Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
- Authors
- Wray, Naomi R; Ripke, Stephan; Mattheisen, Manuel; Trzaskowski, Maciej; Byrne, Enda M; Abdellaoui, Abdel; Adams, Mark J; Agerbo, Esben; Air, Tracy M; Andlauer, Till M F; Bacanu, Silviu-Alin; Bækvad-Hansen, Marie; Beekman, Aartjan F T; Bigdeli, Tim B; Binder, Elisabeth B; Blackwood, Douglas R H; Bryois, Julien; Buttenschøn, Henriette N; Bybjerg-Grauholm, Jonas; Cai, Na; Castelao, Enrique; Christensen, Jane Hvarregaard; Clarke, Toni-Kim; Coleman, Jonathan I R; Colodro-Conde, Lucía; Couvy-Duchesne, Baptiste; Craddock, Nick; Crawford, Gregory E; Crowley, Cheynna A; Dashti, Hassan S; Davies, Gail; Deary, Ian J; Degenhardt, Franziska; Derks, Eske M; Direk, Nese; Dolan, Conor V; Dunn, Erin C; Eley, Thalia C; Eriksson, Nicholas; Escott-Price, Valentina; Kiadeh, Farnush Hassan Farhadi; Finucane, Hilary K; Forstner, Andreas J; Frank, Josef; Gaspar, Héléna A; Gill, Michael; Giusti-Rodríguez, Paola; Goes, Fernando S; Gordon, Scott D; Grove, Jakob; Hall, Lynsey S; Hannon, Eilis; Hansen, Christine Søholm; Hansen, Thomas F; Herms, Stefan; Hickie, Ian B; Hoffmann, Per; Homuth, Georg; Horn, Carsten; Hottenga, Jouke-Jan; Hougaard, David M; Hu, Ming; Hyde, Craig L; Ising, Marcus; Jansen, Rick; Jin, Fulai; Jorgenson, Eric; Knowles, James A; Kohane, Isaac S; Kraft, Julia; Kretzschmar, Warren W; Krogh, Jesper; Kutalik, Zoltán; Lane, Jacqueline M; Li, Yihan; Li, Yun; Lind, Penelope A; Liu, Xiaoxiao; Lu, Leina; MacIntyre, Donald J; MacKinnon, Dean F; Maier, Robert M; Maier, Wolfgang; Marchini, Jonathan; Mbarek, Hamdi; McGrath, Patrick; McGuffin, Peter; Medland, Sarah E; Mehta, Divya; Middeldorp, Christel M; Mihailov, Evelin; Milaneschi, Yuri; Milani, Lili; Mill, Jonathan; Mondimore, Francis M; Montgomery, Grant W; Mostafavi, Sara; Mullins, Niamh; Nauck, Matthias; Ng, Bernard; Nivard, Michel G; Nyholt, Dale R; O'Reilly, Paul F; Oskarsson, Hogni; Owen, Michael J; Painter, Jodie N; Pedersen, Carsten Bøcker; Pedersen, Marianne Giørtz; Peterson, Roseann E; Pettersson, Erik; Peyrot, Wouter J; Pistis, Giorgio; Posthuma, Danielle; Purcell, Shaun M; Quiroz, Jorge A; Qvist, Per; Rice, John P; Riley, Brien P; Rivera, Margarita; Saeed Mirza, Saira; Saxena, Richa; Schoevers, Robert; Schulte, Eva C; Shen, Ling; Shi, Jianxin; Shyn, Stanley I; Sigurdsson, Engilbert; Sinnamon, Grant B C; Smit, Johannes H; Smith, Daniel J; Stefansson, Hreinn; Steinberg, Stacy; Stockmeier, Craig A; Streit, Fabian; Strohmaier, Jana; Tansey, Katherine E; Teismann, Henning; Teumer, Alexander; Thompson, Wesley; Thomson, Pippa A; Thorgeirsson, Thorgeir E; Tian, Chao; Traylor, Matthew; Treutlein, Jens; Trubetskoy, Vassily; Uitterlinden, André G; Umbricht, Daniel; Van der Auwera, Sandra; van Hemert, Albert M; Viktorin, Alexander; Visscher, Peter M; Wang, Yunpeng; Webb, Bradley T; Weinsheimer, Shantel Marie; Wellmann, Jürgen; Willemsen, Gonneke; Witt, Stephanie H; Wu, Yang; Xi, Hualin S; Yang, Jian; Zhang, Futao; eQTLGen; 23andMe; Arolt, Volker; Baune, Bernhard T; Berger, Klaus; Boomsma, Dorret I; Cichon, Sven; Dannlowski, Udo; de Geus, E C J; DePaulo, J Raymond; Domenici, Enrico; Domschke, Katharina; Esko, Tõnu; Grabe, Hans J; Hamilton, Steven P; Hayward, Caroline; Heath, Andrew C; Hinds, David