Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and informs causal proteins for Alzheimer's disease
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Abstract
The integration of quantitative trait loci (QTL) with disease genome-wide association studies (GWAS) has proven successful at prioritizing candidate genes at disease-associated loci. QTL mapping has mainly been focused on multi-tissue expression QTL or plasma protein QTL (pQTL). Here we generated the largest-to-date cerebrospinal fluid (CSF) pQTL atlas by analyzing 7,028 proteins in 3,107 samples. We identified 3,373 independent study-wide associations for 1,961 proteins, including 2,448 novel pQTLs of which 1,585 are unique to CSF, demonstrating unique genetic regulation of the CSF proteome. In addition to the established chr6p22.2-21.32 HLA region, we identified pleiotropic regions on chr3q28 near OSTN and chr19q13.32 near APOE that were enriched for neuron-specificity and neurological development. We also integrated this pQTL atlas with the latest Alzheimer’s disease (AD) GWAS through PWAS, colocalization and Mendelian Randomization and identified 42 putative causal proteins for AD, 15 of which have drugs available. Finally, we developed a proteomics-based risk score for AD that outperforms genetics-based polygenic risk scores. These findings will be instrumental to further understand the biology and identify causal and druggable proteins for brain and neurological traits.
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Previous studies found overlapping and tissue-specific pQTLs between plasma and CSF.
“Previous studies found overlapping and tissue‐specific pQTLs between plasma and CSF, 10 , 28 which was also found in this study.”
From A genetic and proteomic comparison of key AD biomarkers across tissues (2024) · cites this paper as [10]
Biomarker status was available for the CSF samples and has been used before in previous studies stratifying by biomarker.
“Biomarker status was available for the CSF samples and has been used before in previous studies stratifying by biomarker. 10 , 11 , 13 , 48”
From A genetic and proteomic comparison of key AD biomarkers across tissues (2024) · cites this paper as [10]
Plasma and CSF shared pQTLs in several loci.
“Plasma and CSF shared pQTLs in several loci, as reported previously. 10 , 28”
From A genetic and proteomic comparison of key AD biomarkers across tissues (2024) · cites this paper as [10]
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- [1]Yengo L. A saturated map of common genetic variants associated with human height. Nature 610, 704– 712 (2022). 10.1038/s41586-022-05275-y 36224396 PMC9605867 · DOI · Paper page
- [2]Fernandez-Rozadilla C. Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries. Nature Genetics (2022). 10.1038/s41588-022-01222-9 PMC10094749 36539618 · DOI
- [3]Tcheandjieu C. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nature Medicine 28, 1679– 1692 (2022). 10.1038/s41591-022-01891-3 PMC9419655 35915156 · DOI
- [4]Consortium T. G. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science, 1318– 1330 (2020). 10.1126/science.abl4290 PMC7737656 32913098 · DOI
- [5]Võsa U. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nature Genetics 53, 1300– 1310 (2021). 10.1038/s41588-021-00913-z 34475573 PMC8432599 · DOI · Paper page
- [6]De Klein N. Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases (Cold Spring Harbor Laboratory, 2021). PMC10011140 36823318
- [7]Deming Y. The MS4A gene cluster is a key modulator of soluble TREM2 and Alzheimer’s disease risk. Science Translational Medicine 14, eaau229 (2019). 10.1126/scitranslmed.aau2291 PMC6697053 31413141 · DOI · Paper page
- [8]Vogel C. & Marcotte E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nature Reviews Genetics 13, 227– 232 (2012). 10.1038/nrg3185 PMC3654667 22411467 · DOI · Paper page
- [9]Johnson E. C. B. Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level. Nature Neuroscience 25, 213– 225 (2022). 10.1038/s41593-021-00999-y 35115731 PMC8825285 · DOI · Paper page
- [10]Yang C. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nature Neuroscience 24, 1302– 1312 (2021). 10.1038/s41593-021-00886-6 34239129 PMC8521603 · DOI · Paper page
- [11]Ferkingstad E. Large-scale integration of the plasma proteome with genetics and disease. Nature Genetics 53, 1712– 1721 (2021). 10.1038/s41588-021-00978-w 34857953 · DOI · Paper page
- [12]Pietzner M. Mapping the proteo-genomic convergence of human diseases. Science 374 (2021). 10.1126/science.abj1541 PMC9904207 34648354 · DOI · Paper page
- [13]Sun B. B. Genomic atlas of the human plasma proteome. Nature 558, 73– 79 (2018). 10.1038/s41586-018-0175-2 29875488 PMC6697541 · DOI · Paper page
- [14]Katz D. H. Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease. Circulation 145, 357– 370 (2022). 10.1161/circulationaha.121.055117 34814699 PMC9158509 · DOI · Paper page
