Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

An Author Correction to this article was published on 15 August 2019

This article has been updated

Abstract

Risk for late-onset Alzheimer’s disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer’s or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer’s disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10−7), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education.

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Fig. 1: Manhattan plot of meta-analysis of Stage 1, 2, and 3 results for genome-wide association with Alzheimer’s disease.
Fig. 2: Top prioritized genes of 400 genes located in genome-wide-significant loci.

Data availability

Genome-wide summary statistics for the Stage 1 discovery have been deposited in The National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS)—a NIA/NIH-sanctioned qualified-access data repository, under accession NG00075. Stage 1 data (individual level) for the GERAD cohort can be accessed by applying directly to Cardiff University. Stage 1 ADGC data are deposited in NIAGADS. Stage 1 CHARGE data are accessible by applying to dbGaP for all US cohorts and to Erasmus University for Rotterdam data. AGES primary data are not available owing to Icelandic laws. Stage 2 and Stage 3 primary data are available upon request.

Change history

  • 15 August 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 1.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Adams, P. M. et al. Assessment of the genetic variance of late-onset Alzheimer’s disease. Neurobiol. Aging. 41, 1–8 (2016).

    Google Scholar 

  3. 3.

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

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Naj, A. C. et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat. Genet. 43, 436–441 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Seshadri, S. et al. Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA 303, 1832–1840 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Hollingworth, P. et al. Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat. Genet. 43, 429–435 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Jun, G. et al. Meta-analysis confirms CR1, CLU, and PICALM as alzheimer disease risk loci and reveals interactions with APOE genotypes. Arch. Neurol. 67, 1473–1484 (2010).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Harold, D. et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat. Genet. 41, 1088–1093 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Lambert, J. C. et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat. Genet. 41, 1094–1099 (2009).

    CAS  PubMed  Google Scholar 

  12. 12.

    Zheng, J. et al. LD Hub: A centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 051094 (2017).

    Google Scholar 

  13. 13.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Sims, R. C. et al. Novel rare coding variants in PLCG2, ABI3 and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nat. Genet. 49, 1373–1387 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Liu, J. Z. et al. Case-control association mapping by proxy using family history of disease. Nat. Genet . 49, 325–331 (2017).

    CAS  PubMed  Google Scholar 

  16. 16.

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

  17. 17.

    Jun, G. R. et al. Transethnic genome-wide scan identifies novel Alzheimer’s disease loci. Alzheimers Dement. 13, 727–738 (2017).

  18. 18.

    Vassar, R. ADAM10 prodomain mutations cause late-onset Alzheimer’s disease: not just the latest FAD. Neuron 80, 250–253 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Kim, M. et al. Potential late-onset Alzheimer’s disease-associated mutations in the ADAM10 gene attenuate alpha-secretase activity. Hum. Mol. Genet. 18, 3987–3996 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Kehoe, P. G. et al. Variation in DCP1, encoding ACE, is associated with susceptibility to Alzheimer disease. Nat. Genet. 21, 71–72 (1999).

    CAS  PubMed  Google Scholar 

  21. 21.

    Meng, Y. et al. Association of polymorphisms in the Angiotensin-converting enzyme gene with Alzheimer disease in an Israeli Arab community. Am. J. Hum. Genet. 78, 871–877 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Lehmann, D. J. et al. Large meta-analysis establishes the ACE insertion-deletion polymorphism as a marker of Alzheimer’s disease. Am. J. Epidemiol. 162, 305–317 (2005).

    PubMed  Google Scholar 

  23. 23.

    Wang, X.-B. et al. Angiotensin-converting enzyme insertion/deletion polymorphism is not a major determining factor in the development of sporadic Alzheimer disease: evidence from an updated meta-analysis. PLoS ONE 9, e111406 (2014).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Cai, G. et al. Evidence against a role for rare ADAM10 mutations in sporadic Alzheimer disease. Neurobiol. Aging. 33, 416–417.e3 (2012).

    PubMed  Google Scholar 

  25. 25.

