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Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses

Nature Medicine volume 29, pages 1832–1844 (2023)Cite this article

Subjects

Abstract

Depression is a common psychiatric disorder and a leading cause of disability worldwide. Here we conducted a genome-wide association study meta-analysis of six datasets, including >1.3 million individuals (371,184 with depression) and identified 243 risk loci. Overall, 64 loci were new, including genes encoding glutamate and GABA receptors, which are targets for antidepressant drugs. Intersection with functional genomics data prioritized likely causal genes and revealed new enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found depression to be highly polygenic, with ~11,700 variants explaining 90% of the single-nucleotide polymorphism heritability, estimating that >95% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and attention deficit hyperactivity disorder) were influencing depression risk when both concordant and discordant variants were considered, and nearly all depression risk variants influenced educational attainment. Additionally, depression genetic risk was associated with impaired complex cognition domains. We dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across subgroups of depression and demonstrating significantly increased absolute risks for recurrence and psychiatric comorbidity among cases of depression with the highest polygenic burden, with considerable sex differences. The risks were up to 5- and 32-fold higher than cases with the lowest polygenic burden and the background population, respectively. These results deepen the understanding of the biology underlying depression, its disease progression and inform precision medicine approaches to treatment.

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Fig. 1: Manhattan plots of GWAS, gene-based analysis and TWAS of DEP.
Fig. 2: Genetic overlap between DEP and selected phenotypes using MiXeR.
Fig. 3: Cell-type enrichments.
Fig. 4: Association of DEP PRS with cognitive abilities.
Fig. 5: Absolute risks of recurrency and developing comorbid disorders stratified by polygenic scores.

Data availability

Supplementary Figs. 1–15 and 24–28 are available at https://doi.org/10.6084/m9.figshare.22139849. The GWAS meta-analysis summary statistics from this publication, not including 23andMe, are available at https://ipsych.dk/en/research/downloads/. To access the summary statistics from the meta-analysis of all cohorts, including 23andMe, a data transfer agreement is required from 23andMe (dataset-request@23andMe.com) before a request is made to the corresponding authors. See https://research.23andme.com/collaborate/#dataset-access/ for more information and to apply for access to the data.

All relevant iPSYCH data are available from the authors after approval by the iPSYCH Data Access Committee and can only be accessed on the secured Danish server (GenomeDK, https://genome.au.dk) as the data are protected by Danish legislation. For data access please contact A.D.B.

The downloadable data of the HRC were used for imputation: http://www.haplotype-reference-consortium.org/.

Data used for brain transcriptome model generation are available from PsychENCODE (http://resource.psychencode.org/); genotypes are controlled data and access instructions are provided at https://www.synapse.org/#!Synapse:syn4921369/wiki/477467.

Resources for the colocalization analysis are available at PsychENCODE (http://resource.psychencode.org/) and ROSMAP.

Note that some datasets have been indirectly accessed at the FUMA website. In general, PsychENCODE (http://resource.psychencode.org/) was used for SNP annotations (enhancer, H3K27ac markers), eQTLs and HiC-based enhancer–promoter interactions. GTEx v.6/v.7/v.8 eQTLs and gene expression used in the pipeline were obtained from GTEx (http://www.gtexportal.org/home/). The following eQTL datasets in FUMA were used for gene mapping: BrainSeq (http://eqtl.brainseq.org/), PsychENCODE eQTLs (http://resource.psychencode.org/), Common Mind Consortium (https://www.synapse.org//#!Synapse:syn5585484), BRAINEAC (http://www.braineac.org/) and GTEx/V8/Brain (https://www.gtexportal.org/home/datasets/). Chromatin interaction datasets in FUMA used for gene mapping were PsychENCODE eQTLs and HiC-based enhancer–promoter interactions (http://resource.psychencode.org/), HiC (https://doi.org/10.1101/406330, GSE87112) and Roadmap–brain (https://egg2.wustl.edu/roadmap/web_portal/DNase_reg.html). The following single-cell RNA-seq datasets were used in the cell-type specific analyses in FUMA (https://fuma.ctglab.nl/tutorial#datasets): PsychENCODE human developmental and adult brain samples (http://resource.psychencode.org/), Allen Brain Atlas Cell Type (http://celltypes.brain-map.org/api/v2/well_known_file_download/694416667), DroNc human brain samples (hippocampus) (https://portals.broadinstitute.org/single_cell#study-dronc-seq-single-nucleus-rna-seq-on-human-archived-brain, https://www.gtexportal.org/), human prefrontal cortex brain samples (GSE104276), human cortex brain samples (GSE67835), Linarsson human temporal cortex brain samples (GSE101601) and human midbrain samples (GSE76381).

