Genes play a strong role in Alzheimer’s disease (AD), with late-onset AD showing heritability of 58–79% and early-onset AD showing over 90%. Genetic association provides a robust platform to build our understanding of the etiology of this complex disease. Over 50 loci are now implicated for AD, suggesting that AD is a disease of multiple components, as supported by pathway analyses (immunity, endocytosis, cholesterol transport, ubiquitination, amyloid-β and tau processing). Over 50% of late-onset AD heritability has been captured, allowing researchers to calculate the accumulation of AD genetic risk through polygenic risk scores. A polygenic risk score predicts disease with up to 90% accuracy and is an exciting tool in our research armory that could allow selection of those with high polygenic risk scores for clinical trials and precision medicine. It could also allow cellular modelling of the combined risk. Here we propose the multiplex model as a new perspective from which to understand AD. The multiplex model reflects the combination of some, or all, of these model components (genetic and environmental), in a tissue-specific manner, to trigger or sustain a disease cascade, which ultimately results in the cell and synaptic loss observed in AD.
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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).
Rogaev, E. I. et al. Familial Alzheimer’s disease in kindreds with missense mutations in a gene on chromosome 1 related to the Alzheimer’s disease type 3 gene. Nature 376, 775–778 (1995).
Hardy, J. & Selkoe, D. J. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 297, 353–356 (2002).
Ricciarelli, R. & Fedele, E. The amyloid cascade hypothesis in Alzheimer’s disease: it’s time to change our mind. Curr. Neuropharmacol. 15, 926–935 (2017).
Doody, R. S., Farlow, M. & Aisen, P. S., Alzheimer’s Disease Cooperative Study Data Analysis and Publication Committee. Phase 3 trials of solanezumab and bapineuzumab for Alzheimer’s disease. N. Engl. J. Med. 370, 1460 (2014).
Honig, L. S. et al. Trial of solanezumab for mild dementia due to Alzheimer’s disease. N. Engl. J. Med. 378, 321–330 (2018).
Galimberti, D. & Scarpini, E. Disease-modifying treatments for Alzheimer’s disease. Ther. Adv. Neurol. Disord. 4, 203–216 (2011).
Yiannopoulou, K. G. & Papageorgiou, S. G. Current and future treatments for Alzheimer’s disease. Ther. Adv. Neurol. Disord. 6, 19–33 (2013).
Jagust, W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat. Rev. Neurosci. 19, 687–700 (2018).
Rajan, K. B., Wilson, R. S., Weuve, J., Barnes, L. L. & Evans, D. A. Cognitive impairment 18 years before clinical diagnosis of Alzheimer disease dementia. Neurology 85, 898–904 (2015).
Rosenblum, W. I. Why Alzheimer trials fail: removing soluble oligomeric beta amyloid is essential, inconsistent, and difficult. Neurobiol. Aging 35, 969–974 (2014).
Gatz, M. et al. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry 63, 168–174 (2006).
Wingo, T. S., Lah, J. J., Levey, A. I. & Cutler, D. J. Autosomal recessive causes likely in early-onset Alzheimer disease. Arch. Neurol. 69, 59–64 (2012).
Saunders, A. M. et al. Apolipoprotein E epsilon 4 allele distributions in late-onset Alzheimer’s disease and in other amyloid-forming diseases. Lancet 342, 710–711 (1993).
Strittmatter, W. J. et al. Apolipoprotein E: high-avidity binding to beta-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc. Natl Acad. Sci. USA 90, 1977–1981 (1993).
Corder, E. H. et al. Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat. Genet. 7, 180–184 (1994).
Liao, F., Yoon, H. & Kim, J. Apolipoprotein E metabolism and functions in brain and its role in Alzheimer’s disease. Curr. Opin. Lipidol. 28, 60–67 (2017).
Deane, R. et al. ApoE isoform-specific disruption of amyloid beta peptide clearance from mouse brain. J. Clin. Invest. 118, 4002–4013 (2008).
Verghese, P. B., Castellano, J. M. & Holtzman, D. M. Apolipoprotein E in Alzheimer’s disease and other neurological disorders. Lancet Neurol. 10, 241–252 (2011).
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).
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).
Seshadri, S. et al. Genome-wide analysis of genetic loci associated with Alzheimer disease. J. Am. Med. Assoc. 303, 1832–1840 (2010).
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).
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).
