Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Deconstructing and targeting the genomic architecture of human neurodegeneration

Abstract

The field of neurodegenerative disease research has seen tremendous advances over the last two decades as new technologies and analytic methods have enabled well-powered human genomic studies. Driven first by genetic studies and more recently by transcriptomic and epigenomic studies of proper size, we have uncovered a large repertoire of loci, genes, and molecular features that are implicated in discrete, syndromically defined neurodegenerative conditions, such as Alzheimer’s disease, amyotrophic lateral sclerosis, frontotemporal dementia, multiple sclerosis, and Parkinson’s disease. As we begin to understand the impact of these genomic features in each disease, we also appreciate that many aging individuals accumulate each of these pathologies without fulfilling criteria for syndromic diagnoses, that other pathologies are common in individuals with a given diagnosis, and that there may be shared protective factors against central nervous system injury. Thus, we now need to bring these disparate observations together into a person-centered approach that considers all neurodegenerative and protective processes simultaneously to modulate the trajectory of cognitive and functional decline that comes with brain aging.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overlap among the results of GWAS for AD, FTD, MS, and PD.
Fig. 2: Allelic and phenotypic heterogeneity in the UNC5C locus.
Fig. 3: An initial network map of the aging brain provides a framework for integrating additional factors influencing the function of older individuals.
Fig. 4: Functional and pathologic heterogeneity of brains in the older population.

Similar content being viewed by others

References

  1. Boyle, P. A. et al. Person-specific contribution of neuropathologies to cognitive loss in old age. Ann. Neurol. 83, 74–83 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Hafler, D. A. et al. Risk alleles for multiple sclerosis identified by a genomewide study. N. Engl. J. Med. 357, 851–862 (2007).

    CAS  PubMed  Google Scholar 

  3. Consortium, I.M.S.G. The multiple sclerosis genomic map: role of peripheral leukocytes and resident microglia in susceptibility. Preprint at bioRxiv https://doi.org/10.1101/143933 (2017).

  4. Chang, D. et al. A meta-analysis of genome-wide association studies identifies 17 new Parkinson’s disease risk loci. Nat. Genet. 49, 1511–1516 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Nalls, M. A. et al. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease. Nat. Genet. 46, 989–993 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Roses, A. D. et al. A TOMM40 variable-length polymorphism predicts the age of late-onset Alzheimer’s disease. Pharmacogenomics J. 10, 375–384 (2010).

    CAS  PubMed  Google Scholar 

  7. Simón-Sánchez, J. et al. Genome-wide association study reveals genetic risk underlying Parkinson’s disease. Nat. Genet. 41, 1308–1312 (2009).

    PubMed  PubMed Central  Google Scholar 

  8. Jun, G. et al. A novel Alzheimer disease locus located near the gene encoding tau protein. Mol. Psychiatry 21, 108–117 (2016).

    CAS  PubMed  Google Scholar 

  9. Desikan, R. S. et al. Genetic overlap between Alzheimer’s disease and Parkinson’s disease at the MAPT locus. Mol. Psychiatry 20, 1588–1595 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Vardarajan, B. N. et al. Rare coding mutations identified by sequencing of Alzheimer disease genome-wide association studies loci. Ann. Neurol. 78, 487–498 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Beecham, G. W. et al. Genome-wide association meta-analysis of neuropathologic features of Alzheimer’s disease and related dementias. PLoS Genet. 10, e1004606 (2014).

    PubMed  PubMed Central  Google Scholar 

  12. Yu, L., Boyle, P. A., Leurgans, S., Schneider, J. A. & Bennett, D. A. Disentangling the effects of age and APOE on neuropathology and late life cognitive decline. Neurobiol. Aging 35, 819–826 (2014).

    CAS  PubMed  Google Scholar 

  13. Buchman, A. S. et al. Apolipoprotein E e4 allele is associated with more rapid motor decline in older persons. Alzheimer Dis. Assoc. Disord. 23, 63–69 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Greenberg, S. M., Rebeck, G. W., Vonsattel, J. P., Gomez-Isla, T. & Hyman, B. T. Apolipoprotein E epsilon 4 and cerebral hemorrhage associated with amyloid angiopathy. Ann. Neurol. 38, 254–259 (1995).