GO of Biological Process and Cellular Components datasets of MSigDB v.7.0 (https://www.gsea-msigdb.org/gsea/msigdb) were used for the gene-set enrichment analysis in FUMA’s GENE2FUNC module. Please refer to https://fuma.ctglab.nl/links and https://fuma.ctglab.nl/tutorial#datasets for additional information on availability of datasets.

Code availability

Software and analytical methods used in data analyses include:

Ricopili: https://sites.google.com/a/broadinstitute.org/ricopili/.

Eigensoft v.6.1.4: https://www.hsph.harvard.edu/alkes-price/software/ and https://github.com/chrchang/eigensoft.

Eagel v.2.3.5: https://github.com/poruloh/Eagle.

Minimac3: https://github.com/Santy-8128/Minimac3.

METAL v.2011-03-05: https://genome.sph.umich.edu/wiki/METAL_Documentation.

Genotyping and imputation with the Finnish population-specific SISu v.3 reference panel: https://www.protocols.io/view/genotype-imputation-workflow-v3-0-xbgfijw.

SAIGE v.0.20: Scalable and Accurate Implementation of GEneralized mixed model v.0.20: https://github.com/weizhouUMICH/SAIGE/.

LDsc v.1.0.1: https://github.com/bulik/ldsc.

MiXeR v.1.3: https://github.com/precimed/mixer.

LAVA 2022-09-29 https://ctg.cncr.nl/software/lava and https://github.com/josefin-werme/LAVA. https://github.com/josefin-werme/LAVA. The method and code used for genome partitioning used for the LAVA analyses is available at https://github.com/cadeleeuw/lava-partitioning.

GCTA v.1.93.2: https://yanglab.westlake.edu.cn/software/gcta/#Overview.

LDpred2: https://privefl.github.io/bigsnpr/articles/LDpred2.html.

FUMA v.1.3.7: https://fuma.ctglab.nl/.

S-PrediXcan: https://github.com/hakyimlab/MetaXcan.

eCAVIAR: http://zarlab.cs.ucla.edu/tag/ecaviar/.

coloc v.2: https://github.com/Stahl-Lab-MSSM/coloc2.

CAUSALdb finemapping pipeline: https://github.com/mulinlab/CAUSALdb-finemapping-pip.

Variance partition fraction for GLM with linear, logistic or probit regression used for PNC analysis: https://bioconductor.org/packages/variancePartition

Cox proportional hazard modeling: the R packages survival v.3.4-0 (https://CRAN.R-project.org/package=survival).

R v.4.2.2 was used in general for statistical analyses and plotting (https://www.Rproject.org).

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Acknowledgements

The iPSYCH team was supported by grants from the Lundbeck Foundation (R102-A9118, R155-2014-1724 and R248-2017-2003), the EU H2020 Program (grant no. 667302, ‘CoCA’), National Institutes of Health/National Institute of Mental Health (1U01MH109514-01 and 1R01MH124851-01 to A.D.B.) and the universities and university hospitals of Aarhus and Copenhagen. The Danish National Biobank resource was supported by the Novo Nordisk Foundation. High-performance computer capacity for the handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility (https://genome.au.dk/) was provided by the Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing, iSEQ, Aarhus University, Denmark (grant to A.D.B.). We thank the research participants and employees of 23andMe, Inc. for making this work possible. We thank all members of the iPSYCH-Broad Consortium for their efforts and collaborative spirit. The team at the Center for Disease Neurogenomics at the Icahn School of Medicine at Mount Sinai was supported by the National Institutes of Health (K08MH122911 to G.V.; T32MH087004 to K.T.; and R01MH125246, R01AG067025, U01MH116442 and R01MH109677 to P.R.). D.F.L. was funded by a Veterans Affairs Office of Research and Development Career Development Award (IK2BX005058). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Biomedicine, Aarhus University, Aarhus, Denmark