    Belbin, O. et al. A multi-center study of ACE and the risk of late-onset Alzheimer’s disease. J. Alzheimers. Dis. 24, 587–597 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

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

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Chang, J.-Y. & Chang, N.-S. WWOX dysfunction induces sequential aggregation of TRAPPC6AΔ, TIAF1, tau and amyloid β, and causes apoptosis. Cell Death Discov. 1, 15003 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Sze, C. I. et al. Down-regulation of WW domain-containing oxidoreductase induces tau phosphorylation in vitro: a potential role in Alzheimer’s disease. J. Biol. Chem. 279, 30498–30506 (2004).

    CAS  PubMed  Google Scholar 

  30. 30.

    Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Bai, Z. et al. AlzBase: an integrative database for gene dysregulation in Alzheimer’s disease. Mol. Neurobiol. 53, 310–319 (2016).

    CAS  PubMed  Google Scholar 

  32. 32.

    Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Olah, M. et al. A transcriptomic atlas of aged human microglia. Nat. Commun. 9, 539 (2018).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Corder, E. H. et al. Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat. Genet. 7, 180–184 (1994).

    CAS  PubMed  Google Scholar 

  36. 36.

    Kim, J., Basak, J. M. & Holtzman, D. M. The role of apolipoprotein E in Alzheimer’s disease. Neuron 63, 287–303 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Vasquez, J. B., Fardo, D. W. & Estus, S. ABCA7 expression is associated with Alzheimer’s disease polymorphism and disease status. Neurosci. Lett. 556, 58–62 (2013).

    CAS  PubMed  Google Scholar 

  39. 39.

    De Roeck, A. et al. An intronic VNTR affects splicing of ABCA7 and increases risk of Alzheimer’s disease. Acta Neuropathol. 135, 827–837 (2018).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    De Roeck, A. et al. Deleterious ABCA7 mutations and transcript rescue mechanisms in early onset Alzheimer’s disease. Acta Neuropathol. 134, 475–487 (2017).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Chapuis, J. et al. Increased expression of BIN1 mediates Alzheimer genetic risk by modulating tau pathology. Mol. Psychiatry 18, 1225–1234 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Rogaeva, E. et al. The neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer disease. Nat. Genet. 39, 168–177 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Vardarajan, B. N. et al. Coding mutations in SORL 1 and Alzheimer disease. Ann. Neurol. 77, 215–227 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Huang, K. et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat. Neurosci. 20, 1052–1061 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Brouwers, N. et al. Alzheimer risk associated with a copy number variation in the complement receptor 1 increasing C3b/C4b binding sites. Mol. Psychiatry 17, 223–233 (2012).

    CAS  PubMed  Google Scholar 

  47. 47.

    Flister, M. J. et al. Identifying multiple causative genes at a single GWAS locus. Genome Res. 23, 1996–2002 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2014).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Bis, J. C. et al. Whole exome sequencing study identifies novel rare and common Alzheimer’s-associated variants involved in immune response and transcriptional regulation. Mol. Psychiatry https://doi.org/10.1038/s41380-018-0112-7 (2018).

  50. 50.

    Vardarajan, B. N. et al. Coding mutations in SORL1 and Alzheimer disease. Ann. Neurol. 77, 215–227 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Verheijen, J. et al. A comprehensive study of the genetic impact of rare variants in SORL1 in European early-onset Alzheimer’s disease. Acta Neuropathol. 132, 213–224 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Bellenguez, C. et al. Contribution to Alzheimer’s disease risk of rare variants in TREM2, SORL1, and ABCA7 in 1779 cases and 1273 controls. Neurobiol. Aging. 59, 220.e1–220.e9 (2017).

    CAS  Google Scholar 

  53. 53.

    Kunkle, B. W. et al. Targeted sequencing of ABCA7 identifies splicing, stop-gain and intronic risk variants for Alzheimer disease. Neurosci. Lett. 649, 124–129 (2017).

    CAS  PubMed  Google Scholar 

  54. 54.

    May, P. et al. Rare ABCA7 variants in 2 German families with Alzheimer disease. Neurol. Genet. 4, e224 (2018).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Guennec, K. Le et al. ABCA7 rare variants and Alzheimer disease risk. Neurology 86, 1–4 (2016).