    CAS  PubMed  Google Scholar 

  15. Yu, L. et al. APOE and cerebral amyloid angiopathy in community-dwelling older persons. Neurobiol. Aging 36, 2946–2953 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Yang, H. S. et al. Evaluation of TDP-43 proteinopathy and hippocampal sclerosis in relation to APOE ε4 haplotype status: a community-based cohort study. Lancet Neurol. 17, 773–781 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Farfel, J. M. et al. Relation of genomic variants for Alzheimer disease dementia to common neuropathologies. Neurology 87, 489–496 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Lill, C. M. et al. The role of TREM2 R47H as a risk factor for Alzheimer’s disease, frontotemporal lobar degeneration, amyotrophic lateral sclerosis, and Parkinson’s disease. Alzheimers Dement. 11, 1407–1416 (2015).

    PubMed  PubMed Central  Google Scholar 

  19. Hamza, T. H. et al. Common genetic variation in the HLA region is associated with late-onset sporadic Parkinson’s disease. Nat. Genet. 42, 781–785 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Ferrari, R. et al. Frontotemporal dementia and its subtypes: a genome-wide association study. Lancet Neurol. 13, 686–699 (2014).

    PubMed  PubMed Central  Google Scholar 

  21. 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 

  22. 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 

  23. Chan, G. et al. Trans-pQTL study identifies immune crosstalk between Parkinson and Alzheimer loci. Neurol. Genet. 2, e90 (2016).

    PubMed  PubMed Central  Google Scholar 

  24. Felsky, D. et al. Polygenic analysis of inflammatory disease variants and effects on microglia in the aging brain. Mol. Neurodegener. 13, 38 (2018).

    PubMed  PubMed Central  Google Scholar 

  25. Cotsapas, C. et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet. 7, e1002254 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Lee, S. H. et al. Cross-Disorder Group of the Psychiatric Genomics. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).

    CAS  PubMed  Google Scholar 

  27. Wetzel-Smith, M. K. et al. A rare mutation in UNC5C predisposes to late-onset Alzheimer’s disease and increases neuronal cell death. Nat. Med. 20, 1452–1457 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. White, C. C. et al. Identification of genes associated with dissociation of cognitive performance and neuropathological burden: multistep analysis of genetic, epigenetic, and transcriptional data. PLoS Med. 14, e1002287 (2017).

    PubMed  PubMed Central  Google Scholar 

  29. Yang, H. S. et al. UNC5C variants are associated with cerebral amyloid angiopathy. Neurol. Genet. 3, e176 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).

    CAS  PubMed  Google Scholar 

  33. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Zou, F. et al. Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants. PLoS Genet. 8, e1002707 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Ottoboni, L. et al. Clinical relevance and functional consequences of the TNFRSF1A multiple sclerosis locus. Neurology 81, 1891–1899 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Raj, T. et al. Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s disease. Preprint at bioRxv https://doi.org/10.1101/174565 (2017).

  37. Battle, A., Brown, C. D., Engelhardt, B. E. & Montgomery, S. B. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    PubMed  Google Scholar 

  38. Fernández, J. M. et al. The BLUEPRINT data analysis portal. Cell Syst. 3, 491–495.e5 (2016).

    PubMed  PubMed Central  Google Scholar 

  39. Lee, M. N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  41. Ryan, K. J. et al. A human microglia-like cellular model for assessing the effects of neurodegenerative disease gene variants. Sci. Transl. Med. 9, eaai7635 (2017).

    PubMed  PubMed Central  Google Scholar 

  42. McVicker, G. et al. Identification of genetic variants that affect histone modifications in human cells. Science 342, 747–749 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Gibbs, J. R. et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 6, e1000952 (2010).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Gao, F. B., Almeida, S. & Lopez-Gonzalez, R. Dysregulated molecular pathways in amyotrophic lateral sclerosis-frontotemporal dementia spectrum disorder. EMBO J. 36, 2931–2950 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. De Jager, P. L. et al. Alzheimer’s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat. Neurosci. 17, 1156–1163 (2014).