    Thomas D. Als, Jakob Grove, Trine Tollerup Nielsen, Ditte Demontis, Per Qvist, Veera M. Rajagopal, Leila Farajzadeh, Janne Thirstrup & Anders D. Børglum

  2. The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark

    Thomas D. Als, Jakob Grove, Trine Tollerup Nielsen, Jonas Bybjerg-Grauholm, Ditte Demontis, Anders Rosengren, Georgios Athanasiadis, Marie Bækved-Hansen, Per Qvist, Katherine L. Musliner, Veera M. Rajagopal, Leila Farajzadeh, Janne Thirstrup, Bjarni J. Vilhjálmsson, Manuel Mattheisen, Sandra Meier, Esben Agerbo, Merete Nordentoft, Thomas Werge, David M. Hougaard, Preben B. Mortensen, Ole Mors & Anders D. Børglum

  3. Center for Genomics and Personalized Medicine, Aarhus, Denmark

    Thomas D. Als, Jakob Grove, Trine Tollerup Nielsen, Ditte Demontis, Per Qvist, Veera M. Rajagopal, Leila Farajzadeh, Janne Thirstrup & Anders D. Børglum

  4. Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland

    Mitja I. Kurki, Mark J. Daly & Aarno Palotie

  5. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Mitja I. Kurki, Kumar Veerapen & Mark J. Daly

  6. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Mitja I. Kurki, Kumar Veerapen & Mark J. Daly

  7. Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark

    Jakob Grove & Bjarni J. Vilhjálmsson

  8. Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Georgios Voloudakis, Karen Therrien, Jaroslav Bendl, Biao Zeng & Panos Roussos

  9. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Georgios Voloudakis, Karen Therrien, Jaroslav Bendl, Biao Zeng & Panos Roussos

  10. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Georgios Voloudakis, Karen Therrien, Jaroslav Bendl, Biao Zeng & Panos Roussos

  11. Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Georgios Voloudakis, Karen Therrien, Jaroslav Bendl, Biao Zeng & Panos Roussos

  12. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Georgios Voloudakis, Karen Therrien, Jaroslav Bendl, Biao Zeng & Panos Roussos

  13. Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J Peters VA Medical Center, Bronx, NY, USA

    Georgios Voloudakis, Karen Therrien & Panos Roussos

  14. Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Karen Therrien

  15. Department of Psychology and Logopedics, SleepWell Research Program, University of Helsinki, Helsinki, Finland

    Elisa Tasanko, Joonas Naamanka & Iiris Hovatta

  16. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Kumar Veerapen & Mark J. Daly

  17. Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

    Daniel F. Levey & Joel Gelernter

  18. Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA

    Daniel F. Levey & Joel Gelernter

  19. Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark

    Jonas Bybjerg-Grauholm, Marie Bækved-Hansen & David M. Hougaard

  20. Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark

    Anders Rosengren, Georgios Athanasiadis & Thomas Werge

  21. Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Spain

    Georgios Athanasiadis

  22. deCODE Genetics/Amgen, Reykjavik, Iceland

    G. Bragi Walters, Thorgeir Thorgeirsson, Hreinn Stefánsson & Kári Stefánsson

  23. National Centre for Register-Based Research (NCRR), Business and Social Sciences, Aarhus University, Aarhus, Denmark

    Katherine L. Musliner, Esben Agerbo & Preben B. Mortensen

  24. Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark

    Katherine L. Musliner

  25. The Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Katherine L. Musliner

  26. National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark

    Bjarni J. Vilhjálmsson & John J. McGrath

  27. Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, Queensland, Australia

    John J. McGrath

  28. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia

    John J. McGrath

  29. Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany

    Manuel Mattheisen

  30. Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada

    Manuel Mattheisen & Sandra Meier

  31. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Manuel Mattheisen & Sandra Meier