A; Kendler, Kenneth S; Kloiber, Stefan; Lewis, Glyn; Li, Qingqin S; Lucae, Susanne; Madden, Pamela F A; Magnusson, Patrik K; Martin, Nicholas G; McIntosh, Andrew M; Metspalu, Andres; Mors, Ole; Mortensen, Preben Bo; Müller-Myhsok, Bertram; Nordentoft, Merete; Nöthen, Markus M; O'Donovan, Michael C; Paciga, Sara A; Pedersen, Nancy L; Penninx, Brenda W J H; Perlis, Roy H; Porteous, David J; Potash, James B; Preisig, Martin; Rietschel, Marcella; Schaefer, Catherine; Schulze, Thomas G; Smoller, Jordan W; Stefansson, Kari; Tiemeier, Henning; Uher, Rudolf; Völzke, Henry; Weissman, Myrna M; Werge, Thomas; Winslow, Ashley R; Lewis, Cathryn M; Levinson, Douglas F; Breen, Gerome; Børglum, Anders D; Sullivan, Patrick F; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
- Year
- 2018
- Journal
- Nature genetics
- PMID
- 29700475
- DOI
- 10.1038/s41588-018-0090-3
- PMCID
- PMC5934326
Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.
Results of GWA meta-analysis of seven cohorts for major depression(a) Relation between adding cohorts and number of genome-wide significant genomic regions (before the rigorous vetting used to define the final 44 regions). Beginning with the largest cohort (#1 on the x-axis), added the next largest cohort (#2) until all cohorts were included (#7). The number next to each point shows the total effective sample size equivalent to sample size where the numbers of cases and controls are equal. (b) Association test quantile-quantile plot showing a marked departure from a null model of no associations (y-axis truncated 10−12). (c) Manhattan plot with x-axis showing genomic position (chr1-chr22 plus chrX), and the y-axis showing statistical significance as –log10(P) t-statistic; threshold for significance accounting for multiple testing shown by horizontal line. Association test from meta-analysis of 135,458 major depression cases and 344,901 controls. The red line shows the genome-wide significance threshold (P=5×10−8).
Genetic risk score (GRS) prediction analyses into PGC29 MDD target samples(a) Variance explained (liability scale) based on different discovery samples for three target samples: PGC29 (16,823 cases, 25,632 controls), iPSYCH (a nationally representative sample of 18,629 cases and 17,841 controls,) and a clinical cohort from Münster not included in the GWA analysis (845 MDD inpatient cases, 834 controls). PGC29-LOO: Target sample is one of the PGC29 samples, with discovery sample the remaining 28 PGC29 samples, hence, leave-one-out. (b) Odds ratios of major depression per GRS decile relative to the first decile for iPSYCH and PGC29 target samples. (c) Odds ratios of major depression in GRS standard deviation (SD): 3,950 early onset vs 3,950 late onset cases earlier age at onset; 4,958 severe vs 3,976 moderate cases defined by count of endorsed MDD symptom criteria; 5,574 cases recurrent MDD vs 12,968 single episode cases; severity defined as chronic/unremitting MDD 610 “Stage IV” cases vs 499 “Stage II” or 332 first-episode MDD 77 used the NESDA sample from PGC29. Error bars represent 95% confidence intervals. Logistic regression association test p-values in the target sample for GRS generated from SNPs with p-value < 0.05 in the discovery sample.