    PubMed  PubMed Central  Google Scholar 

  47. Lunnon, K. et al. Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer’s disease. Nat. Neurosci. 17, 1164–1170 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Klein, H. U., Bennett, D. A. & De Jager, P. L. The epigenome in Alzheimer’s disease: current state and approaches for a new path to gene discovery and understanding disease mechanism. Acta Neuropathol. 132, 503–514 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Yu, L. et al. Methylation profiles in peripheral blood CD4+ lymphocytes versus brain: The relation to Alzheimer’s disease pathology. Alzheimers Dement. 12, 942–951 (2016).

    PubMed  PubMed Central  Google Scholar 

  50. Yang, J. et al. Association of DNA methylation in the brain with age in older persons is confounded by common neuropathologies. Int. J. Biochem. Cell Biol. 67, 58–64 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Gao, X., Jia, M., Zhang, Y., Breitling, L. P. & Brenner, H. DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies. Clin. Epigenetics 7, 113 (2015).

    PubMed  PubMed Central  Google Scholar 

  52. Horvath, S. et al. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 13, R97 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Klein, H. U. & De Jager, P. L. Uncovering the role of the methylome in dementia and neurodegeneration. Trends Mol. Med. 22, 687–700 (2016).

    CAS  PubMed  Google Scholar 

  54. Taskesen, E. et al. Susceptible genes and disease mechanisms identified in frontotemporal dementia and frontotemporal dementia with amyotrophic lateral sclerosis by DNA-methylation and GWAS. Sci. Rep. 7, 8899 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Klein, H.a. Epigenome-wide study uncovers tau pathology-driven changes of chromatin organization in the aging human brain. Preprint at bioRxiv https://doi.org/10.1101/273789 (2018).

  56. Frost, B., Hemberg, M., Lewis, J. & Feany, M. B. Tau promotes neurodegeneration through global chromatin relaxation. Nat. Neurosci. 17, 357–366 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Bai, B. et al. U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer’s disease. Proc. Natl. Acad. Sci. USA 110, 16562–16567 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 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 

  59. 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  Google Scholar 

  60. Allen, M. et al. Conserved brain myelination networks are altered in Alzheimer’s and other neurodegenerative diseases. Alzheimers Dement. 14, 352–366 (2018).

    PubMed  Google Scholar 

  61. Seyfried, N. T. et al. A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer’s disease. Cell Syst. 4, 60–72.e4 (2017).

    CAS  PubMed  Google Scholar 

  62. Mostafavi, S. et al. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease. Nat. Neurosci. 21, 811–819 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Bennett, D. A., Schneider, J. A., Arvanitakis, Z. & Wilson, R. S. Overview and findings from the religious orders study. Curr. Alzheimer Res. 9, 628–645 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Bennett, D. A. et al. Overview and findings from the Rush Memory and Aging Project. Curr. Alzheimer Res. 9, 646–663 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Patrick, E. A cortical immune network map identifies a subset of human microglia involved in Tau pathology. Preprint at bioRxiv https://doi.org/10.1101/234351 (2017).

  66. De Jager, P. L. et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci. Data 5, 180142 (2018).

    PubMed  PubMed Central  Google Scholar 

  67. Schneider, J. A., Arvanitakis, Z., Bang, W. & Bennett, D. A. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology 69, 2197–2204 (2007).

    PubMed  Google Scholar 

  68. James, B. D. et al. TDP-43 stage, mixed pathologies, and clinical Alzheimer’s-type dementia. Brain 139, 2983–2993 (2016).

    PubMed  PubMed Central  Google Scholar 

  69. Schneider, J. A. et al. Cognitive impairment, decline and fluctuations in older community-dwelling subjects with Lewy bodies. Brain 135, 3005–3014 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Arvanitakis, Z., Capuano, A. W., Leurgans, S. E., Bennett, D. A. & Schneider, J. A. Relation of cerebral vessel disease to Alzheimer’s disease dementia and cognitive function in elderly people: a cross-sectional study. Lancet Neurol. 15, 934–943 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Josephs, K. A. et al. TDP-43 is a key player in the clinical features associated with Alzheimer’s disease. Acta Neuropathol. 127, 811–824 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Kapasi, A., DeCarli, C. & Schneider, J. A. Impact of multiple pathologies on the threshold for clinically overt dementia. Acta Neuropathol. 134, 171–186 (2017).

    PubMed  PubMed Central  Google Scholar 

  73. Weiner, H. L. A shift from adaptive to innate immunity: a potential mechanism of disease progression in multiple sclerosis. J. Neurol. 255(Suppl 1), 3–11 (2008).

