A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease

  • Nature Neuroscience volume 20, pages 10521061 (2017)
  • doi:10.1038/nn.4587
  • Download Citation
Published online:


A genome-wide survival analysis of 14,406 Alzheimer's disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and 14 novel loci associated with age at onset. Linkage disequilibrium score regression of 220 cell types implicated the regulation of myeloid gene expression in AD risk. The minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, showed association with delayed AD onset and lower expression of SPI1 in monocytes and macrophages. SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability was enriched within the PU.1 cistrome, implicating a myeloid PU.1 target gene network in AD. Finally, experimentally altered PU.1 levels affected the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells. Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.

  • Subscribe to Nature Neuroscience for full access:



Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.


  1. 1.

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

  2. 2.

    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).

  3. 3.

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

  4. 4.

    et al. Genome-wide analysis of genetic loci associated with Alzheimer disease. J. Am. Med. Assoc. 303, 1832–1840 (2010).

  5. 5.

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

  6. 6.

    et al. Effects of multiple genetic loci on age at onset in late-onset Alzheimer disease: a genome-wide association study. JAMA Neurol. 71, 1394–1404 (2014).

  7. 7.

    et al. Genome-wide association analysis of age-at-onset in Alzheimer's disease. Mol. Psychiatry 17, 1340–1346 (2012).

  8. 8.

    et al. Evidence that the APOE locus influences rate of disease progression in late onset familial Alzheimer's Disease but is not causative. Am. J. Med. Genet. 60, 1–6 (1995).

  9. 9.

    et al. Risk estimates of dementia by apolipoprotein E genotypes from a population-based incidence study: the Rotterdam Study. Arch. Neurol. 55, 964–968 (1998).

  10. 10.

    , & Alzheimer's disease risk genes and the age-at-onset phenotype. Neurobiol. Aging 34, 2696 e1–2696.e5 (2013).

  11. 11.

    et al. Evidence that PICALM affects age at onset of Alzheimer's dementia in Down syndrome. Neurobiol. Aging 34, 2441 e1–2441.e5 (2013).

  12. 12.

    et al. GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer's disease. Neuron 78, 256–268 (2013).

  13. 13.

    et al. Alzheimer's disease risk variants show association with cerebrospinal fluid amyloid beta. Neurogenetics 10, 13–17 (2009).

  14. 14.

    et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

  15. 15.

    et al. Variant of TREM2 associated with the risk of Alzheimer's disease. N. Engl. J. Med. 368, 107–116 (2013).

  16. 16.

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

  17. 17.

    et al. CD33 Alzheimer's disease locus: altered monocyte function and amyloid biology. Nat. Neurosci. 16, 848–850 (2013).

  18. 18.

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

  19. 19.

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

  20. 20.

    et al. CD33 modulates TREM2: convergence of Alzheimer loci. Nat. Neurosci. 18, 1556–1558 (2015).

  21. 21.

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

  22. 22.

    et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

  23. 23.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  24. 24.

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

  25. 25.

    Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

  26. 26.

    et al. Genome-wide haplotype analysis of cis expression quantitative trait loci in monocytes. PLoS Genet. 9, e1003240 (2013).

  27. 27.

    et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).

  28. 28.

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

  29. 29.

    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).

  30. 30.

    et al. New tools for studying microglia in the mouse and human CNS. Proc. Natl. Acad. Sci. USA 113, E1738–E1746 (2016).

  31. 31.

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

  32. 32.

    et al. Association of a functional polymorphism in the 3′-untranslated region of SPI1 with systemic lupus erythematosus. Arthritis Rheum. 63, 755–763 (2011).

  33. 33.

    & 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).

  34. 34.

    et al. Dynamic epigenetic enhancer signatures reveal key transcription factors associated with monocytic differentiation states. Blood 119, e161–e171 (2012).

  35. 35.

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

  36. 36.

    et al.; ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  37. 37.

    et al. A neuronal VLDLR variant lacking the third complement-type repeat exhibits high capacity binding of apoE containing lipoproteins. Brain Res. 1276, 11–21 (2009).

  38. 38.

    , , & A model for modulation of leptin activity by association with clusterin. FASEB J. 17, 1505–1507 (2003).

  39. 39.

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

  40. 40.

    et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 2624–2634 (2011).