Comparisons of the major depression GWA meta-analysis(a) Enrichment in bulk tissue mRNA-seq from GTEx; t-statistic, sample sizes in GTEx range from N=75–564. Threshold for significance accounting for multiple testing shown by vertical line. (b) Major depression results and enrichment in three major brain cell types; t-statistic; threshold for significance accounting for multiple testing shown by horizontal line. Sample sizes vary as these data are aggregated from many different sources. (c) Partitioned LDSC to evaluate enrichment of the major depression GWA findings in over 50 functional genomic annotations (Supplementary Table 8); enrichment statistic; threshold for significance accounting for multiple testing given by horizontal dashed line. Sample sizes vary as these data are aggregated from many different sources.
Generative topographic mapping of the 19 significant pathway results. The average position of each pathway on the map is represented by a point. The map is colored by the –log10(P) obtained using MAGMA. The X and Y coordinates result from a kernel generative topographic mapping algorithm (GTM) that reduces high dimensional gene sets to a two-dimensional scatterplot by accounting for gene overlap between gene sets. Each point represents a gene set. Nearby points are more similar in gene overlap than more distant points. The color surrounding each point (gene set) indicates significance per the scale on the right. The significant pathways (Supplementary Table 11) fall into nine main clusters as described in the text.
| Name | Type |
|---|---|
| 10,000 SNPs local | variant |
| 1000 Genomes Project | cohort |
| 23andMe | cohort |
| 23andMeD local | cohort |
| 23andMeD cohort local | cohort |
| ADHD | phenotype |
| adiposity | phenotype |
| adult onset psychiatric disorder local | phenotype |
| affective disorders | phenotype |
| African ancestry fetal brains local | cohort |
| age at first birth | phenotype |
| age at menarche | phenotype |
| anterior cingulate cortex | anatomy |
| Anthropometric risk factors local | phenotype |
| antidepressants | drug |
| antipsychotics | drug |
| astrocytes | phenotype |
| autism | phenotype |
| BIOS | cohort |
| bipolar disorder | phenotype |
| body mass index | phenotype |
| brain cortical samples local | anatomy |
| brain tissue | anatomy |
| CACNA1E | gene |
| CACNA2D1 | gene |
| CAD | drug |
| Carefully phenotyped individuals (smaller cohort) local | cohort |
| CELF4 | gene |
| CHARGE | cohort |
| CHARGE meta-analysis local | cohort |
| childhood onset psychiatric disorder local | phenotype |
| chronic hypothalamic-pituitary-adrenal axis hyperactivation local | phenotype |
| clinical cohort | cohort |
| Common Mind Consortium | cohort |
| Conserved regions across 29 Eutherian mammals local | drug |
| controls | cohort |
| CONVERGE local | cohort |
| coronary artery disease | phenotype |
| cortex | anatomy |
| Daytime sleepiness | phenotype |
| deCODE | cohort |
| depressive symptom local | phenotype |
| depressive symptoms | phenotype |
| Depressive symptoms meta-analysis local | cohort |
| dorsolateral prefrontal cortex | anatomy |
| DRD2 | gene |
| Early-onset MDD | phenotype |
| educational attainment | phenotype |
| eQTLGen Consortium | cohort |
| European ancestry | cohort |
| European-ancestry local | cohort |
| European population | cohort |
| Exonic regions local | drug |
| female reproductive behavior local | phenotype |
| FMex23 local | cohort |
| FMRP | gene |
| Full discovery sample meta-analysis local | cohort |
| Full meta-analyzed cohort local | cohort |
| future GWA studies local | cohort |
| Generation Scotland | cohort |
| genes bound by antidepressant medications local | gene |
| genetic risk | cohort |
| Genotyped individuals (large cohort) local | cohort |
| GenScot local | cohort |
| GERA | cohort |
| GEUVADIS local | cohort |
| GRIK5 | gene |
| GRM5 | gene |
| GTEx | cohort |
| GTEx project | cohort |
| H3K4me1 | drug |
| Han Chinese women study local | cohort |
| hSNP2 local | variant |
| Hyde et al. local | cohort |
| Hyde et al. cohort local | cohort |
| insomnia | phenotype |
| Intelligence quotient | phenotype |
| iPSYCH | cohort |
| IQ | phenotype |
| LD-Hub local | cohort |
| LRFN5 | gene |
| lung cancer | phenotype |
| Major depression cohorts local | cohort |
| Major depression GRS local | phenotype |
