P et al. | — | 2026 | → |
| Probing neuropsychiatric disorders through in vivo CRISPR screening. | Shi T et al. | — | 2026 | → |
| Progress in understanding the biological basis of polygenic disorders. | Wray NR et al. | — | 2026 | → |
| Reduced striatal dopamine transmission as a transdiagnostic substrate of psychomotor retardation. | Leong IL et al. | — | 2026 | → |
| Schizophrenia Genetics Modulates Clinical Depressive Features. | Serretti A et al. | — | 2026 | → |
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| A Genetic Atlas of Relationships Between Circulating Metabolites and Liability to Psychiatric Conditions | Kiltschewskij DJ et al. | — | 2025 | — |
| A history of metaphorical brain talk in psychiatry. | Kendler KS | — | 2025 | → |
| ANK3 as a Novel Genetic Biomarker for Liafensine in Treatment-Resistant Depression: The ENLIGHTEN Randomized Clinical Trial. | Wang G et al. | — | 2025 | → |
| Antidepressant Switching as a Proxy Phenotype for Drug Nonresponse: Investigating Clinical, Demographic, and Genetic Characteristics. | Lo CWH et al. | — | 2025 | → |
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| Current Insight into Biological Markers of Depressive Disorder in Children and Adolescents: A Narrative Review. | Trebatická J et al. | — | 2025 | → |
| Depression Polygenicity and Disease Activity and Disability Worsening in Multiple Sclerosis. | Manouchehrinia A et al. | — | 2025 | → |
| Dietary intake and five types of mental disorders: a bidirectional Mendelian randomization study. | Zhang Y et al. | — | 2025 | → |
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| Estimating effects of serum vitamin B12 levels on psychiatric disorders and cognitive impairment: a Mendelian randomization study. | Lu T et al. | — | 2025 | → |
| Evidence of bidirectional relationship between type 2 diabetes and depression; a Mendelian randomization study. | Bala R et al. | — | 2025 | → |
| Gain of Alternative Allele Expression of LINC02449 at rs149707223 in Schizophrenia and Bipolar Disorder: Inducing Synaptic Transmission and Behavioral Deficits in Mice. | Yang T et al. | — | 2025 | → |
| Genetic factors predicting risk of mood disorders in adolescents. | Gangaraju SH et al. | — | 2025 | → |
| Genetic influence and neural pathways underlying the dose-response relationships between wearable-measured physical activity and mental health in adolescence. | Yu G et al. | — | 2025 | → |
| Genome-wide analyses identify 30 loci associated with obsessive-compulsive disorder. | Strom NI et al. | — | 2025 | → |
| Genome-wide association analyses identify distinct genetic architectures for early-onset and late-onset depression. | Shorter JR et al. | — | 2025 | → |
| Genomic risk prediction for depression in a large prospective study of older adults of European descent. | Yu C et al. | — | 2025 | → |
| Global, regional and national burden of depressive disorders in adolescents and young adults, 1990-2021: systematic analysis of the global burden of disease study 2021. | Zhao L et al. | — | 2025 | → |
| Identifying gene-environment interactions across genome-wide, twin, and polygenic risk score approaches. | Verhulst B | — | 2025 | → |
| Individual and Comorbid Influences of Chronic Stress and a Western Diet on Allostatic Loads and Cardiac Resilience, Adaptation and Proteome Profiles in Male Mice. | Nicholas M et al. | — | 2025 | → |
| Integrating brain proteomes and genetics to identify novel risk genes in chronic widespread musculoskeletal pain. | Dai Z et al. | — | 2025 | → |
| Integrating Multi-Omics Summary Data Identifies Candidate Molecular Mechanisms for Major Depression. | Nisbet L et al. | — | 2025 | → |
| Integration of Metabolomic and Brain Imaging Data Highlights Pleiotropy Among Posttraumatic Stress Disorder, Glycoprotein Acetyls, and Pallidum Structure. | Løkhammer S et al. | — | 2025 | → |
| Limitations and Potential of Polygenic Risk Scores for Major Psychiatric Disorders. | Shen X | — | 2025 | → |
| Moving toward precision and personalized treatment strategies in psychiatry. | Comai S et al. | — | 2025 | → |
| New Genomics Discoveries Across the Bipolar Disorder Spectrum Implicate Neurobiological and Developmental Pathways. | O'Connell KS et al. | — | 2025 | → |
| Novel Gene-Informed Regional Brain Targets for Clinical Screening for Major Depression. | Odierna GL et al. | — | 2025 | → |
| Polygenic Contributions to Lithium Augmentation Outcomes in Unipolar Depression. | Kraft J et al. | — | 2025 | → |
| Polygenic risk scores for severe psychiatric disorders in bipolar disorders: associations with the clinical and dimensional expression, interactions with childhood maltreatment and mediation models. | Etain B et al. | — | 2025 | → |
| Polygenic scores and antidepressant treatment outcomes in major depression: a critical integrative review. | Serretti A et al. | — | 2025 | → |
| Protein Associations With Alcohol Consumption and Genetic Risk for Alcohol-Related Sociomedical Conditions. | Drouard G et al. | — | 2025 | → |
| Psychiatric genetics in the diverse landscape of Latin American populations. | Bruxel EM et al. | — | 2025 | → |
| Rapid-acting NMDA and GABAergic Modulators in Mood Disorders: From Synaptic Mechanisms to Clinical Practice. | Serretti A | — | 2025 | → |
| Recommendations for responsible use of population descriptors in polygenic risk score development. | Smith JL et al. | — | 2025 | → |
| Sex-stratified genome-wide association meta-analysis of major depressive disorder. | Thomas JT et al. | — | 2025 | → |
| Sleep and psychiatric disorders: Bidirectional interactions and shared neurobiological mechanisms. | Hyndych A et al. | — | 2025 | → |
| SynaptopathyDB integrates synaptic proteomes, genetic and phenotypic data to advance research on nervous system disorders. | Sorokina O et al. | — | 2025 | → |
| The causal association between psychiatric disorders and gynecological cancer: a bidirectional Mendelian randomization study. | Wang Y et al. | — | 2025 | → |
| The emerging landscape of brain glycosylation: from molecular complexity to therapeutic potential. | Seo Y et al. | — | 2025 | → |
| The Psychiatric Genomics Consortium: discoveries and directions. | Agrawal A et al. | — | 2025 | → |
| The Role of Genetic Data in Dissecting Depression Heterogeneity. | Mitchell BL et al. | — | 2025 | → |
| The role of social and genetic factors in partnership trajectories and later life health. | Lin MJ | — | 2025 | → |
| What clinicians should know about the contribution of modern behavioral genetics to psychiatric problems. | Plomin R et al. | — | 2025 | → |
| Youth depression: An overview of genetic findings and the challenge of heterogeneity. | Thapar A et al. | — | 2025 | → |