- [15]Sun B. B. Genetic regulation of the human plasma proteome in 54,306 UK Biobank participants. bioRxiv, 2022.2006.2017.496443 (2022). 10.1101/2022.06.17.496443 · DOI · Paper page
- [16]Yang C. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat Neurosci(2021). 10.1038/s41593-021-00886-6 PMC8521603 34239129 · DOI · Paper page
- [17]Sasayama D. Genome-wide quantitative trait loci mapping of the human cerebrospinal fluid proteome. Human Molecular Genetics 26, 44– 51 (2017). 10.1093/hmg/ddw366 28031287 · DOI · Paper page
- [18]Hansson O. The genetic regulation of protein expression in cerebrospinal fluid. EMBO Molecular Medicine 15 (2023). 10.15252/emmm.202216359 PMC9832827 36504281 · DOI · Paper page
- [20]Kauwe J. S. K. Genome-Wide Association Study of CSF Levels of 59 Alzheimer’s Disease Candidate Proteins: Significant Associations with Proteins Involved in Amyloid Processing and Inflammation. PLoS Genetics 10, e1004758 (2014). 10.1371/journal.pgen.1004758 25340798 PMC4207667 · DOI · Paper page
- [21]Gatz M. Role of Genes and Environments for Explaining Alzheimer Disease. Archives of General Psychiatry 63, 168 (2006). 10.1001/archpsyc.63.2.168 16461860 · DOI · Paper page
- [22]Bellenguez C. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nature Genetics 54, 412– 436 (2022). 10.1038/s41588-022-01024-z 35379992 PMC9005347 · DOI · Paper page
- [23]Gold L. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One 5, e15004 (2010). 10.1371/journal.pone.0015004 21165148 PMC3000457 · DOI · Paper page
- [25]Jack C. R. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 87, 539– 547 (2016). 10.1212/wnl.0000000000002923 27371494 PMC4970664 · DOI · Paper page
- [26]Yang J. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nature Genetics 44, 369– 375 (2012). 10.1038/ng.2213 22426310 PMC3593158 · DOI · Paper page
- [27]Yang J., Lee S. H., Goddard M. E. & Visscher P. M. GCTA: A Tool for Genome-wide Complex Trait Analysis. The American Journal of Human Genetics 88, 76– 82 (2011). 10.1016/j.ajhg.2010.11.011 21167468 PMC3014363 · DOI · Paper page
- [28]McLaren W. The Ensembl Variant Effect Predictor. Genome Biology 17, 122 (2016). 10.1186/s13059-016-0974-4 27268795 PMC4893825 · DOI · Paper page
- [29]Lopes K. D. P. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nature Genetics 54, 4– 17 (2022). 10.1038/s41588-021-00976-y 34992268 PMC9245609 · DOI · Paper page
- [30]Sollis E. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Research 51, D977– D985 (2023). 10.1093/nar/gkac1010 36350656 PMC9825413 · DOI · Paper page
- [31]Bennett M. K., Calakos N. & Scheller R. H. Syntaxin: A Synaptic Protein Implicated in Docking of Synaptic Vesicles at Presynaptic Active Zones. Science 257, 255– 259 (1992). 10.1126/science.1321498 1321498 · DOI · Paper page
- [32]Dines M. & Lamprecht R. The Role of Ephs and Ephrins in Memory Formation. International Journal of Neuropsychopharmacology 19, pyv106 (2016). 10.1093/ijnp/pyv106 26371183 PMC4851260 · DOI · Paper page
- [33]Washburn H. R., Chander P, Srikanth K. D. & Dalva M. B. Transsynaptic Signaling of Ephs in Synaptic Development, Plasticity, and Disease. Neuroscience 508, 137– 152 (2023). 10.1016/j.neuroscience.2022.11.030 36460219 · DOI · Paper page
- [34]Zhang Y. Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional Differences with Mouse. Neuron 89, 37– 53 (2016). 10.1016/j.neuron.2015.11.013 26687838 PMC4707064 · DOI · Paper page
- [35]van der Meer D. The genetic architecture of human cortical folding. Science Advances 7, eabj9446 (2021). 10.1126/sciadv.abj9446 34910505 PMC8673767 · DOI
- [36]Zhao B. Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706). Molecular Psychiatry 26, 3943– 3955 (2021). 10.1038/s41380-019-0569-z 31666681 PMC7190426 · DOI
- [37]Shadrin A. A. Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology. NeuroImage 244, 118603 (2021). 10.1016/j.neuroimage.2021.118603 34560273 PMC8785963 · DOI
- [38]Jansen I. E. Genome-wide meta-analysis for Alzheimer’s disease cerebrospinal fluid biomarkers. Acta Neuropathologica 144, 821– 842 (2022). 10.1007/s00401-022-02454-z 36066633 PMC9547780 · DOI · Paper page
- [39]Karlsson M. A single-cell type transcriptomics map of human tissues. Science Advances 7, eabh2169 (2021). 10.1126/sciadv.abh2169 34321199 PMC8318366 · DOI · Paper page
- [40]Ataman B. Evolution of Osteocrin as an activity-regulated factor in the primate brain. Nature 539, 242– 247 (2016). 10.1038/nature20111 27830782 PMC5499253 · DOI · Paper page
- [41]Horton R. Gene map of the extended human MHC. Nature Reviews Genetics 5, 889– 899 (2004). 10.1038/nrg1489 15573121 · DOI
- [42]Zhu Z. Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank. Journal of Allergy and Clinical Immunology 145, 537– 549 (2020). 10.1016/j.jaci.2019.09.035 31669095 PMC7010560 · DOI