| major depressive disorder | phenotype |
| MDD cases | cohort |
| MDD controls | cohort |
| MDD SNP instruments local | variant |
| Medical conditions local | phenotype |
| MHC region | gene |
| mood disorders | phenotype |
| Neanderthal introgressed regions local | drug |
| Neanderthal introgressed regions local | variant |
| NEGR1 | gene |
| neuroinflammation | phenotype |
| neurons | phenotype |
| neuroticism | phenotype |
| NIH NeuroBiobank local | cohort |
| NTR | cohort |
| number of children | phenotype |
| obesity | phenotype |
| OLFM4 | gene |
| oligodendrocytes | phenotype |
| Open chromatin in human brain local | drug |
| Open chromatin regions local | variant |
| Other Brain Tissues local | anatomy |
| OtherCohorts local | cohort |
| Other traits and disorders local | phenotype |
| outcome | phenotype |
| PCLO | gene |
| PGC | cohort |
| PGC29 local | cohort |
| PGC29 samples local | cohort |
| PGC MDD mega-analysis local | cohort |
| Pitt–Hopkins syndrome local | phenotype |
| population | cohort |
| Postpartum MDD local | phenotype |
| prefrontal cortex | anatomy |
| psychiatric disorders | phenotype |
| RBFOX1 | gene |
| RBFOX2 local | gene |
| RBFOX3 | gene |
| recurrence | phenotype |
| recurrent major depressive disorder | phenotype |
| RERE | gene |
| retinoid X receptor local | drug |
| ricopili pipeline | drug |
| Rosmap | cohort |
| rs12129573 local | variant |
| rs12415800 local | variant |
| rs1432639 local | variant |
| rs159963 local | variant |
| rs35936514 local | variant |
| rs4074723 local | variant |
| rs7198928 local | variant |
| rs8063603 local | variant |
| schizoaffective disorder | phenotype |
| schizophrenia | phenotype |
| Self-reported major depression study local | cohort |
| seven cohorts local | cohort |
| severe depression | phenotype |
| Severe major depressive disorder local | phenotype |
| smoking initiation | phenotype |
| SNP | cohort |
| Somatic Tissues local | anatomy |
| SOX5 | gene |
| SSGAC | cohort |
| study cohort | cohort |
| subjective well-being | phenotype |
| suicide | phenotype |
| TCF7L2 | gene |
| Tiredness | phenotype |
| treatment response | phenotype |
| UK Biobank | cohort |
| voltage-gated calcium channel | drug |
| Volunteer cohort local | cohort |
| whole blood | anatomy |
| years of education | phenotype |
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| Exploring the Genetic Link Between Thyroid Dysfunction and Common Psychiatric Disorders: A Specific Hormonal or a General Autoimmune Comorbidity. | Soheili-Nezhad S et al. | — | 2023 | → |
| Exploring the overlap between alopecia areata and major depressive disorder: Epidemiological and genetic perspectives. | Foo JC et al. | — | 2023 | → |
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| Investigating the shared genetic architecture and causal relationship between pain and neuropsychiatric disorders. | Chen M et al. | — | 2023 | → |
| Investigation of the interaction between Genetic Risk Score (GRS) and fatty acid quality indices on mental health among overweight and obese women. | Rasaei N et al. | — | 2023 | → |
| Is adjustment disorder genetically correlated with depression, anxiety, or risk-tolerant personality trait? | Ohi K et al. | — | 2023 | → |
| Leveraging a genetically-informative study design to explore depression as a risk factor for type 2 diabetes: Rationale and participant characteristics of the Mood and Immune Regulation in Twins Study. | Mezuk B et al. | — | 2023 | → |
| LINE-1 retrotransposons drive human neuronal transcriptome complexity and functional diversification. | Garza R et al. | — | 2023 | → |
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| Long-Term Air Pollution, Genetic Susceptibility, and the Risk of Depression and Anxiety: A Prospective Study in the UK Biobank Cohort. | Gao X et al. | — | 2023 | → |
| LRFN5 and OLFM4 as novel potential biomarkers for major depressive disorder: a pilot study. | Xu K et al. | — | 2023 | → |
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| Melancholic features and typical neurovegetative symptoms of major depressive disorder show specific polygenic patterns. | Oliva V et al. | — | 2023 | → |
| Mendelian randomisation study of body composition and depression in people of East Asian ancestry highlights potential setting-specific causality. | O'Loughlin J et al. | — | 2023 | → |