  32. Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada

    Sandra Meier

  33. Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark

    Esben Agerbo & Preben B. Mortensen

  34. Mental Health Centre Copenhagen, Capital Region of Denmark, Copenhagen University Hospital, Copenhagen, Denmark

    Merete Nordentoft

  35. Institute of Clinical Sciences and GLOBE Institute, LF Center for GeoGenetics, University of Copenhagen, Copenhagen, Denmark

    Thomas Werge

  36. Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA

    Murray B. Stein

  37. Departments of Psychiatry and Herbert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA

    Murray B. Stein

  38. Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA

    Panos Roussos

  39. Psychosis Research Unit, Aarhus University Hospital-Psychiatry, Aarhus, Denmark

    Ole Mors

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  1. Thomas D. Als
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Contributions

T.D.A., M.I.K., J.G., G.V., K.T., E.T., T.T.N., J.N., K.V., D.F.L., J.B. and B.Z. were responsible for analysis. Sample and/or data provider and processing was carried out by J.B.G., D.D., A.R., G.A., M.B.H., P.Q., G.B.W., T.T., H.S., K.L.M., V.M.R., L.F., J.T., B.J.V., J.J.M., M.M., S.M., iPSYCH-Broad Consortium, E.A., K.S., M.M., T.W., D.M.H., P.B.M., M.B.S., J.G., I.H., P.R., M.J.D., O.M., A.P. and A.D.B. Supervision was carried out by P.Q., K.L.M., B.J.V., J.J.M, I.H., P.R., M.J.D., O.M., A.P. and A.D.B. Writing was the responsibility of T.D.A. and A.D.B. Study design and direction was the responsibility of A.D.B. All authors contributed to critical revision of the paper.

Corresponding authors

Correspondence to Thomas D. Als or Anders D. Børglum.

Ethics declarations

Competing interests

B.J.V. is a member of the advisory board for Allelica. D.D. has received a speaker fee from Takeda. B.W., T.T., H.S. and K.S. are employed at deCODE/Amgen. M.J.D. is a founder of Maze Therapeutics and is on the Scientific Advisory Board of RBNC Therapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Gabriëlla Blokland, Gerome Breen and Steven Kushner for their contribution to the peer review of this work. Primary Handling Editor: Ming Yang, in collaboration with the Nature Medicine team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Highlighted Regional Miami plots of GWAS and TWAS results.

corresponding to the genomic region of (a) GABRA1, (b) CYP7B1, (c) DCC, (d) CTTNBP2, (e) FURIN and (f) GIGYF2 genes/transcripts (1Mbp window from start site). Top panels: GWAS results (dots colored according to European ancestry linkage disequilibrium r2 with top SNP): The x-axis shows genomic position, and the y axis shows significance as –log10(P) of z statistics (two-sided nominal P values); blue line corresponds to P = 1 × 10−5, orange line to P = 5 × 10−8 (genome-wide significance). Bottom panels: TWAS results: The x-axis shows genomic position. The y axis shows significance as –log10(P) of z statistics (two-sided nominal P values) for genes represented by both gene expression and isoform expression. Black triangles facing upwards or downwards for a positive or negative association z-score (Wald test; two-sided P values) respectively (up- or down-regulation); transcripts with Bonferroni-adjusted (for all reliably imputed transcripts) P value < 0.1 are labeled; orange line corresponds to Bonferroni-adjusted P = 0.05. Each Bonferroni-significant transcript is connected with lines to the SNPs contributing to its transcriptomic imputation model; lines are gray when the SNPs have a P>1 × 10−5, blue when P < 1 × 10−5 but orange when P < 5 × 10−8. The SNPs that are above the blue line and contribute to the transcriptomic imputation models of significant transcripts are labeled. See Supplementary Fig. 10-1 to 10-88 and Supplementary Table 10.