    Google Scholar 

  56. 56.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).

    CAS  PubMed  Google Scholar 

  58. 58.

    Zerbino, D. R., Wilder, S. P., Johnson, N., Juettemann, T. & Flicek, P. R. The Ensembl Regulatory Build. Genome. Biol. 16, 56 (2015).

    PubMed  PubMed Central  Google Scholar 

  59. 59.

    Huang, D. et al. GWAS4D: multidimensional analysis of context-specific regulatory variant for human complex diseases and traits. Nucleic Acids Res. 46, W114–W120 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518, 365–369 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: Generalized Gene-Set Analysis of GWAS Data. PLoS Comput. Biol. 11, 1–19 (2015).

    Google Scholar 

  62. 62.

    Stefanis, L. alpha-Synuclein in Parkinson’s disease. Cold Spring Harb. Perspect. Med. 2, 1–23 (2012).

    Google Scholar 

  63. 63.

    Takeda, A. et al. C-terminal alpha-synuclein immunoreactivity in structures other than Lewy bodies in neurodegenerative disorders. Acta Neuropathol. 99, 296–304 (2000).

    CAS  PubMed  Google Scholar 

  64. 64.

    Campion, D., Pottier, C., Nicolas, G., Le Guennec, K. & Rovelet-Lecrux, A. Alzheimer disease: modeling an Aβ-centered biological network. Mol. Psychiatry 7, 861–871 (2016).

    Google Scholar 

  65. 65.

    Yeh, F. L., Wang, Y., Tom, I., Gonzalez, L. C. & Sheng, M. TREM2 binds to apolipoproteins, including APOE and CLU/APOJ, and thereby facilitates uptake of amyloid-beta by microglia. Neuron 91, 328–340 (2016).

    CAS  PubMed  Google Scholar 

  66. 66.

    Fritsche, L. G. et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet. 48, 134–143 (2015).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Haass, C., Kaether, C., Thinakaran, G. & Sisodia, S. Trafficking and proteolytic processing of APP. Cold Spring Harb. Perspect. Med. 2, a006270 (2012).

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Kleinberger, G. et al. TREM2 mutations implicated in neurodegeneration impair cell surface transport and phagocytosis. Sci. Transl. Med. 6, 243ra86 (2014).

    PubMed  Google Scholar 

  69. 69.

    Postina, R. et al. A disintegrin-metalloproteinase prevents amyloid plaque formation and hippocampal defects in an Alzheimer disease mouse model. J. Clin. Invest. 113, 1456–1464 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Hinney, A. et al. Genetic variation at the CELF1 (CUGBP, elav-like family member 1 gene) locus is genome-wide associated with Alzheimer’s disease and obesity. Am. J. Med. Genet. B. 165B, 283–293 (2014).

    CAS  Google Scholar 

  71. 71.

    Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Kurabayashi, N., Nguyen, M. D. & Sanada, K. The G protein-coupled receptor GPRC5B contributes to neurogenesis in the developing mouse neocortex. Development 140, 4335–4346 (2013).

    CAS  PubMed  Google Scholar 

  73. 73.

    Cool, B. H. et al. A flanking gene problem leads to the discovery of a Gprc5b splice variant predominantly expressed in C57BL/6J mouse brain and in maturing neurons. PLoS ONE 5, e10351 (2010).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Kim, Y.-J., Sano, T., Nabetani, T., Asano, Y. & Hirabayashi, Y. GPRC5B activates obesity-associated inflammatory signaling in adipocytes. Sci. Signal. 5, ra85–ra85 (2012).

    PubMed  Google Scholar 

  75. 75.

    Bhat, K. et al. The 19S proteasome ATPase Sug1 plays a critical role in regulating MHC class II transcription. Mol. Immunol. 45, 2214–2224 (2008).

    CAS  PubMed  Google Scholar 

  76. 76.