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| Mendelian randomization investigation highlights different roles of selenium status in mental disorders. | Guo X et al. | — | 2023 | → |
| Mendelian randomization study on the causal effects of systemic lupus erythematosus on major depressive disorder. | Li W et al. | — | 2023 | → |
| Meta-Analyses of Genome-Wide Association Studies for Postpartum Depression. | Guintivano J et al. | — | 2023 | → |
| Metabolic parameters and thyroid hormones in relation to suicide attempts in patients with first-episode and drug-naive major depressive disorder with comorbid glucose disturbances: a large cross-sectional study. | Chen SW et al. | — | 2023 | → |
| Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids. | Davyson E et al. | — | 2023 | → |
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| Molecular basis of breast cancer with comorbid depression and the mechanistic insights of Xiaoyaosan in treating breast cancer-associated depression. | Chen G | — | 2023 | → |
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| Molecular signatures of post-traumatic stress disorder in war-zone-exposed veteran and active-duty soldiers. | Muhie S et al. | — | 2023 | → |
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| Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Remission During 2-Year Follow-up. | Habets PC et al. | — | 2023 | → |
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| Polygenic risk score-based phenome-wide association study identifies novel associations for Tourette syndrome. | Jain P et al. | — | 2023 | → |
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| Genetic risk factors have a substantial impact on healthy life years. | Jukarainen S et al. | — | 2022 | → |
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| Identify novel, shared and disorder-specific genetic architecture of major depressive disorder, insomnia and chronic pain. | Zheng H et al. | — | 2022 | → |
| Investigating the causal risk factors for self-harm by integrating Mendelian randomisation within twin modelling. | Lim KX et al. | — | 2022 | → |
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| Is there a causal relationship between executive function and liability to mental health and substance use? A Mendelian randomization approach. | Burton SMI et al. | — | 2022 | → |
| Leveraging genetic discoveries for sleep to determine causal relationships with common complex traits. | Sonti S et al. | — | 2022 | → |
| Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease. | Harvey J et al. | — | 2022 | → |
| Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives. | van der Meer D et al. | — | 2022 | → |
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| Mendelian randomization analyses support causal relationships between brain imaging-derived phenotypes and risk of psychiatric disorders. | Guo J et al. | — | 2022 | → |
| Neandertal introgression partitions the genetic landscape of neuropsychiatric disorders and associated behavioral phenotypes. | Dannemann M et al. | — | 2022 | → |
| Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia, Bipolar Disorder, and Depression Among Adults in the US Veterans Affairs Health Care System. | Bigdeli TB et al. | — | 2022 | → |
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| scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies. | Jia P et al. | — | 2022 | → |
| The Genetic Basis for the Increased Prevalence of Metabolic Syndrome among Post-Traumatic Stress Disorder Patients. | Misganaw B et al. | — | 2022 | → |
| The genetic structure of pain in depression patients: A genome-wide association study and proteome-wide association study. | Zhang Z et al. | — | 2022 | → |
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| The next 10 years of behavioural genomic research. | Plomin R | — | 2022 | → |
| The protective effect of relative carbohydrate intake on depression. | — | — | 2022 | → |
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| Use of Selective Serotonin Reuptake Inhibitors Is Associated with a Lower Risk of Colorectal Cancer among People with Family History. | Zhang N et al. | — | 2022 | → |
| Using genetic designs to identify likely causal environmental contributions to psychopathology. | Sellers R et al. | — | 2022 | → |