  41. 41.

    , , & A comprehensive profile of ChIP-seq-based PU.1/Spi1 target genes in microglia. Gene Regul. Syst. Bio. 8, 127–139 (2014).

  42. 42.

    et al. The active enhancer network operated by liganded RXR supports angiogenic activity in macrophages. Genes Dev. 28, 1562–1577 (2014).

  43. 43.

    et al. Targeted disruption of the PU.1 gene results in multiple hematopoietic abnormalities. EMBO J. 15, 5647–5658 (1996).

  44. 44.

    et al. Wild-type microglia extend survival in PU.1 knockout mice with familial amyotrophic lateral sclerosis. Proc. Natl. Acad. Sci. USA 103, 16021–16026 (2006).

  45. 45.

    , , & PU.1 and haematopoietic cell fate: dosage matters. Int. J. Cell Biol. 2011, 808524 (2011).

  46. 46.

    et al. The transcription factor PU.1 is critical for viability and function of human brain microglia. Glia 61, 929–942 (2013).

  47. 47.

    et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).

  48. 48.

    et al. IL-34 is a tissue-restricted ligand of CSF1R required for the development of Langerhans cells and microglia. Nat. Immunol. 13, 753–760 (2012).

  49. 49.

    et al. Colony-stimulating factor 1 receptor inhibition prevents microglial plaque association and improves cognition in 3xTg-AD mice. J. Neuroinflammation 12, 139 (2015).

  50. 50.

    et al.; 3C Study Group. Vascular factors and risk of dementia: design of the Three-City Study and baseline characteristics of the study population. Neuroepidemiology 22, 316–325 (2003).

  51. 51.

    et al. A polymorphism in CALHM1 influences Ca2+ homeostasis, Abeta levels, and Alzheimer's disease risk. Cell 133, 1149–1161 (2008).

  52. 52.

    et al. Evaluation of dementia in the cardiovascular health cognition study. Neuroepidemiology 22, 1–12 (2003).

  53. 53.

    & The Framingham study. An epidemiological approach to coronary heart disease. Circulation 34, 553–555 (1966).

  54. 54.

    , , , & The Framingham offspring study. Design and preliminary data. Prev. Med. 4, 518–525 (1975).

  55. 55.

    et al. The third generation cohort of the National Heart, Lung, and Blood Institute's Framingham heart study: design, recruitment, and initial examination. Am. J. Epidemiol. 165, 1328–1335 (2007).

  56. 56.

    , , , & Computing estimates of incidence, including lifetime risk: Alzheimer's disease in the Framingham study. the Practical Incidence Estimators (PIE) macro. Stat. Med. 19, 1495–1522 (2000).

  57. 57.

    et al. Incidence of dementia and probable Alzheimer's disease in a general population: the Framingham Study. Neurology 43, 515–519 (1993).

  58. 58.

    et al. Neuropsychological test performance in Framingham: a descriptive study. Psychol. Rep. 60, 1023–1040 (1987).

  59. 59.

    et al. Measures of brain morphology and infarction in the Framingham heart study: establishing what is normal. Neurobiol. Aging 26, 491–510 (2005).

  60. 60.

    et al. New norms for a new generation: cognitive performance in the framingham offspring cohort. Exp. Aging Res. 30, 333–358 (2004).

  61. 61.

    & Lifetime risk of stroke and dementia: current concepts, and estimates from the Framingham Study. Lancet Neurol. 6, 1106–1114 (2007).

  62. 62.

    et al. The Rotterdam Study: 2014 objectives and design update. Eur. J. Epidemiol. 28, 889–926 (2013).

  63. 63.

    et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann. Neurol. 59, 512–519 (2006).

  64. 64.

    , , & Safety of lumbar puncture procedures in patients with Alzheimer's disease. Curr. Alzheimer Res. 6, 290–292 (2009).

  65. 65.

    et al. Beta amyloid in Alzheimer's disease: increased deposition in brain is reflected in reduced concentration in cerebrospinal fluid. Biol. Psychiatry 65, 927–934 (2009).

  66. 66.

    , , & Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat. Rev. Neurol. 6, 131–144 (2010).

  67. 67.

    , & METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

  68. 68.

    et al. Age and apolipoprotein E*4 allele effects on cerebrospinal fluid beta-amyloid 42 in adults with normal cognition. Arch. Neurol. 63, 936–939 (2006).

  69. 69.

    et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

  70. 70.

    , & A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

  71. 71.

    Regression Models and Life-Tables. J. R. Stat. Soc. B. 34, 187–220 (1972).

  72. 72.

    & Retrospective studies and failure time models. Biometrika 65, 153–158 (1978).

  73. 73.

    et al. Cox proportional hazards models have more statistical power than logistic regression models in cross-sectional genetic association studies. Eur. J. Hum. Genet. 16, 1111–1116 (2008).

  74. 74.

    et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

  75. 75.

    et al. Exploring long-range genome interactions using the WashU Epigenome Browser. Nat. Methods 10, 375–376 (2013).

  76. 76.

    et al. Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape. Nucleic Acids Res. 43, e27 (2015).

  77. 77.

    et al. ENCODE data at the ENCODE portal. Nucleic Acids Res. 44, D726–D732 (2016).

  78. 78.

    , , , & BET bromodomain inhibition suppresses the function of hematopoietic transcription factors in acute myeloid leukemia. Mol. Cell 58, 1028–1039 (2015).

  79. 79.

    & A PCR primer bank for quantitative gene expression analysis. Nucleic Acids Res. 31, e154 (2003).

Download references


We thank the patients, control subjects and their family members for participating in and supporting the research projects included in this study. We thank M. Diamond (UT Southwestern Medical Center) for the BV2 cell line and the Flow Cytometry CORE at the Icahn School of Medicine at Mount Sinai Hospital.

IGAP: This study incorporated imputed summary results from the GERAD1 genome-wide association study. GERAD was supported by the Wellcome Trust, the MRC, Alzheimer's Research UK (ARUK) and the Welsh government. ADGC and CHARGE were supported by the US National Institutes of Health, National Institute on Aging (NIH-NIA), including grants U01 AG032984 and R01 AG033193. CHARGE was also supported by Erasmus Medical Center and Erasmus University.

Cardiff University was supported by the Wellcome Trust, Medical Research Council (MRC), Alzheimer's Research UK (ARUK) and the Welsh Assembly Government. Cambridge University and Kings College London acknowledge support from the MRC. ARUK supported sample collections at the South West Dementia Bank and the Universities of Nottingham, Manchester and Belfast. The Belfast group acknowledges support from the Alzheimer′s Society, Ulster Garden Villages, N. Ireland R&D Office and the Royal College of Physicians/Dunhill Medical Trust. The MRC and Mercer's Institute for Research on Aging supported the Trinity College group. The South West Dementia Brain Bank acknowledges support from Bristol Research into Alzheimer's and Care of the Elderly. The Charles Wolfson Charitable Trust supported the OPTIMA group. Washington University was funded by NIH grants, Barnes Jewish Foundation and by the Charles and Joanne Knight Alzheimer's Research Initiative. Patient recruitment for the MRC Prion Unit/UCL Department of Neurodegenerative Disease collection was supported by the UCLH/UCL Biomedical Centre and NIHR Queen Square Dementia Biomedical Research Unit. LASER-AD was funded by Lundbeck SA. The Bonn group was supported by the German Federal Ministry of Education and Research (BMBF), Competence Network Dementia and Competence Network Degenerative Dementia and by the Alfried Krupp von Bohlen und Halbach-Stiftung. The GERAD Consortium also used samples ascertained by the NIMH AD Genetics Initiative.

The KORA F4 studies were financed by Helmholtz Zentrum München; German Research Center for Environmental Health; BMBF; German National Genome Research Network and the Munich Center of Health Sciences. The Heinz Nixdorf Recall cohort was funded by the Heinz Nixdorf Foundation (Dr. jur. G.Schmidt, Chairman) and BMBF. Coriell Cell Repositories is supported by NINDS and the Intramural Research Program of the National Institute on Aging. We acknowledge use of genotype data from the 1958 Birth Cohort collection, funded by the MRC and the Wellcome Trust, which was genotyped by the Wellcome Trust Case Control Consortium and the Type-1 Diabetes Genetics Consortium, sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development and Juvenile Diabetes Research Foundation International.