    Inostroza-Nieves, Y., Venkatraman, P. & Zavala-Ruiz, Z. Role of Sug1, a 19S proteasome ATPase, in the transcription of MHC I and the atypical MHC II molecules, HLA-DM and HLA-DO. Immunol. Lett. 147, 67–74 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Kim, K., Duramad, O., Qin, X. F. & Su, B. MEKK3 is essential for lipopolysaccharide-induced interleukin-6 and granulocyte-macrophage colony-stimulating factor production in macrophages. Immunology 120, 242–250 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Yamazaki, K. et al. Two mechanistically and temporally distinct NF-κB activation pathways in IL-1 signaling. Sci. Signal. 2, 1–12 (2009).

    Google Scholar 

  79. 79.

    Farrer, L. A. et al. Association between angiotensin-converting enzyme and Alzheimer disease. New Engl. J. Med. 57, 210–214 (2000).

    CAS  Google Scholar 

  80. 80.

    Miners, J. S. et al. Angiotensin-converting enzyme levels and activity in Alzheimer’s disease: differences in brain and CSF ACE and association with ACE1 genotypes. Am. J. Transl. Res. 1, 163–177 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Jochemsen, H. M. et al. The association of angiotensin-converting enzyme with biomarkers for Alzheimer’s disease. Alzheimers Res. Ther. 6, 1–10 (2014).

    Google Scholar 

  82. 82.

    Kauwe, J. S. K. et al. Genome-wide association study of CSFl Levels of 59 Alzheimer’s disease candidate proteins: significant associations with proteins involved in amyloid processing and inflammation. PLoS Genet. 10, e1004758 (2014).

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    Baranello, R. J. et al. Amyloid-beta protein clearance and degradation (ABCD) pathways and their role in Alzheimer’s disease. Curr. Alzheimers Res 12, 32–46 (2015).

    CAS  Google Scholar 

  84. 84.

    Kehoe, P. G. The coming of age of the angiotensin hypothesis in Alzheimer’s disease: progress toward disease prevention and treatment? J. Alzheimers. Dis. 62, 1443–1466 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Kehoe, P. G. et al. The rationale and design of the reducing pathology in Alzheimer’s disease through Angiotensin TaRgeting (RADAR) Trial. J. Alzheimers. Dis. 61, 803–814 (2017).

    Google Scholar 

  86. 86.

    Miguel, R. F., Pollak, A. & Lubec, G. Metalloproteinase ADAMTS-1 but not ADAMTS-5 is manifold overexpressed in neurodegenerative disorders as Down syndrome, Alzheimer’s and Pick’s disease. Brain. Res. Mol. Brain. Res. 133, 1–5 (2005).

    CAS  PubMed  Google Scholar 

  87. 87.

    Suttkus, A. et al. Aggrecan, link protein and tenascin-R are essential components of the perineuronal net to protect neurons against iron-induced oxidative stress. Cell Death Dis. 5, e1119 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Végh, M. J. et al. Reducing hippocampal extracellular matrix reverses early memory deficits in a mouse model of Alzheimer’s disease. Acta Neuropathol. Commun. 2, 76 (2014).

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    Morawski, M., Filippov, M., Tzinia, A., Tsilibary, E. & Vargova, L. ECM in brain aging and dementia. Prog. Brain. Res. 214, 207–227 (2014).

    PubMed  Google Scholar 

  90. 90.

    Wilcock, D. M. Neuroinflammation in the aging down syndrome brain; lessons from Alzheimer’s disease. Curr. Gerontol. Geriatr. Res. 2012, 170276 (2012).

    PubMed  PubMed Central  Google Scholar 

  91. 91.

    Wang, K. et al. A genome-wide association study on obesity and obesity-related traits. PLoS ONE 6, 3–8 (2011).

    Google Scholar 

  92. 92.

    Kang, K. et al. Interferon-γ represses M2 gene expression in human macrophages by disassembling enhancers bound by the transcription factor MAF. Immunity 47, 235–250.e4 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Cao, S., Liu, J., Song, L. & Ma, X. The protooncogene c-Maf Is an essential transcription factor for IL-10 gene expression in macrophages. J. Immunol. 174, 3484–3492 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Lee, J. C. et al. WW-domain-containing oxidoreductase is associated with low plasma HDL-C levels. Am. J. Hum. Genet. 83, 180–192 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Saez, M. E. et al. WWOX gene is associated with HDL cholesterol and triglyceride levels. BMC. Med. Genet. 11, 148 (2010).