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).
Desikan, R. S. et al. Genetic assessment of age-associated Alzheimer disease risk: development and validation of a polygenic hazard score. PLoS Med. 14, e1002258 (2017).
Johnson, E. C. B. et al. Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease. Mol. Neurodegener. 13, 52 (2018).
Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
Ruiz, A. et al. Follow-up of loci from the International Genomics of Alzheimer’s Disease Project identifies TRIP4 as a novel susceptibility gene. Transl. Psychiatry 4, e358 (2014).
Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).
Jun, G. R. et al. Transethnic genome-wide scan identifies novel Alzheimer’s disease loci. Alzheimers Dement. 13, 727–738 (2017).
Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).
de Rojas, I., et al. Common variants in Alzheimer’s disease: novel association of six genetic variants with AD and risk stratification by polygenic risk scores. Preprint at medRxiv https://doi.org/10.1101/19012021 (2019).
Escott-Price, V. et al. Gene-wide analysis detects two new susceptibility genes for Alzheimer’s disease. PLoS One 9, e94661 (2014).
Baker, E. et al. Gene-based analysis in HRC imputed genome wide association data identifies three novel genes for Alzheimer’s disease. PLoS One 14, e0218111 (2019).
Marioni, R. E. et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 8, 99 (2018).
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).
So, H. C., Gui, A. H., Cherny, S. S. & Sham, P. C. Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases. Genet. Epidemiol. 35, 310–317 (2011).
Ridge, P. G., Mukherjee, S., Crane, P. K. & Kauwe, J. S. Alzheimer’s Disease Genetics Consortium. Alzheimer’s disease: analyzing the missing heritability. PLoS One 8, e79771 (2013).
Meng, W. et al. A genome-wide association study finds genetic associations with broadly-defined headache in UK Biobank (N=223,773). EBioMedicine 28, 180–186 (2018).
Kunkle, B. et al. Meta-analysis of genetic association with diagnosed Alzheimer’s disease identifies novel risk loci and implicates Abeta, Tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).
Guerreiro, R. et al. TREM2 variants in Alzheimer’s disease. N. Engl. J. Med. 368, 117–127 (2013).
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).
Jonsson, T. et al. Variant of TREM2 associated with the risk of Alzheimer’s disease. N. Engl. J. Med. 368, 107–116 (2013).
Jonsson, T. et al. A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline. Nature 488, 96–99 (2012).
Kunkle, B. W. et al. Early-onset Alzheimer disease and candidate risk genes involved in endolysosomal transport. JAMA Neurol. 74, 1113–1122 (2017).
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).
Louwersheimer, E. et al. Influence of genetic variants in SORL1 gene on the manifestation of Alzheimer’s disease. Neurobiol. Aging 36, 1605.e13–1605.e20 (2015).
Steinberg, S. et al. Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease. Nat. Genet. 47, 445–447 (2015).
Cruchaga, C. et al. Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease. Nature 505, 550–554 (2014).
Logue, M. W. et al. Two rare AKAP9 variants are associated with Alzheimer’s disease in African Americans. Alzheimers Dement. 10, 609–618.e11 (2014).
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).
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).
Magno, L. et al. Alzheimer’s disease phospholipase C-gamma-2 (PLCG2) protective variant is a functional hypermorph. Alzheimers Res. Ther. 11, 16 (2019).
Gusareva, E. S. et al. Male-specific epistasis between WWC1 and TLN2 genes is associated with Alzheimer’s disease. Neurobiol. Aging 72, 188.e3–188.e12 (2018).
Ridge, P. G. et al. Linkage, whole genome sequence, and biological data implicate variants in RAB10 in Alzheimer’s disease resilience. Genome Med. 9, 100 (2017).
Ropacki, S. A. & Jeste, D. V. Epidemiology of and risk factors for psychosis of Alzheimer’s disease: a review of 55 studies published from 1990 to 2003. Am. J. Psychiatry 162, 2022–2030 (2005).
Shin, I. S., Carter, M., Masterman, D., Fairbanks, L. & Cummings, J. L. Neuropsychiatric symptoms and quality of life in Alzheimer disease. Am. J. Geriatr. Psychiatry 13, 469–474 (2005).
Wilkosz, P. A. et al. Trajectories of cognitive decline in Alzheimer’s disease. Int. Psychogeriatr. 22, 281–290 (2010).