- [43]Miller F. W. Genome-wide association study identifies HLA 8.1 ancestral haplotype alleles as major genetic risk factors for myositis phenotypes. Genes & Immunity 16, 470– 480 (2015). 10.1038/gene.2015.28 26291516 PMC4840953 · DOI
- [44]Pickrell J. K. Detection and interpretation of shared genetic influences on 42 human traits. Nature Genetics 48, 709– 717 (2016). 10.1038/ng.3570 27182965 PMC5207801 · DOI · Paper page
- [45]Wightman D. P. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nature Genetics 53, 1276– 1282 (2021). 10.1038/s41588-021-00921-z 34493870 PMC10243600 · DOI · Paper page
- [46]Hammond T. R., Marsh S. E. & Stevens B. Immune Signaling in Neurodegeneration. Immunity 50, 955– 974 (2019). 10.1016/j.immuni.2019.03.016 30995509 PMC6822103 · DOI · Paper page
- [47]Liu C.-C., Kanekiyo T., Xu H. & Bu G. Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nature Reviews Neurology 9, 106– 118 (2013). 10.1038/nrneurol.2012.263 23296339 PMC3726719 · DOI · Paper page
- [48]Lumsden A. L., Mulugeta A., Zhou A. & Hyppönen E. Apolipoprotein E (APOE) genotype-associated disease risks: a phenome-wide, registry-based, case-control study utilising the UK Biobank. eBioMedicine 59, 102954 (2020). 10.1016/j.ebiom.2020.102954 32818802 PMC7452404 · DOI · Paper page
- [49]Bader J. M. Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer’s disease. Molecular Systems Biology 16 (2020). 10.15252/msb.20199356 PMC7266499 32485097 · DOI · Paper page
- [50]Gaetani L. Neurofilament light chain as a biomarker in neurological disorders. Journal of Neurology Neurosurgery & Psychiatry 90, 870 (2019). 10.1136/jnnp-2018-320106 30967444 · DOI · Paper page
- [51]Cruchaga C. SNPs Associated with Cerebrospinal Fluid Phospho-Tau Levels Influence Rate of Decline in Alzheimer’s Disease. PLoS Genetics 6, e1001101 (2010). 10.1371/journal.pgen.1001101 20862329 PMC2940763 · DOI · Paper page
- [52]Zhao J. APOE4 exacerbates synapse loss and neurodegeneration in Alzheimer’s disease patient iPSC-derived cerebral organoids. Nature Communications 11 (2020). 10.1038/s41467-020-19264-0 PMC7608683 33139712 · DOI · Paper page
- [53]Wang C. Gain of toxic apolipoprotein E4 effects in human iPSC-derived neurons is ameliorated by a small-molecule structure corrector. Nature Medicine 24, 647– 657 (2018). 10.1038/s41591-018-0004-z PMC5948154 29632371 · DOI · Paper page
- [54]Grainger D. J., Reckless J. & McKilligin E. Apolipoprotein E Modulates Clearance of Apoptotic Bodies In Vitro and In Vivo, Resulting in a Systemic Proinflammatory State in Apolipoprotein E-Deficient Mice. The Journal of Immunology 173, 6366– 6375 (2004). 15528376
- [55]Gusev A. Integrative approaches for large-scale transcriptome-wide association studies. Nature Genetics 48, 245– 252 (2016). 10.1038/ng.3506 26854917 PMC4767558 · DOI · Paper page
- [56]Wingo A. P. Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis. Nature Genetics 53, 143– 146 (2021). 10.1038/s41588-020-00773-z 33510477 PMC8130821 · DOI · Paper page
- [57]Hemani G. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7 (2018). 10.7554/elife.34408 PMC5976434 29846171 · DOI · Paper page
- [58]Giambartolomei C. Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics. PLoS Genetics 10, e1004383 (2014). 10.1371/journal.pgen.1004383 24830394 PMC4022491 · DOI · Paper page
- [59]Wallace C. A more accurate method for colocalisation analysis allowing for multiple causal variants. PLOS Genetics 17, e1009440 (2021). 10.1371/journal.pgen.1009440 34587156 PMC8504726 · DOI · Paper page
- [60]Hemani G., Tilling K. & Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLOS Genetics 13, e1007081 (2017). 10.1371/journal.pgen.1007081 29149188 PMC5711033 · DOI · Paper page
- [61]Fonseca M. I. Analysis of the Putative Role of CR1 in Alzheimer’s Disease: Genetic Association, Expression and Function. PLOS ONE 11, e0149792 (2016). 10.1371/journal.pone.0149792 26914463 PMC4767815 · DOI · Paper page
- [62]Kucukkilic E. Complement receptor 1 gene (CR1) intragenic duplication and risk of Alzheimer’s disease. Human Genetics 137, 305– 314 (2018). 10.1007/s00439-018-1883-2 29675612 PMC5937907 · DOI · Paper page
- [63]Brouwers N. Alzheimer risk associated with a copy number variation in the complement receptor 1 increasing C3b/C4b binding sites. Molecular Psychiatry 17, 223– 233 (2012). 10.1038/mp.2011.24 21403675 PMC3265835 · DOI · Paper page
- [64]Vandendreissche S., Cambier S., Proost P. & Marques P. E. Complement Receptors and Their Role in Leukocyte Recruitment and Phagocytosis. Frontiers in Cell and Developmental Biology 9 (2021). 10.3389/fcell.2021.624025 PMC7905230 33644062 · DOI · Paper page
- [65]Heneka M. T. Neuroinflammation in Alzheimer’s disease. The Lancet Neurology 14, 388– 405 (2015). 10.1016/s1474-4422(15)70016-5 25792098 PMC5909703 · DOI · Paper page
- [66]Küçükali F. ZCWPW1 loss-of-function variants in Alzheimer’s Disease. medRxiv, 2021.2008.2013.21261426 (2021). 10.1101/2021.08.13.21261426 · DOI
- [67]Rathore N. Paired Immunoglobulin-like Type 2 Receptor Alpha G78R variant alters ligand binding and confers protection to Alzheimer’s disease. PLOS Genetics 14, e1007427 (2018). 10.1371/journal.pgen.1007427 30388101 PMC6235402 · DOI · Paper page
- [68]Schwartzentruber J. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes. Nature Genetics 53, 392– 402 (2021). 10.1038/s41588-020-00776-w 33589840 PMC7610386 · DOI · Paper page
- [69]Martinez Wanda M. & Spear Patricia G. Structural Features of Nectin-2 (HveB) Required for Herpes Simplex Virus Entry. Journal of Virology 75, 11185– 11195 (2001). 10.1128/JVI.75.22.11185-11195.2001 11602758 PMC114698 · DOI
- [70]Itzhaki R. F. Overwhelming Evidence for a Major Role for Herpes Simplex Virus Type 1 (HSV1) in Alzheimer’s Disease (AD); Underwhelming Evidence against. Vaccines 9, 679 (2021). 10.3390/vaccines9060679 34205498 PMC8234998 · DOI
- [71]Lee Y. H. Association between FCGR3B copy number variations and susceptibility to autoimmune diseases: a meta-analysis. Inflammation Research 64, 983– 991 (2015). 10.1007/s00011-015-0882-1 26407570 · DOI
- [72]Basak I., Hansen R. A., Ward M. E. & Hughes S. M. Deficiency of the Lysosomal Protein CLN5 Alters Lysosomal Function and Movement. Biomolecules 11, 1412 (2021). 10.3390/biom11101412 34680045 PMC8533494 · DOI · Paper page
- [73]Qureshi Y. H. An Alzheimer’s Disease-Linked Loss-of-Function CLN5 Variant Impairs Cathepsin D Maturation, Consistent with a Retromer Trafficking Defect. Molecular and Cellular Biology 38 (2018). 10.1128/mcb.00011-18 PMC6168984 30037983 · DOI
- [74]Muhammad A. Retromer deficiency observed in Alzheimer’s disease causes hippocampal dysfunction, neurodegeneration, and Aβ accumulation. Proceedings of the National Academy of Sciences 105, 7327– 7332 (2008). 10.1073/pnas.0802545105 PMC2386077 18480253 · DOI · Paper page
- [75]Ding X. Loss of microglial SIRPα promotes synaptic pruning in preclinical models of neurodegeneration. Nature Communications 12 (2021). 10.1038/s41467-021-22301-1 PMC8016980 33795678 · DOI · Paper page
- [76]Phongpreecha T. Single-synapse analyses of Alzheimer’s disease implicate pathologic tau, DJ1, CD47, and ApoE. Science Advances 7 (2021). 10.1126/sciadv.abk0473 PMC8673771 34910503 · DOI
- [77]Chapuis J. Increased expression of BIN1 mediates Alzheimer genetic risk by modulating tau pathology. Molecular Psychiatry 18, 1225– 1234 (2013). 10.1038/mp.2013.1 23399914 PMC3807661 · DOI · Paper page
- [78]Haaker J. Higher anxiety and larger amygdala volumes in carriers of a TMEM132D risk variant for panic disorder. Translational Psychiatry 4, e357– e357 (2014). 10.1038/tp.2014.1 24495968 PMC3944634 · DOI
- [79]Lane J. M. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nature Genetics 49, 274– 281 (2017). 10.1038/ng.3749 27992416 PMC5491693 · DOI
- [80]Sanchez-Pulido L. & Ponting C. P TMEM132: an ancient architecture of cohesin and immunoglobulin domains define a new family of neural adhesion molecules. Bioinformatics 34, 721– 724 (2018). 10.1093/bioinformatics/btx689 29088312 PMC6030884 · DOI
- [81]Polla D. L. Phenotypic spectrum associated with a CRADD founder variant underlying frontotemporal predominant pachygyria in the Finnish population. European Journal of Human Genetics 27, 1235– 1243 (2019). 10.1038/s41431-019-0383-8 30914828 PMC6777631 · DOI
- [82]Jabado O. RAIDD aggregation facilitates apoptotic death of PC12 cells and sympathetic neurons. Cell Death & Differentiation 11, 618– 630 (2004). 10.1038/sj.cdd.4401397 14765136 · DOI
- [83]Jiang P. The HOPS complex mediates autophagosome–lysosome fusion through interaction with syntaxin 17. Molecular Biology of the Cell 25, 1327– 1337 (2014). 10.1091/mbc.e13-08-0447 24554770 PMC3982997 · DOI · Paper page
- [84]Zhang L. BRUCE silencing leads to axonal dystrophy by repressing autophagosome-lysosome fusion in Alzheimer’s disease. Translational Psychiatry 11 (2021). 10.1038/s41398-021-01427-2 PMC8342531 34354038 · DOI
- [85]Kögel D., Deller T. & Behl C. Roles of amyloid precursor protein family members in neuroprotection, stress signaling and aging. Experimental Brain Research 217, 471– 479 (2012). 10.1007/s00221-011-2932-4 22086493 · DOI
- [86]Postina R. A disintegrin-metalloproteinase prevents amyloid plaque formation and hippocampal defects in an Alzheimer disease mouse model. Journal of Clinical Investigation 113, 1456– 1464 (2004). 10.1172/jci20864 15146243 PMC406531 · DOI · Paper page
- [87]Schmitt U., Hiemke C., Fahrenholz F. & Schroeder A. Over-expression of two different forms of the α-secretase ADAM10 affects learning and memory in mice. Behavioural Brain Research 175, 278– 284 (2006). 10.1016/j.bbr.2006.08.030 17014917 · DOI
- [88]Jones M. E. A genetic variant of the Wnt receptor LRP6 accelerates synapse degeneration during aging and in Alzheimer’s disease. Science Advances 9, eabo7421 10.1126/sciadv.abo7421 PMC10624429 36638182 · DOI
- [89]Deczkowska A. Disease-Associated Microglia: A Universal Immune Sensor of Neurodegeneration. Cell 173, 1073– 1081 (2018). 10.1016/j.cell.2018.05.003 29775591 · DOI · Paper page
- [90]Schlepckow K. An Alzheimer-associated TREM2 variant occurs at the ADAM cleavage site and affects shedding and phagocytic function. EMBO Molecular Medicine 9, 1356– 1365 (2017). 10.15252/emmm.201707672 28855300 PMC5623859 · DOI
- [91]Konishi H. & Kiyama H. Microglial TREM2/DAP12 Signaling: A Double-Edged Sword in Neural Diseases. Frontiers in Cellular Neuroscience 12 (2018). 10.3389/fncel.2018.00206 PMC6087757 30127720 · DOI · Paper page
- [92]Mizuno T. Interleukin-34 Selectively Enhances the Neuroprotective Effects of Microglia to Attenuate Oligomeric Amyloid-β Neurotoxicity. The American Journal of Pathology 179, 2016– 2027 (2011). 10.1016/j.ajpath.2011.06.011 21872563 PMC3181379 · DOI · Paper page
- [93]Fournier N. FDF03, a Novel Inhibitory Receptor of the Immunoglobulin Superfamily, Is Expressed by Human Dendritic and Myeloid Cells1. The Journal of Immunology 165, 1197– 1209 (2000). 10.4049/jimmunol.165.3.1197 10903717 · DOI · Paper page
- [94]Paul S. P, Taylor L. S., Stansbury E. K. & McVicar D. W. Myeloid specific human CD33 is an inhibitory receptor with differential ITIM function in recruiting the phosphatases SHP-1 and SHP-2. Blood 96, 483– 490 (2000). 10.1182/blood.V96.2.483 10887109 · DOI
- [95]Zhang J. Q., Nicoll G., Jones C. & Crocker P. R. Siglec-9, a Novel Sialic Acid Binding Member of the Immunoglobulin Superfamily Expressed Broadly on Human Blood Leukocytes. Journal of Biological Chemistry 275, 22121– 22126 (2000). 10.1074/jbc.m002788200 10801862 · DOI · Paper page
- [96]Linnartz B., Wang Y. & Neumann H. Microglial Immunoreceptor Tyrosine-Based Activation and Inhibition Motif Signaling in Neuroinflammation. International Journal of Alzheimer’s Disease 2010, 1– 7 (2010). 10.4061/2010/587463 PMC2915791 20721346 · DOI · Paper page
- [97]Asanomi Y. A rare functional variant of SHARPIN attenuates the inflammatory response and associates with increased risk of late-onset Alzheimer’s disease. Molecular Medicine 25 (2019). 10.1186/s10020-019-0090-5 PMC6585023 31216982 · DOI · Paper page
- [98]Spycher S. E., Nick H. & Rickli E. E. Human complement component C1−s. European Journal of Biochemistry 156, 49– 55 (1986). 10.1111/j.1432-1033.1986.tb09546.x 3007145 · DOI
- [99]Trouw L. A. C4b-binding protein in Alzheimer’s disease: Binding to Aβ1–42 and to dead cells. Molecular Immunology 45, 3649– 3660 (2008). 10.1016/j.molimm.2008.04.025 18556068 · DOI
- [100]Van Acker Z. P., Bretou M. & Annaert W. Endo-lysosomal dysregulations and late-onset Alzheimer’s disease: impact of genetic risk factors. Molecular Neurodegeneration 14, 20 (2019). 10.1186/s13024-019-0323-7 31159836 PMC6547588 · DOI · Paper page
- [101]Nixon R. A. Autophagy, amyloidogenesis and Alzheimer disease. Journal of Cell Science 120, 4081– 4091 (2007). 10.1242/jcs.019265 18032783 · DOI · Paper page
- [102]Itakura E., Kishi-Itakura C. & Mizushima N. The hairpin-type tail-anchored SNARE syntaxin 17 targets to autophagosomes for fusion with endosomes/lysosomes. Cell 151, 1256– 1269 (2012). 10.1016/j.cell.2012.11.001 23217709 · DOI
- [103]Li Y. Functional genomics identify causal variant underlying the protective CTSH locus for Alzheimer’s disease. Neuropsychopharmacology (2023). 10.1038/s41386-023-01542-2 PMC10516988 36739351 · DOI · Paper page
- [104]Brix K., Dunkhorst A., Mayer K. & Jordans S. Cysteine cathepsins: Cellular roadmap to different functions. Biochimie 90, 194– 207 (2008). 10.1016/j.biochi.2007.07.024 17825974 · DOI
- [105]Haves-Zburof D. Cathepsins and their endogenous inhibitors cystatins: expression and modulation in multiple sclerosis. Journal of Cellular and Molecular Medicine 15, 2421– 2429 (2011). 10.1111/j.1582-4934.2010.01229.x 21143385 PMC3822953 · DOI · Paper page
- [106]Van Deerlin V. M. Common variants at 7p21 are associated with frontotemporal lobar degeneration with TDP-43 inclusions. Nature Genetics 42, 234– 239 (2010). 10.1038/ng.536 20154673 PMC2828525 · DOI · Paper page
- [107]Baker M. Mutations in progranulin cause tau-negative frontotemporal dementia linked to chromosome 17. Nature 442, 916– 919 (2006). 10.1038/nature05016 16862116 · DOI · Paper page
- [108]Paushter D. H., Du H., Feng T. & Hu F. The lysosomal function of progranulin, a guardian against neurodegeneration. Acta Neuropathologica 136, 1– 17 (2018). 10.1007/s00401-018-1861-8 29744576 PMC6117207 · DOI · Paper page
- [109]Feng T. Loss of TMEM106B and PGRN leads to severe lysosomal abnormalities and neurodegeneration in mice. EMBO reports 21, e50219 (2020). 10.15252/embr.202050219 32852886 PMC7534636 · DOI · Paper page
- [110]Wishart D. S. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research 46, D1074– D1082 (2017). 10.1093/nar/gkx1037 PMC5753335 29126136 · DOI · Paper page
- [111]Akushevich I., Yashkin A. P., Kravchenko J. & Kertai M. D. Chemotherapy and the Risk of Alzheimer’s Disease in Colorectal Cancer Survivors: Evidence From the Medicare System. JCO Oncology Practice 17, e1649– e1659 (2021). 10.1200/op.20.00729 33630665 PMC8600506 · DOI · Paper page
- [112]Wang R. & Reddy P. H. Role of Glutamate and NMDA Receptors in Alzheimer’s Disease. Journal of Alzheimer’s Disease 57, 1041– 1048 (2017). 10.3233/JAD-160763 PMC5791143 27662322 · DOI · Paper page
- [113]Fiermonte G. Identification of the Mitochondrial Glutamate Transporter: BACTERIAL EXPRESSION, RECONSTITUTION, FUNCTIONAL CHARACTERIZATION, AND TISSUE DISTRIBUTION OF TWO HUMAN ISOFORMS*. Journal of Biological Chemistry 277, 19289– 19294 (2002). 10.1074/jbc.M201572200 11897791 · DOI · Paper page
- [114]Ge M. Role of Calcium Homeostasis in Alzheimer’s Disease. Neuropsychiatric Disease and Treatment Volume 18, 487– 498 (2022). 10.2147/ndt.s350939 35264851 PMC8901263 · DOI · Paper page
- [115]Solesio M. E. Carbonic anhydrase inhibition selectively prevents amyloid β neurovascular mitochondrial toxicity. Aging Cell 17, e12787 (2018). 10.1111/acel.12787 29873184 PMC6052473 · DOI · Paper page
- [116]Marigorta U. M. Transcriptional risk scores link GWAS to eQTLs and predict complications in Crohn’s disease. Nature Genetics 49, 1517– 1521 (2017). 10.1038/ng.3936 28805827 PMC5745037 · DOI · Paper page
- [117]Leonenko G. Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores. Nature Communications 12 (2021). 10.1038/s41467-021-24082-z PMC8302739 34301930 · DOI
- [118]Moll M. Development of a Blood-based Transcriptional Risk Score for Chronic Obstructive Pulmonary Disease. American Journal of Respiratory and Critical Care Medicine 205 (2021). 10.1164/rccm.202107-1584OC PMC8787248 34739356 · DOI
- [119]Collaborators G. N. Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurology 18, 459– 480 (2019). 10.1016/S1474-4422(18)30499-X 30879893 PMC6459001 · DOI · Paper page
- [120]Carroll W. M. The global burden of neurological disorders. The Lancet Neurology 18, 418– 419 (2019). 10.1016/s1474-4422(19)30029-8 30879892 · DOI
- [123]Van Dyck C. H. Lecanemab in Early Alzheimer’s Disease. New England Journal of Medicine 388, 9– 21 (2023). 10.1056/nejmoa2212948 36449413 · DOI · Paper page
- [124]Zaragozá R. Transport of Amino Acids Across the Blood-Brain Barrier. Frontiers in Physiology 11 (2020). 10.3389/fphys.2020.00973 PMC7538855 33071801 · DOI · Paper page
- [125]Regeniter A. A modern approach to CSF analysis: Pathophysiology, clinical application, proof of concept and laboratory reporting. Clinial Neurology and Neurosurgery 111, 313– 318 (2009). 10.1016/j.clineuro.2008.12.004 19185983 · DOI · Paper page
- [126]Jones L. Genetic Evidence Implicates the Immune System and Cholesterol Metabolism in the Aetiology of Alzheimer’s Disease. PLoS ONE 5, e13950 (2010). 10.1371/journal.pone.0013950 21085570 PMC2981526 · DOI · Paper page
- [127]Yokoyama J. S. Association Between Genetic Traits for Immune-Mediated Diseases and Alzheimer Disease. JAMA Neurology 73, 691 (2016). 10.1001/jamaneurol.2016.0150 27088644 PMC4905783 · DOI · Paper page
- [128]Haage V. & De Jager P. L. Neuroimmune contributions to Alzheimer’s disease: a focus on human data. Molecular Psychiatry 27, 3164– 3181 (2022). 10.1038/s41380-022-01637-0 35668160 PMC9168642 · DOI · Paper page
- [129]Jin S. C. Coding variants in TREM2 increase risk for Alzheimer’s disease. Human Molecular Genetics 23 (2014). 10.1093/hmg/ddu277 PMC4189899 24899047 · DOI · Paper page
- [130]Phillips B. Proteome Wide Association Studies of LRRK2 variants identify novel causal and druggable for Parkinson’s disease (2023). PMC10329646 37422510
- [131]Morris J. C. The Uniform Data Set (UDS): Clinical and Cognitive Variables and Descriptive Data From Alzheimer Disease Centers. Alzheimer Disease & Associated Disorders 20 (2006). 17132964
- [132]Day G. S. Differentiating cognitive impairment due to corticobasal degeneration and Alzheimer disease. Neurology 88, 1273– 1281 (2017). 10.1212/wnl.0000000000003770 28235814 PMC5373780 · DOI
- [133]McKhann G. Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939– 939 (1984). 10.1212/wnl.34.7.939 6610841 · DOI · Paper page
- [134]McKhann G. M. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia 7, 263– 269 (2011). 10.1016/j.jalz.2011.03.005 PMC3312024 21514250 · DOI · Paper page
- [135]Román G. C. Vascular cognitive disorder: a new diagnostic category updating vascular cognitive impairment and vascular dementia. Journal of the Neurological Sciences 226, 81– 87 (2004). 10.1016/j.jns.2004.09.016 15537526 · DOI
- [136]Moreno-Grau S. Genome-wide association analysis of dementia and its clinical endophenotypes reveal novel loci associated with Alzheimer’s disease and three causality networks: The GR@ACE project. Alzheimer’s & Dementia 15, 1333– 1347 (2019). 10.1016/j.jalz.2019.06.4950 31473137 · DOI
- [137]De Rojas I. Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores. Nature Communications 12 (2021). 10.1038/s41467-021-22491-8 PMC8184987 34099642 · DOI · Paper page
- [138]Candia J. Assessment of Variability in the SOMAscan Assay. Scientific Reports 7 (2017). 10.1038/s41598-017-14755-5 29079756 PMC5660188 · DOI
- [139]Chang C. C. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4(2015). 10.1186/s13742-015-0047-8 PMC4342193 25722852 · DOI · Paper page
- [140]Wilier C. J., Li Y. & Abecasis G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190– 2191 (2010). 10.1093/bioinformatics/btq340 20616382 PMC2922887 · DOI · Paper page
- [141]Scrucca L., Fop M., Murphy T. B. & Raftery A. E. mclust 5: Clustering, Classification and Density Estimation using Gaussian Finite Mixture Models. The R Journal 8/1, 289– 317 (2016). PMC5096736 27818791
- [142]Orellana A. Establishing In-House Cutoffs of CSF Alzheimer’s Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort. International Journal of Molecular Sciences 23, 6891 (2022). 10.3390/ijms23136891 35805894 PMC9266894 · DOI
- [143]Consortium T. U. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Research 51, D523– D531 (2023). 10.1093/nar/gkac1052 36408920 PMC9825514 · DOI · Paper page
- [144]Kent W. J. The Human Genome Browser at UCSC. Genome Research 12, 996– 1006 (2002). 10.1101/gr.229102 12045153 PMC186604 · DOI · Paper page
- [145]Newcombe R. G. Interval estimation for the difference between independent proportions: comparison of eleven methods. Statistics in Medicine (1998). 10.1002/(SICI)1097-0258(19980430)17:8<873::AID-SIM779>3.0.CO;2-I 9595617 · DOI · Paper page
- [146]Durinck S., Spellman P. T., Birney E. & Huber W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature Protocols 4, 1184– 1191 (2009). 10.1038/nprot.2009.97 19617889 PMC3159387 · DOI
- [147]Gu Z., Gu L., Eils R., Schlesner M. & Brors B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811– 2812 (2014). 10.1093/bioinformatics/btu393 24930139 · DOI
- [148]Barrett J. C., Fry B., Mailer J. & Daly M. J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263– 265 (2005). 10.1093/bioinformatics/bth457 15297300 · DOI
- [149]Benjamini Y. & Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Statistical Methodology) (1995). 10.1111/j.2517-6161.1995.tb02031.x · DOI · Paper page
- [150]Wang G., Sarkar A., Carbonetto P. & Stephens M. A simple new approach to variable selection in regression, with application to genetic fine mapping. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82, 1273– 1300 (2020). 10.1111/rssb.12388 37220626 PMC10201948 · DOI · Paper page
- [151]Wilcoxon F. Individual Comparisons by Ranking Methods. Biometrics Bulletin 1, 80– 83 (1945). 10.2307/3001968 · DOI · Paper page
- [152]Friedman J., Hastie T. & Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 33 (2010). PMC2929880 20808728
- [153]Choi S. W. & O’Reilly P. F. PRSice-2: Polygenic Risk Score software for biobank-scale data. GigaScience 8 (2019). 10.1093/gigascience/giz082 PMC6629542 31307061 · DOI
- [154]Robin X. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011). 10.1186/1471-2105-12-77 21414208 PMC3068975 · DOI · Paper page
- [155]Yu G., Wang L.-G., Han Y. & He Q.-Y. clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. OMICS: A Journal of Integrative Biology 16, 284– 287 (2012). 10.1089/omi.2011.0118 22455463 PMC3339379 · DOI · Paper page
- [156]Yu G., Wang L.-G., Yan G.-R. & He Q.-Y. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31, 608– 609 (2015). 10.1093/bioinformatics/btu684 25677125 · DOI
- [157]Yu G. & He Q.-Y. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 12, 477– 479 (2016). 10.1039/c5mb00663e 26661513 · DOI
Cited by
Sorted by citations
Vance, J. M., Farrer, L. A., Huang, Y., Cruchaga, C., Hyman, B. T., Pericak‐Vance, M. A., Goate, A. M., Greicius, M. D., Griswold, A. J., Haines, J. L., et al. (2024) · Annals of Neurology · doi:10.1002/ana.26864 · Cited by 52 · see citing works
Self-citationA genetic and proteomic comparison of key AD biomarkers across tissues
Marsh, T. W., Western, D., Timsina, J., Gorijala, P., Yang, C., Pastor, P., Liu, M., Morris, J. C., Bateman, R. J., Schindler, S. E., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14139 · Cited by 6 · see citing works
Self-citation
- Authors
- Cruchaga, C., Western, D., Timsina, J., Wang, L., Wang, C., Yang, C., Ali, M., Beric, A., Gorijala, P., Kohlfeld, P., Budde, J., Levey, A., Morris, J., Perrin, R., Ruiz, A., Marquié, M., Boada, M., de Rojas, I., Rutledge, J., Oh, H., Wilson, E., Guen, Y. L., Alvarez, I., Aguilar, M., Greicius, M., Pastor, P., Pulford, D., Ibanez, L., Wyss-Coray, T., Sung, Y. J., & Phillips, B.
- Affiliations
- Washington University in St. Louis (US)
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- Washington University School of Medicine, St Louis, MO, USA
- Emory University (US)
- Fundació ACE
- Instituto de Salud Carlos III (ES)
- Memory Clinic of Fundaciò ACE, Catalan Institute of Applied Neurosciences
- Universitat Internacional de Catalunya
- Stanford University (US)
- Stanford Medicine (US)
- Fundació Docència i Recerca MútuaTerrassa, Terrassa, Barcelona, Spain
- University Hospital Mútua de Terrassa (ES)
- GlaxoSmithKline (United Kingdom) (GB)
- University Hospital Germans Trias i Pujol
- Washington University in St. Louis
- Stanford University
- Corresponding author
- Cruchaga, Carlos
- ORCID
- Cruchaga, Carlos
- Western, Dan
- Timsina, Jigyasha
- Wang, Lihua
- Wang, Ciyang
- Yang, Chengran
- Ali, Muhammad
- Beric, Aleksandra
- Gorijala, Priyanka
- Kohlfeld, Patsy
- Budde, John
- Levey, Allan
- Morris, John
- Perrin, Richard
- Ruiz, Agustín
- Marquié, Marta
- Rutledge, Jarod
- Oh, Hamilton
- Wilson, Edward
- Guen, Yann Le
- Alvarez, Ignacio
- Greicius, Michael
- Pastor, Pau
- Pulford, David
- Wyss-Coray, Tony
- Sung, Yun Ju
- Phillips, Bridget
- Venue
- Nature Genetics
- Published
- 2023-06-09
- Volume
- 56
- Issue
- 12
- Pages
- 2672-2684
- ISSN
- 1061-4036 (ISSN-L)
- PMC
- PMC10275048
- OpenAlex
- W4404224780
- Cited by
- 112
- References
- 0
- Citations by year
- 2026: 48 · 2025: 41 · 2024: 22 · 2023: 1
- FWCI
- 27.27
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Genetics (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH1220012, W81XWH2010849, AG024904)
- U.S. Department of Health and Human Services (RF1AG058501, RF1 AG053303, U01AG058922, P01AG003991, R01 AG044546)
- Michael J. Fox Foundation for Parkinson's Research
- Alzheimer's Association (ZEN-22-848604, SG-20-690363-DIAN, U01 AG024904, W81XWH-12-2-0012)
- Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
- GlaxoSmithKline (P01AG026276, P30AG066444, P01AG03991)
- Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
- F. Hoffmann-La Roche (W81XWH-12-2-0012, AG024904, U01 AG024904)
- Alzheimer's Disease Neuroimaging Initiative (P01AG026276, W81XWH-12-2–0012, U01AG024904, AG024904, W81XWH-12-2-, Department of Defense award number W81XWH-12-2-0012, P01AG03991)
- BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
- Fondation Brain Canada
- Japan Agency for Medical Research and Development
- Hope Center for Neurological Disorders
- Chan Zuckerberg Initiative
- ZonMw
- Korea Health Industry Development Institute
- Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Deutsches Zentrum für Neurodegenerative Erkrankungen
- Alzheimer Nederland (WE.03-2018-05)
- IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
- Fleni
- Amsterdam Neuroscience
- National Institutes of Health (3u01ag024904-10s2, W81XWH, W81XWH-12-2-0012, U01-AG024904, Grant U01 AG024904, AG024904, AG066515)
- Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
- Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
- Instituto de Salud Carlos III
- National Institute on Aging (U01-AG-024904, W81XWH-12-2–0012, AG 024904, RF1AG053303, R01AG044546, AG066515, P01AG003991, R01AG064614, RF1AG071706, U19AG032438, R00AG062723, RF1AG074007, U01AG058922, RF1AG058501, R01AG058501, R01AG074007, R01AG078964, P30AG066444, P30AG066515, P01AG026276)
- National Institute of Biomedical Imaging and Bioengineering (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- cerebrospinal fluid, proteome-wide association study, Alzheimer's disease, causal proteins, pleiotropy, apolipoprotein E, Mendelian randomization, colocalization, disease prediction model, druggable targets
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