Extended Data Fig. 2 MvPRS analyses of depression cases with/without anxiety (ANX).

Depression-subphenotype is shown on the x-axis (NDEPwoANX = 22114, NDEPwANX = 7044 and Nctrls = 38142). The slope (β) of the linear regression (95% CI) for each depression subphenotype is shown on the y axis. Significant difference between β for depression without/with an additional diagnosis is indicated with horizontal line with nominal two-sided P value above, that is the Wald test of equal group effect (see Supplementary Table 12a). Overall two-sided P value = 1.2 × 10−21. Cases with BP were excluded. The polygenic risk scores analyzed are (a) PRS for depression (DEP-PRS), (b) PRS for anxiety (ANX-PRS), (c) PRS for bipolar disorder (BP-PRS), (d) PRS for schizophrenia (SZ-PRS), (e) PRS for ADHD (ADHD-PRS), (f) PRS for autism (ASD-PRS), (g) PRS for neuroticism (Neuroticism PRS), (h) PRS for substance use (SU-PRS), and (i) PRS for substance use disorder (SUD-PRS). See Supplementary Table 18b, 18c, Supplementary Fig. 19-1 and 19-2 for sex-stratified analyses.

Extended Data Fig. 3 MvPRS analyses of depression cases with/without bipolar disorder (BP).

Depression-subphenotype is shown on the x-axis (NDEPwoBP = 29158, DEPwBP, N = 1460 and Nctrls = 38200). The slope (β) of the linear regression (95% CI) for each depression subphenotype is shown on the y axis. Significant difference between β for depression without/with an additional diagnosis is indicated with horizontal line with nominal two-sided P value above, that is the Wald test of equal group effect (See Supplementary Table 12d). Overall two-sided P value = 1.5 × 10−15. The polygenic risk scores analyzed are (a) PRS for depression (DEP-PRS), (b) PRS for anxiety (ANX-PRS), (c) PRS for bipolar disorder (BP-PRS), (d) PRS for schizophrenia (SZ-PRS), (e) PRS for ADHD (ADHD-PRS), (f) PRS for autism (ASD-PRS), (g) PRS for neuroticism (Neuroticism PRS), (h) PRS for substance use (SU-PRS), and (i) PRS for substance use disorder (SUD-PRS). See Supplementary Table 18e, S18f, Supplementary Fig. 19-1 and 19-2 for sex-stratified analyses.

Extended Data Fig. 4 MvPRS analyses of depression cases with/without schizophrenia (SZ).

Depression-subphenotype is shown on the x-axis (NDEPwoSZ = 25253, NDEPwSZ = 3905 and Nctrls = 38142). The slope (β) of the linear regression (95% CI) for each depression subphenotype is shown on the y axis. Significant difference between β for depression without/with an additional diagnosis is indicated with horizontal line with nominal two-sided P value above, that is the Wald test of equal group effect (see Supplementary Table 12g). Overall two-sided P value = 1.7 × 10−15. Cases with BP were excluded. The polygenic risk scores analyzed are (a) PRS for depression (DEP-PRS), (b) PRS for anxiety (ANX-PRS), (c) PRS for bipolar disorder (BP-PRS), (d) PRS for schizophrenia (SZ-PRS), (e) PRS for ADHD (ADHD-PRS), (f) PRS for autism (ASD-PRS), (g) PRS for neuroticism (Neuroticism PRS), (h) PRS for substance use (SU-PRS), and (i) PRS for substance use disorder (SUD-PRS). See Supplementary Table 18h, 18i, Supplementary Fig. 19-1 and 19-2 for sex-stratified analyses.

Extended Data Fig. 5 MvPRS analyses of depression cases with/without substance use disorder (SUD).