ADNI: Data collection and sharing for this project was funded by the Alzheimer′s Disease Neuroimaging Initiative (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company, Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

We thank the Cardiogenics (European Project reference LSHM-CT-2006-037593) project for providing summary statistics for the cis-eQTL-based analyses. We also thank the ENCODE Consortium and R. Myers' lab (HAIB) for providing ChIP-seq datasets.

This work was supported by grants from the National Institutes of Health (U01 AG049508, R01-AG035083 and RF-AG054011 (to A.M.G.) and R01-AG044546 and RF1AG053303 (to C.C.)), the JPB Foundation (to A.M.G.) and F Prime (to A.M.G.). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P50 AG05681, P01 AG03991 and P01 AG026276. Kuan-lin Huang received fellowship funding in part from the Ministry of Education in Taiwan and the Lucille P. Markey Special Emphasis Pathway in Human Pathobiology. Ke Hao is partially supported by the National Natural Science Foundation of China (Grant Nos. 21477087 and 91643201) and by the Ministry of Science and Technology of China (Grant No. 2016YFC0206507). This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

Author information

Author notes

    • Kuan-lin Huang
    •  & Edoardo Marcora

    These authors contributed equally to this work.


  1. Department of Medicine and McDonnell Genome Institute, Washington University in St. Louis, Saint Louis, Missouri, USA.

    • Kuan-lin Huang
  2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York,New York, USA.

    • Edoardo Marcora
    • , Antonio F Di Narzo
    • , Manav Kapoor
    • , Andrew McKenzie
    • , Towfique Raj
    • , Bin Zhang
    • , Ke Hao
    •  & Alison M Goate
  3. Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York,New York, USA.

    • Edoardo Marcora
    • , Anna A Pimenova
    • , Manav Kapoor
    • , Sarah Bertelsen
    • , Towfique Raj
    • , Alan E Renton
    •  & Alison M Goate
  4. Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA.

    • Sheng Chih Jin
  5. Department of Psychiatry, Washington University in St. Louis, Saint Louis, Missouri, USA.

    • Oscar Harari
    • , Yuetiva Deming
    • , John Budde
    • , Jorge L Del-Aguila
    • , Maria Victoria Fernandez
    • , Laura Ibañez
    •  & Carlos Cruchaga
  6. Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

    • Benjamin P Fairfax
  7. Department of Genetics, Washington University in St. Louis, Saint Louis, Missouri, USA.

    • Jake Czajkowski
    •  & Ingrid Borecki
  8. Department of Neurology, Boston University School of Medicine, Boston, Massachussets, USA.

    • Vincent Chouraki
    •  & Sudha Seshadri
  9. Inserm, U1167, RID-AGE –Risk factors and molecular determinants of aging-related diseases, Lille, France.

    • Benjamin Grenier-Boley
    • , Céline Bellenguez
    • , Jean Charles Lambert
    •  & Philippe Amouyel
  10. Univ. Lille - Excellence laboratory Labex DISTALZ, Lille, France.

    • Benjamin Grenier-Boley
    • , Céline Bellenguez
    • , Jean Charles Lambert
    •  & Philippe Amouyel
  11. Institut Pasteur de Lille, Lille, France.

    • Benjamin Grenier-Boley
    • , Céline Bellenguez
    • , Jean Charles Lambert
    •  & Philippe Amouyel
  12. Icelandic Heart Association, Faculty of Medicine, University of Iceland, Reykjavik, Iceland.

    • Albert Smith
  13. Department of Epidemiology, University of Washington, Seattle, Washington, USA.

    • Annette Fitzpatrick
  14. Department of Medicine, University of Washington, Seattle, Washington, USA.

    • Joshua C Bis
  15. Department of Biostatistics, Boston University School of Public Health, Boston, Massachussetts, USA.

    • Anita DeStefano
    •  & Lindsay A Farrer
  16. Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.

    • Hieab H H Adams
    • , M Arfan Ikram
    • , Sven van der Lee
    •  & Cornelia van Duijn
  17. Psychological Medicine and Clinical Neurosciences, Medical Research Council (MRC) Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.

    • Rebecca Sims
    • , Valentina Escott-Price
    •  & Julie Williams
  18. Taub Institute on Alzheimer's Disease and the Aging Brain, Gertrude H. Sergievsky Center, and Department of Neurology, Columbia University, New York,New York, USA.

