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.
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Full acknowledgments are in the Supplementary Note. We are deeply indebted to the investigators who comprise the PGC, and to the hundreds of thousands of subjects who have shared their life experiences with PGC investigators. A full list of funding is in the Supplementary Note. Major funding for the PGC is from the US National Institutes of Health (U01 MH109528 and U01 MH109532). Statistical analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org/) hosted by SURFsara. The iPSYCH team acknowledges funding from the Lundbeck Foundation (grants R102-A9118 and R155-2014-1724), the Stanley Medical Research Institute, the European Research Council (project 294838), the Novo Nordisk Foundation for supporting the Danish National Biobank resource, and Aarhus and Copenhagen Universities and University Hospitals, including support to the iSEQ Center, the GenomeDK HPC facility, and the CIRRAU Center. This research has been conducted using the UK Biobank Resource (see URLs), including applications 4844 and 6818. Finally, we thank the members of the eQTLGen Consortium for allowing us to use their very large eQTL database ahead of publication. Its members are listed in Supplementary Table 14.
Some data used in this study were obtained from dbGaP (see URLs). dbGaP accession phs000021: funding support for the Genome-Wide Association of Schizophrenia Study was provided by the National Institute of Mental Health (R01 MH67257, R01 MH59588, R01 MH59571, R01 MH59565, R01 MH59587, R01 MH60870, R01 MH59566, R01 MH59586, R01 MH61675, R01 MH60879, R01 MH81800, U01 MH46276, U01 MH46289, U01 MH46318, U01 MH79469, and U01 MH79470), and the genotyping of samples was provided through the Genetic Association Information Network (GAIN). Samples and associated phenotype data for the Genome-Wide Association of Schizophrenia Study were provided by the Molecular Genetics of Schizophrenia Collaboration (principal investigator P. V. Gejman, Evanston Northwestern Healthcare (ENH) and Northwestern University, Evanston, IL, USA). dbGaP accession phs000196: this work used in part data from the NINDS dbGaP database from the CIDR:NGRC PARKINSON’S DISEASE STUDY. dbGaP accession phs000187: High-Density SNP Association Analysis of Melanoma: Case–Control and Outcomes Investigation. Research support to collect data and develop an application to support this project was provided by P50 CA093459, P50 CA097007, R01 ES011740, and R01 CA133996 from the NIH.
A.T.F.B. is on speaker’s bureaus for Lundbeck and GlaxoSmithKline. G.C. is a cofounder of Element Genomics. E.D. was an employee of Hoffmann–La Roche at the time this study was conducted and a consultant to Roche and Pierre-Fabre. N.E. is employed by 23andMe, Inc., and owns stock in 23andMe, Inc. D.H. is an employee of and owns stock options in 23andMe, Inc. S.P. is an employee of Pfizer, Inc. C.L.H. is an employee of Pfizer, Inc. A.R.W. was a former employee and stockholder of Pfizer, Inc. J.A.Q. was an employee of Hoffmann–La Roche at the time this study was conducted. H.S. is an employee of deCODE Genetics/Amgen. K.S. is an employee of deCODE Genetics/Amgen. S.S. is an employee of deCODE Genetics/Amgen. P.F.S. is on the scientific advisory board for Pfizer, Inc., and the advisory committee for Lundbeck. T.E.T. is an employee of deCODE Genetics/Amgen. C.T. is an employee of and owns stock options in 23andMe, Inc.
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Supplementary Figures 1–4 and Supplementary Note
Supplementary Tables 1–15
Regional association plots of the 44 regions with genome-wide significant loci associated with major depression. Association test from meta-analysis of 135,458 major depression cases and 344,901 controls.
Regional association plots of genomic regions identified from SMR analysis of major depression genome-wide association and eQTL results. SMR analysis helps to prioritize specific genes in a region of association for follow-up functional studies. Figures appear in the same order as the results reported in Supplementary Table 9. In the top plot, gray dots represent the major depression genome-wide association P values, diamonds show P values for probes from the SMR test, and triangles are probes without a cis-eQTL (at PeQTL < 5 × 10–8). Genes that pass SMR and heterogeneity tests (designed to remove loci with more than one causal association) are highlighted in red. The eQTL P values of SNPs are shown in the bottom plot.
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Wray, N.R., Ripke, S., Mattheisen, M. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 50, 668–681 (2018). https://doi.org/10.1038/s41588-018-0090-3
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