    PubMed  PubMed Central  Google Scholar 

  96. 96.

    Chang, H. T. et al. WW domain-containing oxidoreductase in neuronal injury and neurological diseases. Oncotarget 5, 11792–11799 (2014).

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    Lee, M. H. et al. Zfra restores memory deficits in Alzheimer’s disease triple-transgenic mice by blocking aggregation of TRAPPC6AΔ, SH3GLB2, tau, and amyloid β, and inflammatory NF-κB activation. Alzheimers Dement. Transl. Res. Clin. Interv 3, 189–204 (2017).

    Google Scholar 

  98. 98.

    Dourlen, P. et al. Functional screening of Alzheimer risk loci identifies PTK2B as an in vivo modulator and early marker of Tau pathology. Mol. Psychiatry 22, 874–883 (2017).

    CAS  PubMed  Google Scholar 

  99. 99.

    Chapuis, J. et al. Genome-wide, high-content siRNA screening identifies the Alzheimer’s genetic risk factor FERMT2 as a major modulator of APP metabolism. Acta Neuropathol. 133, 955–966 (2017).

    CAS  PubMed  Google Scholar 

  100. 100.

    Shulman, J. M. et al. Functional screening in Drosophila identifies Alzheimer’s disease susceptibility genes and implicates tau-mediated mechanisms. Hum. Mol. Genet. 23, 870–877 (2014).

    CAS  PubMed  Google Scholar 

  101. 101.

    Zhao, Z. et al. Central role for PICALM in amyloid-β blood-brain barrier transcytosis and clearance. Nat. Neurosci. 18, 978–987 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Aguet, F. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Google Scholar 

  103. 103.

    Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Knickmeyer, R. C. & Ross, M. E. Imaging and rare APOE alleles. Neurology 87, 558–559 (2016).

    PubMed  Google Scholar 

  105. 105.

    Douaud, G. et al. A common brain network links development, aging, and vulnerability to disease. Proc. Natl Acad. Sci. USA 111, 17648–17653 (2014).

    CAS  Google Scholar 

  106. 106.

    Steele, N. Z. et al. Fine-mapping of the human leukocyte antigen locus as a risk factor for Alzheimer disease: a case-control study. PLoS Med. 14, 1–25 (2017).

    Google Scholar 

  107. 107.

    Fekih Mrissa, N. et al. Association of HLA-DR-DQ polymorphisms with diabetes in Tunisian patients. Transfus. Apher. Sci. 49, 200–204 (2013).

    PubMed  Google Scholar 

  108. 108.

    Pugliese, A. et al. HLA-DRB1 15:01-DQA1 01:02-DQB1 06:02 haplotype protects autoantibody-positive relatives from type 1 diabetes throughout the stages of disease progression. Diabetes 65, 1109–1119 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. 109.

    Patsopoulos, Na et al. Fine-mapping the genetic association of the major histocompatibility complex in multiple sclerosis: HLA and non-HLA effects. PLoS Genet. 9, e1003926 (2013).

    PubMed  PubMed Central  Google Scholar 

  110. 110.

    Schmidt, H., Williamson, D. & Ashley-Koch, A. HLA-DR15 haplotype and multiple sclerosis: a HuGE review. Am. J. Epidemiol. 165, 1097–1109 (2007).

    PubMed  Google Scholar 

  111. 111.

    Karnes, J. H. et al. Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants. Sci. Transl. Med. 9, 1–14 (2017).

    Google Scholar 

  112. 112.

    Wissemann, W. T. et al. Association of Parkinson disease with structural and regulatory variants in the HLA region. Am. J. Hum. Genet. 93, 984–993 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Misra, M. K., Damotte, V. & Hollenbach, J. A. The immunogenetics of neurological disease. Immunology 153, 399–414 (2018).

    CAS  PubMed  Google Scholar 

  114. 114.

    Tan, Z. S. Thyroid function and the risk of Alzheimer disease: the Framingham study. Arch. Intern. Med. 168, 1514 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115.

    Dendrou, C. A., Petersen, J., Rossjohn, J. & Fugger, L. HLA variation and disease. Nat. Rev. Immunol. 18, 325–339 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  117. 117.

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

    PubMed  PubMed Central  Google Scholar 

  118. 118.

    Cadar, D. et al. Individual and area-based socioeconomic factors associated with dementia incidence in England: evidencefrom a 12-year follow-up in the English longitudinal study of ageing. JAMA Psychiatry 75, 723–732 (2018).

    PubMed  PubMed Central  Google Scholar 

  119. 119.

    Marden, J. R., Tchetgen Tchetgen, E. J., Kawachi, I. & Glymour, M. M. Contribution of socioeconomic status at 3 life-course periods to late-life memory function and decline: early and late predictors of dementia risk. Am. J. Epidemiol. 186, 805–814 (2017).

    PubMed  PubMed Central  Google Scholar 

  120. 120.

    Østergaard, S. D. S. D. et al. Associations between potentially modifiable risk factors and Alzheimer disease: a Mendelian randomization study. PLoS Med. 12, e1001841 (2015).

    PubMed  PubMed Central  Google Scholar 

  121. 121.

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

    PubMed  PubMed Central  Google Scholar 

  122. 122.

    Baumgart, M. et al. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: a population-based perspective. Alzheimers Dement. 11, 1–9 (2015).

    Google Scholar 

  123. 123.

    Larsson, S. C., Traylor, M., Burgess, S. & Markus, H. S. Genetically-predicted adult height and Alzheimer’s disease. J. Alzheimers. Dis. 60, 691–698 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. 124.

    Helzner, E. P. et al. Contribution of vascular risk factors to the progression in Alzheimer disease. Arch. Neurol. 66, 343–348 (2009).

    PubMed  PubMed Central  Google Scholar 

  125. 125.

    Reitz, C. et al. Association of higher levels of high-density lipoprotein cholesterol in elderly individuals and lower risk of late-onset Alzheimer disease. Arch. Neurol. 67, 1491–1497 (2010).

    PubMed  PubMed Central  Google Scholar 

  126. 126.

    Mukherjee, S. et al. Genetically predicted body mass index and Alzheimer’s disease-related phenotypes in three large samples: Mendelian randomization analyses. Alzheimers Dement. 11, (2015).

    Google Scholar 

  127. 127.

    Arvanitakis, Z. et al. Late-life blood pressure association with cerebrovascular and Alzheimer disease pathology. Neurology 91, e517–e525 (2018).

    PubMed  PubMed Central  Google Scholar 

  128. 128.

    Kuźma, E. et al. Which risk factors causally influence dementia? A systematic review of mendelian randomization studies. J. Alzheimers. Dis. 36, 215–221 (2018).

    Google Scholar 

  129. 129.

    Murray, M. E. et al. Clinicopathologic and 11C-Pittsburgh compound B implications of Thal amyloid phase across the Alzheimer’s disease spectrum. Brain 138, 1370–1381 (2015).

    PubMed  PubMed Central  Google Scholar 

  130. 130.

    Shi, Y. et al. ApoE4 markedly exacerbates tau-mediated neurodegeneration in a mouse model of tauopathy. Nature 549, 523–527 (2017).

    PubMed  PubMed Central  Google Scholar 

  131. 131.

    Brier, M. R. M. R. et al. Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease. Sci. Transl. Med. 8, 338ra66 (2016).

    PubMed  PubMed Central  Google Scholar 

  132. 132.

    Genomes Project, C.. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Google Scholar 

  133. 133.

    Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    PubMed  PubMed Central  Google Scholar 

  134. 134.

    Delaneau, O., Marchini, J. & Zagury, J. F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2012).

    CAS  Google Scholar 

  135. 135.

    Li, Y., Willer, C. J., Ding, J., Scheet, P. & Abecasis, G. R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes.. Genet. Epidemiol. 34, 816–834 (2010).

  136. 136.

    Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. 137.

    Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 1, 457–470 (2011).

    PubMed  PubMed Central  Google Scholar 

  138. 138.

    Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. 139.

    Ma, C. et al. Recommended joint and meta-analysis strategies for case-control association testing of single low-count variants. Genet. Epidemiol. 37, 539–550 (2013).

    PubMed  PubMed Central  Google Scholar 

  140. 140.

    Chen, M.-H. H. & Yang, Q. GWAF: an R package for genome-wide association analyses with family data. Bioinformatics 26, 580–581 (2010).

    Google Scholar 

  141. 141.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Aulchenko, Y. S., Ripke, S., Isaacs, A. & van Duijn, C. M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).

    CAS  PubMed  Google Scholar 

  143. 143.

    Sudlow, C. et al. UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, 1–10 (2015).

    Google Scholar 

  144. 144.

    Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).

    PubMed  PubMed Central  Google Scholar 

  145. 145.

    Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).

    PubMed  PubMed Central  Google Scholar 

  146. 146.

    Machiela, M. J. & Chanock, S. J. LDlink: A web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. 147.

    Zhang, X. et al. Synthesis of 53 tissue and cell line expression QTL datasets reveals master eQTLs. BMC Genomics 15, 532 (2014).

    PubMed  PubMed Central  Google Scholar 

  148. 148.

    Pruitt, K. D., Tatusova, T., Brown, G. R. & Maglott, D. R. NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res. 40, D130–D135 (2012).

    CAS  PubMed  Google Scholar 

  149. 149.

    Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. 150.

    Arnold, M., Raffler, J., Pfeufer, a, Suhre, K. & Kastenmuller, G. SNiPA: an interactive, genetic variant-centered annotation browser. Bioinformatics 31, 1334–1336 (2014).

    PubMed  PubMed Central  Google Scholar 

  151. 151.

    McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

  152. 152.

    Cargill, M. et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat. Genet. 22, 231–238 (1999).

    CAS  PubMed  Google Scholar 

  153. 153.

    Ng, P. C. & Henikoff, S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. 154.

    Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. 155.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  156. 156.

    Gonzalez-Perez, A. & Lopez-Bigas, N. Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel. Am. J. Hum. Genet. 88, 440–449 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. 157.

    Samocha, K. E. et al. Regional missense constraint improves variant deleteriousness prediction. Preprint at https://doi.org/10.1101/148353 (2017).

  158. 158.

    Ionita-Laza, I., McCallum, K., Xu, B. & Buxbaum, J. D. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat. Genet. 48, 214–220 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  159. 159.

    Yeo, G. & Burge, C. B. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol. 11, 377–394 (2004).

    CAS  Google Scholar 

  160. 160.

    Amlie-Wolf, A. et al. INFERNO—INFERring the molecular mechanisms of NOncoding genetic variants. Nucleic Acids Res. 46, 8740–8753 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  161. 161.

    Ward, L. D. & Kellis, M. HaploRegv4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 44, D877–D881 (2015).

    PubMed  PubMed Central  Google Scholar 

  162. 162.

    Thériault, P., ElAli, A. & Rivest, S. The dynamics of monocytes and microglia in Alzheimer’s disease. Alzheimers Res. Ther. 7, 41 (2015).

    PubMed  PubMed Central  Google Scholar 

  163. 163.

    Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. 164.

    Qi, T. et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat. Commun. 9, 2282 (2018).

  165. 165.

    Schramm, K. et al. Mapping the genetic architecture of gene regulation in whole blood. PLoS ONE 9, e93844 (2014).

  166. 166.

    Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    PubMed  PubMed Central  Google Scholar 

  167. 167.

    Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–489 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  168. 168.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  169. 169.

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  170. 170.

    Blake, J. A. et al. Gene ontology consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).

    CAS  Google Scholar 

  171. 171.

    Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  172. 172.

    Ogata, H. et al. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27, 29–34 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  173. 173.

    Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 44, D481–D487 (2016).

    CAS  PubMed  Google Scholar