    • Richard Mayeux
  19. Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA.

    • Jonathan L Haines
  20. Department of Ophthalmology, Boston University School of Medicine, Boston, Massachussets, USA.

    • Lindsay A Farrer
  21. Department of Epidemiology, Boston University School of Public Health, Boston, Massachussetts, USA.

    • Lindsay A Farrer
  22. The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida, USA.

    • Lindsay A Farrer
    •  & Margaret A Pericak-Vance
  23. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, Florida, USA.

    • Margaret A Pericak-Vance
  24. Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, Maryland, USA.

    • Lenore Launer
  25. Centre Hospitalier Universitaire de Lille, U1167, Lille, France.

    • Philippe Amouyel
  26. Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

    • Gerard D Schellenberg
  27. Department of Biology, Brigham Young University, Provo, Utah, USA.

    • John S K Kauwe


  1. The International Genomics of Alzheimer's Project

    A full list of members and affiliations appears in the Supplementary Note.

  2. The Alzheimer's Disease Neuroimaging Initiative

    A full list of members and affiliations appears in the Supplementary Note.


  1. Search for Kuan-lin Huang in:

  2. Search for Edoardo Marcora in:

  3. Search for Anna A Pimenova in:

  4. Search for Antonio F Di Narzo in:

  5. Search for Manav Kapoor in:

  6. Search for Sheng Chih Jin in:

  7. Search for Oscar Harari in:

  8. Search for Sarah Bertelsen in:

  9. Search for Benjamin P Fairfax in:

  10. Search for Jake Czajkowski in:

  11. Search for Vincent Chouraki in:

  12. Search for Benjamin Grenier-Boley in:

  13. Search for Céline Bellenguez in:

  14. Search for Yuetiva Deming in:

  15. Search for Andrew McKenzie in:

  16. Search for Towfique Raj in:

  17. Search for Alan E Renton in:

  18. Search for John Budde in:

  19. Search for Albert Smith in:

  20. Search for Annette Fitzpatrick in:

  21. Search for Joshua C Bis in:

  22. Search for Anita DeStefano in:

  23. Search for Hieab H H Adams in:

  24. Search for M Arfan Ikram in:

  25. Search for Sven van der Lee in:

  26. Search for Jorge L Del-Aguila in:

  27. Search for Maria Victoria Fernandez in:

  28. Search for Laura Ibañez in:

  29. Search for Rebecca Sims in:

  30. Search for Valentina Escott-Price in:

  31. Search for Richard Mayeux in:

  32. Search for Jonathan L Haines in:

  33. Search for Lindsay A Farrer in:

  34. Search for Margaret A Pericak-Vance in:

  35. Search for Jean Charles Lambert in:

  36. Search for Cornelia van Duijn in:

  37. Search for Lenore Launer in:

  38. Search for Sudha Seshadri in:

  39. Search for Julie Williams in:

  40. Search for Philippe Amouyel in:

  41. Search for Gerard D Schellenberg in:

  42. Search for Bin Zhang in:

  43. Search for Ingrid Borecki in:

  44. Search for John S K Kauwe in:

  45. Search for Carlos Cruchaga in:

  46. Search for Ke Hao in:

  47. Search for Alison M Goate in:


A.M.G., E.M. and K.-L.H. conceived and designed the experiments. K.-L.H., S.C.J., O.H., A.D., M.K., J.C., J.C.L., V.C., C.B., B.G.-B., Y.D., A.M., T.R., A.E.R., J.L.D.-A., M.V.F, L.I., B.Z., I.B., C.C. and E.M. performed data analysis. A.A.P. performed phagocytosis assays, western blotting and qPCR validation. S.B., B.P.F., J.B., R.S., V.E.-P., R.M., J.L.H., L.A.F., M.A.P.-V., S.S., J.W., P.A., G.D.S., J.S.K.K., K.H. and C.C. provided and processed the data. A.M.G. supervised data analysis and functional experiments. K.-L.H., A.A.P., E.M. and A.M.G. wrote and edited the manuscript. All authors read and approved the manuscript.

Competing interests

A.M.G. is on the scientific advisory board for Denali Therapeutics and has served as a consultant for AbbVie and Cognition Therapeutics.