Depression-subphenotype is shown on the x-axis (NDEPwoSUD = 25620, NDEPwSUD = 3538 and Nctrls = 38142). The slope (β) of the linear regression (95% CI) for each depression subphenotype is shown on the y axis. Significant difference between β for depression without/with an additional diagnosis is indicated with horizontal line with nominal two-sided P value above, that is the Wald test of equal group effect (see Supplementary Table 12j). Overall two-sided P value = 6.4 × 10−97. Cases with BP were excluded. The polygenic risk scores analyzed are (a) PRS for depression (DEP-PRS), (b) PRS for anxiety (ANX-PRS), (c) PRS for bipolar disorder (BP-PRS), (d) PRS for schizophrenia (SZ-PRS), (e) PRS for ADHD (ADHD-PRS), (f) PRS for autism (ASD-PRS), (g) PRS for neuroticism (Neuroticism PRS), (h) PRS for substance use (SU-PRS), and (i) PRS for substance use disorder (SUD-PRS). See Supplementary Table 18k, 18l, Supplementary Fig. 19-1 and 19-2 for sex-stratified analyses.

Extended Data Fig. 6 Absolute risk and HRR of developing anxiety.

Left subpanels: Absolute risk (95%-CI) of developing anxiety since first depression episode for three groups of PRS deciles (1st, 2nd-to-9th and 10th) among 25124 depression cases with/without (NDEPwANX = 3010, NDEPwoANX = 22114) anxiety. The absolute risk (95%-CI) of anxiety in light blue for the iPSYCH2015 subcohort (random population sample) excluding all depression cases, aligned to match the endpoint for the depression-cohort. Right subpanels: HRRs (95%) for 1st and 10th decile using 2nd-to-9th decile as reference (see Supplementary Table 13a) in colors matching the absolute risks curves. For deciles of (a) Depression DEP-PRS, (b) Anxiety ANX-PRS, (c) Bipolar disorder BP-PRS, (d) Schizophrenia SZ-PRS, (e) ADHD-PRS, (f) Autism ASD-PRS, (g) Neuroticism PRS, (h) Substance Use SU-PRS, (i) Substance Use Disorder SUD-PRS and (j) sum of PRSs. The SUM-PRS was calculated by adding PRSs for multiple phenotypes weighted by log(OR) with the aim of optimizing prediction (see Methods for details). See Supplementary Fig. 20-1–20-2 and Supplementary Table 19b – 19c for sex-stratified analyses.

Extended Data Fig. 7 Absolute risk and HRR of transitioning into bipolar disorder.

Left subpanels: Absolute risk (95%-CI) of transitioning into bipolar disorder since first depression episode for three groups of PRS deciles (1st, 2nd-to-9th and 10th) among 30300 depression cases with/without (NDEPwBP = 1142, NDEPwoBP = 29158) anxiety. The absolute risk (95%-CI) of anxiety in light blue for the iPSYCH2015 subcohort (random population sample) excluding all depression cases, aligned to match the endpoint for the depression-cohort. Right subpanels: HRRs (95%) for 1st and 10th decile using 2nd-to-9th decile as reference (see Supplementary Table 14a) in colors matching the absolute risks curves. For deciles of (a) Depression DEP-PRS, (b) Anxiety ANX-PRS, (c) Bipolar disorder BP-PRS, (d) Schizophrenia SZ-PRS, (e) ADHD-PRS, (f) Autism ASD-PRS, (g) Neuroticism PRS, (h) Substance Use SU-PRS, (i) Substance Use Disorder SUD-PRS and (j) sum of PRSs. The SUM-PRS was calculated by adding PRSs for multiple phenotypes weighted by log(OR) with the aim of optimizing prediction (see Methods for details). See Supplementary Fig. 21-1–21-2 Supplementary Tables 20b – 20c for sex-stratified analyses.

Extended Data Fig. 8 Absolute risk and HRR of developing schizophrenia.

Left subpanels: Absolute risk (95%-CI) of developing schizophrenia since first depression episode for three groups of PRS deciles (1st, 2nd-to-9th and 10th) among 28714 depression cases with/without (NDEPwSZ = 1606, NDEPwoSZ = 27108) anxiety. The absolute risk (95%-CI) of anxiety in light blue for the iPSYCH2015 subcohort (random population sample) excluding all depression cases, aligned to match the endpoint for the depression-cohort. Right subpanels: HRRs (95%) for 1st and 10th decile using 2nd-to-9th decile as reference (see Supplementary Table 15a) in colors matching the absolute risks curves. For deciles of (a) Depression DEP-PRS, (b) Anxiety ANX-PRS, (c) Bipolar disorder BP-PRS, (d) Schizophrenia SZ-PRS, (e) ADHD-PRS, (f) Autism ASD-PRS, (g) Neuroticism PRS, (h) Substance Use SU-PRS, (i) Substance Use Disorder SUD-PRS and (j) sum of PRSs. The SUM-PRS was calculated by adding PRSs for multiple phenotypes weighted by log(OR) with the aim of optimizing prediction (see Methods for details). See Supplementary Fig. 22-1 – 22-2 and Supplementary Table 21b – 21c for sex-stratified analyses.

Extended Data Fig. 9 Absolute risk and HRR of developing SUD.

Left subpanels: Absolute risk (95%-CI) of developing SUD since first depression episode for three groups of PRS deciles (1st, 2nd-to-9th and 10th) among 27249 depression cases with/without (NDEPwSUD = 1629, NDEPwoSUD = 25620) anxiety. The absolute risk (95%-CI) of anxiety in light blue for the iPSYCH2015 subcohort (random population sample) excluding all depression cases, aligned to match the endpoint for the depression-cohort. Right subpanels: HRRs (95%) for 1st and 10th decile using 2nd-to-9th decile as reference (see Supplementary Table 16a) in colors matching the absolute risks curves. For deciles of (a) Depression DEP-PRS, (b) Anxiety ANX-PRS, (c) Bipolar disorder BP-PRS, (d) Schizophrenia SZ-PRS, (e) ADHD-PRS, (f) Autism ASD-PRS, (g) Neuroticism PRS, (h) Substance Use SU-PRS, (i) Substance Use Disorder SUD-PRS and (j) sum of PRSs. The SUM-PRS was calculated by adding PRSs for multiple phenotypes weighted by log(OR) with the aim of optimizing prediction (see Methods for details). See Supplementary Fig. 23-1 – 23-2 and Supplementary Tables 22b – 22c for sex-stratified analyses.

Extended Data Table 1 Number of comorbid diagnoses among depression subtypes: single-episode and recurrent depression
Full size table

Supplementary information

Supplementary Information

Supplementary Tables 1–28 and Supplementary Figs. 16–23.

Reporting Summary

Supplementary Table 1

a, Independent index SNPs of the primary DEP meta-analysis. b, Results from the stepwise selection procedure selecting independently associated SNPs implemented in GCTA-COJO.

Supplementary Table 5

a, Genetic correlation (rG) of the primary and narrow DEP meta-analysis and DEP-narrow meta-analysis verus published non-UKB GWAS summary statistics available at LD Hub or locally. b, Genetic correlation (rG) of the primary and narrow DEP meta-analysis and DEP-narrow meta-analysis versus published UKB GWAS summary statistics available at LD Hub.

Supplementary Table 7

a, Datasets used in FUMA SNP2GENE analyses. b, Genome-wide-significant variants mapped to genes using FUMA. c, All candidate SNPs with annotations. d, Results from MAGMA gene-based association analysis.

Supplementary Table 8

a, List of parameters in the FUMA GENE2FUNC analysis. b, Results of the gene-to-function analysis in FUMA.

Supplementary Table 9

a, TWAS summary. b, List of Bonferroni-significant genes and transcripts by LD block. c, List of FDR significant genes. d, List of FDR significant transcripts.

Supplementary Table 13

Independent index SNPs of the narrow DEP GWAS meta-analysis.

Supplementary Table 15

Genetic correlations of other traits with DEP subtypes within iPSYCH2015. a, Based on non-UKB GWAS summary statistics available at LD Hub or locally. b, Based on UKB GWAS summary statistics available at LD Hub.

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Als, T.D., Kurki, M.I., Grove, J. et al. Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses. Nat Med 29, 1832–1844 (2023). https://doi.org/10.1038/s41591-023-02352-1

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