Lopez, O. L. et al. Long-term effects of the concomitant use of memantine with cholinesterase inhibition in Alzheimer disease. J. Neurol. Neurosurg. Psychiatry 80, 600–607 (2009).
Hollingworth, P. et al. Genome-wide association study of Alzheimer’s disease with psychotic symptoms. Mol. Psychiatry 17, 1316–1327 (2012).
Del-Aguila, J. L. et al. Assessment of the genetic architecture of Alzheimer’s disease risk in rate of memory decline. J. Alzheimers Dis. 62, 745–756 (2018).
Criswell, L. A. et al. Analysis of families in the multiple autoimmune disease genetics consortium (MADGC) collection: the PTPN22 620W allele associates with multiple autoimmune phenotypes. Am. J. Hum. Genet. 76, 561–571 (2005).
Eaton, W. W., Rose, N. R., Kalaydjian, A., Pedersen, M. G. & Mortensen, P. B. Epidemiology of autoimmune diseases in Denmark. J. Autoimmun. 29, 1–9 (2007).
Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M. & Smoller, J. W. Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013).
Sivakumaran, S. et al. Abundant pleiotropy in human complex diseases and traits. Am. J. Hum. Genet. 89, 607–618 (2011).
Moskvina, V. et al. Analysis of genome-wide association studies of Alzheimer disease and of Parkinson disease to determine if these 2 diseases share a common genetic risk. JAMA Neurol. 70, 1268–1276 (2013).
Guerreiro, R. et al. Genome-wide analysis of genetic correlation in dementia with Lewy bodies, Parkinson’s and Alzheimer’s diseases. Neurobiol. Aging 38, 214.e7–214.e10 (2016).
Geiger, J. T. et al. Next-generation sequencing reveals substantial genetic contribution to dementia with Lewy bodies. Neurobiol. Dis. 94, 55–62 (2016).
Liu, G. et al. Cardiovascular disease contributes to Alzheimer’s disease: evidence from large-scale genome-wide association studies. Neurobiol. Aging 35, 786–792 (2014).
Holmans, P. et al. Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder. Am. J. Hum. Genet. 85, 13–24 (2009).
Lee, P. H., O’Dushlaine, C., Thomas, B. & Purcell, S. M. INRICH: interval-based enrichment analysis for genome-wide association studies. Bioinformatics 28, 1797–1799 (2012).
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).
International Genomics of Alzheimer’s Disease Consortium (IGAP). Convergent genetic and expression data implicate immunity in Alzheimer’s disease. Alzheimers Dement. 11, 658–671 (2015).
Yu, C. H., Pal, L. R. & Moult, J. Consensus genome-wide expression quantitative trait loci and their relationship with human complex trait disease. OMICS 20, 400–414 (2016).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).
Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 50, 1584–1592 (2018).
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).
Akbarian, S. et al. The PsychENCODE project. Nat. Neurosci. 18, 1707–1712 (2015).
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).
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).
Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).
Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271.e6 (2019).
Li, Q. et al. Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single-cell RNA sequencing. Neuron 101, 207–223.e10 (2019).
Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).
Farlik, M. et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep. 10, 1386–1397 (2015).
Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Olah, M. et al. A single cell-based atlas of human microglial states reveals associations with neurological disorders and histopathological features of the aging brain. Preprint at bioRxiv https://doi.org/10.1101/343780 (2018).
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).
Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013).
Salih, D.A. et al. Genetic variability in response to amyloid beta deposition influences Alzheimer’s risk. Brain Commun. 1, fcz022 (2019).
Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518, 365–369 (2015).
Lu, Q. et al. Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease. PLoS Genet. 13, e1006933 (2017).
Novikova, G. et al. Integration of Alzheimer’s disease genetics and myeloid cell genomics identifies novel causal variants, regulatory elements, genes and pathways. Preprint at bioRxiv https://doi.org/10.1101/694281 (2019).
Amlie-Wolf, A. et al. Inferring the molecular mechanisms of noncoding Alzheimer’s disease-associated genetic variants. J. Alzheimers Dis. 72, 301–318 (2019).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Huang, K. L. 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).
Tansey, K. E., Cameron, D. & Hill, M. J. Genetic risk for Alzheimer’s disease is concentrated in specific macrophage and microglial transcriptional networks. Genome Med. 10, 14 (2018).
Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017).
Matarin, M. et al. A genome-wide gene-expression analysis and database in transgenic mice during development of amyloid or tau pathology. Cell Rep. 10, 633–644 (2015).
Liu, X., Li, Y. I. & Pritchard, J. K. Trans effects on gene expression can drive omnigenic inheritance. Cell 177, 1022–1034.e6 (2019).
Alasoo, K. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50, 424–431 (2018).
Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).
McConnell, M. J. et al. Intersection of diverse neuronal genomes and neuropsychiatric disease: the brain somatic mosaicism network. Science 356, eaal1641 (2017).
Arendt, T., Brückner, M. K., Mosch, B. & Lösche, A. Selective cell death of hyperploid neurons in Alzheimer’s disease. Am. J. Pathol. 177, 15–20 (2010).
Bushman, D. M. et al. Genomic mosaicism with increased amyloid precursor protein (APP) gene copy number in single neurons from sporadic Alzheimer’s disease brains. eLife 4, e05116 (2015).
Lee, M. H. et al. Somatic APP gene recombination in Alzheimer’s disease and normal neurons. Nature 563, 639–645 (2018).
Lee, S. H. et al. Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer’s disease, multiple sclerosis and endometriosis. Hum. Mol. Genet. 22, 832–841 (2013).
Escott-Price, V. et al. Common polygenic variation enhances risk prediction for Alzheimer’s disease. Brain 138, 3673–3684 (2015).
Escott-Price, V. & Jones, L. Genomic profiling and diagnostic biomarkers in Alzheimer’s disease. Lancet Neurol. 16, 582–583 (2017).
Escott-Price, V., Shoai, M., Pither, R., Williams, J. & Hardy, J. Polygenic score prediction captures nearly all common genetic risk for Alzheimer’s disease. Neurobiol. Aging 49, 214.e7–214.e11 (2017).
Ridge, P. G. et al. Assessment of the genetic variance of late-onset Alzheimer’s disease. Neurobiol. Aging 41, 200.e13–200.e20 (2016).
Lewis, C. M. & Vassos, E. Prospects for using risk scores in polygenic medicine. Genome Med. 9, 96 (2017).
Escott-Price, V., Myers, A. J., Huentelman, M. & Hardy, J. Polygenic risk score analysis of pathologically confirmed Alzheimer disease. Ann. Neurol. 82, 311–314 (2017).
Ahmad, S. et al. Disentangling the biological pathways involved in early features of Alzheimer’s disease in the Rotterdam Study. Alzheimers Dement. 14, 848–857 (2018).
Biffi, A. et al. Genetic variation and neuroimaging measures in Alzheimer disease. Arch. Neurol. 67, 677–685 (2010).
Braskie, M. N., Ringman, J. M. & Thompson, P. M. Neuroimaging measures as endophenotypes in Alzheimer’s disease. Int. J. Alzheimers Dis. 2011, 490140 (2011).
Saykin, A. J. et al. Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: genetics core aims, progress, and plans. Alzheimers Dement. 6, 265–273 (2010).
Mormino, E. C. et al. Polygenic risk of Alzheimer disease is associated with early- and late-life processes. Neurology 87, 481–488 (2016).
Bralten, J. et al. CR1 genotype is associated with entorhinal cortex volume in young healthy adults. Neurobiol. Aging 32, 2106.e7–2106.e11 (2011).
Lancaster, T. M. et al. Alzheimer’s disease risk variant in CLU is associated with neural inefficiency in healthy individuals. Alzheimers Dement. 11, 1144–1152 (2015).
Foley, S. F. et al. Multimodal brain imaging reveals structural differences in Alzheimer’s disease polygenic risk carriers: a study in healthy young adults. Biol. Psychiatry 81, 154–161 (2017).
Axelrud, L. K. et al. Polygenic risk score for Alzheimer’s disease: implications for memory performance and hippocampal volumes in early life. Am. J. Psychiatry 175, 555–563 (2018).
Chandler, H. L. et al. Polygenic impact of common genetic risk loci for Alzheimer’s disease on cerebral blood flow in young individuals. Sci. Rep. 9, 467 (2019).
Lupton, M. K. et al. The effect of increased genetic risk for Alzheimer’s disease on hippocampal and amygdala volume. Neurobiol. Aging 40, 68–77 (2016).
Chauhan, G. et al. Association of Alzheimer’s disease GWAS loci with MRI markers of brain aging. Neurobiol. Aging 36, 1765.e7–1765.e16 (2015).
Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 8, 13624 (2017).
Satizabal, C.L. et al. Genetic architecture of subcortical brain structures in 38,851 individuals. Nat. Genet. 51, 1624–1636 (2019).
Thompson, P. M. et al. ENIGMA and the individual: predicting factors that affect the brain in 35 countries worldwide. Neuroimage 145, 389–408 (2017). Pt B.
Lancaster, T. M., Hill, M. J., Sims, R. & Williams, J. Microglia - mediated immunity partly contributes to the genetic association between Alzheimer’s disease and hippocampal volume. Brain Behav. Immun. 79, 267–273 (2019).
Esquerda-Canals, G., Montoliu-Gaya, L., Güell-Bosch, J. & Villegas, S. Mouse models of Alzheimer’s disease. J. Alzheimers Dis. 57, 1171–1183 (2017).
Bouleau, S. & Tricoire, H. Drosophila models of Alzheimer’s disease: advances, limits, and perspectives. J. Alzheimers Dis. 45, 1015–1038 (2015).
Yagi, T. et al. [Modeling familial Alzheimer’s disease with induced pluripotent stem cells]. Rinsho Shinkeigaku 52, 1134–1136 (2012).
Israel, M. A. et al. Probing sporadic and familial Alzheimer’s disease using induced pluripotent stem cells. Nature 482, 216–220 (2012).
Garcia-Reitboeck, P. et al. Human induced pluripotent stem cell-derived microglia-like cells harboring TREM2 missense mutations show specific deficits in phagocytosis. Cell Rep. 24, 2300–2311 (2018).
Claes, C. et al. Human stem cell-derived monocytes and microglia-like cells reveal impaired amyloid plaque clearance upon heterozygous or homozygous loss of TREM2. Alzheimers Dement. 15, 453–464 (2019).
Zhao, J. et al. APOE ε4/ε4 diminishes neurotrophic function of human iPSC-derived astrocytes. Hum. Mol. Genet. 26, 2690–2700 (2017).
Lin, Y. T. et al. APOE4 causes widespread molecular and cellular alterations associated with Alzheimer’s disease phenotypes in human iPSC-derived brain cell types. Neuron 98, 1141–1154.e7 (2018).
Zhao, Z. et al. Central role for PICALM in amyloid-β blood-brain barrier transcytosis and clearance. Nat. Neurosci. 18, 978–987 (2015).
Robbins, J. P. et al. Clusterin is required for β-amyloid toxicity in human iPSC-derived neurons. Front. Neurosci. 12, 504 (2018).
Huang, Y. A., Zhou, B., Wernig, M. & Südhof, T. C. ApoE2, ApoE3, and ApoE4 differentially stimulate APP transcription and Aβ secretion. Cell 168, 427–441.e21 (2017).
Arber, C., Lovejoy, C. & Wray, S. Stem cell models of Alzheimer’s disease: progress and challenges. Alzheimers Res. Ther. 9, 42 (2017).
Rius-Pérez, S., Tormos, A. M., Pérez, S. & Taléns-Visconti, R. Vascular pathology: cause or effect in Alzheimer disease? Neurologia 33, 112–120 (2018).
Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).
Cardiff University was supported by the UK Dementia Research Institute at Cardiff (MC_PC_17112), Medical Research Council MR/K013041/1), the Alzheimer’s Society, Alzheimer’s Research UK (ARUK-NC2018-WAL), Dementia Platform UK (HQR00720), the European Joint Programme for Neurodegenerative Disease (MR/N029402/1), the Welsh Assembly Government, Centre for Ageing & Dementia Research (SGR544:CADR) and a donation from the Moondance Charitable Foundation. We thank T. D. Cushion, G. Leonenko, J. Harwood and B. Lan-Leung for their support preparing this manuscript. Finally, we thank all patients and affected families for their continued generosity and willingness to participate in medical research; without their involvement, there would be no advancement in our knowledge of disease genetics.
The authors declare no competing interests.
Peer review information Nature Neuroscience thanks Mina Ryten and Bryan Traynor for their contribution to the peer review of this work.
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Sims, R., Hill, M. & Williams, J. The multiplex model of the genetics of Alzheimer’s disease. Nat Neurosci 23, 311–322 (2020). https://doi.org/10.1038/s41593-020-0599-5
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