Corresponding author

Correspondence to Alison M Goate.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–9.

  2. 2.

    Supplementary Methods Checklist

  3. 3.

    Supplementary Note

Excel files

  1. 1.

    Supplementary Table 1

    Quality control of the age-at-onset survival GWAS. The number of filtered SNPs and samples are shown for each of the 7 cohort included for the IGAP meta-analysis. Additionally, the Schoenfeld P values were recorded for each of the 22 top AAOS GWAS SNPs across the 7 IGAP cohorts, and the covariates in Cox models for the ADGC and GERAD cohorts.

  2. 2.

    Supplementary Table 2

    Association results from the previous IGAP logistic regression and the matching cohort logistic regression of the 22 top AD-associated SNPs in AAOS GWAS.

  3. 3.

    Supplementary Table 3

    The 22 top SNPs significantly associated with age-at-onset defined survival in AD and their tag SNPs (R2 ≥ 0.8).

  4. 4.

    Supplementary Table 4

    Significant cis-eQTL associations between the 22 survival-associated SNPs across 10 brain regions in the BRAINEAC dataset.Bonferroni-corrected threshold: P=0.05/292,000 probes = 1.7x10-7.

  5. 5.

    Supplementary Table 5

    Significant cis-eQTL associations between the 22 survival-associated SNPs across tissues in the GTEx dataset.

  6. 6.

    Supplementary Table 6

    LD score regression analysis results for IGAP AD and PGC SCZ GWAS SNP heritability partitioned using 220 human cell types (CT, and 10 cell type groups, CTG)-specific functional annotations as described by Finucane et al.

  7. 7.

    Supplementary Table 7

    Replication of monocyte/macrophage cis-eQTL associations in CD14+ human monocytes from 432 individuals of European ancestry.Bonferroni-corrected threshold: P=0.05/15421 probes = 3.24x10-6.

  8. 8.

    Supplementary Table 8

    Coloc analysis results for the SPI1/CELF1, MS4A and SELL loci, using AAOS GWAS SNPs and Cardiogenics monocyte (MC) or macrophage (MP) cis-eQTL datasets.Estimated posterior probabilities for the following mutually exclusive hypotheses are shown in bold when surpassing the threshold PP ≥ 0.8: H0, neither trait has a genetic association in the region; H1: only trait 1 (cis-eQTL) has a genetic association in the region; H2: only trait 2 (GWAS) has a genetic association in the region; H3: both traits are associated, but with different causal variants; H4: both traits are associated and share a single causal variant.

  9. 9.

    Supplementary Table 9

    Conditional analysis of monocyte/macrophage SPI1 eQTL associations of 6 SNPs of interest: rs1057233, rs10838698, rs10838699, rs7928163, rs1377416, and rs10838725.Bonferroni-corrected threshold: P=0.05/6 SNPs = 0.00833.

  10. 10.

    Supplementary Table 10

    SMR/HEIDI analysis results for 6 SNPs of interest in the SPI1/CELF1 locus: rs1057233, rs10838698, rs10838699, rs7928163, rs1377416 and rs10838725, using AAOS GWAS SNPs and Cardiogenics monocyte (MC) or macrophage (MP) cis-eQTL datasets.Bonferroni-corrected threshold: SMR P=0.05/(6 SNPs x 17 probes) = 0.00049 [green cells]. A nominal significance threshold P ≥ 0.05 [red cells] is used for the HEIDI test as motivated by Zhu et al. Only probes with nominally significant SMR P are shown.

  11. 11.

    Supplementary Table 11

    AD-associated genes expressed in human brain myeloid cells with nearby SPI1 (PU.1) binding sites in human blood myeloid cells.

  12. 12.

    Supplementary Table 12

    LD score regression analysis results for IGAP AD and PGC SCZ GWAS SNP heritability partitioned using SPI1 (PU.1) ChIP-Seq binding sites in human monocytes and macrophages, as well as SPI1 (PU.1) and POLR2AphosphoS5 ChIP-Seq binding sites in human HL60 cells.

  13. 13.

    Supplementary Table 13

    Genes regulated in BV2 microglial cells with differential expression of Spi1.

  14. 14.

    Supplementary Table 14

    Primers used for qPCR validation of gene expression in BV2 microglial cells.