Article | Published:

Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk

Nature Genetics (2019) | Download Citation


Alzheimer’s disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Data availability

Summary statistics will be made available for download upon publication (

Additional information

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


  1. 1.

    Prince, M. et al. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement. 9, 63–75.e2 (2013).

  2. 2.

    Gatz, M. et al. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry 63, 168–174 (2006).

  3. 3.

    Cacace, R., Sleegers, K. & Van Broeckhoven, C. Molecular genetics of early-onset Alzheimer’s disease revisited. Alzheimers Dement. 12, 733–748 (2016).

  4. 4.

    Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).

  5. 5.

    Goate, A. et al. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature 349, 704–706 (1991).

  6. 6.

    Sherrington, R. et al. Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature 375, 754–760 (1995).

  7. 7.

    Sherrington, R. et al. Alzheimer’s disease associated with mutations in presenilin 2 is rare and variably penetrant. Hum. Mol. Genet. 5, 985–988 (1996).

  8. 8.

    Karran, E., Mercken, M. & De Strooper, B. The amyloid cascade hypothesis for Alzheimer’s disease: an appraisal for the development of therapeutics. Nat. Rev. Drug Discov. 10, 698–712 (2011).

  9. 9.

    Jonsson, T. et al. Variant of TREM2 associated with the risk of Alzheimer’s disease. N. Engl. J. Med. 368, 107–116 (2013).

  10. 10.

    Steinberg, S. et al. Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease. Nat. Genet. 47, 445–447 (2015).

  11. 11.

    Liu, C. C., Liu, C. C., Kanekiyo, T., Xu, H. & Bu, G. Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nat. Rev. Neurol. 9, 106–118 (2013).

  12. 12.

    Liu, J. Z., Erlich, Y. & Pickrell, J. K. Case-control association mapping by proxy using family history of disease. Nat. Genet. 49, 325–331 (2017).

  13. 13.

    Marioni, R. E. et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 8, 99 (2018).

  14. 14.

    Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

  15. 15.

    de Bakker, P. I. W. et al. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum. Mol. Genet. 17, R122–R128 (2008).

  16. 16.

    Guerreiro, R. et al. TREM2 variants in Alzheimer’s disease. N. Engl. J. Med. 368, 117–127 (2013).

  17. 17.

    Desikan, R. S. et al. Polygenic overlap between C-reactive protein, plasma lipids, and Alzheimer disease. Circulation 131, 2061–2069 (2015).

  18. 18.

    Sims, R. et al. Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nat. Genet. 49, 1373–1384 (2017).

  19. 19.

    Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).

  20. 20.

    Steinthorsdottir, V. et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet. 46, 294–298 (2014).

  21. 21.

    Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: polygenic risk score software. Bioinformatics 31, 1466–1468 (2015).

  22. 22.

    Valentina, E. P., J., M. A., Matt, H. & John, H. Polygenic risk score analysis of pathologically confirmed Alzheimer disease. Ann. Neurol. 82, 311–314 (2017).

  23. 23.

    Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

  24. 24.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  25. 25.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

  26. 26.

    Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

  27. 27.

    Fagerberg, L. et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol. Cell. Proteomics 13, 397–406 (2014).

  28. 28.

    Gurses, M. S., Ural, M. N., Gulec, M. A., Akyol, O. & Akyol, S. Pathophysiological function of ADAMTS enzymes on molecular mechanism of Alzheimer’s disease. Aging Dis. 7, 479–490 (2016).

  29. 29.

    Suh, J. et al. ADAM10 missense mutations potentiate beta-amyloid accumulation by impairing prodomain chaperone function. Neuron 80, 385–401 (2013).

  30. 30.

    Dries, D. R. & Yu, G. Assembly, maturation, and trafficking of the gamma-secretase complex in Alzheimer’s disease. Curr. Alzheimer Res. 5, 132–146 (2008).

  31. 31.

    Dumitriu, A. et al. Integrative analyses of proteomics and RNA transcriptomics implicate mitochondrial processes, protein folding pathways and GWAS loci in Parkinson disease. BMC Med. Genomics 9, 5 (2016).

  32. 32.

    de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

  33. 33.

    The Gene Ontology Consortium. Expansion of the gene ontology knowledgebase and resources. Nucleic Acids Res. 45, D331–D338 (2017).

  34. 34.

    Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

  35. 35.

    Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).

  36. 36.

    Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

  37. 37.

    Skene, N. G. & Grant, S. G. Identification of vulnerable cell types in major brain disorders using single cell transcriptomes and expression weighted cell type enrichment. Front. Neurosci. 10, 16 (2016).

  38. 38.

    Kang, J. & Rivest, S. Lipid metabolism and neuroinflammation in Alzheimer’s disease: a role for liver X receptors. Endocr. Rev. 33, 715–746 (2012).

  39. 39.

    Loewendorf, A., Fonteh, A., Mg, H. & Me, C. Inflammation in Alzheimer’s disease: cross-talk between lipids and innate immune cells of the brain. J. Immun. Res. 2, 1022 (2015).

  40. 40.

    Stern, Y. Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurol. 11, 1006–1012 (2012).

  41. 41.

    Satizabal, C., Beiser, A. S. & Seshadri, S. Incidence of dementia over three decades in the Framingham Heart Study. N. Engl. J. Med. 375, 93–94 (2016).

  42. 42.

    Adams, H. H. et al. Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nat. Neurosci. 19, 1569–1582 (2016).

  43. 43.

    Ikram, M. A. et al. Common variants at 6q22 and 17q21 are associated with intracranial volume. Nat. Genet. 44, 539–544 (2012).

  44. 44.

    Graves, A. B. et al. Head circumference as a measure of cognitive reserve. Association with severity of impairment in Alzheimer’s disease. Br. J. Psychiatry 169, 86–92 (1996).

  45. 45.

    Abbott, R. D. et al. Height as a marker of childhood development and late-life cognitive function: the Honolulu-Asia Aging Study. Pediatrics 102, 602–609 (1998).

  46. 46.

    Giuffrida, M. L. et al. Beta-amyloid monomer and insulin/IGF-1 signaling in Alzheimer’s disease. Mol. Neurobiol. 46, 605–613 (2012).

  47. 47.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

  48. 48.

    Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

  49. 49.

    Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

  50. 50.

    Lovestone, S. et al. AddNeuroMed—the European collaboration for the discovery of novel biomarkers for Alzheimer’s disease. Ann. N. Y. Acad. Sci. 1180, 36–46 (2009).

  51. 51.

    Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).

  52. 52.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

  53. 53.

    Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

  54. 54.

    Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

  55. 55.

    Roadmap Epigenomics Consortium, Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  56. 56.

    Amendola, L. M. et al. Actionable exomic incidental findings in 6503 participants: challenges of variant classification. Genome Res. 25, 305–315 (2015).

  57. 57.

    Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  58. 58.

    Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

  59. 59.

    Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).

  60. 60.

    Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).

  61. 61.

    Ramasamy, A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418–1428 (2014).

  62. 62.

    Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).

  63. 63.

    Ng, B. et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat. Neurosci. 20, 1418–1426 (2017).

  64. 64.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

  65. 65.

    Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

Download references


This work was funded by the Netherlands Organization for Scientific Research (NWO VICI 453-14-005). The analyses were carried out on the Genetic Cluster Computer, which is financed by the Netherlands Scientific Organization (NWO: 480-05-003), by the VU University, Amsterdam, the Netherlands, and by the Dutch Brain Foundation, and is hosted by the Dutch National Computing and Networking Services SurfSARA. The work was also funded by the Research Council of Norway (grant nos. 251134, 248778, 223273, 213837, and 225989), KG Jebsen Stiftelsen, the Norwegian Health Association, European Community’s JPND Program, ApGeM RCN grant no. 237250, and the European Community’s grant no. PIAPP-GA-2011-286213 PsychDPC. This research has been conducted using the UK Biobank resource under application number 16406 and the public ADSP data set, obtained through the Database of Genotypes and Phenotypes under accession number phs000572. Full acknowledgments for the studies that contributed data can be found in the Supplementary Note. We thank the numerous participants, researchers, and staff from many studies who collected and contributed to the data.

Author information

Author notes

  1. These authors contributed equally: I.E. Jansen, J.E. Savage.

  2. These authors jointly supervised this work: S. Ripke, O.A. Andreassen, D. Posthuma.


  1. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University, Amsterdam, the Netherlands

    • Iris E. Jansen
    • , Jeanne E. Savage
    • , Kyoko Watanabe
    • , Sven Stringer
    • , Christiaan de Leeuw
    •  & Danielle Posthuma
  2. Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands

    • Iris E. Jansen
    •  & Wiesje M. van der Flier
  3. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    • Julien Bryois
    • , Dylan M. Williams
    • , Ida K. Karlsson
    • , Sara Hägg
    • , Patrick F. Sullivan
    •  & Nancy L. Pedersen
  4. deCODE Genetics/Amgen, Reykjavik, Iceland

    • Stacy Steinberg
    • , Hreinn Stefansson
    •  & Kari Stefansson
  5. Interdisciplinary Graduate Program, Vanderbilt University, Nashville, TN, USA

    • Julia Sealock
  6. Institute of Gerontology and Aging Research Network–Jönköping (ARN-J), School of Health and Welfare, Jönköping University, Jönköping, Sweden

    • Ida K. Karlsson
  7. NORMENT, K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    • Lavinia Athanasiu
    • , Aree Witoelar
    • , Francesco Bettella
    • , Srdjan Djurovic
    • , Yunpeng Wang
    •  & Ole A. Andreassen
  8. Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

    • Lavinia Athanasiu
  9. Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    • Nicola Voyle
    • , Petroula Proitsi
    • , Dag Aarsland
    •  & Richard Dobson
  10. Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    • Aree Witoelar
    • , Francesco Bettella
    • , Yunpeng Wang
    •  & Ole A. Andreassen
  11. Center for Age-Related Diseases, Stavanger University Hospital, Stavanger, Norway

    • Dag Aarsland
  12. Department of Neurology, Akershus University Hospital, Lørenskog, Norway

    • Ina S. Almdahl
    •  & Tormod Fladby
  13. AHUS Campus, University of Oslo, Oslo, Norway

    • Ina S. Almdahl
    •  & Tormod Fladby
  14. Department of Psychiatry of Old Age, Oslo University Hospital, Oslo, Norway

    • Ina S. Almdahl
  15. Department of Community Medicine, University of Tromsø, Tromsø, Norway

    • Fred Andersen
  16. Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway

    • Sverre Bergh
    • , Anne Brækhus
    •  & Geir Selbæk
  17. Centre for Old Age Psychiatry Research, Innlandet Hospital Trust, Ottestad, Norway

    • Sverre Bergh
  18. Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland

    • Sigurbjorn Bjornsson
    • , Palmi V. Jonsson
    •  & Jon Snaedal
  19. Geriatric Department, Oslo University Hospital, Oslo, Norway

    • Anne Brækhus
  20. Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway

    • Geir Bråthen
    • , Ingvild Saltvedt
    • , Sigrid B. Sando
    •  & Linda R. White
  21. Department of Neurology, St Olav’s Hospital, Trondheim University Hospital, Trondheim, Norway

    • Geir Bråthen
    • , Sigrid B. Sando
    •  & Linda R. White
  22. Neuroradiology Section, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA

    • Rahul S. Desikan
  23. Department of Medical Genetics, Oslo University Hospital, Oslo, Norway

    • Srdjan Djurovic
  24. Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA

    • Logan Dumitrescu
    •  & Timothy J. Hohman
  25. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA

    • Logan Dumitrescu
    • , Timothy J. Hohman
    •  & Lea K. Davis
  26. Faculty of Medicine, University of Iceland, Reykjavik, Iceland

    • Palmi V. Jonsson
  27. MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK

    • Steven J. Kiddle
  28. Department of Research and Innovation, Helse Fonna, Haugesund, Norway

    • Arvid Rongve
  29. Department of Clinical Medicine, University of Bergen, Bergen, Norway

    • Arvid Rongve
  30. Department of Geriatrics, St. Olav’s Hospital, Trondheim University Hospital, Trondheim, Norway

    • Ingvild Saltvedt
  31. Institute of Health and Society, University of Oslo, Oslo, Norway

    • Geir Selbæk
  32. Department of Neurodegenerative Disorders, Institute of Neurology, UCL, London, UK

    • Maryam Shoai
    •  & John Hardy
  33. Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

    • Nathan G. Skene
    •  & Jens Hjerling-Leffler
  34. Department of Neuromuscular Diseases, Institute of Neurology, University College London, London, UK

    • Nathan G. Skene
  35. Department of Psychiatry, Namsos Hospital, Namsos, Norway

    • Eystein Stordal
  36. Department of Mental Health, Norwegian University of Science and Technology, Trondheim, Norway

    • Eystein Stordal
  37. Memory Clinic, Geriatric Department, Oslo University Hospital, Oslo, Norway

    • Ingun D. Ulstein
  38. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

    • Patrick F. Sullivan
  39. Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA

    • Patrick F. Sullivan
  40. NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, UK

    • Richard Dobson
  41. Institute of Health Informatics Research, University College London, London, UK

    • Richard Dobson
  42. Health Data Research UK London, University College London, London, UK

    • Richard Dobson
  43. Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    • Lea K. Davis
  44. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    • Stephan Ripke
  45. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Stephan Ripke
  46. Department of Psychiatry and Psychotherapy, Charité–Universitätsmedizin, Berlin, Germany

    • Stephan Ripke
  47. Department of Clinical Genetics, VU University Medical Center, Amsterdam, the Netherlands

    • Danielle Posthuma


  1. Search for Iris E. Jansen in:

  2. Search for Jeanne E. Savage in:

  3. Search for Kyoko Watanabe in:

  4. Search for Julien Bryois in:

  5. Search for Dylan M. Williams in:

  6. Search for Stacy Steinberg in:

  7. Search for Julia Sealock in:

  8. Search for Ida K. Karlsson in:

  9. Search for Sara Hägg in:

  10. Search for Lavinia Athanasiu in:

  11. Search for Nicola Voyle in:

  12. Search for Petroula Proitsi in:

  13. Search for Aree Witoelar in:

  14. Search for Sven Stringer in:

  15. Search for Dag Aarsland in:

  16. Search for Ina S. Almdahl in:

  17. Search for Fred Andersen in:

  18. Search for Sverre Bergh in:

  19. Search for Francesco Bettella in:

  20. Search for Sigurbjorn Bjornsson in:

  21. Search for Anne Brækhus in:

  22. Search for Geir Bråthen in:

  23. Search for Christiaan de Leeuw in:

  24. Search for Rahul S. Desikan in:

  25. Search for Srdjan Djurovic in:

  26. Search for Logan Dumitrescu in:

  27. Search for Tormod Fladby in:

  28. Search for Timothy J. Hohman in:

  29. Search for Palmi V. Jonsson in:

  30. Search for Steven J. Kiddle in:

  31. Search for Arvid Rongve in:

  32. Search for Ingvild Saltvedt in:

  33. Search for Sigrid B. Sando in:

  34. Search for Geir Selbæk in:

  35. Search for Maryam Shoai in:

  36. Search for Nathan G. Skene in:

  37. Search for Jon Snaedal in:

  38. Search for Eystein Stordal in:

  39. Search for Ingun D. Ulstein in:

  40. Search for Yunpeng Wang in:

  41. Search for Linda R. White in:

  42. Search for John Hardy in:

  43. Search for Jens Hjerling-Leffler in:

  44. Search for Patrick F. Sullivan in:

  45. Search for Wiesje M. van der Flier in:

  46. Search for Richard Dobson in:

  47. Search for Lea K. Davis in:

  48. Search for Hreinn Stefansson in:

  49. Search for Kari Stefansson in:

  50. Search for Nancy L. Pedersen in:

  51. Search for Stephan Ripke in:

  52. Search for Ole A. Andreassen in:

  53. Search for Danielle Posthuma in:


I.E.J. and J.E.S. performed the analyses. D.P. and O.A.A. conceived the idea for the study. D.P. and S.R. supervised analyses. Sv.St. performed quality control on the UK Biobank data and wrote the analysis pipeline. K.W. constructed and applied the FUMA pipeline for performing follow-up analyses. J.B. conducted the single-cell enrichment analyses. J.H.L. and N.S. contributed data. M.S. and J.H. performed polygenic score analyses. D.P. and I.E.J. wrote the first draft of the paper. All other authors contributed data and critically reviewed the paper.

Competing interests

The authors report the following potentially competing financial interests. P.F.S.: Lundbeck (advisory committee), Pfizer (Scientific Advisory Board member), and Roche (grant recipient, speaker reimbursement). J.H.L.: Cartana (Scientific Advisor) and Roche (grant recipient). O.A.A.: Lundbeck (speaker’s honorarium). St.St., H.S., and K.S. are employees of deCODE Genetics/Amgen. J.H. is a cograntee of Cytox from Innovate UK (UK Department of Business). D.A. has received research support and/or honoraria from Astra-Zeneca, Lundbeck, Novartis Pharmaceuticals, and GE Health, and serves as a paid consultant for Lundbeck, Eisai, Heptares, and Axovant. All other authors declare no financial interests or potential conflicts of interest.

Corresponding author

Correspondence to Danielle Posthuma.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Note and Supplementary Figures 1–7

  2. Reporting Summary

  3. Supplementary Tables

    Supplementary Tables 1–27

About this article

Publication history