Abstract
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.
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Data availability
Summary statistics will be made available for download upon publication (https://ctg.cncr.nl/software/summary_statistics).
Change history
06 February 2020
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
References
Prince, M. et al. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement. 9, 63–75.e2 (2013).
Gatz, M. et al. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry 63, 168–174 (2006).
Cacace, R., Sleegers, K. & Van Broeckhoven, C. Molecular genetics of early-onset Alzheimer’s disease revisited. Alzheimers Dement. 12, 733–748 (2016).
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).
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).
Sherrington, R. et al. Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature 375, 754–760 (1995).
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).
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).
Jonsson, T. et al. Variant of TREM2 associated with the risk of Alzheimer’s disease. N. Engl. J. Med. 368, 107–116 (2013).
Steinberg, S. et al. Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease. Nat. Genet. 47, 445–447 (2015).
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).
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).
Marioni, R. E. et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 8, 99 (2018).
Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
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).
Guerreiro, R. et al. TREM2 variants in Alzheimer’s disease. N. Engl. J. Med. 368, 117–127 (2013).
Desikan, R. S. et al. Polygenic overlap between C-reactive protein, plasma lipids, and Alzheimer disease. Circulation 131, 2061–2069 (2015).
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).
Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).
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).
Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: polygenic risk score software. Bioinformatics 31, 1466–1468 (2015).
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).
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
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).
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).
Suh, J. et al. ADAM10 missense mutations potentiate beta-amyloid accumulation by impairing prodomain chaperone function. Neuron 80, 385–401 (2013).
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).
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).
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).
The Gene Ontology Consortium. Expansion of the gene ontology knowledgebase and resources. Nucleic Acids Res. 45, D331–D338 (2017).
Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
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).
Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).
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).
Kang, J. & Rivest, S. Lipid metabolism and neuroinflammation in Alzheimer’s disease: a role for liver X receptors. Endocr. Rev. 33, 715–746 (2012).
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).
Stern, Y. Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurol. 11, 1006–1012 (2012).
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).
Adams, H. H. et al. Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nat. Neurosci. 19, 1569–1582 (2016).
Ikram, M. A. et al. Common variants at 6q22 and 17q21 are associated with intracranial volume. Nat. Genet. 44, 539–544 (2012).
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).
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).
Giuffrida, M. L. et al. Beta-amyloid monomer and insulin/IGF-1 signaling in Alzheimer’s disease. Mol. Neurobiol. 46, 605–613 (2012).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
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).
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).
Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).
Roadmap Epigenomics Consortium, Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Amendola, L. M. et al. Actionable exomic incidental findings in 6503 participants: challenges of variant classification. Genome Res. 25, 305–315 (2015).
Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).
Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).
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).
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).
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
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).
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).
Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).
Acknowledgements
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.
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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.
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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.
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Jansen, I.E., Savage, J.E., Watanabe, K. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet 51, 404–413 (2019). https://doi.org/10.1038/s41588-018-0311-9
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DOI: https://doi.org/10.1038/s41588-018-0311-9
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