    CAS  PubMed  Google Scholar 

  74. George, M. F. et al. Multiple sclerosis risk loci and disease severity in 7,125 individuals from 10 studies. Neurol. Genet. 2, e87 (2016).

    PubMed  PubMed Central  Google Scholar 

  75. Boyle, P. A. et al. Much of late life cognitive decline is not due to common neurodegenerative pathologies. Ann. Neurol. 74, 478–489 (2013).

    PubMed  Google Scholar 

  76. Boyle, P. A., Yu, L., Wilson, R. S., Schneider, J. A. & Bennett, D. A. Relation of neuropathology with cognitive decline among older persons without dementia. Front. Aging Neurosci. 5, 50 (2013).

    PubMed  PubMed Central  Google Scholar 

  77. Yu, L. et al. Residual decline in cognition after adjustment for common neuropathologic conditions. Neuropsychology 29, 335–343 (2015).

    PubMed  Google Scholar 

  78. Negash, S. et al. Resilient brain aging: characterization of discordance between Alzheimer’s disease pathology and cognition. Curr. Alzheimer Res. 10, 844–851 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Katzman, R. et al. Clinical, pathological, and neurochemical changes in dementia: a subgroup with preserved mental status and numerous neocortical plaques. Ann. Neurol. 23, 138–144 (1988).

    CAS  PubMed  Google Scholar 

  80. Wilson, R. S. et al. Neural reserve, neuronal density in the locus coeruleus, and cognitive decline. Neurology 80, 1202–1208 (2013).

    PubMed  PubMed Central  Google Scholar 

  81. Wilson, R. S. et al. Participation in cognitively stimulating activities and risk of incident Alzheimer disease. J. Am. Med. Assoc. 287, 742–748 (2002).

    Google Scholar 

  82. Stern, Y. et al. Influence of education and occupation on the incidence of Alzheimer’s disease. J. Am. Med. Assoc. 271, 1004–1010 (1994).

    CAS  Google Scholar 

  83. Snowdon, D. A. et al. Brain infarction and the clinical expression of Alzheimer disease. The Nun Study. J. Am. Med. Assoc. 277, 813–817 (1997).

    CAS  Google Scholar 

  84. Perez-Nievas, B. G. et al. Dissecting phenotypic traits linked to human resilience to Alzheimer’s pathology. Brain 136, 2510–2526 (2013).

    PubMed  PubMed Central  Google Scholar 

  85. Schofield, P. W., Logroscino, G., Andrews, H. F., Albert, S. & Stern, Y. An association between head circumference and Alzheimer’s disease in a population-based study of aging and dementia. Neurology 49, 30–37 (1997).

    CAS  PubMed  Google Scholar 

  86. Honer, W. G. et al. Cognitive reserve, presynaptic proteins and dementia in the elderly. Transl. Psychiatry 2, e114 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Sumowski, J. F. et al. Brain reserve and cognitive reserve in multiple sclerosis: what you’ve got and how you use it. Neurology 80, 2186–2193 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Wilson, R. S., Segawa, E., Boyle, P. A. & Bennett, D. A. Influence of late-life cognitive activity on cognitive health. Neurology 78, 1123–1129 (2012).

    PubMed  PubMed Central  Google Scholar 

  89. Walsh, D. M. et al. Naturally secreted oligomers of amyloid beta protein potently inhibit hippocampal long-term potentiation in vivo. Nature 416, 535–539 (2002).

    CAS  PubMed  Google Scholar 

  90. Yu, L. et al. Targeted brain proteomics uncover multiple pathways to Alzheimer’s dementia. Ann. Neurol. (2018).

  91. Bennett, D. A. et al. Religious Orders Study and Rush Memory and Aging Project. J. Alzheimers Dis. 64(s1), S161–S189 (2018).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank C. White and D. Felsky for their help in preparing Figs. 1 and 4.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Philip L. De Jager.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De Jager, P.L., Yang, HS. & Bennett, D.A. Deconstructing and targeting the genomic architecture of human neurodegeneration. Nat Neurosci 21, 1310–1317 (2018). https://doi.org/10.1038/s41593-018-0240-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-018-0240-z

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing