The DOI matches its Crossref record; 91 of the 91 references it cites were found in a registry or an index.
The MS4A gene cluster is a key modulator of soluble TREM2 and Alzheimer’s disease risk
Open accessPublished version of a preprint
Abstract
Common variants in the microglia-specific MS4A gene cluster modify risk for late-onset Alzheimer’s disease and modulate extracellular soluble TREM2.
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In the citing paper
“TREM2 function is regulated via interactions and crosstalk with other receptors, including the AD risk hits CD33, and the Ms4A gene-cluster products [23, 59].”
From TREM2/PLCγ2 signalling in immune cells: function, structural insight, and potential therapeutic modulation (2021) · cites this paper as [59]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“As Ms4a genes can regulate the level of soluble TREM2 and modify AD risk39, we used immunoblot analysis to rule out the possibility that myelin dysfunction affects TREM2 protein levels or cleavage through increased Ms4a expression (Extended Data Fig. 9a).”
From Myelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease (2023) · cites this paper as [39]
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In the citing paper
“Targeting CSF proteins has proven successful at elucidating causal genes at some disease GWAS loci, including an association for the TREM2 protein at the MS4A locus potentially an Alzheimer’s Disease (AD) risk locus7,20 and an association for GRN protein levels near LRRK2 potentially relevant for Parkinson’s Disease (PD)21.”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [7]
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In the citing paper
“supporting a strong immunological role in AD93–95 including TREM27,96, CD33, and IL34.”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [7]
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In the citing paper
“MS4A6A, a gene related to AD risk22,68,69, was one of the genes that showed a region-specific effect of age68,70.”
From Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies (2022) · cites this paper as [69]
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In the citing paper
“Common variants in the MS4A gene cluster were shown to significantly regulate sTREM2 levels in CSF [134], demonstrating regulation of this endophenotype is orchestrated by genetic variants that also modify risk for disease.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [134]
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Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · cited 2×
“Gene counts data were transformed to stabilize expression variances along the range of mean values and normalized according to library size using the variance-stabilizing transformation (VST) function in DESeq2 (79).”
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · cited 1×
“2b (64), and reads alignment was further evaluated by applying Picard CollectRnaSeqMetrics v2.”
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,611 · cited 1×
“Bioinformatics annotation All variants below the threshold for suggestive significance (P < 1 × 10−5) were taken forward for functional annotation using ANNOVAR version 2015-06-17 (81) and examined for potential regulatory functions using HaploReg v4.”
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700 · cited 1×
“2 (78) transcript expression quantification of the coding transcripts of H.”
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248 · cited 2×
“EIGENSTRAT ( 66 ) was used to calculate principal components.”
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723 · cited 1×
“5 (65) was used to determine the read count for each transcript and quantified transcript abundance as transcripts per kilobase per million reads mapped (TPM), using gene annotation of Homo sapiens reference genome (GENCODE GRCh38) with the following parameter: -t 10 -b 100.”
The Ensembl Variant Effect Predictor
McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016) · Genome Biology · doi:10.1186/s13059-016-0974-4 · Cited by 8,099 · cited 1×
“The Ensembl Variant Effect Predictor (VEP; http://grch37.ensembl.org/Homo_sapiens/ Tools/VEP) ( 86 ) was queried for the predicted effects of identified coding variants, including any applicable SIFT and PolyPhen scores.”
METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205 · cited 2×
“Meta-analyses were performed using a fixed effect model in METAL (77).”
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062
Cited by
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Scheltens, P., De Strooper, B., Kivipelto, M., Holstege, H., Chételat, G., Teunissen, C. E., Cummings, J., & van der Flier, W. M. (2021) · The Lancet · doi:10.1016/s0140-6736(20)32205-4 · Cited by 5,465
Large-scale integration of the plasma proteome with genetics and disease
Ferkingstad, E., Sulem, P., Atlason, B. A., Sveinbjornsson, G., Magnusson, M. I., Styrmisdottir, E. L., Gunnarsdottir, K., Helgason, A., Oddsson, A., Halldorsson, B. V., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00978-w · Cited by 1,697
The Physiology, Pathology, and Potential Therapeutic Applications of the TREM2 Signaling Pathway
Deczkowska, A., Weiner, A., & Amit, I. (2020) · Cell · doi:10.1016/j.cell.2020.05.003 · Cited by 648
Myelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease
Depp, C., Sun, T., Sasmita, A. O., Spieth, L., Berghoff, S. A., Nazarenko, T., Overhoff, K., Steixner-Kumar, A. A., Subramanian, S., Arinrad, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06120-6 · Cited by 493
Self-citationMeningeal lymphatics affect microglia responses and anti-Aβ immunotherapy
Da Mesquita, S., Papadopoulos, Z., Dykstra, T., Brase, L., Farias, F. G., Wall, M., Jiang, H., Kodira, C. D., de Lima, K. A., Herz, J., et al. (2021) · Nature · doi:10.1038/s41586-021-03489-0 · Cited by 431
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Metadata
- Authors
- Deming, Y., Filipello, F., Cignarella, F., Cantoni, C., Hsu, S., Mikesell, R., Li, Z., Del-Aguila, J. L., Dube, U., Farias, F. G., Bradley, J., Budde, J., Ibanez, L., Fernandez, M. V., Blennow, K., Zetterberg, H., Heslegrave, A., Johansson, P. M., Svensson, J., Nellgård, B., Lleo, A., Alcolea, D., Clarimon, J., Rami, L., Molinuevo, J. L., Suárez-Calvet, M., Morenas-Rodríguez, E., Kleinberger, G., Ewers, M., Harari, O., Haass, C., Brett, T. J., Benitez, B. A., Karch, C. M., Piccio, L., & Cruchaga, C.
- Affiliations
- University of Wisconsin–Madison (US)
- Washington University in St. Louis (US)
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA.
- Lund University (SE)
- Sahlgrenska University Hospital (SE)
- Universitat Autònoma de Barcelona (ES)
- Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.
- Pasqual Maragall Foundation (ES)
- Munich Cluster for Systems Neurology (DE)
- IDIBAPS, Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, ICN Hospital Clinic, Barcelona, Spain.
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain.
- Biomedical Center (BMC), Biochemistry, Ludwig-Maximilians-Universität München, Munich, Germany.
- German Center for Neurodegenerative Diseases (DE)
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA.
- Corresponding author
- Deming, Yuetiva
- Filipello, Fabia
- Cignarella, Francesca
- Harari, Oscar
- Haass, Christian
- Karch, Celeste M.
- ORCID
- Deming, Yuetiva
- Filipello, Fabia
- Cignarella, Francesca
- Cantoni, Claudia
- Hsu, Simon
- Mikesell, Robert
- Li, Zeran
- Del-Aguila, Jorge L
- Dube, Umber
- Farias, Fabiana Geraldo
- Bradley, Joseph
- Budde, John
- Ibanez, Laura
- Fernandez, Maria Victoria
- Blennow, Kaj
- Zetterberg, Henrik
- Heslegrave, Amanda
- Johansson, Per M
- Svensson, Johan
- Nellgård, Bengt
- Alcolea, Daniel
- Clarimon, Jordi
- Rami, Lorena
- Suárez-Calvet, Marc
- Kleinberger, Gernot
- Ewers, Michael
- Harari, Oscar
- Haass, Christian
- Benitez, Bruno A.
- Karch, Celeste M.
- Piccio, Laura
- Cruchaga, Carlos
- Venue
- Science Translational Medicine
- Published
- 2019-08-14
- Volume
- 11
- Issue
- 505
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- PMC
- PMC6697053
- OpenAlex
- W2968181515
- Cited by
- 271
- References
- 90
- Citations by year
- 2026: 21 · 2025: 37 · 2024: 46 · 2023: 43 · 2022: 48 · 2021: 40 · 2020: 21 · 2019: 15
- FWCI
- 13.58
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Science Translational Medicine (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- DZNE Pub (submittedVersion)
- Funders
- National Institutes of Health (R01AG044546, P01AG003991, RF1AG053303, R01AG058501, R01AG035083)
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- soluble TREM2, Alzheimer's disease risk, late-onset Alzheimer's disease, microglia, common genetic variants, genetic risk factors
- MeSH terms
- Alzheimer Disease, Female, Multigene Family, Humans, Macrophages, Male, Membrane Glycoproteins, Membrane Proteins, Receptors, Immunologic, Meta-Analysis as Topic, Genome-Wide Association Study
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